imbd.ipynb

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      "source": [
        "## 1.Problem Definition\n",
        "\n",
        "Need to work on IMDB data to prepare insights on Titles (movies and TV shows)\n",
        "To analyse data to bright out the following insights:\n",
        "\n",
        "1. Which Genre gets the maximum votes?\n",
        "* Drama, with votes 504932745\n",
        "2. Which actor - director pair is most successful (in terms of IMDB ratings)?\n",
        "* director:Rian Johnson,\n",
        "*  actors: Aaron Paul,Bryan Cranston,Dean Norris\n",
        "*  title: Ozymandias with a rating of 10.0 and 112607 votes\n",
        "3. Male leads are most successful in which age bracket?\n",
        "* (16, 107) if the deathYear is not available it is assumned as present year  and success as top imbd rating\n",
        "4. For TV shows, what is the avg episode duration of the best TV shows?\n",
        "* 29.675 minutes\n",
        "5. Best TV shows last for how many seasons?\n",
        "\ttconst\ttitle\tNumber of seasons\n",
        "* 0\ttt10233774\tRusty Camel\t1\n",
        "* 1\ttt10243828\tLive from the 8th Dimension\t6\n",
        "* 2\ttt10276912\tThat's My Chair, That Is\t1\n",
        "* 3\ttt10293068\tÀ Brasileira\t2\n",
        "* 4\ttt10361854\tTunadas\t1\n",
        "* 5\ttt10622458\tWords Written Well\t5\n",
        "* 6\ttt10758248\tFSE Moments (Frustrated Software Engineer Mome...\t1\n",
        "* 7\ttt11004858\tBlind Wave: Black Sails Reaction\t4\n",
        "* 8\ttt11466824\tState of Decay\t1\n",
        "* 9\ttt1165334\tRendezvous mit Jo\t\\N\n",
        "* 10\ttt11863100\tBoss Hoggin Road Trip Series\t1\n",
        "* 11\ttt11869314\tPain: Fibromyalgia Stories\t1\n",
        "* 12\ttt2360408\tMy Brother's Keeper\t1\n",
        "* 13\ttt5190996\tCaroline the Job Hunter\t1\n",
        "* 14\ttt6572396\tBariga Kvitila\t1\n",
        "* 15\ttt7131158\tGray Ground\t1\n",
        "\n",
        "6. In which year maximum TV shows ended?\n",
        "* 2017 with 2863 shows\n",
        "7.  What factors lead to higher rating\n",
        "\n",
        "## 2. Data\n",
        "\n",
        "Link for data:\n",
        "https://www.imdb.com/interfaces/\n",
        "\n",
        "**title.tsv / title.akas.tsv.gz - Contains the following information for titles:**\n",
        "\n",
        "* titleId (string) - a tconst, an alphanumeric unique identifier of the title\n",
        "* ordering (integer) – a number to uniquely identify rows for a given titleId\n",
        "* title (string) – the localized title\n",
        "* region (string) - the region for this version of the title\n",
        "* language (string) - the language of the title\n",
        "* types (array) - Enumerated set of attributes for this alternative title. One or more of the following: \"alternative\", \"dvd\", \"festival\", \"tv\", \"video\", \"working\", \"original\", \"imdbDisplay\". New values may be added in the future without warning\n",
        "* attributes (array) - Additional terms to describe this alternative title, not enumerated\n",
        "* isOriginalTitle (boolean) – 0: not original title; 1: original title\n",
        "\n",
        "**genres-runtime.tsv / title.basics.tsv.gz - Contains the following information for titles:**\n",
        "* tconst (string) - alphanumeric unique identifier of the title\n",
        "* titleType (string) – the type/format of the title (e.g. movie, short, tvseries, tvepisode, video, etc)\n",
        "* primaryTitle (string) – the more popular title / the title used by the filmmakers on promotional materials at the point of release\n",
        "* originalTitle (string) - original title, in the original language\n",
        "* isAdult (boolean) - 0: non-adult title; 1: adult title\n",
        "* startYear (YYYY) – represents the release year of a title. In the case of TV Series, it is the series start year\n",
        "* endYear (YYYY) – TV Series end year. ‘\\N’ for all other title types\n",
        "* runtimeMinutes – primary runtime of the title, in minutes\n",
        "* genres (string array) – includes up to three genres associated with the title\n",
        "\n",
        "**director.tsv / title.crew.tsv.gz – Contains the director and writer information for all the titles in IMDb. Fields include:**\n",
        "* tconst (string) - alphanumeric unique identifier of the title\n",
        "* directors (array of nconsts) - director(s) of the given title\n",
        "* writers (array of nconsts) – writer(s) of the given title\n",
        "\n",
        "**episode.tsv / title.episode.tsv.gz – Contains the tv episode information. Fields include:**\n",
        "* tconst (string) - alphanumeric identifier of episode\n",
        "* parentTconst (string) - alphanumeric identifier of the parent TV Series\n",
        "* seasonNumber (integer) – season number the episode belongs to\n",
        "* episodeNumber (integer) – episode number of the tconst in the TV series\n",
        "\n",
        "**cast-crew.tsv/ title.principals.tsv.gz – Contains the principal cast/crew for titles**\n",
        "* tconst (string) - alphanumeric unique identifier of the title\n",
        "* ordering (integer) – a number to uniquely identify rows for a given titleId\n",
        "* nconst (string) - alphanumeric unique identifier of the name/person\n",
        "* category (string) - the category of job that person was in\n",
        "* job (string) - the specific job title if applicable, else '\\N'\n",
        "* characters (string) - the name of the character played if applicable, else '\\N'\n",
        "\n",
        "**ratings.tsv/ title.ratings.tsv.gz – Contains the IMDb rating and votes information for titles**\n",
        "* tconst (string) - alphanumeric unique identifier of the title\n",
        "* averageRating – weighted average of all the individual user ratings\n",
        "* numVotes - number of votes the title has received\n",
        "\n",
        "**names.tsv / *name.basics.tsv.gz – Contains the following information for names:**\n",
        "* nconst (string) - alphanumeric unique identifier of the name/person\n",
        "* primaryName (string)– name by which the person is most often credited\n",
        "* birthYear – in YYYY format\n",
        "* deathYear – in YYYY format if applicable, else '\\N'\n",
        "* primaryProfession (array of strings)– the top-3 professions of the person\n",
        "* knownForTitles (array of tconsts) – titles the person is known for\n",
        "\n",
        "## 3. Submisson\n",
        "\n",
        "* A ppt in pdf format with answers to the questions with reasoning and graphs and explain what methods are used to analyse the data and why\n",
        "* Maximum 7 slides\n",
        "\n",
        "* Deadline : 23nd - April - 2020\n",
        "\n",
        "* Fill up this google form and upload your excel :\n",
        "https://forms.gle/yow2qT4Bgn3nyF8E6"
      ]
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        ""
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      "cell_type": "markdown",
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        "id": "IkjKhbof15TB",
        "colab_type": "text"
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      "source": [
        "## 1. Top Genre in terms of votes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsrMvlCjc9_n",
        "colab_type": "code",
        "outputId": "a53a3416-7c42-431d-960e-e383c2cdee47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "with gzip.open('/content/drive/My Drive/Data/title.tsv.gz') as f:\n",
        "  title = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/genres.tsv.gz') as f:\n",
        "  genres = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/ratings.tsv.gz') as f:\n",
        "  ratings = pd.read_csv(f,sep='\\t')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n",
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sr8YrL6OEMQ4",
        "colab_type": "code",
        "outputId": "757b05d0-7714-45b6-c57c-f68765739df2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        }
      },
      "source": [
        "genres.loc[genres.endYear != '\\\\N']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34987</th>\n",
              "      <td>tt0035599</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Voice of Firestone Televues</td>\n",
              "      <td>Voice of Firestone Televues</td>\n",
              "      <td>0</td>\n",
              "      <td>1943</td>\n",
              "      <td>1947</td>\n",
              "      <td>15</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35188</th>\n",
              "      <td>tt0035803</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>The German Weekly Review</td>\n",
              "      <td>Die Deutsche Wochenschau</td>\n",
              "      <td>0</td>\n",
              "      <td>1940</td>\n",
              "      <td>1945</td>\n",
              "      <td>12</td>\n",
              "      <td>Documentary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37617</th>\n",
              "      <td>tt0038276</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>You Are an Artist</td>\n",
              "      <td>You Are an Artist</td>\n",
              "      <td>0</td>\n",
              "      <td>1946</td>\n",
              "      <td>1950</td>\n",
              "      <td>15</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38451</th>\n",
              "      <td>tt0039120</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Americana</td>\n",
              "      <td>Americana</td>\n",
              "      <td>0</td>\n",
              "      <td>1947</td>\n",
              "      <td>1949</td>\n",
              "      <td>30</td>\n",
              "      <td>Family,Game-Show</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38452</th>\n",
              "      <td>tt0039121</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Birthday Party</td>\n",
              "      <td>Birthday Party</td>\n",
              "      <td>0</td>\n",
              "      <td>1947</td>\n",
              "      <td>1949</td>\n",
              "      <td>30</td>\n",
              "      <td>Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709347</th>\n",
              "      <td>tt9915338</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Aunty Donna: Camp Bush Camp!</td>\n",
              "      <td>Aunty Donna: Camp Bush Camp!</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709441</th>\n",
              "      <td>tt9915530</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>The Land F.A.U.</td>\n",
              "      <td>The Land F.A.U.</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Crime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709570</th>\n",
              "      <td>tt9915822</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Ichhapyaari Naagin</td>\n",
              "      <td>Ichhapyaari Naagin</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>2017</td>\n",
              "      <td>20</td>\n",
              "      <td>Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709752</th>\n",
              "      <td>tt9916216</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Kalyanam Mudhal Kadhal Varai</td>\n",
              "      <td>Kalyanam Mudhal Kadhal Varai</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>2017</td>\n",
              "      <td>22</td>\n",
              "      <td>Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709753</th>\n",
              "      <td>tt9916218</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Lost in Food</td>\n",
              "      <td>Lost in Food</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>2017</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Talk-Show</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>58662 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst titleType  ... runtimeMinutes            genres\n",
              "34987    tt0035599  tvSeries  ...             15                \\N\n",
              "35188    tt0035803  tvSeries  ...             12       Documentary\n",
              "37617    tt0038276  tvSeries  ...             15                \\N\n",
              "38451    tt0039120  tvSeries  ...             30  Family,Game-Show\n",
              "38452    tt0039121  tvSeries  ...             30            Family\n",
              "...            ...       ...  ...            ...               ...\n",
              "6709347  tt9915338  tvSeries  ...              5            Comedy\n",
              "6709441  tt9915530  tvSeries  ...             \\N             Crime\n",
              "6709570  tt9915822  tvSeries  ...             20           Fantasy\n",
              "6709752  tt9916216  tvSeries  ...             22           Romance\n",
              "6709753  tt9916218  tvSeries  ...             \\N         Talk-Show\n",
              "\n",
              "[58662 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QChPdf3JOdum",
        "colab_type": "code",
        "outputId": "b64f8334-3beb-4c28-b292-c6932a794465",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "source": [
        "df = pd.merge(genres,ratings,on='tconst')\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      tconst titleType  ... averageRating numVotes\n",
              "0  tt0000001     short  ...           5.6     1600\n",
              "1  tt0000002     short  ...           6.0      196\n",
              "2  tt0000003     short  ...           6.5     1275\n",
              "3  tt0000004     short  ...           6.1      121\n",
              "4  tt0000005     short  ...           6.1     2038\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BohuPdhIziGS",
        "colab_type": "text"
      },
      "source": [
        "Genres column has upto three genre for each title hence after merging genres table with ratings table it is exlpded based on genres column\n",
        "then the resulting dataframe is grouped by genres with sum to get number of votes for each genre type"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4Q8gtdaOdq1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.genres = df.genres.str.split(',')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
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              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
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              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
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              "      <td>Carmencita</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>[Documentary, Short]</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>[Animation, Short]</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>[Animation, Comedy, Romance]</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>[Animation, Short]</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
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              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>[Comedy, Short]</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      tconst titleType  ... averageRating numVotes\n",
              "0  tt0000001     short  ...           5.6     1600\n",
              "1  tt0000002     short  ...           6.0      196\n",
              "2  tt0000003     short  ...           6.5     1275\n",
              "3  tt0000004     short  ...           6.1      121\n",
              "4  tt0000005     short  ...           6.1     2038\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hk8Cnm3TPf0E",
        "colab_type": "code",
        "outputId": "f7694068-5b2e-4877-fcd8-42be8b60a410",
        "colab": {
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          "height": 34
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      "source": [
        "len(df)"
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1024884"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "dZJtZw91P6um",
        "colab_type": "code",
        "outputId": "7a9fb848-1d40-48e4-f73c-22a80fb84b1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
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      "source": [
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
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              "      <th>originalTitle</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>\\N</td>\n",
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              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>[Animation, Comedy, Romance]</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>[Animation, Short]</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>[Comedy, Short]</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>tt9916576</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Destinee's Story</td>\n",
              "      <td>Destinee's Story</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>85</td>\n",
              "      <td>[Reality-TV]</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>tt9916578</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>The Trial of Joan Collins</td>\n",
              "      <td>The Trial of Joan Collins</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[Adventure, Biography, Comedy]</td>\n",
              "      <td>8.4</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
              "      <td>short</td>\n",
              "      <td>The Nun 2</td>\n",
              "      <td>The Nun 2</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>[Comedy, Horror, Mystery]</td>\n",
              "      <td>5.5</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>43</td>\n",
              "      <td>[Family, Reality-TV]</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Escape</td>\n",
              "      <td>Escape</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ... averageRating numVotes\n",
              "0        tt0000001      short  ...           5.6     1600\n",
              "1        tt0000002      short  ...           6.0      196\n",
              "2        tt0000003      short  ...           6.5     1275\n",
              "3        tt0000004      short  ...           6.1      121\n",
              "4        tt0000005      short  ...           6.1     2038\n",
              "...            ...        ...  ...           ...      ...\n",
              "1024879  tt9916576  tvEpisode  ...           6.0        9\n",
              "1024880  tt9916578  tvEpisode  ...           8.4       17\n",
              "1024881  tt9916720      short  ...           5.5       48\n",
              "1024882  tt9916766  tvEpisode  ...           6.8       13\n",
              "1024883  tt9916778  tvEpisode  ...           7.2       21\n",
              "\n",
              "[1024884 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bV863VsXOXhQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2 = df.explode('genres')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03FQwAQbOXef",
        "colab_type": "code",
        "outputId": "b846e9a1-4308-4a4c-b281-892d1ea07076",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
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      },
      "source": [
        "len(df2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2005513"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 20
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "p5vTFq6COXLM",
        "colab_type": "code",
        "outputId": "94458f41-aa63-478e-dd40-0c62812ded81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df2"
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      "execution_count": 0,
      "outputs": [
        {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>tconst</th>\n",
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              "      <td>\\N</td>\n",
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              "      <td>Le clown et ses chiens</td>\n",
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              "      <td>\\N</td>\n",
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              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
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              "      <td>The Nun 2</td>\n",
              "      <td>The Nun 2</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Horror</td>\n",
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              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
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              "      <td>The Nun 2</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Mystery</td>\n",
              "      <td>5.5</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>43</td>\n",
              "      <td>Family</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>43</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Escape</td>\n",
              "      <td>Escape</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2005513 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ... averageRating numVotes\n",
              "0        tt0000001      short  ...           5.6     1600\n",
              "0        tt0000001      short  ...           5.6     1600\n",
              "1        tt0000002      short  ...           6.0      196\n",
              "1        tt0000002      short  ...           6.0      196\n",
              "2        tt0000003      short  ...           6.5     1275\n",
              "...            ...        ...  ...           ...      ...\n",
              "1024881  tt9916720      short  ...           5.5       48\n",
              "1024881  tt9916720      short  ...           5.5       48\n",
              "1024882  tt9916766  tvEpisode  ...           6.8       13\n",
              "1024882  tt9916766  tvEpisode  ...           6.8       13\n",
              "1024883  tt9916778  tvEpisode  ...           7.2       21\n",
              "\n",
              "[2005513 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYD7508KOXWC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final = df2.groupby(by='genres').sum().sort_values(by='numVotes',ascending=False).reset_index()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gIAjI37Wb3QO",
        "colab_type": "code",
        "outputId": "8c62c067-7655-4fbd-d7dd-948388b1f9e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "final"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>genres</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Drama</td>\n",
              "      <td>1439</td>\n",
              "      <td>2364229.1</td>\n",
              "      <td>504932745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Comedy</td>\n",
              "      <td>1568</td>\n",
              "      <td>2246709.8</td>\n",
              "      <td>300147902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Action</td>\n",
              "      <td>219</td>\n",
              "      <td>828576.1</td>\n",
              "      <td>295762583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adventure</td>\n",
              "      <td>191</td>\n",
              "      <td>728462.8</td>\n",
              "      <td>249998965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Crime</td>\n",
              "      <td>437</td>\n",
              "      <td>742312.5</td>\n",
              "      <td>189050575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Thriller</td>\n",
              "      <td>148</td>\n",
              "      <td>261107.6</td>\n",
              "      <td>152475371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Romance</td>\n",
              "      <td>1121</td>\n",
              "      <td>509915.6</td>\n",
              "      <td>118551562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Sci-Fi</td>\n",
              "      <td>220</td>\n",
              "      <td>194832.1</td>\n",
              "      <td>117579178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Mystery</td>\n",
              "      <td>139</td>\n",
              "      <td>336140.3</td>\n",
              "      <td>100081828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Fantasy</td>\n",
              "      <td>414</td>\n",
              "      <td>287782.5</td>\n",
              "      <td>94428579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Horror</td>\n",
              "      <td>255</td>\n",
              "      <td>258508.8</td>\n",
              "      <td>86172958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Animation</td>\n",
              "      <td>216</td>\n",
              "      <td>794145.3</td>\n",
              "      <td>69682766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Biography</td>\n",
              "      <td>12</td>\n",
              "      <td>141740.0</td>\n",
              "      <td>56031584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Family</td>\n",
              "      <td>1</td>\n",
              "      <td>548832.6</td>\n",
              "      <td>53337896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>History</td>\n",
              "      <td>20</td>\n",
              "      <td>184391.7</td>\n",
              "      <td>29596801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Music</td>\n",
              "      <td>33</td>\n",
              "      <td>328984.6</td>\n",
              "      <td>19517954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>War</td>\n",
              "      <td>13</td>\n",
              "      <td>81434.3</td>\n",
              "      <td>18688645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Sport</td>\n",
              "      <td>22</td>\n",
              "      <td>120364.5</td>\n",
              "      <td>16294220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Documentary</td>\n",
              "      <td>155</td>\n",
              "      <td>863308.5</td>\n",
              "      <td>15985552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Western</td>\n",
              "      <td>49</td>\n",
              "      <td>97582.9</td>\n",
              "      <td>8692881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Musical</td>\n",
              "      <td>17</td>\n",
              "      <td>70025.3</td>\n",
              "      <td>8496209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Short</td>\n",
              "      <td>261</td>\n",
              "      <td>914198.0</td>\n",
              "      <td>6342072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Film-Noir</td>\n",
              "      <td>0</td>\n",
              "      <td>5189.6</td>\n",
              "      <td>3039702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>36</td>\n",
              "      <td>319313.7</td>\n",
              "      <td>2353419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Talk-Show</td>\n",
              "      <td>3</td>\n",
              "      <td>213364.3</td>\n",
              "      <td>1702602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>News</td>\n",
              "      <td>1</td>\n",
              "      <td>90767.8</td>\n",
              "      <td>1085755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Game-Show</td>\n",
              "      <td>0</td>\n",
              "      <td>134657.2</td>\n",
              "      <td>1025838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>\\N</td>\n",
              "      <td>382</td>\n",
              "      <td>131591.1</td>\n",
              "      <td>462495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Adult</td>\n",
              "      <td>17937</td>\n",
              "      <td>113460.4</td>\n",
              "      <td>384655</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         genres  isAdult  averageRating   numVotes\n",
              "0         Drama     1439      2364229.1  504932745\n",
              "1        Comedy     1568      2246709.8  300147902\n",
              "2        Action      219       828576.1  295762583\n",
              "3     Adventure      191       728462.8  249998965\n",
              "4         Crime      437       742312.5  189050575\n",
              "5      Thriller      148       261107.6  152475371\n",
              "6       Romance     1121       509915.6  118551562\n",
              "7        Sci-Fi      220       194832.1  117579178\n",
              "8       Mystery      139       336140.3  100081828\n",
              "9       Fantasy      414       287782.5   94428579\n",
              "10       Horror      255       258508.8   86172958\n",
              "11    Animation      216       794145.3   69682766\n",
              "12    Biography       12       141740.0   56031584\n",
              "13       Family        1       548832.6   53337896\n",
              "14      History       20       184391.7   29596801\n",
              "15        Music       33       328984.6   19517954\n",
              "16          War       13        81434.3   18688645\n",
              "17        Sport       22       120364.5   16294220\n",
              "18  Documentary      155       863308.5   15985552\n",
              "19      Western       49        97582.9    8692881\n",
              "20      Musical       17        70025.3    8496209\n",
              "21        Short      261       914198.0    6342072\n",
              "22    Film-Noir        0         5189.6    3039702\n",
              "23   Reality-TV       36       319313.7    2353419\n",
              "24    Talk-Show        3       213364.3    1702602\n",
              "25         News        1        90767.8    1085755\n",
              "26    Game-Show        0       134657.2    1025838\n",
              "27           \\N      382       131591.1     462495\n",
              "28        Adult    17937       113460.4     384655"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9Vss-EflW6k",
        "colab_type": "code",
        "outputId": "ff290060-0f19-46a5-f483-b3773d82023d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1024884 entries, 0 to 1024883\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   tconst          1024884 non-null  object \n",
            " 1   titleType       1024884 non-null  object \n",
            " 2   primaryTitle    1024884 non-null  object \n",
            " 3   originalTitle   1024884 non-null  object \n",
            " 4   isAdult         1024884 non-null  int64  \n",
            " 5   startYear       1024884 non-null  object \n",
            " 6   endYear         1024884 non-null  object \n",
            " 7   runtimeMinutes  1024884 non-null  object \n",
            " 8   genres          1024882 non-null  object \n",
            " 9   averageRating   1024884 non-null  float64\n",
            " 10  numVotes        1024884 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(8)\n",
            "memory usage: 93.8+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vguSNSQJbjFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ueu5jKlYpQjX",
        "colab_type": "code",
        "outputId": "6eb46e22-4ba5-43b3-eb1d-7a291b0ed761",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "ax = final[:10].plot(x='genres',y='numVotes',kind='barh',figsize=(15,8),legend=None,color=\"mediumslateblue\",fontsize=14)\n",
        "ax.set_xlabel('Number of Votes',fontsize=16)\n",
        "ax.set_ylabel('Genre',fontsize=16)\n",
        "ax.ticklabel_format(axis='x',style='plain')\n",
        "ax.invert_yaxis()\n",
        "ax.set_xticklabels(['{:,}'.format(int(x)) for x in ax.get_xticks().tolist()])\n",
        "ax.set_title('Top 10 Genres in terms of Votes',fontsize=18)\n",
        "\n",
        "for i in ax.patches:\n",
        "  width = i.get_width()\n",
        "  s='{:,}'.format(width)\n",
        "  ax.annotate(s,xy=(i.get_width()-80000000,i.get_y()+0.1+(i.get_height())/2),color=\"black\",fontsize=12)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvJSjJPqct4Q",
        "colab_type": "text"
      },
      "source": [
        "## 2. Director-Actor pair based on rating\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cuxUpJactSZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "with gzip.open('/content/drive/My Drive/Data/crew.tsv.gz') as f:\n",
        "  crew = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/ratings.tsv.gz') as f:\n",
        "  ratings = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/name.basics.tsv.gz') as f:\n",
        "  names = pd.read_csv(f,sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBofhoWcc2qx",
        "colab_type": "code",
        "outputId": "e87144ea-f356-4f31-8bc8-72a6bd6aa4b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "crew"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>job</th>\n",
              "      <th>characters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>1</td>\n",
              "      <td>nm1588970</td>\n",
              "      <td>self</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Self\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0374658</td>\n",
              "      <td>cinematographer</td>\n",
              "      <td>director of photography</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0721526</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>2</td>\n",
              "      <td>nm1335271</td>\n",
              "      <td>composer</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728844</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>5</td>\n",
              "      <td>nm0996406</td>\n",
              "      <td>director</td>\n",
              "      <td>principal director</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728845</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>6</td>\n",
              "      <td>nm1482639</td>\n",
              "      <td>writer</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728846</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>7</td>\n",
              "      <td>nm2586970</td>\n",
              "      <td>writer</td>\n",
              "      <td>books</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728847</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>8</td>\n",
              "      <td>nm1594058</td>\n",
              "      <td>producer</td>\n",
              "      <td>producer</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728848</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>9</td>\n",
              "      <td>nm1052583</td>\n",
              "      <td>actress</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Mum\",\"Tidy Ted\",\"Fang\"]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>38728849 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             tconst  ...                 characters\n",
              "0         tt0000001  ...                   [\"Self\"]\n",
              "1         tt0000001  ...                         \\N\n",
              "2         tt0000001  ...                         \\N\n",
              "3         tt0000002  ...                         \\N\n",
              "4         tt0000002  ...                         \\N\n",
              "...             ...  ...                        ...\n",
              "38728844  tt9916880  ...                         \\N\n",
              "38728845  tt9916880  ...                         \\N\n",
              "38728846  tt9916880  ...                         \\N\n",
              "38728847  tt9916880  ...                         \\N\n",
              "38728848  tt9916880  ...  [\"Mum\",\"Tidy Ted\",\"Fang\"]\n",
              "\n",
              "[38728849 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teKitvMegA7K",
        "colab_type": "code",
        "outputId": "93d25ba3-ba9e-456a-d4f8-aa08631366e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "import numpy as np\n",
        "np.array(crew.category.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['self', 'director', 'cinematographer', 'composer', 'producer',\n",
              "       'editor', 'actor', 'actress', 'writer', 'production_designer',\n",
              "       'archive_footage', 'archive_sound'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhzSrdXdc2j8",
        "colab_type": "code",
        "outputId": "412ec9f2-2deb-4126-f6c1-23da454df81a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "ratings"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>tt9916576</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>tt9916578</td>\n",
              "      <td>8.4</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
              "      <td>5.5</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  averageRating  numVotes\n",
              "0        tt0000001            5.6      1600\n",
              "1        tt0000002            6.0       196\n",
              "2        tt0000003            6.5      1275\n",
              "3        tt0000004            6.1       121\n",
              "4        tt0000005            6.1      2038\n",
              "...            ...            ...       ...\n",
              "1024879  tt9916576            6.0         9\n",
              "1024880  tt9916578            8.4        17\n",
              "1024881  tt9916720            5.5        48\n",
              "1024882  tt9916766            6.8        13\n",
              "1024883  tt9916778            7.2        21\n",
              "\n",
              "[1024884 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LQjcW5Lc2Q5",
        "colab_type": "code",
        "outputId": "69c39eb8-e862-4487-8e4e-9c4d0c13f6a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "names"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nconst</th>\n",
              "      <th>primaryName</th>\n",
              "      <th>birthYear</th>\n",
              "      <th>deathYear</th>\n",
              "      <th>primaryProfession</th>\n",
              "      <th>knownForTitles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nm0000001</td>\n",
              "      <td>Fred Astaire</td>\n",
              "      <td>1899</td>\n",
              "      <td>1987</td>\n",
              "      <td>soundtrack,actor,miscellaneous</td>\n",
              "      <td>tt0072308,tt0043044,tt0050419,tt0053137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>nm0000002</td>\n",
              "      <td>Lauren Bacall</td>\n",
              "      <td>1924</td>\n",
              "      <td>2014</td>\n",
              "      <td>actress,soundtrack</td>\n",
              "      <td>tt0117057,tt0037382,tt0038355,tt0071877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nm0000003</td>\n",
              "      <td>Brigitte Bardot</td>\n",
              "      <td>1934</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actress,soundtrack,producer</td>\n",
              "      <td>tt0059956,tt0049189,tt0054452,tt0057345,tt0054...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nm0000004</td>\n",
              "      <td>John Belushi</td>\n",
              "      <td>1949</td>\n",
              "      <td>1982</td>\n",
              "      <td>actor,soundtrack,writer</td>\n",
              "      <td>tt0078723,tt0080455,tt0072562,tt0077975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>nm0000005</td>\n",
              "      <td>Ingmar Bergman</td>\n",
              "      <td>1918</td>\n",
              "      <td>2007</td>\n",
              "      <td>writer,director,actor</td>\n",
              "      <td>tt0050976,tt0069467,tt0083922,tt0050986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10012295</th>\n",
              "      <td>nm9993714</td>\n",
              "      <td>Romeo del Rosario</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>animation_department,art_department</td>\n",
              "      <td>tt2455546,tt2455546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10012296</th>\n",
              "      <td>nm9993716</td>\n",
              "      <td>Essias Loberg</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10012297</th>\n",
              "      <td>nm9993717</td>\n",
              "      <td>Harikrishnan Rajan</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>cinematographer</td>\n",
              "      <td>tt8736744,tt8736744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10012298</th>\n",
              "      <td>nm9993718</td>\n",
              "      <td>Aayush Nair</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>cinematographer</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10012299</th>\n",
              "      <td>nm9993719</td>\n",
              "      <td>Andre Hill</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10012300 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             nconst  ...                                     knownForTitles\n",
              "0         nm0000001  ...            tt0072308,tt0043044,tt0050419,tt0053137\n",
              "1         nm0000002  ...            tt0117057,tt0037382,tt0038355,tt0071877\n",
              "2         nm0000003  ...  tt0059956,tt0049189,tt0054452,tt0057345,tt0054...\n",
              "3         nm0000004  ...            tt0078723,tt0080455,tt0072562,tt0077975\n",
              "4         nm0000005  ...            tt0050976,tt0069467,tt0083922,tt0050986\n",
              "...             ...  ...                                                ...\n",
              "10012295  nm9993714  ...                                tt2455546,tt2455546\n",
              "10012296  nm9993716  ...                                                 \\N\n",
              "10012297  nm9993717  ...                                tt8736744,tt8736744\n",
              "10012298  nm9993718  ...                                                 \\N\n",
              "10012299  nm9993719  ...                                                 \\N\n",
              "\n",
              "[10012300 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3NKpVqoc2JX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "actors = crew.loc[crew.category == 'actor']\n",
        "directors = crew.loc[crew.category == 'director']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lc4Sdst2c2Bd",
        "colab_type": "code",
        "outputId": "cb22a273-8689-42d4-d91c-49e24b2734aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "actors"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>job</th>\n",
              "      <th>characters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0443482</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Blacksmith\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0653042</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Assistant\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>tt0000007</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0179163</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>tt0000007</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0183947</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>tt0000008</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0653028</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Sneezing Man\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728822</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>1</td>\n",
              "      <td>nm5519557</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Cetin Ertas\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728823</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>2</td>\n",
              "      <td>nm8825009</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Umut\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728833</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>3</td>\n",
              "      <td>nm10538646</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Stephan\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728840</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>1</td>\n",
              "      <td>nm1483166</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Rude Ralph\",\"Mischievous Mike\",\"Jolly Josh\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728842</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0286175</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Dad\",\"Aerobic Al\",\"Nasty Nicola\"]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8978390 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             tconst  ...                                      characters\n",
              "11        tt0000005  ...                                  [\"Blacksmith\"]\n",
              "12        tt0000005  ...                                   [\"Assistant\"]\n",
              "16        tt0000007  ...                                              \\N\n",
              "17        tt0000007  ...                                              \\N\n",
              "21        tt0000008  ...                                [\"Sneezing Man\"]\n",
              "...             ...  ...                                             ...\n",
              "38728822  tt9916852  ...                                 [\"Cetin Ertas\"]\n",
              "38728823  tt9916852  ...                                        [\"Umut\"]\n",
              "38728833  tt9916856  ...                                     [\"Stephan\"]\n",
              "38728840  tt9916880  ...  [\"Rude Ralph\",\"Mischievous Mike\",\"Jolly Josh\"]\n",
              "38728842  tt9916880  ...             [\"Dad\",\"Aerobic Al\",\"Nasty Nicola\"]\n",
              "\n",
              "[8978390 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFIYPdvac15v",
        "colab_type": "code",
        "outputId": "550d49d3-6e38-44be-94c4-d35ca0f9e73b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "directors"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>job</th>\n",
              "      <th>characters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0721526</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0721526</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0721526</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728817</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>6</td>\n",
              "      <td>nm5519375</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728826</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>5</td>\n",
              "      <td>nm5519454</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728827</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>6</td>\n",
              "      <td>nm5519375</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728835</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>5</td>\n",
              "      <td>nm10538645</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38728844</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>5</td>\n",
              "      <td>nm0996406</td>\n",
              "      <td>director</td>\n",
              "      <td>principal director</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4470015 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             tconst  ordering  ...                 job characters\n",
              "1         tt0000001         2  ...                  \\N         \\N\n",
              "3         tt0000002         1  ...                  \\N         \\N\n",
              "5         tt0000003         1  ...                  \\N         \\N\n",
              "9         tt0000004         1  ...                  \\N         \\N\n",
              "13        tt0000005         3  ...                  \\N         \\N\n",
              "...             ...       ...  ...                 ...        ...\n",
              "38728817  tt9916850         6  ...                  \\N         \\N\n",
              "38728826  tt9916852         5  ...                  \\N         \\N\n",
              "38728827  tt9916852         6  ...                  \\N         \\N\n",
              "38728835  tt9916856         5  ...                  \\N         \\N\n",
              "38728844  tt9916880         5  ...  principal director         \\N\n",
              "\n",
              "[4470015 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQf1u28Ic1yK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pairs = pd.merge(directors[['tconst','nconst']],actors[['tconst','nconst']],on='tconst')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dRKdQKbPc1qX",
        "colab_type": "code",
        "outputId": "a13bed6d-8c51-480e-d89c-b5d02653bc59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "pairs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>nconst_x</th>\n",
              "      <th>nconst_y</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>nm0443482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>nm0653042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000007</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>nm0179163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000007</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>nm0183947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000007</td>\n",
              "      <td>nm0374658</td>\n",
              "      <td>nm0179163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7723903</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>nm5519375</td>\n",
              "      <td>nm5519557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7723904</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>nm5519375</td>\n",
              "      <td>nm8825009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7723905</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>nm10538645</td>\n",
              "      <td>nm10538646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7723906</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>nm0996406</td>\n",
              "      <td>nm1483166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7723907</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>nm0996406</td>\n",
              "      <td>nm0286175</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7723908 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst    nconst_x    nconst_y\n",
              "0        tt0000005   nm0005690   nm0443482\n",
              "1        tt0000005   nm0005690   nm0653042\n",
              "2        tt0000007   nm0005690   nm0179163\n",
              "3        tt0000007   nm0005690   nm0183947\n",
              "4        tt0000007   nm0374658   nm0179163\n",
              "...            ...         ...         ...\n",
              "7723903  tt9916852   nm5519375   nm5519557\n",
              "7723904  tt9916852   nm5519375   nm8825009\n",
              "7723905  tt9916856  nm10538645  nm10538646\n",
              "7723906  tt9916880   nm0996406   nm1483166\n",
              "7723907  tt9916880   nm0996406   nm0286175\n",
              "\n",
              "[7723908 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67b8JAkweNZ6",
        "colab_type": "code",
        "outputId": "bb9113cd-4d46-417f-adf8-42b330c67ff0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "pair_rating = pd.merge(pairs,ratings,on='tconst').sort_values(by='averageRating',ascending=False)\n",
        "pair_rating"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>nconst_x</th>\n",
              "      <th>nconst_y</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1697307</th>\n",
              "      <td>tt6452096</td>\n",
              "      <td>nm7870411</td>\n",
              "      <td>nm5092260</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1682968</th>\n",
              "      <td>tt6255510</td>\n",
              "      <td>nm7870411</td>\n",
              "      <td>nm5092260</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294949</th>\n",
              "      <td>tt2121942</td>\n",
              "      <td>nm1341249</td>\n",
              "      <td>nm4577205</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294950</th>\n",
              "      <td>tt2121942</td>\n",
              "      <td>nm1341249</td>\n",
              "      <td>nm1162129</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1294951</th>\n",
              "      <td>tt2121942</td>\n",
              "      <td>nm1341249</td>\n",
              "      <td>nm4530933</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1620445</th>\n",
              "      <td>tt5493492</td>\n",
              "      <td>nm6169193</td>\n",
              "      <td>nm7946704</td>\n",
              "      <td>1.0</td>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1175421</th>\n",
              "      <td>tt1613600</td>\n",
              "      <td>nm0666362</td>\n",
              "      <td>nm0250616</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1175420</th>\n",
              "      <td>tt1613600</td>\n",
              "      <td>nm0666362</td>\n",
              "      <td>nm0191520</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1175419</th>\n",
              "      <td>tt1613600</td>\n",
              "      <td>nm0666362</td>\n",
              "      <td>nm0063710</td>\n",
              "      <td>1.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1426622</th>\n",
              "      <td>tt3185040</td>\n",
              "      <td>nm1823684</td>\n",
              "      <td>nm3049870</td>\n",
              "      <td>1.0</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1864350 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst   nconst_x   nconst_y  averageRating  numVotes\n",
              "1697307  tt6452096  nm7870411  nm5092260           10.0         9\n",
              "1682968  tt6255510  nm7870411  nm5092260           10.0         6\n",
              "1294949  tt2121942  nm1341249  nm4577205           10.0        12\n",
              "1294950  tt2121942  nm1341249  nm1162129           10.0        12\n",
              "1294951  tt2121942  nm1341249  nm4530933           10.0        12\n",
              "...            ...        ...        ...            ...       ...\n",
              "1620445  tt5493492  nm6169193  nm7946704            1.0       176\n",
              "1175421  tt1613600  nm0666362  nm0250616            1.0         6\n",
              "1175420  tt1613600  nm0666362  nm0191520            1.0         6\n",
              "1175419  tt1613600  nm0666362  nm0063710            1.0         6\n",
              "1426622  tt3185040  nm1823684  nm3049870            1.0       276\n",
              "\n",
              "[1864350 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeL-Ss6ZeNVK",
        "colab_type": "code",
        "outputId": "9f26082d-b1bd-4c63-897f-0dbbfbb954f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "top_pairs = pair_rating.loc[pair_rating.averageRating == 10.0].sort_values(by='numVotes',ascending=False)\n",
        "top_pairs"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>nconst_x</th>\n",
              "      <th>nconst_y</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1328897</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1328895</th>\n",
              "      <td>tt2301451</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1328896</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>966624</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>966625</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>nm0492226</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1831248</th>\n",
              "      <td>tt8971000</td>\n",
              "      <td>nm1852837</td>\n",
              "      <td>nm0387752</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466093</th>\n",
              "      <td>tt3581396</td>\n",
              "      <td>nm1121943</td>\n",
              "      <td>nm6341141</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1466092</th>\n",
              "      <td>tt3581396</td>\n",
              "      <td>nm1121943</td>\n",
              "      <td>nm6341140</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1802959</th>\n",
              "      <td>tt8323554</td>\n",
              "      <td>nm5807365</td>\n",
              "      <td>nm2286983</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308791</th>\n",
              "      <td>tt2190331</td>\n",
              "      <td>nm4860131</td>\n",
              "      <td>nm4860639</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2418 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst   nconst_x   nconst_y  averageRating  numVotes\n",
              "1328897  tt2301451  nm0426059  nm0606487           10.0    112607\n",
              "1328895  tt2301451  nm0426059  nm0186505           10.0    112607\n",
              "1328896  tt2301451  nm0426059  nm0666739           10.0    112607\n",
              "966624   tt1069840  nm0035106  nm0805189           10.0       162\n",
              "966625   tt1069840  nm0035106  nm0492226           10.0       162\n",
              "...            ...        ...        ...            ...       ...\n",
              "1831248  tt8971000  nm1852837  nm0387752           10.0         5\n",
              "1466093  tt3581396  nm1121943  nm6341141           10.0         5\n",
              "1466092  tt3581396  nm1121943  nm6341140           10.0         5\n",
              "1802959  tt8323554  nm5807365  nm2286983           10.0         5\n",
              "1308791  tt2190331  nm4860131  nm4860639           10.0         5\n",
              "\n",
              "[2418 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTu2vl7ieNQh",
        "colab_type": "code",
        "outputId": "f2d15699-2642-418f-bcaa-c7f019d51227",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df = pd.merge(top_pairs,names[['nconst','primaryName']],left_on='nconst_x',right_on='nconst')\n",
        "df1 = pd.merge(df.drop('nconst_x',axis=1),names[['nconst','primaryName']],left_on='nconst_y',right_on='nconst',suffixes=('','_2'))\n",
        "df1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>nconst_y</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>nconst</th>\n",
              "      <th>primaryName</th>\n",
              "      <th>nconst_2</th>\n",
              "      <th>primaryName_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>Dean Norris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>Aaron Paul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>Allan Arkush</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt1576812</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>Allan Arkush</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2412</th>\n",
              "      <td>tt4554168</td>\n",
              "      <td>nm5539980</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>nm5981759</td>\n",
              "      <td>Victor Lockhart</td>\n",
              "      <td>nm5539980</td>\n",
              "      <td>Colin Cunninghame</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2413</th>\n",
              "      <td>tt1958076</td>\n",
              "      <td>nm3935246</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>nm3925995</td>\n",
              "      <td>Wes Sutton</td>\n",
              "      <td>nm3935246</td>\n",
              "      <td>Mike Henderson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2414</th>\n",
              "      <td>tt1958076</td>\n",
              "      <td>nm3837412</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>nm3925995</td>\n",
              "      <td>Wes Sutton</td>\n",
              "      <td>nm3837412</td>\n",
              "      <td>Jonathan O'Rear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2415</th>\n",
              "      <td>tt1223960</td>\n",
              "      <td>nm0246977</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>nm2120311</td>\n",
              "      <td>Alex Schmidt</td>\n",
              "      <td>nm0246977</td>\n",
              "      <td>Nils Düwell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2416</th>\n",
              "      <td>tt5337428</td>\n",
              "      <td>nm7367750</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>nm7813853</td>\n",
              "      <td>Nuel Caslo</td>\n",
              "      <td>nm7367750</td>\n",
              "      <td>Roberto Arrizon</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2417 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst   nconst_y  ...   nconst_2      primaryName_2\n",
              "0     tt2301451  nm0606487  ...  nm0606487        Dean Norris\n",
              "1     tt2301451  nm0186505  ...  nm0186505     Bryan Cranston\n",
              "2     tt2301451  nm0666739  ...  nm0666739         Aaron Paul\n",
              "3     tt1069840  nm0805189  ...  nm0805189  Max Elliott Slade\n",
              "4     tt1576812  nm0805189  ...  nm0805189  Max Elliott Slade\n",
              "...         ...        ...  ...        ...                ...\n",
              "2412  tt4554168  nm5539980  ...  nm5539980  Colin Cunninghame\n",
              "2413  tt1958076  nm3935246  ...  nm3935246     Mike Henderson\n",
              "2414  tt1958076  nm3837412  ...  nm3837412    Jonathan O'Rear\n",
              "2415  tt1223960  nm0246977  ...  nm0246977        Nils Düwell\n",
              "2416  tt5337428  nm7367750  ...  nm7367750    Roberto Arrizon\n",
              "\n",
              "[2417 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5GWikg1KeNME",
        "colab_type": "code",
        "outputId": "6d6a7b20-4538-46f9-9813-0add1de6bf30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "pd.DataFrame({'Title_id':df1.tconst,\n",
        "              'Director_id':df1.nconst,\n",
        "              \"Director\":df1.primaryName,\n",
        "              'Actor_id':df1.nconst_y,\n",
        "              'Actor':df1.primaryName_2\n",
        "              })"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Title_id</th>\n",
              "      <th>Director_id</th>\n",
              "      <th>Director</th>\n",
              "      <th>Actor_id</th>\n",
              "      <th>Actor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>Dean Norris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>Aaron Paul</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>Allan Arkush</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt1576812</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>Allan Arkush</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2412</th>\n",
              "      <td>tt4554168</td>\n",
              "      <td>nm5981759</td>\n",
              "      <td>Victor Lockhart</td>\n",
              "      <td>nm5539980</td>\n",
              "      <td>Colin Cunninghame</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2413</th>\n",
              "      <td>tt1958076</td>\n",
              "      <td>nm3925995</td>\n",
              "      <td>Wes Sutton</td>\n",
              "      <td>nm3935246</td>\n",
              "      <td>Mike Henderson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2414</th>\n",
              "      <td>tt1958076</td>\n",
              "      <td>nm3925995</td>\n",
              "      <td>Wes Sutton</td>\n",
              "      <td>nm3837412</td>\n",
              "      <td>Jonathan O'Rear</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2415</th>\n",
              "      <td>tt1223960</td>\n",
              "      <td>nm2120311</td>\n",
              "      <td>Alex Schmidt</td>\n",
              "      <td>nm0246977</td>\n",
              "      <td>Nils Düwell</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2416</th>\n",
              "      <td>tt5337428</td>\n",
              "      <td>nm7813853</td>\n",
              "      <td>Nuel Caslo</td>\n",
              "      <td>nm7367750</td>\n",
              "      <td>Roberto Arrizon</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2417 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Title_id Director_id         Director   Actor_id              Actor\n",
              "0     tt2301451   nm0426059     Rian Johnson  nm0606487        Dean Norris\n",
              "1     tt2301451   nm0426059     Rian Johnson  nm0186505     Bryan Cranston\n",
              "2     tt2301451   nm0426059     Rian Johnson  nm0666739         Aaron Paul\n",
              "3     tt1069840   nm0035106     Allan Arkush  nm0805189  Max Elliott Slade\n",
              "4     tt1576812   nm0035106     Allan Arkush  nm0805189  Max Elliott Slade\n",
              "...         ...         ...              ...        ...                ...\n",
              "2412  tt4554168   nm5981759  Victor Lockhart  nm5539980  Colin Cunninghame\n",
              "2413  tt1958076   nm3925995       Wes Sutton  nm3935246     Mike Henderson\n",
              "2414  tt1958076   nm3925995       Wes Sutton  nm3837412    Jonathan O'Rear\n",
              "2415  tt1223960   nm2120311     Alex Schmidt  nm0246977        Nils Düwell\n",
              "2416  tt5337428   nm7813853       Nuel Caslo  nm7367750    Roberto Arrizon\n",
              "\n",
              "[2417 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0A_3VcXg1T1z",
        "colab_type": "text"
      },
      "source": [
        "## 2. Director-Actor pair"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAhUZjGEx-3N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "with gzip.open('/content/drive/My Drive/Data/crew.tsv.gz') as f:\n",
        "  crew = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/ratings.tsv.gz') as f:\n",
        "  ratings = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/name.basics.tsv.gz') as f:\n",
        "  names = pd.read_csv(f,sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZ_4glMrhF7E",
        "colab_type": "code",
        "outputId": "444f9ea4-37da-4853-871a-80744f288548",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "crew.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>job</th>\n",
              "      <th>characters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>1</td>\n",
              "      <td>nm1588970</td>\n",
              "      <td>self</td>\n",
              "      <td>\\N</td>\n",
              "      <td>[\"Self\"]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0374658</td>\n",
              "      <td>cinematographer</td>\n",
              "      <td>director of photography</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0721526</td>\n",
              "      <td>director</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>2</td>\n",
              "      <td>nm1335271</td>\n",
              "      <td>composer</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      tconst  ordering  ...                      job characters\n",
              "0  tt0000001         1  ...                       \\N   [\"Self\"]\n",
              "1  tt0000001         2  ...                       \\N         \\N\n",
              "2  tt0000001         3  ...  director of photography         \\N\n",
              "3  tt0000002         1  ...                       \\N         \\N\n",
              "4  tt0000002         2  ...                       \\N         \\N\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKFBnKbtfnqJ",
        "colab_type": "text"
      },
      "source": [
        "**Each title might have multiple directors and multiple actors**\n",
        "* A rating of 10.0 is considered the highest rating. Titles with rating 10.0 are first extrated from ratings table and named top_title.\n",
        "\n",
        "* Form crew table it is found that Each title might have multiple directors and multiple actors.\n",
        "\n",
        "* Two Tables with only actors,their id and directors,their id for top_titles are  created.\n",
        "\n",
        "* actors and directors table are merged by inner join and named as pairs\n",
        "\n",
        "* paris table is merged with names table to get top_pairs table which has names which when sorted by b=number of votes gives"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTHrCbjKQS3V",
        "colab_type": "code",
        "outputId": "8bf28d95-51aa-4c57-f9c9-42c7aa37e5c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "ratings.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      tconst  averageRating  numVotes\n",
              "0  tt0000001            5.6      1600\n",
              "1  tt0000002            6.0       196\n",
              "2  tt0000003            6.5      1275\n",
              "3  tt0000004            6.1       121\n",
              "4  tt0000005            6.1      2038"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6zFft5yQc_P",
        "colab_type": "code",
        "outputId": "cb4086ba-327b-4320-ed2a-96dd1c79dc28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "names.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nconst</th>\n",
              "      <th>primaryName</th>\n",
              "      <th>birthYear</th>\n",
              "      <th>deathYear</th>\n",
              "      <th>primaryProfession</th>\n",
              "      <th>knownForTitles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>nm0000001</td>\n",
              "      <td>Fred Astaire</td>\n",
              "      <td>1899</td>\n",
              "      <td>1987</td>\n",
              "      <td>soundtrack,actor,miscellaneous</td>\n",
              "      <td>tt0072308,tt0043044,tt0050419,tt0053137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>nm0000002</td>\n",
              "      <td>Lauren Bacall</td>\n",
              "      <td>1924</td>\n",
              "      <td>2014</td>\n",
              "      <td>actress,soundtrack</td>\n",
              "      <td>tt0117057,tt0037382,tt0038355,tt0071877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>nm0000003</td>\n",
              "      <td>Brigitte Bardot</td>\n",
              "      <td>1934</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actress,soundtrack,producer</td>\n",
              "      <td>tt0059956,tt0049189,tt0054452,tt0057345,tt0054...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>nm0000004</td>\n",
              "      <td>John Belushi</td>\n",
              "      <td>1949</td>\n",
              "      <td>1982</td>\n",
              "      <td>actor,soundtrack,writer</td>\n",
              "      <td>tt0078723,tt0080455,tt0072562,tt0077975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>nm0000005</td>\n",
              "      <td>Ingmar Bergman</td>\n",
              "      <td>1918</td>\n",
              "      <td>2007</td>\n",
              "      <td>writer,director,actor</td>\n",
              "      <td>tt0050976,tt0069467,tt0083922,tt0050986</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      nconst  ...                                     knownForTitles\n",
              "0  nm0000001  ...            tt0072308,tt0043044,tt0050419,tt0053137\n",
              "1  nm0000002  ...            tt0117057,tt0037382,tt0038355,tt0071877\n",
              "2  nm0000003  ...  tt0059956,tt0049189,tt0054452,tt0057345,tt0054...\n",
              "3  nm0000004  ...            tt0078723,tt0080455,tt0072562,tt0077975\n",
              "4  nm0000005  ...            tt0050976,tt0069467,tt0083922,tt0050986\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcpfSowoQhIB",
        "colab_type": "code",
        "outputId": "e2696c61-e529-400a-ce58-4a453302314e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(crew),len(ratings),len(names)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38728849, 1024884, 10012300)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5JI_oGMQg3T",
        "colab_type": "code",
        "outputId": "7c20df98-7e49-487e-a6a9-f67e92e23aa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(crew.tconst.unique()),len(ratings.tconst.unique()),len(names.nconst.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6071698, 1024884, 10012300)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tX5_9NJ0UyLZ",
        "colab_type": "text"
      },
      "source": [
        "* There is no unique key for crew table\n",
        "* tconst is key for ratings table\n",
        "* nconst is key for names table"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vndWd8XWNTU1",
        "colab_type": "code",
        "outputId": "cb645894-d559-4917-a17a-7bdca5bca977",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# creating a dataframe of top rating titles\n",
        "top_rating = ratings[ratings['averageRating'] == 10.0].sort_values(by='numVotes',ascending=False)\n",
        "top_rating.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671254</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>867391</th>\n",
              "      <td>tt5658362</td>\n",
              "      <td>10.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460174</th>\n",
              "      <td>tt10867894</td>\n",
              "      <td>10.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454487</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568217</th>\n",
              "      <td>tt1576808</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  averageRating  numVotes\n",
              "671254   tt2301451           10.0    112607\n",
              "867391   tt5658362           10.0       199\n",
              "460174  tt10867894           10.0       175\n",
              "454487   tt1069840           10.0       162\n",
              "568217   tt1576808           10.0       161"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vI0V0tuCNhQT",
        "colab_type": "code",
        "outputId": "46a7a69e-b4a2-475d-c4bf-da2629ed2a62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(top_rating),len(top_rating.tconst.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3241, 3241)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQekXmBdSzmb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pairs = pd.merge(crew[crew.category.isin(['actor','director'])].drop(['job','characters'],axis=1),top_rating,on='tconst')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFFW3-BSSzid",
        "colab_type": "code",
        "outputId": "1c874c53-9d28-4ff3-afc4-e46278b60089",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "pairs.sort_values(by='numVotes',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2208</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>4</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2209</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>5</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2206</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2207</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4109</th>\n",
              "      <td>tt5658362</td>\n",
              "      <td>2</td>\n",
              "      <td>nm5047343</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1967</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>2</td>\n",
              "      <td>nm2311954</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1966</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>1</td>\n",
              "      <td>nm1193100</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1937</th>\n",
              "      <td>tt2047551</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0556296</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1930</th>\n",
              "      <td>tt2016188</td>\n",
              "      <td>5</td>\n",
              "      <td>nm4573433</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5482</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>5</td>\n",
              "      <td>nm3336494</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5483 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  ordering     nconst  category  averageRating  numVotes\n",
              "2208  tt2301451         4  nm0606487     actor           10.0    112607\n",
              "2209  tt2301451         5  nm0426059  director           10.0    112607\n",
              "2206  tt2301451         1  nm0186505     actor           10.0    112607\n",
              "2207  tt2301451         3  nm0666739     actor           10.0    112607\n",
              "4109  tt5658362         2  nm5047343  director           10.0       199\n",
              "...         ...       ...        ...       ...            ...       ...\n",
              "1967  tt2076145         2  nm2311954     actor           10.0         5\n",
              "1966  tt2076145         1  nm1193100     actor           10.0         5\n",
              "1937  tt2047551         1  nm0556296     actor           10.0         5\n",
              "1930  tt2016188         5  nm4573433  director           10.0         5\n",
              "5482  tt9889618         5  nm3336494  director           10.0         5\n",
              "\n",
              "[5483 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZWudKlkSzdM",
        "colab_type": "code",
        "outputId": "965f95a5-3570-4d96-f8c0-5816a4504908",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(pairs),len(pairs.tconst.unique()),len(pairs.nconst.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5483, 2424, 2965)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UuOgCoaiSzRy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "actors = pairs[pairs.category.isin(['actor'])]\n",
        "director = pairs[pairs.category.isin(['director'])]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ykf6h_gudD5G",
        "colab_type": "code",
        "outputId": "25d7dbba-b161-429c-daf0-35e68ad9b8de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "actors.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0297293</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0917076</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>8</td>\n",
              "      <td>nm2080718</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>9</td>\n",
              "      <td>nm6948643</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>tt0061857</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0182015</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      tconst  ordering     nconst category  averageRating  numVotes\n",
              "0  tt0050536         2  nm0297293    actor           10.0         6\n",
              "1  tt0050536         3  nm0917076    actor           10.0         6\n",
              "3  tt0050536         8  nm2080718    actor           10.0         6\n",
              "4  tt0050536         9  nm6948643    actor           10.0         6\n",
              "5  tt0061857         2  nm0182015    actor           10.0         6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a9B6l2mGdD0z",
        "colab_type": "code",
        "outputId": "1918176f-9fe2-4579-9aee-d0f79e5a7419",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "director.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>5</td>\n",
              "      <td>nm0928929</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>tt0061857</td>\n",
              "      <td>5</td>\n",
              "      <td>nm0351746</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>tt0143594</td>\n",
              "      <td>5</td>\n",
              "      <td>nm3229973</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>tt0165902</td>\n",
              "      <td>5</td>\n",
              "      <td>nm0334131</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>tt0176240</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0025955</td>\n",
              "      <td>director</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       tconst  ordering     nconst  category  averageRating  numVotes\n",
              "2   tt0050536         5  nm0928929  director           10.0         6\n",
              "6   tt0061857         5  nm0351746  director           10.0         6\n",
              "15  tt0143594         5  nm3229973  director           10.0         5\n",
              "17  tt0165902         5  nm0334131  director           10.0         5\n",
              "18  tt0176240         1  nm0025955  director           10.0         5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQjudI-jdDtJ",
        "colab_type": "code",
        "outputId": "972b8f07-b336-4613-fce6-fda1b93ca8a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(actors),len(director),len(actors.tconst.unique()),len(director.tconst.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4118, 1365, 2111, 1132)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_aHYp7tRc0E",
        "colab_type": "code",
        "outputId": "3ff549d2-3f9a-4352-dc33-d126ab3c2214",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df = pd.merge(actors,names,on='nconst')\n",
        "act_name = pd.DataFrame({'tconst':df.tconst,\n",
        "                        'actorId':df.nconst,\n",
        "                        'actorName':df.primaryName,\n",
        "                         'averageRating':df.averageRating,\n",
        "                         'votes':df.numVotes})\n",
        "act_name"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>actorId</th>\n",
              "      <th>actorName</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>votes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm0297293</td>\n",
              "      <td>Joachim Fuchsberger</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm0917076</td>\n",
              "      <td>Peter Weck</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm2080718</td>\n",
              "      <td>Horst Kreuter</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm6948643</td>\n",
              "      <td>Walter Stark</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0061857</td>\n",
              "      <td>nm0182015</td>\n",
              "      <td>Hans Cossy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4109</th>\n",
              "      <td>tt9448190</td>\n",
              "      <td>nm1242398</td>\n",
              "      <td>Kitodar Todorov</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4110</th>\n",
              "      <td>tt9448196</td>\n",
              "      <td>nm1242398</td>\n",
              "      <td>Kitodar Todorov</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4111</th>\n",
              "      <td>tt9461190</td>\n",
              "      <td>nm4590958</td>\n",
              "      <td>Zayn Alexander</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4112</th>\n",
              "      <td>tt9524000</td>\n",
              "      <td>nm10363929</td>\n",
              "      <td>Jason Nawrocki</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4113</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>nm10526891</td>\n",
              "      <td>Carl Dubbeling</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4114 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst     actorId            actorName  averageRating  votes\n",
              "0     tt0050536   nm0297293  Joachim Fuchsberger           10.0      6\n",
              "1     tt0050536   nm0917076           Peter Weck           10.0      6\n",
              "2     tt0050536   nm2080718        Horst Kreuter           10.0      6\n",
              "3     tt0050536   nm6948643         Walter Stark           10.0      6\n",
              "4     tt0061857   nm0182015           Hans Cossy           10.0      6\n",
              "...         ...         ...                  ...            ...    ...\n",
              "4109  tt9448190   nm1242398      Kitodar Todorov           10.0     11\n",
              "4110  tt9448196   nm1242398      Kitodar Todorov           10.0      9\n",
              "4111  tt9461190   nm4590958       Zayn Alexander           10.0      5\n",
              "4112  tt9524000  nm10363929       Jason Nawrocki           10.0      7\n",
              "4113  tt9889618  nm10526891       Carl Dubbeling           10.0      5\n",
              "\n",
              "[4114 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XcewTm49Xqpp",
        "colab_type": "code",
        "outputId": "e647df8f-be81-4cff-a8a4-b234aedf5d22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df = pd.merge(director,names,on='nconst')\n",
        "dic_name = pd.DataFrame({'tconst':df.tconst,\n",
        "                        'directorId':df.nconst,\n",
        "                        'directorName':df.primaryName})\n",
        "dic_name"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>directorId</th>\n",
              "      <th>directorName</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm0928929</td>\n",
              "      <td>Kurt Wilhelm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0061857</td>\n",
              "      <td>nm0351746</td>\n",
              "      <td>Wilm ten Haaf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0812403</td>\n",
              "      <td>nm0351746</td>\n",
              "      <td>Wilm ten Haaf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0143594</td>\n",
              "      <td>nm3229973</td>\n",
              "      <td>Charles Goodwin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0165902</td>\n",
              "      <td>nm0334131</td>\n",
              "      <td>John Graham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1360</th>\n",
              "      <td>tt9165892</td>\n",
              "      <td>nm5389223</td>\n",
              "      <td>Pavel Vazquez</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1361</th>\n",
              "      <td>tt9179308</td>\n",
              "      <td>nm0809332</td>\n",
              "      <td>Micah Smith</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1362</th>\n",
              "      <td>tt9206890</td>\n",
              "      <td>nm9437750</td>\n",
              "      <td>Bernardo Covarrubias Villarreal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1363</th>\n",
              "      <td>tt9257804</td>\n",
              "      <td>nm9614025</td>\n",
              "      <td>Onur Erdurak</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1364</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>nm3336494</td>\n",
              "      <td>Aaron M. Lane</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1365 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst directorId                     directorName\n",
              "0     tt0050536  nm0928929                     Kurt Wilhelm\n",
              "1     tt0061857  nm0351746                    Wilm ten Haaf\n",
              "2     tt0812403  nm0351746                    Wilm ten Haaf\n",
              "3     tt0143594  nm3229973                  Charles Goodwin\n",
              "4     tt0165902  nm0334131                      John Graham\n",
              "...         ...        ...                              ...\n",
              "1360  tt9165892  nm5389223                    Pavel Vazquez\n",
              "1361  tt9179308  nm0809332                      Micah Smith\n",
              "1362  tt9206890  nm9437750  Bernardo Covarrubias Villarreal\n",
              "1363  tt9257804  nm9614025                     Onur Erdurak\n",
              "1364  tt9889618  nm3336494                    Aaron M. Lane\n",
              "\n",
              "[1365 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o1-9aSX9Xqj7",
        "colab_type": "code",
        "outputId": "e8d84c20-98ca-40a8-ac68-b70a0789c8c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "top_pair = pd.merge(dic_name,act_name,on='tconst')\n",
        "top_pair"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>directorId</th>\n",
              "      <th>directorName</th>\n",
              "      <th>actorId</th>\n",
              "      <th>actorName</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>votes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm0928929</td>\n",
              "      <td>Kurt Wilhelm</td>\n",
              "      <td>nm0297293</td>\n",
              "      <td>Joachim Fuchsberger</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm0928929</td>\n",
              "      <td>Kurt Wilhelm</td>\n",
              "      <td>nm0917076</td>\n",
              "      <td>Peter Weck</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm0928929</td>\n",
              "      <td>Kurt Wilhelm</td>\n",
              "      <td>nm2080718</td>\n",
              "      <td>Horst Kreuter</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>nm0928929</td>\n",
              "      <td>Kurt Wilhelm</td>\n",
              "      <td>nm6948643</td>\n",
              "      <td>Walter Stark</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0061857</td>\n",
              "      <td>nm0351746</td>\n",
              "      <td>Wilm ten Haaf</td>\n",
              "      <td>nm0182015</td>\n",
              "      <td>Hans Cossy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2412</th>\n",
              "      <td>tt9206890</td>\n",
              "      <td>nm9437750</td>\n",
              "      <td>Bernardo Covarrubias Villarreal</td>\n",
              "      <td>nm9827202</td>\n",
              "      <td>Sebastian Ortiz Wilkins</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2413</th>\n",
              "      <td>tt9257804</td>\n",
              "      <td>nm9614025</td>\n",
              "      <td>Onur Erdurak</td>\n",
              "      <td>nm10249432</td>\n",
              "      <td>Vid Cerjak</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2414</th>\n",
              "      <td>tt9257804</td>\n",
              "      <td>nm9614025</td>\n",
              "      <td>Onur Erdurak</td>\n",
              "      <td>nm10249435</td>\n",
              "      <td>Bedri Erdurak</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2415</th>\n",
              "      <td>tt9257804</td>\n",
              "      <td>nm9614025</td>\n",
              "      <td>Onur Erdurak</td>\n",
              "      <td>nm1727208</td>\n",
              "      <td>Kemal Erdurak</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2416</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>nm3336494</td>\n",
              "      <td>Aaron M. Lane</td>\n",
              "      <td>nm10526891</td>\n",
              "      <td>Carl Dubbeling</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2417 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst directorId  ... averageRating votes\n",
              "0     tt0050536  nm0928929  ...          10.0     6\n",
              "1     tt0050536  nm0928929  ...          10.0     6\n",
              "2     tt0050536  nm0928929  ...          10.0     6\n",
              "3     tt0050536  nm0928929  ...          10.0     6\n",
              "4     tt0061857  nm0351746  ...          10.0     6\n",
              "...         ...        ...  ...           ...   ...\n",
              "2412  tt9206890  nm9437750  ...          10.0     7\n",
              "2413  tt9257804  nm9614025  ...          10.0     5\n",
              "2414  tt9257804  nm9614025  ...          10.0     5\n",
              "2415  tt9257804  nm9614025  ...          10.0     5\n",
              "2416  tt9889618  nm3336494  ...          10.0     5\n",
              "\n",
              "[2417 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q27slZVvqIwN",
        "colab_type": "code",
        "outputId": "7bbb42f8-53d6-4469-d618-9f5084f816e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "top_pair.sort_values(by='votes',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>directorId</th>\n",
              "      <th>directorName</th>\n",
              "      <th>actorId</th>\n",
              "      <th>actorName</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>votes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1033</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>Aaron Paul</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1034</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>Dean Norris</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1032</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>nm0426059</td>\n",
              "      <td>Rian Johnson</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>408</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>Allan Arkush</td>\n",
              "      <td>nm0492226</td>\n",
              "      <td>Zachary La Voy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>nm0035106</td>\n",
              "      <td>Allan Arkush</td>\n",
              "      <td>nm0000893</td>\n",
              "      <td>Ed Begley Jr.</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1062</th>\n",
              "      <td>tt2399912</td>\n",
              "      <td>nm4698955</td>\n",
              "      <td>Tim Ellrich</td>\n",
              "      <td>nm5276592</td>\n",
              "      <td>Jozo Rados</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1061</th>\n",
              "      <td>tt2399912</td>\n",
              "      <td>nm4698955</td>\n",
              "      <td>Tim Ellrich</td>\n",
              "      <td>nm5277044</td>\n",
              "      <td>Lindor Isenaj</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1060</th>\n",
              "      <td>tt2399912</td>\n",
              "      <td>nm4698955</td>\n",
              "      <td>Tim Ellrich</td>\n",
              "      <td>nm7937198</td>\n",
              "      <td>Thomas Erlmoser</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1059</th>\n",
              "      <td>tt2395754</td>\n",
              "      <td>nm5315067</td>\n",
              "      <td>Frances Hughes</td>\n",
              "      <td>nm5501565</td>\n",
              "      <td>Presley Cole Carlson</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2416</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>nm3336494</td>\n",
              "      <td>Aaron M. Lane</td>\n",
              "      <td>nm10526891</td>\n",
              "      <td>Carl Dubbeling</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2417 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst directorId  ... averageRating   votes\n",
              "1033  tt2301451  nm0426059  ...          10.0  112607\n",
              "1034  tt2301451  nm0426059  ...          10.0  112607\n",
              "1032  tt2301451  nm0426059  ...          10.0  112607\n",
              "408   tt1069840  nm0035106  ...          10.0     162\n",
              "406   tt1069840  nm0035106  ...          10.0     162\n",
              "...         ...        ...  ...           ...     ...\n",
              "1062  tt2399912  nm4698955  ...          10.0       5\n",
              "1061  tt2399912  nm4698955  ...          10.0       5\n",
              "1060  tt2399912  nm4698955  ...          10.0       5\n",
              "1059  tt2395754  nm5315067  ...          10.0       5\n",
              "2416  tt9889618  nm3336494  ...          10.0       5\n",
              "\n",
              "[2417 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WlR38vfeQVHI",
        "colab_type": "code",
        "outputId": "427187ae-8b1b-48f9-e6aa-0f3bee5e62c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "with gzip.open('/content/drive/My Drive/Data/title.tsv.gz') as f:\n",
        "  title = pd.read_csv(f,sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ScLZ9coQUwf",
        "colab_type": "code",
        "outputId": "3d4397f7-6965-45ff-c1a6-8a0b525449d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "title[title.titleId == 'tt2301451']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>titleId</th>\n",
              "      <th>ordering</th>\n",
              "      <th>title</th>\n",
              "      <th>region</th>\n",
              "      <th>language</th>\n",
              "      <th>types</th>\n",
              "      <th>attributes</th>\n",
              "      <th>isOriginalTitle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10471945</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>1</td>\n",
              "      <td>Ozymandias</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>original</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10471946</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>2</td>\n",
              "      <td>Ozymandias</td>\n",
              "      <td>US</td>\n",
              "      <td>\\N</td>\n",
              "      <td>imdbDisplay</td>\n",
              "      <td>\\N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            titleId  ordering  ... attributes isOriginalTitle\n",
              "10471945  tt2301451         1  ...         \\N               1\n",
              "10471946  tt2301451         2  ...         \\N               0\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "axyHsavEatbJ",
        "colab_type": "text"
      },
      "source": [
        "## Age of successful male lead roles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MN2hdgZQUex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "with gzip.open('/content/drive/My Drive/Data/crew.tsv.gz') as f:\n",
        "  crew = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/ratings.tsv.gz') as f:\n",
        "  ratings = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/name.basics.tsv.gz') as f:\n",
        "  names = pd.read_csv(f,sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAw04OkNypxQ",
        "colab_type": "code",
        "outputId": "9170e9bf-ff6e-4d8a-c779-32c69f3d98e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# creating a dataframe of top rating titles\n",
        "top_rating = ratings[ratings['averageRating'] == 10.0].sort_values(by='numVotes',ascending=False)\n",
        "top_rating.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671254</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>867391</th>\n",
              "      <td>tt5658362</td>\n",
              "      <td>10.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460174</th>\n",
              "      <td>tt10867894</td>\n",
              "      <td>10.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454487</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568217</th>\n",
              "      <td>tt1576808</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  averageRating  numVotes\n",
              "671254   tt2301451           10.0    112607\n",
              "867391   tt5658362           10.0       199\n",
              "460174  tt10867894           10.0       175\n",
              "454487   tt1069840           10.0       162\n",
              "568217   tt1576808           10.0       161"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OlsOkcJL8Au8",
        "colab_type": "code",
        "outputId": "2a7a4bec-ac33-43a0-c6b7-de5f360ca35b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "top_rating"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671254</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>867391</th>\n",
              "      <td>tt5658362</td>\n",
              "      <td>10.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460174</th>\n",
              "      <td>tt10867894</td>\n",
              "      <td>10.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454487</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568217</th>\n",
              "      <td>tt1576808</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>818979</th>\n",
              "      <td>tt4720314</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>818978</th>\n",
              "      <td>tt4720312</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>818977</th>\n",
              "      <td>tt4720310</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>818975</th>\n",
              "      <td>tt4720302</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024238</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3241 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             tconst  averageRating  numVotes\n",
              "671254    tt2301451           10.0    112607\n",
              "867391    tt5658362           10.0       199\n",
              "460174   tt10867894           10.0       175\n",
              "454487    tt1069840           10.0       162\n",
              "568217    tt1576808           10.0       161\n",
              "...             ...            ...       ...\n",
              "818979    tt4720314           10.0         5\n",
              "818978    tt4720312           10.0         5\n",
              "818977    tt4720310           10.0         5\n",
              "818975    tt4720302           10.0         5\n",
              "1024238   tt9889618           10.0         5\n",
              "\n",
              "[3241 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKRDBxbo8Mv9",
        "colab_type": "code",
        "outputId": "ea4845d3-8689-4c10-8a4a-fc731c6fb877",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(ratings.loc[ratings.averageRating == 10.0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3241"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UX3Li0pyrSn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_actor = pd.merge(crew[crew.category.isin(['actor'])].drop(['job','characters'],axis=1),top_rating,on='tconst')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FAMDYBIcaMa",
        "colab_type": "code",
        "outputId": "fd214f46-7467-4d72-df09-bbf087bacc8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "top_actor"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0297293</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0917076</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>8</td>\n",
              "      <td>nm2080718</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>9</td>\n",
              "      <td>nm6948643</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0061857</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0182015</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4113</th>\n",
              "      <td>tt9448190</td>\n",
              "      <td>1</td>\n",
              "      <td>nm1242398</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4114</th>\n",
              "      <td>tt9448196</td>\n",
              "      <td>1</td>\n",
              "      <td>nm1242398</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4115</th>\n",
              "      <td>tt9461190</td>\n",
              "      <td>1</td>\n",
              "      <td>nm4590958</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4116</th>\n",
              "      <td>tt9524000</td>\n",
              "      <td>4</td>\n",
              "      <td>nm10363929</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4117</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>2</td>\n",
              "      <td>nm10526891</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4118 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  ordering      nconst category  averageRating  numVotes\n",
              "0     tt0050536         2   nm0297293    actor           10.0         6\n",
              "1     tt0050536         3   nm0917076    actor           10.0         6\n",
              "2     tt0050536         8   nm2080718    actor           10.0         6\n",
              "3     tt0050536         9   nm6948643    actor           10.0         6\n",
              "4     tt0061857         2   nm0182015    actor           10.0         6\n",
              "...         ...       ...         ...      ...            ...       ...\n",
              "4113  tt9448190         1   nm1242398    actor           10.0        11\n",
              "4114  tt9448196         1   nm1242398    actor           10.0         9\n",
              "4115  tt9461190         1   nm4590958    actor           10.0         5\n",
              "4116  tt9524000         4  nm10363929    actor           10.0         7\n",
              "4117  tt9889618         2  nm10526891    actor           10.0         5\n",
              "\n",
              "[4118 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCJysJGu8560",
        "colab_type": "code",
        "outputId": "e9c8f5bb-8fc5-4370-c53b-037128b6fd13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(top_actor),len(top_actor.nconst),len(top_actor.tconst.unique()),len(top_actor.nconst.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4118, 4118, 2111, 2166)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "baEjVUBO9TfJ",
        "colab_type": "code",
        "outputId": "59f5839a-125f-4090-ea6c-b0e7ccc42509",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "top_actor.sort_values(by='nconst')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1298</th>\n",
              "      <td>tt1576810</td>\n",
              "      <td>10</td>\n",
              "      <td>nm0000274</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1308</th>\n",
              "      <td>tt1576814</td>\n",
              "      <td>10</td>\n",
              "      <td>nm0000274</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458</th>\n",
              "      <td>tt1994734</td>\n",
              "      <td>8</td>\n",
              "      <td>nm0000317</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>tt0659278</td>\n",
              "      <td>10</td>\n",
              "      <td>nm0000489</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>tt0726124</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0000792</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4043</th>\n",
              "      <td>tt8594682</td>\n",
              "      <td>4</td>\n",
              "      <td>nm9924252</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4044</th>\n",
              "      <td>tt8594682</td>\n",
              "      <td>5</td>\n",
              "      <td>nm9924253</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>tt0834174</td>\n",
              "      <td>3</td>\n",
              "      <td>nm9949524</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>448</th>\n",
              "      <td>tt10529798</td>\n",
              "      <td>1</td>\n",
              "      <td>nm9955575</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4047</th>\n",
              "      <td>tt8726206</td>\n",
              "      <td>4</td>\n",
              "      <td>nm9985886</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4118 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          tconst  ordering     nconst category  averageRating  numVotes\n",
              "1298   tt1576810        10  nm0000274    actor           10.0       161\n",
              "1308   tt1576814        10  nm0000274    actor           10.0       161\n",
              "1458   tt1994734         8  nm0000317    actor           10.0         5\n",
              "132    tt0659278        10  nm0000489    actor           10.0         6\n",
              "148    tt0726124         1  nm0000792    actor           10.0         5\n",
              "...          ...       ...        ...      ...            ...       ...\n",
              "4043   tt8594682         4  nm9924252    actor           10.0         6\n",
              "4044   tt8594682         5  nm9924253    actor           10.0         6\n",
              "195    tt0834174         3  nm9949524    actor           10.0         5\n",
              "448   tt10529798         1  nm9955575    actor           10.0        10\n",
              "4047   tt8726206         4  nm9985886    actor           10.0         5\n",
              "\n",
              "[4118 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OiXENiN3-V-D",
        "colab_type": "code",
        "outputId": "2d316a04-bd0e-4074-e54d-b72ddc86cd44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "# titles and actors are not unique, make nconst column unique\n",
        "top_actor = top_actor.drop_duplicates(subset='nconst')\n",
        "top_actor"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0297293</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0917076</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>8</td>\n",
              "      <td>nm2080718</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0050536</td>\n",
              "      <td>9</td>\n",
              "      <td>nm6948643</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0061857</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0182015</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4110</th>\n",
              "      <td>tt9257804</td>\n",
              "      <td>3</td>\n",
              "      <td>nm1727208</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4111</th>\n",
              "      <td>tt9448172</td>\n",
              "      <td>1</td>\n",
              "      <td>nm1242398</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4115</th>\n",
              "      <td>tt9461190</td>\n",
              "      <td>1</td>\n",
              "      <td>nm4590958</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4116</th>\n",
              "      <td>tt9524000</td>\n",
              "      <td>4</td>\n",
              "      <td>nm10363929</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4117</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>2</td>\n",
              "      <td>nm10526891</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2166 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  ordering      nconst category  averageRating  numVotes\n",
              "0     tt0050536         2   nm0297293    actor           10.0         6\n",
              "1     tt0050536         3   nm0917076    actor           10.0         6\n",
              "2     tt0050536         8   nm2080718    actor           10.0         6\n",
              "3     tt0050536         9   nm6948643    actor           10.0         6\n",
              "4     tt0061857         2   nm0182015    actor           10.0         6\n",
              "...         ...       ...         ...      ...            ...       ...\n",
              "4110  tt9257804         3   nm1727208    actor           10.0         5\n",
              "4111  tt9448172         1   nm1242398    actor           10.0        11\n",
              "4115  tt9461190         1   nm4590958    actor           10.0         5\n",
              "4116  tt9524000         4  nm10363929    actor           10.0         7\n",
              "4117  tt9889618         2  nm10526891    actor           10.0         5\n",
              "\n",
              "[2166 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeQNBdzqyrOA",
        "colab_type": "code",
        "outputId": "6eb5e7c4-8a07-4f46-a5a1-463620013ec9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "#merging with names table which has birth and death year\n",
        "df = pd.merge(top_actor,names,on='nconst').sort_values(by='numVotes',ascending=False)\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>primaryName</th>\n",
              "      <th>birthYear</th>\n",
              "      <th>deathYear</th>\n",
              "      <th>primaryProfession</th>\n",
              "      <th>knownForTitles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Aaron Paul</td>\n",
              "      <td>1979</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,soundtrack</td>\n",
              "      <td>tt0903747,tt0844708,tt2369135,tt9243946,tt0844...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>4</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Dean Norris</td>\n",
              "      <td>1963</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,soundtrack,producer</td>\n",
              "      <td>tt1567609,tt1553656,tt0100802,tt0903747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1025</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "      <td>1956</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0903747,tt1024648,tt3203606,tt0212671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "      <td>1980</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0109015,tt0103596,tt0098067,tt0112384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0000893</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Ed Begley Jr.</td>\n",
              "      <td>1949</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0310281,tt0910936,tt1178663,tt1289401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>956</th>\n",
              "      <td>tt2148877</td>\n",
              "      <td>1</td>\n",
              "      <td>nm4734262</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>B.K-McKee</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,set_decorator,miscellaneous</td>\n",
              "      <td>tt2258130,tt2449810,tt2518370,tt2413816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>955</th>\n",
              "      <td>tt2144164</td>\n",
              "      <td>3</td>\n",
              "      <td>nm5032893</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Ben McMath</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt2384975,tt2384975,tt2144164,tt2144164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>954</th>\n",
              "      <td>tt2144164</td>\n",
              "      <td>2</td>\n",
              "      <td>nm2956456</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>David Haydn</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt1913178,tt5338744,tt2048905,tt2048905,tt5338...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>946</th>\n",
              "      <td>tt2113096</td>\n",
              "      <td>6</td>\n",
              "      <td>nm1459548</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tom Moore</td>\n",
              "      <td>1972</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,director,writer</td>\n",
              "      <td>tt1706508,tt9181908,tt9181908,tt3357640,tt1081...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2161</th>\n",
              "      <td>tt9889618</td>\n",
              "      <td>2</td>\n",
              "      <td>nm10526891</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Carl Dubbeling</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2162 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  ...                                     knownForTitles\n",
              "1026  tt2301451  ...  tt0903747,tt0844708,tt2369135,tt9243946,tt0844...\n",
              "1027  tt2301451  ...            tt1567609,tt1553656,tt0100802,tt0903747\n",
              "1025  tt2301451  ...            tt0903747,tt1024648,tt3203606,tt0212671\n",
              "419   tt1069840  ...            tt0109015,tt0103596,tt0098067,tt0112384\n",
              "418   tt1069840  ...            tt0310281,tt0910936,tt1178663,tt1289401\n",
              "...         ...  ...                                                ...\n",
              "956   tt2148877  ...            tt2258130,tt2449810,tt2518370,tt2413816\n",
              "955   tt2144164  ...            tt2384975,tt2384975,tt2144164,tt2144164\n",
              "954   tt2144164  ...  tt1913178,tt5338744,tt2048905,tt2048905,tt5338...\n",
              "946   tt2113096  ...  tt1706508,tt9181908,tt9181908,tt3357640,tt1081...\n",
              "2161  tt9889618  ...                                                 \\N\n",
              "\n",
              "[2162 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sx6pCbbDh0nN",
        "colab_type": "code",
        "outputId": "da9e3d8d-4f46-4c97-edeb-74bf108e28e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(df.loc[df.deathYear != '\\\\N'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "125"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-8JmWoryrJc",
        "colab_type": "code",
        "outputId": "9f9c72c0-62bf-41da-8b5a-8759194dd457",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2162 entries, 1026 to 2161\n",
            "Data columns (total 11 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   tconst             2162 non-null   object \n",
            " 1   ordering           2162 non-null   int64  \n",
            " 2   nconst             2162 non-null   object \n",
            " 3   category           2162 non-null   object \n",
            " 4   averageRating      2162 non-null   float64\n",
            " 5   numVotes           2162 non-null   int64  \n",
            " 6   primaryName        2162 non-null   object \n",
            " 7   birthYear          2162 non-null   object \n",
            " 8   deathYear          2162 non-null   object \n",
            " 9   primaryProfession  2110 non-null   object \n",
            " 10  knownForTitles     2162 non-null   object \n",
            "dtypes: float64(1), int64(2), object(8)\n",
            "memory usage: 202.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GjfRLpDyrFS",
        "colab_type": "code",
        "outputId": "d91b5fde-b490-42c9-9a44-37d01d663ff8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "source": [
        "df = df[df.birthYear != '\\\\N']\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>primaryName</th>\n",
              "      <th>birthYear</th>\n",
              "      <th>deathYear</th>\n",
              "      <th>primaryProfession</th>\n",
              "      <th>knownForTitles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Aaron Paul</td>\n",
              "      <td>1979</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,soundtrack</td>\n",
              "      <td>tt0903747,tt0844708,tt2369135,tt9243946,tt0844...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>4</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Dean Norris</td>\n",
              "      <td>1963</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,soundtrack,producer</td>\n",
              "      <td>tt1567609,tt1553656,tt0100802,tt0903747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1025</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "      <td>1956</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0903747,tt1024648,tt3203606,tt0212671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "      <td>1980</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0109015,tt0103596,tt0098067,tt0112384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0000893</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Ed Begley Jr.</td>\n",
              "      <td>1949</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0310281,tt0910936,tt1178663,tt1289401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>932</th>\n",
              "      <td>tt2087670</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0854584</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Petar Temelkovski</td>\n",
              "      <td>1946</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0179196,tt0110423,tt0083306,tt0179196,tt0110...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>4</td>\n",
              "      <td>nm2792891</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>David Yuzuk</td>\n",
              "      <td>1968</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,writer</td>\n",
              "      <td>tt1552112,tt6547170,tt7291412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>2</td>\n",
              "      <td>nm2311954</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Robert Gordon Spencer</td>\n",
              "      <td>1969</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,writer</td>\n",
              "      <td>tt1653083,tt6397524,tt1189041,tt6397818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>962</th>\n",
              "      <td>tt2156462</td>\n",
              "      <td>2</td>\n",
              "      <td>nm1066970</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Rico E. Anderson</td>\n",
              "      <td>1968</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,producer,miscellaneous</td>\n",
              "      <td>tt6111130,tt7587890,tt5691552,tt7821582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>946</th>\n",
              "      <td>tt2113096</td>\n",
              "      <td>6</td>\n",
              "      <td>nm1459548</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tom Moore</td>\n",
              "      <td>1972</td>\n",
              "      <td>\\N</td>\n",
              "      <td>actor,director,writer</td>\n",
              "      <td>tt1706508,tt9181908,tt9181908,tt3357640,tt1081...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  ...                                     knownForTitles\n",
              "1026  tt2301451  ...  tt0903747,tt0844708,tt2369135,tt9243946,tt0844...\n",
              "1027  tt2301451  ...            tt1567609,tt1553656,tt0100802,tt0903747\n",
              "1025  tt2301451  ...            tt0903747,tt1024648,tt3203606,tt0212671\n",
              "419   tt1069840  ...            tt0109015,tt0103596,tt0098067,tt0112384\n",
              "418   tt1069840  ...            tt0310281,tt0910936,tt1178663,tt1289401\n",
              "...         ...  ...                                                ...\n",
              "932   tt2087670  ...  tt0179196,tt0110423,tt0083306,tt0179196,tt0110...\n",
              "929   tt2076145  ...                      tt1552112,tt6547170,tt7291412\n",
              "928   tt2076145  ...            tt1653083,tt6397524,tt1189041,tt6397818\n",
              "962   tt2156462  ...            tt6111130,tt7587890,tt5691552,tt7821582\n",
              "946   tt2113096  ...  tt1706508,tt9181908,tt9181908,tt3357640,tt1081...\n",
              "\n",
              "[530 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-GvoXM3lICD",
        "colab_type": "code",
        "outputId": "36bd1167-f9d8-4da9-be68-7ed4dd995feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "# setting death year to 2020 \n",
        "df.loc[df.deathYear == '\\\\N','deathYear'] = 2020"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:966: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self.obj[item] = s\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7E11uO5GX5R",
        "colab_type": "code",
        "outputId": "c9e4146e-2a8a-40d9-f8a4-85902a30b3d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>actor</td>\n",
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              "      <td>Aaron Paul</td>\n",
              "      <td>1979</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,soundtrack</td>\n",
              "      <td>tt0903747,tt0844708,tt2369135,tt9243946,tt0844...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>4</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Dean Norris</td>\n",
              "      <td>1963</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,soundtrack,producer</td>\n",
              "      <td>tt1567609,tt1553656,tt0100802,tt0903747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1025</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "      <td>1956</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0903747,tt1024648,tt3203606,tt0212671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "      <td>1980</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0109015,tt0103596,tt0098067,tt0112384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0000893</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Ed Begley Jr.</td>\n",
              "      <td>1949</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0310281,tt0910936,tt1178663,tt1289401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>932</th>\n",
              "      <td>tt2087670</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0854584</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Petar Temelkovski</td>\n",
              "      <td>1946</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0179196,tt0110423,tt0083306,tt0179196,tt0110...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>4</td>\n",
              "      <td>nm2792891</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>David Yuzuk</td>\n",
              "      <td>1968</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,writer</td>\n",
              "      <td>tt1552112,tt6547170,tt7291412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>2</td>\n",
              "      <td>nm2311954</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Robert Gordon Spencer</td>\n",
              "      <td>1969</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,writer</td>\n",
              "      <td>tt1653083,tt6397524,tt1189041,tt6397818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>962</th>\n",
              "      <td>tt2156462</td>\n",
              "      <td>2</td>\n",
              "      <td>nm1066970</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Rico E. Anderson</td>\n",
              "      <td>1968</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,miscellaneous</td>\n",
              "      <td>tt6111130,tt7587890,tt5691552,tt7821582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>946</th>\n",
              "      <td>tt2113096</td>\n",
              "      <td>6</td>\n",
              "      <td>nm1459548</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tom Moore</td>\n",
              "      <td>1972</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,director,writer</td>\n",
              "      <td>tt1706508,tt9181908,tt9181908,tt3357640,tt1081...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  ...                                     knownForTitles\n",
              "1026  tt2301451  ...  tt0903747,tt0844708,tt2369135,tt9243946,tt0844...\n",
              "1027  tt2301451  ...            tt1567609,tt1553656,tt0100802,tt0903747\n",
              "1025  tt2301451  ...            tt0903747,tt1024648,tt3203606,tt0212671\n",
              "419   tt1069840  ...            tt0109015,tt0103596,tt0098067,tt0112384\n",
              "418   tt1069840  ...            tt0310281,tt0910936,tt1178663,tt1289401\n",
              "...         ...  ...                                                ...\n",
              "932   tt2087670  ...  tt0179196,tt0110423,tt0083306,tt0179196,tt0110...\n",
              "929   tt2076145  ...                      tt1552112,tt6547170,tt7291412\n",
              "928   tt2076145  ...            tt1653083,tt6397524,tt1189041,tt6397818\n",
              "962   tt2156462  ...            tt6111130,tt7587890,tt5691552,tt7821582\n",
              "946   tt2113096  ...  tt1706508,tt9181908,tt9181908,tt3357640,tt1081...\n",
              "\n",
              "[530 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzwKirMFlH9S",
        "colab_type": "code",
        "outputId": "1a421727-7bbc-4640-c2eb-baeb88b892ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(df.loc[df.deathYear == '2020']),len(df.loc[df.deathYear == '\\\\N']),len(df.loc[df.deathYear != '\\\\N'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(408, 0, 530)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4dSY-EulH4C",
        "colab_type": "code",
        "outputId": "7f426c38-1e0c-43f9-c207-07628c8a39a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>primaryName</th>\n",
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              "      <th>deathYear</th>\n",
              "      <th>primaryProfession</th>\n",
              "      <th>knownForTitles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Aaron Paul</td>\n",
              "      <td>1979</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,soundtrack</td>\n",
              "      <td>tt0903747,tt0844708,tt2369135,tt9243946,tt0844...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>4</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Dean Norris</td>\n",
              "      <td>1963</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,soundtrack,producer</td>\n",
              "      <td>tt1567609,tt1553656,tt0100802,tt0903747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1025</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "      <td>1956</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0903747,tt1024648,tt3203606,tt0212671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "      <td>1980</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0109015,tt0103596,tt0098067,tt0112384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0000893</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Ed Begley Jr.</td>\n",
              "      <td>1949</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0310281,tt0910936,tt1178663,tt1289401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>932</th>\n",
              "      <td>tt2087670</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0854584</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Petar Temelkovski</td>\n",
              "      <td>1946</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0179196,tt0110423,tt0083306,tt0179196,tt0110...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>929</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>4</td>\n",
              "      <td>nm2792891</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>David Yuzuk</td>\n",
              "      <td>1968</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,writer</td>\n",
              "      <td>tt1552112,tt6547170,tt7291412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>928</th>\n",
              "      <td>tt2076145</td>\n",
              "      <td>2</td>\n",
              "      <td>nm2311954</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Robert Gordon Spencer</td>\n",
              "      <td>1969</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,writer</td>\n",
              "      <td>tt1653083,tt6397524,tt1189041,tt6397818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>962</th>\n",
              "      <td>tt2156462</td>\n",
              "      <td>2</td>\n",
              "      <td>nm1066970</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Rico E. Anderson</td>\n",
              "      <td>1968</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,miscellaneous</td>\n",
              "      <td>tt6111130,tt7587890,tt5691552,tt7821582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>946</th>\n",
              "      <td>tt2113096</td>\n",
              "      <td>6</td>\n",
              "      <td>nm1459548</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tom Moore</td>\n",
              "      <td>1972</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,director,writer</td>\n",
              "      <td>tt1706508,tt9181908,tt9181908,tt3357640,tt1081...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>530 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  ...                                     knownForTitles\n",
              "1026  tt2301451  ...  tt0903747,tt0844708,tt2369135,tt9243946,tt0844...\n",
              "1027  tt2301451  ...            tt1567609,tt1553656,tt0100802,tt0903747\n",
              "1025  tt2301451  ...            tt0903747,tt1024648,tt3203606,tt0212671\n",
              "419   tt1069840  ...            tt0109015,tt0103596,tt0098067,tt0112384\n",
              "418   tt1069840  ...            tt0310281,tt0910936,tt1178663,tt1289401\n",
              "...         ...  ...                                                ...\n",
              "932   tt2087670  ...  tt0179196,tt0110423,tt0083306,tt0179196,tt0110...\n",
              "929   tt2076145  ...                      tt1552112,tt6547170,tt7291412\n",
              "928   tt2076145  ...            tt1653083,tt6397524,tt1189041,tt6397818\n",
              "962   tt2156462  ...            tt6111130,tt7587890,tt5691552,tt7821582\n",
              "946   tt2113096  ...  tt1706508,tt9181908,tt9181908,tt3357640,tt1081...\n",
              "\n",
              "[530 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vE112fs9lHvS",
        "colab_type": "code",
        "outputId": "b054edad-f1ca-4e06-bf18-d271301d7423",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        }
      },
      "source": [
        "df.birthYear = df.birthYear.astype(int)\n",
        "df.deathYear = df.deathYear.astype(int)\n",
        "df[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:5303: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>ordering</th>\n",
              "      <th>nconst</th>\n",
              "      <th>category</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>primaryName</th>\n",
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              "      <th>deathYear</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1026</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0666739</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Aaron Paul</td>\n",
              "      <td>1979</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,soundtrack</td>\n",
              "      <td>tt0903747,tt0844708,tt2369135,tt9243946,tt0844...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1027</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>4</td>\n",
              "      <td>nm0606487</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Dean Norris</td>\n",
              "      <td>1963</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,soundtrack,producer</td>\n",
              "      <td>tt1567609,tt1553656,tt0100802,tt0903747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1025</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0186505</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "      <td>1956</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0903747,tt1024648,tt3203606,tt0212671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>3</td>\n",
              "      <td>nm0805189</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "      <td>1980</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor</td>\n",
              "      <td>tt0109015,tt0103596,tt0098067,tt0112384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>1</td>\n",
              "      <td>nm0000893</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Ed Begley Jr.</td>\n",
              "      <td>1949</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,director</td>\n",
              "      <td>tt0310281,tt0910936,tt1178663,tt1289401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>8</td>\n",
              "      <td>nm0492226</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "      <td>Zachary La Voy</td>\n",
              "      <td>1986</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,writer</td>\n",
              "      <td>tt3587736,tt0098887,tt2262221,tt0098067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>925</th>\n",
              "      <td>tt2061693</td>\n",
              "      <td>4</td>\n",
              "      <td>nm0233079</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "      <td>Ryan Donowho</td>\n",
              "      <td>1980</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,soundtrack</td>\n",
              "      <td>tt0412019,tt1457764,tt0475268,tt0373024,tt1457...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>791</th>\n",
              "      <td>tt1576810</td>\n",
              "      <td>10</td>\n",
              "      <td>nm0000274</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "      <td>David Arquette</td>\n",
              "      <td>1971</td>\n",
              "      <td>2020</td>\n",
              "      <td>producer,actor,director</td>\n",
              "      <td>tt0151738,tt0120082,tt0271367,tt0134084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>tt10471404</td>\n",
              "      <td>2</td>\n",
              "      <td>nm0005000</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>87</td>\n",
              "      <td>Tony Hawk</td>\n",
              "      <td>1968</td>\n",
              "      <td>2020</td>\n",
              "      <td>actor,producer,stunts</td>\n",
              "      <td>tt0295701,tt0355702,tt1528854,tt1528854,tt0355...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>tt11281576</td>\n",
              "      <td>1</td>\n",
              "      <td>nm11128343</td>\n",
              "      <td>actor</td>\n",
              "      <td>10.0</td>\n",
              "      <td>66</td>\n",
              "      <td>Hazar Deniz BG</td>\n",
              "      <td>1998</td>\n",
              "      <td>2020</td>\n",
              "      <td>director,editor,writer</td>\n",
              "      <td>tt11281950,tt11437496,tt11437496,tt11281950,tt...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          tconst  ...                                     knownForTitles\n",
              "1026   tt2301451  ...  tt0903747,tt0844708,tt2369135,tt9243946,tt0844...\n",
              "1027   tt2301451  ...            tt1567609,tt1553656,tt0100802,tt0903747\n",
              "1025   tt2301451  ...            tt0903747,tt1024648,tt3203606,tt0212671\n",
              "419    tt1069840  ...            tt0109015,tt0103596,tt0098067,tt0112384\n",
              "418    tt1069840  ...            tt0310281,tt0910936,tt1178663,tt1289401\n",
              "420    tt1069840  ...            tt3587736,tt0098887,tt2262221,tt0098067\n",
              "925    tt2061693  ...  tt0412019,tt1457764,tt0475268,tt0373024,tt1457...\n",
              "791    tt1576810  ...            tt0151738,tt0120082,tt0271367,tt0134084\n",
              "354   tt10471404  ...  tt0295701,tt0355702,tt1528854,tt1528854,tt0355...\n",
              "557   tt11281576  ...  tt11281950,tt11437496,tt11437496,tt11281950,tt...\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "us_oqAHxz3Wr",
        "colab_type": "code",
        "outputId": "43c0a582-d642-4397-f480-9d4a7dd8602c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 530 entries, 1026 to 946\n",
            "Data columns (total 11 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   tconst             530 non-null    object \n",
            " 1   ordering           530 non-null    int64  \n",
            " 2   nconst             530 non-null    object \n",
            " 3   category           530 non-null    object \n",
            " 4   averageRating      530 non-null    float64\n",
            " 5   numVotes           530 non-null    int64  \n",
            " 6   primaryName        530 non-null    object \n",
            " 7   birthYear          530 non-null    int64  \n",
            " 8   deathYear          530 non-null    int64  \n",
            " 9   primaryProfession  529 non-null    object \n",
            " 10  knownForTitles     530 non-null    object \n",
            "dtypes: float64(1), int64(4), object(6)\n",
            "memory usage: 49.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYn1nTeC4iKK",
        "colab_type": "code",
        "outputId": "81701e35-57f3-4518-fe0d-6223a14d70f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "age_df = (pd.DataFrame({'numVotes':df.numVotes,\n",
        "                       'actor':df.primaryName,\n",
        "                       'age':(df.deathYear - df.birthYear)})).reset_index(drop=True)\n",
        "age_df[:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>numVotes</th>\n",
              "      <th>actor</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>112607</td>\n",
              "      <td>Aaron Paul</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>112607</td>\n",
              "      <td>Dean Norris</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>112607</td>\n",
              "      <td>Bryan Cranston</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>162</td>\n",
              "      <td>Max Elliott Slade</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>162</td>\n",
              "      <td>Ed Begley Jr.</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>162</td>\n",
              "      <td>Zachary La Voy</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>161</td>\n",
              "      <td>Ryan Donowho</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>161</td>\n",
              "      <td>David Arquette</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>87</td>\n",
              "      <td>Tony Hawk</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>66</td>\n",
              "      <td>Hazar Deniz BG</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   numVotes              actor  age\n",
              "0    112607         Aaron Paul   41\n",
              "1    112607        Dean Norris   57\n",
              "2    112607     Bryan Cranston   64\n",
              "3       162  Max Elliott Slade   40\n",
              "4       162      Ed Begley Jr.   71\n",
              "5       162     Zachary La Voy   34\n",
              "6       161       Ryan Donowho   40\n",
              "7       161     David Arquette   49\n",
              "8        87          Tony Hawk   52\n",
              "9        66     Hazar Deniz BG   22"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYcgAhtwvx_B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGbGFs-QvTFw",
        "colab_type": "code",
        "outputId": "d5fae67b-03d6-42b9-ce5b-664e2b39894b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(15,3))\n",
        "ax.set_xlim(age_df.age.min()-10,age_df.age.max()+10)\n",
        "ax.set_ylim(0,10)\n",
        "\n",
        "# draw lines\n",
        "xmin = age_df.age.min()-5\n",
        "xmax = age_df.age.max()+5\n",
        "y = 5\n",
        "height = 1\n",
        "\n",
        "ax.hlines(y, xmin, xmax)\n",
        "ax.vlines(xmin, y - height / 2., y + height / 2.)\n",
        "ax.vlines(xmax, y - height / 2., y + height / 2.)\n",
        "ax.plot(age_df.age,[5]*len(age_df),'bo')\n",
        "ax.annotate('Min Age:'+str(age_df.age.min()),xy=(age_df.age.min(),5),\n",
        "            xytext=(age_df.age.min()-8,7),\n",
        "            arrowprops=dict(facecolor='black', shrink=0.01),fontsize=16)\n",
        "ax.annotate('Max Age:\\n'+str(age_df.age.max()),xy=(age_df.age.max(),5),\n",
        "            xytext=(age_df.age.max(),7),\n",
        "            arrowprops=dict(facecolor='black', shrink=0.01),fontsize=16)\n",
        "ax.text(30,7,'Age of Actors whose titles got a rating of 10.0',fontsize=16)\n",
        "ax.text(26,2.85,'Age',fontsize=16)\n",
        "ax.text(50,2.85,'Range = (16,107)',fontsize=16)\n",
        "ax.annotate('',xy=(25,3),\n",
        "            xytext=(15,3),\n",
        "            arrowprops=dict(facecolor='black', width=1),fontsize=16)\n",
        "ax.set_yticklabels([])\n",
        "ax.set_yticks([])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LFHBGebgCLZU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpQyUJk4sCcx",
        "colab_type": "code",
        "outputId": "75f7c454-d078-401e-a3a2-f0c7c58d4dc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "ax = age_df[:10].plot(x='actor',y='age',kind='barh',figsize=(15,8),legend=None,color=\"darkcyan\",fontsize=14)\n",
        "ax.set_xlabel('Age',fontsize=16)\n",
        "ax.set_ylabel('Actor',fontsize=16)\n",
        "\n",
        "ax.invert_yaxis()\n",
        "\n",
        "ax.set_title('Age of Top 10 Actors in terms of Rating & Votes',fontsize=18)\n",
        "for i, rows in age_df[:10].iterrows():\n",
        "  plt.annotate(rows[\"age\"], xy=(rows[\"age\"],i+0.1), rotation=0,fontsize=14, color=\"black\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V20VJevZ4h8b",
        "colab_type": "code",
        "outputId": "b1aaa2dc-939a-4f9e-f56f-5380e6d9e380",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "age_df.age.min(),age_df.age.max()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 107)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fEo0I29H4hkR",
        "colab_type": "code",
        "outputId": "2c36600b-c7ac-4689-e0a7-0c4447e5958a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "age_df.loc[age_df.age == 16]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>numVotes</th>\n",
              "      <th>actor</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>7</td>\n",
              "      <td>Freddie Beddington</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     numVotes               actor  age\n",
              "162         7  Freddie Beddington   16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6w43LZjzD3x",
        "colab_type": "text"
      },
      "source": [
        "## Avg episode duration of the best TV shows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmyXiTIPzD0n",
        "colab_type": "code",
        "outputId": "06bc66ce-a144-4da2-9ba3-917d300b0979",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "with gzip.open('/content/drive/My Drive/Data/genres.tsv.gz') as f:\n",
        "  genres = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/ratings.tsv.gz') as f:\n",
        "  ratings = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/episode.tsv.gz') as f:\n",
        "  episodes = pd.read_csv(f,sep='\\t')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TcFnDoiicFyd",
        "colab_type": "text"
      },
      "source": [
        "* Best is defined by rating, which is equal to 10.0\n",
        "* From genre table titles with tvSeries titletype are extracted and named as tv_shows, which is merged with ratings table whose rating is 10.0 to get top_shows table\n",
        "* From top_shows table we get following"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5JzWfEbzDxr",
        "colab_type": "code",
        "outputId": "5f1e1c3a-f552-422a-b003-3b0e5a7806a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "genres"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710056</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710057</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710058</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710059</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>short</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>\\N</td>\n",
              "      <td>27</td>\n",
              "      <td>Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710060</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6710061 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ... runtimeMinutes                    genres\n",
              "0        tt0000001      short  ...              1         Documentary,Short\n",
              "1        tt0000002      short  ...              5           Animation,Short\n",
              "2        tt0000003      short  ...              4  Animation,Comedy,Romance\n",
              "3        tt0000004      short  ...             12           Animation,Short\n",
              "4        tt0000005      short  ...              1              Comedy,Short\n",
              "...            ...        ...  ...            ...                       ...\n",
              "6710056  tt9916848  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710057  tt9916850  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710058  tt9916852  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710059  tt9916856      short  ...             27                     Short\n",
              "6710060  tt9916880  tvEpisode  ...             10   Animation,Comedy,Family\n",
              "\n",
              "[6710061 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SLoqt1Xnw7W",
        "colab_type": "code",
        "outputId": "d56cb206-879e-4480-b947-92f8d9662927",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "tv_shows = genres.loc[genres.titleType == 'tvSeries']\n",
        "tv_shows"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34987</th>\n",
              "      <td>tt0035599</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Voice of Firestone Televues</td>\n",
              "      <td>Voice of Firestone Televues</td>\n",
              "      <td>0</td>\n",
              "      <td>1943</td>\n",
              "      <td>1947</td>\n",
              "      <td>15</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35188</th>\n",
              "      <td>tt0035803</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>The German Weekly Review</td>\n",
              "      <td>Die Deutsche Wochenschau</td>\n",
              "      <td>0</td>\n",
              "      <td>1940</td>\n",
              "      <td>1945</td>\n",
              "      <td>12</td>\n",
              "      <td>Documentary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37617</th>\n",
              "      <td>tt0038276</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>You Are an Artist</td>\n",
              "      <td>You Are an Artist</td>\n",
              "      <td>0</td>\n",
              "      <td>1946</td>\n",
              "      <td>1950</td>\n",
              "      <td>15</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38451</th>\n",
              "      <td>tt0039120</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Americana</td>\n",
              "      <td>Americana</td>\n",
              "      <td>0</td>\n",
              "      <td>1947</td>\n",
              "      <td>1949</td>\n",
              "      <td>30</td>\n",
              "      <td>Family,Game-Show</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38452</th>\n",
              "      <td>tt0039121</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Birthday Party</td>\n",
              "      <td>Birthday Party</td>\n",
              "      <td>0</td>\n",
              "      <td>1947</td>\n",
              "      <td>1949</td>\n",
              "      <td>30</td>\n",
              "      <td>Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709749</th>\n",
              "      <td>tt9916210</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rumpole of the Bailey</td>\n",
              "      <td>Rumpole of the Bailey</td>\n",
              "      <td>0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709752</th>\n",
              "      <td>tt9916216</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Kalyanam Mudhal Kadhal Varai</td>\n",
              "      <td>Kalyanam Mudhal Kadhal Varai</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>2017</td>\n",
              "      <td>22</td>\n",
              "      <td>Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709753</th>\n",
              "      <td>tt9916218</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Lost in Food</td>\n",
              "      <td>Lost in Food</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>2017</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Talk-Show</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709833</th>\n",
              "      <td>tt9916380</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Meie aasta Aafrikas</td>\n",
              "      <td>Meie aasta Aafrikas</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>43</td>\n",
              "      <td>Adventure,Comedy,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709976</th>\n",
              "      <td>tt9916678</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Acelerados</td>\n",
              "      <td>Acelerados</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>181832 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst titleType  ... runtimeMinutes                   genres\n",
              "34987    tt0035599  tvSeries  ...             15                       \\N\n",
              "35188    tt0035803  tvSeries  ...             12              Documentary\n",
              "37617    tt0038276  tvSeries  ...             15                       \\N\n",
              "38451    tt0039120  tvSeries  ...             30         Family,Game-Show\n",
              "38452    tt0039121  tvSeries  ...             30                   Family\n",
              "...            ...       ...  ...            ...                      ...\n",
              "6709749  tt9916210  tvSeries  ...             \\N                       \\N\n",
              "6709752  tt9916216  tvSeries  ...             22                  Romance\n",
              "6709753  tt9916218  tvSeries  ...             \\N                Talk-Show\n",
              "6709833  tt9916380  tvSeries  ...             43  Adventure,Comedy,Family\n",
              "6709976  tt9916678  tvSeries  ...             \\N                   Comedy\n",
              "\n",
              "[181832 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PwEEsox3zDue",
        "colab_type": "code",
        "outputId": "c83bb2f5-3d21-41bd-afcb-311d30f375cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# creating a dataframe of top rating titles\n",
        "top_rating = ratings[ratings['averageRating'] == 10.0].sort_values(by='numVotes',ascending=False)\n",
        "top_rating.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671254</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>867391</th>\n",
              "      <td>tt5658362</td>\n",
              "      <td>10.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460174</th>\n",
              "      <td>tt10867894</td>\n",
              "      <td>10.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454487</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568217</th>\n",
              "      <td>tt1576808</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  averageRating  numVotes\n",
              "671254   tt2301451           10.0    112607\n",
              "867391   tt5658362           10.0       199\n",
              "460174  tt10867894           10.0       175\n",
              "454487   tt1069840           10.0       162\n",
              "568217   tt1576808           10.0       161"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRKGtPoWzDqd",
        "colab_type": "code",
        "outputId": "bac455e3-fb8d-4d84-8e38-97ab30386c97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        }
      },
      "source": [
        "top_tv = pd.merge(tv_shows,top_rating,on='tconst')\n",
        "top_tv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt10243828</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Live from the 8th Dimension</td>\n",
              "      <td>Live from the 8th Dimension</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt10361854</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Tunadas</td>\n",
              "      <td>Tunadas</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt10462950</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Ommi Traki, nas mlah</td>\n",
              "      <td>Ommi Traki, nas mlah</td>\n",
              "      <td>0</td>\n",
              "      <td>1969</td>\n",
              "      <td>1971</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>tt10622458</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>15</td>\n",
              "      <td>Short</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>tt11004858</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>30</td>\n",
              "      <td>Talk-Show</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>tt11006412</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Surajya Sanhita</td>\n",
              "      <td>Surajya Sanhita</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>History</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>tt11051072</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>El Harba</td>\n",
              "      <td>El Harba</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>tt1165334</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>0</td>\n",
              "      <td>1963</td>\n",
              "      <td>1965</td>\n",
              "      <td>60</td>\n",
              "      <td>Music,Talk-Show</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>tt11863100</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Boss Hoggin Road Trip Series</td>\n",
              "      <td>Boss Hoggin Road Trip Series</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        tconst titleType  ... averageRating numVotes\n",
              "0   tt10233774  tvSeries  ...          10.0        9\n",
              "1   tt10243828  tvSeries  ...          10.0        5\n",
              "2   tt10293068  tvSeries  ...          10.0        9\n",
              "3   tt10361854  tvSeries  ...          10.0        9\n",
              "4   tt10462950  tvSeries  ...          10.0        5\n",
              "5   tt10622458  tvSeries  ...          10.0        5\n",
              "6   tt11004858  tvSeries  ...          10.0        6\n",
              "7   tt11006412  tvSeries  ...          10.0        8\n",
              "8   tt11051072  tvSeries  ...          10.0        8\n",
              "9    tt1165334  tvSeries  ...          10.0        5\n",
              "10  tt11863100  tvSeries  ...          10.0        7\n",
              "11   tt5190996  tvSeries  ...          10.0        5\n",
              "\n",
              "[12 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cA-t0IRwdgf_",
        "colab_type": "code",
        "outputId": "2a301544-8608-4920-a8eb-2d43cec7b3fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "df = top_tv.loc[top_tv.runtimeMinutes != '\\\\N'].reset_index()\n",
        "df.runtimeMinutes = pd.to_numeric(df.runtimeMinutes)\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>tt10622458</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>15</td>\n",
              "      <td>Short</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>tt11004858</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>30</td>\n",
              "      <td>Talk-Show</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>tt1165334</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>0</td>\n",
              "      <td>1963</td>\n",
              "      <td>1965</td>\n",
              "      <td>60</td>\n",
              "      <td>Music,Talk-Show</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index      tconst titleType  ...           genres averageRating  numVotes\n",
              "0      2  tt10293068  tvSeries  ...      Documentary          10.0         9\n",
              "1      5  tt10622458  tvSeries  ...            Short          10.0         5\n",
              "2      6  tt11004858  tvSeries  ...        Talk-Show          10.0         6\n",
              "3      9   tt1165334  tvSeries  ...  Music,Talk-Show          10.0         5\n",
              "4     11   tt5190996  tvSeries  ...           Comedy          10.0         5\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xLUI25tBe0L3",
        "colab_type": "code",
        "outputId": "741e7b10-5627-466d-a463-8d5f00afe628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5 entries, 2 to 11\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   tconst          5 non-null      object \n",
            " 1   titleType       5 non-null      object \n",
            " 2   primaryTitle    5 non-null      object \n",
            " 3   originalTitle   5 non-null      object \n",
            " 4   isAdult         5 non-null      int64  \n",
            " 5   startYear       5 non-null      object \n",
            " 6   endYear         5 non-null      object \n",
            " 7   runtimeMinutes  5 non-null      int64  \n",
            " 8   genres          5 non-null      object \n",
            " 9   averageRating   5 non-null      float64\n",
            " 10  numVotes        5 non-null      int64  \n",
            "dtypes: float64(1), int64(3), object(7)\n",
            "memory usage: 480.0+ bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyFSuwC3dgaw",
        "colab_type": "code",
        "outputId": "64a39c89-3cc1-4543-e64b-6f14dedada41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "source": [
        "ax = df.plot(x='originalTitle',y='runtimeMinutes',\n",
        "             figsize=(15,10),kind='bar',color='mediumseagreen',legend=False,fontsize=14)\n",
        "ax.tick_params(labelbottom=False,bottom=False)\n",
        "ax.set_xlabel('Title',fontsize=16)\n",
        "ax.set_ylabel('Avg Episode Duration',fontsize=16)\n",
        "ind = 0\n",
        "for i in ax.patches:\n",
        "  height = i.get_height()\n",
        "  ax.annotate(s = str(height) + ' Min',xy=(i.get_x()+0.1,height-2),fontsize=15,color='aliceblue')\n",
        "  ax.annotate(s = df.originalTitle[ind],xy=(i.get_x()-0.1,1),fontsize=15,rotation=90)\n",
        "  ind = ind+1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eCyEcuDPnlyRJkiSlZLrgy9NAzOYXhxCOBIYBb1cZGk5qgZnBwAzgZ8CTIYRuMcZV2cwgSZIkSUmRUfmLMQ7O5peGEHYD/gAMBUZWOB6AC4EbY4x/Sh8bBJQAZwB3ZTOHJEmSJCVFprd9ZtvdwKMxxmerHO8CtAP+tflAjHEdMAk4On/xJEmSJKluybj8hRAODiE8GkJYEkLYlP79cAjh4B35whDCOcDewIitDLdL/15c5fjiCmOSJEmSpB2U0W2fIYSewPPAOuCvwCJSZexk4KQQQu8Y42sZnKcbcD1wTIxx406nrnzOYaSeHaRjx47ZOKUkSZIk1TmZLvhyA/AOcELFRVdCCM2Ap9Lj/TI4z1FAK+Dd1ON9ABQDvUMIPwA2r+rZFphX4XNtSRXOLcQY7yZ1Gyk9evTI6qI0kiRJklRXZHrb55HADVVX20y/H0Oq1GXiL8DBQPcKP1OBB9OvZ5IqeX03fyCE0AjoBbyU4XdIkiRJkqrI9Mrf9q6oZXTFLcb4CfBJxWMhhDXA8hjjO+n3twFXhBCmkyqDI4DVwAMZZpUkSZIkVZFp+XuVVCF7qsptn02Ay4BXspjpJmAX4A5g9/R393OPP0mSJEnaeZmWvyuA54C5IYSJwMekFnzpDzQGjtvZADHG46q8j8Co9I8kSZIkKQsy3eR9SgjhSOBnwFeAFsBy4FngmhjjtNxFlCRJkiR9Xple+SPG+DbwrRxmkSRJkiTlSMabvEuSJEmSaq9tXvkLIYwjdUvn7PTr6sQY43ezG02SJEmSlC3V3fZ5PPCL9Os+VL+dg5urS5IkSVINts3yF2PsUuF157ykkSRJkiTlREbP/IUQeocQmm5jrEkIoXd2Y0mSJEmSsinTBV+eBQ7Yxth+6XFJkiRJUg2VafkL1Yw1BEqzkEWSJEmSlCPVrfbZGeha4VCPrdz6uQswFJiX9WSSJEmSpKypbrXPQcBIUit5RuB2Kl8BjOn3m4Af5SqgJEmSJOnzq678jQeeI1XwniFV8N6rMmcDMDPGuDwX4SRJkiRJ2VHdVg9zgbkAIYTjgddijKvzFUySJEmSlD3VXfkrF2N8PtdBJEmSJEm5k1H5Awgh9AN+CHQDGlUZjjHGL2QzmCRJkiQpezLd5L0/8A+gMal9/aaTWuGzA1AGTMpVQEmSJEnS55fpPn9XAXcA/dPvR8QYjwMOBIpJFUNJkiRJUg2VafnbD3ic1FW+SPp20RjjTGAUqXIoSZIkSaqhMi1/ZcCmGGMElgAdK4wtBHzeT5IkSZJqsEzL3wygc/r1VODCEMIeIYTWwCXAnOxHkyRJkiRlS6arff4B2D/9eiTwFLAg/b4UOCPLuSRJkiRJWZTpPn93VHj9WgjhYOCrpFb/fCrG+F6O8kmSJEmSsmC75S+E0IDU/n5PxxjfAYgxLgB+m+NskiRJkqQs2e4zfzHGz4AbgRa5jyNJkiRJyoVMF3x5H+iayyCSJEmSpNzJtPz9DLgq/ayfJEmSJKmWyXS1z8uApsAbIYQ5wMekNnvfLMYYj81yNkmSJElSlmRa/koBV/SUJEmSpFoq060ejstxDkmSJElSDmX6zJ8kSZIkqRbL6MpfCKH39ubEGCd9/jiSJEmSpFzI9Jm/56i8wMvWFH++KJIkSZKkXMm0/B2/lWMtgQHAscCPs5ZIkiRJkpR1mS748vw2hv4cQvg5cDLwj6ylkiRJkiRlVTYWfPkbcGoWziNJkiRJypFslL9uQFkWziNJkiRJypFMV/s8eyuHGwAHAd8F/pzNUJIkSZKk7Mp0wZfx2zi+AXgIuCAraSRJkiRJOZFp+euylWPrY4yLsxlGkiRJkpQbma72OTfXQSRJkiRJuZPpM39dgC8De5Da7P1j4AVLoSRJkiTVDtWWvxBCS2Acqc3cQ5XhGEJ4DBgWY1yeo3ySJEmSpCzYZvkLIdQHniK1lcMvgL8D80iVwA6kCuH3gX+GEI6KMW7KfVxJkiRJ0s6o7srf2cD+wLExxlerjM0AngohPAQ8A3yHba8IKkmSJEkqsOo2ef8GcP9Wil+5GOPLwO+Ab2U7mCRJkiQpe6orf4cAT2Zwjn8Bh2YnjiRJkiQpF6orfy2BhRmc4+P0XEmSJElSDVVd+WsEbMzgHJuAhtmJI0mSJEnKhe3t89cjhNB0O3MOyFYYSZIkSVJubK/83c6W+/tVFNPjMWuJJEmSJElZV135Oz5vKSRJkiRJObXN8hdjfD6fQSRJkiRJuVPdgi+SJEmSpDrC8idJkiRJCWD5kyRJkqQEsPxJkiRJUgJY/iRJkiQpAXa4/IUQmoYQOoUQ6ucikCRJkiQp+zIufyGEASGE14FPgVnAwenjvw0hnJGjfJIkSZKkLMio/IUQTgEmAEuBy6p8bjYwKPvRJEmSJEnZkumVv5HAvTHGfsBtVcbeAQ7KaipJkiRJUlZlWv72Bx5Kv45VxlYALbOWSJIkSZKUdZmWv5VAq22MdQaWZCWNJEmSJCknMi1/TwKXhxCaVzgWQwgNgR8D/8h6MkmSJElS1tTLcN6VwBRgBvB3Urd+/hQ4BNgNOCUn6SRJkiRJWZHRlb8Y4xzgi8BEoC9QCvQGXgGOiDEuzFVASZIkSdLnl+mVP2KMC4Dv5jCLJEmSJClHMt7kXZIkSZJUe23zyl8IYdwOnCfGGL0qKEmSJEk1VHW3ffah8p5+zUkt7rIJWEZqb796wKek9vqTJEmSJNVQ27ztM8bYOcbYJcbYBTgLWA2cBuwSY9wD2AU4HVgFfCcfYSVJkiRJOyfTBV9uBW6IMT68+UCMsRR4KITQCrgN+FIO8kmSJEmSsiDTBV8OBv6zjbEPgIOyE0eSJEmSlAuZlr9FwKnbGDsNWJydOJIkSZKkXMj0ts/bgJ+HEPYAHiFV9tqSKoRfAS7MTTxJkiRJUjZkdOUvxvgL4BxSt3+OA/6W/n0gcE6M8fZMzhNC+FEI4e0Qwsr0z8shhJMqjIcQwqgQwsIQwroQwnMhhAN3+K+SJEmSJFWS8SbvMcZ7gE7pnyPTvzvHGHdkP8AFwGXAF4EewDPAX0IIh6THhwOXAOcBPYES4MkQQrMd+A5JkiRJUhWZ3vYJpHZyB+anf3ZYjHFClUNXhhB+CBwVQphG6vbRG2OMfwIIIQwiVQDPAO7ame+UJEmSJO3Alb8QwsEhhEdDCEtCCJvSvx8OIRy8M18cQigOIZwGNAVeAroA7YB/bZ4TY1wHTAKO3pnvkCRJkiSlZHTlL4TQE3geWAf8ldTqn+2Ak4GTQgi9Y4yvZXiug4GXgUakNo4fGGOcFkLYXPCqrhy6GNgrk3NLkiRJkrYu09s+bwDeAU6IMa7afDD9LN5T6fF+GZ5rBtAd2A34FnBfCOG4TANXFUIYBgwD6Nix486eRpJUg/WaeHGhI9QpkwfcWugIkqQCyPS2zyOBGyoWP4D0+zHAUZl+YYzxsxjjf2KMr8UYLwfeBC4idTURUltIVNS2wtjWznd3jLFHjLFH69atM40hSZIkSYmSafmLn3N8exkaArNJlby+mwdCCI2AXqSeCZQkSZIk7aRMb/t8FbgihPBUlds+m5DauuGVTE4SQriR1B6B84FmpFbxPA44KcYYQwi3pb9nOjATGEHqucAHMswpSZIkSdqKTMvfFcBzwNwQwkTgY1ILvvQHGpMqcJloB/w+/ftT4G3gazHGf6bHbwJ2Ae4AdidVOvtVvd1UkiRJkrRjMip/McYpIYQjgZ8BXwFaAMuBZ4FrYozTMjzP4O2MR2BU+keSpKz6cptDOKXTsezVuA2NihtQsn4Fz348lT/NeZZNsbTS3G93PoH+HY5m1/pN+WDlPO6a/hizVy+s9vw3HH4uB7fYm39+9Aq3v/dwpbFWDZszrtcIikIRl0+9g2krZgFwzzEjeHHxW4z74PHs/rGSJFWR8SbvMca3Sa3OKUlSrdSsfhPeWv4f/jznOVZvWse+u3XkjK5fYfcGu/KbGX8un/ftzidwWtd+3PvB48xfs5iBnY7j2sN/wI9evplPPqv+ZpS1m9ZzdJtDuPP9RymNZeXHe7c7jPWln9G4XqNK8697615WbVyT3T9UkqStyHiT96pCCAeEEL4ZQtgzm4EkScqVJz56md/P+gcvL5nGtBX/4U9znuEvc5/juD0OL59Tv6ge3+rch0fmPM3E+S/w1vIPuOGt+wAY0OGY7X7HOytmUUTgiy33q3S8d7vuTFny7hbzP1z1EUvWf/I5/zJJkrYvo/IXQvhVCOE3Fd5/g9Tzeo8A76U3gZckqdZZtXEt9YqKy9/vv1tnmtTfhRcWvVl+bEPZZ0xZ8i49Wu23tVNUsrFsE68ueYfe7Q4rP7Zn41bsvWsHJi16Y4v59xwzgqH7nFz+/sIDT+PnR1xE9xb7cvuRl/JonxsY0+PHdGxSdSckSZJ2TKZX/r5G5e0WrgYeBw4FpgAjs5xLkqScKSLQsKg+BzTvwskde/GPBf/9T1z7Jm0oLStl4dollT4zf00J7Zu0yej8zy96gyNaH0iDovoA9G73RaZ/MpfF65Zn9PnWjZozdN+TeXj2U9w07fc0b9CUyw45O8O/TpKkrcv0mb89gDkAIYT2wIHAd2OM00IIvwTuyU08SVJds3DhQiZOnMiCBQtYv359pbEQAmPGjMl5hkf73EiD4lQxe3rhvxk387+LrTSt35h1pZ9RVmUL29Wb1tKouCH1QvEWi8NU9cbymWws20TPVgfwYslb9GrbnX9+lNGuSAA0q9eY4f++nYVrlwKpsjqi+1DaN27DgrUlGZ9HkqSKMi1/a4Gm6dfHAiuBqen3q0nt2SdJUrUee+wxTj/9dEpLS2nTpg0NGjSoNJ6v8veTf/+ShsUN2HfXjpzWtR8/2O8b/Hr6n7J2/rJYxksl0+jd7jA+Wpu6YvjC4jdpVq9xRp9fvH5FefEDmLdmMQAtG+1m+ZMk7bRMy9/rwI9CCPOAHwFPxli+hFkXUvv+SZJUrSuuuIJ+/foxfvx4WrRoUbAcs1Z9BMB7n8xm5cY1XHzQGTw29zkWrVvG6o1r2aW4AUWESlf/mtZrzPrSDdu96rfZpEVvMOqwc1ixYSXvrfiQ5RtWZlz+1mxaV+n9prJNAOW3kUqStDMyfebvSuBI4C2gG3BNhbFTSD33J0lStebPn8/5559f0OJX1ayVCwBou0sq04I1JRQXFbNH41aV5rVv0oYFazK/6vbOilms3rSWr3U4mkmLt1zoRZKkfMuo/MUY/w10BL4EdEnv+bfZ3bjgiyQpA0cffTQzZswodIxK9m/eBaB8MZb3P53Dmo3rOKbtoeVzGhbV50utD2Dq0ukZnzcSeWT200xZ8i4vLn57+x+QJCnHdmST9zXAa1s5/resJpIk1Vm33norZ555Jk2bNqVv3740b958izmNG2d2a+TOuPqwYby5fCbzVi+iLJaxf/MuDOx0HJMWvcGidcuA1FYNj855hv/t2pfVG9exYG0Jp3Q8lkBg4vzJO/R9E+e/wMT5L+TiT5EkaYdts/yFEM4G/hZjXJZ+Xa0Y4/1ZTSZJqnMOOeQQAIYMGUIIYatzSksze6ZuZ3ywch4n7tmTNo1aUBrLWLRuGff952+VtnoAeGTO04QQ+HaXE2hWvwn/WTmfq16/i08+W52zbJIk5VqIMW59IIQy4MgY45T06+rEGGPxdubkXI8ePeLUqVO3P1GSVBDjx4/fZunbbNCgQVsc6zXx4lxFSqTJA24tdIQ6xf99Zpf/+5Q+nxDCazHGHlsbq+62z4qreHbJeipJUuIMHjy40BEkSUqsbZa/GOPcrb2WJOnzWrhwIS+//DLLly+nRYsWHHXUUey5556FjiVJUp2W8YIvACGEvUmt+LkX8BHwaoxxVi6CSZLqntLSUs477zzGjh1b6dm+4uJihg0bxu23305RUaa7EEmSpB2R0X9hQwiNQgjjgPeB3wNj0r+nhxB+G0JomMOMkqQ6YuTIkYwbN47rr7+eOXPmsG7dOubMmcP111/PuHHjGDVqVKEjSpJUZ2V65e8W4ExS+/k9CCwG2gKnAz8D1gLn5yKgJKnuuP/++7n22mu59NJLy4917NiRn/zkJ4QQ+OUvf8no0aMLmFCSpLor0/J3GnB1jPH6Csc+BO/ZqkMAACAASURBVK5Lr9p2EZY/SdJ2lJSUlG/3UNUhhxxCSUlJnhNJkpQcmT5Y0RCYso2xV4EG2YkjSarL9t13Xx588MGtjj344IN069Ytz4kkSUqOTK/8PQX0S/+uqh/wTNYSSZLqrBEjRnDaaacxb948vvWtb9G2bVtKSkp45JFHePbZZ7dZDCVJ0ueXafm7FfhdCKEJ8Aj/febvVKA/8J0QQtfNk2OMH2Y7qCSp9jv11FNp3rw5I0eO5IILLmDjxo3Ur1+fww8/nCeeeIK+ffsWOqIkSXVWpuXv+fTvHwI/qHA8VBnfrPjzhJIk1V39+vWjX79+lJWVsXTpUlq1auX2DpIk5UGm5W9ITlNIkhKnqKiINm3aFDqGJEmJkVH5izHel+sgkqS6afjw4Zx//vm0b9+e4cOHVzs3hMCYMWPylEySpGTJ9MrfNoUQioDmMcblWcgjSapjHnnkEc4880zat2/Pww8/THqLoK2y/EmSlDvbLH8hhOXAiTHG19PvAzABuLDKgi49gZfwOT9J0lbMnj27/PWcOXMKF0SSpISr7gn75lQuh0XAgPRxSZJ22P3338+yZcu2OrZ8+XLuv//+PCeSJCk5XF5NkpQ3Q4YMYdasWVsdmz17NkOGuL6YJEm5YvmTJOVNjHGbY8uWLWPXXXfNYxpJkpLlcy/4IklSdSZMmMCECRPK319zzTW0bt260pz169czefJkevbsme94kiQlxvbK314hhK7p18UVjn1SYU777MeSJNUVJSUlTJs2rfz9rFmzWLRoUaU5DRo0oF+/fowYMSLf8SRJSoztlb9Ht3LsL1XeB2Db9/FIkhLtnHPO4ZxzzgHg+OOP584772T//fcvcCpJkpKnuvLnU/eSpKx69tlnCx1BkqTE2mb5izHel88gkqS6b+jQoaxdu5YHH3xwi7HTTz+dpk2bMnbs2AIkkySp7nO1T0lS3jz55JN885vf3OrYN7/5Tf75z3/mOZEkSclh+ZMk5c2SJUto0aLFVsd23313SkpK8pxIkqTksPxJkvKmU6dOTJo0aatjkyZNon17F5CWJClX3OevAHpNvLjQEeqMyQNuLXQESTtg8ODBXH311bRp04ZBgwbRtGlTVq9ezf33389NN93EyJEjCx1RkqQ6y/InScqbyy67jFmzZnHeeedx/vnn06RJE9asWUOMkWHDhnHZZZcVOqIkSXWW5U+SlDdFRUX89re/5Sc/+QnPPPMMy5cvp2XLlvTp04d999230PEkSarTMip/IYSzqxkuAz4F3ogxLshKKklSndatWze6detW6BiSJCVKplf+xgMx/TpUOF7xWFkI4SFgSIzxs+zEkyTVRQsWLGDmzJmsX79+i7H+/fsXIJEkSXVfpuXvy8AfgMeBR4HFQFvgVGAAcC5wIDAamAtckfWkquTLbQ6lz56Hs3ezDjSu14iP1pbw57nPMWnRG5Xm3XD4uRzcYu8tPj/w6eFsLNu0zfNfeOBpnLjnl3hz2UxGvP6bSmMNiurz+2OvpnG9Rvz8nT/y9Mf/Lv+ulRvXcMPb92XhL5RUF61atYpTTz2Vf/3rXwDEmPo3xBD++++KpaWlBckmSVJdl2n5uxR4MMZYsdTNBCaHEFYBw2KMA0MIuwFnYvnLuYGdjmXRuuWMnfkXVn62hh6t9mf4wWexa/0mTJz/QqW5by3/gPv/8/dKx6orfput3bSeg3f/As0bNOWTz1aXH+/Z6oCtzr9z+p8ojf6fNknbdvnllzNv3jwmT57MMcccw2OPPcbuu+/O73//e5555hn++Mc/FjqiJEl1Vqblrx9w5zbGngF+nH49CRj+eUNp+0a/eQ8rN64pf//2iv/QouFunNLp2C3K3+qNa5nx6dwd/o6P1i5hl+KGHNO2e6Vz9m53GFOWvMtxexxeaf78NYt3+DskJcvf//53rr32Wo444ggA9txzT3r27Env3r255JJLuPnmm3n44YcLnFKSpLop003eNwCHb2PscGDzM35FwJptzFMWVSx+m3246iNaNtwtq98zefGb9Grbvfz9LsUN6dFq/y1uL4XUbZ+XHzKo/P0ZXb/CH44dTddme3FLzwt4tM+N/OKIizmweZesZpRUeyxevJgOHTpQXFxMkyZNWL58eflY//79y28HlSRJ2Zdp+XsEuDqEcEkIoVMIYZf070uBUcBD6XndgRk5yKkM7LdbJz5as2SL44e17MajfW7k0T43MvqwYXRuukfG53x+0evs37wzrRs1B+CoNgezetNapq2YldHnGxY34KIDT+eJj17ihrfGs7GslCsOHULDovoZZ5BUd3To0IGlS5cCsM8++zBx4sTysVdffZVGjRoVKpokSXVeprd9Xgw0A25K/1T0AHBJ+vU7wMvZiaYdcWiLfTiyzUH84t2HKh2ftmIWT3/8bz5eu5TWjVrwv11OZEyPH3PeK7dQsn7Fds+7YE0Jc1cvolfbw/jz3Gfp3e4wXlj0FrF8odfqNSpuwNgZf+HtFf8BYPmGldx+1KUcuPsXeH3Z9B3/QyXVan379uWpp55i4MCBXHTRRQwaNIjXXnuNhg0bMmnSJC655JLtn0SSJO2UjMpfjHEd8J0QwmjgCGAP4GNgSoxxRoV5f8tJSlWrTaPdufSg7/DqknfLV97c7IEP/1nh3WzeXD6T3xz9U/6n47GMnfmXjM4/adEb9G7XnScXvsqhLfbhjx9mflvWxrJNla4Sbn4usFWj7N6eKql2GDNmDGvXrgXgrLPOomnTpjz66KOsW7eOX/3qV3z/+98vcEJJkuquTDd5L44xlsYYZ5Ja5VM1RNN6jbn6sGEsWb+CW6b9frvzP/lsFe9/Mpsv7LpXxt8xadEbDNrnJL7d+QSWbfiUGZ/OpVFxg4w+u3bT+kpXCTelVwNt4G2fUiI1btyYxo0bl78fOHAgAwcOLGAiSZKSI9Nn/haGEG4LIWxr0RcVQMOi+ow87LvUKyrm6jd+y4ayjRl9LmZ802bK4vXLmfHpXP6n07FMXvTmzoWVpAr+8Y9/cM011zBs2DDmzZsHwKRJk1i4cGGBk0mSVHdlWv7+BHwHmBJCeC+E8NMQQocc5tJ2FIUifnrIIPZs3JqRb9zNpxtXb/9DQPMGzTigeVdmrZy/Q9/32NznmLLkXZ75eOrOxJUkILXa5xFHHMHJJ5/Mfffdxz333FO+AMy9997LNddcU+CEkiTVXZk+83duCOEC4CTgLGAkcG0IYTJwH/CnGOOq3MVUVefu9016tj6Au6Y/RrP6Tei2W5PysVkrF7ApltK56R4M2vskXlj8FiXrV9C6UXO+3eUEIpEJ8ybt0Pe9sPgtXlj8Vrb/DEkJc95557F69WqmT59O586dadDgv7eQn3jiiVx99dUFTCdJUt2W6WqfxBg3An8B/hJC2A34X1JXA38L/ApompOE2qrDWnYD4Pv7bfmszNDJ11CyfgWrNq4lhMCgfU6iWf3GrNu0gWkrZnHdf+5lyfpP8h1ZknjiiSe477772HvvvSktLa001r59ez766KMCJZMkqe7LuPxVFGP8NITwD6Al0JXU6p/Ko+++cO125yzb8Cmj3hi7U+e/7d0Hqx1fX/oZA568uNKxy1+7s9L7Bz78Z5XVRlOqfk5SstSrt/X/9CxdupRddtklz2kkSUqOTJ/5AyCE0CyEMDSE8CwwGxgBTAZOzkU4SVLd0qtXL375y19WuuoXQgBg3Lhx9OnTp1DRJEmq8zLd6mEAqVs8TwYaAZOAYcAjPusnScrUmDFjOOaYYzjooIMYOHAgIQTGjh3Lu+++y7Rp03jllVcKHVGSpDor0yt/fwUOBa4DusQYj48xjrP4SZJ2xEEHHcRrr71Gjx49GD9+PMXFxfz5z3+mffv2vPrqq+y7776FjihJUp2V6TN/X4oxbnWN/xDCscCgGOPQ7MWSJNVVX/jCF/jd73631bGPPvqIvfbaK8+JJElKhoyu/FUtfiGEvUMIo0MIs4FngVNzEU6SlAzvvPMOgwYNomvXroWOIklSnZXxgi8hhN1CCMNCCC8CM4ArgRXAD4E9c5RPklQHPPDAA3z1q1/lwAMPZMCAAbz44osATJs2jZNOOolDDz2Uv/3tb4wYMaLASSVJqruqLX8hhKIQQv8QwkPAx8BvgE7AHekpF8YY74oxrsxxTklSLXXPPffwne98h/nz53PwwQezfPlyTjzxRH71q1/Ro0cPpkyZwpgxY5g7dy5XXXVVoeNKklRnbfOZvxDC/wFnAG2A9cBjwH3AU8CuwI/zEVCSVLvdfvvtnH322YwfP7782G233cYFF1zA0UcfzeOPP07z5s0LF1CSpISo7srfRaSK39+BjjHGM2OM/4oxlgExL+kkSbXerFmzOOussyodGzx4MDFGrrzySoufJEl5Ul35uwdYBZwEzAgh/CqE8KX8xJIk1RVr1qyhWbNmlY5tft+mTZtCRJIkKZG2edtnjPGcEMJ5wEBgEPB94IchhJmkbgH16p8kKSMvvfQSS5cuLX9fVlZGCIEXX3yRRYsWVZrbv3//fMeTJCkRqt3nL8a4Hvgj8McQwh7AWcDZwE/TU24MIdwJPJqeK0nSFi6++OKtHr/gggsqvQ8hUFpamo9IkiQlTqabvBNj/Bi4CbgphNCD1NXA04D7gduB3XOSUJJUq82ePbvQESRJEjtQ/ipKb/o+NYRwMTCA1NVASZK20KlTp0JHkCRJ7GT52yzGuJHU83+PZSeOJEmSJCkXqt3kXZIkSZJUN1j+JEmSpBpq48aNvPjiiyxcuLDQUVQHWP4kSZKkGqq4uJg+ffowffr0QkdRHWD5kyRJkmqooqIi9tlnny32RJV2huVPkpQ3kydPZsKECeXvly5dyhlnnEH37t255JJL2LhxYwHTSVLNdN111zF69GimTZtW6Ciq5T7Xap+SJO2I4cOHM2DAAP7nf/4HSG3y/vTTTzNw4EDGjx9Pw4YNuf766wucUpJqlmuvvZZly5bRvXt39tprL9q2bUsIodKcKVOmFCidapO8lr8QwuXAN4BuwAbgFeDyGOM7FeYEYCQwjNTG8a8CP4oxvpvPrJKk7JsxYwYjR44EYO3atTz22GOMGzeO0047jZ49e3L99ddb/iSpioMOOoiDDjqo0DFUB+T7yt9xwJ3Av4EAjAaeCiEcEGNcnp4zHLgEGAzMAH4GPBlC6BZjXJXnvJKkLPrss89o1KgRAC+++CKbNm3ipJNOAmDffffl448/LmQ8SaqR7r333kJHUB2R12f+YoxfiTHeG2N8J8Y4DTgLaA18Gcqv+l0I3Bhj/FP6iuAgoBlwRj6zSpKyb7/99uOJJ54A4A9/+ANHHXUUzZo1A2DhwoW0aNGikPEkqUaLMTJ//nxeeukl1qxZU+g4qoUKveBLs3SGFen3XYB2wL82T4gxrgMmAUfnPZ0kKat+9rOf8fOf/5zWrVvzwAMP8NOf/rR87IknnuCwww4rYDpJqrnuvPNO9tprLzp16kSvXr2YMWMGAN/4xje47bbbCpxOtUWhy98vgDeBl9Pv26V/L64yb3GFsUpCCMNCCFNDCFOXLFmSm5SSpKz4+te/zvvvv89vfvMb3nnnHb72ta+Vjx111FFceeWVBUwnSTXTzTffzMUXX8w555zDM888Q4yxfOy4447joYceKmA61SYFW+0zhHArcAxwTIyxdGfPE2O8G7gboEePHnE70yVJBda1a1e6du26xfFhw4YVII0k1Xx33HEHo0ePZvjw4ZSWVv6/zd26dWPmzJkFSqbapiDlL4Twc+A04PgY44cVhjbvXtkWmFfheNsKY5KkWuTvf//7Ds3v379/jpJIUu20aNEiDj/88K2OFRUVsX79+jwnUm2V9/IXQvgF8L+kit/0KsOzSZW8vqRWBCWE0AjoBfwknzklSdkxYMAAQgiVblPalhDCFv+qLUlJt/fee/P8889zwgknbDE2adIkDjjggAKkUm2U733+7iC1wucpwIoQwubn+FbHGFfHGGMI4TbgihDCdGAmMAJYDTyQz6ySpOyYPXt2oSNIUq124YUXcu6559KgQQO+9a1vAVBSUsI999zDrbfeytixYwucULVFvq/8nZv+/XSV41cDo9KvbwJ2Ae7gv5u893OPP0mqnTp16lToCJJUq33ve99jxYoVjB49mpEjRwKpW+QbN27MqFGjOOMMd0RTZvJa/mKMIYM5kVQRHJXrPJKk3Fu7di2NGzcuf709m+dKkv7rJz/5CT/4wQ94+eWXWbp0KS1atOCoo45it912K3Q01SIFW+1TkpQMzZo14+WXX+ZLX/oSTZs2JYTq/x3QZ/4kqbL777+fk046iZYtW9KvX79KY8uXL2fixImcffbZBUqn2sTyJ0nKqXHjxvGFL3yh/PX2yp8kqbIhQ4bw8ssv07Jlyy3GZs+ezZAhQyx/yojlT5KUU4MGDSp/PXjw4MIFkaRaqrrVkpctW8auu+6axzSqzSx/kiRJUg0zYcIEJkyYUP7+mmuuoXXr1pXmrF+/nsmTJ9OzZ898x1MtZfmTJOXVQw89xNixY5k5c+ZWNyYuKSkpQCpJqllKSkqYNm1a+ftZs2axaNGiSnMaNGhAv379GDFiRL7jqZay/EmS8uaBBx5g6NChDB48mGeeeYahQ4dSVlbGX//6V5o3b+4zK5KUds4553DOOecAcPzxx/PrX/+a/fbbr8CpVNsVFTqAJCk5br75Zq666iruuOMOAM4991zGjRvH7NmzadWqlds8SNJWPPvssxY/ZYVX/iRJefPBBx/w5S9/meLiYoqLi1m5ciWQ2g7isssu46KLLuLSSy8tcEpJqnkWLlzIxIkTWbBgwRa3zIcQGDNmTIGSqTax/EmS8mbXXXdlw4YNAOy11168//77HHfccUBqNbtly5YVMJ0k1UyPPfYYp59+OqWlpbRp04YGDRpUGrf8KVOWP0lS3vTs2ZO3336br3zlK3z9619n9OjR1KtXjwYNGjB69GiOPPLIQkeUpBrniiuuoF+/fowfP54WLVoUOo5qMcufJClvLr/8cubOnQvA6NGjmTt3Lj/84Q8pKyujZ8+e3HXXXQVOKEk1z/z587n99tstfvrcLH+SpLw58sgjy6/uNW/enAkTJrBhwwY2bNjgJsWStA1HH300M2bM4MQTTyx0FNVylj9JUkGtXLmS3XbbrdAxJKnGuvXWWznzzDNp2rQpffv2pXnz5lvMcbVkZcKtHiRJOfXCCy9w8803b3H87rvvplWrVrRr147mzZtz4YUXUlZWVoCEklSzHXLIIUybNo0hQ4bQoUMHmjVrtsWPlAmv/EmScuqWW26huLi40rGnn36aH/7wh3Tv3p1Ro0Yxc+ZM7rjjDg444ACGDRtWoKSSVDONGzeOEEKhY6gOsPxJknLq9ddf55prrql07Ne//jWNGzfmySefLF/AoGHDhowdO9byJ0lVDB48uNARVEd426ckKaeWLFlC586dy9/HGHnyySc54YQTKq1c17dvX2bNmlWAhJIkJYNX/iRJOdWmTRsWLlxY/v6NN95g1apV9O7du9K8+vXrU1pamu94klTjtW7deru3fZaUlOQpjWozy58kKad69+7N//3f/9GvXz923313brnlFoqKijjllFMqzXvrrbfo0KFDgVJKUs31ox/9aIvyt2LFCp5++mlWrlzJ0KFDC5RMtY3lT6oDMvkXwYr810Hl03XXXccRRxxB27ZtadCgAevXr+fiiy+ma9eulebdf//9HH/88QVKKUk116hRo7Z6PMbIqaeeSv369fMbSLWW5U+qA7b2L4JSTdGxY0fef/99HnnkET799FO++MUv0qdPn0pzli5dyplnnkn//v0LlFKSap8QAt/73vcYMmQIV1xxRaHjqBaw/El1wLb+RfD/2bvvsCrr/4/jzxvZW1CGAWZuHDlA4eBWSBFXhrNyj1yVuTDTxNRclJmjzIU5cGampuVWcGs/Z5iYijJcONmc3x98PUVggum5gfN+XJdX59yf+3hedN0ezvu+P/f7I0RhYW9vT79+/Z46XqpUKT766CM9JhJCiOIhJiaGtLQ0tWOIIkKKPyGEEEIIIQqxefPm5dqWlpbG+fPnWbFiBcHBwSqkEkWRFH9CFAOdOnUq0P5r1qx5SUmEEKLoiomJYcaMGRw4cIA7d+7g4OBAw4YNGTFiRK57VIXQpyFDhuTaZmZmhpubG4MGDWLChAkqpBJFkRR/QhQDN2/eVDuCEEIUacePH6dp06aYm5sTFBSEs7MzCQkJrF+/nhUrVrB7927q1KmjdkxhoLKystSOIIoJKf6EKAZ2796tdgQhhCjSRowYQe3atdm2bRuWlpa67Y8fPyYwMJARI0awa9cuFRMKIcR/J8WfEMWYVqslLi4OJycnjI3ln7tQX0xMzL9On9u5cyfNmzfXYyIhsh05coQ1a9bkKPwALC0tGTFiBJ07d1YpmTBUW7duLdD+0i1Z5Id8GxSiGNq6dSsTJ07k1KlTZGRkcPToUerUqUO/fv1o3Lgxb7/9ttoRhYFq1qwZBw4cwM3NLdfYpk2b6Nq1K48fP1YhmTB0FhYW3L59O8+xO3fuYG5urudEwtAFBQWhKAparVa37Z/P/749MzNTn/FEEWWkdgAhxIsVHh5O27ZtqVKlCt9++22OXxKVKlVi0aJFKqYThq5OnTo0bdqU+Pj4HNtXrlxJcHAwI0eOVCmZMHStW7dmzJgxHDhwIMf2AwcOEBISQps2bVRKJgzV5cuXiYmJ4fLly1y+fJmLFy+i1WrZvHmzbtuTPzExMWrHFUWEXPkTopiZPHkyI0eOZOrUqWRmZtKrVy/dWLVq1Zg5c6aK6YShi4iIoH379jRr1ox9+/ZRqlQpFixYwNChQ5k6dSojRoxQO6IwUGFhYbRr147GjRvj5OSEk5MTiYmJJCYm4uvry6xZs9SOKAxM2bJlczx/cmXP1dU115gQ+SXFnxDFzJUrV/D3989zzNzcnPv37+s5kRB/MTExYcOGDbRu3ZoWLVrQoUMHJk2axNdff83AgQPVjicMmKOjIwcOHODnn3/m6NGjxMXF4erqSv369QkICFA7nhBCvBBS/AlRzLi7u3Py5EmaNWuWa+zYsWNUqFBBhVRC/MXMzIzNmzfTsmVLJk+ezLJly+jevbvasYQAoGXLlrRs2VLtGEII8VJI8SdEMdOnTx8mTpyIs7Mz7du3B7K7fu7cuZPp06czfvx4lRMKQ+Pt7Y2iKLm2P3z4EEtLS2bPns3s2bN1248cOaLPeMKA5be5kKIomJqaUqJEiZecSAghXi4p/oQoZkaPHs21a9fo0aOH7ouKRqMhMzOTAQMGMGzYMJUTCkNTrVq1PIs/IdRmbW1doGPTxcWFDh06MH369FxLQgjxos2bNy/H86ysLBRFYe3atRw6dCjHmKIovPfee/qMJ4ooKf6EKGYURWHu3Ll8+OGH7Ny5k9u3b+Pg4ECzZs2oVKmS2vGEAVq6dKnaEYTI0+LFi/Nd/CUnJ/PHH3+wZMkS0tPT+eabb15yOmHohgwZkuf2adOm5domxZ/ILyn+hCgGqlSpQoMGDfD19cXPz48qVapQoUIFub9PFDoPHjzg4cOHuLq65hqLi4vDxsYGa2trFZIJQ9SzZ88Cv6ZmzZqMGTNGij/x0mVlZakdQRRDUvwJUQzUrVuXXbt26c5ilyxZEl9fXxo0aICfnx9eXl75WqC44U/D9ZDWcOwPClM7QqHTp08f7OzsWLhwYa6xTz/9lHv37rF69WoVkgmRPw0aNJCGMEKIIkuKPyGKgRUrVgAQHx/PwYMHiYqKIjIykgkTJpCWloaJiQm1a9fGz88PjUZDx44dVU4sDNW+fftYsGBBnmOBgYEybUkUeq+99hqLFy9WO4YQQjwXKf6EKEZcXFzo2LGjrrhLS0vj2LFjREZG8uuvv/Lll1/y5Zdf6haKFULf7t2799RGGebm5ty9e1fPiYQQQgjDIcWfEMVUbGwskZGRREZGcvDgQX777TcsLCzw9vZWO5owYBUrVmTLli15Lpq9detWypcvr0IqIYQQwjBI8SdEMZCZmcnJkyd1xV5kZCSxsbG89tpr+Pr60qtXL3x9falZs6asUyVUNXToUAYOHIipqSk9e/bE1dWVuLg4li1bxty5c5k/f77aEYUQQohiS4o/IYoBW1tbAOrXr4+vry/du3fH19eXUqVKqZxMiJz69etHQkICU6dOJSzsr4Y45ubmfPbZZ/Tr10/FdMKQlShRgqioKOrVq5dr7Pjx49SrV0+mzAshijwp/oQoBmxsbLh58yYJCQkkJiZy8+ZNbt26pVrx52pRijdfbUoVu7J4WLtw7m4MIcfn5dpvUYNxOFs45Nh2N/U+7+z79F///ql1B1HDoQLbrx9izrk1OcZKmdmzuOE4jBQjQo7N5fTdS7r3OpjwG4svbv5vP5z4z8aNG8fQoUOJiori9u3bODo64uvri52dndrRhAHTarVPHUtPT8fYWL4yCXUlJiby5ZdfcuTIEeLi4nB1daV+/foMGzYMZ2dnteOJIkI+yYQoBuLj44mJidF1+fz6668ZMGAANjY2+Pj44Ovri6+vL/Xr18fGxual5/GwdsarVBV+v3cFY+Xfp5nuiTvO5msHdM8zsjLy9R6PM1LQONVk3vl1ZGr/WgupkUttUjLTsDTOubTF5N+W8CD9UQF+CvEy2dnZSbt8obqrV6/y559/6p6fPHmSlJSUHPukpKSwbNkyypUrp+d0Qvzl4MGDBAYGYmxsjL+/P56eniQmJrJgwQLmzJnDtm3b8PPzUzumKAKk+BOimHjttdd47bXX6N69OwAPHz7k8OHDHDp0iMjISGbPnk1SUhLVqlXjt99+e6lZjtw8x+GbZwEIqdkDWxOrp+57J/U+v9+7UuD3OHP3EtXsX6OOYxWO3jqn297IpRZHbp6liWvdHPvHPLhe4PcQLHjZVgAAIABJREFUL8+BAweIjo7O9UUbYNCgQSokEoZoyZIlTJw4EUVRUBTlqUuNWFhY8N133+k5nRB/GTJkCHXr1mXz5s1YWf31O/Xhw4cEBQUxdOhQTpw4oWJCUVRI8SdEMWVtbU2NGjV48OAB9+7d4+7duxw+fJgzZ8689PfW8vTpUy9KelYGh2+eoZFLbV3xV8ayFBVs3Vl5aXuu4u+f0z4/qNaFstauLLu4hT6V2uJq6cil+9eZe34tVx8lvPT8hiohIYHmzZtz7tw5FEXRTbVTFEW3jxR/Ql8GDRrEW2+9hVarpWbNmqxYsYKaNWvm2MfU1BQPDw/MzMxUSikEXLhwgXXr1uUo/CD7d/2IESMIDg5WKZkoaqT4E6KY0Gq1nD59OkfHz8uXL6PVailZsiQ+Pj6EhoYWumkhAa/Up41HQ9Iy0zl1J5rvon/kZkr+1nrbG3+S0TXfxdTIhLSsdBq51OFC0hUSku/k6/Wlze3pXakNay7/SmpWOn0qtmF0zXcZHDXjv/xI4l989NFH2NnZce3aNdzd3Tl8+DDOzs58//33hIeHs2XLFrUjCgNSunRpSpcuDcDly5dxdXXF1NRU5VRC5Obp6Ul8fHyeY3FxcVSpUkXPiURRJcWfEMVAQEAAR44c4cGDB2i1WipWrEijRo0YM2YMGo0GT09PtSPm6fDNM1y4d4XbKUm4WTnT7bU3mOY1hCGHZvA4I/d0wH86eSea9KwMvEt5cjDxNxo612L79UP5fn8bY0tGHZ3Djce3ADBCYVyt3rhZOhH7OPG5fy7xdHv37mX27Nm4uroC2SctPDw8GDt2LFlZWQwaNIjt27ernFIYisePH2NpaQlkF4IZGRlkZDz9vuMn+wqhb3PmzOGdd97B2tqa9u3bY2ZmRmpqKhs3buTzzz8nPDxc7YiiiJDiT4hiICUlhYEDB+Ln54dGo8HR0VHtSPny7e8/6B6fTbrMhaQ/+crnI1qUqcePV/c98/VZ2iwiE0/TyKU21x8n4mblxIGEU9gY5+8LWkLKXV3hB+imezqa20nx95IkJSVRunRpjIyMsLW1JTHxr//PGo2GadOmqZhOGBobGxvd8g7W1tY5ph/nRZZ6EPpUunTpHMfko0eP6NatG5A93fPhw4dA9lI5HTp0yPF5KsTTSPEnRDGwb9+zC6Wi4MqjeGIf36SCzSv5fs2++JN8Wrsfd1Pvc+5uDHdS7+e7+HuUkZzj+ZNOo6ZGJvkPLQqkXLlyxMXFAVCtWjVWrFhBUFAQAJs3b8bBweHfXi7EC7V48WLKly+ve/ys4k8IfRo8eLAck+KFk+JPCFHIFKxdzJm7l3iY8ZhW7hoWXFj/0lKJF6N169bs2LGDTp06MW7cONq1a4ebmxsmJiZcvXpVrvwJverRo4fucc+ePdULIkQePv30U7UjiGJIij8hRKFR1soFN0snfo7N/317WrSsvbyT1x0qcjDh/15iOvEiTJ06Vfe4VatWREZGsnHjRpKTk/H396dVq1YqphNCiMItLS2N06dPc+fOHRwcHKhRo4Y0KRIFIsWfEOKFMzMywatUVQAczeywMDbHzym7ffqxW+dJzUrHq1RVmrrW5ejNc9xOvY+blRNdyvlzMyWJX28cKdD7/XTtAD/9baF4UXR4eXnh5eWldgwhSEtL48svv2Tjxo1cv349zzUo5Z4qoabp06czdepU7t+/r1smx87OjrFjxzJy5EiV04miQoo/IcQLZ2dqTcjrPXNse/K89/5JJKbc5VZKEvYm1vSr3B4rYwsepD/i+O0LhP+xleTMVP2HFi/N48ePC7S/dFQUanjvvfdYsWIF7dq1o1mzZnI1RRQqX375JSEhIQwcOJDOnTvj7OxMQkICERERhISEYGZmxrBhw9SOKYoA5cmZg+LAy8tLe+zYMbVjPFPDn4arHaHY2B8UpnaEYkWOzRdLjs9sRkZGBWpakFdHRTk2Xyw5NnMrWbIkU6dOZeDAgQV+rRyfL5Ycn7lVrFiRTp06MXny5FxjH3/8MREREfzxxx+5xuTYfLGKyrGpKMpxrVab57QaufInRDF048YNfvrpJ2JjY3NNXVIURZpqCL2SLoqiKHBwcMDDw0PtGELk6dq1azRt2jTPsSZNmjBr1iw9JxJFlRR/QhQzGzdupGvXrmRmZuLk5JRr6pIUf0LfpIuiKArGjx/PrFmzaNy4MVZWVmrHESIHDw8PduzYQYsWLXKN/fLLL3LiQuSbFH9CFDNjx44lICCApUuXypppoki4ffs2jo6OascQBq5Hjx6cO3cODw8P6tati729fY5xRVGIiIhQKZ0wdMOGDWPYsGHcuXOHt956C2dnZxITE1m7di1Lly5l9uzZakcURYQUf0IUM9euXWPOnDlS+IlCJTIykh9//JH09HTat29Pw4YNCQ8PZ+TIkdy6dQsLCwsGDBjAtGnTMDaWX01C/2bNmsWMGTNwcXHh0aNHpKenqx1JCJ0hQ4ZgZmbGxIkTdVPptVotZcqUYcGCBfTt21ftiKKIkN+wQhQzGo2G33//Pc+pIUKoYePGjQQHB+Pk5ISlpSVfffUV06dPZ+zYsfTv35+qVaty+vRpvv76a1xcXKRluVDF559/zrBhw/jiiy/kHlVRKPXr14++ffsSGxtLXFwcrq6uuLm56eV4dbUoxZuvNqWKXVk8rF04dzeGkOPzcuxjrJTgo+rdqWjrTkkzW1IyU7l4/xrL/9jGpQex//r3T607iBoOFdh+/RBzzq3JMVbKzJ7FDcdhpBgRcmwup+9eAmBRg3EcTPiNxRc3v9gftpiT4k+IYiYsLIzu3btjbW2Nv79/rqlLIK30hX5NnTqVLl26sHz5chRFYebMmYwaNYrQ0FBCQkJ0+zk7O7Ns2TIp/oQqtFotQUFBUviJQk1RFNzd3XF3d9fr+3pYO+NVqgq/37uCsVIiz32MFCO0aFn7507iHt/C0ticdmUbM6Xueww7PIuE5Dv/+h6PM1LQONVk3vl1ZGqzdNsbudQmJTMNS2PzHPtP/m0JD9If/fcfzsBI8SdEMVOzZvZi6r169Xrql5i8WukL8bJcuHCBKVOm6I7HPn36MGrUKBo2bJhjv0aNGkkzIqGanj17sn79epk1IQqNefPmPXsnsgtCU1NTPDw8aNiwIebm5s9+UQEduXmOwzfPAhBSswe2JrmbIqVlpTP99PIc207diWZVk8/wLV2DH67u/df3OHP3EtXsX6OOYxWO3jqn297IpRZHbp6liWvdHPvHPLj+vD+OQZPiT4hiRtrqi8Lm4cOH2Nra6p4/efzPK9AWFha5liYRQl/c3NwICwujRYsWNGvWLM+GL++9955K6YQhGjJkSIFfU6pUKXbs2EGtWrVeaBYtz7cueEpmGmmZ6Rgb5X218O/SszI4fPMMjVxq64q/MpalqGDrzspL23MVf/+c9vlBtS6UtXZl2cUt9KnUFldLRy7dv87c82u5+ijhufIXR1L8CVHMSFt9URjldUJCTlKIwmT48OzFsGNjY9m1a1eucSn+hL5lZWU9e6f/SUlJ4Y8//uD9999n6NCh7N+//yUmezYjxQhbEys6lG1CFlr2xp/M1+v2xp9kdM13MTUyIS0rnUYudbiQdOWZU0afKG1uT+9KbVhz+VdSs9LpU7ENo2u+y+CoGf/lxylWpPgTopg6d+4cx48f59q1a/Tu3RsXFxf++OMPnJ2dsbGxUTueMDA9e/bMtXbaO++8k+Pq36NHcu+GUE9BvmgLUdiYm5tTvXp1xowZQ4cOHVTN8tarzehZMQiApLQHfHpyITdT7ubrtSfvRJOelYF3KU8OJv5GQ+dabL9+KN/vbWNsyaijc7jx+BYARiiMq9UbN0snYh8nFvyHKYak+BOimHn48CG9e/dm3bp1mJiYkJGRQcuWLXFxcWHs2LF4eHgwc+ZMtWMKA9KjR49c26pVq5bnvvXq1XvZcYQQotjy8fFhy5Ytqmb49cZRTt2JxsHUlkB3P8bX6sOYY3O5lo+pl1naLCITT9PIpTbXHyfiZuXEgYRT2Bjnr1FdQspdXeEH6KZ7OprbSfH3P1L8CVHMDB8+nMjISHbu3Imfn1+OG78DAwOZOXOmFH9Cr5YsWaJ2BCGEMAg2NjY0btxY1QxJaQ9ISnsAwLHbF5jnO4rgV5sRdnZVvl6/L/4kn9bux93U+5y7G8Od1Pv5Lv4eZSTneJ6RlQGAqZFJAX6C4s1I7QBCiBdrw4YNTJs2jaZNm1KiRM4brMuWLcuVK1dUSiaEEEIIQ5KlzeLKwzicLRzz/Zozdy/xMOMxrdw17EvI372CIv+k+BOimElOTsbRMe8P2QcPHuQqCIUQQgghXgYTI2PK27jlu2ELZHcWXXt5J0dunuVgwv+9xHSGSaZ9ClHMeHt7Ex4eTsuWLXONrVu3Do1Go0IqIYQQQhRVZkYmeJWqCoCjmR0Wxub4OWWvK3zs1nlSs9Jp5FIbL8cqHL/9O3dS71HSzJbWbn6UNLN95hp///TTtQP8dO3AC/85hBR/QhQ7kyZNwt/fnxYtWhAcHIyiKGzdupUvvviCdevWsW/fPrUjCiFEkZGUlJRrzT8h1PB///d/TJ48mWPHjhEbG0tUVBR16tTh448/pkGDBrRq1eqlvbedqTUhr/fMse3J8977J5GYcpfYR4k0dalL30ptsTax5E7qfX6/d4W5h2WdvcJEij8hipmGDRuyc+dOxowZw5AhQ9BqtUyYMAEfHx9+/fVXvL291Y4ohBCFzvz583nw4AGjRo0C4NSpUwQFBREXF0etWrXYtGkTbm5uKqcUhmrbtm20bdsWjUbDu+++y8SJE3VjZmZmzJkz56UWf4kpdwn6Zfi/7hPz4DoTT333XH9/yPF5/zp+5VF8rvfvc+CzHM+/PLs61+vyk9vQyD1/QhRDfn5+7N+/n/v37xMbG8uDBw84ePAgfn5+akcTQohCac6cOdja2uqeDxs2jDJlyrBixQqysrIYM2aMiumEoQsJCaFnz57s3buXjz/+OMdYrVq1OHXqlErJRFEjxZ8QxUzv3r25fPkyABYWFpQpU0a3kPaVK1fo3bu3mvGEIDw8nBUrVuTa/v3337N8+XIVEgkBV69epXLlygDcvHmTgwcPMn36dLp06cInn3zCrl27VE4oDNmFCxfo3LkzAIqi5BiztbXlzp38N1QRhk2KPyGKmaVLl3Lz5s08x27dusWyZcv0nEiInHr27EmvXr1ybe/Vqxc9e/bUfyAhyJ46l5aWBsDu3buxtLSkYcOGADg4OJCUlKRmPGHgnJyciImJyXPs7NmzeHh46DmRKKr0XvwpitJIUZQfFUW5riiKVlGUnv8YVxRF+VRRlBuKoiQrirJHUZRq+s4pRFH2z7OCT5w5c4bSpUvrOY0QOV2+fJk//vgj1/ZLly499cuNEC9bvXr1mDt3LmfPnuWrr76iZcuWuqVxYmJiKFOmjMoJhSHr0qUL48eP58CBvzpgKopCdHQ006ZNo3v37iqmE0WJGg1frIEzQPj//vzTKOAjoCfwOzAe+EVRlMparfaBvkLmR0REBAsXLiQ6OpqUlJRc44mJiSqkEoZo9uzZzJ49G8j+ZdC+fXvMzMxy7JOSkkJCQoJcWRGqK1u2bJ7b5cy1UNOsWbNo06YNNWrUwN3dncWLF+vGIiIi5J5poapJkyZx7tw5GjdujIuLCwDt2rUjPj6egIAAxo4dq3JCUVTovfjTarVbga0AiqIs/fuYkn254gPgc61Wu/5/23oAiUA34Bu9hv0XK1eupHfv3vTs2ZNdu3bRu3dvsrKy+PHHH7G3t+fdd99VO6IwIJ6ennTs2BGtVktYWBhNmzbF1dU1xz6mpqZUqVKFTp06qZRSiL/cvXuXM2fOcO3aNVq1akXJkiVJSUnB1NQUIyO5I0Hon6enJ5cuXeL27ds4ODjkmEExc+ZM3RduIdRgZmbGTz/9xM6dO9m5cye3bt3CwcGB5s2b4+/vr3Y8UYQUtqUeygEuwI4nG7RabbKiKPsADYWo+JsxYwaffPIJY8aM4dtvv2XQoEHUqVOHBw8e4O/vr2uwIYQ++Pv76z78bWxs6Nevn0xREoVSZmYmISEhzJ07l+TkZBRF4ejRo5QsWZKOHTvi5eWVo4W5EPrm6OiYa1uNGjVUSCJEbs2bN6d58+ZqxxBFWGEr/p6cVvvnSpAJwCt6zvKvLl68iJ+fHyVKlKBEiRLcv38fyP7iPXr0aD788ENGjBihckphiPbu3Uvnzp3zLP6io6MZOHCgdK0Tqhk7diwLFy7k66+/pmnTprz22mu6sXbt2rFgwQIp/oQq8jMrYs2aNXpIIsTTpaamcv369TxvN/L09FQhkShqClvxV2CKovQH+oN+7xextbUlNTUVgFdeeYXz58/TpEkTALRaLbdv39ZbFiH+bs+ePbqTEf90//599u3bp+dEQvwlPDyczz//nF69epGZmZljrHz58tLwRagmry7Jd+/e5cKFCzg6OuqWgRBCDTdu3KB///5s27Yt15hWq0VRlFyfqULkpbAVf/H/+68zcPVv253/NpaDVqv9FvgWwMvLS/tS0/2Nt7c3//d//8cbb7xB27ZtCQ0NxdjYGFNTU0JDQ/Hx8dFXFCFyyavbZ1paGrt27ZL7VoSqkpKSKF++fJ5jaWlp8uVFqGb37t15br927RodOnTgww8/1HMiIf7St29fTpw4QVhYGJ6enpiamqodSRRRha34u0x2kecPHAVQFMUcaAiMVDFXLiEhIVy5cgWA0NBQrly5wnvvvUdWVhbe3t58802huT1RGICJEycSGhoKZBd+/3byYeTIQvVPSRiY6tWrs2nTJlq0aJFrbNu2bdSpU0eFVEI8nbu7OyEhIYwaNYo2bdqoHUcYqIMHD7Jw4UJp2ib+M70Xf4qiWAMV/vfUCPBQFKUWcEer1V5VFOVLYKyiKBeAaGAc8BBYqe+s/8bHx0f3Bdve3p5NmzaRmppKamoqtra2KqcThiYwMJBSpUqh1WoZNmwYH330Ea+++mqOfZ50+3yyaLEQahg3bhwdO3YkOTmZ4OBgFEXh1KlTbNy4kW+++YYff/xR7YhC5FKiRAliY2PVjiEMmJOTExYWFmrHEMWAGlf+vIC/z62Y+L8/y8he2286YAHMBUoCh4GAwrTGX0pKCnZ2dkRERNC+fXvddjMzs1xrqwmhD97e3nh7ewPZTYdat25NqVKlVE4lRG7t2rVj5cqVjBo1SreOWt++fXnllVdYvnw5b7zxhsoJhaE6d+5crm1paWmcP3+eTz75RPcZK4QaQkNDmTZtGo0bN5aLDOI/UWOdvz1A7huS/hrXAp/+70+hZG5ujpOTE8bGhW3WrBDQo0cPtSMI8a86depEp06diI6O1q1VVbly5TzvVRVCX6pXr57nMajVavHy8uK7775TIZUwZP+c4nn16lXKli2Lt7c39vb2OcYURSEiIkKf8UQRJdXLcxowYABfffUVb7zxBiYmJmrHEQauXr16LF26FE9PT+rVq/fM/Y8cOaKHVELktmvXLpo2bYqiKFSqVIlKlSqpHUkIIO+GL+bm5ri5ufHKK4VqtSlhIP7ZgfZJs6z09PQ8u9MKkR9S/D2npKQkzpw5w6uvvkrz5s1xdnbOccZQURSmTZumYkJhSKpVq6a7F8DT01OuoIhCq0WLFri4uPDWW2/RpUsXNBqN2pGEAKBx48ZqRxAih6d1oBXiv5Di7zmtX79ed3/f/v37c41L8Sf0acmSJbrHS5cuVS+IEM9w+vRpIiIiWLNmDV9//TXu7u506tSJLl26ULduXbXjCQOXkZHB+vXrOXDgAHfu3MHBwYGGDRvy5ptvyq0eQlWhoaH07duXMmXK5BqLi4tj4cKFjB8/XoVkoqgxUjtAUXX58uV//SMLFQs1pKSkYGZmxg8//KB2FCHyVK1aNUJDQ7lw4QInTpygW7dubNy4EW9vbypUqMC4cePUjigMVGJiIl5eXnTt2pUtW7YQExPDli1b6NKlC97e3jLNTqhq4sSJT+04e+PGDSZOnKjnRKKokuJPiGJEmhGJoqRWrVpMnTqVP/74gx9//JHk5GSmTp2qdixhoIYPH87t27c5dOgQMTExREVFERMTw+HDh7l9+zbDhw9XO6IwYFqt9qm3dMTGxlKyZEk9JxJFlXxDLICtW7fSoEEDbG1t2bp16zP3DwwM1EMqIXKSZkSiqLh79y7r168nIiKCvXv3YmFhQbdu3dSOJQzU1q1b+frrr3M1zfL29mbq1KkMHTpUpWTCUC1btoxly5YB2bcTvffee7mWeUhJSeH06dMEBASoEVEUQVL8FUBQUBCHDh2iXr16BAUFoSgK2StT5KYoCpmZmXpOKIQ0IxKF2/3799m4cSMRERHs3LkTY2NjWrduzerVqwkMDMTc3FztiMJApaamYmNjk+eYjY0NaWlpek4kDJ2lpSWOjo5A9pU/Ozs7HBwccuxjampKq1atGDRokBoRRREkxV8BXL58GVdXV91jIQojaUYkCrPSpUtjZGTEG2+8wdKlS2nbti1WVlZqxxICHx8fpk2bRrNmzXIck48ePWLatGn4+PiomE4YouDgYIKDgwHo1asX48ePp1y5ciqnEkWdFH8FULZs2TwfC1GYyIkJUZh9++23tG/fHjs7O7WjCJHDrFmzaNq0Ke7u7gQEBODs7ExiYiLbt29Hq9WyZ88etSMKA/b3rt5C/BfS8OU/SE1NZf78+fTp04eAgAAuXrwIQEREBOfPn1c5nTAkLVu25LPPPmPnzp08evRI7ThCPFWPHj2k8BOFUq1atbh48SL9+/fn5s2b/PLLLyQmJjJw4EAuXrzI66+/rnZEIYT4z+TK33OKjo7G39+fe/fuUbduXfbs2cODBw+A7Kl2W7ZsITw8XOWUwlA8ePCAyZMnk5qairGxMTVq1ECj0eDn54efnx/u7u5qRxRC588//+T7778nOjqalJSUXONr1qxRIZUQUKpUKT7//HO1YwghxEsjxd9zGjZsGB4eHmzevBlra2tMTU11Y40bN2b06NEqphOG5uDBg6Snp3P8+HEiIyOJjIxkw4YNzJ07F0VReOWVV3TFoEajkcW0hWqOHz9Oo0aN8PDwIDo6mpo1a3Lv3j3+/PNP3NzcqFChgtoRhYF699136dq1KwEBAZQoUULtOEII8VLItM/ntH//fkJCQrC3t8+17oqzszNxcXEqJROGysTEBB8fH4YPH866deu4fv06MTExLF++nLZt2xIdHc2HH35I/fr11Y4qDNjIkSMJDg7mzJkzaLVaFi1aRExMDAcOHEBRFEaNGqV2RGGgzp8/T+vWrXF2dqZfv37s2rXrqR29hRCiqJLi7zmZm5uTnJyc59j169ext7fXcyIhcsrIyCAxMZHExEQSEhJITEwkKytLpoAKVZ06dYquXbtiZJT96+fJtE+NRsOECRMYM2aMmvGEATt69CiXLl3io48+4tixY7Ro0QJXV1eGDBmSZ+dkIdRw7tw5li9fzpQpU4iPjwfgjz/+0N16JMSzSPH3nPz9/ZkyZQr37t3TbVMUhdTUVObMmSMLvAu9u3XrFj/++CNjxoyhUaNG2NnZ0aRJE9avX0+5cuWYM2cON27ckG6gQlWKomBqaoqiKDg5OXHlyhXdmLu7u65xlhBqKFeuHCEhIZw8eZLz588zaNAg9uzZQ5MmTfDw8FA7njBgDx8+pFOnTtSoUYO+ffvyySefcOPGDQDGjh3LxIkTVU4oigq55+85zZgxAz8/PypUqIC/vz+KohAaGsrZs2dJS0tjw4YNakcUBqRSpUpcunSJsmXL4uvrS3BwMGFhYdSqVQtjY/lnLgoPT09PLl26RNOmTfH19eWLL77Ay8sLU1NTpk+fTvny5dWOKAQAlStXpnfv3lhZWREWFsb169fVjiQM2PDhw4mMjOTXX3/Fz88Pc3Nz3VhgYCAzZ85k5syZKiYURYV8K3xO7u7u/Pbbb4SFhbFz507Kly9PXFwcwcHBDB8+HEdHR7UjCgMSExODhYUFVatWxdPTk2rVqlGlShUp/ESh079/f93VvilTphAQEECVKlUAsLKyYt26dWrGE4L4+HjWrl1LREQEhw4dwt7eng4dOtClSxe1owkDtmHDBmbPnk3Tpk3JzMzMMVa2bNkcsyiE+DfyzfA/KFmyJJMmTWLSpElqRxEG7t69exw+fJioqCgiIyMJCwvj/v37VKtWDY1Gg6+vL76+vtJJUagiPj4eFxcXAN555x3d9qpVq3L+/HmioqJITk7Gx8cHJycntWIKAzd//nzWrFnDgQMHsLKyol27dowdO5aAgAA5kSZUl5yc/NQLCw8ePJAOtSLf5NNMiGLAysqKZs2a0axZM922J1+qo6Ki+Pzzz/n9999xcHDA19eXTZs2qZhWGJoyZcpQrlw5NBoNDRo0wM/Pj+rVqwNgbW2Nv7+/ygmFyO5EGxQUxNq1a2nVqhVmZmZqRxJCx9vbm/DwcFq2bJlrbN26dWg0GhVSiaJIir8C8Pb2zrWsw785cuTIS0wjxL+rWrUqVapUoU6dOtSqVYutW7fy888/89NPP6kdTRiYb775RndVesWKFSiKgo2NDT4+Prq1J318fLCyslI7qjBgiYmJWFpaqh1DiDxNmjQJf39/WrRoQXBwMIqisHXrVr744gvWrVvHvn371I4oiggp/gqgWrVqBSr+hNC3e/fu6b5kR0ZGcuTIER49egRAlSpV6N27N35+fiqnFIamX79+9OvXD4A7d+5w8OBBIiMjiYqKYtq0aTx+/BhjY2Nq1KiBn58fX331lcqJhSEKCgqicuXKhIWFYWFhkWPs/PnzDB48mF27dqmUThi6hg0bsnPnTsaMGcOQIUPQarVMmDABHx8ffv31V7y9vdWOKIoIKf4KYOnSpWpHECJP/fv3JzIykgsXLpCVlYWlpSX16tVj2LBBHCC+AAAbdklEQVRhaDQaNBqNrD0pCgUHBwfatGlDmzZtAMjMzGTv3r2EhYWxbds2Tp06JcWfUMWePXs4dOgQUVFRbNy4kXLlyunG7t+/z969e1VMJwT4+fmxf/9+kpOTuXv3Lvb29nK1WhSYFH9CFAM///wzGo2GAQMGoNFoqFWrltz8LQqtmJgY3dXpyMhIzp49q7v3z9fXV+14woCtWrWKGTNmULduXb7//ntZs1cUShYWFrmuTguRX1L8FcCoUaMYNmwYbm5ujBo16pn7T58+XQ+phICrV6+qHUGIp/p7oRcZGcnNmzepWrUqPj4+DB06FF9fXzw9PdWOKQSurq7s2bOHoUOH0rZtW8aPH8/48ePVjiUEAMeOHWPDhg3ExsaSkpKSY0xRFCIiIlRKJooSKf4KYO3atXTv3h03NzfWrl37r/sqiiLFnxBCAA0aNMDKyop3332X5cuXU69ePezs7NSOJUSejI2NmT9/Pl5eXgwZMoTjx48zePBgtWMJAzd//nyGDBmCo6MjFStWxNTUVO1IooiS4q8ALl++nOdjIYQQTxccHExUVBTz589nx44dunUnNRoNNWvWlEZaolDq06cP1atX56233qJz585qxxEGbubMmfTq1YsFCxbIupPiP5GjRwghxEv1ZCrS9evXdV0+ly1bxgcffICZmRne3t66gtDHx+epCxkL8TL16NGD0qVL59hWv359jh07xjvvvMPFixdVSiZE9lIkXbt2lcJP/GdGagcoqtavX8+iRYt0zy9fvqzrqNixY0eSkpJUTCeEEIXPK6+8QnBwMGFhYRw6dIj79++zbds2WrZsydGjR2nXrh3Ozs5qxxQGasmSJTk6fD7h7OzMjh07ZMaPUFWrVq04fPiw2jFEMSCnD57TZ599xrvvvqt7PnToUG7dusWYMWP45ptv+Pjjj5k7d66KCYUQovCKjY3N0QTm1KlTZGVlyX0sQlWpqaksXryYY8eOce3aNebOnUvFihWJiIigZs2aVK1aVe2IwkANHjyY/v37k56ejr+/f57LN0njLJEfUvw9p5iYGGrUqAFkL6y9Y8cONm7cSOvWrfHw8GDMmDFS/AlVrF+/nqSkJPr06QNkX5Xu3r07586do3nz5ixatEjW/BN6lZmZycmTJ3WFXlRUFLGxsWi1WkqXLo1GoyE4OBiNRoOXl5facYWBio6Oxt/fn3v37lG3bl327NnDgwcPANi/fz9btmwhPDxc5ZTCUDVt2hSAiRMnEhoammNMq9WiKAqZmZlqRBNFjBR//8GTJgV79+6lRIkStGjRAgA3Nzdu3rypZjRhwOSqtChsbGxsSE1NBbLPTLds2RI/Pz80Gg0VKlRQOZ0Q2YYNG4aHhwebN2/G2to6x1Xoxo0bM3r0aBXTCUO3e/dutSOIYkKKv+f0+uuvs2LFCnx8fPjuu+9o2rQpZmZmQPaaa05OTionFIZKrkqLwmbEiBFoNBp8fX1liQdRaO3fv5+1a9dib2+f6wqKs7MzcXFxKiUTIvsEhBAvghR/z2nKlCm0adOGZcuWYW1tzS+//KIb++GHH6hfv76K6YShk6vSojD55xQlIQojc3NzkpOT8xy7fv26TJcXQhQLUvw9pwYNGnD16lWio6MpX758jl8KvXv3lqlMQjVyVVoIIQrO39+fKVOm0KJFC6ytrYHsE2mpqanMmTOHwMBAlRMKQ+Pk5MT27dupXbs2pUuXfuaaqImJiXpKJooyKf7+AxsbG+rWrZtru/yCEGqSq9JCCFFwM2bMwM/PjwoVKuDv74+iKISGhnL27FnS0tLYsGGD2hGFgRk8eLBu+ZvBgwc/s/gTIj+k+PsPHjx4wKZNm4iOjiYlJSXX+PTp01VIJQydXJUWQoiCc3d357fffiMsLIydO3dSvnx54uLiCA4OZvjw4Tg6OqodURiYCRMm6B5/+umn6gURxYoUf8/p0qVLaDQakpOTefToEaVLl+bOnTtkZGRQsmRJ7OzspPgTqpGr0kIIUXAlS5Zk0qRJTJo0Se0oQgjxUkjx95w+/PBDvL29Wbt2LVZWVmzdupXXX3+diIgIQkJCiIiIUDuiMCAFbagxfvz4l5RECCGEEC9Cp06dCrT/mjVrXlISUZxI8fecjhw5wnfffadrpJGWlkaJEiXo1q0bt27d4v333ycyMlLllMJQzJkzJ8fz5ORkHj9+DIC1tTUPHz4EwNLSEktLSyn+hF41a9asQPvv2rXrJSURIqdy5coV6D6qmJiYl5hGiJykO7d4GaT4e04pKSnY2tpiZGSEg4MDN27c0I1Vr16d3377TcV0wtD8/RdEVFQU3bt357PPPqNDhw5YWFiQnJzMhg0b+OSTT1ixYoWKSYUh+ue9UlFRUSQkJFC3bl2cnJxITEzkxIkTODs74+vrq1JKYYg6duyYo/hbvXo1jx8/xt/fX3ds/vLLL1hZWdGlSxcVkwpDJAu7i5dBir/nVKlSJa5cuQJA7dq1WbBgAYGBgZQoUYJFixZRpkwZlRMKQzVs2DDGjh1Lt27ddNssLCzo3r07jx49YvDgwZw4cULFhMLQrF27Vvd40aJF/P7770RGRuLh4aHbfvXqVYKCgvD391cjojBQM2fO1D2eMmUK5cuXZ8uWLVhZWem2P3z4kKCgIGxtbdWIKESe0tPTMTExUTuGKIKM1A5QVHXp0oVTp04BMGnSJA4fPoytrS02NjasWbNGujIJ1Zw5c+apJx9eeeUVzp8/r+dEQvxl8uTJhIaG5ij8ADw8PPj000+ZMmWKSsmEoZs7dy4jR47MUfhB9tT5ESNGMHfuXJWSCZEtMjKSVq1aYWNjg7m5OTY2NgQGBhIVFaV2NFGEyJW/5zR8+HDdYx8fH86cOcPPP/9McnIyzZo1o3r16iqmE4asUqVKhIWF0bx5c909qZA9VTksLIzKlSurmE4Yuvj4eFJTU/McS0tLk0WKhWru379PQkJCnmPx8fG6e6eFUMMvv/xC69atqVy5MiNHjsTZ2ZmEhATWrVtHkyZN2LJlCy1atFA7pigCpPh7DikpKQwdOpQ+ffrg4+MDZK8P1K9fP5WTCZHd/CUwMBA3N7dc9608fvyYbdu2qR1RGLAmTZowevRoypcvj5eXl2770aNHGT16NI0bN1YxnTBkbdq0YeTIkdja2tK2bVtMTU1JS0tj06ZNjB49mjZt2qgdURiwjz/+mLZt27J27doc96mOHz+ejh07MnbsWCn+RL7ItM/nYG5uzurVq/Nc2F0ItTVq1IiLFy/Sq1cv4uLi2L59O3FxcfTq1YuLFy/SqFEjtSMKA/btt9/i4OBA/fr1KVOmDLVq1aJMmTL4+Pjg4ODAt99+q3ZEYaDmz59Po0aN6NSpExYWFtjb22NhYUHnzp1p2LAh8+fPVzuiMGCnT5+mX79+eXan7d+/P6dPn1YhlSiK5Mrfc2rWrBm7d++mSZMmakcRQic1NZWZM2cSFBTE9OnT1Y4jRC5ubm6cOHGCrVu3cvToUeLj43FxccHb25vAwEC14wkDZmdnx8aNGzl37lyuY9PT01PteMLA2dvbc+nSpTzHLl26hL29vZ4TiaJKir/nNHjwYPr27cujR48IDAzE2dk519kY+WUh9M3MzIzJkyfToEEDtaMI8a8CAwOl2BOFkqenp/z+FoVOcHAwISEh2Nra8tZbb2Fubk5KSgrr1q1j7Nix9OjRQ+2IooiQ4u85tWzZEoCwsDDCwsJyFH5arRZFUcjMzFQrnjBg9evX58SJE3LvlCjUUlNTuX79ep7T5+WLt1BTdHQ0sbGxeR6bcsJCqGXatGncvn2bHj160KNHD6ytrXVNiLp27cq0adNUTiiKCin+npMsvCkKq+nTp9OtWzdMTEyeelXa0tJSpXTC0N24cYP+/fvn2XhITpwJNZ07d44uXbpw9uxZtFptrnE5NoWaLCwsWLFiBZ988glHjx4lLi4OV1dXvL29qVKlitrxRBEixd9zkqsqorCqX78+kL3Y+/vvv5/nPvIFRqilb9++nDhxgrCwMDw9PTE1NVU7khAADBgwgNTUVDZs2CDHpihUUlJSsLOzIyIigvbt20uxJ/4TKf5ekKSkJC5duoS7uztOTk5qxxEGbPHixXl2AxOiMDh48CALFy6kU6dOakcRIoeTJ0+yevVqgoKC1I4iRA7m5uY4OTlhbCxf28V/J0dRAa1evZoffviB9PR03nzzTbp3705oaChTp04lLS0NgPbt2xMeHo6VlZXKaYUh6tmzp9oRhHgqJycnLCws1I4hRC7ly5eXJZxEoTVgwAC++uor3njjDUxMTNSOI4owKf4KYOHChQwYMABvb29sbGzo1asXx44dY+nSpUyZMoWqVaty+vRpJk+ezOTJk5kyZYrakYUBu3HjBlFRUdy5cwcHBwd8fX0pU6aM2rGEgQsNDWXatGk0btwYW1tbteMIoTNr1ixGjRpFnTp1eO2119SOI0QOSUlJnDlzhldffZXmzZvnup9fURRp+iLyRYq/ApgzZw4ffPABYWFhAHz//ff06NGD2bNnM2TIECC7C6ixsTELFiyQ4k+oIjMzk6FDh7Jw4cIc9/aVKFGC/v37M2fOHIyMjFRMKAzZhg0buHr1KmXLlsXb2zvX2lSKohAREaFSOmHIQkJCuH79OlWqVOHVV1/Nc920I0eOqJBMCFi/fj1mZmYA7N+/P9e4FH8iv6T4K4BLly4xe/Zs3fN27dqh1WqpW7dujv28vLy4cuWKvuMJAcCECRNYvHgxU6ZMoXPnzjg7O5OQkEBERATjx4/H0dGR0NBQtWMKA3Xr1i3Kly8PQHp6Ojdv3lQ5kRDZqlevTvXq1dWOIUSeLl++rHYEUUxI8VcAycnJOe7je9Iu/8mZmCdMTU1JT0/XazYhnggPD+ezzz5jxIgRum0eHh6MHDkSRVH46quvpPgTqpFlckRhtWTJErUjCCHESyfFXwHl1UVROiuKwiQxMZGaNWvmOVazZk0SExP1nEgIIYoOrVZLbGws165d4/XXX5fmbaJQOXDgANHR0Xk2Jxo0aJAKiURRI8VfAb3xxhu5Wu02b948x7aMjAx9xxJCp1KlSqxevZqAgIBcY6tXr6Zy5coqpBKGbN68eQQHB1O6dGnmzZv3zP3lC4xQy7x58/jss8+Ij49HURSOHj1KnTp1ePPNN2nUqBEffPCB2hGFgUpISKB58+acO3cORVHQarVAzgsQ8tkp8kOKvwKYMGGC2hGEeKZx48bRpUsXrl69yltvvYWzszOJiYmsXbuW3bt3s3r1arUjCgMzZMgQvLy8KF26tK451tMoiiJfYIQqZsyYwSeffMLo0aNp2rQpzZo10401adKEVatWSfEnVPPRRx9hZ2fHtWvXcHd35/Dhwzg7O/P9998THh7Oli1b1I4oiggp/gpAij9RFHTq1Al7e3smTJjA+++/T3p6OiYmJtStW5eff/4Zf39/tSMKA5OVlZXnYyEKk7lz5xIaGsqoUaNydEoGqFy5MtHR0SolEwL27t3L7NmzcXV1BbKnJ3t4eDB27FiysrIYNGgQ27dvVzmlKAqk37sQxcChQ4dyNBkKCAggKiqK5ORk4uPjSU5OJjIyUgo/IYR4ivj4+Fzdu58wMjKSBeCFqpKSkihdujRGRkbY2trmuH9fo9EQGRmpYjpRlMiVPyGKAY1Gg7m5OXXr1sXPzw8/Pz80Gg2Ojo44OTmpHU8YuHPnzhVof09Pz5eURIinq1ChAnv37qV58+a5xvbt2yfHpVBVuXLliIuLA6BatWqsWLGCoKAgADZv3oyDg4Oa8UQRIsWfEMXA9u3biYqKIjIykm+++Ybp06ejKAoVK1bUFYJ+fn5UqVJF7ajCAFWvXj1fXZG1Wi2KouSacieEPnzwwQcMGjQIU1NT3nrrLSC7e/KiRYsICwtj4cKFKicUhqx169bs2LGDTp06MW7cONq1a4ebmxsmJiZcvXpVFngX+SbFnxDFgL+/v25Kp1ar5ezZsxw8eJCoqCj279/PkiVLUBSFkiVLotFo+PHHH1VOLAyJrO0nioK+ffty9+5dQkNDdff4BwYGYmlpyaeffkq3bt1UTigM2dSpU3WPW7VqRWRkJBs3biQ5ORl/f39atWqlYjpRlEjx95I8abIhhL4pikL16tWpXr06AwYMAGDPnj3MnDmTbdu2SUcwoXeNGzdWO4IQ+TJy5EgGDhxIVFQUt27dwsHBAV9fX+zs7NSOJkQOXl5eeHl5qR1DFEHS8OUF0mq17Ny5k759++Ls7Kx2HGGgHj16xK5du/jss88IDAzEwcEBf39/rl27Rr9+/Vi8eLHaEYXQSUpK4vjx4zmaFwihJhsbGwICAujWrRstW7bEzs6O3bt3y5UVoXdxcXF07NjxX7t4bt++nY4dO8pnqMg3ufL3Ahw6dIhVq1axdu1aEhIScHBwoGvXrmrHEgZk5cqVREZGEhkZyenTp7Gzs8PHxweNRsNHH31EvXr1sLGxUTumMGCrV6/mhx9+ID09nTfffJPu3bsTGhrK1KlTSUtLA6B9+/aEh4djZWWlclphSJKSkvj555+5du0a5cqVo127drqZO2vXrmXatGmcOHGCSpUqqZxUGJqZM2cSExNDQEDAU/cJCAggJCSEWbNmyX1/Il+k+HtOp0+fZtWqVaxevZorV65gampKWloaYWFhDB48GGNj+V8r9Oftt9/GysqKd999l+XLl1OtWjW1Iwmhs3DhQgYMGIC3tzc2Njb06tWLY8eOsXTpUqZMmULVqlU5ffo0kydPZvLkyUyZMkXtyMJAnD59moCAABISEnTb6tSpw/r16+nWrRtRUVG6zoqdO3dWMakwRD/99BPDhw//14ZZiqIwYMAAvvjiCyn+RL5IhZIPTzrQxcTEsGrVKlatWsX58+cxNjYmICCASZMm0bhxYzw8PKhdu7YUfkLvRo4cSVRUFEuWLGHp0qXUrVsXX19ffH190Wg0styDUNWcOXP44IMPCAsLA+D777+nR48ezJ49myFDhgDQsmVLjI2NWbBggRR/Qm/Gjh2Lra0tP/zwA6+//jpXrlxh6NCheHt7k5qaSnh4OG+//bbaMYWBunLlSr6WGKlatSp//vnnyw8kigWpUp7h6tWrvP322+zbt48KFSqgKAr169fnm2++oWPHjpQsWRKAe/fuqZxUGLInZ/syMjI4efKkbtmHVatWERsbS7ly5XTFoK+vL3Xq1FE5sTAkly5dYvbs2brn7dq1Q6vV5lpQ28vLiytXrug7njBgx44dY/bs2dSvXx+AypUrM3/+fCpWrMi3334rhZ9QlYWFBffv33/mfg8fPsTCwkIPiURxIA1fnsHLy4uePXsCULZsWbRaLWfOnGHPnj1ERkaSkZGhbkAh/sbY2Bhvb2+GDRvG6tWruXr1KteuXePzzz8nKSmJ999/n3r16qkdUxiY5OTkHPfxWVpaAmBmZpZjP1NTU9LT0/WaTRi2hIQEXn311Rzbnjx//fXX9R9IiL+pU6dOvpZm2rRpk5zUFfkmV/6eYc2aNTRp0gSAy5cvc+jQIVauXMnatWtZuXIlJUuW5M0336RVq1b5WsRYCH1ITU3l6NGjuiYwUVFR3Lx5EwAPDw+V0wlDlNfno3xmisLgaceh3MIh1DZo0CA6d+6MRqOhR48eee4THh7OkiVLiIiI0HM6UVTJJ9szPCn8nvDx8cHHx4cvv/ySXbt2sWrVKtavX8+iRYtQFIWFCxdiaWkpa68Ivbpx44au0IuMjOTUqVOkpaVhbGxMrVq16NatGxqNBo1GwyuvvKJ2XGGA3njjjVxfpps3b55jm8ykEGrI69iE3McnIO30hV517NiR999/n169evH111/TsmVLPDw8UBSFq1evsn37do4dO8aHH35Ihw4d1I4riggp/p6TkZERLVq0oEWLFixYsICtW7eyevVqNm7cyMqVK6lUqRLnz59XO6YwEG5ubiiKQsmSJfH19WXChAn4+fnh7e0t9wEI1U2YMEHtCELkSY5NUdjNmjWLJk2a8OWXXzJz5kxSU1OB7Gnzfn5+bNq0iaCgIJVTiqJEir8XwMTEhHbt2tGuXTseP37MDz/8wOrVq9WOJQzId999h0ajoUqVKmpHESIX+YItCis5NkVR0KZNG9q0aUNGRga3b98GwNHRUaYmi+dSaBu+KIoySFGUy4qipCiKclxRlIZqZ8oPS0tLunXrlq8bdIV4UXr37i2FnxBCCFGMGRsb4+zsjLOzsxR+4rkVyuJPUZTOwGxgClAbiAS2KYoinSqEEEIIIYQQ4jkUyuIPGA4s1Wq1C7Va7XmtVjsUiAPeUzmXEEIIIYQQQhRJha74UxTFFKgL7PjH0A5Ao/9EQgghhBBCCFH0FbriDygFlAAS/rE9AXDRfxwhhBBCCCGEKPoUrVardoYcFEUpA1wHGmu12n1/2z4e6K7Vaiv/Y//+QP//Pa0M/K6vrEIIIYQQQghRyJTVarWl8xoojK2CbgGZgPM/tjsD8f/cWavVfgt8q4dcQgghhBBCCFFkFbppn1qtNg04Dvj/Y8if7K6fQgghhBBCCCEKqDBe+QMIA5YrinIEOAgMBMoAC1RNJYQQQgghhBBFVKG78geg1WojgA+AccApoAEQqNVqr6gaTAghRLGlKIo2H3/+VBTl1f897vm31/ZUFKV3Hn9nz//t+6oefxQhhBAiT4X1yh9arXYeME/tHEIIIQyG7z+eb/z/9u6eNYooCgPwezClNgo2om0a/4AKaqOdta2VQiwljZWKhWnFMlppbx/ECBZapon4kco2IiRNUui12FXWYFRYdxO8zwPDsGcuy+mGlzkzN8lKklsjte0M9p09lWRtpH4lg3vqo8m1BwDj2bfhDwCmqbX2avR3VW0nWd9ZH/pVDQD2tX059gkA+9XOsc+qWk5yLsmZkfHQ5T/8x9WqWqmqrapar6qHVXV44s0D0DVP/gBgPHNJHic5kOTasLax2+KqupfkRpL7SeaTHEtyN8nJqjrdWvsy2XYB6JXwBwBjaK2tVtVGkpldRkR/GH74ZT7J7dbanZH6uyQvk1xK8nRy3QLQM2OfADA9FzK49z6pqpnvR5LXSTaTnN3T7gD4r3nyBwDTc3R4/rDL9SPTagSA/gh/ADA9n4bni0k+/+Y6APxzwh8AjG87yaG/WLeU5GuSE621pcm2BAA/E/4AYHyrSeaq6nIGm79vttbe7lzUWlurqoUkD6pqNsmLJFtJjmfwPuBia+35FPsGoCPCHwCMbyHJbJLFJAczCHXnf7WwtXazqt4kuT48WpKPSZ4leT+NZgHoU7XW9roHAAAAJsxWDwAAAB0Q/gAAADog/AEAAHRA+AMAAOiA8AcAANAB4Q8AAKADwh8AAEAHhD8AAIAOCH8AAAAd+Aa2LpuuCgMPwgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N4ap4TIOvLad",
        "colab_type": "code",
        "outputId": "fa799d5d-0fd8-45f8-b054-9b9056b3af1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "episodes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>parentTconst</th>\n",
              "      <th>seasonNumber</th>\n",
              "      <th>episodeNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0041951</td>\n",
              "      <td>tt0041038</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0042816</td>\n",
              "      <td>tt0989125</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0042889</td>\n",
              "      <td>tt0989125</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0043426</td>\n",
              "      <td>tt0040051</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0043631</td>\n",
              "      <td>tt0989125</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768792</th>\n",
              "      <td>tt9916846</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768793</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768794</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768795</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768796</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tt0985991</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4768797 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst parentTconst seasonNumber episodeNumber\n",
              "0        tt0041951    tt0041038            1             9\n",
              "1        tt0042816    tt0989125            1            17\n",
              "2        tt0042889    tt0989125           \\N            \\N\n",
              "3        tt0043426    tt0040051            3            42\n",
              "4        tt0043631    tt0989125            2            16\n",
              "...            ...          ...          ...           ...\n",
              "4768792  tt9916846    tt1289683            3            18\n",
              "4768793  tt9916848    tt1289683            3            17\n",
              "4768794  tt9916850    tt1289683            3            19\n",
              "4768795  tt9916852    tt1289683            3            20\n",
              "4768796  tt9916880    tt0985991            4             2\n",
              "\n",
              "[4768797 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KkT-qAeutHI",
        "colab_type": "code",
        "outputId": "ca72335b-cfb3-4f74-872d-ba3f79657aec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "top_episodes = pd.merge(top_tv,episodes,left_on='tconst',right_on='parentTconst')\n",
        "top_episodes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst_x</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>tconst_y</th>\n",
              "      <th>parentTconst</th>\n",
              "      <th>seasonNumber</th>\n",
              "      <th>episodeNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10492486</td>\n",
              "      <td>tt10233774</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10492490</td>\n",
              "      <td>tt10233774</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10492492</td>\n",
              "      <td>tt10233774</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10492496</td>\n",
              "      <td>tt10233774</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10492498</td>\n",
              "      <td>tt10233774</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228646</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228652</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228660</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228670</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228688</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>142 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tconst_x titleType  ... seasonNumber episodeNumber\n",
              "0    tt10233774  tvSeries  ...            1             1\n",
              "1    tt10233774  tvSeries  ...            1             2\n",
              "2    tt10233774  tvSeries  ...            1             3\n",
              "3    tt10233774  tvSeries  ...            1             4\n",
              "4    tt10233774  tvSeries  ...            1             5\n",
              "..          ...       ...  ...          ...           ...\n",
              "137   tt5190996  tvSeries  ...            1             4\n",
              "138   tt5190996  tvSeries  ...            1             5\n",
              "139   tt5190996  tvSeries  ...            1             6\n",
              "140   tt5190996  tvSeries  ...            1             7\n",
              "141   tt5190996  tvSeries  ...            1             8\n",
              "\n",
              "[142 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhmJQtzUzDTM",
        "colab_type": "code",
        "outputId": "1b92b12f-5a02-436a-a55d-66031283c046",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(top_episodes.loc[top_episodes.runtimeMinutes != '\\\\N'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yKn0Z3K6BVR",
        "colab_type": "code",
        "outputId": "82741e21-8f6a-4ff6-8f9d-9e6ebe4ed1b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "top_episodes = top_episodes.loc[top_episodes.runtimeMinutes != '\\\\N']\n",
        "top_episodes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst_x</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>tconst_y</th>\n",
              "      <th>parentTconst</th>\n",
              "      <th>seasonNumber</th>\n",
              "      <th>episodeNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303142</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303336</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303338</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303340</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303344</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228646</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228652</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228660</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228670</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228688</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tconst_x titleType  ... seasonNumber episodeNumber\n",
              "46   tt10293068  tvSeries  ...            1             1\n",
              "47   tt10293068  tvSeries  ...            1             2\n",
              "48   tt10293068  tvSeries  ...            1             3\n",
              "49   tt10293068  tvSeries  ...            1             4\n",
              "50   tt10293068  tvSeries  ...            1             5\n",
              "..          ...       ...  ...          ...           ...\n",
              "137   tt5190996  tvSeries  ...            1             4\n",
              "138   tt5190996  tvSeries  ...            1             5\n",
              "139   tt5190996  tvSeries  ...            1             6\n",
              "140   tt5190996  tvSeries  ...            1             7\n",
              "141   tt5190996  tvSeries  ...            1             8\n",
              "\n",
              "[80 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5l7IVR_V7SpC",
        "colab_type": "code",
        "outputId": "032dfeea-fe76-4397-a842-4449115d5597",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "top_episodes.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 80 entries, 46 to 141\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   tconst_x        80 non-null     object \n",
            " 1   titleType       80 non-null     object \n",
            " 2   primaryTitle    80 non-null     object \n",
            " 3   originalTitle   80 non-null     object \n",
            " 4   isAdult         80 non-null     int64  \n",
            " 5   startYear       80 non-null     object \n",
            " 6   endYear         80 non-null     object \n",
            " 7   runtimeMinutes  80 non-null     int64  \n",
            " 8   genres          80 non-null     object \n",
            " 9   averageRating   80 non-null     float64\n",
            " 10  numVotes        80 non-null     int64  \n",
            " 11  tconst_y        80 non-null     object \n",
            " 12  parentTconst    80 non-null     object \n",
            " 13  seasonNumber    80 non-null     object \n",
            " 14  episodeNumber   80 non-null     object \n",
            "dtypes: float64(1), int64(3), object(11)\n",
            "memory usage: 10.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sr0pO7yJoFES",
        "colab_type": "code",
        "outputId": "b56981aa-2435-433b-a583-999a63bd3ba7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "top_episodes.runtimeMinutes = top_episodes.runtimeMinutes.astype(int)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py:5303: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self[name] = value\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cjadiLnoFAf",
        "colab_type": "code",
        "outputId": "e788cf6f-ca7b-4528-fea9-52f70de06cdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "top_episodes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst_x</th>\n",
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              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
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              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>tconst_y</th>\n",
              "      <th>parentTconst</th>\n",
              "      <th>seasonNumber</th>\n",
              "      <th>episodeNumber</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303142</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
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              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303336</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303338</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303340</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303344</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228646</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228652</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228660</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228670</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228688</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>80 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       tconst_x titleType  ... seasonNumber episodeNumber\n",
              "46   tt10293068  tvSeries  ...            1             1\n",
              "47   tt10293068  tvSeries  ...            1             2\n",
              "48   tt10293068  tvSeries  ...            1             3\n",
              "49   tt10293068  tvSeries  ...            1             4\n",
              "50   tt10293068  tvSeries  ...            1             5\n",
              "..          ...       ...  ...          ...           ...\n",
              "137   tt5190996  tvSeries  ...            1             4\n",
              "138   tt5190996  tvSeries  ...            1             5\n",
              "139   tt5190996  tvSeries  ...            1             6\n",
              "140   tt5190996  tvSeries  ...            1             7\n",
              "141   tt5190996  tvSeries  ...            1             8\n",
              "\n",
              "[80 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3szP1CaTLQgz",
        "colab_type": "code",
        "outputId": "48666ac2-01ce-4775-8e48-29fe1e167907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(top_episodes.tconst_x.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RewYAyl8KRZO",
        "colab_type": "code",
        "outputId": "bbca3e89-a807-4130-9cc8-7c2f12721d9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "df = top_episodes.drop_duplicates(subset='tconst_x').reset_index(drop=True)\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst_x</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>tconst_y</th>\n",
              "      <th>parentTconst</th>\n",
              "      <th>seasonNumber</th>\n",
              "      <th>episodeNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9</td>\n",
              "      <td>tt10303142</td>\n",
              "      <td>tt10293068</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt10622458</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>15</td>\n",
              "      <td>Short</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt11122160</td>\n",
              "      <td>tt10622458</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt11004858</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>30</td>\n",
              "      <td>Talk-Show</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6</td>\n",
              "      <td>tt11015298</td>\n",
              "      <td>tt11004858</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt1165334</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>0</td>\n",
              "      <td>1963</td>\n",
              "      <td>1965</td>\n",
              "      <td>60</td>\n",
              "      <td>Music,Talk-Show</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt11118796</td>\n",
              "      <td>tt1165334</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>10.0</td>\n",
              "      <td>5</td>\n",
              "      <td>tt5228560</td>\n",
              "      <td>tt5190996</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     tconst_x titleType  ... seasonNumber episodeNumber\n",
              "0  tt10293068  tvSeries  ...            1             1\n",
              "1  tt10622458  tvSeries  ...            5             1\n",
              "2  tt11004858  tvSeries  ...            1             1\n",
              "3   tt1165334  tvSeries  ...           \\N            \\N\n",
              "4   tt5190996  tvSeries  ...            1             1\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwyCL-OdoE4W",
        "colab_type": "code",
        "outputId": "e5643759-4521-4db6-fc2d-e2d451504241",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "top_episodes.runtimeMinutes.mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.675"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7wltwIdjnAz",
        "colab_type": "code",
        "outputId": "bd68318d-b4ba-44f7-c5e5-2ceecab5d7c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.primaryTitle[46]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'À Brasileira'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1O6D3yY8Z5HA",
        "colab_type": "code",
        "outputId": "661dfacf-5b5e-4d8e-d2eb-8840ef3dc1cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        }
      },
      "source": [
        "ax = df.plot(x='originalTitle',y='runtimeMinutes',\n",
        "             figsize=(15,10),kind='bar',color='mediumseagreen',legend=False,fontsize=14)\n",
        "ax.tick_params(labelbottom=False,bottom=False)\n",
        "ax.set_xlabel('Title',fontsize=16)\n",
        "ax.set_ylabel('Avg Episode Duration',fontsize=16)\n",
        "ind = 0\n",
        "for i in ax.patches:\n",
        "  height = i.get_height()\n",
        "  ax.annotate(s = str(height) + ' Min',xy=(i.get_x()+0.1,height-2),fontsize=15,color='aliceblue')\n",
        "  ax.annotate(s = df.originalTitle[ind],xy=(i.get_x()-0.1,1),fontsize=15,rotation=90)\n",
        "  ind = ind+1\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jj2zWVjGMoSn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ax = df.plot(x='originalTitle',y='runtimeMinutes',kind='barh',figsize=(15,8),legend=None,color=\"darkcyan\",fontsize=14)\n",
        "# ax.set_xlabel('Avg Episode Duration',fontsize=16)\n",
        "# ax.set_ylabel('Title',fontsize=16)\n",
        "\n",
        "# for rects in ax.patches:\n",
        "#       ax.annotate(str(rects.get_width())+' Minutes',xy=(rects.get_width()-len(str(rects.get_width())+' Minutes'),\n",
        "#                                                         rects.get_y()+0.2),fontsize=15)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ei2Ap9aDljoH",
        "colab_type": "text"
      },
      "source": [
        "## Number of seasons of Best TV shows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_X2CrhWljgQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "with gzip.open('/content/drive/My Drive/Data/episode.tsv.gz') as f:\n",
        "  episodes = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/ratings.tsv.gz') as f:\n",
        "  ratings = pd.read_csv(f,sep='\\t')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhJml-yhirqG",
        "colab_type": "text"
      },
      "source": [
        "* the top_shows table from frevious question is merged with episodes table which has seasonNumber.\n",
        "* table obtained above is grouped by titleId and max\n",
        "* rows whose data is not available are dropped to get following"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yToWIO_ljVD",
        "colab_type": "code",
        "outputId": "925d7ebf-dc17-45d4-d03a-4434b4b95640",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "episodes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>parentTconst</th>\n",
              "      <th>seasonNumber</th>\n",
              "      <th>episodeNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0041951</td>\n",
              "      <td>tt0041038</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0042816</td>\n",
              "      <td>tt0989125</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0042889</td>\n",
              "      <td>tt0989125</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0043426</td>\n",
              "      <td>tt0040051</td>\n",
              "      <td>3</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0043631</td>\n",
              "      <td>tt0989125</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768792</th>\n",
              "      <td>tt9916846</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768793</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768794</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768795</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tt1289683</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4768796</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tt0985991</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4768797 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst parentTconst seasonNumber episodeNumber\n",
              "0        tt0041951    tt0041038            1             9\n",
              "1        tt0042816    tt0989125            1            17\n",
              "2        tt0042889    tt0989125           \\N            \\N\n",
              "3        tt0043426    tt0040051            3            42\n",
              "4        tt0043631    tt0989125            2            16\n",
              "...            ...          ...          ...           ...\n",
              "4768792  tt9916846    tt1289683            3            18\n",
              "4768793  tt9916848    tt1289683            3            17\n",
              "4768794  tt9916850    tt1289683            3            19\n",
              "4768795  tt9916852    tt1289683            3            20\n",
              "4768796  tt9916880    tt0985991            4             2\n",
              "\n",
              "[4768797 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgBA9qZWXA0Z",
        "colab_type": "code",
        "outputId": "f99e41e9-6220-435e-e22e-dc0ac2c08543",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# creating a dataframe of top rating titles\n",
        "top_rating = ratings[ratings['averageRating'] == 10.0].sort_values(by='numVotes',ascending=False)\n",
        "top_rating.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>671254</th>\n",
              "      <td>tt2301451</td>\n",
              "      <td>10.0</td>\n",
              "      <td>112607</td>\n",
              "    </tr>\n",
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              "      <th>867391</th>\n",
              "      <td>tt5658362</td>\n",
              "      <td>10.0</td>\n",
              "      <td>199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460174</th>\n",
              "      <td>tt10867894</td>\n",
              "      <td>10.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454487</th>\n",
              "      <td>tt1069840</td>\n",
              "      <td>10.0</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568217</th>\n",
              "      <td>tt1576808</td>\n",
              "      <td>10.0</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  averageRating  numVotes\n",
              "671254   tt2301451           10.0    112607\n",
              "867391   tt5658362           10.0       199\n",
              "460174  tt10867894           10.0       175\n",
              "454487   tt1069840           10.0       162\n",
              "568217   tt1576808           10.0       161"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 172
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UX_5HL7JXAlx",
        "colab_type": "code",
        "outputId": "b83b8fe8-36b3-4e7c-a60d-0c9d7c8e3605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "top_shows = pd.merge(episodes[['parentTconst','seasonNumber']],top_rating[['tconst']],left_on='parentTconst',right_on='tconst')\n",
        "top_shows = top_shows.drop('tconst',axis=1)\n",
        "n_seasons = top_shows.loc[top_shows.seasonNumber != '\\\\N'].groupby(by='parentTconst').max()\n",
        "n_seasons"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             seasonNumber\n",
              "parentTconst             \n",
              "tt10233774              1\n",
              "tt10243828              6\n",
              "tt10276912              1\n",
              "tt10293068              2\n",
              "tt10361854              1\n",
              "tt10622458              5\n",
              "tt10758248              1\n",
              "tt11004858              4\n",
              "tt11466824              1\n",
              "tt11863100              1\n",
              "tt11869314              1\n",
              "tt2360408               1\n",
              "tt5190996               1\n",
              "tt6572396               1\n",
              "tt7131158               1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcK7i35oXAfU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_shows.drop(['tconst_x','tconst_y','averageRating','episodeNumber','numVotes'],axis=1,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeCe3jkzVK4N",
        "colab_type": "code",
        "outputId": "e066ef3b-5288-452b-926b-c949c829e559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        }
      },
      "source": [
        "top_shows.tail(20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    parentTconst seasonNumber\n",
              "148    tt2360408            1\n",
              "149    tt2360408            1\n",
              "150    tt2360408            1\n",
              "151    tt5190996            1\n",
              "152    tt5190996            1\n",
              "153    tt5190996            1\n",
              "154    tt5190996            1\n",
              "155    tt5190996            1\n",
              "156    tt5190996            1\n",
              "157    tt5190996            1\n",
              "158    tt5190996            1\n",
              "159    tt6572396            1\n",
              "160    tt7131158            1\n",
              "161    tt7131158            1\n",
              "162    tt7131158            1\n",
              "163    tt7131158            1\n",
              "164    tt7131158            1\n",
              "165    tt7131158            1\n",
              "166    tt7131158            1\n",
              "167    tt7131158            1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8o1jWIQVK1v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_shows = top_shows.groupby('parentTconst').max()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xwcObqbHJX9",
        "colab_type": "code",
        "outputId": "5324535e-85d3-455d-ea91-fd6dda0fd548",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        }
      },
      "source": [
        "top_shows"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>1</td>\n",
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              "      <th>tt7131158</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             seasonNumber\n",
              "parentTconst             \n",
              "tt10233774              1\n",
              "tt10243828              6\n",
              "tt10276912              1\n",
              "tt10293068              2\n",
              "tt10361854              1\n",
              "tt10622458              5\n",
              "tt10758248              1\n",
              "tt11004858              4\n",
              "tt11466824              1\n",
              "tt1165334              \\N\n",
              "tt11863100              1\n",
              "tt11869314              1\n",
              "tt2360408               1\n",
              "tt5190996               1\n",
              "tt6572396               1\n",
              "tt7131158               1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOCOWfzuHRu7",
        "colab_type": "code",
        "outputId": "95eeab41-95df-4d24-b347-5aba78b34b83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(top_shows)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Jp1AXJ-VKyk",
        "colab_type": "code",
        "outputId": "b4527cd7-59c4-4ae7-a6b7-e1374f8586f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "with gzip.open('/content/drive/My Drive/Data/genres.tsv.gz') as f:\n",
        "  genres = pd.read_csv(f,sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t87_Jvz8VKva",
        "colab_type": "code",
        "outputId": "f3968a61-3552-4d5c-bdbb-9091bd3c5a99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "genres"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710056</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710057</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710058</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710059</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>short</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>\\N</td>\n",
              "      <td>27</td>\n",
              "      <td>Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710060</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6710061 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ... runtimeMinutes                    genres\n",
              "0        tt0000001      short  ...              1         Documentary,Short\n",
              "1        tt0000002      short  ...              5           Animation,Short\n",
              "2        tt0000003      short  ...              4  Animation,Comedy,Romance\n",
              "3        tt0000004      short  ...             12           Animation,Short\n",
              "4        tt0000005      short  ...              1              Comedy,Short\n",
              "...            ...        ...  ...            ...                       ...\n",
              "6710056  tt9916848  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710057  tt9916850  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710058  tt9916852  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710059  tt9916856      short  ...             27                     Short\n",
              "6710060  tt9916880  tvEpisode  ...             10   Animation,Comedy,Family\n",
              "\n",
              "[6710061 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3glz2jPIVKsM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_shows = pd.merge(genres,top_shows,left_on='tconst',right_on='parentTconst')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48bTFpNzJAn0",
        "colab_type": "code",
        "outputId": "bab1395f-7f6c-40f5-823e-a90aaac67f72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        }
      },
      "source": [
        "top_shows"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>seasonNumber</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt10243828</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Live from the 8th Dimension</td>\n",
              "      <td>Live from the 8th Dimension</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt10276912</td>\n",
              "      <td>tvMiniSeries</td>\n",
              "      <td>That's My Chair, That Is</td>\n",
              "      <td>That's My Chair, That Is</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>9</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>25</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt10361854</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Tunadas</td>\n",
              "      <td>Tunadas</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>tt10622458</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>15</td>\n",
              "      <td>Short</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>tt10758248</td>\n",
              "      <td>tvMiniSeries</td>\n",
              "      <td>FSE Moments (Frustrated Software Engineer Mome...</td>\n",
              "      <td>FSE Moments (Frustrated Software Engineer Mome...</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>tt11004858</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>30</td>\n",
              "      <td>Talk-Show</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>tt11466824</td>\n",
              "      <td>tvMiniSeries</td>\n",
              "      <td>State of Decay</td>\n",
              "      <td>State of Decay</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>2020</td>\n",
              "      <td>9</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>tt1165334</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>Rendezvous mit Jo</td>\n",
              "      <td>0</td>\n",
              "      <td>1963</td>\n",
              "      <td>1965</td>\n",
              "      <td>60</td>\n",
              "      <td>Music,Talk-Show</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>tt11863100</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Boss Hoggin Road Trip Series</td>\n",
              "      <td>Boss Hoggin Road Trip Series</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>tt11869314</td>\n",
              "      <td>tvMiniSeries</td>\n",
              "      <td>Pain: Fibromyalgia Stories</td>\n",
              "      <td>Pain: Fibromyalgia Stories</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>\\N</td>\n",
              "      <td>32</td>\n",
              "      <td>Documentary</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>tt2360408</td>\n",
              "      <td>tvMiniSeries</td>\n",
              "      <td>My Brother's Keeper</td>\n",
              "      <td>My Brother's Keeper</td>\n",
              "      <td>0</td>\n",
              "      <td>2012</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Horror,Musical</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>\\N</td>\n",
              "      <td>13</td>\n",
              "      <td>Comedy</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>tt6572396</td>\n",
              "      <td>tvMiniSeries</td>\n",
              "      <td>Bariga Kvitila</td>\n",
              "      <td>Bariga Kvitila</td>\n",
              "      <td>0</td>\n",
              "      <td>2017</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Mystery</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>tt7131158</td>\n",
              "      <td>tvMiniSeries</td>\n",
              "      <td>Gray Ground</td>\n",
              "      <td>Gray Ground</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Crime,Drama,Mystery</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        tconst     titleType  ...               genres seasonNumber\n",
              "0   tt10233774      tvSeries  ...               Comedy            1\n",
              "1   tt10243828      tvSeries  ...               Comedy            6\n",
              "2   tt10276912  tvMiniSeries  ...               Comedy            1\n",
              "3   tt10293068      tvSeries  ...          Documentary            2\n",
              "4   tt10361854      tvSeries  ...          Documentary            1\n",
              "5   tt10622458      tvSeries  ...                Short            5\n",
              "6   tt10758248  tvMiniSeries  ...               Comedy            1\n",
              "7   tt11004858      tvSeries  ...            Talk-Show            4\n",
              "8   tt11466824  tvMiniSeries  ...               Comedy            1\n",
              "9    tt1165334      tvSeries  ...      Music,Talk-Show           \\N\n",
              "10  tt11863100      tvSeries  ...           Reality-TV            1\n",
              "11  tt11869314  tvMiniSeries  ...          Documentary            1\n",
              "12   tt2360408  tvMiniSeries  ...       Horror,Musical            1\n",
              "13   tt5190996      tvSeries  ...               Comedy            1\n",
              "14   tt6572396  tvMiniSeries  ...              Mystery            1\n",
              "15   tt7131158  tvMiniSeries  ...  Crime,Drama,Mystery            1\n",
              "\n",
              "[16 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5Qjwx0I-we8",
        "colab_type": "code",
        "outputId": "d828dae2-8b1d-4da7-bae9-6bdb657fe3fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "top_seasons = pd.DataFrame({'title_Id':top_shows.tconst,\n",
        "                            'title':top_shows.originalTitle,\n",
        "                            'Number of seasons':top_shows.seasonNumber})\n",
        "top_seasons = top_seasons.loc[top_seasons['Number of seasons'] != '\\\\N'].reset_index(drop=True)\n",
        "top_seasons"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title_Id</th>\n",
              "      <th>title</th>\n",
              "      <th>Number of seasons</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt10233774</td>\n",
              "      <td>Rusty Camel</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt10243828</td>\n",
              "      <td>Live from the 8th Dimension</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt10276912</td>\n",
              "      <td>That's My Chair, That Is</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt10293068</td>\n",
              "      <td>À Brasileira</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt10361854</td>\n",
              "      <td>Tunadas</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>tt10622458</td>\n",
              "      <td>Words Written Well</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>tt10758248</td>\n",
              "      <td>FSE Moments (Frustrated Software Engineer Mome...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>tt11004858</td>\n",
              "      <td>Blind Wave: Black Sails Reaction</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>tt11466824</td>\n",
              "      <td>State of Decay</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>tt11863100</td>\n",
              "      <td>Boss Hoggin Road Trip Series</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>tt11869314</td>\n",
              "      <td>Pain: Fibromyalgia Stories</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>tt2360408</td>\n",
              "      <td>My Brother's Keeper</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>tt5190996</td>\n",
              "      <td>Caroline the Job Hunter</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>tt6572396</td>\n",
              "      <td>Bariga Kvitila</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>tt7131158</td>\n",
              "      <td>Gray Ground</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      title_Id  ... Number of seasons\n",
              "0   tt10233774  ...                 1\n",
              "1   tt10243828  ...                 6\n",
              "2   tt10276912  ...                 1\n",
              "3   tt10293068  ...                 2\n",
              "4   tt10361854  ...                 1\n",
              "5   tt10622458  ...                 5\n",
              "6   tt10758248  ...                 1\n",
              "7   tt11004858  ...                 4\n",
              "8   tt11466824  ...                 1\n",
              "9   tt11863100  ...                 1\n",
              "10  tt11869314  ...                 1\n",
              "11   tt2360408  ...                 1\n",
              "12   tt5190996  ...                 1\n",
              "13   tt6572396  ...                 1\n",
              "14   tt7131158  ...                 1\n",
              "\n",
              "[15 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XFHPKDeAJggf",
        "colab_type": "text"
      },
      "source": [
        "## Year in which maximum tv shows ended"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sx8y5sTWKnCC",
        "colab_type": "code",
        "outputId": "7005b09d-0ba7-462d-adc3-0649348a4eeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "with gzip.open('/content/drive/My Drive/Data/genres.tsv.gz') as f:\n",
        "  genres = pd.read_csv(f,sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9F-g2AN8K3cw",
        "colab_type": "code",
        "outputId": "3d4ab3c2-b6b7-4e80-c64b-c7c58af623b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "genres"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710056</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710057</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710058</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710059</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>short</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>\\N</td>\n",
              "      <td>27</td>\n",
              "      <td>Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710060</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6710061 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ... runtimeMinutes                    genres\n",
              "0        tt0000001      short  ...              1         Documentary,Short\n",
              "1        tt0000002      short  ...              5           Animation,Short\n",
              "2        tt0000003      short  ...              4  Animation,Comedy,Romance\n",
              "3        tt0000004      short  ...             12           Animation,Short\n",
              "4        tt0000005      short  ...              1              Comedy,Short\n",
              "...            ...        ...  ...            ...                       ...\n",
              "6710056  tt9916848  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710057  tt9916850  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710058  tt9916852  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710059  tt9916856      short  ...             27                     Short\n",
              "6710060  tt9916880  tvEpisode  ...             10   Animation,Comedy,Family\n",
              "\n",
              "[6710061 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqEi-q1sK5Ej",
        "colab_type": "code",
        "outputId": "29858aae-c737-446a-8844-f1ec0c5d5173",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "tv_shows = genres.loc[genres.titleType == 'tvSeries'].loc[genres.endYear != '\\\\N']\n",
        "tv_shows.endYear = tv_shows.endYear.astype(int)\n",
        "tv_shows.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 48856 entries, 34987 to 6709753\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype \n",
            "---  ------          --------------  ----- \n",
            " 0   tconst          48856 non-null  object\n",
            " 1   titleType       48856 non-null  object\n",
            " 2   primaryTitle    48856 non-null  object\n",
            " 3   originalTitle   48856 non-null  object\n",
            " 4   isAdult         48856 non-null  int64 \n",
            " 5   startYear       48856 non-null  object\n",
            " 6   endYear         48856 non-null  int64 \n",
            " 7   runtimeMinutes  48856 non-null  object\n",
            " 8   genres          48856 non-null  object\n",
            "dtypes: int64(2), object(7)\n",
            "memory usage: 3.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVTbBhAaLOQ2",
        "colab_type": "code",
        "outputId": "9f0335c8-76e3-4930-a23a-5ad16b04a443",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "source": [
        "tv_shows"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34987</th>\n",
              "      <td>tt0035599</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Voice of Firestone Televues</td>\n",
              "      <td>Voice of Firestone Televues</td>\n",
              "      <td>0</td>\n",
              "      <td>1943</td>\n",
              "      <td>1947</td>\n",
              "      <td>15</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35188</th>\n",
              "      <td>tt0035803</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>The German Weekly Review</td>\n",
              "      <td>Die Deutsche Wochenschau</td>\n",
              "      <td>0</td>\n",
              "      <td>1940</td>\n",
              "      <td>1945</td>\n",
              "      <td>12</td>\n",
              "      <td>Documentary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37617</th>\n",
              "      <td>tt0038276</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>You Are an Artist</td>\n",
              "      <td>You Are an Artist</td>\n",
              "      <td>0</td>\n",
              "      <td>1946</td>\n",
              "      <td>1950</td>\n",
              "      <td>15</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38451</th>\n",
              "      <td>tt0039120</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Americana</td>\n",
              "      <td>Americana</td>\n",
              "      <td>0</td>\n",
              "      <td>1947</td>\n",
              "      <td>1949</td>\n",
              "      <td>30</td>\n",
              "      <td>Family,Game-Show</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38452</th>\n",
              "      <td>tt0039121</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Birthday Party</td>\n",
              "      <td>Birthday Party</td>\n",
              "      <td>0</td>\n",
              "      <td>1947</td>\n",
              "      <td>1949</td>\n",
              "      <td>30</td>\n",
              "      <td>Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709347</th>\n",
              "      <td>tt9915338</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Aunty Donna: Camp Bush Camp!</td>\n",
              "      <td>Aunty Donna: Camp Bush Camp!</td>\n",
              "      <td>0</td>\n",
              "      <td>2018</td>\n",
              "      <td>2018</td>\n",
              "      <td>5</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709441</th>\n",
              "      <td>tt9915530</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>The Land F.A.U.</td>\n",
              "      <td>The Land F.A.U.</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Crime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709570</th>\n",
              "      <td>tt9915822</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Ichhapyaari Naagin</td>\n",
              "      <td>Ichhapyaari Naagin</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>2017</td>\n",
              "      <td>20</td>\n",
              "      <td>Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709752</th>\n",
              "      <td>tt9916216</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Kalyanam Mudhal Kadhal Varai</td>\n",
              "      <td>Kalyanam Mudhal Kadhal Varai</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>2017</td>\n",
              "      <td>22</td>\n",
              "      <td>Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6709753</th>\n",
              "      <td>tt9916218</td>\n",
              "      <td>tvSeries</td>\n",
              "      <td>Lost in Food</td>\n",
              "      <td>Lost in Food</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>2017</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Talk-Show</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48856 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst titleType  ... runtimeMinutes            genres\n",
              "34987    tt0035599  tvSeries  ...             15                \\N\n",
              "35188    tt0035803  tvSeries  ...             12       Documentary\n",
              "37617    tt0038276  tvSeries  ...             15                \\N\n",
              "38451    tt0039120  tvSeries  ...             30  Family,Game-Show\n",
              "38452    tt0039121  tvSeries  ...             30            Family\n",
              "...            ...       ...  ...            ...               ...\n",
              "6709347  tt9915338  tvSeries  ...              5            Comedy\n",
              "6709441  tt9915530  tvSeries  ...             \\N             Crime\n",
              "6709570  tt9915822  tvSeries  ...             20           Fantasy\n",
              "6709752  tt9916216  tvSeries  ...             22           Romance\n",
              "6709753  tt9916218  tvSeries  ...             \\N         Talk-Show\n",
              "\n",
              "[48856 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pcm_W5UMAOQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "year = tv_shows.groupby('endYear').count()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Snd2hLWCM2T9",
        "colab_type": "code",
        "outputId": "10d35e65-92d7-4b1c-eb24-c802837d7328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "year"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>endYear</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1924</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1932</th>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1933</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1935</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1936</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2025</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2026</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2027</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  titleType  primaryTitle  ...  startYear  runtimeMinutes  genres\n",
              "endYear                                   ...                                   \n",
              "1924          1          1             1  ...          1               1       1\n",
              "1932         11         11            11  ...         11              11      11\n",
              "1933          2          2             2  ...          2               2       2\n",
              "1935          1          1             1  ...          1               1       1\n",
              "1936          3          3             3  ...          3               3       3\n",
              "...         ...        ...           ...  ...        ...             ...     ...\n",
              "2022          1          1             1  ...          1               1       1\n",
              "2023          1          1             1  ...          1               1       1\n",
              "2025          1          1             1  ...          1               1       1\n",
              "2026          1          1             1  ...          1               1       1\n",
              "2027          1          1             1  ...          1               1       1\n",
              "\n",
              "[93 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRJdC_W6sWTG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "year.reset_index(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "769sCtRMr3FC",
        "colab_type": "code",
        "outputId": "1ea72183-c10c-4f80-a956-eab414d5f0cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "year.plot(x='endYear',y='tconst')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f174bfe5be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 192
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUh5E21ZztGp",
        "colab_type": "code",
        "outputId": "a6d5019d-c09a-45a8-9fd7-24245ed521ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "year"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>endYear</th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1924</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1932</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1933</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1935</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1936</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>2022</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>2023</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>2025</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>2026</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>2027</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    endYear  tconst  titleType  ...  startYear  runtimeMinutes  genres\n",
              "0      1924       1          1  ...          1               1       1\n",
              "1      1932      11         11  ...         11              11      11\n",
              "2      1933       2          2  ...          2               2       2\n",
              "3      1935       1          1  ...          1               1       1\n",
              "4      1936       3          3  ...          3               3       3\n",
              "..      ...     ...        ...  ...        ...             ...     ...\n",
              "88     2022       1          1  ...          1               1       1\n",
              "89     2023       1          1  ...          1               1       1\n",
              "90     2025       1          1  ...          1               1       1\n",
              "91     2026       1          1  ...          1               1       1\n",
              "92     2027       1          1  ...          1               1       1\n",
              "\n",
              "[93 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QIO20mvSzmV-",
        "colab_type": "code",
        "outputId": "a751540c-0d1b-4713-bd47-027e2bf24784",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "x = year.tconst\n",
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83    2863\n",
              "Name: tconst, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0IjAnMH0jYh",
        "colab_type": "code",
        "outputId": "036bbdbf-26d3-4dc7-8e40-fec6919e21c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "max_x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2017"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsd0HnoUsvg6",
        "colab_type": "code",
        "outputId": "d2d7a38e-1e29-43d2-aaf7-98a3ad2d2bad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 631
        }
      },
      "source": [
        "fig,ax=plt.subplots(figsize=(15,10))\n",
        "ax.plot(year.endYear,year.tconst)\n",
        "max_x = int(year.endYear[year.tconst== year.tconst.max()])\n",
        "max_y = year.tconst.max()\n",
        "ax.annotate(f'Maximum shows Ended\\nyear = {max_x },shows={max_y}',\n",
        "            xy=(max_x,max_y),xytext=(1970,2500),arrowprops=dict(facecolor='black'),\n",
        "            fontsize=16)\n",
        "ax.set_xlabel('Year',fontsize=16)\n",
        "ax.set_ylabel('Number of Shows Ended',fontsize=16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Shows Ended')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 229
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NDHmpiAM5J2",
        "colab_type": "code",
        "outputId": "47375df6-2660-4ff7-9a40-431430e95b03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        }
      },
      "source": [
        "\n",
        "year.sort_values('tconst',ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>endYear</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>2508</td>\n",
              "      <td>2508</td>\n",
              "      <td>2508</td>\n",
              "      <td>2508</td>\n",
              "      <td>2508</td>\n",
              "      <td>2508</td>\n",
              "      <td>2508</td>\n",
              "      <td>2508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>2365</td>\n",
              "      <td>2365</td>\n",
              "      <td>2365</td>\n",
              "      <td>2365</td>\n",
              "      <td>2365</td>\n",
              "      <td>2365</td>\n",
              "      <td>2365</td>\n",
              "      <td>2365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>2101</td>\n",
              "      <td>2101</td>\n",
              "      <td>2101</td>\n",
              "      <td>2101</td>\n",
              "      <td>2101</td>\n",
              "      <td>2101</td>\n",
              "      <td>2101</td>\n",
              "      <td>2101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>2021</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021</td>\n",
              "      <td>2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1924</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1944</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1937</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1935</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2027</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>93 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  titleType  primaryTitle  ...  startYear  runtimeMinutes  genres\n",
              "endYear                                   ...                                   \n",
              "2017       2863       2863          2863  ...       2863            2863    2863\n",
              "2016       2508       2508          2508  ...       2508            2508    2508\n",
              "2018       2365       2365          2365  ...       2365            2365    2365\n",
              "2015       2101       2101          2101  ...       2101            2101    2101\n",
              "2019       2021       2021          2021  ...       2021            2021    2021\n",
              "...         ...        ...           ...  ...        ...             ...     ...\n",
              "1924          1          1             1  ...          1               1       1\n",
              "1944          1          1             1  ...          1               1       1\n",
              "1937          1          1             1  ...          1               1       1\n",
              "1935          1          1             1  ...          1               1       1\n",
              "2027          1          1             1  ...          1               1       1\n",
              "\n",
              "[93 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQrtwhiyMJsN",
        "colab_type": "code",
        "outputId": "5a9c79d9-9121-4717-dc41-93aeb49447d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "year.loc[year.tconst == max(year.tconst)]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>endYear</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "      <td>2863</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         tconst  titleType  primaryTitle  ...  startYear  runtimeMinutes  genres\n",
              "endYear                                   ...                                   \n",
              "2017       2863       2863          2863  ...       2863            2863    2863\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WX0bFUTnMx1Z",
        "colab_type": "text"
      },
      "source": [
        "## Factors for higher rating"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SrDEJP4NfAE",
        "colab_type": "code",
        "outputId": "da60e4c2-95e1-4338-a9fe-29bc84741e06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "import gzip\n",
        "import pandas as pd\n",
        "\n",
        "with gzip.open('/content/drive/My Drive/Data/ratings.tsv.gz') as f:\n",
        "  ratings = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/title.tsv.gz') as f:\n",
        "  title = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/genres.tsv.gz') as f:\n",
        "  genres = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/crew.tsv.gz') as f:\n",
        "  crew = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/director.tsv.gz') as f:\n",
        "  director = pd.read_csv(f,sep='\\t')\n",
        "with gzip.open('/content/drive/My Drive/Data/episode.tsv.gz') as f:\n",
        "  episode = pd.read_csv(f,sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n",
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DazMEJyWUIm9",
        "colab_type": "text"
      },
      "source": [
        "* ratings :done\n",
        "* title :\n",
        "* genres : done\n",
        "* crew :\n",
        "* episode : done"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKjYy5xWNxMm",
        "colab_type": "code",
        "outputId": "72827f07-5313-4d7e-93cf-9829b137f4e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "ratings"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>tt9916576</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>tt9916578</td>\n",
              "      <td>8.4</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
              "      <td>5.5</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  averageRating  numVotes\n",
              "0        tt0000001            5.6      1600\n",
              "1        tt0000002            6.0       196\n",
              "2        tt0000003            6.5      1275\n",
              "3        tt0000004            6.1       121\n",
              "4        tt0000005            6.1      2038\n",
              "...            ...            ...       ...\n",
              "1024879  tt9916576            6.0         9\n",
              "1024880  tt9916578            8.4        17\n",
              "1024881  tt9916720            5.5        48\n",
              "1024882  tt9916766            6.8        13\n",
              "1024883  tt9916778            7.2        21\n",
              "\n",
              "[1024884 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfQM_fDCRXI6",
        "colab_type": "code",
        "outputId": "e11825f9-7055-4f19-915a-ff062b796c3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "genres"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710056</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>Episode #3.17</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710057</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>Episode #3.19</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710058</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>Episode #3.20</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710059</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>short</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>\\N</td>\n",
              "      <td>27</td>\n",
              "      <td>Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710060</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>Horrid Henry Knows It All</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6710061 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ... runtimeMinutes                    genres\n",
              "0        tt0000001      short  ...              1         Documentary,Short\n",
              "1        tt0000002      short  ...              5           Animation,Short\n",
              "2        tt0000003      short  ...              4  Animation,Comedy,Romance\n",
              "3        tt0000004      short  ...             12           Animation,Short\n",
              "4        tt0000005      short  ...              1              Comedy,Short\n",
              "...            ...        ...  ...            ...                       ...\n",
              "6710056  tt9916848  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710057  tt9916850  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710058  tt9916852  tvEpisode  ...             \\N       Action,Drama,Family\n",
              "6710059  tt9916856      short  ...             27                     Short\n",
              "6710060  tt9916880  tvEpisode  ...             10   Animation,Comedy,Family\n",
              "\n",
              "[6710061 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8a1bjHFmP6qX",
        "colab_type": "text"
      },
      "source": [
        "out of 6710061 unique titles from genres table 1024884 unique titles in rating are there"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G72MWkbKRaJQ",
        "colab_type": "code",
        "outputId": "4ec8f158-e4f7-4c3c-9745-d5fd9cabcf37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "g = genres.drop(['primaryTitle','originalTitle'],axis=1)\n",
        "g"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710056</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710057</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710058</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710059</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>\\N</td>\n",
              "      <td>27</td>\n",
              "      <td>Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710060</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6710061 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ...  runtimeMinutes                    genres\n",
              "0        tt0000001      short  ...               1         Documentary,Short\n",
              "1        tt0000002      short  ...               5           Animation,Short\n",
              "2        tt0000003      short  ...               4  Animation,Comedy,Romance\n",
              "3        tt0000004      short  ...              12           Animation,Short\n",
              "4        tt0000005      short  ...               1              Comedy,Short\n",
              "...            ...        ...  ...             ...                       ...\n",
              "6710056  tt9916848  tvEpisode  ...              \\N       Action,Drama,Family\n",
              "6710057  tt9916850  tvEpisode  ...              \\N       Action,Drama,Family\n",
              "6710058  tt9916852  tvEpisode  ...              \\N       Action,Drama,Family\n",
              "6710059  tt9916856      short  ...              27                     Short\n",
              "6710060  tt9916880  tvEpisode  ...              10   Animation,Comedy,Family\n",
              "\n",
              "[6710061 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SkCZWydRgOB",
        "colab_type": "code",
        "outputId": "a2f010c5-d832-4c24-c9ca-5c67bb46ffad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df = pd.merge(g,ratings)\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>tt9916576</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>85</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>tt9916578</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Adventure,Biography,Comedy</td>\n",
              "      <td>8.4</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Comedy,Horror,Mystery</td>\n",
              "      <td>5.5</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>43</td>\n",
              "      <td>Family,Reality-TV</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ...  averageRating numVotes\n",
              "0        tt0000001      short  ...            5.6     1600\n",
              "1        tt0000002      short  ...            6.0      196\n",
              "2        tt0000003      short  ...            6.5     1275\n",
              "3        tt0000004      short  ...            6.1      121\n",
              "4        tt0000005      short  ...            6.1     2038\n",
              "...            ...        ...  ...            ...      ...\n",
              "1024879  tt9916576  tvEpisode  ...            6.0        9\n",
              "1024880  tt9916578  tvEpisode  ...            8.4       17\n",
              "1024881  tt9916720      short  ...            5.5       48\n",
              "1024882  tt9916766  tvEpisode  ...            6.8       13\n",
              "1024883  tt9916778  tvEpisode  ...            7.2       21\n",
              "\n",
              "[1024884 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mceT1Ei70kas",
        "colab_type": "code",
        "outputId": "156228f2-269f-4e51-a511-08dc1424ca8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df.replace('\\\\N', np.nan,inplace=True)\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
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              "      <td>1894</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "      <td>5.6</td>\n",
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              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>tt9916576</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>tt9916578</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Adventure,Biography,Comedy</td>\n",
              "      <td>8.4</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>Comedy,Horror,Mystery</td>\n",
              "      <td>5.5</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>43</td>\n",
              "      <td>Family,Reality-TV</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ...  averageRating numVotes\n",
              "0        tt0000001      short  ...            5.6     1600\n",
              "1        tt0000002      short  ...            6.0      196\n",
              "2        tt0000003      short  ...            6.5     1275\n",
              "3        tt0000004      short  ...            6.1      121\n",
              "4        tt0000005      short  ...            6.1     2038\n",
              "...            ...        ...  ...            ...      ...\n",
              "1024879  tt9916576  tvEpisode  ...            6.0        9\n",
              "1024880  tt9916578  tvEpisode  ...            8.4       17\n",
              "1024881  tt9916720      short  ...            5.5       48\n",
              "1024882  tt9916766  tvEpisode  ...            6.8       13\n",
              "1024883  tt9916778  tvEpisode  ...            7.2       21\n",
              "\n",
              "[1024884 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BUvOZfxx5f0",
        "colab_type": "code",
        "outputId": "c4033f0d-a005-4dd7-f9fb-4cea6d82d0f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1024884 entries, 0 to 1024883\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   tconst          1024884 non-null  object \n",
            " 1   titleType       1024884 non-null  object \n",
            " 2   isAdult         1024884 non-null  int64  \n",
            " 3   startYear       1024753 non-null  object \n",
            " 4   endYear         23130 non-null    object \n",
            " 5   runtimeMinutes  748918 non-null   object \n",
            " 6   genres          1004372 non-null  object \n",
            " 7   averageRating   1024884 non-null  float64\n",
            " 8   numVotes        1024884 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(6)\n",
            "memory usage: 78.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGTbyb7-72wk",
        "colab_type": "code",
        "outputId": "54d2c1d8-a0b1-4ccc-8ac2-f9d6ab8b3050",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "df - df[df.runtimeMinutes.isin(['Talk-Show','Reality-TV']) == True]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mna_arithmetic_op\u001b[0;34m(left, right, op, str_rep)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, op_str, a, b, use_numexpr)\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0muse_numexpr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'float'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-102-d986a3a3cfa1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mruntimeMinutes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Talk-Show'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Reality-TV'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, other, axis, level, fill_value)\u001b[0m\n\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m             \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"outer\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 767\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpass_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    768\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    769\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_combine_frame\u001b[0;34m(self, other, func, fill_value, level)\u001b[0m\n\u001b[1;32m   5298\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_series_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5299\u001b[0m             \u001b[0;31m# iterate over columns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5300\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_to_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_arith_op\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5301\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5302\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mdispatch_to_series\u001b[0;34m(left, right, func, str_rep, axis)\u001b[0m\n\u001b[1;32m    417\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m     \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    420\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(op, op_str, a, b, use_numexpr)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0muse_numexpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muse_numexpr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m_bool_arith_check\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0muse_numexpr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b)\u001b[0m\n\u001b[1;32m     68\u001b[0m         \u001b[0m_store_test_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mcolumn_op\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcolumn_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    386\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mcolumn_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 388\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    389\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/common.py\u001b[0m in \u001b[0;36mnew_method\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitem_from_zerodim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/__init__.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m    501\u001b[0m         \u001b[0mlvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m         \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextract_numpy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_construct_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mres_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36marithmetic_op\u001b[0;34m(left, right, op, str_rep)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m             \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mna_arithmetic_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mna_arithmetic_op\u001b[0;34m(left, right, op, str_rep)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr_rep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmasked_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_fill_zeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops/array_ops.py\u001b[0m in \u001b[0;36mmasked_arith_op\u001b[0;34m(x, y, op)\u001b[0m\n\u001b[1;32m     92\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m                 \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myrav\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'str'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWZSvtPB8Qa2",
        "colab_type": "code",
        "outputId": "332a7278-e826-42e1-9fd2-0d248e6b975f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "df.loc[df.runtimeMinutes == 'Reality-TV']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>779847</th>\n",
              "      <td>tt3984412</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>2014</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           tconst  titleType  isAdult  ... genres averageRating numVotes\n",
              "779847  tt3984412  tvEpisode     2014  ...    NaN           8.0        5\n",
              "\n",
              "[1 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O_yCbVMv3hOb",
        "colab_type": "code",
        "outputId": "475ad57d-734e-404b-eb1d-2831a971fcb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "tmp = df.dropna(subset=['runtimeMinutes'])\n",
        "tmp.runtimeMinutes.astype(int)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-87-c6b00b51ee0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'runtimeMinutes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mruntimeMinutes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5696\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5697\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5698\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5699\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5700\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    580\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    581\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, filter, **kwargs)\u001b[0m\n\u001b[1;32m    440\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 442\u001b[0;31m                 \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    443\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    623\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    624\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    626\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    627\u001b[0m                 \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m    872\u001b[0m         \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m         \u001b[0;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Talk-Show'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9-YQV2u7Zuh",
        "colab_type": "code",
        "outputId": "4153e839-f448-4201-cffa-1d61ecf5bbd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "np.array(df.runtimeMinutes.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1', '5', '4', '12', '45', '2', nan, '0', '3', '20', '13', '6',\n",
              "       '11', '10', '8', '15', '21', '16', '14', '7', '24', '100', '9',\n",
              "       '17', '70', '25', '120', '38', '58', '50', '19', '44', '23', '18',\n",
              "       '51', '40', '28', '60', '56', '53', '30', '26', '41', '48', '68',\n",
              "       '34', '33', '31', '29', '43', '46', '27', '32', '85', '55', '121',\n",
              "       '69', '22', '54', '90', '35', '150', '64', '96', '49', '61', '300',\n",
              "       '99', '110', '39', '88', '52', '86', '72', '78', '75', '450',\n",
              "       '148', '37', '59', '63', '112', '1428', '84', '65', '42', '105',\n",
              "       '310', '73', '81', '199', '109', '74', '80', '82', '67', '195',\n",
              "       '440', '77', '62', '71', '57', '47', '36', '421', '360', '180',\n",
              "       '163', '93', '400', '95', '83', '116', '66', '125', '97', '138',\n",
              "       '350', '374', '91', '87', '108', '136', '94', '117', '92', '76',\n",
              "       '418', '330', '223', '489', '119', '122', '115', '207', '166',\n",
              "       '106', '111', '145', '167', '103', '130', '157', '104', '107',\n",
              "       '248', '79', '235', '184', '89', '102', '131', '380', '98', '200',\n",
              "       '242', '128', '143', '137', '101', '183', '210', '133', '135',\n",
              "       '124', '141', '162', '153', '132', '129', '123', '144', '155',\n",
              "       '151', '114', '170', '140', '113', '359', '118', '126', '179',\n",
              "       '230', '168', '250', '240', '142', '139', '185', '156', '127',\n",
              "       '178', '215', '213', '149', '165', '147', '266', '152', '220',\n",
              "       '217', '172', '158', '226', '164', '194', '225', '216', '227',\n",
              "       '231', '218', '246', '243', '281', '208', '228', '229', '212',\n",
              "       '236', '306', '257', '245', '197', '255', '219', '323', '305',\n",
              "       '328', '269', '176', '232', '258', '256', '303', '290', '308',\n",
              "       '224', '294', '252', '268', '260', '204', '298', '299', '264',\n",
              "       '160', '211', '302', '238', '263', '320', '265', '205', '154',\n",
              "       '237', '285', '146', '293', '267', '247', '186', '241', '289',\n",
              "       '177', '283', '134', '270', '251', '278', '196', '279', '261',\n",
              "       '275', '280', '244', '189', '169', '284', '295', '201', '221',\n",
              "       '173', '192', '171', '193', '287', '161', '181', '175', '174',\n",
              "       '315', '159', '182', '188', '497', '209', '190', '327', '317',\n",
              "       '191', '480', '234', '288', '438', '422', '378', '316', '202',\n",
              "       '340', '203', '253', '206', '344', '540', '476', '451', '321',\n",
              "       '348', '890', '309', '653', '239', '431', '390', '187', '370',\n",
              "       '396', '282', '669', '467', '286', '274', '763', '770', '311',\n",
              "       '198', '500', '373', '382', '596', '588', '750', '273', '442',\n",
              "       '599', '333', '614', '1256', '385', '780', '510', '475', '271',\n",
              "       '600', '291', '366', '840', '742', '931', '676', '547', '673',\n",
              "       '358', '394', '301', '233', '381', '659', '752', '504', '410',\n",
              "       '520', '720', '460', '883', '313', '420', '778', '413', '402',\n",
              "       '700', '272', '222', '924', '391', '561', '428', '455', '437',\n",
              "       '416', '367', '566', '307', '304', '570', '569', '371', '415',\n",
              "       '376', '870', '312', '572', '407', '331', '375', '436', '355',\n",
              "       '353', '357', '873', '384', '1290', '345', '1620', '318', '335',\n",
              "       '680', '262', '398', '567', '606', '214', '501', '322', '834',\n",
              "       '1140', '522', '361', '276', '314', '515', '762', '624', '528',\n",
              "       '623', '537', '336', '341', '568', '571', '556', '386', '383',\n",
              "       '650', '292', '343', '277', '698', '401', '377', '672', '465',\n",
              "       '702', '660', '622', '356', '296', '1352', '1320', '395', '583',\n",
              "       '339', '259', '388', '325', '1120', '254', '554', '612', '616',\n",
              "       '349', '1500', '627', '630', '594', '485', '495', '347', '417',\n",
              "       '1800', '577', '458', '2800', '1440', '491', '1100', '406', '729',\n",
              "       '369', '297', '445', '435', '960', '615', '535', '392', '663',\n",
              "       '693', '580', '425', '544', '5220', '877', '1080', '389', '545',\n",
              "       '640', '453', '446', '372', '629', '678', '1300', '352', '689',\n",
              "       '793', '2925', '608', '2880', '462', '1084', '457', '351', '338',\n",
              "       '550', '701', '249', '530', '346', '738', '354', '551', '1020',\n",
              "       '1200', '646', '579', '820', '704', '408', '552', '710', '715',\n",
              "       '514', '1664', '1035', '1370', '1350', '1410', '1380', '1430',\n",
              "       '585', '730', '364', '516', '705', '3600', '1196', '765', '502',\n",
              "       '541', '414', '652', '1040', '1442', '598', '526', '334', '1248',\n",
              "       '543', '1932', '466', '538', '4800', '3900', '2150', '403', '434',\n",
              "       '1170', '527', '636', '575', '564', '379', '506', '800', '900',\n",
              "       '441', '519', '469', '719', '490', '319', '10062', 'Talk-Show',\n",
              "       '368', '337', '329', '412', '365', '387', '454', '13319', '584',\n",
              "       '324', '479', '690', '521', '668', '712', '736', '470', '1452',\n",
              "       '342', '473', '397', '1680', '362', '1568', '5700', '1050', '326',\n",
              "       '845', '853', '990', '1210', '463', '724', '9000', '411', '557',\n",
              "       '525', '14400', '532', '404', '795', '1470', '546', '2288', '3000',\n",
              "       'Reality-TV', '536', '487', '740', '363', '1000', '607', '439',\n",
              "       '511', '582', '430', '447', '492', '542', '625', '3825', '697',\n",
              "       '691', '443', '773', '1329', '1532', '5760', '746', '51420', '601',\n",
              "       '808'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cLZ6ulLL6stv",
        "colab_type": "code",
        "outputId": "f6396c0c-dee7-4e9e-d388-d7f8b264e611",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
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              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "      <td>6.5</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>tt9916576</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>85</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>tt9916578</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Adventure,Biography,Comedy</td>\n",
              "      <td>8.4</td>\n",
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              "    </tr>\n",
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              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
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              "    </tr>\n",
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              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>tvEpisode</td>\n",
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              "      <td>2019</td>\n",
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              "      <td>43</td>\n",
              "      <td>Family,Reality-TV</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>tvEpisode</td>\n",
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              "      <td>2019</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ...  averageRating numVotes\n",
              "0        tt0000001      short  ...            5.6     1600\n",
              "1        tt0000002      short  ...            6.0      196\n",
              "2        tt0000003      short  ...            6.5     1275\n",
              "3        tt0000004      short  ...            6.1      121\n",
              "4        tt0000005      short  ...            6.1     2038\n",
              "...            ...        ...  ...            ...      ...\n",
              "1024879  tt9916576  tvEpisode  ...            6.0        9\n",
              "1024880  tt9916578  tvEpisode  ...            8.4       17\n",
              "1024881  tt9916720      short  ...            5.5       48\n",
              "1024882  tt9916766  tvEpisode  ...            6.8       13\n",
              "1024883  tt9916778  tvEpisode  ...            7.2       21\n",
              "\n",
              "[1024884 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        }
      },
      "source": [
        "df.fillna('runtimeMinutes').median()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "isAdult           0.0\n",
              "averageRating     7.1\n",
              "numVotes         20.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
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        "colab": {
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      },
      "source": [
        "df1 = df.drop('endYear',1)\n",
        "df1 = df1.loc[df1.runtimeMinutes != '\\\\N']\n",
        "df1"
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      "execution_count": 0,
      "outputs": [
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              "<p>748918 rows × 8 columns</p>\n",
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            "text/plain": [
              "            tconst  titleType  ...  averageRating numVotes\n",
              "0        tt0000001      short  ...            5.6     1600\n",
              "1        tt0000002      short  ...            6.0      196\n",
              "2        tt0000003      short  ...            6.5     1275\n",
              "3        tt0000004      short  ...            6.1      121\n",
              "4        tt0000005      short  ...            6.1     2038\n",
              "...            ...        ...  ...            ...      ...\n",
              "1024877  tt9916538      movie  ...            8.4        5\n",
              "1024878  tt9916544      short  ...            7.2       19\n",
              "1024879  tt9916576  tvEpisode  ...            6.0        9\n",
              "1024881  tt9916720      short  ...            5.5       48\n",
              "1024882  tt9916766  tvEpisode  ...            6.8       13\n",
              "\n",
              "[748918 rows x 8 columns]"
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        "colab_type": "code",
        "colab": {}
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4768797 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst parentTconst seasonNumber episodeNumber\n",
              "0        tt0041951    tt0041038            1             9\n",
              "1        tt0042816    tt0989125            1            17\n",
              "2        tt0042889    tt0989125           \\N            \\N\n",
              "3        tt0043426    tt0040051            3            42\n",
              "4        tt0043631    tt0989125            2            16\n",
              "...            ...          ...          ...           ...\n",
              "4768792  tt9916846    tt1289683            3            18\n",
              "4768793  tt9916848    tt1289683            3            17\n",
              "4768794  tt9916850    tt1289683            3            19\n",
              "4768795  tt9916852    tt1289683            3            20\n",
              "4768796  tt9916880    tt0985991            4             2\n",
              "\n",
              "[4768797 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "outputId": "2f0e873b-d54c-4a44-f004-c3c8e5fc7085",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(episode.tconst.unique())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4768797"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7rxzjyewSMMw",
        "colab_type": "text"
      },
      "source": [
        "merge df and episode table in a way all of df data is present and column from episode are added whereever applicable (left outer join)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arsn02GuSMII",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "df1 = pd.merge(df,episode,how='left')\n",
        "df1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-6cb8ede82221>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepisode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'left'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQnirN-JSMDr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7_lxpRFSL5P",
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        "outputId": "a485db5c-52c8-4117-e1c3-d3441b46d4e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "director"
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      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>directors</th>\n",
              "      <th>writers</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>nm0005690</td>\n",
              "      <td>\\N</td>\n",
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              "    <tr>\n",
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              "      <th>...</th>\n",
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              "      <th>6710056</th>\n",
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              "      <td>nm5519454,nm5519375</td>\n",
              "      <td>nm6182221,nm1628284,nm2921377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710057</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>nm5519454,nm5519375</td>\n",
              "      <td>nm6182221,nm1628284,nm2921377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710058</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>nm5519375,nm5519454</td>\n",
              "      <td>nm6182221,nm1628284,nm2921377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710059</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>nm10538645</td>\n",
              "      <td>nm6951431</td>\n",
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              "    <tr>\n",
              "      <th>6710060</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>nm0996406</td>\n",
              "      <td>nm1482639,nm2586970</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6710061 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst            directors                        writers\n",
              "0        tt0000001            nm0005690                             \\N\n",
              "1        tt0000002            nm0721526                             \\N\n",
              "2        tt0000003            nm0721526                             \\N\n",
              "3        tt0000004            nm0721526                             \\N\n",
              "4        tt0000005            nm0005690                             \\N\n",
              "...            ...                  ...                            ...\n",
              "6710056  tt9916848  nm5519454,nm5519375  nm6182221,nm1628284,nm2921377\n",
              "6710057  tt9916850  nm5519454,nm5519375  nm6182221,nm1628284,nm2921377\n",
              "6710058  tt9916852  nm5519375,nm5519454  nm6182221,nm1628284,nm2921377\n",
              "6710059  tt9916856           nm10538645                      nm6951431\n",
              "6710060  tt9916880            nm0996406            nm1482639,nm2586970\n",
              "\n",
              "[6710061 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6z2UQPfVcXD",
        "colab_type": "code",
        "outputId": "1c203c00-4d30-45cb-a08b-8b2f3f962898",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "df2 = pd.merge(df1,director,on='tconst')\n",
        "df2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-64fc8c0c259b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdirector\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'tconst'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df1' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQHwr5TB3HLf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hI7gKbm-3HE-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qb5j3tN3G6h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjSoqVM43Gy9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5VEoIOK3Go5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBj33NoH3Ggn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAYrTI_J3GYN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzIAWbWA3F-e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDcz-JFV3F3V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aw162Tnb3FvG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UM9aQzTO3FXh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfFUkD0Og6JI",
        "colab_type": "code",
        "outputId": "73be1c0f-94d1-4a73-dd09-895a63907159",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(df2.genres.str.split(',')[2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4krqbCi8g6Co",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_of_directors = []\n",
        "for i,rows in df2.iterrows():\n",
        "  num_of_directors.append(len(df2.directors.str.split(',')[i]))\n",
        "num_of_directors"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guVpYa4Og572",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vyOI0kYg5kN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkIK3NF_WHCu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAwmppwEVcBv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = list(crew.category.unique())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BR9dEM0WaI7D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = [pd.Series(np.ones(len(df1)))] * len(x)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRwy8dWnbMGC",
        "colab_type": "code",
        "outputId": "bc74ddd3-c8bc-42bf-d5b1-3ec43c32209a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        }
      },
      "source": [
        "pd.DataFrame(dict(zip(x,y)),columns=x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 12 columns</p>\n",
              "</div>"
            ],
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              "         self  director  ...  archive_footage  archive_sound\n",
              "0         1.0       1.0  ...              1.0            1.0\n",
              "1         1.0       1.0  ...              1.0            1.0\n",
              "2         1.0       1.0  ...              1.0            1.0\n",
              "3         1.0       1.0  ...              1.0            1.0\n",
              "4         1.0       1.0  ...              1.0            1.0\n",
              "...       ...       ...  ...              ...            ...\n",
              "1024879   1.0       1.0  ...              1.0            1.0\n",
              "1024880   1.0       1.0  ...              1.0            1.0\n",
              "1024881   1.0       1.0  ...              1.0            1.0\n",
              "1024882   1.0       1.0  ...              1.0            1.0\n",
              "1024883   1.0       1.0  ...              1.0            1.0\n",
              "\n",
              "[1024884 rows x 12 columns]"
            ]
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      "outputs": [
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          "data": {
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              "['self',\n",
              " 'director',\n",
              " 'cinematographer',\n",
              " 'composer',\n",
              " 'producer',\n",
              " 'editor',\n",
              " 'actor',\n",
              " 'actress',\n",
              " 'writer',\n",
              " 'production_designer',\n",
              " 'archive_footage',\n",
              " 'archive_sound']"
            ]
          },
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        }
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              "      <td>9</td>\n",
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              "      <td>[\"Mum\",\"Tidy Ted\",\"Fang\"]</td>\n",
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              "<p>38728849 rows × 6 columns</p>\n",
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              "             tconst  ...                 characters\n",
              "0         tt0000001  ...                   [\"Self\"]\n",
              "1         tt0000001  ...                         \\N\n",
              "2         tt0000001  ...                         \\N\n",
              "3         tt0000002  ...                         \\N\n",
              "4         tt0000002  ...                         \\N\n",
              "...             ...  ...                        ...\n",
              "38728844  tt9916880  ...                         \\N\n",
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              "38728846  tt9916880  ...                         \\N\n",
              "38728847  tt9916880  ...                         \\N\n",
              "38728848  tt9916880  ...  [\"Mum\",\"Tidy Ted\",\"Fang\"]\n",
              "\n",
              "[38728849 rows x 6 columns]"
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        "len(director.tconst.unique())"
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        ""
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              "      <td>tt9916852</td>\n",
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              "      <td>tt9916852</td>\n",
              "      <td>7</td>\n",
              "      <td>एपिसोड #3.20</td>\n",
              "      <td>IN</td>\n",
              "      <td>hi</td>\n",
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              "      <td>\\N</td>\n",
              "      <td>0</td>\n",
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              "      <td>tt9916856</td>\n",
              "      <td>1</td>\n",
              "      <td>The Wind</td>\n",
              "      <td>DE</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21226848 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            titleId  ordering  ...     attributes isOriginalTitle\n",
              "0         tt0000001         1  ...             \\N               0\n",
              "1         tt0000001         2  ...  literal title               0\n",
              "2         tt0000001         3  ...             \\N               0\n",
              "3         tt0000001         4  ...             \\N               0\n",
              "4         tt0000001         5  ...             \\N               0\n",
              "...             ...       ...  ...            ...             ...\n",
              "21226843  tt9916852         4  ...             \\N               0\n",
              "21226844  tt9916852         5  ...             \\N               0\n",
              "21226845  tt9916852         6  ...             \\N               0\n",
              "21226846  tt9916852         7  ...             \\N               0\n",
              "21226847  tt9916856         1  ...             \\N               0\n",
              "\n",
              "[21226848 rows x 8 columns]"
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      "source": [
        "t= title.drop(['ordering','title','types','attributes','isOriginalTitle'],axis=1)\n",
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              "      <th>1</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>DE</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>HU</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>GR</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>RU</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21226843</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>JP</td>\n",
              "      <td>ja</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21226844</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>PT</td>\n",
              "      <td>pt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21226845</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>IT</td>\n",
              "      <td>it</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21226846</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>IN</td>\n",
              "      <td>hi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21226847</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>DE</td>\n",
              "      <td>\\N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21226848 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            titleId region language\n",
              "0         tt0000001     UA       \\N\n",
              "1         tt0000001     DE       \\N\n",
              "2         tt0000001     HU       \\N\n",
              "3         tt0000001     GR       \\N\n",
              "4         tt0000001     RU       \\N\n",
              "...             ...    ...      ...\n",
              "21226843  tt9916852     JP       ja\n",
              "21226844  tt9916852     PT       pt\n",
              "21226845  tt9916852     IT       it\n",
              "21226846  tt9916852     IN       hi\n",
              "21226847  tt9916856     DE       \\N\n",
              "\n",
              "[21226848 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2R3VubMwq-wQ",
        "colab_type": "code",
        "outputId": "bf99dfa1-f887-4e01-abba-9fec4a704982",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        }
      },
      "source": [
        "g = genres.drop(['primaryTitle','originalTitle'],axis=1)\n",
        "g"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710056</th>\n",
              "      <td>tt9916848</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710057</th>\n",
              "      <td>tt9916850</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710058</th>\n",
              "      <td>tt9916852</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2010</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Action,Drama,Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710059</th>\n",
              "      <td>tt9916856</td>\n",
              "      <td>short</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>\\N</td>\n",
              "      <td>27</td>\n",
              "      <td>Short</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6710060</th>\n",
              "      <td>tt9916880</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>0</td>\n",
              "      <td>2014</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6710061 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ...  runtimeMinutes                    genres\n",
              "0        tt0000001      short  ...               1         Documentary,Short\n",
              "1        tt0000002      short  ...               5           Animation,Short\n",
              "2        tt0000003      short  ...               4  Animation,Comedy,Romance\n",
              "3        tt0000004      short  ...              12           Animation,Short\n",
              "4        tt0000005      short  ...               1              Comedy,Short\n",
              "...            ...        ...  ...             ...                       ...\n",
              "6710056  tt9916848  tvEpisode  ...              \\N       Action,Drama,Family\n",
              "6710057  tt9916850  tvEpisode  ...              \\N       Action,Drama,Family\n",
              "6710058  tt9916852  tvEpisode  ...              \\N       Action,Drama,Family\n",
              "6710059  tt9916856      short  ...              27                     Short\n",
              "6710060  tt9916880  tvEpisode  ...              10   Animation,Comedy,Family\n",
              "\n",
              "[6710061 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZQOkOifBB4D",
        "colab_type": "code",
        "outputId": "8c29f0b9-23fd-4481-bab5-c79942bc932f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(genres.tconst.unique()),len(genres)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6710061, 6710061)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iI25sF34Q1tx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IP0obcIGQ8LW",
        "colab_type": "code",
        "outputId": "9e62d3e7-b512-48b6-dba4-b9393df90a05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>short</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>Carmencita</td>\n",
              "      <td>0</td>\n",
              "      <td>1894</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Documentary,Short</td>\n",
              "      <td>5.6</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>short</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>Le clown et ses chiens</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>5</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.0</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tt0000003</td>\n",
              "      <td>short</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>Pauvre Pierrot</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Romance</td>\n",
              "      <td>6.5</td>\n",
              "      <td>1275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tt0000004</td>\n",
              "      <td>short</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>Un bon bock</td>\n",
              "      <td>0</td>\n",
              "      <td>1892</td>\n",
              "      <td>\\N</td>\n",
              "      <td>12</td>\n",
              "      <td>Animation,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>short</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>Blacksmith Scene</td>\n",
              "      <td>0</td>\n",
              "      <td>1893</td>\n",
              "      <td>\\N</td>\n",
              "      <td>1</td>\n",
              "      <td>Comedy,Short</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024879</th>\n",
              "      <td>tt9916576</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Destinee's Story</td>\n",
              "      <td>Destinee's Story</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>85</td>\n",
              "      <td>Reality-TV</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024880</th>\n",
              "      <td>tt9916578</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>The Trial of Joan Collins</td>\n",
              "      <td>The Trial of Joan Collins</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Adventure,Biography,Comedy</td>\n",
              "      <td>8.4</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024881</th>\n",
              "      <td>tt9916720</td>\n",
              "      <td>short</td>\n",
              "      <td>The Nun 2</td>\n",
              "      <td>The Nun 2</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>10</td>\n",
              "      <td>Comedy,Horror,Mystery</td>\n",
              "      <td>5.5</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024882</th>\n",
              "      <td>tt9916766</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>Episode #10.15</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>43</td>\n",
              "      <td>Family,Reality-TV</td>\n",
              "      <td>6.8</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1024883</th>\n",
              "      <td>tt9916778</td>\n",
              "      <td>tvEpisode</td>\n",
              "      <td>Escape</td>\n",
              "      <td>Escape</td>\n",
              "      <td>0</td>\n",
              "      <td>2019</td>\n",
              "      <td>\\N</td>\n",
              "      <td>\\N</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.2</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1024884 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            tconst  titleType  ... averageRating numVotes\n",
              "0        tt0000001      short  ...           5.6     1600\n",
              "1        tt0000002      short  ...           6.0      196\n",
              "2        tt0000003      short  ...           6.5     1275\n",
              "3        tt0000004      short  ...           6.1      121\n",
              "4        tt0000005      short  ...           6.1     2038\n",
              "...            ...        ...  ...           ...      ...\n",
              "1024879  tt9916576  tvEpisode  ...           6.0        9\n",
              "1024880  tt9916578  tvEpisode  ...           8.4       17\n",
              "1024881  tt9916720      short  ...           5.5       48\n",
              "1024882  tt9916766  tvEpisode  ...           6.8       13\n",
              "1024883  tt9916778  tvEpisode  ...           7.2       21\n",
              "\n",
              "[1024884 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXKiOOosQ9Ug",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}