F22 EPSY5122 PFSSR Week11.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EPSY 5122: Programming for Social Science Researchers\n",
    "## Week 11\n",
    "### Wednesday, November 16, 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy.random as npr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mov = pd.read_csv('in_data/tmdb_5000_movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.avatarmovie.com/</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>2787965087</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>300000000</td>\n",
       "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
       "      <td>285</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2007-05-19</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>At the end of the world, the adventure begins.</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>245000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
       "      <td>206647</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
       "      <td>[{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>880674609</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
       "      <td>Released</td>\n",
       "      <td>A Plan No One Escapes</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>250000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>http://www.thedarkknightrises.com/</td>\n",
       "      <td>49026</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>1084939099</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Legend Ends</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>260000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>http://movies.disney.com/john-carter</td>\n",
       "      <td>49529</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>284139100</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Lost in our world, found in another.</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      budget                                             genres  \\\n",
       "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
       "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
       "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
       "\n",
       "                                       homepage      id  \\\n",
       "0                   http://www.avatarmovie.com/   19995   \n",
       "1  http://disney.go.com/disneypictures/pirates/     285   \n",
       "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
       "3            http://www.thedarkknightrises.com/   49026   \n",
       "4          http://movies.disney.com/john-carter   49529   \n",
       "\n",
       "                                            keywords original_language  \\\n",
       "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
       "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
       "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
       "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
       "\n",
       "                             original_title  \\\n",
       "0                                    Avatar   \n",
       "1  Pirates of the Caribbean: At World's End   \n",
       "2                                   Spectre   \n",
       "3                     The Dark Knight Rises   \n",
       "4                               John Carter   \n",
       "\n",
       "                                            overview  popularity  \\\n",
       "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3  Following the death of District Attorney Harve...  112.312950   \n",
       "4  John Carter is a war-weary, former military ca...   43.926995   \n",
       "\n",
       "                                production_companies  \\\n",
       "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
       "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
       "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   \n",
       "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   \n",
       "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]   \n",
       "\n",
       "                                production_countries release_date     revenue  \\\n",
       "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
       "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   961000000   \n",
       "2  [{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...   2015-10-26   880674609   \n",
       "3  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-07-16  1084939099   \n",
       "4  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-03-07   284139100   \n",
       "\n",
       "   runtime                                   spoken_languages    status  \\\n",
       "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
       "1    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "2    148.0  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
       "3    165.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "4    132.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
       "\n",
       "                                          tagline  \\\n",
       "0                     Enter the World of Pandora.   \n",
       "1  At the end of the world, the adventure begins.   \n",
       "2                           A Plan No One Escapes   \n",
       "3                                 The Legend Ends   \n",
       "4            Lost in our world, found in another.   \n",
       "\n",
       "                                      title  vote_average  vote_count  \n",
       "0                                    Avatar           7.2       11800  \n",
       "1  Pirates of the Caribbean: At World's End           6.9        4500  \n",
       "2                                   Spectre           6.3        4466  \n",
       "3                     The Dark Knight Rises           7.6        9106  \n",
       "4                               John Carter           6.1        2124  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Challenge 1**: *Find the descriptive stats for each numeric column.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-289f4412284f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmov\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'budget'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'revenue'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\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[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(self, percentiles, include, exclude)\u001b[0m\n\u001b[1;32m  10263\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mexclude\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10264\u001b[0m             \u001b[0;31m# when some numerics are found, keep only numerics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 10265\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\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  10266\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10267\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mselect_dtypes\u001b[0;34m(self, include, exclude)\u001b[0m\n\u001b[1;32m   3425\u001b[0m         \u001b[0;31m# the \"union\" of the logic of case 1 and case 2:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3426\u001b[0m         \u001b[0;31m# we get the included and excluded, and return their logical and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3427\u001b[0;31m         \u001b[0minclude_these\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\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[0m\n\u001b[0m\u001b[1;32m   3428\u001b[0m         \u001b[0mexclude_these\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mnot\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\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[0m\n\u001b[1;32m   3429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.7/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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    310\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--> 311\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\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[0mraise_cast_failure\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    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\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/opt/anaconda3/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36msanitize_array\u001b[0;34m(data, index, dtype, copy, raise_cast_failure)\u001b[0m\n\u001b[1;32m    710\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_cast_to_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 712\u001b[0;31m             \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_1d_arraylike_from_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    713\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    714\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/opt/anaconda3/lib/python3.7/site-packages/pandas/core/dtypes/cast.py\u001b[0m in \u001b[0;36mconstruct_1d_arraylike_from_scalar\u001b[0;34m(value, length, dtype)\u001b[0m\n\u001b[1;32m   1231\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1233\u001b[0;31m         \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1234\u001b[0m         \u001b[0msubarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type"
     ]
    }
   ],
   "source": [
    "mov[['budget', 'revenue']].describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Challenge 2**: *Find which columns have missing data (and how many missing data)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget                     0\n",
       "genres                     0\n",
       "homepage                3091\n",
       "id                         0\n",
       "keywords                   0\n",
       "original_language          0\n",
       "original_title             0\n",
       "overview                   3\n",
       "popularity                 0\n",
       "production_companies       0\n",
       "production_countries       0\n",
       "release_date               1\n",
       "revenue                    0\n",
       "runtime                    2\n",
       "spoken_languages           0\n",
       "status                     0\n",
       "tagline                  844\n",
       "title                      0\n",
       "vote_average               0\n",
       "vote_count                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# challenge 2 code here\n",
    "mov.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mov2 = mov.drop(['homepage', 'tagline'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "budget                  0\n",
       "genres                  0\n",
       "id                      0\n",
       "keywords                0\n",
       "original_language       0\n",
       "original_title          0\n",
       "overview                3\n",
       "popularity              0\n",
       "production_companies    0\n",
       "production_countries    0\n",
       "release_date            1\n",
       "revenue                 0\n",
       "runtime                 2\n",
       "spoken_languages        0\n",
       "status                  0\n",
       "title                   0\n",
       "vote_average            0\n",
       "vote_count              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mov3 = mov2.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.799000e+03\n",
       "mean     2.906593e+07\n",
       "std      4.073251e+07\n",
       "min      0.000000e+00\n",
       "25%      8.000000e+05\n",
       "50%      1.500000e+07\n",
       "75%      4.000000e+07\n",
       "max      3.800000e+08\n",
       "Name: budget, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov3['budget'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4.799000e+03\n",
       "mean     8.232920e+07\n",
       "std      1.629076e+08\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      1.918402e+07\n",
       "75%      9.295652e+07\n",
       "max      2.787965e+09\n",
       "Name: revenue, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov3['revenue'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mov_budg_rev = mov3[(mov3.budget > 0) & (mov3.revenue > 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>production_countries</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>237000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>19995</td>\n",
       "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>150.437577</td>\n",
       "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>2787965087</td>\n",
       "      <td>162.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Avatar</td>\n",
       "      <td>7.2</td>\n",
       "      <td>11800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>300000000</td>\n",
       "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
       "      <td>285</td>\n",
       "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
       "      <td>139.082615</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2007-05-19</td>\n",
       "      <td>961000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Pirates of the Caribbean: At World's End</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>245000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>206647</td>\n",
       "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
       "      <td>en</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
       "      <td>107.376788</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
       "      <td>[{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>880674609</td>\n",
       "      <td>148.0</td>\n",
       "      <td>[{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Spectre</td>\n",
       "      <td>6.3</td>\n",
       "      <td>4466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>250000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>49026</td>\n",
       "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
       "      <td>en</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>Following the death of District Attorney Harve...</td>\n",
       "      <td>112.312950</td>\n",
       "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>1084939099</td>\n",
       "      <td>165.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "      <td>9106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>260000000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
       "      <td>49529</td>\n",
       "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
       "      <td>en</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>John Carter is a war-weary, former military ca...</td>\n",
       "      <td>43.926995</td>\n",
       "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2012-03-07</td>\n",
       "      <td>284139100</td>\n",
       "      <td>132.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>John Carter</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4769</td>\n",
       "      <td>27000</td>\n",
       "      <td>[{\"id\": 35, \"name\": \"Comedy\"}]</td>\n",
       "      <td>2292</td>\n",
       "      <td>[{\"id\": 1361, \"name\": \"salesclerk\"}, {\"id\": 30...</td>\n",
       "      <td>en</td>\n",
       "      <td>Clerks</td>\n",
       "      <td>Convenience and video store clerks Dante and R...</td>\n",
       "      <td>19.748658</td>\n",
       "      <td>[{\"name\": \"Miramax Films\", \"id\": 14}, {\"name\":...</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>1994-09-13</td>\n",
       "      <td>3151130</td>\n",
       "      <td>92.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Clerks</td>\n",
       "      <td>7.4</td>\n",
       "      <td>755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4784</td>\n",
       "      <td>12000</td>\n",
       "      <td>[{\"id\": 27, \"name\": \"Horror\"}, {\"id\": 35, \"nam...</td>\n",
       "      <td>692</td>\n",
       "      <td>[{\"id\": 237, \"name\": \"gay\"}, {\"id\": 900, \"name...</td>\n",
       "      <td>en</td>\n",
       "      <td>Pink Flamingos</td>\n",
       "      <td>Notorious Baltimore criminal and underground f...</td>\n",
       "      <td>4.553644</td>\n",
       "      <td>[{\"name\": \"Dreamland Productions\", \"id\": 407}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>1972-03-12</td>\n",
       "      <td>6000000</td>\n",
       "      <td>93.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Pink Flamingos</td>\n",
       "      <td>6.2</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4788</td>\n",
       "      <td>20000</td>\n",
       "      <td>[{\"id\": 80, \"name\": \"Crime\"}, {\"id\": 27, \"name...</td>\n",
       "      <td>36095</td>\n",
       "      <td>[{\"id\": 233, \"name\": \"japan\"}, {\"id\": 549, \"na...</td>\n",
       "      <td>ja</td>\n",
       "      <td>キュア</td>\n",
       "      <td>A wave of gruesome murders is sweeping Tokyo. ...</td>\n",
       "      <td>0.212443</td>\n",
       "      <td>[{\"name\": \"Daiei Studios\", \"id\": 881}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"JP\", \"name\": \"Japan\"}]</td>\n",
       "      <td>1997-11-06</td>\n",
       "      <td>99000</td>\n",
       "      <td>111.0</td>\n",
       "      <td>[{\"iso_639_1\": \"ja\", \"name\": \"\\u65e5\\u672c\\u8a...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Cure</td>\n",
       "      <td>7.4</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4792</td>\n",
       "      <td>7000</td>\n",
       "      <td>[{\"id\": 878, \"name\": \"Science Fiction\"}, {\"id\"...</td>\n",
       "      <td>14337</td>\n",
       "      <td>[{\"id\": 1448, \"name\": \"distrust\"}, {\"id\": 2101...</td>\n",
       "      <td>en</td>\n",
       "      <td>Primer</td>\n",
       "      <td>Friends/fledgling entrepreneurs invent a devic...</td>\n",
       "      <td>23.307949</td>\n",
       "      <td>[{\"name\": \"Thinkfilm\", \"id\": 446}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
       "      <td>2004-10-08</td>\n",
       "      <td>424760</td>\n",
       "      <td>77.0</td>\n",
       "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Primer</td>\n",
       "      <td>6.9</td>\n",
       "      <td>658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4794</td>\n",
       "      <td>220000</td>\n",
       "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
       "      <td>9367</td>\n",
       "      <td>[{\"id\": 5616, \"name\": \"united states\\u2013mexi...</td>\n",
       "      <td>es</td>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>El Mariachi just wants to play his guitar and ...</td>\n",
       "      <td>14.269792</td>\n",
       "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}]</td>\n",
       "      <td>[{\"iso_3166_1\": \"MX\", \"name\": \"Mexico\"}, {\"iso...</td>\n",
       "      <td>1992-09-04</td>\n",
       "      <td>2040920</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>El Mariachi</td>\n",
       "      <td>6.6</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3229 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         budget                                             genres      id  \\\n",
       "0     237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   19995   \n",
       "1     300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...     285   \n",
       "2     245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...  206647   \n",
       "3     250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   49026   \n",
       "4     260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   49529   \n",
       "...         ...                                                ...     ...   \n",
       "4769      27000                     [{\"id\": 35, \"name\": \"Comedy\"}]    2292   \n",
       "4784      12000  [{\"id\": 27, \"name\": \"Horror\"}, {\"id\": 35, \"nam...     692   \n",
       "4788      20000  [{\"id\": 80, \"name\": \"Crime\"}, {\"id\": 27, \"name...   36095   \n",
       "4792       7000  [{\"id\": 878, \"name\": \"Science Fiction\"}, {\"id\"...   14337   \n",
       "4794     220000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...    9367   \n",
       "\n",
       "                                               keywords original_language  \\\n",
       "0     [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
       "1     [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
       "2     [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
       "3     [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
       "4     [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
       "...                                                 ...               ...   \n",
       "4769  [{\"id\": 1361, \"name\": \"salesclerk\"}, {\"id\": 30...                en   \n",
       "4784  [{\"id\": 237, \"name\": \"gay\"}, {\"id\": 900, \"name...                en   \n",
       "4788  [{\"id\": 233, \"name\": \"japan\"}, {\"id\": 549, \"na...                ja   \n",
       "4792  [{\"id\": 1448, \"name\": \"distrust\"}, {\"id\": 2101...                en   \n",
       "4794  [{\"id\": 5616, \"name\": \"united states\\u2013mexi...                es   \n",
       "\n",
       "                                original_title  \\\n",
       "0                                       Avatar   \n",
       "1     Pirates of the Caribbean: At World's End   \n",
       "2                                      Spectre   \n",
       "3                        The Dark Knight Rises   \n",
       "4                                  John Carter   \n",
       "...                                        ...   \n",
       "4769                                    Clerks   \n",
       "4784                            Pink Flamingos   \n",
       "4788                                       キュア   \n",
       "4792                                    Primer   \n",
       "4794                               El Mariachi   \n",
       "\n",
       "                                               overview  popularity  \\\n",
       "0     In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
       "1     Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
       "2     A cryptic message from Bond’s past sends him o...  107.376788   \n",
       "3     Following the death of District Attorney Harve...  112.312950   \n",
       "4     John Carter is a war-weary, former military ca...   43.926995   \n",
       "...                                                 ...         ...   \n",
       "4769  Convenience and video store clerks Dante and R...   19.748658   \n",
       "4784  Notorious Baltimore criminal and underground f...    4.553644   \n",
       "4788  A wave of gruesome murders is sweeping Tokyo. ...    0.212443   \n",
       "4792  Friends/fledgling entrepreneurs invent a devic...   23.307949   \n",
       "4794  El Mariachi just wants to play his guitar and ...   14.269792   \n",
       "\n",
       "                                   production_companies  \\\n",
       "0     [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
       "1     [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
       "2     [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   \n",
       "3     [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   \n",
       "4           [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]   \n",
       "...                                                 ...   \n",
       "4769  [{\"name\": \"Miramax Films\", \"id\": 14}, {\"name\":...   \n",
       "4784     [{\"name\": \"Dreamland Productions\", \"id\": 407}]   \n",
       "4788             [{\"name\": \"Daiei Studios\", \"id\": 881}]   \n",
       "4792                 [{\"name\": \"Thinkfilm\", \"id\": 446}]   \n",
       "4794           [{\"name\": \"Columbia Pictures\", \"id\": 5}]   \n",
       "\n",
       "                                   production_countries release_date  \\\n",
       "0     [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10   \n",
       "1     [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   \n",
       "2     [{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...   2015-10-26   \n",
       "3     [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-07-16   \n",
       "4     [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-03-07   \n",
       "...                                                 ...          ...   \n",
       "4769  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   1994-09-13   \n",
       "4784  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   1972-03-12   \n",
       "4788            [{\"iso_3166_1\": \"JP\", \"name\": \"Japan\"}]   1997-11-06   \n",
       "4792  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2004-10-08   \n",
       "4794  [{\"iso_3166_1\": \"MX\", \"name\": \"Mexico\"}, {\"iso...   1992-09-04   \n",
       "\n",
       "         revenue  runtime                                   spoken_languages  \\\n",
       "0     2787965087    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...   \n",
       "1      961000000    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "2      880674609    148.0  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...   \n",
       "3     1084939099    165.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "4      284139100    132.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "...          ...      ...                                                ...   \n",
       "4769     3151130     92.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "4784     6000000     93.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "4788       99000    111.0  [{\"iso_639_1\": \"ja\", \"name\": \"\\u65e5\\u672c\\u8a...   \n",
       "4792      424760     77.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
       "4794     2040920     81.0      [{\"iso_639_1\": \"es\", \"name\": \"Espa\\u00f1ol\"}]   \n",
       "\n",
       "        status                                     title  vote_average  \\\n",
       "0     Released                                    Avatar           7.2   \n",
       "1     Released  Pirates of the Caribbean: At World's End           6.9   \n",
       "2     Released                                   Spectre           6.3   \n",
       "3     Released                     The Dark Knight Rises           7.6   \n",
       "4     Released                               John Carter           6.1   \n",
       "...        ...                                       ...           ...   \n",
       "4769  Released                                    Clerks           7.4   \n",
       "4784  Released                            Pink Flamingos           6.2   \n",
       "4788  Released                                      Cure           7.4   \n",
       "4792  Released                                    Primer           6.9   \n",
       "4794  Released                               El Mariachi           6.6   \n",
       "\n",
       "      vote_count  \n",
       "0          11800  \n",
       "1           4500  \n",
       "2           4466  \n",
       "3           9106  \n",
       "4           2124  \n",
       "...          ...  \n",
       "4769         755  \n",
       "4784         110  \n",
       "4788          63  \n",
       "4792         658  \n",
       "4794         238  \n",
       "\n",
       "[3229 rows x 18 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov_budg_rev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mov_budg_rev.to_csv(\"mid_data/mov_data_cleanish.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = sns.regplot(mov_budg_rev.budget, mov_budg_rev.revenue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot.get_figure().savefig(\"out_plots/regression.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = smf.ols(formula = \"revenue ~ budget\", data = mov_budg_rev).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    902540.953295\n",
       "budget            2.960080\n",
       "dtype: float64"
      ]
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     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "lm.params"
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  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HC0_se',\n",
       " 'HC1_se',\n",
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       " '_get_robustcov_results',\n",
       " '_is_nested',\n",
       " '_wexog_singular_values',\n",
       " 'aic',\n",
       " 'bic',\n",
       " 'bse',\n",
       " 'centered_tss',\n",
       " 'compare_f_test',\n",
       " 'compare_lm_test',\n",
       " 'compare_lr_test',\n",
       " 'condition_number',\n",
       " 'conf_int',\n",
       " 'conf_int_el',\n",
       " 'cov_HC0',\n",
       " 'cov_HC1',\n",
       " 'cov_HC2',\n",
       " 'cov_HC3',\n",
       " 'cov_kwds',\n",
       " 'cov_params',\n",
       " 'cov_type',\n",
       " 'df_model',\n",
       " 'df_resid',\n",
       " 'eigenvals',\n",
       " 'el_test',\n",
       " 'ess',\n",
       " 'f_pvalue',\n",
       " 'f_test',\n",
       " 'fittedvalues',\n",
       " 'fvalue',\n",
       " 'get_influence',\n",
       " 'get_prediction',\n",
       " 'get_robustcov_results',\n",
       " 'initialize',\n",
       " 'k_constant',\n",
       " 'llf',\n",
       " 'load',\n",
       " 'model',\n",
       " 'mse_model',\n",
       " 'mse_resid',\n",
       " 'mse_total',\n",
       " 'nobs',\n",
       " 'normalized_cov_params',\n",
       " 'outlier_test',\n",
       " 'params',\n",
       " 'predict',\n",
       " 'pvalues',\n",
       " 'remove_data',\n",
       " 'resid',\n",
       " 'resid_pearson',\n",
       " 'rsquared',\n",
       " 'rsquared_adj',\n",
       " 'save',\n",
       " 'scale',\n",
       " 'ssr',\n",
       " 'summary',\n",
       " 'summary2',\n",
       " 't_test',\n",
       " 't_test_pairwise',\n",
       " 'tvalues',\n",
       " 'uncentered_tss',\n",
       " 'use_t',\n",
       " 'wald_test',\n",
       " 'wald_test_terms',\n",
       " 'wresid']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "dir(lm)"
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Intercept</td>\n",
       "      <td>-5.277035e+06</td>\n",
       "      <td>7.082117e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>budget</td>\n",
       "      <td>2.857418e+00</td>\n",
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       "                      0             1\n",
       "Intercept -5.277035e+06  7.082117e+06\n",
       "budget     2.857418e+00  3.062742e+00"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "lm.conf_int()"
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   "execution_count": 36,
   "metadata": {},
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     "execution_count": 36,
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    "lm.predict(pd.DataFrame({'budget': [10000]}))"
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   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Intercept    0.774617\n",
       "budget       0.000000\n",
       "dtype: float64"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "lm.pvalues"
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   "execution_count": 38,
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     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "lm.rsquared"
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>revenue</td>     <th>  R-squared:         </th> <td>   0.498</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.497</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3196.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 16 Nov 2022</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>17:11:58</td>     <th>  Log-Likelihood:    </th> <td> -64959.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  3229</td>      <th>  AIC:               </th> <td>1.299e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  3227</td>      <th>  BIC:               </th> <td>1.299e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td> 9.025e+05</td> <td> 3.15e+06</td> <td>    0.286</td> <td> 0.775</td> <td>-5.28e+06</td> <td> 7.08e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>budget</th>    <td>    2.9601</td> <td>    0.052</td> <td>   56.533</td> <td> 0.000</td> <td>    2.857</td> <td>    3.063</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2528.122</td> <th>  Durbin-Watson:     </th>  <td>   1.504</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>129017.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 3.276</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>33.266</td>  <th>  Cond. No.          </th>  <td>8.16e+07</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 8.16e+07. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                revenue   R-squared:                       0.498\n",
       "Model:                            OLS   Adj. R-squared:                  0.497\n",
       "Method:                 Least Squares   F-statistic:                     3196.\n",
       "Date:                Wed, 16 Nov 2022   Prob (F-statistic):               0.00\n",
       "Time:                        17:11:58   Log-Likelihood:                -64959.\n",
       "No. Observations:                3229   AIC:                         1.299e+05\n",
       "Df Residuals:                    3227   BIC:                         1.299e+05\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept   9.025e+05   3.15e+06      0.286      0.775   -5.28e+06    7.08e+06\n",
       "budget         2.9601      0.052     56.533      0.000       2.857       3.063\n",
       "==============================================================================\n",
       "Omnibus:                     2528.122   Durbin-Watson:                   1.504\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           129017.819\n",
       "Skew:                           3.276   Prob(JB):                         0.00\n",
       "Kurtosis:                      33.266   Cond. No.                     8.16e+07\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 8.16e+07. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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