exploration_template.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# How COVID has influenced Bike use in the Bay Area\n",
    "## by Grant Glass\n",
    "\n",
    "## Preliminary Wrangling\n",
    "\n",
    "> Bay Wheels is a regional public bicycle sharing system in the San Francisco Bay Area, California operated by Motivate in a partnership with the Metropolitan Transportation Commission and the Bay Area Air Quality Management District. Bay Wheels is the first regional and large-scale bicycle sharing system deployed in California and on the West Coast of the United States. It was established as Bay Area Bike Share in August 2013. As of January 2018, the Bay Wheels system had over 2,600 bicycles in 262 stations across San Francisco, East Bay and San Jose. On June 28, 2017, the system officially re-launched as Ford GoBike in a partnership with Ford Motor Company. After Motivate's acquisition by Lyft, the system was subsequently renamed to Bay Wheels in June 2019. The system is expected to expand to 7,000 bicycles around 540 stations in San Francisco, Oakland, Berkeley, Emeryville, and San Jose.\n",
    "\n",
    "\n",
    ">On Mar 17, 2020 San Francisco enacted a shelter in place, which drastically reduced car traffic in the bay area. I am going to investigate whether or not this caused an increase in ridership.\n",
    "\n",
    "\n",
    ">This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. I will merge both January, Februrary, and March's data together. The dataset includes the following features : Trip Duration in seconds , Start Time and Date , End Time and Date , Start Station ID , Start Station Name , Start Station Latitude , Start Station Longitude , End Station ID , End Station Name, End Station Latitude , End Station Longitude , Bike ID , User Type , and  Rental Access Method. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mat\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style \n",
    "import seaborn as sb\n",
    "import os\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Load in your dataset and describe its properties through the questions below.\n",
    "Try and motivate your exploration goals through this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/grantglass/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (13) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83118</td>\n",
       "      <td>2020-01-31 15:23:47.7330</td>\n",
       "      <td>2020-02-01 14:29:06.2630</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>13052</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68239</td>\n",
       "      <td>2020-01-31 15:40:31.6160</td>\n",
       "      <td>2020-02-01 10:37:51.0000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>12235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55407</td>\n",
       "      <td>2020-01-31 17:48:16.6350</td>\n",
       "      <td>2020-02-01 09:11:44.3170</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>12822</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54929</td>\n",
       "      <td>2020-01-31 17:53:03.4130</td>\n",
       "      <td>2020-02-01 09:08:32.6340</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>11705</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55700</td>\n",
       "      <td>2020-01-31 17:12:33.4600</td>\n",
       "      <td>2020-02-01 08:40:53.6460</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>371.0</td>\n",
       "      <td>Lombard St at Columbus Ave</td>\n",
       "      <td>37.802746</td>\n",
       "      <td>-122.413579</td>\n",
       "      <td>3673</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "   duration_sec                start_time                  end_time  \\\n",
       "0         83118  2020-01-31 15:23:47.7330  2020-02-01 14:29:06.2630   \n",
       "1         68239  2020-01-31 15:40:31.6160  2020-02-01 10:37:51.0000   \n",
       "2         55407  2020-01-31 17:48:16.6350  2020-02-01 09:11:44.3170   \n",
       "3         54929  2020-01-31 17:53:03.4130  2020-02-01 09:08:32.6340   \n",
       "4         55700  2020-01-31 17:12:33.4600  2020-02-01 08:40:53.6460   \n",
       "\n",
       "   start_station_id             start_station_name  start_station_latitude  \\\n",
       "0             400.0  Buchanan St at North Point St               37.804272   \n",
       "1              99.0           Folsom St at 15th St               37.767037   \n",
       "2             197.0    El Embarcadero at Grand Ave               37.808848   \n",
       "3             197.0    El Embarcadero at Grand Ave               37.808848   \n",
       "4              12.0    Pier 1/2 at The Embarcadero               37.796389   \n",
       "\n",
       "   start_station_longitude  end_station_id               end_station_name  \\\n",
       "0              -122.433537           400.0  Buchanan St at North Point St   \n",
       "1              -122.415443            99.0           Folsom St at 15th St   \n",
       "2              -122.249680           197.0    El Embarcadero at Grand Ave   \n",
       "3              -122.249680           197.0    El Embarcadero at Grand Ave   \n",
       "4              -122.394586           371.0     Lombard St at Columbus Ave   \n",
       "\n",
       "   end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "0             37.804272            -122.433537    13052    Customer   \n",
       "1             37.767037            -122.415443    12235  Subscriber   \n",
       "2             37.808848            -122.249680    12822    Customer   \n",
       "3             37.808848            -122.249680    11705    Customer   \n",
       "4             37.802746            -122.413579     3673    Customer   \n",
       "\n",
       "  rental_access_method  \n",
       "0                  NaN  \n",
       "1                  NaN  \n",
       "2                  NaN  \n",
       "3                  NaN  \n",
       "4                  NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge all the files together\n",
    "currentDir = os.getcwd()\n",
    "path = currentDir+'/data/'\n",
    "text_files = os.listdir(path)\n",
    "\n",
    "# loop to get all files together\n",
    "file_list = []\n",
    "for text_file in text_files:\n",
    "    filename = path+text_file\n",
    "    df = pd.read_csv(filename)\n",
    "    file_list.append(df)\n",
    "df_merge = pd.concat(file_list) \n",
    "df_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error seems to be caused by the blank in the 13th column. We will address that in cleanup. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>2020-04-01 10:42:35</td>\n",
       "      <td>2020-04-01 10:43:37</td>\n",
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       "      <td>37.848476</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-122.266063</td>\n",
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       "      <td>2020-04-01 13:13:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848530</td>\n",
       "      <td>-122.266013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848497</td>\n",
       "      <td>-122.266029</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176798</th>\n",
       "      <td>333</td>\n",
       "      <td>2020-03-23 16:39:57</td>\n",
       "      <td>2020-03-23 16:45:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848625</td>\n",
       "      <td>-122.266660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848446</td>\n",
       "      <td>-122.266022</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        duration_sec           start_time             end_time  \\\n",
       "176794            61  2020-04-01 10:42:35  2020-04-01 10:43:37   \n",
       "176795            78  2020-03-18 17:10:42  2020-03-18 17:12:01   \n",
       "176796           122  2020-04-01 13:32:28  2020-04-01 13:34:31   \n",
       "176797           340  2020-04-01 13:07:34  2020-04-01 13:13:14   \n",
       "176798           333  2020-03-23 16:39:57  2020-03-23 16:45:30   \n",
       "\n",
       "        start_station_id start_station_name  start_station_latitude  \\\n",
       "176794               NaN                NaN               37.848493   \n",
       "176795               NaN                NaN               37.848499   \n",
       "176796               NaN                NaN               37.848507   \n",
       "176797               NaN                NaN               37.848530   \n",
       "176798               NaN                NaN               37.848625   \n",
       "\n",
       "        start_station_longitude  end_station_id end_station_name  \\\n",
       "176794              -122.265797             NaN              NaN   \n",
       "176795              -122.265794             NaN              NaN   \n",
       "176796              -122.266010             NaN              NaN   \n",
       "176797              -122.266013             NaN              NaN   \n",
       "176798              -122.266660             NaN              NaN   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "176794             37.848476            -122.265779   999960  Subscriber   \n",
       "176795             37.848477            -122.265815   999960  Subscriber   \n",
       "176796             37.848485            -122.266063   633095  Subscriber   \n",
       "176797             37.848497            -122.266029   633095  Subscriber   \n",
       "176798             37.848446            -122.266022   633095  Subscriber   \n",
       "\n",
       "       rental_access_method  \n",
       "176794                  app  \n",
       "176795                  app  \n",
       "176796                  app  \n",
       "176797                  app  \n",
       "176798                  app  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 905007 entries, 0 to 176798\n",
      "Data columns (total 14 columns):\n",
      "duration_sec               905007 non-null int64\n",
      "start_time                 905007 non-null object\n",
      "end_time                   905007 non-null object\n",
      "start_station_id           418705 non-null float64\n",
      "start_station_name         419938 non-null object\n",
      "start_station_latitude     905007 non-null float64\n",
      "start_station_longitude    905007 non-null float64\n",
      "end_station_id             419225 non-null float64\n",
      "end_station_name           420499 non-null object\n",
      "end_station_latitude       905007 non-null float64\n",
      "end_station_longitude      905007 non-null float64\n",
      "bike_id                    905007 non-null int64\n",
      "user_type                  905007 non-null object\n",
      "rental_access_method       617858 non-null object\n",
      "dtypes: float64(6), int64(2), object(6)\n",
      "memory usage: 103.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_merge.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                 int64\n",
       "start_time                  object\n",
       "end_time                    object\n",
       "start_station_id           float64\n",
       "start_station_name          object\n",
       "start_station_latitude     float64\n",
       "start_station_longitude    float64\n",
       "end_station_id             float64\n",
       "end_station_name            object\n",
       "end_station_latitude       float64\n",
       "end_station_longitude      float64\n",
       "bike_id                      int64\n",
       "user_type                   object\n",
       "rental_access_method        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> The dataset is the result of merging 3 files(January, February, March (2020)). It has 14 features and 905,007 rows. \n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> Did Covid19 influence ridership? Are people biking for longer?\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> Trip Duration, Start Time and Date , End Time and Date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "\n",
    "> In this section, investigate distributions of individual variables. If\n",
    "you see unusual points or outliers, take a deeper look to clean things up\n",
    "and prepare yourself to look at relationships between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'Duration in seconds')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VQ3tkZmZ6fe90Oi/YvgFcfA0NDfx364fcv9llbv0Pc+u/mFv/FRER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\nmiRJZoQQQgghhBBN0v8BAgRHk36oFHwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3fc30790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at the average trip length\n",
    "style.use('seaborn-poster') #sets the size of the charts\n",
    "style.use('ggplot')\n",
    "bin_edges = np.arange(0, df_merge['duration_sec'].max() + 200 , 200)\n",
    "plt.hist(data = df_merge , x = 'duration_sec' , normed=True, bins = bin_edges)\n",
    "plt.xlim(0,3000)\n",
    "plt.title('Trip Durations Distrobution')\n",
    "plt.xlabel('Duration in seconds')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph is a little hard to look at, maybe minutes could create a better looking graph?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.016667      1152\n",
       "7.866667      1138\n",
       "6.783333      1137\n",
       "6.166667      1129\n",
       "6.566667      1128\n",
       "5.916667      1123\n",
       "5.600000      1121\n",
       "6.416667      1121\n",
       "6.733333      1118\n",
       "5.933333      1115\n",
       "6.266667      1113\n",
       "6.350000      1112\n",
       "6.183333      1112\n",
       "5.733333      1112\n",
       "6.050000      1108\n",
       "5.583333      1108\n",
       "6.150000      1106\n",
       "5.883333      1105\n",
       "6.366667      1104\n",
       "7.266667      1103\n",
       "6.083333      1103\n",
       "5.900000      1103\n",
       "6.766667      1103\n",
       "5.950000      1102\n",
       "6.433333      1101\n",
       "5.800000      1101\n",
       "6.300000      1100\n",
       "6.833333      1096\n",
       "6.516667      1096\n",
       "6.900000      1094\n",
       "              ... \n",
       "125.200000       1\n",
       "99.550000        1\n",
       "99.950000        1\n",
       "238.950000       1\n",
       "80.883333        1\n",
       "126.850000       1\n",
       "285.400000       1\n",
       "75.300000        1\n",
       "147.083333       1\n",
       "207.733333       1\n",
       "95.650000        1\n",
       "223.816667       1\n",
       "130.850000       1\n",
       "184.450000       1\n",
       "185.550000       1\n",
       "143.816667       1\n",
       "142.183333       1\n",
       "95.183333        1\n",
       "130.150000       1\n",
       "136.616667       1\n",
       "212.216667       1\n",
       "148.833333       1\n",
       "331.816667       1\n",
       "160.016667       1\n",
       "894.483333       1\n",
       "84.250000        1\n",
       "177.966667       1\n",
       "111.266667       1\n",
       "928.333333       1\n",
       "93.083333        1\n",
       "Name: duration_min, Length: 8742, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge['duration_min'] = df_merge['duration_sec'] / 60\n",
    "df_merge['duration_min'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there are a few super users that are taking long trips. It is probably better to look at seconds rather than min. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at time. First we need to extract the date data from the start_time column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_merge['start_time'] = pd.to_datetime(df_merge['start_time'])\n",
    "df_merge['start_year'] = df_merge['start_time'].dt.year\n",
    "df_merge['start_month'] = df_merge['start_time'].dt.month\n",
    "df_merge['start_day'] = df_merge['start_time'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                        int64\n",
       "start_time                 datetime64[ns]\n",
       "end_time                           object\n",
       "start_station_id                  float64\n",
       "start_station_name                 object\n",
       "start_station_latitude            float64\n",
       "start_station_longitude           float64\n",
       "end_station_id                    float64\n",
       "end_station_name                   object\n",
       "end_station_latitude              float64\n",
       "end_station_longitude             float64\n",
       "bike_id                             int64\n",
       "user_type                          object\n",
       "rental_access_method               object\n",
       "duration_min                      float64\n",
       "start_year                          int64\n",
       "start_month                         int64\n",
       "start_day                           int64\n",
       "start_day_month                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-82-2cf43e6f7e7c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_merge\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timing'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_merge\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'start_month'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'start_day'\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/Users/grantglass/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1962\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1963\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1964\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1966\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1969\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1970\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1971\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1972\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1973\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1641\u001b[0m         \u001b[0;34m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1642\u001b[0m         \u001b[0mcache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1643\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1644\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1645\u001b[0m             \u001b[0mvalues\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[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
     ]
    }
   ],
   "source": [
    "df_merge['timing'] = pd.concat(df_merge[['start_month'], ['start_day']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_merge.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Using arbitrary long args with data is not supported due to ambiguity of arguments.\nUse multiple plotting calls instead.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-f7c2899fc8e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbin_edges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.1\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_merge\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'start_month'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'duration_sec'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbin_edges\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The Trip Duration Trend over Each Month '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Month'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3238\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3239\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3240\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m   3241\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3242\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1646\u001b[0m                     \u001b[0;31m# update the information about replace names and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m                     \u001b[0;31m# label position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1648\u001b[0;31m                     \u001b[0m_arg_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpositional_parameter_names\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1649\u001b[0m                     if (label_namer and  # we actually want a label here ...\n\u001b[1;32m   1650\u001b[0m                             \u001b[0m_arg_names\u001b[0m \u001b[0;32mand\u001b[0m  \u001b[0;31m# and we can find a label in *args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36m_plot_args_replacer\u001b[0;34m(args, data)\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"x\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         raise ValueError(\"Using arbitrary long args with data is not \"\n\u001b[0m\u001b[1;32m     87\u001b[0m                          \u001b[0;34m\"supported due to ambiguity of arguments.\\nUse \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m                          \"multiple plotting calls instead.\")\n",
      "\u001b[0;31mValueError\u001b[0m: Using arbitrary long args with data is not supported due to ambiguity of arguments.\nUse multiple plotting calls instead."
     ]
    },
    {
     "data": {
      "image/png": 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kwAEAoJAABwCAQgIcAAAKCXAAACgkwAEAoJAABwCAQgIcAAAKCXAAACgkwAEAoJAABwCA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\n6MBUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2af99ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12,12])\n",
    "\n",
    "\n",
    "plt.plot(data = df_merge , x = 'start_month' , y = 'duration_sec', index=pd.date_range('1/1/2000', periods=1000)\n",
    "plt.title('The Trip Duration Trend over Each Month ')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Trip Duration(min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?\n",
    "\n",
    "> Your answer here!\n",
    "\n",
    "### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?\n",
    "\n",
    "> Your answer here!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "> In this section, investigate relationships between pairs of variables in your\n",
    "data. Make sure the variables that you cover here have been introduced in some\n",
    "fashion in the previous section (univariate exploration)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "> Your answer here!\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> Your answer here!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "> Create plots of three or more variables to investigate your data even\n",
    "further. Make sure that your investigations are justified, and follow from\n",
    "your work in the previous sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "\n",
    "> Your answer here!\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> Your answer here!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> At the end of your report, make sure that you export the notebook as an\n",
    "html file from the `File > Download as... > HTML` menu. Make sure you keep\n",
    "track of where the exported file goes, so you can put it in the same folder\n",
    "as this notebook for project submission. Also, make sure you remove all of\n",
    "the quote-formatted guide notes like this one before you finish your report!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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