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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "markdown",
  5.    "metadata": {},
  6.    "source": [
  7.     "# Data Exploration and Analysis of NYC Taxi Trips                \n",
  8.     "#### Yağmur Duman 25133                                                                                                                           "
  9.    ]
  10.   },
  11.   {
  12.    "cell_type": "markdown",
  13.    "metadata": {},
  14.    "source": [
  15.     "## Data Exploration\n",
  16.     "### Reading and Printing the File"
  17.    ]
  18.   },
  19.   {
  20.    "cell_type": "code",
  21.    "execution_count": 1,
  22.    "metadata": {},
  23.    "outputs": [
  24.     {
  25.      "data": {
  26.       "text/html": [
  27.        "<div>\n",
  28.        "<style scoped>\n",
  29.        "    .dataframe tbody tr th:only-of-type {\n",
  30.        "        vertical-align: middle;\n",
  31.        "    }\n",
  32.        "\n",
  33.        "    .dataframe tbody tr th {\n",
  34.        "        vertical-align: top;\n",
  35.        "    }\n",
  36.        "\n",
  37.        "    .dataframe thead th {\n",
  38.        "        text-align: right;\n",
  39.        "    }\n",
  40.        "</style>\n",
  41.        "<table border=\"1\" class=\"dataframe\">\n",
  42.        "  <thead>\n",
  43.        "    <tr style=\"text-align: right;\">\n",
  44.        "      <th></th>\n",
  45.        "      <th>id</th>\n",
  46.        "      <th>vendor_id</th>\n",
  47.        "      <th>pickup_datetime</th>\n",
  48.        "      <th>dropoff_datetime</th>\n",
  49.        "      <th>passenger_count</th>\n",
  50.        "      <th>pickup_longitude</th>\n",
  51.        "      <th>pickup_latitude</th>\n",
  52.        "      <th>dropoff_longitude</th>\n",
  53.        "      <th>dropoff_latitude</th>\n",
  54.        "      <th>store_and_fwd_flag</th>\n",
  55.        "      <th>trip_duration</th>\n",
  56.        "    </tr>\n",
  57.        "  </thead>\n",
  58.        "  <tbody>\n",
  59.        "    <tr>\n",
  60.        "      <th>0</th>\n",
  61.        "      <td>id2875421</td>\n",
  62.        "      <td>2</td>\n",
  63.        "      <td>2016-03-14 17:24:55</td>\n",
  64.        "      <td>2016-03-14 17:32:30</td>\n",
  65.        "      <td>1</td>\n",
  66.        "      <td>-73.982155</td>\n",
  67.        "      <td>40.767937</td>\n",
  68.        "      <td>-73.964630</td>\n",
  69.        "      <td>40.765602</td>\n",
  70.        "      <td>N</td>\n",
  71.        "      <td>455</td>\n",
  72.        "    </tr>\n",
  73.        "    <tr>\n",
  74.        "      <th>1</th>\n",
  75.        "      <td>id0012891</td>\n",
  76.        "      <td>2</td>\n",
  77.        "      <td>2016-03-10 21:45:01</td>\n",
  78.        "      <td>2016-03-10 22:05:26</td>\n",
  79.        "      <td>1</td>\n",
  80.        "      <td>-73.981049</td>\n",
  81.        "      <td>40.744339</td>\n",
  82.        "      <td>-73.973000</td>\n",
  83.        "      <td>40.789989</td>\n",
  84.        "      <td>N</td>\n",
  85.        "      <td>1225</td>\n",
  86.        "    </tr>\n",
  87.        "    <tr>\n",
  88.        "      <th>2</th>\n",
  89.        "      <td>id3361153</td>\n",
  90.        "      <td>1</td>\n",
  91.        "      <td>2016-03-11 07:11:23</td>\n",
  92.        "      <td>2016-03-11 07:20:09</td>\n",
  93.        "      <td>1</td>\n",
  94.        "      <td>-73.994560</td>\n",
  95.        "      <td>40.750526</td>\n",
  96.        "      <td>-73.978500</td>\n",
  97.        "      <td>40.756191</td>\n",
  98.        "      <td>N</td>\n",
  99.        "      <td>526</td>\n",
  100.        "    </tr>\n",
  101.        "    <tr>\n",
  102.        "      <th>3</th>\n",
  103.        "      <td>id2129090</td>\n",
  104.        "      <td>1</td>\n",
  105.        "      <td>2016-03-14 14:05:39</td>\n",
  106.        "      <td>2016-03-14 14:28:05</td>\n",
  107.        "      <td>1</td>\n",
  108.        "      <td>-73.975090</td>\n",
  109.        "      <td>40.758766</td>\n",
  110.        "      <td>-73.953201</td>\n",
  111.        "      <td>40.765068</td>\n",
  112.        "      <td>N</td>\n",
  113.        "      <td>1346</td>\n",
  114.        "    </tr>\n",
  115.        "    <tr>\n",
  116.        "      <th>4</th>\n",
  117.        "      <td>id0256505</td>\n",
  118.        "      <td>1</td>\n",
  119.        "      <td>2016-03-14 15:04:38</td>\n",
  120.        "      <td>2016-03-14 15:16:13</td>\n",
  121.        "      <td>1</td>\n",
  122.        "      <td>-73.994484</td>\n",
  123.        "      <td>40.745087</td>\n",
  124.        "      <td>-73.998993</td>\n",
  125.        "      <td>40.722710</td>\n",
  126.        "      <td>N</td>\n",
  127.        "      <td>695</td>\n",
  128.        "    </tr>\n",
  129.        "  </tbody>\n",
  130.        "</table>\n",
  131.        "</div>"
  132.       ],
  133.       "text/plain": [
  134.        "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
  135.        "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
  136.        "1  id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
  137.        "2  id3361153          1  2016-03-11 07:11:23  2016-03-11 07:20:09   \n",
  138.        "3  id2129090          1  2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
  139.        "4  id0256505          1  2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
  140.        "\n",
  141.        "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
  142.        "0                1        -73.982155        40.767937         -73.964630   \n",
  143.        "1                1        -73.981049        40.744339         -73.973000   \n",
  144.        "2                1        -73.994560        40.750526         -73.978500   \n",
  145.        "3                1        -73.975090        40.758766         -73.953201   \n",
  146.        "4                1        -73.994484        40.745087         -73.998993   \n",
  147.        "\n",
  148.        "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
  149.        "0         40.765602                  N            455  \n",
  150.        "1         40.789989                  N           1225  \n",
  151.        "2         40.756191                  N            526  \n",
  152.        "3         40.765068                  N           1346  \n",
  153.        "4         40.722710                  N            695  "
  154.       ]
  155.      },
  156.      "execution_count": 1,
  157.      "metadata": {},
  158.      "output_type": "execute_result"
  159.     }
  160.    ],
  161.    "source": [
  162.     "#importing necessary libraries\n",
  163.     "import pandas as pd\n",
  164.     "import reverse_geocoder as rg \n",
  165.     "import numpy as np\n",
  166.     "import seaborn as sns\n",
  167.     "import matplotlib.pyplot as plt\n",
  168.     "from datetime import datetime\n",
  169.     "from datetime import date\n",
  170.     "from datetime import time\n",
  171.     "from geopy.distance import great_circle\n",
  172.     "from scipy import stats\n",
  173.     "import csv\n",
  174.     "%matplotlib inline\n",
  175.     "\n",
  176.     "#reading the file\n",
  177.     "df = pd.read_csv('/Users/mehmetahkemoglu/Desktop/taxi-trips.csv')\n",
  178.     "\n",
  179.     "df.head()"
  180.    ]
  181.   },
  182.   {
  183.    "cell_type": "code",
  184.    "execution_count": 2,
  185.    "metadata": {},
  186.    "outputs": [
  187.     {
  188.      "name": "stdout",
  189.      "output_type": "stream",
  190.      "text": [
  191.       "Requirement already satisfied: reverse_geocoder in ./anaconda3/lib/python3.7/site-packages (1.5.1)\n",
  192.       "Requirement already satisfied: scipy>=0.17.1 in ./anaconda3/lib/python3.7/site-packages (from reverse_geocoder) (1.1.0)\n",
  193.       "Requirement already satisfied: numpy>=1.11.0 in ./anaconda3/lib/python3.7/site-packages (from reverse_geocoder) (1.15.4)\n",
  194.       "Requirement already satisfied: geopy in ./anaconda3/lib/python3.7/site-packages (1.18.1)\n",
  195.       "Requirement already satisfied: geographiclib<2,>=1.49 in ./anaconda3/lib/python3.7/site-packages (from geopy) (1.49)\n",
  196.       "Requirement already satisfied: matplotlib in ./anaconda3/lib/python3.7/site-packages (3.0.2)\n",
  197.       "Requirement already satisfied: numpy>=1.10.0 in ./anaconda3/lib/python3.7/site-packages (from matplotlib) (1.15.4)\n",
  198.       "Requirement already satisfied: cycler>=0.10 in ./anaconda3/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
  199.       "Requirement already satisfied: kiwisolver>=1.0.1 in ./anaconda3/lib/python3.7/site-packages (from matplotlib) (1.0.1)\n",
  200.       "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in ./anaconda3/lib/python3.7/site-packages (from matplotlib) (2.3.0)\n",
  201.       "Requirement already satisfied: python-dateutil>=2.1 in ./anaconda3/lib/python3.7/site-packages (from matplotlib) (2.7.5)\n",
  202.       "Requirement already satisfied: six in ./anaconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
  203.       "Requirement already satisfied: setuptools in ./anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.6.3)\n"
  204.      ]
  205.     }
  206.    ],
  207.    "source": [
  208.     "!pip install reverse_geocoder #installing necessary libraries\n",
  209.     "!pip install geopy #installing necessary libraries\n",
  210.     "!pip install matplotlib #installing necessary libraries\n"
  211.    ]
  212.   },
  213.   {
  214.    "cell_type": "markdown",
  215.    "metadata": {},
  216.    "source": [
  217.     "### Basic information on shape, data types and descriptive statistics that summarize columns"
  218.    ]
  219.   },
  220.   {
  221.    "cell_type": "code",
  222.    "execution_count": 3,
  223.    "metadata": {},
  224.    "outputs": [
  225.     {
  226.      "name": "stdout",
  227.      "output_type": "stream",
  228.      "text": [
  229.       "Index(['id', 'vendor_id', 'pickup_datetime', 'dropoff_datetime',\n",
  230.       "       'passenger_count', 'pickup_longitude', 'pickup_latitude',\n",
  231.       "       'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',\n",
  232.       "       'trip_duration'],\n",
  233.       "      dtype='object')\n"
  234.      ]
  235.     }
  236.    ],
  237.    "source": [
  238.     "print(df.columns)"
  239.    ]
  240.   },
  241.   {
  242.    "cell_type": "code",
  243.    "execution_count": 4,
  244.    "metadata": {},
  245.    "outputs": [
  246.     {
  247.      "name": "stdout",
  248.      "output_type": "stream",
  249.      "text": [
  250.       "number of rows: 118185, number of columns: 11\n"
  251.      ]
  252.     }
  253.    ],
  254.    "source": [
  255.     "n_rows, n_columns = df.shape  \n",
  256.     "print(\"number of rows: {}, number of columns: {}\".format(n_rows, n_columns))"
  257.    ]
  258.   },
  259.   {
  260.    "cell_type": "code",
  261.    "execution_count": 5,
  262.    "metadata": {},
  263.    "outputs": [
  264.     {
  265.      "data": {
  266.       "text/plain": [
  267.        "id                     object\n",
  268.        "vendor_id               int64\n",
  269.        "pickup_datetime        object\n",
  270.        "dropoff_datetime       object\n",
  271.        "passenger_count         int64\n",
  272.        "pickup_longitude      float64\n",
  273.        "pickup_latitude       float64\n",
  274.        "dropoff_longitude     float64\n",
  275.        "dropoff_latitude      float64\n",
  276.        "store_and_fwd_flag     object\n",
  277.        "trip_duration           int64\n",
  278.        "dtype: object"
  279.       ]
  280.      },
  281.      "execution_count": 5,
  282.      "metadata": {},
  283.      "output_type": "execute_result"
  284.     }
  285.    ],
  286.    "source": [
  287.     "df.dtypes"
  288.    ]
  289.   },
  290.   {
  291.    "cell_type": "code",
  292.    "execution_count": 6,
  293.    "metadata": {},
  294.    "outputs": [
  295.     {
  296.      "data": {
  297.       "text/html": [
  298.        "<div>\n",
  299.        "<style scoped>\n",
  300.        "    .dataframe tbody tr th:only-of-type {\n",
  301.        "        vertical-align: middle;\n",
  302.        "    }\n",
  303.        "\n",
  304.        "    .dataframe tbody tr th {\n",
  305.        "        vertical-align: top;\n",
  306.        "    }\n",
  307.        "\n",
  308.        "    .dataframe thead th {\n",
  309.        "        text-align: right;\n",
  310.        "    }\n",
  311.        "</style>\n",
  312.        "<table border=\"1\" class=\"dataframe\">\n",
  313.        "  <thead>\n",
  314.        "    <tr style=\"text-align: right;\">\n",
  315.        "      <th></th>\n",
  316.        "      <th>vendor_id</th>\n",
  317.        "      <th>passenger_count</th>\n",
  318.        "      <th>pickup_longitude</th>\n",
  319.        "      <th>pickup_latitude</th>\n",
  320.        "      <th>dropoff_longitude</th>\n",
  321.        "      <th>dropoff_latitude</th>\n",
  322.        "      <th>trip_duration</th>\n",
  323.        "    </tr>\n",
  324.        "  </thead>\n",
  325.        "  <tbody>\n",
  326.        "    <tr>\n",
  327.        "      <th>count</th>\n",
  328.        "      <td>118185.000000</td>\n",
  329.        "      <td>118185.000000</td>\n",
  330.        "      <td>118185.000000</td>\n",
  331.        "      <td>118185.000000</td>\n",
  332.        "      <td>118185.000000</td>\n",
  333.        "      <td>118185.000000</td>\n",
  334.        "      <td>118185.000000</td>\n",
  335.        "    </tr>\n",
  336.        "    <tr>\n",
  337.        "      <th>mean</th>\n",
  338.        "      <td>1.534958</td>\n",
  339.        "      <td>1.657148</td>\n",
  340.        "      <td>-73.973971</td>\n",
  341.        "      <td>40.751392</td>\n",
  342.        "      <td>-73.973538</td>\n",
  343.        "      <td>40.752212</td>\n",
  344.        "      <td>927.186310</td>\n",
  345.        "    </tr>\n",
  346.        "    <tr>\n",
  347.        "      <th>std</th>\n",
  348.        "      <td>0.498779</td>\n",
  349.        "      <td>1.313844</td>\n",
  350.        "      <td>0.040456</td>\n",
  351.        "      <td>0.027958</td>\n",
  352.        "      <td>0.039192</td>\n",
  353.        "      <td>0.032284</td>\n",
  354.        "      <td>3118.710246</td>\n",
  355.        "    </tr>\n",
  356.        "    <tr>\n",
  357.        "      <th>min</th>\n",
  358.        "      <td>1.000000</td>\n",
  359.        "      <td>0.000000</td>\n",
  360.        "      <td>-79.487900</td>\n",
  361.        "      <td>40.225803</td>\n",
  362.        "      <td>-79.487900</td>\n",
  363.        "      <td>40.225800</td>\n",
  364.        "      <td>1.000000</td>\n",
  365.        "    </tr>\n",
  366.        "    <tr>\n",
  367.        "      <th>25%</th>\n",
  368.        "      <td>1.000000</td>\n",
  369.        "      <td>1.000000</td>\n",
  370.        "      <td>-73.991875</td>\n",
  371.        "      <td>40.737835</td>\n",
  372.        "      <td>-73.991394</td>\n",
  373.        "      <td>40.736462</td>\n",
  374.        "      <td>393.000000</td>\n",
  375.        "    </tr>\n",
  376.        "    <tr>\n",
  377.        "      <th>50%</th>\n",
  378.        "      <td>2.000000</td>\n",
  379.        "      <td>1.000000</td>\n",
  380.        "      <td>-73.981796</td>\n",
  381.        "      <td>40.754501</td>\n",
  382.        "      <td>-73.979759</td>\n",
  383.        "      <td>40.754848</td>\n",
  384.        "      <td>652.000000</td>\n",
  385.        "    </tr>\n",
  386.        "    <tr>\n",
  387.        "      <th>75%</th>\n",
  388.        "      <td>2.000000</td>\n",
  389.        "      <td>2.000000</td>\n",
  390.        "      <td>-73.967575</td>\n",
  391.        "      <td>40.768471</td>\n",
  392.        "      <td>-73.962990</td>\n",
  393.        "      <td>40.770077</td>\n",
  394.        "      <td>1048.000000</td>\n",
  395.        "    </tr>\n",
  396.        "    <tr>\n",
  397.        "      <th>max</th>\n",
  398.        "      <td>2.000000</td>\n",
  399.        "      <td>6.000000</td>\n",
  400.        "      <td>-73.425018</td>\n",
  401.        "      <td>41.292198</td>\n",
  402.        "      <td>-73.055977</td>\n",
  403.        "      <td>41.292198</td>\n",
  404.        "      <td>86366.000000</td>\n",
  405.        "    </tr>\n",
  406.        "  </tbody>\n",
  407.        "</table>\n",
  408.        "</div>"
  409.       ],
  410.       "text/plain": [
  411.        "           vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
  412.        "count  118185.000000    118185.000000     118185.000000    118185.000000   \n",
  413.        "mean        1.534958         1.657148        -73.973971        40.751392   \n",
  414.        "std         0.498779         1.313844          0.040456         0.027958   \n",
  415.        "min         1.000000         0.000000        -79.487900        40.225803   \n",
  416.        "25%         1.000000         1.000000        -73.991875        40.737835   \n",
  417.        "50%         2.000000         1.000000        -73.981796        40.754501   \n",
  418.        "75%         2.000000         2.000000        -73.967575        40.768471   \n",
  419.        "max         2.000000         6.000000        -73.425018        41.292198   \n",
  420.        "\n",
  421.        "       dropoff_longitude  dropoff_latitude  trip_duration  \n",
  422.        "count      118185.000000     118185.000000  118185.000000  \n",
  423.        "mean          -73.973538         40.752212     927.186310  \n",
  424.        "std             0.039192          0.032284    3118.710246  \n",
  425.        "min           -79.487900         40.225800       1.000000  \n",
  426.        "25%           -73.991394         40.736462     393.000000  \n",
  427.        "50%           -73.979759         40.754848     652.000000  \n",
  428.        "75%           -73.962990         40.770077    1048.000000  \n",
  429.        "max           -73.055977         41.292198   86366.000000  "
  430.       ]
  431.      },
  432.      "execution_count": 6,
  433.      "metadata": {},
  434.      "output_type": "execute_result"
  435.     }
  436.    ],
  437.    "source": [
  438.     "df.describe()"
  439.    ]
  440.   },
  441.   {
  442.    "cell_type": "markdown",
  443.    "metadata": {},
  444.    "source": [
  445.     "### Creating two new columns: ”pickup district” and ”dropoff district”"
  446.    ]
  447.   },
  448.   {
  449.    "cell_type": "code",
  450.    "execution_count": 7,
  451.    "metadata": {},
  452.    "outputs": [
  453.     {
  454.      "name": "stdout",
  455.      "output_type": "stream",
  456.      "text": [
  457.       "Loading formatted geocoded file...\n"
  458.      ]
  459.     }
  460.    ],
  461.    "source": [
  462.     "pickupcoord= df[[\"pickup_latitude\", \"pickup_longitude\"]].values\n",
  463.     "dropoffcoord= df[[\"dropoff_latitude\", \"dropoff_longitude\"]].values\n",
  464.     "\n",
  465.     "\n",
  466.     "pickuploc = []\n",
  467.     "for i in pickupcoord:\n",
  468.     "    pickuploc.append(tuple(i))\n",
  469.     "dropoffloc = []\n",
  470.     "for i in dropoffcoord:\n",
  471.     "    dropoffloc.append(tuple(i))\n",
  472.     "        \n",
  473.     "pickup = rg.search(pickuploc)\n",
  474.     "dropoff = rg.search(dropoffloc)"
  475.    ]
  476.   },
  477.   {
  478.    "cell_type": "code",
  479.    "execution_count": 8,
  480.    "metadata": {},
  481.    "outputs": [
  482.     {
  483.      "data": {
  484.       "text/html": [
  485.        "<div>\n",
  486.        "<style scoped>\n",
  487.        "    .dataframe tbody tr th:only-of-type {\n",
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  490.        "\n",
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  499.        "<table border=\"1\" class=\"dataframe\">\n",
  500.        "  <thead>\n",
  501.        "    <tr style=\"text-align: right;\">\n",
  502.        "      <th></th>\n",
  503.        "      <th>id</th>\n",
  504.        "      <th>vendor_id</th>\n",
  505.        "      <th>pickup_datetime</th>\n",
  506.        "      <th>dropoff_datetime</th>\n",
  507.        "      <th>passenger_count</th>\n",
  508.        "      <th>pickup_longitude</th>\n",
  509.        "      <th>pickup_latitude</th>\n",
  510.        "      <th>dropoff_longitude</th>\n",
  511.        "      <th>dropoff_latitude</th>\n",
  512.        "      <th>store_and_fwd_flag</th>\n",
  513.        "      <th>trip_duration</th>\n",
  514.        "      <th>pickup_district</th>\n",
  515.        "      <th>dropoff_district</th>\n",
  516.        "    </tr>\n",
  517.        "  </thead>\n",
  518.        "  <tbody>\n",
  519.        "    <tr>\n",
  520.        "      <th>0</th>\n",
  521.        "      <td>id2875421</td>\n",
  522.        "      <td>2</td>\n",
  523.        "      <td>2016-03-14 17:24:55</td>\n",
  524.        "      <td>2016-03-14 17:32:30</td>\n",
  525.        "      <td>1</td>\n",
  526.        "      <td>-73.982155</td>\n",
  527.        "      <td>40.767937</td>\n",
  528.        "      <td>-73.964630</td>\n",
  529.        "      <td>40.765602</td>\n",
  530.        "      <td>N</td>\n",
  531.        "      <td>455</td>\n",
  532.        "      <td>Manhattan</td>\n",
  533.        "      <td>Manhattan</td>\n",
  534.        "    </tr>\n",
  535.        "    <tr>\n",
  536.        "      <th>1</th>\n",
  537.        "      <td>id0012891</td>\n",
  538.        "      <td>2</td>\n",
  539.        "      <td>2016-03-10 21:45:01</td>\n",
  540.        "      <td>2016-03-10 22:05:26</td>\n",
  541.        "      <td>1</td>\n",
  542.        "      <td>-73.981049</td>\n",
  543.        "      <td>40.744339</td>\n",
  544.        "      <td>-73.973000</td>\n",
  545.        "      <td>40.789989</td>\n",
  546.        "      <td>N</td>\n",
  547.        "      <td>1225</td>\n",
  548.        "      <td>Long Island City</td>\n",
  549.        "      <td>Manhattan</td>\n",
  550.        "    </tr>\n",
  551.        "    <tr>\n",
  552.        "      <th>2</th>\n",
  553.        "      <td>id3361153</td>\n",
  554.        "      <td>1</td>\n",
  555.        "      <td>2016-03-11 07:11:23</td>\n",
  556.        "      <td>2016-03-11 07:20:09</td>\n",
  557.        "      <td>1</td>\n",
  558.        "      <td>-73.994560</td>\n",
  559.        "      <td>40.750526</td>\n",
  560.        "      <td>-73.978500</td>\n",
  561.        "      <td>40.756191</td>\n",
  562.        "      <td>N</td>\n",
  563.        "      <td>526</td>\n",
  564.        "      <td>Weehawken</td>\n",
  565.        "      <td>Manhattan</td>\n",
  566.        "    </tr>\n",
  567.        "    <tr>\n",
  568.        "      <th>3</th>\n",
  569.        "      <td>id2129090</td>\n",
  570.        "      <td>1</td>\n",
  571.        "      <td>2016-03-14 14:05:39</td>\n",
  572.        "      <td>2016-03-14 14:28:05</td>\n",
  573.        "      <td>1</td>\n",
  574.        "      <td>-73.975090</td>\n",
  575.        "      <td>40.758766</td>\n",
  576.        "      <td>-73.953201</td>\n",
  577.        "      <td>40.765068</td>\n",
  578.        "      <td>N</td>\n",
  579.        "      <td>1346</td>\n",
  580.        "      <td>Manhattan</td>\n",
  581.        "      <td>Long Island City</td>\n",
  582.        "    </tr>\n",
  583.        "    <tr>\n",
  584.        "      <th>4</th>\n",
  585.        "      <td>id0256505</td>\n",
  586.        "      <td>1</td>\n",
  587.        "      <td>2016-03-14 15:04:38</td>\n",
  588.        "      <td>2016-03-14 15:16:13</td>\n",
  589.        "      <td>1</td>\n",
  590.        "      <td>-73.994484</td>\n",
  591.        "      <td>40.745087</td>\n",
  592.        "      <td>-73.998993</td>\n",
  593.        "      <td>40.722710</td>\n",
  594.        "      <td>N</td>\n",
  595.        "      <td>695</td>\n",
  596.        "      <td>New York City</td>\n",
  597.        "      <td>New York City</td>\n",
  598.        "    </tr>\n",
  599.        "  </tbody>\n",
  600.        "</table>\n",
  601.        "</div>"
  602.       ],
  603.       "text/plain": [
  604.        "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
  605.        "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
  606.        "1  id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
  607.        "2  id3361153          1  2016-03-11 07:11:23  2016-03-11 07:20:09   \n",
  608.        "3  id2129090          1  2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
  609.        "4  id0256505          1  2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
  610.        "\n",
  611.        "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
  612.        "0                1        -73.982155        40.767937         -73.964630   \n",
  613.        "1                1        -73.981049        40.744339         -73.973000   \n",
  614.        "2                1        -73.994560        40.750526         -73.978500   \n",
  615.        "3                1        -73.975090        40.758766         -73.953201   \n",
  616.        "4                1        -73.994484        40.745087         -73.998993   \n",
  617.        "\n",
  618.        "   dropoff_latitude store_and_fwd_flag  trip_duration   pickup_district  \\\n",
  619.        "0         40.765602                  N            455         Manhattan   \n",
  620.        "1         40.789989                  N           1225  Long Island City   \n",
  621.        "2         40.756191                  N            526         Weehawken   \n",
  622.        "3         40.765068                  N           1346         Manhattan   \n",
  623.        "4         40.722710                  N            695     New York City   \n",
  624.        "\n",
  625.        "   dropoff_district  \n",
  626.        "0         Manhattan  \n",
  627.        "1         Manhattan  \n",
  628.        "2         Manhattan  \n",
  629.        "3  Long Island City  \n",
  630.        "4     New York City  "
  631.       ]
  632.      },
  633.      "execution_count": 8,
  634.      "metadata": {},
  635.      "output_type": "execute_result"
  636.     }
  637.    ],
  638.    "source": [
  639.     "plist = []\n",
  640.     "for i in pickup:\n",
  641.     "    plist.append(i[\"name\"])\n",
  642.     "dlist = []\n",
  643.     "for j in dropoff:\n",
  644.     "    dlist.append(j[\"name\"])\n",
  645.     "\n",
  646.     "df['pickup_district'] = plist\n",
  647.     "df['dropoff_district'] = dlist\n",
  648.     "\n",
  649.     "df.head()"
  650.    ]
  651.   },
  652.   {
  653.    "cell_type": "markdown",
  654.    "metadata": {},
  655.    "source": [
  656.     "### Top 5 districts where passengers prefer to leave and arrive"
  657.    ]
  658.   },
  659.   {
  660.    "cell_type": "code",
  661.    "execution_count": 9,
  662.    "metadata": {},
  663.    "outputs": [
  664.     {
  665.      "name": "stdout",
  666.      "output_type": "stream",
  667.      "text": [
  668.       "Manhattan           45329\n",
  669.       "New York City       34625\n",
  670.       "Long Island City    17787\n",
  671.       "Weehawken           11334\n",
  672.       "The Bronx            2777\n",
  673.       "Name: pickup_district, dtype: int64\n"
  674.      ]
  675.     }
  676.    ],
  677.    "source": [
  678.     "print( df['pickup_district'].value_counts()[:5])"
  679.    ]
  680.   },
  681.   {
  682.    "cell_type": "markdown",
  683.    "metadata": {},
  684.    "source": [
  685.     "### Creating a new column: ”distance”"
  686.    ]
  687.   },
  688.   {
  689.    "cell_type": "code",
  690.    "execution_count": 10,
  691.    "metadata": {},
  692.    "outputs": [],
  693.    "source": [
  694.     "distancelist= []\n",
  695.     "k= 0\n",
  696.     "while k< df.shape[0]:\n",
  697.     "    distancelist.append(great_circle(pickuploc[k], dropoffloc[k]).miles)\n",
  698.     "    k += 1"
  699.    ]
  700.   },
  701.   {
  702.    "cell_type": "code",
  703.    "execution_count": 11,
  704.    "metadata": {},
  705.    "outputs": [
  706.     {
  707.      "data": {
  708.       "text/html": [
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  714.        "\n",
  715.        "    .dataframe tbody tr th {\n",
  716.        "        vertical-align: top;\n",
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  718.        "\n",
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  720.        "        text-align: right;\n",
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  722.        "</style>\n",
  723.        "<table border=\"1\" class=\"dataframe\">\n",
  724.        "  <thead>\n",
  725.        "    <tr style=\"text-align: right;\">\n",
  726.        "      <th></th>\n",
  727.        "      <th>id</th>\n",
  728.        "      <th>vendor_id</th>\n",
  729.        "      <th>pickup_datetime</th>\n",
  730.        "      <th>dropoff_datetime</th>\n",
  731.        "      <th>passenger_count</th>\n",
  732.        "      <th>pickup_longitude</th>\n",
  733.        "      <th>pickup_latitude</th>\n",
  734.        "      <th>dropoff_longitude</th>\n",
  735.        "      <th>dropoff_latitude</th>\n",
  736.        "      <th>store_and_fwd_flag</th>\n",
  737.        "      <th>trip_duration</th>\n",
  738.        "      <th>pickup_district</th>\n",
  739.        "      <th>dropoff_district</th>\n",
  740.        "      <th>distance</th>\n",
  741.        "    </tr>\n",
  742.        "  </thead>\n",
  743.        "  <tbody>\n",
  744.        "    <tr>\n",
  745.        "      <th>0</th>\n",
  746.        "      <td>id2875421</td>\n",
  747.        "      <td>2</td>\n",
  748.        "      <td>2016-03-14 17:24:55</td>\n",
  749.        "      <td>2016-03-14 17:32:30</td>\n",
  750.        "      <td>1</td>\n",
  751.        "      <td>-73.982155</td>\n",
  752.        "      <td>40.767937</td>\n",
  753.        "      <td>-73.964630</td>\n",
  754.        "      <td>40.765602</td>\n",
  755.        "      <td>N</td>\n",
  756.        "      <td>455</td>\n",
  757.        "      <td>Manhattan</td>\n",
  758.        "      <td>Manhattan</td>\n",
  759.        "      <td>0.931139</td>\n",
  760.        "    </tr>\n",
  761.        "    <tr>\n",
  762.        "      <th>1</th>\n",
  763.        "      <td>id0012891</td>\n",
  764.        "      <td>2</td>\n",
  765.        "      <td>2016-03-10 21:45:01</td>\n",
  766.        "      <td>2016-03-10 22:05:26</td>\n",
  767.        "      <td>1</td>\n",
  768.        "      <td>-73.981049</td>\n",
  769.        "      <td>40.744339</td>\n",
  770.        "      <td>-73.973000</td>\n",
  771.        "      <td>40.789989</td>\n",
  772.        "      <td>N</td>\n",
  773.        "      <td>1225</td>\n",
  774.        "      <td>Long Island City</td>\n",
  775.        "      <td>Manhattan</td>\n",
  776.        "      <td>3.182147</td>\n",
  777.        "    </tr>\n",
  778.        "    <tr>\n",
  779.        "      <th>2</th>\n",
  780.        "      <td>id3361153</td>\n",
  781.        "      <td>1</td>\n",
  782.        "      <td>2016-03-11 07:11:23</td>\n",
  783.        "      <td>2016-03-11 07:20:09</td>\n",
  784.        "      <td>1</td>\n",
  785.        "      <td>-73.994560</td>\n",
  786.        "      <td>40.750526</td>\n",
  787.        "      <td>-73.978500</td>\n",
  788.        "      <td>40.756191</td>\n",
  789.        "      <td>N</td>\n",
  790.        "      <td>526</td>\n",
  791.        "      <td>Weehawken</td>\n",
  792.        "      <td>Manhattan</td>\n",
  793.        "      <td>0.927234</td>\n",
  794.        "    </tr>\n",
  795.        "    <tr>\n",
  796.        "      <th>3</th>\n",
  797.        "      <td>id2129090</td>\n",
  798.        "      <td>1</td>\n",
  799.        "      <td>2016-03-14 14:05:39</td>\n",
  800.        "      <td>2016-03-14 14:28:05</td>\n",
  801.        "      <td>1</td>\n",
  802.        "      <td>-73.975090</td>\n",
  803.        "      <td>40.758766</td>\n",
  804.        "      <td>-73.953201</td>\n",
  805.        "      <td>40.765068</td>\n",
  806.        "      <td>N</td>\n",
  807.        "      <td>1346</td>\n",
  808.        "      <td>Manhattan</td>\n",
  809.        "      <td>Long Island City</td>\n",
  810.        "      <td>1.225473</td>\n",
  811.        "    </tr>\n",
  812.        "    <tr>\n",
  813.        "      <th>4</th>\n",
  814.        "      <td>id0256505</td>\n",
  815.        "      <td>1</td>\n",
  816.        "      <td>2016-03-14 15:04:38</td>\n",
  817.        "      <td>2016-03-14 15:16:13</td>\n",
  818.        "      <td>1</td>\n",
  819.        "      <td>-73.994484</td>\n",
  820.        "      <td>40.745087</td>\n",
  821.        "      <td>-73.998993</td>\n",
  822.        "      <td>40.722710</td>\n",
  823.        "      <td>N</td>\n",
  824.        "      <td>695</td>\n",
  825.        "      <td>New York City</td>\n",
  826.        "      <td>New York City</td>\n",
  827.        "      <td>1.564023</td>\n",
  828.        "    </tr>\n",
  829.        "  </tbody>\n",
  830.        "</table>\n",
  831.        "</div>"
  832.       ],
  833.       "text/plain": [
  834.        "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
  835.        "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
  836.        "1  id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
  837.        "2  id3361153          1  2016-03-11 07:11:23  2016-03-11 07:20:09   \n",
  838.        "3  id2129090          1  2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
  839.        "4  id0256505          1  2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
  840.        "\n",
  841.        "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
  842.        "0                1        -73.982155        40.767937         -73.964630   \n",
  843.        "1                1        -73.981049        40.744339         -73.973000   \n",
  844.        "2                1        -73.994560        40.750526         -73.978500   \n",
  845.        "3                1        -73.975090        40.758766         -73.953201   \n",
  846.        "4                1        -73.994484        40.745087         -73.998993   \n",
  847.        "\n",
  848.        "   dropoff_latitude store_and_fwd_flag  trip_duration   pickup_district  \\\n",
  849.        "0         40.765602                  N            455         Manhattan   \n",
  850.        "1         40.789989                  N           1225  Long Island City   \n",
  851.        "2         40.756191                  N            526         Weehawken   \n",
  852.        "3         40.765068                  N           1346         Manhattan   \n",
  853.        "4         40.722710                  N            695     New York City   \n",
  854.        "\n",
  855.        "   dropoff_district  distance  \n",
  856.        "0         Manhattan  0.931139  \n",
  857.        "1         Manhattan  3.182147  \n",
  858.        "2         Manhattan  0.927234  \n",
  859.        "3  Long Island City  1.225473  \n",
  860.        "4     New York City  1.564023  "
  861.       ]
  862.      },
  863.      "execution_count": 11,
  864.      "metadata": {},
  865.      "output_type": "execute_result"
  866.     }
  867.    ],
  868.    "source": [
  869.     "df[\"distance\"]= distancelist\n",
  870.     "df.head()"
  871.    ]
  872.   },
  873.   {
  874.    "cell_type": "markdown",
  875.    "metadata": {},
  876.    "source": [
  877.     "### Creating a new column: ”time of day”"
  878.    ]
  879.   },
  880.   {
  881.    "cell_type": "code",
  882.    "execution_count": 12,
  883.    "metadata": {},
  884.    "outputs": [],
  885.    "source": [
  886.     "datetimelist = []\n",
  887.     "for date in df.pickup_datetime:\n",
  888.     "    datetimelist.append(date.split())\n",
  889.     "    \n",
  890.     "timelist=[]\n",
  891.     "for p in range (df.shape[0]):\n",
  892.     "    timelist.append(datetimelist[p][1])"
  893.    ]
  894.   },
  895.   {
  896.    "cell_type": "code",
  897.    "execution_count": 13,
  898.    "metadata": {},
  899.    "outputs": [],
  900.    "source": [
  901.     "timeofday=[]\n",
  902.     "count= 0\n",
  903.     "for h in range (df.shape[0]):\n",
  904.     "    (h, m, s) = (timelist[h]).split(':')\n",
  905.     "    decimaltime = int(h) * 3600 + int(m) * 60 + int(s)\n",
  906.     "    if ((7*3600)<=decimaltime<(9*3600)):\n",
  907.     "        timeofday.append(\"rush hour morning\")\n",
  908.     "    elif ((9*3600)<=decimaltime<(16*3600)):\n",
  909.     "        timeofday.append(\"afternoon\")\n",
  910.     "    elif ((16*3600)<=decimaltime<(18*3600)):\n",
  911.     "        timeofday.append(\"rush hour evening\")\n",
  912.     "    elif ((18*3600)<=decimaltime<(23*3600)):\n",
  913.     "        timeofday.append(\"evening\")\n",
  914.     "    elif ((23*3600)<=decimaltime<=(24*3600) or (0<= decimaltime < (7*3600))):\n",
  915.     "        timeofday.append(\"late night\")\n",
  916.     "    count+= 1"
  917.    ]
  918.   },
  919.   {
  920.    "cell_type": "code",
  921.    "execution_count": 14,
  922.    "metadata": {},
  923.    "outputs": [
  924.     {
  925.      "data": {
  926.       "text/html": [
  927.        "<div>\n",
  928.        "<style scoped>\n",
  929.        "    .dataframe tbody tr th:only-of-type {\n",
  930.        "        vertical-align: middle;\n",
  931.        "    }\n",
  932.        "\n",
  933.        "    .dataframe tbody tr th {\n",
  934.        "        vertical-align: top;\n",
  935.        "    }\n",
  936.        "\n",
  937.        "    .dataframe thead th {\n",
  938.        "        text-align: right;\n",
  939.        "    }\n",
  940.        "</style>\n",
  941.        "<table border=\"1\" class=\"dataframe\">\n",
  942.        "  <thead>\n",
  943.        "    <tr style=\"text-align: right;\">\n",
  944.        "      <th></th>\n",
  945.        "      <th>id</th>\n",
  946.        "      <th>vendor_id</th>\n",
  947.        "      <th>pickup_datetime</th>\n",
  948.        "      <th>dropoff_datetime</th>\n",
  949.        "      <th>passenger_count</th>\n",
  950.        "      <th>pickup_longitude</th>\n",
  951.        "      <th>pickup_latitude</th>\n",
  952.        "      <th>dropoff_longitude</th>\n",
  953.        "      <th>dropoff_latitude</th>\n",
  954.        "      <th>store_and_fwd_flag</th>\n",
  955.        "      <th>trip_duration</th>\n",
  956.        "      <th>pickup_district</th>\n",
  957.        "      <th>dropoff_district</th>\n",
  958.        "      <th>distance</th>\n",
  959.        "      <th>time_of_day</th>\n",
  960.        "    </tr>\n",
  961.        "  </thead>\n",
  962.        "  <tbody>\n",
  963.        "    <tr>\n",
  964.        "      <th>0</th>\n",
  965.        "      <td>id2875421</td>\n",
  966.        "      <td>2</td>\n",
  967.        "      <td>2016-03-14 17:24:55</td>\n",
  968.        "      <td>2016-03-14 17:32:30</td>\n",
  969.        "      <td>1</td>\n",
  970.        "      <td>-73.982155</td>\n",
  971.        "      <td>40.767937</td>\n",
  972.        "      <td>-73.964630</td>\n",
  973.        "      <td>40.765602</td>\n",
  974.        "      <td>N</td>\n",
  975.        "      <td>455</td>\n",
  976.        "      <td>Manhattan</td>\n",
  977.        "      <td>Manhattan</td>\n",
  978.        "      <td>0.931139</td>\n",
  979.        "      <td>rush hour evening</td>\n",
  980.        "    </tr>\n",
  981.        "    <tr>\n",
  982.        "      <th>1</th>\n",
  983.        "      <td>id0012891</td>\n",
  984.        "      <td>2</td>\n",
  985.        "      <td>2016-03-10 21:45:01</td>\n",
  986.        "      <td>2016-03-10 22:05:26</td>\n",
  987.        "      <td>1</td>\n",
  988.        "      <td>-73.981049</td>\n",
  989.        "      <td>40.744339</td>\n",
  990.        "      <td>-73.973000</td>\n",
  991.        "      <td>40.789989</td>\n",
  992.        "      <td>N</td>\n",
  993.        "      <td>1225</td>\n",
  994.        "      <td>Long Island City</td>\n",
  995.        "      <td>Manhattan</td>\n",
  996.        "      <td>3.182147</td>\n",
  997.        "      <td>evening</td>\n",
  998.        "    </tr>\n",
  999.        "    <tr>\n",
  1000.        "      <th>2</th>\n",
  1001.        "      <td>id3361153</td>\n",
  1002.        "      <td>1</td>\n",
  1003.        "      <td>2016-03-11 07:11:23</td>\n",
  1004.        "      <td>2016-03-11 07:20:09</td>\n",
  1005.        "      <td>1</td>\n",
  1006.        "      <td>-73.994560</td>\n",
  1007.        "      <td>40.750526</td>\n",
  1008.        "      <td>-73.978500</td>\n",
  1009.        "      <td>40.756191</td>\n",
  1010.        "      <td>N</td>\n",
  1011.        "      <td>526</td>\n",
  1012.        "      <td>Weehawken</td>\n",
  1013.        "      <td>Manhattan</td>\n",
  1014.        "      <td>0.927234</td>\n",
  1015.        "      <td>rush hour morning</td>\n",
  1016.        "    </tr>\n",
  1017.        "    <tr>\n",
  1018.        "      <th>3</th>\n",
  1019.        "      <td>id2129090</td>\n",
  1020.        "      <td>1</td>\n",
  1021.        "      <td>2016-03-14 14:05:39</td>\n",
  1022.        "      <td>2016-03-14 14:28:05</td>\n",
  1023.        "      <td>1</td>\n",
  1024.        "      <td>-73.975090</td>\n",
  1025.        "      <td>40.758766</td>\n",
  1026.        "      <td>-73.953201</td>\n",
  1027.        "      <td>40.765068</td>\n",
  1028.        "      <td>N</td>\n",
  1029.        "      <td>1346</td>\n",
  1030.        "      <td>Manhattan</td>\n",
  1031.        "      <td>Long Island City</td>\n",
  1032.        "      <td>1.225473</td>\n",
  1033.        "      <td>afternoon</td>\n",
  1034.        "    </tr>\n",
  1035.        "    <tr>\n",
  1036.        "      <th>4</th>\n",
  1037.        "      <td>id0256505</td>\n",
  1038.        "      <td>1</td>\n",
  1039.        "      <td>2016-03-14 15:04:38</td>\n",
  1040.        "      <td>2016-03-14 15:16:13</td>\n",
  1041.        "      <td>1</td>\n",
  1042.        "      <td>-73.994484</td>\n",
  1043.        "      <td>40.745087</td>\n",
  1044.        "      <td>-73.998993</td>\n",
  1045.        "      <td>40.722710</td>\n",
  1046.        "      <td>N</td>\n",
  1047.        "      <td>695</td>\n",
  1048.        "      <td>New York City</td>\n",
  1049.        "      <td>New York City</td>\n",
  1050.        "      <td>1.564023</td>\n",
  1051.        "      <td>afternoon</td>\n",
  1052.        "    </tr>\n",
  1053.        "    <tr>\n",
  1054.        "      <th>5</th>\n",
  1055.        "      <td>id0970832</td>\n",
  1056.        "      <td>1</td>\n",
  1057.        "      <td>2016-03-12 20:39:39</td>\n",
  1058.        "      <td>2016-03-12 21:05:40</td>\n",
  1059.        "      <td>1</td>\n",
  1060.        "      <td>-74.008247</td>\n",
  1061.        "      <td>40.747353</td>\n",
  1062.        "      <td>-73.979446</td>\n",
  1063.        "      <td>40.718750</td>\n",
  1064.        "      <td>N</td>\n",
  1065.        "      <td>1561</td>\n",
  1066.        "      <td>Hoboken</td>\n",
  1067.        "      <td>New York City</td>\n",
  1068.        "      <td>2.485830</td>\n",
  1069.        "      <td>evening</td>\n",
  1070.        "    </tr>\n",
  1071.        "    <tr>\n",
  1072.        "      <th>6</th>\n",
  1073.        "      <td>id2049424</td>\n",
  1074.        "      <td>2</td>\n",
  1075.        "      <td>2016-03-02 20:15:07</td>\n",
  1076.        "      <td>2016-03-02 20:37:43</td>\n",
  1077.        "      <td>1</td>\n",
  1078.        "      <td>-73.963890</td>\n",
  1079.        "      <td>40.773651</td>\n",
  1080.        "      <td>-74.005112</td>\n",
  1081.        "      <td>40.751492</td>\n",
  1082.        "      <td>N</td>\n",
  1083.        "      <td>1356</td>\n",
  1084.        "      <td>Manhattan</td>\n",
  1085.        "      <td>Weehawken</td>\n",
  1086.        "      <td>2.645357</td>\n",
  1087.        "      <td>evening</td>\n",
  1088.        "    </tr>\n",
  1089.        "    <tr>\n",
  1090.        "      <th>7</th>\n",
  1091.        "      <td>id0038484</td>\n",
  1092.        "      <td>2</td>\n",
  1093.        "      <td>2016-03-09 13:41:11</td>\n",
  1094.        "      <td>2016-03-09 13:53:27</td>\n",
  1095.        "      <td>2</td>\n",
  1096.        "      <td>-73.972855</td>\n",
  1097.        "      <td>40.764400</td>\n",
  1098.        "      <td>-73.971809</td>\n",
  1099.        "      <td>40.757889</td>\n",
  1100.        "      <td>N</td>\n",
  1101.        "      <td>736</td>\n",
  1102.        "      <td>Manhattan</td>\n",
  1103.        "      <td>Manhattan</td>\n",
  1104.        "      <td>0.453228</td>\n",
  1105.        "      <td>afternoon</td>\n",
  1106.        "    </tr>\n",
  1107.        "    <tr>\n",
  1108.        "      <th>8</th>\n",
  1109.        "      <td>id3092788</td>\n",
  1110.        "      <td>2</td>\n",
  1111.        "      <td>2016-03-03 22:01:32</td>\n",
  1112.        "      <td>2016-03-03 22:17:44</td>\n",
  1113.        "      <td>2</td>\n",
  1114.        "      <td>-73.984772</td>\n",
  1115.        "      <td>40.710571</td>\n",
  1116.        "      <td>-73.989410</td>\n",
  1117.        "      <td>40.730148</td>\n",
  1118.        "      <td>N</td>\n",
  1119.        "      <td>972</td>\n",
  1120.        "      <td>New York City</td>\n",
  1121.        "      <td>New York City</td>\n",
  1122.        "      <td>1.374282</td>\n",
  1123.        "      <td>evening</td>\n",
  1124.        "    </tr>\n",
  1125.        "    <tr>\n",
  1126.        "      <th>9</th>\n",
  1127.        "      <td>id3863815</td>\n",
  1128.        "      <td>2</td>\n",
  1129.        "      <td>2016-03-14 04:24:36</td>\n",
  1130.        "      <td>2016-03-14 04:37:11</td>\n",
  1131.        "      <td>3</td>\n",
  1132.        "      <td>-73.944359</td>\n",
  1133.        "      <td>40.714489</td>\n",
  1134.        "      <td>-73.910530</td>\n",
  1135.        "      <td>40.709492</td>\n",
  1136.        "      <td>N</td>\n",
  1137.        "      <td>755</td>\n",
  1138.        "      <td>Long Island City</td>\n",
  1139.        "      <td>East New York</td>\n",
  1140.        "      <td>1.805032</td>\n",
  1141.        "      <td>late night</td>\n",
  1142.        "    </tr>\n",
  1143.        "    <tr>\n",
  1144.        "      <th>10</th>\n",
  1145.        "      <td>id1832737</td>\n",
  1146.        "      <td>2</td>\n",
  1147.        "      <td>2016-03-06 10:53:26</td>\n",
  1148.        "      <td>2016-03-06 10:59:30</td>\n",
  1149.        "      <td>1</td>\n",
  1150.        "      <td>-73.984711</td>\n",
  1151.        "      <td>40.760181</td>\n",
  1152.        "      <td>-73.979561</td>\n",
  1153.        "      <td>40.752705</td>\n",
  1154.        "      <td>N</td>\n",
  1155.        "      <td>364</td>\n",
  1156.        "      <td>Manhattan</td>\n",
  1157.        "      <td>Long Island City</td>\n",
  1158.        "      <td>0.582684</td>\n",
  1159.        "      <td>afternoon</td>\n",
  1160.        "    </tr>\n",
  1161.        "    <tr>\n",
  1162.        "      <th>11</th>\n",
  1163.        "      <td>id2718231</td>\n",
  1164.        "      <td>1</td>\n",
  1165.        "      <td>2016-03-08 02:44:19</td>\n",
  1166.        "      <td>2016-03-08 03:04:35</td>\n",
  1167.        "      <td>1</td>\n",
  1168.        "      <td>-73.992500</td>\n",
  1169.        "      <td>40.740444</td>\n",
  1170.        "      <td>-73.840111</td>\n",
  1171.        "      <td>40.719517</td>\n",
  1172.        "      <td>N</td>\n",
  1173.        "      <td>1216</td>\n",
  1174.        "      <td>New York City</td>\n",
  1175.        "      <td>Borough of Queens</td>\n",
  1176.        "      <td>8.108848</td>\n",
  1177.        "      <td>late night</td>\n",
  1178.        "    </tr>\n",
  1179.        "    <tr>\n",
  1180.        "      <th>12</th>\n",
  1181.        "      <td>id3956459</td>\n",
  1182.        "      <td>2</td>\n",
  1183.        "      <td>2016-03-05 10:23:45</td>\n",
  1184.        "      <td>2016-03-05 10:45:52</td>\n",
  1185.        "      <td>1</td>\n",
  1186.        "      <td>-73.986908</td>\n",
  1187.        "      <td>40.761608</td>\n",
  1188.        "      <td>-74.008408</td>\n",
  1189.        "      <td>40.711620</td>\n",
  1190.        "      <td>N</td>\n",
  1191.        "      <td>1327</td>\n",
  1192.        "      <td>Manhattan</td>\n",
  1193.        "      <td>New York City</td>\n",
  1194.        "      <td>3.632609</td>\n",
  1195.        "      <td>afternoon</td>\n",
  1196.        "    </tr>\n",
  1197.        "    <tr>\n",
  1198.        "      <th>13</th>\n",
  1199.        "      <td>id2393811</td>\n",
  1200.        "      <td>1</td>\n",
  1201.        "      <td>2016-03-10 18:52:40</td>\n",
  1202.        "      <td>2016-03-10 19:08:43</td>\n",
  1203.        "      <td>1</td>\n",
  1204.        "      <td>-73.970581</td>\n",
  1205.        "      <td>40.799046</td>\n",
  1206.        "      <td>-73.989815</td>\n",
  1207.        "      <td>40.767246</td>\n",
  1208.        "      <td>N</td>\n",
  1209.        "      <td>963</td>\n",
  1210.        "      <td>Manhattan</td>\n",
  1211.        "      <td>Guttenberg</td>\n",
  1212.        "      <td>2.416584</td>\n",
  1213.        "      <td>evening</td>\n",
  1214.        "    </tr>\n",
  1215.        "    <tr>\n",
  1216.        "      <th>14</th>\n",
  1217.        "      <td>id2808378</td>\n",
  1218.        "      <td>1</td>\n",
  1219.        "      <td>2016-03-09 17:11:16</td>\n",
  1220.        "      <td>2016-03-09 17:28:43</td>\n",
  1221.        "      <td>1</td>\n",
  1222.        "      <td>-73.978645</td>\n",
  1223.        "      <td>40.740932</td>\n",
  1224.        "      <td>-74.012695</td>\n",
  1225.        "      <td>40.701588</td>\n",
  1226.        "      <td>N</td>\n",
  1227.        "      <td>1047</td>\n",
  1228.        "      <td>Long Island City</td>\n",
  1229.        "      <td>New York City</td>\n",
  1230.        "      <td>3.251044</td>\n",
  1231.        "      <td>rush hour evening</td>\n",
  1232.        "    </tr>\n",
  1233.        "    <tr>\n",
  1234.        "      <th>15</th>\n",
  1235.        "      <td>id1295254</td>\n",
  1236.        "      <td>1</td>\n",
  1237.        "      <td>2016-03-06 11:01:27</td>\n",
  1238.        "      <td>2016-03-06 11:08:29</td>\n",
  1239.        "      <td>1</td>\n",
  1240.        "      <td>-73.975983</td>\n",
  1241.        "      <td>40.757748</td>\n",
  1242.        "      <td>-73.982162</td>\n",
  1243.        "      <td>40.740749</td>\n",
  1244.        "      <td>N</td>\n",
  1245.        "      <td>422</td>\n",
  1246.        "      <td>Manhattan</td>\n",
  1247.        "      <td>Long Island City</td>\n",
  1248.        "      <td>1.218201</td>\n",
  1249.        "      <td>afternoon</td>\n",
  1250.        "    </tr>\n",
  1251.        "    <tr>\n",
  1252.        "      <th>16</th>\n",
  1253.        "      <td>id1660823</td>\n",
  1254.        "      <td>2</td>\n",
  1255.        "      <td>2016-03-01 06:40:18</td>\n",
  1256.        "      <td>2016-03-01 07:01:37</td>\n",
  1257.        "      <td>5</td>\n",
  1258.        "      <td>-73.982140</td>\n",
  1259.        "      <td>40.775326</td>\n",
  1260.        "      <td>-74.009850</td>\n",
  1261.        "      <td>40.721699</td>\n",
  1262.        "      <td>N</td>\n",
  1263.        "      <td>1279</td>\n",
  1264.        "      <td>Manhattan</td>\n",
  1265.        "      <td>New York City</td>\n",
  1266.        "      <td>3.979052</td>\n",
  1267.        "      <td>late night</td>\n",
  1268.        "    </tr>\n",
  1269.        "    <tr>\n",
  1270.        "      <th>17</th>\n",
  1271.        "      <td>id0802391</td>\n",
  1272.        "      <td>1</td>\n",
  1273.        "      <td>2016-03-06 17:44:45</td>\n",
  1274.        "      <td>2016-03-06 17:52:14</td>\n",
  1275.        "      <td>1</td>\n",
  1276.        "      <td>-73.997208</td>\n",
  1277.        "      <td>40.724072</td>\n",
  1278.        "      <td>-74.000618</td>\n",
  1279.        "      <td>40.732155</td>\n",
  1280.        "      <td>N</td>\n",
  1281.        "      <td>449</td>\n",
  1282.        "      <td>New York City</td>\n",
  1283.        "      <td>New York City</td>\n",
  1284.        "      <td>0.586357</td>\n",
  1285.        "      <td>rush hour evening</td>\n",
  1286.        "    </tr>\n",
  1287.        "    <tr>\n",
  1288.        "      <th>18</th>\n",
  1289.        "      <td>id2268459</td>\n",
  1290.        "      <td>1</td>\n",
  1291.        "      <td>2016-03-02 07:02:21</td>\n",
  1292.        "      <td>2016-03-02 07:24:57</td>\n",
  1293.        "      <td>1</td>\n",
  1294.        "      <td>-73.985359</td>\n",
  1295.        "      <td>40.738411</td>\n",
  1296.        "      <td>-73.870422</td>\n",
  1297.        "      <td>40.773682</td>\n",
  1298.        "      <td>N</td>\n",
  1299.        "      <td>1356</td>\n",
  1300.        "      <td>New York City</td>\n",
  1301.        "      <td>The Bronx</td>\n",
  1302.        "      <td>6.490441</td>\n",
  1303.        "      <td>rush hour morning</td>\n",
  1304.        "    </tr>\n",
  1305.        "    <tr>\n",
  1306.        "      <th>19</th>\n",
  1307.        "      <td>id2797773</td>\n",
  1308.        "      <td>1</td>\n",
  1309.        "      <td>2016-03-08 08:33:35</td>\n",
  1310.        "      <td>2016-03-08 08:36:35</td>\n",
  1311.        "      <td>1</td>\n",
  1312.        "      <td>-73.967133</td>\n",
  1313.        "      <td>40.793465</td>\n",
  1314.        "      <td>-73.970390</td>\n",
  1315.        "      <td>40.795750</td>\n",
  1316.        "      <td>N</td>\n",
  1317.        "      <td>180</td>\n",
  1318.        "      <td>Manhattan</td>\n",
  1319.        "      <td>Manhattan</td>\n",
  1320.        "      <td>0.232301</td>\n",
  1321.        "      <td>rush hour morning</td>\n",
  1322.        "    </tr>\n",
  1323.        "    <tr>\n",
  1324.        "      <th>20</th>\n",
  1325.        "      <td>id3817493</td>\n",
  1326.        "      <td>2</td>\n",
  1327.        "      <td>2016-03-14 14:57:56</td>\n",
  1328.        "      <td>2016-03-14 15:15:26</td>\n",
  1329.        "      <td>1</td>\n",
  1330.        "      <td>-73.952881</td>\n",
  1331.        "      <td>40.766468</td>\n",
  1332.        "      <td>-73.978630</td>\n",
  1333.        "      <td>40.761921</td>\n",
  1334.        "      <td>N</td>\n",
  1335.        "      <td>1050</td>\n",
  1336.        "      <td>Manhattan</td>\n",
  1337.        "      <td>Manhattan</td>\n",
  1338.        "      <td>1.383638</td>\n",
  1339.        "      <td>afternoon</td>\n",
  1340.        "    </tr>\n",
  1341.        "    <tr>\n",
  1342.        "      <th>21</th>\n",
  1343.        "      <td>id1971518</td>\n",
  1344.        "      <td>1</td>\n",
  1345.        "      <td>2016-03-12 13:04:28</td>\n",
  1346.        "      <td>2016-03-12 13:14:33</td>\n",
  1347.        "      <td>1</td>\n",
  1348.        "      <td>-73.988976</td>\n",
  1349.        "      <td>40.759205</td>\n",
  1350.        "      <td>-73.973991</td>\n",
  1351.        "      <td>40.760590</td>\n",
  1352.        "      <td>N</td>\n",
  1353.        "      <td>605</td>\n",
  1354.        "      <td>Weehawken</td>\n",
  1355.        "      <td>Manhattan</td>\n",
  1356.        "      <td>0.790009</td>\n",
  1357.        "      <td>afternoon</td>\n",
  1358.        "    </tr>\n",
  1359.        "    <tr>\n",
  1360.        "      <th>22</th>\n",
  1361.        "      <td>id3911487</td>\n",
  1362.        "      <td>1</td>\n",
  1363.        "      <td>2016-03-03 17:56:45</td>\n",
  1364.        "      <td>2016-03-03 18:05:28</td>\n",
  1365.        "      <td>1</td>\n",
  1366.        "      <td>-73.962112</td>\n",
  1367.        "      <td>40.776100</td>\n",
  1368.        "      <td>-73.968521</td>\n",
  1369.        "      <td>40.764408</td>\n",
  1370.        "      <td>N</td>\n",
  1371.        "      <td>523</td>\n",
  1372.        "      <td>Manhattan</td>\n",
  1373.        "      <td>Manhattan</td>\n",
  1374.        "      <td>0.874682</td>\n",
  1375.        "      <td>rush hour evening</td>\n",
  1376.        "    </tr>\n",
  1377.        "    <tr>\n",
  1378.        "      <th>23</th>\n",
  1379.        "      <td>id3276198</td>\n",
  1380.        "      <td>2</td>\n",
  1381.        "      <td>2016-03-14 20:31:12</td>\n",
  1382.        "      <td>2016-03-14 20:36:18</td>\n",
  1383.        "      <td>1</td>\n",
  1384.        "      <td>-73.981911</td>\n",
  1385.        "      <td>40.766880</td>\n",
  1386.        "      <td>-73.982597</td>\n",
  1387.        "      <td>40.777180</td>\n",
  1388.        "      <td>N</td>\n",
  1389.        "      <td>306</td>\n",
  1390.        "      <td>Manhattan</td>\n",
  1391.        "      <td>Manhattan</td>\n",
  1392.        "      <td>0.712547</td>\n",
  1393.        "      <td>evening</td>\n",
  1394.        "    </tr>\n",
  1395.        "    <tr>\n",
  1396.        "      <th>24</th>\n",
  1397.        "      <td>id1527676</td>\n",
  1398.        "      <td>1</td>\n",
  1399.        "      <td>2016-03-07 19:38:25</td>\n",
  1400.        "      <td>2016-03-07 19:54:35</td>\n",
  1401.        "      <td>2</td>\n",
  1402.        "      <td>-73.986130</td>\n",
  1403.        "      <td>40.759720</td>\n",
  1404.        "      <td>-74.001488</td>\n",
  1405.        "      <td>40.736065</td>\n",
  1406.        "      <td>N</td>\n",
  1407.        "      <td>970</td>\n",
  1408.        "      <td>Manhattan</td>\n",
  1409.        "      <td>New York City</td>\n",
  1410.        "      <td>1.821408</td>\n",
  1411.        "      <td>evening</td>\n",
  1412.        "    </tr>\n",
  1413.        "    <tr>\n",
  1414.        "      <th>25</th>\n",
  1415.        "      <td>id1146853</td>\n",
  1416.        "      <td>2</td>\n",
  1417.        "      <td>2016-03-05 02:59:30</td>\n",
  1418.        "      <td>2016-03-05 03:20:50</td>\n",
  1419.        "      <td>4</td>\n",
  1420.        "      <td>-74.005394</td>\n",
  1421.        "      <td>40.740810</td>\n",
  1422.        "      <td>-73.950630</td>\n",
  1423.        "      <td>40.821037</td>\n",
  1424.        "      <td>N</td>\n",
  1425.        "      <td>1280</td>\n",
  1426.        "      <td>New York City</td>\n",
  1427.        "      <td>Edgewater</td>\n",
  1428.        "      <td>6.239842</td>\n",
  1429.        "      <td>late night</td>\n",
  1430.        "    </tr>\n",
  1431.        "    <tr>\n",
  1432.        "      <th>26</th>\n",
  1433.        "      <td>id3714906</td>\n",
  1434.        "      <td>1</td>\n",
  1435.        "      <td>2016-03-01 08:33:57</td>\n",
  1436.        "      <td>2016-03-01 08:40:44</td>\n",
  1437.        "      <td>1</td>\n",
  1438.        "      <td>-73.989494</td>\n",
  1439.        "      <td>40.753677</td>\n",
  1440.        "      <td>-73.988335</td>\n",
  1441.        "      <td>40.745949</td>\n",
  1442.        "      <td>N</td>\n",
  1443.        "      <td>407</td>\n",
  1444.        "      <td>Weehawken</td>\n",
  1445.        "      <td>New York City</td>\n",
  1446.        "      <td>0.537433</td>\n",
  1447.        "      <td>rush hour morning</td>\n",
  1448.        "    </tr>\n",
  1449.        "    <tr>\n",
  1450.        "      <th>27</th>\n",
  1451.        "      <td>id1937745</td>\n",
  1452.        "      <td>2</td>\n",
  1453.        "      <td>2016-03-07 18:51:46</td>\n",
  1454.        "      <td>2016-03-07 18:58:30</td>\n",
  1455.        "      <td>2</td>\n",
  1456.        "      <td>-73.990974</td>\n",
  1457.        "      <td>40.760632</td>\n",
  1458.        "      <td>-73.994720</td>\n",
  1459.        "      <td>40.750450</td>\n",
  1460.        "      <td>N</td>\n",
  1461.        "      <td>404</td>\n",
  1462.        "      <td>Weehawken</td>\n",
  1463.        "      <td>Weehawken</td>\n",
  1464.        "      <td>0.730280</td>\n",
  1465.        "      <td>evening</td>\n",
  1466.        "    </tr>\n",
  1467.        "    <tr>\n",
  1468.        "      <th>28</th>\n",
  1469.        "      <td>id2672200</td>\n",
  1470.        "      <td>1</td>\n",
  1471.        "      <td>2016-03-08 10:59:46</td>\n",
  1472.        "      <td>2016-03-08 11:21:50</td>\n",
  1473.        "      <td>1</td>\n",
  1474.        "      <td>-73.964325</td>\n",
  1475.        "      <td>40.773594</td>\n",
  1476.        "      <td>-73.989769</td>\n",
  1477.        "      <td>40.738483</td>\n",
  1478.        "      <td>N</td>\n",
  1479.        "      <td>1324</td>\n",
  1480.        "      <td>Manhattan</td>\n",
  1481.        "      <td>New York City</td>\n",
  1482.        "      <td>2.767382</td>\n",
  1483.        "      <td>afternoon</td>\n",
  1484.        "    </tr>\n",
  1485.        "    <tr>\n",
  1486.        "      <th>29</th>\n",
  1487.        "      <td>id3200728</td>\n",
  1488.        "      <td>2</td>\n",
  1489.        "      <td>2016-03-03 10:14:57</td>\n",
  1490.        "      <td>2016-03-03 10:32:51</td>\n",
  1491.        "      <td>1</td>\n",
  1492.        "      <td>-73.995880</td>\n",
  1493.        "      <td>40.759190</td>\n",
  1494.        "      <td>-73.979874</td>\n",
  1495.        "      <td>40.752781</td>\n",
  1496.        "      <td>N</td>\n",
  1497.        "      <td>1074</td>\n",
  1498.        "      <td>Weehawken</td>\n",
  1499.        "      <td>Long Island City</td>\n",
  1500.        "      <td>0.947571</td>\n",
  1501.        "      <td>afternoon</td>\n",
  1502.        "    </tr>\n",
  1503.        "    <tr>\n",
  1504.        "      <th>...</th>\n",
  1505.        "      <td>...</td>\n",
  1506.        "      <td>...</td>\n",
  1507.        "      <td>...</td>\n",
  1508.        "      <td>...</td>\n",
  1509.        "      <td>...</td>\n",
  1510.        "      <td>...</td>\n",
  1511.        "      <td>...</td>\n",
  1512.        "      <td>...</td>\n",
  1513.        "      <td>...</td>\n",
  1514.        "      <td>...</td>\n",
  1515.        "      <td>...</td>\n",
  1516.        "      <td>...</td>\n",
  1517.        "      <td>...</td>\n",
  1518.        "      <td>...</td>\n",
  1519.        "      <td>...</td>\n",
  1520.        "    </tr>\n",
  1521.        "    <tr>\n",
  1522.        "      <th>118155</th>\n",
  1523.        "      <td>id2073065</td>\n",
  1524.        "      <td>2</td>\n",
  1525.        "      <td>2016-03-10 21:43:30</td>\n",
  1526.        "      <td>2016-03-10 21:50:55</td>\n",
  1527.        "      <td>1</td>\n",
  1528.        "      <td>-73.989738</td>\n",
  1529.        "      <td>40.756599</td>\n",
  1530.        "      <td>-74.005318</td>\n",
  1531.        "      <td>40.740231</td>\n",
  1532.        "      <td>N</td>\n",
  1533.        "      <td>445</td>\n",
  1534.        "      <td>Weehawken</td>\n",
  1535.        "      <td>New York City</td>\n",
  1536.        "      <td>1.394318</td>\n",
  1537.        "      <td>evening</td>\n",
  1538.        "    </tr>\n",
  1539.        "    <tr>\n",
  1540.        "      <th>118156</th>\n",
  1541.        "      <td>id1042737</td>\n",
  1542.        "      <td>2</td>\n",
  1543.        "      <td>2016-03-10 06:10:29</td>\n",
  1544.        "      <td>2016-03-10 06:13:15</td>\n",
  1545.        "      <td>1</td>\n",
  1546.        "      <td>-73.985954</td>\n",
  1547.        "      <td>40.752129</td>\n",
  1548.        "      <td>-73.978592</td>\n",
  1549.        "      <td>40.752602</td>\n",
  1550.        "      <td>N</td>\n",
  1551.        "      <td>166</td>\n",
  1552.        "      <td>Manhattan</td>\n",
  1553.        "      <td>Long Island City</td>\n",
  1554.        "      <td>0.386736</td>\n",
  1555.        "      <td>late night</td>\n",
  1556.        "    </tr>\n",
  1557.        "    <tr>\n",
  1558.        "      <th>118157</th>\n",
  1559.        "      <td>id0538386</td>\n",
  1560.        "      <td>1</td>\n",
  1561.        "      <td>2016-03-07 18:29:35</td>\n",
  1562.        "      <td>2016-03-07 18:36:43</td>\n",
  1563.        "      <td>1</td>\n",
  1564.        "      <td>-73.976997</td>\n",
  1565.        "      <td>40.755756</td>\n",
  1566.        "      <td>-73.990540</td>\n",
  1567.        "      <td>40.751163</td>\n",
  1568.        "      <td>N</td>\n",
  1569.        "      <td>428</td>\n",
  1570.        "      <td>Manhattan</td>\n",
  1571.        "      <td>Weehawken</td>\n",
  1572.        "      <td>0.776594</td>\n",
  1573.        "      <td>evening</td>\n",
  1574.        "    </tr>\n",
  1575.        "    <tr>\n",
  1576.        "      <th>118158</th>\n",
  1577.        "      <td>id2824253</td>\n",
  1578.        "      <td>1</td>\n",
  1579.        "      <td>2016-03-03 08:09:29</td>\n",
  1580.        "      <td>2016-03-03 09:04:10</td>\n",
  1581.        "      <td>1</td>\n",
  1582.        "      <td>-73.961922</td>\n",
  1583.        "      <td>40.800533</td>\n",
  1584.        "      <td>-74.177269</td>\n",
  1585.        "      <td>40.691124</td>\n",
  1586.        "      <td>N</td>\n",
  1587.        "      <td>3281</td>\n",
  1588.        "      <td>Manhattan</td>\n",
  1589.        "      <td>Elizabeth</td>\n",
  1590.        "      <td>13.572632</td>\n",
  1591.        "      <td>rush hour morning</td>\n",
  1592.        "    </tr>\n",
  1593.        "    <tr>\n",
  1594.        "      <th>118159</th>\n",
  1595.        "      <td>id1333654</td>\n",
  1596.        "      <td>1</td>\n",
  1597.        "      <td>2016-03-05 01:22:46</td>\n",
  1598.        "      <td>2016-03-05 01:34:27</td>\n",
  1599.        "      <td>1</td>\n",
  1600.        "      <td>-73.973228</td>\n",
  1601.        "      <td>40.792824</td>\n",
  1602.        "      <td>-73.945877</td>\n",
  1603.        "      <td>40.777721</td>\n",
  1604.        "      <td>N</td>\n",
  1605.        "      <td>701</td>\n",
  1606.        "      <td>Manhattan</td>\n",
  1607.        "      <td>Manhattan</td>\n",
  1608.        "      <td>1.770954</td>\n",
  1609.        "      <td>late night</td>\n",
  1610.        "    </tr>\n",
  1611.        "    <tr>\n",
  1612.        "      <th>118160</th>\n",
  1613.        "      <td>id2731206</td>\n",
  1614.        "      <td>1</td>\n",
  1615.        "      <td>2016-03-13 20:14:32</td>\n",
  1616.        "      <td>2016-03-13 20:23:39</td>\n",
  1617.        "      <td>1</td>\n",
  1618.        "      <td>-73.981178</td>\n",
  1619.        "      <td>40.753674</td>\n",
  1620.        "      <td>-74.004509</td>\n",
  1621.        "      <td>40.747082</td>\n",
  1622.        "      <td>N</td>\n",
  1623.        "      <td>547</td>\n",
  1624.        "      <td>Manhattan</td>\n",
  1625.        "      <td>Weehawken</td>\n",
  1626.        "      <td>1.303353</td>\n",
  1627.        "      <td>evening</td>\n",
  1628.        "    </tr>\n",
  1629.        "    <tr>\n",
  1630.        "      <th>118161</th>\n",
  1631.        "      <td>id2838932</td>\n",
  1632.        "      <td>1</td>\n",
  1633.        "      <td>2016-03-13 17:03:03</td>\n",
  1634.        "      <td>2016-03-13 17:11:10</td>\n",
  1635.        "      <td>1</td>\n",
  1636.        "      <td>-73.998634</td>\n",
  1637.        "      <td>40.726131</td>\n",
  1638.        "      <td>-73.985001</td>\n",
  1639.        "      <td>40.727985</td>\n",
  1640.        "      <td>N</td>\n",
  1641.        "      <td>487</td>\n",
  1642.        "      <td>New York City</td>\n",
  1643.        "      <td>New York City</td>\n",
  1644.        "      <td>0.725275</td>\n",
  1645.        "      <td>rush hour evening</td>\n",
  1646.        "    </tr>\n",
  1647.        "    <tr>\n",
  1648.        "      <th>118162</th>\n",
  1649.        "      <td>id1486744</td>\n",
  1650.        "      <td>2</td>\n",
  1651.        "      <td>2016-03-09 10:45:19</td>\n",
  1652.        "      <td>2016-03-09 11:18:58</td>\n",
  1653.        "      <td>1</td>\n",
  1654.        "      <td>-73.982903</td>\n",
  1655.        "      <td>40.765659</td>\n",
  1656.        "      <td>-73.872917</td>\n",
  1657.        "      <td>40.774441</td>\n",
  1658.        "      <td>N</td>\n",
  1659.        "      <td>2019</td>\n",
  1660.        "      <td>Manhattan</td>\n",
  1661.        "      <td>The Bronx</td>\n",
  1662.        "      <td>5.787094</td>\n",
  1663.        "      <td>afternoon</td>\n",
  1664.        "    </tr>\n",
  1665.        "    <tr>\n",
  1666.        "      <th>118163</th>\n",
  1667.        "      <td>id0042357</td>\n",
  1668.        "      <td>2</td>\n",
  1669.        "      <td>2016-03-10 20:56:32</td>\n",
  1670.        "      <td>2016-03-10 21:09:55</td>\n",
  1671.        "      <td>1</td>\n",
  1672.        "      <td>-73.993996</td>\n",
  1673.        "      <td>40.741283</td>\n",
  1674.        "      <td>-73.973114</td>\n",
  1675.        "      <td>40.757057</td>\n",
  1676.        "      <td>N</td>\n",
  1677.        "      <td>803</td>\n",
  1678.        "      <td>New York City</td>\n",
  1679.        "      <td>Manhattan</td>\n",
  1680.        "      <td>1.543531</td>\n",
  1681.        "      <td>evening</td>\n",
  1682.        "    </tr>\n",
  1683.        "    <tr>\n",
  1684.        "      <th>118164</th>\n",
  1685.        "      <td>id3542490</td>\n",
  1686.        "      <td>2</td>\n",
  1687.        "      <td>2016-03-07 21:35:25</td>\n",
  1688.        "      <td>2016-03-07 21:47:42</td>\n",
  1689.        "      <td>1</td>\n",
  1690.        "      <td>-73.996368</td>\n",
  1691.        "      <td>40.723660</td>\n",
  1692.        "      <td>-73.975166</td>\n",
  1693.        "      <td>40.689621</td>\n",
  1694.        "      <td>N</td>\n",
  1695.        "      <td>737</td>\n",
  1696.        "      <td>New York City</td>\n",
  1697.        "      <td>New York City</td>\n",
  1698.        "      <td>2.600838</td>\n",
  1699.        "      <td>evening</td>\n",
  1700.        "    </tr>\n",
  1701.        "    <tr>\n",
  1702.        "      <th>118165</th>\n",
  1703.        "      <td>id0998702</td>\n",
  1704.        "      <td>2</td>\n",
  1705.        "      <td>2016-03-06 02:15:18</td>\n",
  1706.        "      <td>2016-03-06 02:24:16</td>\n",
  1707.        "      <td>1</td>\n",
  1708.        "      <td>-73.963203</td>\n",
  1709.        "      <td>40.671833</td>\n",
  1710.        "      <td>-73.960808</td>\n",
  1711.        "      <td>40.648785</td>\n",
  1712.        "      <td>N</td>\n",
  1713.        "      <td>538</td>\n",
  1714.        "      <td>Brooklyn</td>\n",
  1715.        "      <td>Brooklyn</td>\n",
  1716.        "      <td>1.597435</td>\n",
  1717.        "      <td>late night</td>\n",
  1718.        "    </tr>\n",
  1719.        "    <tr>\n",
  1720.        "      <th>118166</th>\n",
  1721.        "      <td>id0480063</td>\n",
  1722.        "      <td>1</td>\n",
  1723.        "      <td>2016-03-05 12:53:30</td>\n",
  1724.        "      <td>2016-03-05 12:57:32</td>\n",
  1725.        "      <td>1</td>\n",
  1726.        "      <td>-73.976250</td>\n",
  1727.        "      <td>40.728737</td>\n",
  1728.        "      <td>-73.989166</td>\n",
  1729.        "      <td>40.734058</td>\n",
  1730.        "      <td>N</td>\n",
  1731.        "      <td>242</td>\n",
  1732.        "      <td>Long Island City</td>\n",
  1733.        "      <td>New York City</td>\n",
  1734.        "      <td>0.769767</td>\n",
  1735.        "      <td>afternoon</td>\n",
  1736.        "    </tr>\n",
  1737.        "    <tr>\n",
  1738.        "      <th>118167</th>\n",
  1739.        "      <td>id2034624</td>\n",
  1740.        "      <td>2</td>\n",
  1741.        "      <td>2016-03-12 20:01:27</td>\n",
  1742.        "      <td>2016-03-12 20:36:01</td>\n",
  1743.        "      <td>5</td>\n",
  1744.        "      <td>-73.781212</td>\n",
  1745.        "      <td>40.644951</td>\n",
  1746.        "      <td>-73.977303</td>\n",
  1747.        "      <td>40.750721</td>\n",
  1748.        "      <td>N</td>\n",
  1749.        "      <td>2074</td>\n",
  1750.        "      <td>Inwood</td>\n",
  1751.        "      <td>Long Island City</td>\n",
  1752.        "      <td>12.606367</td>\n",
  1753.        "      <td>evening</td>\n",
  1754.        "    </tr>\n",
  1755.        "    <tr>\n",
  1756.        "      <th>118168</th>\n",
  1757.        "      <td>id1203726</td>\n",
  1758.        "      <td>2</td>\n",
  1759.        "      <td>2016-03-03 17:19:23</td>\n",
  1760.        "      <td>2016-03-03 17:27:35</td>\n",
  1761.        "      <td>2</td>\n",
  1762.        "      <td>-73.991798</td>\n",
  1763.        "      <td>40.749840</td>\n",
  1764.        "      <td>-73.993942</td>\n",
  1765.        "      <td>40.735722</td>\n",
  1766.        "      <td>N</td>\n",
  1767.        "      <td>492</td>\n",
  1768.        "      <td>Weehawken</td>\n",
  1769.        "      <td>New York City</td>\n",
  1770.        "      <td>0.981909</td>\n",
  1771.        "      <td>rush hour evening</td>\n",
  1772.        "    </tr>\n",
  1773.        "    <tr>\n",
  1774.        "      <th>118169</th>\n",
  1775.        "      <td>id3860980</td>\n",
  1776.        "      <td>2</td>\n",
  1777.        "      <td>2016-03-11 23:59:25</td>\n",
  1778.        "      <td>2016-03-12 00:10:12</td>\n",
  1779.        "      <td>1</td>\n",
  1780.        "      <td>-73.971542</td>\n",
  1781.        "      <td>40.757721</td>\n",
  1782.        "      <td>-73.991043</td>\n",
  1783.        "      <td>40.750568</td>\n",
  1784.        "      <td>N</td>\n",
  1785.        "      <td>647</td>\n",
  1786.        "      <td>Long Island City</td>\n",
  1787.        "      <td>Weehawken</td>\n",
  1788.        "      <td>1.134007</td>\n",
  1789.        "      <td>late night</td>\n",
  1790.        "    </tr>\n",
  1791.        "    <tr>\n",
  1792.        "      <th>118170</th>\n",
  1793.        "      <td>id2924763</td>\n",
  1794.        "      <td>2</td>\n",
  1795.        "      <td>2016-03-04 23:24:33</td>\n",
  1796.        "      <td>2016-03-04 23:31:02</td>\n",
  1797.        "      <td>1</td>\n",
  1798.        "      <td>-73.997643</td>\n",
  1799.        "      <td>40.756622</td>\n",
  1800.        "      <td>-73.984688</td>\n",
  1801.        "      <td>40.761581</td>\n",
  1802.        "      <td>N</td>\n",
  1803.        "      <td>389</td>\n",
  1804.        "      <td>Weehawken</td>\n",
  1805.        "      <td>Manhattan</td>\n",
  1806.        "      <td>0.759656</td>\n",
  1807.        "      <td>late night</td>\n",
  1808.        "    </tr>\n",
  1809.        "    <tr>\n",
  1810.        "      <th>118171</th>\n",
  1811.        "      <td>id0873910</td>\n",
  1812.        "      <td>1</td>\n",
  1813.        "      <td>2016-03-10 12:12:01</td>\n",
  1814.        "      <td>2016-03-10 12:25:52</td>\n",
  1815.        "      <td>2</td>\n",
  1816.        "      <td>-73.973885</td>\n",
  1817.        "      <td>40.764061</td>\n",
  1818.        "      <td>-73.990173</td>\n",
  1819.        "      <td>40.741711</td>\n",
  1820.        "      <td>N</td>\n",
  1821.        "      <td>831</td>\n",
  1822.        "      <td>Manhattan</td>\n",
  1823.        "      <td>New York City</td>\n",
  1824.        "      <td>1.763972</td>\n",
  1825.        "      <td>afternoon</td>\n",
  1826.        "    </tr>\n",
  1827.        "    <tr>\n",
  1828.        "      <th>118172</th>\n",
  1829.        "      <td>id1250471</td>\n",
  1830.        "      <td>1</td>\n",
  1831.        "      <td>2016-03-04 12:21:19</td>\n",
  1832.        "      <td>2016-03-04 12:37:49</td>\n",
  1833.        "      <td>1</td>\n",
  1834.        "      <td>-73.972527</td>\n",
  1835.        "      <td>40.758957</td>\n",
  1836.        "      <td>-73.956093</td>\n",
  1837.        "      <td>40.785572</td>\n",
  1838.        "      <td>N</td>\n",
  1839.        "      <td>990</td>\n",
  1840.        "      <td>Manhattan</td>\n",
  1841.        "      <td>Manhattan</td>\n",
  1842.        "      <td>2.030047</td>\n",
  1843.        "      <td>afternoon</td>\n",
  1844.        "    </tr>\n",
  1845.        "    <tr>\n",
  1846.        "      <th>118173</th>\n",
  1847.        "      <td>id1192201</td>\n",
  1848.        "      <td>1</td>\n",
  1849.        "      <td>2016-03-05 03:56:36</td>\n",
  1850.        "      <td>2016-03-05 04:05:39</td>\n",
  1851.        "      <td>1</td>\n",
  1852.        "      <td>-73.988785</td>\n",
  1853.        "      <td>40.727390</td>\n",
  1854.        "      <td>-73.999474</td>\n",
  1855.        "      <td>40.744106</td>\n",
  1856.        "      <td>N</td>\n",
  1857.        "      <td>543</td>\n",
  1858.        "      <td>New York City</td>\n",
  1859.        "      <td>New York City</td>\n",
  1860.        "      <td>1.283393</td>\n",
  1861.        "      <td>late night</td>\n",
  1862.        "    </tr>\n",
  1863.        "    <tr>\n",
  1864.        "      <th>118174</th>\n",
  1865.        "      <td>id3453691</td>\n",
  1866.        "      <td>2</td>\n",
  1867.        "      <td>2016-03-07 18:11:54</td>\n",
  1868.        "      <td>2016-03-07 18:29:09</td>\n",
  1869.        "      <td>1</td>\n",
  1870.        "      <td>-74.006531</td>\n",
  1871.        "      <td>40.738232</td>\n",
  1872.        "      <td>-73.985970</td>\n",
  1873.        "      <td>40.726978</td>\n",
  1874.        "      <td>N</td>\n",
  1875.        "      <td>1035</td>\n",
  1876.        "      <td>New York City</td>\n",
  1877.        "      <td>New York City</td>\n",
  1878.        "      <td>1.327944</td>\n",
  1879.        "      <td>evening</td>\n",
  1880.        "    </tr>\n",
  1881.        "    <tr>\n",
  1882.        "      <th>118175</th>\n",
  1883.        "      <td>id2086152</td>\n",
  1884.        "      <td>1</td>\n",
  1885.        "      <td>2016-03-11 00:22:18</td>\n",
  1886.        "      <td>2016-03-11 00:29:14</td>\n",
  1887.        "      <td>2</td>\n",
  1888.        "      <td>-73.986481</td>\n",
  1889.        "      <td>40.725826</td>\n",
  1890.        "      <td>-73.987297</td>\n",
  1891.        "      <td>40.736004</td>\n",
  1892.        "      <td>N</td>\n",
  1893.        "      <td>416</td>\n",
  1894.        "      <td>New York City</td>\n",
  1895.        "      <td>New York City</td>\n",
  1896.        "      <td>0.704504</td>\n",
  1897.        "      <td>late night</td>\n",
  1898.        "    </tr>\n",
  1899.        "    <tr>\n",
  1900.        "      <th>118176</th>\n",
  1901.        "      <td>id2525150</td>\n",
  1902.        "      <td>1</td>\n",
  1903.        "      <td>2016-03-08 12:56:58</td>\n",
  1904.        "      <td>2016-03-08 13:20:07</td>\n",
  1905.        "      <td>1</td>\n",
  1906.        "      <td>-73.978241</td>\n",
  1907.        "      <td>40.744911</td>\n",
  1908.        "      <td>-73.870483</td>\n",
  1909.        "      <td>40.773777</td>\n",
  1910.        "      <td>N</td>\n",
  1911.        "      <td>1389</td>\n",
  1912.        "      <td>Long Island City</td>\n",
  1913.        "      <td>The Bronx</td>\n",
  1914.        "      <td>5.981817</td>\n",
  1915.        "      <td>afternoon</td>\n",
  1916.        "    </tr>\n",
  1917.        "    <tr>\n",
  1918.        "      <th>118177</th>\n",
  1919.        "      <td>id3780824</td>\n",
  1920.        "      <td>2</td>\n",
  1921.        "      <td>2016-03-12 01:08:45</td>\n",
  1922.        "      <td>2016-03-12 01:23:02</td>\n",
  1923.        "      <td>5</td>\n",
  1924.        "      <td>-73.991463</td>\n",
  1925.        "      <td>40.719189</td>\n",
  1926.        "      <td>-73.949112</td>\n",
  1927.        "      <td>40.711090</td>\n",
  1928.        "      <td>N</td>\n",
  1929.        "      <td>857</td>\n",
  1930.        "      <td>New York City</td>\n",
  1931.        "      <td>Long Island City</td>\n",
  1932.        "      <td>2.287415</td>\n",
  1933.        "      <td>late night</td>\n",
  1934.        "    </tr>\n",
  1935.        "    <tr>\n",
  1936.        "      <th>118178</th>\n",
  1937.        "      <td>id2669138</td>\n",
  1938.        "      <td>2</td>\n",
  1939.        "      <td>2016-03-05 09:41:26</td>\n",
  1940.        "      <td>2016-03-05 09:52:15</td>\n",
  1941.        "      <td>6</td>\n",
  1942.        "      <td>-73.968597</td>\n",
  1943.        "      <td>40.786320</td>\n",
  1944.        "      <td>-73.981667</td>\n",
  1945.        "      <td>40.754440</td>\n",
  1946.        "      <td>N</td>\n",
  1947.        "      <td>649</td>\n",
  1948.        "      <td>Manhattan</td>\n",
  1949.        "      <td>Manhattan</td>\n",
  1950.        "      <td>2.306377</td>\n",
  1951.        "      <td>afternoon</td>\n",
  1952.        "    </tr>\n",
  1953.        "    <tr>\n",
  1954.        "      <th>118179</th>\n",
  1955.        "      <td>id3087596</td>\n",
  1956.        "      <td>2</td>\n",
  1957.        "      <td>2016-03-13 15:25:46</td>\n",
  1958.        "      <td>2016-03-13 15:34:52</td>\n",
  1959.        "      <td>2</td>\n",
  1960.        "      <td>-73.998871</td>\n",
  1961.        "      <td>40.724781</td>\n",
  1962.        "      <td>-73.983299</td>\n",
  1963.        "      <td>40.743511</td>\n",
  1964.        "      <td>N</td>\n",
  1965.        "      <td>546</td>\n",
  1966.        "      <td>New York City</td>\n",
  1967.        "      <td>Long Island City</td>\n",
  1968.        "      <td>1.529515</td>\n",
  1969.        "      <td>afternoon</td>\n",
  1970.        "    </tr>\n",
  1971.        "    <tr>\n",
  1972.        "      <th>118180</th>\n",
  1973.        "      <td>id3274818</td>\n",
  1974.        "      <td>2</td>\n",
  1975.        "      <td>2016-03-11 21:04:31</td>\n",
  1976.        "      <td>2016-03-11 21:08:41</td>\n",
  1977.        "      <td>2</td>\n",
  1978.        "      <td>-73.978233</td>\n",
  1979.        "      <td>40.763203</td>\n",
  1980.        "      <td>-73.982498</td>\n",
  1981.        "      <td>40.766701</td>\n",
  1982.        "      <td>N</td>\n",
  1983.        "      <td>250</td>\n",
  1984.        "      <td>Manhattan</td>\n",
  1985.        "      <td>Manhattan</td>\n",
  1986.        "      <td>0.328978</td>\n",
  1987.        "      <td>evening</td>\n",
  1988.        "    </tr>\n",
  1989.        "    <tr>\n",
  1990.        "      <th>118181</th>\n",
  1991.        "      <td>id2224211</td>\n",
  1992.        "      <td>1</td>\n",
  1993.        "      <td>2016-03-06 10:42:32</td>\n",
  1994.        "      <td>2016-03-06 10:46:57</td>\n",
  1995.        "      <td>1</td>\n",
  1996.        "      <td>-73.987488</td>\n",
  1997.        "      <td>40.768585</td>\n",
  1998.        "      <td>-73.979660</td>\n",
  1999.        "      <td>40.759151</td>\n",
  2000.        "      <td>N</td>\n",
  2001.        "      <td>265</td>\n",
  2002.        "      <td>Manhattan</td>\n",
  2003.        "      <td>Manhattan</td>\n",
  2004.        "      <td>0.769845</td>\n",
  2005.        "      <td>afternoon</td>\n",
  2006.        "    </tr>\n",
  2007.        "    <tr>\n",
  2008.        "      <th>118182</th>\n",
  2009.        "      <td>id3537077</td>\n",
  2010.        "      <td>2</td>\n",
  2011.        "      <td>2016-03-11 23:48:13</td>\n",
  2012.        "      <td>2016-03-12 00:01:36</td>\n",
  2013.        "      <td>1</td>\n",
  2014.        "      <td>-73.992729</td>\n",
  2015.        "      <td>40.752811</td>\n",
  2016.        "      <td>-73.987862</td>\n",
  2017.        "      <td>40.731930</td>\n",
  2018.        "      <td>N</td>\n",
  2019.        "      <td>803</td>\n",
  2020.        "      <td>Weehawken</td>\n",
  2021.        "      <td>New York City</td>\n",
  2022.        "      <td>1.465113</td>\n",
  2023.        "      <td>late night</td>\n",
  2024.        "    </tr>\n",
  2025.        "    <tr>\n",
  2026.        "      <th>118183</th>\n",
  2027.        "      <td>id3482902</td>\n",
  2028.        "      <td>1</td>\n",
  2029.        "      <td>2016-03-01 07:21:04</td>\n",
  2030.        "      <td>2016-03-01 07:23:36</td>\n",
  2031.        "      <td>1</td>\n",
  2032.        "      <td>-73.974693</td>\n",
  2033.        "      <td>40.756088</td>\n",
  2034.        "      <td>-73.969971</td>\n",
  2035.        "      <td>40.762115</td>\n",
  2036.        "      <td>N</td>\n",
  2037.        "      <td>152</td>\n",
  2038.        "      <td>Long Island City</td>\n",
  2039.        "      <td>Manhattan</td>\n",
  2040.        "      <td>0.484264</td>\n",
  2041.        "      <td>rush hour morning</td>\n",
  2042.        "    </tr>\n",
  2043.        "    <tr>\n",
  2044.        "      <th>118184</th>\n",
  2045.        "      <td>id0469946</td>\n",
  2046.        "      <td>2</td>\n",
  2047.        "      <td>2016-03-06 11:04:48</td>\n",
  2048.        "      <td>2016-03-06 11:17:45</td>\n",
  2049.        "      <td>2</td>\n",
  2050.        "      <td>-74.015572</td>\n",
  2051.        "      <td>40.710892</td>\n",
  2052.        "      <td>-73.996620</td>\n",
  2053.        "      <td>40.743633</td>\n",
  2054.        "      <td>N</td>\n",
  2055.        "      <td>777</td>\n",
  2056.        "      <td>New York City</td>\n",
  2057.        "      <td>New York City</td>\n",
  2058.        "      <td>2.470292</td>\n",
  2059.        "      <td>afternoon</td>\n",
  2060.        "    </tr>\n",
  2061.        "  </tbody>\n",
  2062.        "</table>\n",
  2063.        "<p>118185 rows × 15 columns</p>\n",
  2064.        "</div>"
  2065.       ],
  2066.       "text/plain": [
  2067.        "               id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
  2068.        "0       id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
  2069.        "1       id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
  2070.        "2       id3361153          1  2016-03-11 07:11:23  2016-03-11 07:20:09   \n",
  2071.        "3       id2129090          1  2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
  2072.        "4       id0256505          1  2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
  2073.        "5       id0970832          1  2016-03-12 20:39:39  2016-03-12 21:05:40   \n",
  2074.        "6       id2049424          2  2016-03-02 20:15:07  2016-03-02 20:37:43   \n",
  2075.        "7       id0038484          2  2016-03-09 13:41:11  2016-03-09 13:53:27   \n",
  2076.        "8       id3092788          2  2016-03-03 22:01:32  2016-03-03 22:17:44   \n",
  2077.        "9       id3863815          2  2016-03-14 04:24:36  2016-03-14 04:37:11   \n",
  2078.        "10      id1832737          2  2016-03-06 10:53:26  2016-03-06 10:59:30   \n",
  2079.        "11      id2718231          1  2016-03-08 02:44:19  2016-03-08 03:04:35   \n",
  2080.        "12      id3956459          2  2016-03-05 10:23:45  2016-03-05 10:45:52   \n",
  2081.        "13      id2393811          1  2016-03-10 18:52:40  2016-03-10 19:08:43   \n",
  2082.        "14      id2808378          1  2016-03-09 17:11:16  2016-03-09 17:28:43   \n",
  2083.        "15      id1295254          1  2016-03-06 11:01:27  2016-03-06 11:08:29   \n",
  2084.        "16      id1660823          2  2016-03-01 06:40:18  2016-03-01 07:01:37   \n",
  2085.        "17      id0802391          1  2016-03-06 17:44:45  2016-03-06 17:52:14   \n",
  2086.        "18      id2268459          1  2016-03-02 07:02:21  2016-03-02 07:24:57   \n",
  2087.        "19      id2797773          1  2016-03-08 08:33:35  2016-03-08 08:36:35   \n",
  2088.        "20      id3817493          2  2016-03-14 14:57:56  2016-03-14 15:15:26   \n",
  2089.        "21      id1971518          1  2016-03-12 13:04:28  2016-03-12 13:14:33   \n",
  2090.        "22      id3911487          1  2016-03-03 17:56:45  2016-03-03 18:05:28   \n",
  2091.        "23      id3276198          2  2016-03-14 20:31:12  2016-03-14 20:36:18   \n",
  2092.        "24      id1527676          1  2016-03-07 19:38:25  2016-03-07 19:54:35   \n",
  2093.        "25      id1146853          2  2016-03-05 02:59:30  2016-03-05 03:20:50   \n",
  2094.        "26      id3714906          1  2016-03-01 08:33:57  2016-03-01 08:40:44   \n",
  2095.        "27      id1937745          2  2016-03-07 18:51:46  2016-03-07 18:58:30   \n",
  2096.        "28      id2672200          1  2016-03-08 10:59:46  2016-03-08 11:21:50   \n",
  2097.        "29      id3200728          2  2016-03-03 10:14:57  2016-03-03 10:32:51   \n",
  2098.        "...           ...        ...                  ...                  ...   \n",
  2099.        "118155  id2073065          2  2016-03-10 21:43:30  2016-03-10 21:50:55   \n",
  2100.        "118156  id1042737          2  2016-03-10 06:10:29  2016-03-10 06:13:15   \n",
  2101.        "118157  id0538386          1  2016-03-07 18:29:35  2016-03-07 18:36:43   \n",
  2102.        "118158  id2824253          1  2016-03-03 08:09:29  2016-03-03 09:04:10   \n",
  2103.        "118159  id1333654          1  2016-03-05 01:22:46  2016-03-05 01:34:27   \n",
  2104.        "118160  id2731206          1  2016-03-13 20:14:32  2016-03-13 20:23:39   \n",
  2105.        "118161  id2838932          1  2016-03-13 17:03:03  2016-03-13 17:11:10   \n",
  2106.        "118162  id1486744          2  2016-03-09 10:45:19  2016-03-09 11:18:58   \n",
  2107.        "118163  id0042357          2  2016-03-10 20:56:32  2016-03-10 21:09:55   \n",
  2108.        "118164  id3542490          2  2016-03-07 21:35:25  2016-03-07 21:47:42   \n",
  2109.        "118165  id0998702          2  2016-03-06 02:15:18  2016-03-06 02:24:16   \n",
  2110.        "118166  id0480063          1  2016-03-05 12:53:30  2016-03-05 12:57:32   \n",
  2111.        "118167  id2034624          2  2016-03-12 20:01:27  2016-03-12 20:36:01   \n",
  2112.        "118168  id1203726          2  2016-03-03 17:19:23  2016-03-03 17:27:35   \n",
  2113.        "118169  id3860980          2  2016-03-11 23:59:25  2016-03-12 00:10:12   \n",
  2114.        "118170  id2924763          2  2016-03-04 23:24:33  2016-03-04 23:31:02   \n",
  2115.        "118171  id0873910          1  2016-03-10 12:12:01  2016-03-10 12:25:52   \n",
  2116.        "118172  id1250471          1  2016-03-04 12:21:19  2016-03-04 12:37:49   \n",
  2117.        "118173  id1192201          1  2016-03-05 03:56:36  2016-03-05 04:05:39   \n",
  2118.        "118174  id3453691          2  2016-03-07 18:11:54  2016-03-07 18:29:09   \n",
  2119.        "118175  id2086152          1  2016-03-11 00:22:18  2016-03-11 00:29:14   \n",
  2120.        "118176  id2525150          1  2016-03-08 12:56:58  2016-03-08 13:20:07   \n",
  2121.        "118177  id3780824          2  2016-03-12 01:08:45  2016-03-12 01:23:02   \n",
  2122.        "118178  id2669138          2  2016-03-05 09:41:26  2016-03-05 09:52:15   \n",
  2123.        "118179  id3087596          2  2016-03-13 15:25:46  2016-03-13 15:34:52   \n",
  2124.        "118180  id3274818          2  2016-03-11 21:04:31  2016-03-11 21:08:41   \n",
  2125.        "118181  id2224211          1  2016-03-06 10:42:32  2016-03-06 10:46:57   \n",
  2126.        "118182  id3537077          2  2016-03-11 23:48:13  2016-03-12 00:01:36   \n",
  2127.        "118183  id3482902          1  2016-03-01 07:21:04  2016-03-01 07:23:36   \n",
  2128.        "118184  id0469946          2  2016-03-06 11:04:48  2016-03-06 11:17:45   \n",
  2129.        "\n",
  2130.        "        passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
  2131.        "0                     1        -73.982155        40.767937         -73.964630   \n",
  2132.        "1                     1        -73.981049        40.744339         -73.973000   \n",
  2133.        "2                     1        -73.994560        40.750526         -73.978500   \n",
  2134.        "3                     1        -73.975090        40.758766         -73.953201   \n",
  2135.        "4                     1        -73.994484        40.745087         -73.998993   \n",
  2136.        "5                     1        -74.008247        40.747353         -73.979446   \n",
  2137.        "6                     1        -73.963890        40.773651         -74.005112   \n",
  2138.        "7                     2        -73.972855        40.764400         -73.971809   \n",
  2139.        "8                     2        -73.984772        40.710571         -73.989410   \n",
  2140.        "9                     3        -73.944359        40.714489         -73.910530   \n",
  2141.        "10                    1        -73.984711        40.760181         -73.979561   \n",
  2142.        "11                    1        -73.992500        40.740444         -73.840111   \n",
  2143.        "12                    1        -73.986908        40.761608         -74.008408   \n",
  2144.        "13                    1        -73.970581        40.799046         -73.989815   \n",
  2145.        "14                    1        -73.978645        40.740932         -74.012695   \n",
  2146.        "15                    1        -73.975983        40.757748         -73.982162   \n",
  2147.        "16                    5        -73.982140        40.775326         -74.009850   \n",
  2148.        "17                    1        -73.997208        40.724072         -74.000618   \n",
  2149.        "18                    1        -73.985359        40.738411         -73.870422   \n",
  2150.        "19                    1        -73.967133        40.793465         -73.970390   \n",
  2151.        "20                    1        -73.952881        40.766468         -73.978630   \n",
  2152.        "21                    1        -73.988976        40.759205         -73.973991   \n",
  2153.        "22                    1        -73.962112        40.776100         -73.968521   \n",
  2154.        "23                    1        -73.981911        40.766880         -73.982597   \n",
  2155.        "24                    2        -73.986130        40.759720         -74.001488   \n",
  2156.        "25                    4        -74.005394        40.740810         -73.950630   \n",
  2157.        "26                    1        -73.989494        40.753677         -73.988335   \n",
  2158.        "27                    2        -73.990974        40.760632         -73.994720   \n",
  2159.        "28                    1        -73.964325        40.773594         -73.989769   \n",
  2160.        "29                    1        -73.995880        40.759190         -73.979874   \n",
  2161.        "...                 ...               ...              ...                ...   \n",
  2162.        "118155                1        -73.989738        40.756599         -74.005318   \n",
  2163.        "118156                1        -73.985954        40.752129         -73.978592   \n",
  2164.        "118157                1        -73.976997        40.755756         -73.990540   \n",
  2165.        "118158                1        -73.961922        40.800533         -74.177269   \n",
  2166.        "118159                1        -73.973228        40.792824         -73.945877   \n",
  2167.        "118160                1        -73.981178        40.753674         -74.004509   \n",
  2168.        "118161                1        -73.998634        40.726131         -73.985001   \n",
  2169.        "118162                1        -73.982903        40.765659         -73.872917   \n",
  2170.        "118163                1        -73.993996        40.741283         -73.973114   \n",
  2171.        "118164                1        -73.996368        40.723660         -73.975166   \n",
  2172.        "118165                1        -73.963203        40.671833         -73.960808   \n",
  2173.        "118166                1        -73.976250        40.728737         -73.989166   \n",
  2174.        "118167                5        -73.781212        40.644951         -73.977303   \n",
  2175.        "118168                2        -73.991798        40.749840         -73.993942   \n",
  2176.        "118169                1        -73.971542        40.757721         -73.991043   \n",
  2177.        "118170                1        -73.997643        40.756622         -73.984688   \n",
  2178.        "118171                2        -73.973885        40.764061         -73.990173   \n",
  2179.        "118172                1        -73.972527        40.758957         -73.956093   \n",
  2180.        "118173                1        -73.988785        40.727390         -73.999474   \n",
  2181.        "118174                1        -74.006531        40.738232         -73.985970   \n",
  2182.        "118175                2        -73.986481        40.725826         -73.987297   \n",
  2183.        "118176                1        -73.978241        40.744911         -73.870483   \n",
  2184.        "118177                5        -73.991463        40.719189         -73.949112   \n",
  2185.        "118178                6        -73.968597        40.786320         -73.981667   \n",
  2186.        "118179                2        -73.998871        40.724781         -73.983299   \n",
  2187.        "118180                2        -73.978233        40.763203         -73.982498   \n",
  2188.        "118181                1        -73.987488        40.768585         -73.979660   \n",
  2189.        "118182                1        -73.992729        40.752811         -73.987862   \n",
  2190.        "118183                1        -73.974693        40.756088         -73.969971   \n",
  2191.        "118184                2        -74.015572        40.710892         -73.996620   \n",
  2192.        "\n",
  2193.        "        dropoff_latitude store_and_fwd_flag  trip_duration   pickup_district  \\\n",
  2194.        "0              40.765602                  N            455         Manhattan   \n",
  2195.        "1              40.789989                  N           1225  Long Island City   \n",
  2196.        "2              40.756191                  N            526         Weehawken   \n",
  2197.        "3              40.765068                  N           1346         Manhattan   \n",
  2198.        "4              40.722710                  N            695     New York City   \n",
  2199.        "5              40.718750                  N           1561           Hoboken   \n",
  2200.        "6              40.751492                  N           1356         Manhattan   \n",
  2201.        "7              40.757889                  N            736         Manhattan   \n",
  2202.        "8              40.730148                  N            972     New York City   \n",
  2203.        "9              40.709492                  N            755  Long Island City   \n",
  2204.        "10             40.752705                  N            364         Manhattan   \n",
  2205.        "11             40.719517                  N           1216     New York City   \n",
  2206.        "12             40.711620                  N           1327         Manhattan   \n",
  2207.        "13             40.767246                  N            963         Manhattan   \n",
  2208.        "14             40.701588                  N           1047  Long Island City   \n",
  2209.        "15             40.740749                  N            422         Manhattan   \n",
  2210.        "16             40.721699                  N           1279         Manhattan   \n",
  2211.        "17             40.732155                  N            449     New York City   \n",
  2212.        "18             40.773682                  N           1356     New York City   \n",
  2213.        "19             40.795750                  N            180         Manhattan   \n",
  2214.        "20             40.761921                  N           1050         Manhattan   \n",
  2215.        "21             40.760590                  N            605         Weehawken   \n",
  2216.        "22             40.764408                  N            523         Manhattan   \n",
  2217.        "23             40.777180                  N            306         Manhattan   \n",
  2218.        "24             40.736065                  N            970         Manhattan   \n",
  2219.        "25             40.821037                  N           1280     New York City   \n",
  2220.        "26             40.745949                  N            407         Weehawken   \n",
  2221.        "27             40.750450                  N            404         Weehawken   \n",
  2222.        "28             40.738483                  N           1324         Manhattan   \n",
  2223.        "29             40.752781                  N           1074         Weehawken   \n",
  2224.        "...                  ...                ...            ...               ...   \n",
  2225.        "118155         40.740231                  N            445         Weehawken   \n",
  2226.        "118156         40.752602                  N            166         Manhattan   \n",
  2227.        "118157         40.751163                  N            428         Manhattan   \n",
  2228.        "118158         40.691124                  N           3281         Manhattan   \n",
  2229.        "118159         40.777721                  N            701         Manhattan   \n",
  2230.        "118160         40.747082                  N            547         Manhattan   \n",
  2231.        "118161         40.727985                  N            487     New York City   \n",
  2232.        "118162         40.774441                  N           2019         Manhattan   \n",
  2233.        "118163         40.757057                  N            803     New York City   \n",
  2234.        "118164         40.689621                  N            737     New York City   \n",
  2235.        "118165         40.648785                  N            538          Brooklyn   \n",
  2236.        "118166         40.734058                  N            242  Long Island City   \n",
  2237.        "118167         40.750721                  N           2074            Inwood   \n",
  2238.        "118168         40.735722                  N            492         Weehawken   \n",
  2239.        "118169         40.750568                  N            647  Long Island City   \n",
  2240.        "118170         40.761581                  N            389         Weehawken   \n",
  2241.        "118171         40.741711                  N            831         Manhattan   \n",
  2242.        "118172         40.785572                  N            990         Manhattan   \n",
  2243.        "118173         40.744106                  N            543     New York City   \n",
  2244.        "118174         40.726978                  N           1035     New York City   \n",
  2245.        "118175         40.736004                  N            416     New York City   \n",
  2246.        "118176         40.773777                  N           1389  Long Island City   \n",
  2247.        "118177         40.711090                  N            857     New York City   \n",
  2248.        "118178         40.754440                  N            649         Manhattan   \n",
  2249.        "118179         40.743511                  N            546     New York City   \n",
  2250.        "118180         40.766701                  N            250         Manhattan   \n",
  2251.        "118181         40.759151                  N            265         Manhattan   \n",
  2252.        "118182         40.731930                  N            803         Weehawken   \n",
  2253.        "118183         40.762115                  N            152  Long Island City   \n",
  2254.        "118184         40.743633                  N            777     New York City   \n",
  2255.        "\n",
  2256.        "         dropoff_district   distance        time_of_day  \n",
  2257.        "0               Manhattan   0.931139  rush hour evening  \n",
  2258.        "1               Manhattan   3.182147            evening  \n",
  2259.        "2               Manhattan   0.927234  rush hour morning  \n",
  2260.        "3        Long Island City   1.225473          afternoon  \n",
  2261.        "4           New York City   1.564023          afternoon  \n",
  2262.        "5           New York City   2.485830            evening  \n",
  2263.        "6               Weehawken   2.645357            evening  \n",
  2264.        "7               Manhattan   0.453228          afternoon  \n",
  2265.        "8           New York City   1.374282            evening  \n",
  2266.        "9           East New York   1.805032         late night  \n",
  2267.        "10       Long Island City   0.582684          afternoon  \n",
  2268.        "11      Borough of Queens   8.108848         late night  \n",
  2269.        "12          New York City   3.632609          afternoon  \n",
  2270.        "13             Guttenberg   2.416584            evening  \n",
  2271.        "14          New York City   3.251044  rush hour evening  \n",
  2272.        "15       Long Island City   1.218201          afternoon  \n",
  2273.        "16          New York City   3.979052         late night  \n",
  2274.        "17          New York City   0.586357  rush hour evening  \n",
  2275.        "18              The Bronx   6.490441  rush hour morning  \n",
  2276.        "19              Manhattan   0.232301  rush hour morning  \n",
  2277.        "20              Manhattan   1.383638          afternoon  \n",
  2278.        "21              Manhattan   0.790009          afternoon  \n",
  2279.        "22              Manhattan   0.874682  rush hour evening  \n",
  2280.        "23              Manhattan   0.712547            evening  \n",
  2281.        "24          New York City   1.821408            evening  \n",
  2282.        "25              Edgewater   6.239842         late night  \n",
  2283.        "26          New York City   0.537433  rush hour morning  \n",
  2284.        "27              Weehawken   0.730280            evening  \n",
  2285.        "28          New York City   2.767382          afternoon  \n",
  2286.        "29       Long Island City   0.947571          afternoon  \n",
  2287.        "...                   ...        ...                ...  \n",
  2288.        "118155      New York City   1.394318            evening  \n",
  2289.        "118156   Long Island City   0.386736         late night  \n",
  2290.        "118157          Weehawken   0.776594            evening  \n",
  2291.        "118158          Elizabeth  13.572632  rush hour morning  \n",
  2292.        "118159          Manhattan   1.770954         late night  \n",
  2293.        "118160          Weehawken   1.303353            evening  \n",
  2294.        "118161      New York City   0.725275  rush hour evening  \n",
  2295.        "118162          The Bronx   5.787094          afternoon  \n",
  2296.        "118163          Manhattan   1.543531            evening  \n",
  2297.        "118164      New York City   2.600838            evening  \n",
  2298.        "118165           Brooklyn   1.597435         late night  \n",
  2299.        "118166      New York City   0.769767          afternoon  \n",
  2300.        "118167   Long Island City  12.606367            evening  \n",
  2301.        "118168      New York City   0.981909  rush hour evening  \n",
  2302.        "118169          Weehawken   1.134007         late night  \n",
  2303.        "118170          Manhattan   0.759656         late night  \n",
  2304.        "118171      New York City   1.763972          afternoon  \n",
  2305.        "118172          Manhattan   2.030047          afternoon  \n",
  2306.        "118173      New York City   1.283393         late night  \n",
  2307.        "118174      New York City   1.327944            evening  \n",
  2308.        "118175      New York City   0.704504         late night  \n",
  2309.        "118176          The Bronx   5.981817          afternoon  \n",
  2310.        "118177   Long Island City   2.287415         late night  \n",
  2311.        "118178          Manhattan   2.306377          afternoon  \n",
  2312.        "118179   Long Island City   1.529515          afternoon  \n",
  2313.        "118180          Manhattan   0.328978            evening  \n",
  2314.        "118181          Manhattan   0.769845          afternoon  \n",
  2315.        "118182      New York City   1.465113         late night  \n",
  2316.        "118183          Manhattan   0.484264  rush hour morning  \n",
  2317.        "118184      New York City   2.470292          afternoon  \n",
  2318.        "\n",
  2319.        "[118185 rows x 15 columns]"
  2320.       ]
  2321.      },
  2322.      "execution_count": 14,
  2323.      "metadata": {},
  2324.      "output_type": "execute_result"
  2325.     }
  2326.    ],
  2327.    "source": [
  2328.     "#print(df.shape[0])\n",
  2329.     "#print(len(timeofday))\n",
  2330.     "df[\"time_of_day\"]= timeofday\n",
  2331.     "df"
  2332.    ]
  2333.   },
  2334.   {
  2335.    "cell_type": "markdown",
  2336.    "metadata": {},
  2337.    "source": [
  2338.     "### How does average distance vary as time of the day changes?"
  2339.    ]
  2340.   },
  2341.   {
  2342.    "cell_type": "code",
  2343.    "execution_count": 15,
  2344.    "metadata": {},
  2345.    "outputs": [
  2346.     {
  2347.      "data": {
  2348.       "image/png": 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\n",
  2349.       "text/plain": [
  2350.        "<Figure size 432x288 with 1 Axes>"
  2351.       ]
  2352.      },
  2353.      "metadata": {
  2354.       "needs_background": "light"
  2355.      },
  2356.      "output_type": "display_data"
  2357.     }
  2358.    ],
  2359.    "source": [
  2360.     "timegroups= df.groupby(['time_of_day']).mean()\n",
  2361.     "total_distance = timegroups[\"distance\"]\n",
  2362.     "\n",
  2363.     "ax = total_distance.plot(kind=\"bar\",\n",
  2364.     "                   color=\"lightsalmon\",\n",
  2365.     "                   rot=15)\n",
  2366.     "\n",
  2367.     "plt.ylabel(\"Total Distance\")\n",
  2368.     "plt.title(\"Total Distance-Time\")\n",
  2369.     "plt.show()\n",
  2370.     "\n",
  2371.     "timegroups= df.groupby(['time_of_day']).mean()\n",
  2372.     "tripduration = timegroups[\"trip_duration\"]"
  2373.    ]
  2374.   },
  2375.   {
  2376.    "cell_type": "markdown",
  2377.    "metadata": {},
  2378.    "source": [
  2379.     "### How does trip duration vary as time of the day changes?"
  2380.    ]
  2381.   },
  2382.   {
  2383.    "cell_type": "code",
  2384.    "execution_count": 16,
  2385.    "metadata": {},
  2386.    "outputs": [
  2387.     {
  2388.      "data": {
  2389.       "image/png": 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\n",
  2390.       "text/plain": [
  2391.        "<Figure size 432x288 with 1 Axes>"
  2392.       ]
  2393.      },
  2394.      "metadata": {
  2395.       "needs_background": "light"
  2396.      },
  2397.      "output_type": "display_data"
  2398.     }
  2399.    ],
  2400.    "source": [
  2401.     "ax2 = tripduration.plot(kind=\"bar\",\n",
  2402.     "                   color=\"mediumaquamarine\",\n",
  2403.     "                   rot=15)\n",
  2404.     "\n",
  2405.     "plt.ylabel(\"Trip Duration\")\n",
  2406.     "plt.title(\"Trip Duration-Time\")\n",
  2407.     "plt.show()"
  2408.    ]
  2409.   },
  2410.   {
  2411.    "cell_type": "markdown",
  2412.    "metadata": {},
  2413.    "source": [
  2414.     "## Hypothesis Testing"
  2415.    ]
  2416.   },
  2417.   {
  2418.    "cell_type": "markdown",
  2419.    "metadata": {},
  2420.    "source": [
  2421.     "### Does passenger group size affect the distance?\n",
  2422.     "• Null hypothesis: passenger group size has no effect on the distance."
  2423.    ]
  2424.   },
  2425.   {
  2426.    "cell_type": "markdown",
  2427.    "metadata": {},
  2428.    "source": [
  2429.     "### Pearson Correlation\n",
  2430.     "\n",
  2431.     "A Pearson correlation is a number between -1 and 1 that indicates the extent to which two variables are linearly related."
  2432.    ]
  2433.   },
  2434.   {
  2435.    "cell_type": "code",
  2436.    "execution_count": 17,
  2437.    "metadata": {},
  2438.    "outputs": [
  2439.     {
  2440.      "data": {
  2441.       "text/plain": [
  2442.        "(0.005972277211762019, 0.04005815226892839)"
  2443.       ]
  2444.      },
  2445.      "execution_count": 17,
  2446.      "metadata": {},
  2447.      "output_type": "execute_result"
  2448.     },
  2449.     {
  2450.      "data": {
  2451.       "image/png": 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\n",
  2452.       "text/plain": [
  2453.        "<Figure size 360x360 with 6 Axes>"
  2454.       ]
  2455.      },
  2456.      "metadata": {
  2457.       "needs_background": "light"
  2458.      },
  2459.      "output_type": "display_data"
  2460.     }
  2461.    ],
  2462.    "source": [
  2463.     "cols = [\"passenger_count\", \"distance\"]\n",
  2464.     "\n",
  2465.     "sns.pairplot(data=df, vars=cols)\n",
  2466.     "\n",
  2467.     "stats.pearsonr(df[\"passenger_count\"], df[\"distance\"])\n",
  2468.     "\n"
  2469.    ]
  2470.   },
  2471.   {
  2472.    "cell_type": "markdown",
  2473.    "metadata": {},
  2474.    "source": [
  2475.     "Correlation coefficient that we obtained 0.0059 states that the correlation between passenger count and distance is not signficant."
  2476.    ]
  2477.   },
  2478.   {
  2479.    "cell_type": "code",
  2480.    "execution_count": 18,
  2481.    "metadata": {},
  2482.    "outputs": [
  2483.     {
  2484.      "data": {
  2485.       "text/plain": [
  2486.        "<matplotlib.axes._subplots.AxesSubplot at 0x1a38363ba8>"
  2487.       ]
  2488.      },
  2489.      "execution_count": 18,
  2490.      "metadata": {},
  2491.      "output_type": "execute_result"
  2492.     },
  2493.     {
  2494.      "data": {
  2495.       "image/png": 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\n",
  2496.       "text/plain": [
  2497.        "<Figure size 432x288 with 2 Axes>"
  2498.       ]
  2499.      },
  2500.      "metadata": {
  2501.       "needs_background": "light"
  2502.      },
  2503.      "output_type": "display_data"
  2504.     }
  2505.    ],
  2506.    "source": [
  2507.     "corr = df.corr()  # extract correlations between each column\n",
  2508.     "\n",
  2509.     "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)"
  2510.    ]
  2511.   },
  2512.   {
  2513.    "cell_type": "markdown",
  2514.    "metadata": {},
  2515.    "source": [
  2516.     "The heat map above shows all of the correlations between each columns.\n",
  2517.     "\n",
  2518.     "From this heat map we can say that passenger count and distance are not correlated."
  2519.    ]
  2520.   },
  2521.   {
  2522.    "cell_type": "markdown",
  2523.    "metadata": {},
  2524.    "source": [
  2525.     "### Do trip distances increase in weekends?\n",
  2526.     "• Null hypothesis: The day of the week has no effect on the distance."
  2527.    ]
  2528.   },
  2529.   {
  2530.    "cell_type": "code",
  2531.    "execution_count": 19,
  2532.    "metadata": {},
  2533.    "outputs": [],
  2534.    "source": [
  2535.     "datelist=[]\n",
  2536.     "for l in range (df.shape[0]):\n",
  2537.     "    datelist.append(datetimelist[l][0])\n",
  2538.     "\n",
  2539.     "weekdaylist=[]\n",
  2540.     "daylist=[]\n",
  2541.     "for t in range (df.shape[0]):\n",
  2542.     "    day= datetime.strptime(datelist[t] , '%Y-%m-%d').strftime(\"%A\")\n",
  2543.     "    if(day== 'Monday' or day== 'Tuesday' or day== 'Wednesday' or day=='Thursday' or day== 'Friday'):\n",
  2544.     "        weekdaylist.append(day)\n",
  2545.     "        daylist.append(\"Work\")\n",
  2546.     "    elif(day=='Saturday' or day== 'Sunday'):\n",
  2547.     "        weekdaylist.append(day)\n",
  2548.     "        daylist.append(\"Holiday\")\n",
  2549.     "    #strftime(\"%A\")"
  2550.    ]
  2551.   },
  2552.   {
  2553.    "cell_type": "code",
  2554.    "execution_count": 20,
  2555.    "metadata": {},
  2556.    "outputs": [
  2557.     {
  2558.      "data": {
  2559.       "image/png": 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\n",
  2560.       "text/plain": [
  2561.        "<Figure size 432x288 with 1 Axes>"
  2562.       ]
  2563.      },
  2564.      "metadata": {
  2565.       "needs_background": "light"
  2566.      },
  2567.      "output_type": "display_data"
  2568.     }
  2569.    ],
  2570.    "source": [
  2571.     "df['week_day'] = daylist\n",
  2572.     "weekgroup = df.groupby(by=\"week_day\").mean()\n",
  2573.     "weekdistance = weekgroup[\"distance\"]\n",
  2574.     "\n",
  2575.     "ax3 = weekdistance.plot(kind=\"barh\")\n",
  2576.     "\n",
  2577.     "plt.ylabel(\"Avg. Distance\")\n",
  2578.     "plt.xlabel(\"Week Day\")\n",
  2579.     "plt.title(\"Distance vs Week Day\")\n",
  2580.     "plt.show()"
  2581.    ]
  2582.   },
  2583.   {
  2584.    "cell_type": "markdown",
  2585.    "metadata": {},
  2586.    "source": [
  2587.     "From this graph we can't tell much, so to further prove this we can run the ANOVA test."
  2588.    ]
  2589.   },
  2590.   {
  2591.    "cell_type": "markdown",
  2592.    "metadata": {},
  2593.    "source": [
  2594.     "### Anova (Analysis of Variance)\n",
  2595.     "Analysis of variance (ANOVA) is a statistical technique that is used to check if the means of two or more groups are significantly different from each other. ANOVA checks the impact of one or more factors by comparing the means of different samples."
  2596.    ]
  2597.   },
  2598.   {
  2599.    "cell_type": "code",
  2600.    "execution_count": 21,
  2601.    "metadata": {},
  2602.    "outputs": [
  2603.     {
  2604.      "name": "stderr",
  2605.      "output_type": "stream",
  2606.      "text": [
  2607.       "/Users/mehmetahkemoglu/anaconda3/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
  2608.       "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
  2609.      ]
  2610.     },
  2611.     {
  2612.      "data": {
  2613.       "image/png": 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\n",
  2614.       "text/plain": [
  2615.        "<Figure size 1440x720 with 1 Axes>"
  2616.       ]
  2617.      },
  2618.      "metadata": {
  2619.       "needs_background": "light"
  2620.      },
  2621.      "output_type": "display_data"
  2622.     }
  2623.    ],
  2624.    "source": [
  2625.     "df['day'] = tuple(daylist)\n",
  2626.     "daygroup = df.groupby(by=\"day\").mean()\n",
  2627.     "work = df[df[\"day\"] == \"Work\"][\"distance\"]\n",
  2628.     "holiday = df[df[\"day\"] == \"Holiday\"][\"distance\"]\n",
  2629.     "\n",
  2630.     "fig, ax = plt.subplots(1,1, figsize=(20,10))\n",
  2631.     "\n",
  2632.     "sns.kdeplot(work, shade=True, ax=ax, label=\"Work Day\")\n",
  2633.     "sns.kdeplot(holiday, shade=True, ax=ax, label=\"Holiday\")\n",
  2634.     "\n",
  2635.     "plt.xlabel(\"Average Distance\")\n",
  2636.     "plt.title(\"Workday and Holiday vs Average Distance Distribution \")\n",
  2637.     "plt.show()"
  2638.    ]
  2639.   },
  2640.   {
  2641.    "cell_type": "code",
  2642.    "execution_count": 22,
  2643.    "metadata": {},
  2644.    "outputs": [
  2645.     {
  2646.      "data": {
  2647.       "text/plain": [
  2648.        "F_onewayResult(statistic=31.4326781024453, pvalue=2.0693550758912806e-08)"
  2649.       ]
  2650.      },
  2651.      "execution_count": 22,
  2652.      "metadata": {},
  2653.      "output_type": "execute_result"
  2654.     }
  2655.    ],
  2656.    "source": [
  2657.     "stats.f_oneway(work, holiday)"
  2658.    ]
  2659.   },
  2660.   {
  2661.    "cell_type": "markdown",
  2662.    "metadata": {},
  2663.    "source": [
  2664.     "If we set significance value as 0.05, then we can reject the null hypothesis and say that day of the week has an effect on distance."
  2665.    ]
  2666.   }
  2667.  ],
  2668.  "metadata": {
  2669.   "kernelspec": {
  2670.    "display_name": "Python 3",
  2671.    "language": "python",
  2672.    "name": "python3"
  2673.   },
  2674.   "language_info": {
  2675.    "codemirror_mode": {
  2676.     "name": "ipython",
  2677.     "version": 3
  2678.    },
  2679.    "file_extension": ".py",
  2680.    "mimetype": "text/x-python",
  2681.    "name": "python",
  2682.    "nbconvert_exporter": "python",
  2683.    "pygments_lexer": "ipython3",
  2684.    "version": "3.7.1"
  2685.   }
  2686.  },
  2687.  "nbformat": 4,
  2688.  "nbformat_minor": 2
  2689. }
RAW Paste Data
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