daily pastebin goal
6%
SHARE
TWEET

Untitled

a guest Mar 24th, 2019 79 Never
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "markdown",
  5.    "metadata": {},
  6.    "source": [
  7.     "Aybars Yazıcı 25330 - CS 210 Individual Project"
  8.    ]
  9.   },
  10.   {
  11.    "cell_type": "code",
  12.    "execution_count": 1,
  13.    "metadata": {},
  14.    "outputs": [
  15.     {
  16.      "data": {
  17.       "text/html": [
  18.        "<div>\n",
  19.        "<style scoped>\n",
  20.        "    .dataframe tbody tr th:only-of-type {\n",
  21.        "        vertical-align: middle;\n",
  22.        "    }\n",
  23.        "\n",
  24.        "    .dataframe tbody tr th {\n",
  25.        "        vertical-align: top;\n",
  26.        "    }\n",
  27.        "\n",
  28.        "    .dataframe thead th {\n",
  29.        "        text-align: right;\n",
  30.        "    }\n",
  31.        "</style>\n",
  32.        "<table border=\"1\" class=\"dataframe\">\n",
  33.        "  <thead>\n",
  34.        "    <tr style=\"text-align: right;\">\n",
  35.        "      <th></th>\n",
  36.        "      <th>id</th>\n",
  37.        "      <th>vendor_id</th>\n",
  38.        "      <th>pickup_datetime</th>\n",
  39.        "      <th>dropoff_datetime</th>\n",
  40.        "      <th>passenger_count</th>\n",
  41.        "      <th>pickup_longitude</th>\n",
  42.        "      <th>pickup_latitude</th>\n",
  43.        "      <th>dropoff_longitude</th>\n",
  44.        "      <th>dropoff_latitude</th>\n",
  45.        "      <th>store_and_fwd_flag</th>\n",
  46.        "      <th>trip_duration</th>\n",
  47.        "    </tr>\n",
  48.        "  </thead>\n",
  49.        "  <tbody>\n",
  50.        "    <tr>\n",
  51.        "      <th>0</th>\n",
  52.        "      <td>id2875421</td>\n",
  53.        "      <td>2</td>\n",
  54.        "      <td>2016-03-14 17:24:55</td>\n",
  55.        "      <td>2016-03-14 17:32:30</td>\n",
  56.        "      <td>1</td>\n",
  57.        "      <td>-73.982155</td>\n",
  58.        "      <td>40.767937</td>\n",
  59.        "      <td>-73.964630</td>\n",
  60.        "      <td>40.765602</td>\n",
  61.        "      <td>N</td>\n",
  62.        "      <td>455</td>\n",
  63.        "    </tr>\n",
  64.        "    <tr>\n",
  65.        "      <th>1</th>\n",
  66.        "      <td>id0012891</td>\n",
  67.        "      <td>2</td>\n",
  68.        "      <td>2016-03-10 21:45:01</td>\n",
  69.        "      <td>2016-03-10 22:05:26</td>\n",
  70.        "      <td>1</td>\n",
  71.        "      <td>-73.981049</td>\n",
  72.        "      <td>40.744339</td>\n",
  73.        "      <td>-73.973000</td>\n",
  74.        "      <td>40.789989</td>\n",
  75.        "      <td>N</td>\n",
  76.        "      <td>1225</td>\n",
  77.        "    </tr>\n",
  78.        "    <tr>\n",
  79.        "      <th>2</th>\n",
  80.        "      <td>id3361153</td>\n",
  81.        "      <td>1</td>\n",
  82.        "      <td>2016-03-11 07:11:23</td>\n",
  83.        "      <td>2016-03-11 07:20:09</td>\n",
  84.        "      <td>1</td>\n",
  85.        "      <td>-73.994560</td>\n",
  86.        "      <td>40.750526</td>\n",
  87.        "      <td>-73.978500</td>\n",
  88.        "      <td>40.756191</td>\n",
  89.        "      <td>N</td>\n",
  90.        "      <td>526</td>\n",
  91.        "    </tr>\n",
  92.        "    <tr>\n",
  93.        "      <th>3</th>\n",
  94.        "      <td>id2129090</td>\n",
  95.        "      <td>1</td>\n",
  96.        "      <td>2016-03-14 14:05:39</td>\n",
  97.        "      <td>2016-03-14 14:28:05</td>\n",
  98.        "      <td>1</td>\n",
  99.        "      <td>-73.975090</td>\n",
  100.        "      <td>40.758766</td>\n",
  101.        "      <td>-73.953201</td>\n",
  102.        "      <td>40.765068</td>\n",
  103.        "      <td>N</td>\n",
  104.        "      <td>1346</td>\n",
  105.        "    </tr>\n",
  106.        "    <tr>\n",
  107.        "      <th>4</th>\n",
  108.        "      <td>id0256505</td>\n",
  109.        "      <td>1</td>\n",
  110.        "      <td>2016-03-14 15:04:38</td>\n",
  111.        "      <td>2016-03-14 15:16:13</td>\n",
  112.        "      <td>1</td>\n",
  113.        "      <td>-73.994484</td>\n",
  114.        "      <td>40.745087</td>\n",
  115.        "      <td>-73.998993</td>\n",
  116.        "      <td>40.722710</td>\n",
  117.        "      <td>N</td>\n",
  118.        "      <td>695</td>\n",
  119.        "    </tr>\n",
  120.        "    <tr>\n",
  121.        "      <th>5</th>\n",
  122.        "      <td>id0970832</td>\n",
  123.        "      <td>1</td>\n",
  124.        "      <td>2016-03-12 20:39:39</td>\n",
  125.        "      <td>2016-03-12 21:05:40</td>\n",
  126.        "      <td>1</td>\n",
  127.        "      <td>-74.008247</td>\n",
  128.        "      <td>40.747353</td>\n",
  129.        "      <td>-73.979446</td>\n",
  130.        "      <td>40.718750</td>\n",
  131.        "      <td>N</td>\n",
  132.        "      <td>1561</td>\n",
  133.        "    </tr>\n",
  134.        "    <tr>\n",
  135.        "      <th>6</th>\n",
  136.        "      <td>id2049424</td>\n",
  137.        "      <td>2</td>\n",
  138.        "      <td>2016-03-02 20:15:07</td>\n",
  139.        "      <td>2016-03-02 20:37:43</td>\n",
  140.        "      <td>1</td>\n",
  141.        "      <td>-73.963890</td>\n",
  142.        "      <td>40.773651</td>\n",
  143.        "      <td>-74.005112</td>\n",
  144.        "      <td>40.751492</td>\n",
  145.        "      <td>N</td>\n",
  146.        "      <td>1356</td>\n",
  147.        "    </tr>\n",
  148.        "    <tr>\n",
  149.        "      <th>7</th>\n",
  150.        "      <td>id0038484</td>\n",
  151.        "      <td>2</td>\n",
  152.        "      <td>2016-03-09 13:41:11</td>\n",
  153.        "      <td>2016-03-09 13:53:27</td>\n",
  154.        "      <td>2</td>\n",
  155.        "      <td>-73.972855</td>\n",
  156.        "      <td>40.764400</td>\n",
  157.        "      <td>-73.971809</td>\n",
  158.        "      <td>40.757889</td>\n",
  159.        "      <td>N</td>\n",
  160.        "      <td>736</td>\n",
  161.        "    </tr>\n",
  162.        "    <tr>\n",
  163.        "      <th>8</th>\n",
  164.        "      <td>id3092788</td>\n",
  165.        "      <td>2</td>\n",
  166.        "      <td>2016-03-03 22:01:32</td>\n",
  167.        "      <td>2016-03-03 22:17:44</td>\n",
  168.        "      <td>2</td>\n",
  169.        "      <td>-73.984772</td>\n",
  170.        "      <td>40.710571</td>\n",
  171.        "      <td>-73.989410</td>\n",
  172.        "      <td>40.730148</td>\n",
  173.        "      <td>N</td>\n",
  174.        "      <td>972</td>\n",
  175.        "    </tr>\n",
  176.        "    <tr>\n",
  177.        "      <th>9</th>\n",
  178.        "      <td>id3863815</td>\n",
  179.        "      <td>2</td>\n",
  180.        "      <td>2016-03-14 04:24:36</td>\n",
  181.        "      <td>2016-03-14 04:37:11</td>\n",
  182.        "      <td>3</td>\n",
  183.        "      <td>-73.944359</td>\n",
  184.        "      <td>40.714489</td>\n",
  185.        "      <td>-73.910530</td>\n",
  186.        "      <td>40.709492</td>\n",
  187.        "      <td>N</td>\n",
  188.        "      <td>755</td>\n",
  189.        "    </tr>\n",
  190.        "    <tr>\n",
  191.        "      <th>10</th>\n",
  192.        "      <td>id1832737</td>\n",
  193.        "      <td>2</td>\n",
  194.        "      <td>2016-03-06 10:53:26</td>\n",
  195.        "      <td>2016-03-06 10:59:30</td>\n",
  196.        "      <td>1</td>\n",
  197.        "      <td>-73.984711</td>\n",
  198.        "      <td>40.760181</td>\n",
  199.        "      <td>-73.979561</td>\n",
  200.        "      <td>40.752705</td>\n",
  201.        "      <td>N</td>\n",
  202.        "      <td>364</td>\n",
  203.        "    </tr>\n",
  204.        "    <tr>\n",
  205.        "      <th>11</th>\n",
  206.        "      <td>id2718231</td>\n",
  207.        "      <td>1</td>\n",
  208.        "      <td>2016-03-08 02:44:19</td>\n",
  209.        "      <td>2016-03-08 03:04:35</td>\n",
  210.        "      <td>1</td>\n",
  211.        "      <td>-73.992500</td>\n",
  212.        "      <td>40.740444</td>\n",
  213.        "      <td>-73.840111</td>\n",
  214.        "      <td>40.719517</td>\n",
  215.        "      <td>N</td>\n",
  216.        "      <td>1216</td>\n",
  217.        "    </tr>\n",
  218.        "    <tr>\n",
  219.        "      <th>12</th>\n",
  220.        "      <td>id3956459</td>\n",
  221.        "      <td>2</td>\n",
  222.        "      <td>2016-03-05 10:23:45</td>\n",
  223.        "      <td>2016-03-05 10:45:52</td>\n",
  224.        "      <td>1</td>\n",
  225.        "      <td>-73.986908</td>\n",
  226.        "      <td>40.761608</td>\n",
  227.        "      <td>-74.008408</td>\n",
  228.        "      <td>40.711620</td>\n",
  229.        "      <td>N</td>\n",
  230.        "      <td>1327</td>\n",
  231.        "    </tr>\n",
  232.        "    <tr>\n",
  233.        "      <th>13</th>\n",
  234.        "      <td>id2393811</td>\n",
  235.        "      <td>1</td>\n",
  236.        "      <td>2016-03-10 18:52:40</td>\n",
  237.        "      <td>2016-03-10 19:08:43</td>\n",
  238.        "      <td>1</td>\n",
  239.        "      <td>-73.970581</td>\n",
  240.        "      <td>40.799046</td>\n",
  241.        "      <td>-73.989815</td>\n",
  242.        "      <td>40.767246</td>\n",
  243.        "      <td>N</td>\n",
  244.        "      <td>963</td>\n",
  245.        "    </tr>\n",
  246.        "    <tr>\n",
  247.        "      <th>14</th>\n",
  248.        "      <td>id2808378</td>\n",
  249.        "      <td>1</td>\n",
  250.        "      <td>2016-03-09 17:11:16</td>\n",
  251.        "      <td>2016-03-09 17:28:43</td>\n",
  252.        "      <td>1</td>\n",
  253.        "      <td>-73.978645</td>\n",
  254.        "      <td>40.740932</td>\n",
  255.        "      <td>-74.012695</td>\n",
  256.        "      <td>40.701588</td>\n",
  257.        "      <td>N</td>\n",
  258.        "      <td>1047</td>\n",
  259.        "    </tr>\n",
  260.        "    <tr>\n",
  261.        "      <th>15</th>\n",
  262.        "      <td>id1295254</td>\n",
  263.        "      <td>1</td>\n",
  264.        "      <td>2016-03-06 11:01:27</td>\n",
  265.        "      <td>2016-03-06 11:08:29</td>\n",
  266.        "      <td>1</td>\n",
  267.        "      <td>-73.975983</td>\n",
  268.        "      <td>40.757748</td>\n",
  269.        "      <td>-73.982162</td>\n",
  270.        "      <td>40.740749</td>\n",
  271.        "      <td>N</td>\n",
  272.        "      <td>422</td>\n",
  273.        "    </tr>\n",
  274.        "    <tr>\n",
  275.        "      <th>16</th>\n",
  276.        "      <td>id1660823</td>\n",
  277.        "      <td>2</td>\n",
  278.        "      <td>2016-03-01 06:40:18</td>\n",
  279.        "      <td>2016-03-01 07:01:37</td>\n",
  280.        "      <td>5</td>\n",
  281.        "      <td>-73.982140</td>\n",
  282.        "      <td>40.775326</td>\n",
  283.        "      <td>-74.009850</td>\n",
  284.        "      <td>40.721699</td>\n",
  285.        "      <td>N</td>\n",
  286.        "      <td>1279</td>\n",
  287.        "    </tr>\n",
  288.        "    <tr>\n",
  289.        "      <th>17</th>\n",
  290.        "      <td>id0802391</td>\n",
  291.        "      <td>1</td>\n",
  292.        "      <td>2016-03-06 17:44:45</td>\n",
  293.        "      <td>2016-03-06 17:52:14</td>\n",
  294.        "      <td>1</td>\n",
  295.        "      <td>-73.997208</td>\n",
  296.        "      <td>40.724072</td>\n",
  297.        "      <td>-74.000618</td>\n",
  298.        "      <td>40.732155</td>\n",
  299.        "      <td>N</td>\n",
  300.        "      <td>449</td>\n",
  301.        "    </tr>\n",
  302.        "    <tr>\n",
  303.        "      <th>18</th>\n",
  304.        "      <td>id2268459</td>\n",
  305.        "      <td>1</td>\n",
  306.        "      <td>2016-03-02 07:02:21</td>\n",
  307.        "      <td>2016-03-02 07:24:57</td>\n",
  308.        "      <td>1</td>\n",
  309.        "      <td>-73.985359</td>\n",
  310.        "      <td>40.738411</td>\n",
  311.        "      <td>-73.870422</td>\n",
  312.        "      <td>40.773682</td>\n",
  313.        "      <td>N</td>\n",
  314.        "      <td>1356</td>\n",
  315.        "    </tr>\n",
  316.        "    <tr>\n",
  317.        "      <th>19</th>\n",
  318.        "      <td>id2797773</td>\n",
  319.        "      <td>1</td>\n",
  320.        "      <td>2016-03-08 08:33:35</td>\n",
  321.        "      <td>2016-03-08 08:36:35</td>\n",
  322.        "      <td>1</td>\n",
  323.        "      <td>-73.967133</td>\n",
  324.        "      <td>40.793465</td>\n",
  325.        "      <td>-73.970390</td>\n",
  326.        "      <td>40.795750</td>\n",
  327.        "      <td>N</td>\n",
  328.        "      <td>180</td>\n",
  329.        "    </tr>\n",
  330.        "    <tr>\n",
  331.        "      <th>20</th>\n",
  332.        "      <td>id3817493</td>\n",
  333.        "      <td>2</td>\n",
  334.        "      <td>2016-03-14 14:57:56</td>\n",
  335.        "      <td>2016-03-14 15:15:26</td>\n",
  336.        "      <td>1</td>\n",
  337.        "      <td>-73.952881</td>\n",
  338.        "      <td>40.766468</td>\n",
  339.        "      <td>-73.978630</td>\n",
  340.        "      <td>40.761921</td>\n",
  341.        "      <td>N</td>\n",
  342.        "      <td>1050</td>\n",
  343.        "    </tr>\n",
  344.        "    <tr>\n",
  345.        "      <th>21</th>\n",
  346.        "      <td>id1971518</td>\n",
  347.        "      <td>1</td>\n",
  348.        "      <td>2016-03-12 13:04:28</td>\n",
  349.        "      <td>2016-03-12 13:14:33</td>\n",
  350.        "      <td>1</td>\n",
  351.        "      <td>-73.988976</td>\n",
  352.        "      <td>40.759205</td>\n",
  353.        "      <td>-73.973991</td>\n",
  354.        "      <td>40.760590</td>\n",
  355.        "      <td>N</td>\n",
  356.        "      <td>605</td>\n",
  357.        "    </tr>\n",
  358.        "    <tr>\n",
  359.        "      <th>22</th>\n",
  360.        "      <td>id3911487</td>\n",
  361.        "      <td>1</td>\n",
  362.        "      <td>2016-03-03 17:56:45</td>\n",
  363.        "      <td>2016-03-03 18:05:28</td>\n",
  364.        "      <td>1</td>\n",
  365.        "      <td>-73.962112</td>\n",
  366.        "      <td>40.776100</td>\n",
  367.        "      <td>-73.968521</td>\n",
  368.        "      <td>40.764408</td>\n",
  369.        "      <td>N</td>\n",
  370.        "      <td>523</td>\n",
  371.        "    </tr>\n",
  372.        "    <tr>\n",
  373.        "      <th>23</th>\n",
  374.        "      <td>id3276198</td>\n",
  375.        "      <td>2</td>\n",
  376.        "      <td>2016-03-14 20:31:12</td>\n",
  377.        "      <td>2016-03-14 20:36:18</td>\n",
  378.        "      <td>1</td>\n",
  379.        "      <td>-73.981911</td>\n",
  380.        "      <td>40.766880</td>\n",
  381.        "      <td>-73.982597</td>\n",
  382.        "      <td>40.777180</td>\n",
  383.        "      <td>N</td>\n",
  384.        "      <td>306</td>\n",
  385.        "    </tr>\n",
  386.        "    <tr>\n",
  387.        "      <th>24</th>\n",
  388.        "      <td>id1527676</td>\n",
  389.        "      <td>1</td>\n",
  390.        "      <td>2016-03-07 19:38:25</td>\n",
  391.        "      <td>2016-03-07 19:54:35</td>\n",
  392.        "      <td>2</td>\n",
  393.        "      <td>-73.986130</td>\n",
  394.        "      <td>40.759720</td>\n",
  395.        "      <td>-74.001488</td>\n",
  396.        "      <td>40.736065</td>\n",
  397.        "      <td>N</td>\n",
  398.        "      <td>970</td>\n",
  399.        "    </tr>\n",
  400.        "    <tr>\n",
  401.        "      <th>25</th>\n",
  402.        "      <td>id1146853</td>\n",
  403.        "      <td>2</td>\n",
  404.        "      <td>2016-03-05 02:59:30</td>\n",
  405.        "      <td>2016-03-05 03:20:50</td>\n",
  406.        "      <td>4</td>\n",
  407.        "      <td>-74.005394</td>\n",
  408.        "      <td>40.740810</td>\n",
  409.        "      <td>-73.950630</td>\n",
  410.        "      <td>40.821037</td>\n",
  411.        "      <td>N</td>\n",
  412.        "      <td>1280</td>\n",
  413.        "    </tr>\n",
  414.        "    <tr>\n",
  415.        "      <th>26</th>\n",
  416.        "      <td>id3714906</td>\n",
  417.        "      <td>1</td>\n",
  418.        "      <td>2016-03-01 08:33:57</td>\n",
  419.        "      <td>2016-03-01 08:40:44</td>\n",
  420.        "      <td>1</td>\n",
  421.        "      <td>-73.989494</td>\n",
  422.        "      <td>40.753677</td>\n",
  423.        "      <td>-73.988335</td>\n",
  424.        "      <td>40.745949</td>\n",
  425.        "      <td>N</td>\n",
  426.        "      <td>407</td>\n",
  427.        "    </tr>\n",
  428.        "    <tr>\n",
  429.        "      <th>27</th>\n",
  430.        "      <td>id1937745</td>\n",
  431.        "      <td>2</td>\n",
  432.        "      <td>2016-03-07 18:51:46</td>\n",
  433.        "      <td>2016-03-07 18:58:30</td>\n",
  434.        "      <td>2</td>\n",
  435.        "      <td>-73.990974</td>\n",
  436.        "      <td>40.760632</td>\n",
  437.        "      <td>-73.994720</td>\n",
  438.        "      <td>40.750450</td>\n",
  439.        "      <td>N</td>\n",
  440.        "      <td>404</td>\n",
  441.        "    </tr>\n",
  442.        "    <tr>\n",
  443.        "      <th>28</th>\n",
  444.        "      <td>id2672200</td>\n",
  445.        "      <td>1</td>\n",
  446.        "      <td>2016-03-08 10:59:46</td>\n",
  447.        "      <td>2016-03-08 11:21:50</td>\n",
  448.        "      <td>1</td>\n",
  449.        "      <td>-73.964325</td>\n",
  450.        "      <td>40.773594</td>\n",
  451.        "      <td>-73.989769</td>\n",
  452.        "      <td>40.738483</td>\n",
  453.        "      <td>N</td>\n",
  454.        "      <td>1324</td>\n",
  455.        "    </tr>\n",
  456.        "    <tr>\n",
  457.        "      <th>29</th>\n",
  458.        "      <td>id3200728</td>\n",
  459.        "      <td>2</td>\n",
  460.        "      <td>2016-03-03 10:14:57</td>\n",
  461.        "      <td>2016-03-03 10:32:51</td>\n",
  462.        "      <td>1</td>\n",
  463.        "      <td>-73.995880</td>\n",
  464.        "      <td>40.759190</td>\n",
  465.        "      <td>-73.979874</td>\n",
  466.        "      <td>40.752781</td>\n",
  467.        "      <td>N</td>\n",
  468.        "      <td>1074</td>\n",
  469.        "    </tr>\n",
  470.        "    <tr>\n",
  471.        "      <th>...</th>\n",
  472.        "      <td>...</td>\n",
  473.        "      <td>...</td>\n",
  474.        "      <td>...</td>\n",
  475.        "      <td>...</td>\n",
  476.        "      <td>...</td>\n",
  477.        "      <td>...</td>\n",
  478.        "      <td>...</td>\n",
  479.        "      <td>...</td>\n",
  480.        "      <td>...</td>\n",
  481.        "      <td>...</td>\n",
  482.        "      <td>...</td>\n",
  483.        "    </tr>\n",
  484.        "    <tr>\n",
  485.        "      <th>118155</th>\n",
  486.        "      <td>id2073065</td>\n",
  487.        "      <td>2</td>\n",
  488.        "      <td>2016-03-10 21:43:30</td>\n",
  489.        "      <td>2016-03-10 21:50:55</td>\n",
  490.        "      <td>1</td>\n",
  491.        "      <td>-73.989738</td>\n",
  492.        "      <td>40.756599</td>\n",
  493.        "      <td>-74.005318</td>\n",
  494.        "      <td>40.740231</td>\n",
  495.        "      <td>N</td>\n",
  496.        "      <td>445</td>\n",
  497.        "    </tr>\n",
  498.        "    <tr>\n",
  499.        "      <th>118156</th>\n",
  500.        "      <td>id1042737</td>\n",
  501.        "      <td>2</td>\n",
  502.        "      <td>2016-03-10 06:10:29</td>\n",
  503.        "      <td>2016-03-10 06:13:15</td>\n",
  504.        "      <td>1</td>\n",
  505.        "      <td>-73.985954</td>\n",
  506.        "      <td>40.752129</td>\n",
  507.        "      <td>-73.978592</td>\n",
  508.        "      <td>40.752602</td>\n",
  509.        "      <td>N</td>\n",
  510.        "      <td>166</td>\n",
  511.        "    </tr>\n",
  512.        "    <tr>\n",
  513.        "      <th>118157</th>\n",
  514.        "      <td>id0538386</td>\n",
  515.        "      <td>1</td>\n",
  516.        "      <td>2016-03-07 18:29:35</td>\n",
  517.        "      <td>2016-03-07 18:36:43</td>\n",
  518.        "      <td>1</td>\n",
  519.        "      <td>-73.976997</td>\n",
  520.        "      <td>40.755756</td>\n",
  521.        "      <td>-73.990540</td>\n",
  522.        "      <td>40.751163</td>\n",
  523.        "      <td>N</td>\n",
  524.        "      <td>428</td>\n",
  525.        "    </tr>\n",
  526.        "    <tr>\n",
  527.        "      <th>118158</th>\n",
  528.        "      <td>id2824253</td>\n",
  529.        "      <td>1</td>\n",
  530.        "      <td>2016-03-03 08:09:29</td>\n",
  531.        "      <td>2016-03-03 09:04:10</td>\n",
  532.        "      <td>1</td>\n",
  533.        "      <td>-73.961922</td>\n",
  534.        "      <td>40.800533</td>\n",
  535.        "      <td>-74.177269</td>\n",
  536.        "      <td>40.691124</td>\n",
  537.        "      <td>N</td>\n",
  538.        "      <td>3281</td>\n",
  539.        "    </tr>\n",
  540.        "    <tr>\n",
  541.        "      <th>118159</th>\n",
  542.        "      <td>id1333654</td>\n",
  543.        "      <td>1</td>\n",
  544.        "      <td>2016-03-05 01:22:46</td>\n",
  545.        "      <td>2016-03-05 01:34:27</td>\n",
  546.        "      <td>1</td>\n",
  547.        "      <td>-73.973228</td>\n",
  548.        "      <td>40.792824</td>\n",
  549.        "      <td>-73.945877</td>\n",
  550.        "      <td>40.777721</td>\n",
  551.        "      <td>N</td>\n",
  552.        "      <td>701</td>\n",
  553.        "    </tr>\n",
  554.        "    <tr>\n",
  555.        "      <th>118160</th>\n",
  556.        "      <td>id2731206</td>\n",
  557.        "      <td>1</td>\n",
  558.        "      <td>2016-03-13 20:14:32</td>\n",
  559.        "      <td>2016-03-13 20:23:39</td>\n",
  560.        "      <td>1</td>\n",
  561.        "      <td>-73.981178</td>\n",
  562.        "      <td>40.753674</td>\n",
  563.        "      <td>-74.004509</td>\n",
  564.        "      <td>40.747082</td>\n",
  565.        "      <td>N</td>\n",
  566.        "      <td>547</td>\n",
  567.        "    </tr>\n",
  568.        "    <tr>\n",
  569.        "      <th>118161</th>\n",
  570.        "      <td>id2838932</td>\n",
  571.        "      <td>1</td>\n",
  572.        "      <td>2016-03-13 17:03:03</td>\n",
  573.        "      <td>2016-03-13 17:11:10</td>\n",
  574.        "      <td>1</td>\n",
  575.        "      <td>-73.998634</td>\n",
  576.        "      <td>40.726131</td>\n",
  577.        "      <td>-73.985001</td>\n",
  578.        "      <td>40.727985</td>\n",
  579.        "      <td>N</td>\n",
  580.        "      <td>487</td>\n",
  581.        "    </tr>\n",
  582.        "    <tr>\n",
  583.        "      <th>118162</th>\n",
  584.        "      <td>id1486744</td>\n",
  585.        "      <td>2</td>\n",
  586.        "      <td>2016-03-09 10:45:19</td>\n",
  587.        "      <td>2016-03-09 11:18:58</td>\n",
  588.        "      <td>1</td>\n",
  589.        "      <td>-73.982903</td>\n",
  590.        "      <td>40.765659</td>\n",
  591.        "      <td>-73.872917</td>\n",
  592.        "      <td>40.774441</td>\n",
  593.        "      <td>N</td>\n",
  594.        "      <td>2019</td>\n",
  595.        "    </tr>\n",
  596.        "    <tr>\n",
  597.        "      <th>118163</th>\n",
  598.        "      <td>id0042357</td>\n",
  599.        "      <td>2</td>\n",
  600.        "      <td>2016-03-10 20:56:32</td>\n",
  601.        "      <td>2016-03-10 21:09:55</td>\n",
  602.        "      <td>1</td>\n",
  603.        "      <td>-73.993996</td>\n",
  604.        "      <td>40.741283</td>\n",
  605.        "      <td>-73.973114</td>\n",
  606.        "      <td>40.757057</td>\n",
  607.        "      <td>N</td>\n",
  608.        "      <td>803</td>\n",
  609.        "    </tr>\n",
  610.        "    <tr>\n",
  611.        "      <th>118164</th>\n",
  612.        "      <td>id3542490</td>\n",
  613.        "      <td>2</td>\n",
  614.        "      <td>2016-03-07 21:35:25</td>\n",
  615.        "      <td>2016-03-07 21:47:42</td>\n",
  616.        "      <td>1</td>\n",
  617.        "      <td>-73.996368</td>\n",
  618.        "      <td>40.723660</td>\n",
  619.        "      <td>-73.975166</td>\n",
  620.        "      <td>40.689621</td>\n",
  621.        "      <td>N</td>\n",
  622.        "      <td>737</td>\n",
  623.        "    </tr>\n",
  624.        "    <tr>\n",
  625.        "      <th>118165</th>\n",
  626.        "      <td>id0998702</td>\n",
  627.        "      <td>2</td>\n",
  628.        "      <td>2016-03-06 02:15:18</td>\n",
  629.        "      <td>2016-03-06 02:24:16</td>\n",
  630.        "      <td>1</td>\n",
  631.        "      <td>-73.963203</td>\n",
  632.        "      <td>40.671833</td>\n",
  633.        "      <td>-73.960808</td>\n",
  634.        "      <td>40.648785</td>\n",
  635.        "      <td>N</td>\n",
  636.        "      <td>538</td>\n",
  637.        "    </tr>\n",
  638.        "    <tr>\n",
  639.        "      <th>118166</th>\n",
  640.        "      <td>id0480063</td>\n",
  641.        "      <td>1</td>\n",
  642.        "      <td>2016-03-05 12:53:30</td>\n",
  643.        "      <td>2016-03-05 12:57:32</td>\n",
  644.        "      <td>1</td>\n",
  645.        "      <td>-73.976250</td>\n",
  646.        "      <td>40.728737</td>\n",
  647.        "      <td>-73.989166</td>\n",
  648.        "      <td>40.734058</td>\n",
  649.        "      <td>N</td>\n",
  650.        "      <td>242</td>\n",
  651.        "    </tr>\n",
  652.        "    <tr>\n",
  653.        "      <th>118167</th>\n",
  654.        "      <td>id2034624</td>\n",
  655.        "      <td>2</td>\n",
  656.        "      <td>2016-03-12 20:01:27</td>\n",
  657.        "      <td>2016-03-12 20:36:01</td>\n",
  658.        "      <td>5</td>\n",
  659.        "      <td>-73.781212</td>\n",
  660.        "      <td>40.644951</td>\n",
  661.        "      <td>-73.977303</td>\n",
  662.        "      <td>40.750721</td>\n",
  663.        "      <td>N</td>\n",
  664.        "      <td>2074</td>\n",
  665.        "    </tr>\n",
  666.        "    <tr>\n",
  667.        "      <th>118168</th>\n",
  668.        "      <td>id1203726</td>\n",
  669.        "      <td>2</td>\n",
  670.        "      <td>2016-03-03 17:19:23</td>\n",
  671.        "      <td>2016-03-03 17:27:35</td>\n",
  672.        "      <td>2</td>\n",
  673.        "      <td>-73.991798</td>\n",
  674.        "      <td>40.749840</td>\n",
  675.        "      <td>-73.993942</td>\n",
  676.        "      <td>40.735722</td>\n",
  677.        "      <td>N</td>\n",
  678.        "      <td>492</td>\n",
  679.        "    </tr>\n",
  680.        "    <tr>\n",
  681.        "      <th>118169</th>\n",
  682.        "      <td>id3860980</td>\n",
  683.        "      <td>2</td>\n",
  684.        "      <td>2016-03-11 23:59:25</td>\n",
  685.        "      <td>2016-03-12 00:10:12</td>\n",
  686.        "      <td>1</td>\n",
  687.        "      <td>-73.971542</td>\n",
  688.        "      <td>40.757721</td>\n",
  689.        "      <td>-73.991043</td>\n",
  690.        "      <td>40.750568</td>\n",
  691.        "      <td>N</td>\n",
  692.        "      <td>647</td>\n",
  693.        "    </tr>\n",
  694.        "    <tr>\n",
  695.        "      <th>118170</th>\n",
  696.        "      <td>id2924763</td>\n",
  697.        "      <td>2</td>\n",
  698.        "      <td>2016-03-04 23:24:33</td>\n",
  699.        "      <td>2016-03-04 23:31:02</td>\n",
  700.        "      <td>1</td>\n",
  701.        "      <td>-73.997643</td>\n",
  702.        "      <td>40.756622</td>\n",
  703.        "      <td>-73.984688</td>\n",
  704.        "      <td>40.761581</td>\n",
  705.        "      <td>N</td>\n",
  706.        "      <td>389</td>\n",
  707.        "    </tr>\n",
  708.        "    <tr>\n",
  709.        "      <th>118171</th>\n",
  710.        "      <td>id0873910</td>\n",
  711.        "      <td>1</td>\n",
  712.        "      <td>2016-03-10 12:12:01</td>\n",
  713.        "      <td>2016-03-10 12:25:52</td>\n",
  714.        "      <td>2</td>\n",
  715.        "      <td>-73.973885</td>\n",
  716.        "      <td>40.764061</td>\n",
  717.        "      <td>-73.990173</td>\n",
  718.        "      <td>40.741711</td>\n",
  719.        "      <td>N</td>\n",
  720.        "      <td>831</td>\n",
  721.        "    </tr>\n",
  722.        "    <tr>\n",
  723.        "      <th>118172</th>\n",
  724.        "      <td>id1250471</td>\n",
  725.        "      <td>1</td>\n",
  726.        "      <td>2016-03-04 12:21:19</td>\n",
  727.        "      <td>2016-03-04 12:37:49</td>\n",
  728.        "      <td>1</td>\n",
  729.        "      <td>-73.972527</td>\n",
  730.        "      <td>40.758957</td>\n",
  731.        "      <td>-73.956093</td>\n",
  732.        "      <td>40.785572</td>\n",
  733.        "      <td>N</td>\n",
  734.        "      <td>990</td>\n",
  735.        "    </tr>\n",
  736.        "    <tr>\n",
  737.        "      <th>118173</th>\n",
  738.        "      <td>id1192201</td>\n",
  739.        "      <td>1</td>\n",
  740.        "      <td>2016-03-05 03:56:36</td>\n",
  741.        "      <td>2016-03-05 04:05:39</td>\n",
  742.        "      <td>1</td>\n",
  743.        "      <td>-73.988785</td>\n",
  744.        "      <td>40.727390</td>\n",
  745.        "      <td>-73.999474</td>\n",
  746.        "      <td>40.744106</td>\n",
  747.        "      <td>N</td>\n",
  748.        "      <td>543</td>\n",
  749.        "    </tr>\n",
  750.        "    <tr>\n",
  751.        "      <th>118174</th>\n",
  752.        "      <td>id3453691</td>\n",
  753.        "      <td>2</td>\n",
  754.        "      <td>2016-03-07 18:11:54</td>\n",
  755.        "      <td>2016-03-07 18:29:09</td>\n",
  756.        "      <td>1</td>\n",
  757.        "      <td>-74.006531</td>\n",
  758.        "      <td>40.738232</td>\n",
  759.        "      <td>-73.985970</td>\n",
  760.        "      <td>40.726978</td>\n",
  761.        "      <td>N</td>\n",
  762.        "      <td>1035</td>\n",
  763.        "    </tr>\n",
  764.        "    <tr>\n",
  765.        "      <th>118175</th>\n",
  766.        "      <td>id2086152</td>\n",
  767.        "      <td>1</td>\n",
  768.        "      <td>2016-03-11 00:22:18</td>\n",
  769.        "      <td>2016-03-11 00:29:14</td>\n",
  770.        "      <td>2</td>\n",
  771.        "      <td>-73.986481</td>\n",
  772.        "      <td>40.725826</td>\n",
  773.        "      <td>-73.987297</td>\n",
  774.        "      <td>40.736004</td>\n",
  775.        "      <td>N</td>\n",
  776.        "      <td>416</td>\n",
  777.        "    </tr>\n",
  778.        "    <tr>\n",
  779.        "      <th>118176</th>\n",
  780.        "      <td>id2525150</td>\n",
  781.        "      <td>1</td>\n",
  782.        "      <td>2016-03-08 12:56:58</td>\n",
  783.        "      <td>2016-03-08 13:20:07</td>\n",
  784.        "      <td>1</td>\n",
  785.        "      <td>-73.978241</td>\n",
  786.        "      <td>40.744911</td>\n",
  787.        "      <td>-73.870483</td>\n",
  788.        "      <td>40.773777</td>\n",
  789.        "      <td>N</td>\n",
  790.        "      <td>1389</td>\n",
  791.        "    </tr>\n",
  792.        "    <tr>\n",
  793.        "      <th>118177</th>\n",
  794.        "      <td>id3780824</td>\n",
  795.        "      <td>2</td>\n",
  796.        "      <td>2016-03-12 01:08:45</td>\n",
  797.        "      <td>2016-03-12 01:23:02</td>\n",
  798.        "      <td>5</td>\n",
  799.        "      <td>-73.991463</td>\n",
  800.        "      <td>40.719189</td>\n",
  801.        "      <td>-73.949112</td>\n",
  802.        "      <td>40.711090</td>\n",
  803.        "      <td>N</td>\n",
  804.        "      <td>857</td>\n",
  805.        "    </tr>\n",
  806.        "    <tr>\n",
  807.        "      <th>118178</th>\n",
  808.        "      <td>id2669138</td>\n",
  809.        "      <td>2</td>\n",
  810.        "      <td>2016-03-05 09:41:26</td>\n",
  811.        "      <td>2016-03-05 09:52:15</td>\n",
  812.        "      <td>6</td>\n",
  813.        "      <td>-73.968597</td>\n",
  814.        "      <td>40.786320</td>\n",
  815.        "      <td>-73.981667</td>\n",
  816.        "      <td>40.754440</td>\n",
  817.        "      <td>N</td>\n",
  818.        "      <td>649</td>\n",
  819.        "    </tr>\n",
  820.        "    <tr>\n",
  821.        "      <th>118179</th>\n",
  822.        "      <td>id3087596</td>\n",
  823.        "      <td>2</td>\n",
  824.        "      <td>2016-03-13 15:25:46</td>\n",
  825.        "      <td>2016-03-13 15:34:52</td>\n",
  826.        "      <td>2</td>\n",
  827.        "      <td>-73.998871</td>\n",
  828.        "      <td>40.724781</td>\n",
  829.        "      <td>-73.983299</td>\n",
  830.        "      <td>40.743511</td>\n",
  831.        "      <td>N</td>\n",
  832.        "      <td>546</td>\n",
  833.        "    </tr>\n",
  834.        "    <tr>\n",
  835.        "      <th>118180</th>\n",
  836.        "      <td>id3274818</td>\n",
  837.        "      <td>2</td>\n",
  838.        "      <td>2016-03-11 21:04:31</td>\n",
  839.        "      <td>2016-03-11 21:08:41</td>\n",
  840.        "      <td>2</td>\n",
  841.        "      <td>-73.978233</td>\n",
  842.        "      <td>40.763203</td>\n",
  843.        "      <td>-73.982498</td>\n",
  844.        "      <td>40.766701</td>\n",
  845.        "      <td>N</td>\n",
  846.        "      <td>250</td>\n",
  847.        "    </tr>\n",
  848.        "    <tr>\n",
  849.        "      <th>118181</th>\n",
  850.        "      <td>id2224211</td>\n",
  851.        "      <td>1</td>\n",
  852.        "      <td>2016-03-06 10:42:32</td>\n",
  853.        "      <td>2016-03-06 10:46:57</td>\n",
  854.        "      <td>1</td>\n",
  855.        "      <td>-73.987488</td>\n",
  856.        "      <td>40.768585</td>\n",
  857.        "      <td>-73.979660</td>\n",
  858.        "      <td>40.759151</td>\n",
  859.        "      <td>N</td>\n",
  860.        "      <td>265</td>\n",
  861.        "    </tr>\n",
  862.        "    <tr>\n",
  863.        "      <th>118182</th>\n",
  864.        "      <td>id3537077</td>\n",
  865.        "      <td>2</td>\n",
  866.        "      <td>2016-03-11 23:48:13</td>\n",
  867.        "      <td>2016-03-12 00:01:36</td>\n",
  868.        "      <td>1</td>\n",
  869.        "      <td>-73.992729</td>\n",
  870.        "      <td>40.752811</td>\n",
  871.        "      <td>-73.987862</td>\n",
  872.        "      <td>40.731930</td>\n",
  873.        "      <td>N</td>\n",
  874.        "      <td>803</td>\n",
  875.        "    </tr>\n",
  876.        "    <tr>\n",
  877.        "      <th>118183</th>\n",
  878.        "      <td>id3482902</td>\n",
  879.        "      <td>1</td>\n",
  880.        "      <td>2016-03-01 07:21:04</td>\n",
  881.        "      <td>2016-03-01 07:23:36</td>\n",
  882.        "      <td>1</td>\n",
  883.        "      <td>-73.974693</td>\n",
  884.        "      <td>40.756088</td>\n",
  885.        "      <td>-73.969971</td>\n",
  886.        "      <td>40.762115</td>\n",
  887.        "      <td>N</td>\n",
  888.        "      <td>152</td>\n",
  889.        "    </tr>\n",
  890.        "    <tr>\n",
  891.        "      <th>118184</th>\n",
  892.        "      <td>id0469946</td>\n",
  893.        "      <td>2</td>\n",
  894.        "      <td>2016-03-06 11:04:48</td>\n",
  895.        "      <td>2016-03-06 11:17:45</td>\n",
  896.        "      <td>2</td>\n",
  897.        "      <td>-74.015572</td>\n",
  898.        "      <td>40.710892</td>\n",
  899.        "      <td>-73.996620</td>\n",
  900.        "      <td>40.743633</td>\n",
  901.        "      <td>N</td>\n",
  902.        "      <td>777</td>\n",
  903.        "    </tr>\n",
  904.        "  </tbody>\n",
  905.        "</table>\n",
  906.        "<p>118185 rows × 11 columns</p>\n",
  907.        "</div>"
  908.       ],
  909.       "text/plain": [
  910.        "               id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
  911.        "0       id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
  912.        "1       id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
  913.        "2       id3361153          1  2016-03-11 07:11:23  2016-03-11 07:20:09   \n",
  914.        "3       id2129090          1  2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
  915.        "4       id0256505          1  2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
  916.        "5       id0970832          1  2016-03-12 20:39:39  2016-03-12 21:05:40   \n",
  917.        "6       id2049424          2  2016-03-02 20:15:07  2016-03-02 20:37:43   \n",
  918.        "7       id0038484          2  2016-03-09 13:41:11  2016-03-09 13:53:27   \n",
  919.        "8       id3092788          2  2016-03-03 22:01:32  2016-03-03 22:17:44   \n",
  920.        "9       id3863815          2  2016-03-14 04:24:36  2016-03-14 04:37:11   \n",
  921.        "10      id1832737          2  2016-03-06 10:53:26  2016-03-06 10:59:30   \n",
  922.        "11      id2718231          1  2016-03-08 02:44:19  2016-03-08 03:04:35   \n",
  923.        "12      id3956459          2  2016-03-05 10:23:45  2016-03-05 10:45:52   \n",
  924.        "13      id2393811          1  2016-03-10 18:52:40  2016-03-10 19:08:43   \n",
  925.        "14      id2808378          1  2016-03-09 17:11:16  2016-03-09 17:28:43   \n",
  926.        "15      id1295254          1  2016-03-06 11:01:27  2016-03-06 11:08:29   \n",
  927.        "16      id1660823          2  2016-03-01 06:40:18  2016-03-01 07:01:37   \n",
  928.        "17      id0802391          1  2016-03-06 17:44:45  2016-03-06 17:52:14   \n",
  929.        "18      id2268459          1  2016-03-02 07:02:21  2016-03-02 07:24:57   \n",
  930.        "19      id2797773          1  2016-03-08 08:33:35  2016-03-08 08:36:35   \n",
  931.        "20      id3817493          2  2016-03-14 14:57:56  2016-03-14 15:15:26   \n",
  932.        "21      id1971518          1  2016-03-12 13:04:28  2016-03-12 13:14:33   \n",
  933.        "22      id3911487          1  2016-03-03 17:56:45  2016-03-03 18:05:28   \n",
  934.        "23      id3276198          2  2016-03-14 20:31:12  2016-03-14 20:36:18   \n",
  935.        "24      id1527676          1  2016-03-07 19:38:25  2016-03-07 19:54:35   \n",
  936.        "25      id1146853          2  2016-03-05 02:59:30  2016-03-05 03:20:50   \n",
  937.        "26      id3714906          1  2016-03-01 08:33:57  2016-03-01 08:40:44   \n",
  938.        "27      id1937745          2  2016-03-07 18:51:46  2016-03-07 18:58:30   \n",
  939.        "28      id2672200          1  2016-03-08 10:59:46  2016-03-08 11:21:50   \n",
  940.        "29      id3200728          2  2016-03-03 10:14:57  2016-03-03 10:32:51   \n",
  941.        "...           ...        ...                  ...                  ...   \n",
  942.        "118155  id2073065          2  2016-03-10 21:43:30  2016-03-10 21:50:55   \n",
  943.        "118156  id1042737          2  2016-03-10 06:10:29  2016-03-10 06:13:15   \n",
  944.        "118157  id0538386          1  2016-03-07 18:29:35  2016-03-07 18:36:43   \n",
  945.        "118158  id2824253          1  2016-03-03 08:09:29  2016-03-03 09:04:10   \n",
  946.        "118159  id1333654          1  2016-03-05 01:22:46  2016-03-05 01:34:27   \n",
  947.        "118160  id2731206          1  2016-03-13 20:14:32  2016-03-13 20:23:39   \n",
  948.        "118161  id2838932          1  2016-03-13 17:03:03  2016-03-13 17:11:10   \n",
  949.        "118162  id1486744          2  2016-03-09 10:45:19  2016-03-09 11:18:58   \n",
  950.        "118163  id0042357          2  2016-03-10 20:56:32  2016-03-10 21:09:55   \n",
  951.        "118164  id3542490          2  2016-03-07 21:35:25  2016-03-07 21:47:42   \n",
  952.        "118165  id0998702          2  2016-03-06 02:15:18  2016-03-06 02:24:16   \n",
  953.        "118166  id0480063          1  2016-03-05 12:53:30  2016-03-05 12:57:32   \n",
  954.        "118167  id2034624          2  2016-03-12 20:01:27  2016-03-12 20:36:01   \n",
  955.        "118168  id1203726          2  2016-03-03 17:19:23  2016-03-03 17:27:35   \n",
  956.        "118169  id3860980          2  2016-03-11 23:59:25  2016-03-12 00:10:12   \n",
  957.        "118170  id2924763          2  2016-03-04 23:24:33  2016-03-04 23:31:02   \n",
  958.        "118171  id0873910          1  2016-03-10 12:12:01  2016-03-10 12:25:52   \n",
  959.        "118172  id1250471          1  2016-03-04 12:21:19  2016-03-04 12:37:49   \n",
  960.        "118173  id1192201          1  2016-03-05 03:56:36  2016-03-05 04:05:39   \n",
  961.        "118174  id3453691          2  2016-03-07 18:11:54  2016-03-07 18:29:09   \n",
  962.        "118175  id2086152          1  2016-03-11 00:22:18  2016-03-11 00:29:14   \n",
  963.        "118176  id2525150          1  2016-03-08 12:56:58  2016-03-08 13:20:07   \n",
  964.        "118177  id3780824          2  2016-03-12 01:08:45  2016-03-12 01:23:02   \n",
  965.        "118178  id2669138          2  2016-03-05 09:41:26  2016-03-05 09:52:15   \n",
  966.        "118179  id3087596          2  2016-03-13 15:25:46  2016-03-13 15:34:52   \n",
  967.        "118180  id3274818          2  2016-03-11 21:04:31  2016-03-11 21:08:41   \n",
  968.        "118181  id2224211          1  2016-03-06 10:42:32  2016-03-06 10:46:57   \n",
  969.        "118182  id3537077          2  2016-03-11 23:48:13  2016-03-12 00:01:36   \n",
  970.        "118183  id3482902          1  2016-03-01 07:21:04  2016-03-01 07:23:36   \n",
  971.        "118184  id0469946          2  2016-03-06 11:04:48  2016-03-06 11:17:45   \n",
  972.        "\n",
  973.        "        passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
  974.        "0                     1        -73.982155        40.767937         -73.964630   \n",
  975.        "1                     1        -73.981049        40.744339         -73.973000   \n",
  976.        "2                     1        -73.994560        40.750526         -73.978500   \n",
  977.        "3                     1        -73.975090        40.758766         -73.953201   \n",
  978.        "4                     1        -73.994484        40.745087         -73.998993   \n",
  979.        "5                     1        -74.008247        40.747353         -73.979446   \n",
  980.        "6                     1        -73.963890        40.773651         -74.005112   \n",
  981.        "7                     2        -73.972855        40.764400         -73.971809   \n",
  982.        "8                     2        -73.984772        40.710571         -73.989410   \n",
  983.        "9                     3        -73.944359        40.714489         -73.910530   \n",
  984.        "10                    1        -73.984711        40.760181         -73.979561   \n",
  985.        "11                    1        -73.992500        40.740444         -73.840111   \n",
  986.        "12                    1        -73.986908        40.761608         -74.008408   \n",
  987.        "13                    1        -73.970581        40.799046         -73.989815   \n",
  988.        "14                    1        -73.978645        40.740932         -74.012695   \n",
  989.        "15                    1        -73.975983        40.757748         -73.982162   \n",
  990.        "16                    5        -73.982140        40.775326         -74.009850   \n",
  991.        "17                    1        -73.997208        40.724072         -74.000618   \n",
  992.        "18                    1        -73.985359        40.738411         -73.870422   \n",
  993.        "19                    1        -73.967133        40.793465         -73.970390   \n",
  994.        "20                    1        -73.952881        40.766468         -73.978630   \n",
  995.        "21                    1        -73.988976        40.759205         -73.973991   \n",
  996.        "22                    1        -73.962112        40.776100         -73.968521   \n",
  997.        "23                    1        -73.981911        40.766880         -73.982597   \n",
  998.        "24                    2        -73.986130        40.759720         -74.001488   \n",
  999.        "25                    4        -74.005394        40.740810         -73.950630   \n",
  1000.        "26                    1        -73.989494        40.753677         -73.988335   \n",
  1001.        "27                    2        -73.990974        40.760632         -73.994720   \n",
  1002.        "28                    1        -73.964325        40.773594         -73.989769   \n",
  1003.        "29                    1        -73.995880        40.759190         -73.979874   \n",
  1004.        "...                 ...               ...              ...                ...   \n",
  1005.        "118155                1        -73.989738        40.756599         -74.005318   \n",
  1006.        "118156                1        -73.985954        40.752129         -73.978592   \n",
  1007.        "118157                1        -73.976997        40.755756         -73.990540   \n",
  1008.        "118158                1        -73.961922        40.800533         -74.177269   \n",
  1009.        "118159                1        -73.973228        40.792824         -73.945877   \n",
  1010.        "118160                1        -73.981178        40.753674         -74.004509   \n",
  1011.        "118161                1        -73.998634        40.726131         -73.985001   \n",
  1012.        "118162                1        -73.982903        40.765659         -73.872917   \n",
  1013.        "118163                1        -73.993996        40.741283         -73.973114   \n",
  1014.        "118164                1        -73.996368        40.723660         -73.975166   \n",
  1015.        "118165                1        -73.963203        40.671833         -73.960808   \n",
  1016.        "118166                1        -73.976250        40.728737         -73.989166   \n",
  1017.        "118167                5        -73.781212        40.644951         -73.977303   \n",
  1018.        "118168                2        -73.991798        40.749840         -73.993942   \n",
  1019.        "118169                1        -73.971542        40.757721         -73.991043   \n",
  1020.        "118170                1        -73.997643        40.756622         -73.984688   \n",
  1021.        "118171                2        -73.973885        40.764061         -73.990173   \n",
  1022.        "118172                1        -73.972527        40.758957         -73.956093   \n",
  1023.        "118173                1        -73.988785        40.727390         -73.999474   \n",
  1024.        "118174                1        -74.006531        40.738232         -73.985970   \n",
  1025.        "118175                2        -73.986481        40.725826         -73.987297   \n",
  1026.        "118176                1        -73.978241        40.744911         -73.870483   \n",
  1027.        "118177                5        -73.991463        40.719189         -73.949112   \n",
  1028.        "118178                6        -73.968597        40.786320         -73.981667   \n",
  1029.        "118179                2        -73.998871        40.724781         -73.983299   \n",
  1030.        "118180                2        -73.978233        40.763203         -73.982498   \n",
  1031.        "118181                1        -73.987488        40.768585         -73.979660   \n",
  1032.        "118182                1        -73.992729        40.752811         -73.987862   \n",
  1033.        "118183                1        -73.974693        40.756088         -73.969971   \n",
  1034.        "118184                2        -74.015572        40.710892         -73.996620   \n",
  1035.        "\n",
  1036.        "        dropoff_latitude store_and_fwd_flag  trip_duration  \n",
  1037.        "0              40.765602                  N            455  \n",
  1038.        "1              40.789989                  N           1225  \n",
  1039.        "2              40.756191                  N            526  \n",
  1040.        "3              40.765068                  N           1346  \n",
  1041.        "4              40.722710                  N            695  \n",
  1042.        "5              40.718750                  N           1561  \n",
  1043.        "6              40.751492                  N           1356  \n",
  1044.        "7              40.757889                  N            736  \n",
  1045.        "8              40.730148                  N            972  \n",
  1046.        "9              40.709492                  N            755  \n",
  1047.        "10             40.752705                  N            364  \n",
  1048.        "11             40.719517                  N           1216  \n",
  1049.        "12             40.711620                  N           1327  \n",
  1050.        "13             40.767246                  N            963  \n",
  1051.        "14             40.701588                  N           1047  \n",
  1052.        "15             40.740749                  N            422  \n",
  1053.        "16             40.721699                  N           1279  \n",
  1054.        "17             40.732155                  N            449  \n",
  1055.        "18             40.773682                  N           1356  \n",
  1056.        "19             40.795750                  N            180  \n",
  1057.        "20             40.761921                  N           1050  \n",
  1058.        "21             40.760590                  N            605  \n",
  1059.        "22             40.764408                  N            523  \n",
  1060.        "23             40.777180                  N            306  \n",
  1061.        "24             40.736065                  N            970  \n",
  1062.        "25             40.821037                  N           1280  \n",
  1063.        "26             40.745949                  N            407  \n",
  1064.        "27             40.750450                  N            404  \n",
  1065.        "28             40.738483                  N           1324  \n",
  1066.        "29             40.752781                  N           1074  \n",
  1067.        "...                  ...                ...            ...  \n",
  1068.        "118155         40.740231                  N            445  \n",
  1069.        "118156         40.752602                  N            166  \n",
  1070.        "118157         40.751163                  N            428  \n",
  1071.        "118158         40.691124                  N           3281  \n",
  1072.        "118159         40.777721                  N            701  \n",
  1073.        "118160         40.747082                  N            547  \n",
  1074.        "118161         40.727985                  N            487  \n",
  1075.        "118162         40.774441                  N           2019  \n",
  1076.        "118163         40.757057                  N            803  \n",
  1077.        "118164         40.689621                  N            737  \n",
  1078.        "118165         40.648785                  N            538  \n",
  1079.        "118166         40.734058                  N            242  \n",
  1080.        "118167         40.750721                  N           2074  \n",
  1081.        "118168         40.735722                  N            492  \n",
  1082.        "118169         40.750568                  N            647  \n",
  1083.        "118170         40.761581                  N            389  \n",
  1084.        "118171         40.741711                  N            831  \n",
  1085.        "118172         40.785572                  N            990  \n",
  1086.        "118173         40.744106                  N            543  \n",
  1087.        "118174         40.726978                  N           1035  \n",
  1088.        "118175         40.736004                  N            416  \n",
  1089.        "118176         40.773777                  N           1389  \n",
  1090.        "118177         40.711090                  N            857  \n",
  1091.        "118178         40.754440                  N            649  \n",
  1092.        "118179         40.743511                  N            546  \n",
  1093.        "118180         40.766701                  N            250  \n",
  1094.        "118181         40.759151                  N            265  \n",
  1095.        "118182         40.731930                  N            803  \n",
  1096.        "118183         40.762115                  N            152  \n",
  1097.        "118184         40.743633                  N            777  \n",
  1098.        "\n",
  1099.        "[118185 rows x 11 columns]"
  1100.       ]
  1101.      },
  1102.      "execution_count": 1,
  1103.      "metadata": {},
  1104.      "output_type": "execute_result"
  1105.     }
  1106.    ],
  1107.    "source": [
  1108.     "import reverse_geocoder as rg\n",
  1109.     "import numpy\n",
  1110.     "import scipy\n",
  1111.     "import pandas as pd\n",
  1112.     "from datetime import datetime, time\n",
  1113.     "from geopy.distance import geodesic\n",
  1114.     "from os.path import join\n",
  1115.     "\n",
  1116.     "data_path = \"C:\\\\Users\\\\User\\\\OneDrive - sabanciuniv.edu\\\\Visual_Studio_Stuff\\\\python\\\\homework1\"\n",
  1117.     "filename = \"taxi-trips.csv\"\n",
  1118.     "\n",
  1119.     "df = pd.read_csv(join(data_path, filename), delimiter=\",\")\n",
  1120.     "df"
  1121.    ]
  1122.   },
  1123.   {
  1124.    "cell_type": "code",
  1125.    "execution_count": 2,
  1126.    "metadata": {},
  1127.    "outputs": [],
  1128.    "source": [
  1129.     "pick_coordinates = []\n",
  1130.     "drop_coordinates = []\n",
  1131.     "for i in range(len(df.index)):\n",
  1132.     "    pick_coordinates.append((df.iloc[i][6],df.iloc[i][5]))\n",
  1133.     "    drop_coordinates.append((df.iloc[i][8],df.iloc[i][7]))"
  1134.    ]
  1135.   },
  1136.   {
  1137.    "cell_type": "code",
  1138.    "execution_count": 3,
  1139.    "metadata": {},
  1140.    "outputs": [
  1141.     {
  1142.      "name": "stdout",
  1143.      "output_type": "stream",
  1144.      "text": [
  1145.       "Loading formatted geocoded file...\n"
  1146.      ]
  1147.     }
  1148.    ],
  1149.    "source": [
  1150.     "pick_results = rg.search(pick_coordinates)\n",
  1151.     "drop_results = rg.search(drop_coordinates)"
  1152.    ]
  1153.   },
  1154.   {
  1155.    "cell_type": "code",
  1156.    "execution_count": 5,
  1157.    "metadata": {},
  1158.    "outputs": [],
  1159.    "source": [
  1160.     "pickup_districts = []\n",
  1161.     "dropoff_districts = []\n",
  1162.     "for i in range(len(df.index)):\n",
  1163.     "    pickup_districts.append(pick_results[i].get('name'))\n",
  1164.     "    dropoff_districts.append(drop_results[i].get('name'))"
  1165.    ]
  1166.   },
  1167.   {
  1168.    "cell_type": "code",
  1169.    "execution_count": 143,
  1170.    "metadata": {},
  1171.    "outputs": [],
  1172.    "source": [
  1173.     "df[\"pickup_district\"] = pickup_districts\n",
  1174.     "df[\"dropoff_district\"] = dropoff_districts"
  1175.    ]
  1176.   },
  1177.   {
  1178.    "cell_type": "code",
  1179.    "execution_count": 10,
  1180.    "metadata": {},
  1181.    "outputs": [
  1182.     {
  1183.      "data": {
  1184.       "text/html": [
  1185.        "<div>\n",
  1186.        "<style scoped>\n",
  1187.        "    .dataframe tbody tr th:only-of-type {\n",
  1188.        "        vertical-align: middle;\n",
  1189.        "    }\n",
  1190.        "\n",
  1191.        "    .dataframe tbody tr th {\n",
  1192.        "        vertical-align: top;\n",
  1193.        "    }\n",
  1194.        "\n",
  1195.        "    .dataframe thead th {\n",
  1196.        "        text-align: right;\n",
  1197.        "    }\n",
  1198.        "</style>\n",
  1199.        "<table border=\"1\" class=\"dataframe\">\n",
  1200.        "  <thead>\n",
  1201.        "    <tr style=\"text-align: right;\">\n",
  1202.        "      <th></th>\n",
  1203.        "      <th>id</th>\n",
  1204.        "      <th>vendor_id</th>\n",
  1205.        "      <th>pickup_datetime</th>\n",
  1206.        "      <th>dropoff_datetime</th>\n",
  1207.        "      <th>passenger_count</th>\n",
  1208.        "      <th>pickup_longitude</th>\n",
  1209.        "      <th>pickup_latitude</th>\n",
  1210.        "      <th>dropoff_longitude</th>\n",
  1211.        "      <th>dropoff_latitude</th>\n",
  1212.        "      <th>store_and_fwd_flag</th>\n",
  1213.        "      <th>trip_duration</th>\n",
  1214.        "      <th>pickup_district</th>\n",
  1215.        "      <th>dropoff_district</th>\n",
  1216.        "      <th>distance</th>\n",
  1217.        "    </tr>\n",
  1218.        "  </thead>\n",
  1219.        "  <tbody>\n",
  1220.        "    <tr>\n",
  1221.        "      <th>0</th>\n",
  1222.        "      <td>id2875421</td>\n",
  1223.        "      <td>2</td>\n",
  1224.        "      <td>2016-03-14 17:24:55</td>\n",
  1225.        "      <td>2016-03-14 17:32:30</td>\n",
  1226.        "      <td>1</td>\n",
  1227.        "      <td>-73.982155</td>\n",
  1228.        "      <td>40.767937</td>\n",
  1229.        "      <td>-73.964630</td>\n",
  1230.        "      <td>40.765602</td>\n",
  1231.        "      <td>N</td>\n",
  1232.        "      <td>455</td>\n",
  1233.        "      <td>Manhattan</td>\n",
  1234.        "      <td>Manhattan</td>\n",
  1235.        "      <td>0.933406</td>\n",
  1236.        "    </tr>\n",
  1237.        "    <tr>\n",
  1238.        "      <th>1</th>\n",
  1239.        "      <td>id0012891</td>\n",
  1240.        "      <td>2</td>\n",
  1241.        "      <td>2016-03-10 21:45:01</td>\n",
  1242.        "      <td>2016-03-10 22:05:26</td>\n",
  1243.        "      <td>1</td>\n",
  1244.        "      <td>-73.981049</td>\n",
  1245.        "      <td>40.744339</td>\n",
  1246.        "      <td>-73.973000</td>\n",
  1247.        "      <td>40.789989</td>\n",
  1248.        "      <td>N</td>\n",
  1249.        "      <td>1225</td>\n",
  1250.        "      <td>Long Island City</td>\n",
  1251.        "      <td>Manhattan</td>\n",
  1252.        "      <td>3.178194</td>\n",
  1253.        "    </tr>\n",
  1254.        "    <tr>\n",
  1255.        "      <th>2</th>\n",
  1256.        "      <td>id3361153</td>\n",
  1257.        "      <td>1</td>\n",
  1258.        "      <td>2016-03-11 07:11:23</td>\n",
  1259.        "      <td>2016-03-11 07:20:09</td>\n",
  1260.        "      <td>1</td>\n",
  1261.        "      <td>-73.994560</td>\n",
  1262.        "      <td>40.750526</td>\n",
  1263.        "      <td>-73.978500</td>\n",
  1264.        "      <td>40.756191</td>\n",
  1265.        "      <td>N</td>\n",
  1266.        "      <td>526</td>\n",
  1267.        "      <td>Weehawken</td>\n",
  1268.        "      <td>Manhattan</td>\n",
  1269.        "      <td>0.928961</td>\n",
  1270.        "    </tr>\n",
  1271.        "    <tr>\n",
  1272.        "      <th>3</th>\n",
  1273.        "      <td>id2129090</td>\n",
  1274.        "      <td>1</td>\n",
  1275.        "      <td>2016-03-14 14:05:39</td>\n",
  1276.        "      <td>2016-03-14 14:28:05</td>\n",
  1277.        "      <td>1</td>\n",
  1278.        "      <td>-73.975090</td>\n",
  1279.        "      <td>40.758766</td>\n",
  1280.        "      <td>-73.953201</td>\n",
  1281.        "      <td>40.765068</td>\n",
  1282.        "      <td>N</td>\n",
  1283.        "      <td>1346</td>\n",
  1284.        "      <td>Manhattan</td>\n",
  1285.        "      <td>Long Island City</td>\n",
  1286.        "      <td>1.228003</td>\n",
  1287.        "    </tr>\n",
  1288.        "    <tr>\n",
  1289.        "      <th>4</th>\n",
  1290.        "      <td>id0256505</td>\n",
  1291.        "      <td>1</td>\n",
  1292.        "      <td>2016-03-14 15:04:38</td>\n",
  1293.        "      <td>2016-03-14 15:16:13</td>\n",
  1294.        "      <td>1</td>\n",
  1295.        "      <td>-73.994484</td>\n",
  1296.        "      <td>40.745087</td>\n",
  1297.        "      <td>-73.998993</td>\n",
  1298.        "      <td>40.722710</td>\n",
  1299.        "      <td>N</td>\n",
  1300.        "      <td>695</td>\n",
  1301.        "      <td>New York City</td>\n",
  1302.        "      <td>New York City</td>\n",
  1303.        "      <td>1.562103</td>\n",
  1304.        "    </tr>\n",
  1305.        "    <tr>\n",
  1306.        "      <th>5</th>\n",
  1307.        "      <td>id0970832</td>\n",
  1308.        "      <td>1</td>\n",
  1309.        "      <td>2016-03-12 20:39:39</td>\n",
  1310.        "      <td>2016-03-12 21:05:40</td>\n",
  1311.        "      <td>1</td>\n",
  1312.        "      <td>-74.008247</td>\n",
  1313.        "      <td>40.747353</td>\n",
  1314.        "      <td>-73.979446</td>\n",
  1315.        "      <td>40.718750</td>\n",
  1316.        "      <td>N</td>\n",
  1317.        "      <td>1561</td>\n",
  1318.        "      <td>Hoboken</td>\n",
  1319.        "      <td>New York City</td>\n",
  1320.        "      <td>2.486098</td>\n",
  1321.        "    </tr>\n",
  1322.        "    <tr>\n",
  1323.        "      <th>6</th>\n",
  1324.        "      <td>id2049424</td>\n",
  1325.        "      <td>2</td>\n",
  1326.        "      <td>2016-03-02 20:15:07</td>\n",
  1327.        "      <td>2016-03-02 20:37:43</td>\n",
  1328.        "      <td>1</td>\n",
  1329.        "      <td>-73.963890</td>\n",
  1330.        "      <td>40.773651</td>\n",
  1331.        "      <td>-74.005112</td>\n",
  1332.        "      <td>40.751492</td>\n",
  1333.        "      <td>N</td>\n",
  1334.        "      <td>1356</td>\n",
  1335.        "      <td>Manhattan</td>\n",
  1336.        "      <td>Weehawken</td>\n",
  1337.        "      <td>2.648687</td>\n",
  1338.        "    </tr>\n",
  1339.        "    <tr>\n",
  1340.        "      <th>7</th>\n",
  1341.        "      <td>id0038484</td>\n",
  1342.        "      <td>2</td>\n",
  1343.        "      <td>2016-03-09 13:41:11</td>\n",
  1344.        "      <td>2016-03-09 13:53:27</td>\n",
  1345.        "      <td>2</td>\n",
  1346.        "      <td>-73.972855</td>\n",
  1347.        "      <td>40.764400</td>\n",
  1348.        "      <td>-73.971809</td>\n",
  1349.        "      <td>40.757889</td>\n",
  1350.        "      <td>N</td>\n",
  1351.        "      <td>736</td>\n",
  1352.        "      <td>Manhattan</td>\n",
  1353.        "      <td>Manhattan</td>\n",
  1354.        "      <td>0.452659</td>\n",
  1355.        "    </tr>\n",
  1356.        "    <tr>\n",
  1357.        "      <th>8</th>\n",
  1358.        "      <td>id3092788</td>\n",
  1359.        "      <td>2</td>\n",
  1360.        "      <td>2016-03-03 22:01:32</td>\n",
  1361.        "      <td>2016-03-03 22:17:44</td>\n",
  1362.        "      <td>2</td>\n",
  1363.        "      <td>-73.984772</td>\n",
  1364.        "      <td>40.710571</td>\n",
  1365.        "      <td>-73.989410</td>\n",
  1366.        "      <td>40.730148</td>\n",
  1367.        "      <td>N</td>\n",
  1368.        "      <td>972</td>\n",
  1369.        "      <td>New York City</td>\n",
  1370.        "      <td>New York City</td>\n",
  1371.        "      <td>1.372636</td>\n",
  1372.        "    </tr>\n",
  1373.        "    <tr>\n",
  1374.        "      <th>9</th>\n",
  1375.        "      <td>id3863815</td>\n",
  1376.        "      <td>2</td>\n",
  1377.        "      <td>2016-03-14 04:24:36</td>\n",
  1378.        "      <td>2016-03-14 04:37:11</td>\n",
  1379.        "      <td>3</td>\n",
  1380.        "      <td>-73.944359</td>\n",
  1381.        "      <td>40.714489</td>\n",
  1382.        "      <td>-73.910530</td>\n",
  1383.        "      <td>40.709492</td>\n",
  1384.        "      <td>N</td>\n",
  1385.        "      <td>755</td>\n",
  1386.        "      <td>Long Island City</td>\n",
  1387.        "      <td>East New York</td>\n",
  1388.        "      <td>1.809375</td>\n",
  1389.        "    </tr>\n",
  1390.        "    <tr>\n",
  1391.        "      <th>10</th>\n",
  1392.        "      <td>id1832737</td>\n",
  1393.        "      <td>2</td>\n",
  1394.        "      <td>2016-03-06 10:53:26</td>\n",
  1395.        "      <td>2016-03-06 10:59:30</td>\n",
  1396.        "      <td>1</td>\n",
  1397.        "      <td>-73.984711</td>\n",
  1398.        "      <td>40.760181</td>\n",
  1399.        "      <td>-73.979561</td>\n",
  1400.        "      <td>40.752705</td>\n",
  1401.        "      <td>N</td>\n",
  1402.        "      <td>364</td>\n",
  1403.        "      <td>Manhattan</td>\n",
  1404.        "      <td>Long Island City</td>\n",
  1405.        "      <td>0.582402</td>\n",
  1406.        "    </tr>\n",
  1407.        "    <tr>\n",
  1408.        "      <th>11</th>\n",
  1409.        "      <td>id2718231</td>\n",
  1410.        "      <td>1</td>\n",
  1411.        "      <td>2016-03-08 02:44:19</td>\n",
  1412.        "      <td>2016-03-08 03:04:35</td>\n",
  1413.        "      <td>1</td>\n",
  1414.        "      <td>-73.992500</td>\n",
  1415.        "      <td>40.740444</td>\n",
  1416.        "      <td>-73.840111</td>\n",
  1417.        "      <td>40.719517</td>\n",
  1418.        "      <td>N</td>\n",
  1419.        "      <td>1216</td>\n",
  1420.        "      <td>New York City</td>\n",
  1421.        "      <td>Borough of Queens</td>\n",
  1422.        "      <td>8.128519</td>\n",
  1423.        "    </tr>\n",
  1424.        "    <tr>\n",
  1425.        "      <th>12</th>\n",
  1426.        "      <td>id3956459</td>\n",
  1427.        "      <td>2</td>\n",
  1428.        "      <td>2016-03-05 10:23:45</td>\n",
  1429.        "      <td>2016-03-05 10:45:52</td>\n",
  1430.        "      <td>1</td>\n",
  1431.        "      <td>-73.986908</td>\n",
  1432.        "      <td>40.761608</td>\n",
  1433.        "      <td>-74.008408</td>\n",
  1434.        "      <td>40.711620</td>\n",
  1435.        "      <td>N</td>\n",
  1436.        "      <td>1327</td>\n",
  1437.        "      <td>Manhattan</td>\n",
  1438.        "      <td>New York City</td>\n",
  1439.        "      <td>3.629181</td>\n",
  1440.        "    </tr>\n",
  1441.        "    <tr>\n",
  1442.        "      <th>13</th>\n",
  1443.        "      <td>id2393811</td>\n",
  1444.        "      <td>1</td>\n",
  1445.        "      <td>2016-03-10 18:52:40</td>\n",
  1446.        "      <td>2016-03-10 19:08:43</td>\n",
  1447.        "      <td>1</td>\n",
  1448.        "      <td>-73.970581</td>\n",
  1449.        "      <td>40.799046</td>\n",
  1450.        "      <td>-73.989815</td>\n",
  1451.        "      <td>40.767246</td>\n",
  1452.        "      <td>N</td>\n",
  1453.        "      <td>963</td>\n",
  1454.        "      <td>Manhattan</td>\n",
  1455.        "      <td>Guttenberg</td>\n",
  1456.        "      <td>2.415044</td>\n",
  1457.        "    </tr>\n",
  1458.        "    <tr>\n",
  1459.        "      <th>14</th>\n",
  1460.        "      <td>id2808378</td>\n",
  1461.        "      <td>1</td>\n",
  1462.        "      <td>2016-03-09 17:11:16</td>\n",
  1463.        "      <td>2016-03-09 17:28:43</td>\n",
  1464.        "      <td>1</td>\n",
  1465.        "      <td>-73.978645</td>\n",
  1466.        "      <td>40.740932</td>\n",
  1467.        "      <td>-74.012695</td>\n",
  1468.        "      <td>40.701588</td>\n",
  1469.        "      <td>N</td>\n",
  1470.        "      <td>1047</td>\n",
  1471.        "      <td>Long Island City</td>\n",
  1472.        "      <td>New York City</td>\n",
  1473.        "      <td>3.250545</td>\n",
  1474.        "    </tr>\n",
  1475.        "    <tr>\n",
  1476.        "      <th>15</th>\n",
  1477.        "      <td>id1295254</td>\n",
  1478.        "      <td>1</td>\n",
  1479.        "      <td>2016-03-06 11:01:27</td>\n",
  1480.        "      <td>2016-03-06 11:08:29</td>\n",
  1481.        "      <td>1</td>\n",
  1482.        "      <td>-73.975983</td>\n",
  1483.        "      <td>40.757748</td>\n",
  1484.        "      <td>-73.982162</td>\n",
  1485.        "      <td>40.740749</td>\n",
  1486.        "      <td>N</td>\n",
  1487.        "      <td>422</td>\n",
  1488.        "      <td>Manhattan</td>\n",
  1489.        "      <td>Long Island City</td>\n",
  1490.        "      <td>1.216934</td>\n",
  1491.        "    </tr>\n",
  1492.        "    <tr>\n",
  1493.        "      <th>16</th>\n",
  1494.        "      <td>id1660823</td>\n",
  1495.        "      <td>2</td>\n",
  1496.        "      <td>2016-03-01 06:40:18</td>\n",
  1497.        "      <td>2016-03-01 07:01:37</td>\n",
  1498.        "      <td>5</td>\n",
  1499.        "      <td>-73.982140</td>\n",
  1500.        "      <td>40.775326</td>\n",
  1501.        "      <td>-74.009850</td>\n",
  1502.        "      <td>40.721699</td>\n",
  1503.        "      <td>N</td>\n",
  1504.        "      <td>1279</td>\n",
  1505.        "      <td>Manhattan</td>\n",
  1506.        "      <td>New York City</td>\n",
  1507.        "      <td>3.975872</td>\n",
  1508.        "    </tr>\n",
  1509.        "    <tr>\n",
  1510.        "      <th>17</th>\n",
  1511.        "      <td>id0802391</td>\n",
  1512.        "      <td>1</td>\n",
  1513.        "      <td>2016-03-06 17:44:45</td>\n",
  1514.        "      <td>2016-03-06 17:52:14</td>\n",
  1515.        "      <td>1</td>\n",
  1516.        "      <td>-73.997208</td>\n",
  1517.        "      <td>40.724072</td>\n",
  1518.        "      <td>-74.000618</td>\n",
  1519.        "      <td>40.732155</td>\n",
  1520.        "      <td>N</td>\n",
  1521.        "      <td>449</td>\n",
  1522.        "      <td>New York City</td>\n",
  1523.        "      <td>New York City</td>\n",
  1524.        "      <td>0.585795</td>\n",
  1525.        "    </tr>\n",
  1526.        "    <tr>\n",
  1527.        "      <th>18</th>\n",
  1528.        "      <td>id2268459</td>\n",
  1529.        "      <td>1</td>\n",
  1530.        "      <td>2016-03-02 07:02:21</td>\n",
  1531.        "      <td>2016-03-02 07:24:57</td>\n",
  1532.        "      <td>1</td>\n",
  1533.        "      <td>-73.985359</td>\n",
  1534.        "      <td>40.738411</td>\n",
  1535.        "      <td>-73.870422</td>\n",
  1536.        "      <td>40.773682</td>\n",
  1537.        "      <td>N</td>\n",
  1538.        "      <td>1356</td>\n",
  1539.        "      <td>New York City</td>\n",
  1540.        "      <td>The Bronx</td>\n",
  1541.        "      <td>6.503464</td>\n",
  1542.        "    </tr>\n",
  1543.        "    <tr>\n",
  1544.        "      <th>19</th>\n",
  1545.        "      <td>id2797773</td>\n",
  1546.        "      <td>1</td>\n",
  1547.        "      <td>2016-03-08 08:33:35</td>\n",
  1548.        "      <td>2016-03-08 08:36:35</td>\n",
  1549.        "      <td>1</td>\n",
  1550.        "      <td>-73.967133</td>\n",
  1551.        "      <td>40.793465</td>\n",
  1552.        "      <td>-73.970390</td>\n",
  1553.        "      <td>40.795750</td>\n",
  1554.        "      <td>N</td>\n",
  1555.        "      <td>180</td>\n",
  1556.        "      <td>Manhattan</td>\n",
  1557.        "      <td>Manhattan</td>\n",
  1558.        "      <td>0.232480</td>\n",
  1559.        "    </tr>\n",
  1560.        "    <tr>\n",
  1561.        "      <th>20</th>\n",
  1562.        "      <td>id3817493</td>\n",
  1563.        "      <td>2</td>\n",
  1564.        "      <td>2016-03-14 14:57:56</td>\n",
  1565.        "      <td>2016-03-14 15:15:26</td>\n",
  1566.        "      <td>1</td>\n",
  1567.        "      <td>-73.952881</td>\n",
  1568.        "      <td>40.766468</td>\n",
  1569.        "      <td>-73.978630</td>\n",
  1570.        "      <td>40.761921</td>\n",
  1571.        "      <td>N</td>\n",
  1572.        "      <td>1050</td>\n",
  1573.        "      <td>Manhattan</td>\n",
  1574.        "      <td>Manhattan</td>\n",
  1575.        "      <td>1.386892</td>\n",
  1576.        "    </tr>\n",
  1577.        "    <tr>\n",
  1578.        "      <th>21</th>\n",
  1579.        "      <td>id1971518</td>\n",
  1580.        "      <td>1</td>\n",
  1581.        "      <td>2016-03-12 13:04:28</td>\n",
  1582.        "      <td>2016-03-12 13:14:33</td>\n",
  1583.        "      <td>1</td>\n",
  1584.        "      <td>-73.988976</td>\n",
  1585.        "      <td>40.759205</td>\n",
  1586.        "      <td>-73.973991</td>\n",
  1587.        "      <td>40.760590</td>\n",
  1588.        "      <td>N</td>\n",
  1589.        "      <td>605</td>\n",
  1590.        "      <td>Weehawken</td>\n",
  1591.        "      <td>Manhattan</td>\n",
  1592.        "      <td>0.791979</td>\n",
  1593.        "    </tr>\n",
  1594.        "    <tr>\n",
  1595.        "      <th>22</th>\n",
  1596.        "      <td>id3911487</td>\n",
  1597.        "      <td>1</td>\n",
  1598.        "      <td>2016-03-03 17:56:45</td>\n",
  1599.        "      <td>2016-03-03 18:05:28</td>\n",
  1600.        "      <td>1</td>\n",
  1601.        "      <td>-73.962112</td>\n",
  1602.        "      <td>40.776100</td>\n",
  1603.        "      <td>-73.968521</td>\n",
  1604.        "      <td>40.764408</td>\n",
  1605.        "      <td>N</td>\n",
  1606.        "      <td>523</td>\n",
  1607.        "      <td>Manhattan</td>\n",
  1608.        "      <td>Manhattan</td>\n",
  1609.        "      <td>0.874034</td>\n",
  1610.        "    </tr>\n",
  1611.        "    <tr>\n",
  1612.        "      <th>23</th>\n",
  1613.        "      <td>id3276198</td>\n",
  1614.        "      <td>2</td>\n",
  1615.        "      <td>2016-03-14 20:31:12</td>\n",
  1616.        "      <td>2016-03-14 20:36:18</td>\n",
  1617.        "      <td>1</td>\n",
  1618.        "      <td>-73.981911</td>\n",
  1619.        "      <td>40.766880</td>\n",
  1620.        "      <td>-73.982597</td>\n",
  1621.        "      <td>40.777180</td>\n",
  1622.        "      <td>N</td>\n",
  1623.        "      <td>306</td>\n",
  1624.        "      <td>Manhattan</td>\n",
  1625.        "      <td>Manhattan</td>\n",
  1626.        "      <td>0.711621</td>\n",
  1627.        "    </tr>\n",
  1628.        "    <tr>\n",
  1629.        "      <th>24</th>\n",
  1630.        "      <td>id1527676</td>\n",
  1631.        "      <td>1</td>\n",
  1632.        "      <td>2016-03-07 19:38:25</td>\n",
  1633.        "      <td>2016-03-07 19:54:35</td>\n",
  1634.        "      <td>2</td>\n",
  1635.        "      <td>-73.986130</td>\n",
  1636.        "      <td>40.759720</td>\n",
  1637.        "      <td>-74.001488</td>\n",
  1638.        "      <td>40.736065</td>\n",
  1639.        "      <td>N</td>\n",
  1640.        "      <td>970</td>\n",
  1641.        "      <td>Manhattan</td>\n",
  1642.        "      <td>New York City</td>\n",
  1643.        "      <td>1.820388</td>\n",
  1644.        "    </tr>\n",
  1645.        "    <tr>\n",
  1646.        "      <th>25</th>\n",
  1647.        "      <td>id1146853</td>\n",
  1648.        "      <td>2</td>\n",
  1649.        "      <td>2016-03-05 02:59:30</td>\n",
  1650.        "      <td>2016-03-05 03:20:50</td>\n",
  1651.        "      <td>4</td>\n",
  1652.        "      <td>-74.005394</td>\n",
  1653.        "      <td>40.740810</td>\n",
  1654.        "      <td>-73.950630</td>\n",
  1655.        "      <td>40.821037</td>\n",
  1656.        "      <td>N</td>\n",
  1657.        "      <td>1280</td>\n",
  1658.        "      <td>New York City</td>\n",
  1659.        "      <td>Edgewater</td>\n",
  1660.        "      <td>6.236767</td>\n",
  1661.        "    </tr>\n",
  1662.        "    <tr>\n",
  1663.        "      <th>26</th>\n",
  1664.        "      <td>id3714906</td>\n",
  1665.        "      <td>1</td>\n",
  1666.        "      <td>2016-03-01 08:33:57</td>\n",
  1667.        "      <td>2016-03-01 08:40:44</td>\n",
  1668.        "      <td>1</td>\n",
  1669.        "      <td>-73.989494</td>\n",
  1670.        "      <td>40.753677</td>\n",
  1671.        "      <td>-73.988335</td>\n",
  1672.        "      <td>40.745949</td>\n",
  1673.        "      <td>N</td>\n",
  1674.        "      <td>407</td>\n",
  1675.        "      <td>Weehawken</td>\n",
  1676.        "      <td>New York City</td>\n",
  1677.        "      <td>0.536754</td>\n",
  1678.        "    </tr>\n",
  1679.        "    <tr>\n",
  1680.        "      <th>27</th>\n",
  1681.        "      <td>id1937745</td>\n",
  1682.        "      <td>2</td>\n",
  1683.        "      <td>2016-03-07 18:51:46</td>\n",
  1684.        "      <td>2016-03-07 18:58:30</td>\n",
  1685.        "      <td>2</td>\n",
  1686.        "      <td>-73.990974</td>\n",
  1687.        "      <td>40.760632</td>\n",
  1688.        "      <td>-73.994720</td>\n",
  1689.        "      <td>40.750450</td>\n",
  1690.        "      <td>N</td>\n",
  1691.        "      <td>404</td>\n",
  1692.        "      <td>Weehawken</td>\n",
  1693.        "      <td>Weehawken</td>\n",
  1694.        "      <td>0.729526</td>\n",
  1695.        "    </tr>\n",
  1696.        "    <tr>\n",
  1697.        "      <th>28</th>\n",
  1698.        "      <td>id2672200</td>\n",
  1699.        "      <td>1</td>\n",
  1700.        "      <td>2016-03-08 10:59:46</td>\n",
  1701.        "      <td>2016-03-08 11:21:50</td>\n",
  1702.        "      <td>1</td>\n",
  1703.        "      <td>-73.964325</td>\n",
  1704.        "      <td>40.773594</td>\n",
  1705.        "      <td>-73.989769</td>\n",
  1706.        "      <td>40.738483</td>\n",
  1707.        "      <td>N</td>\n",
  1708.        "      <td>1324</td>\n",
  1709.        "      <td>Manhattan</td>\n",
  1710.        "      <td>New York City</td>\n",
  1711.        "      <td>2.766229</td>\n",
  1712.        "    </tr>\n",
  1713.        "    <tr>\n",
  1714.        "      <th>29</th>\n",
  1715.        "      <td>id3200728</td>\n",
  1716.        "      <td>2</td>\n",
  1717.        "      <td>2016-03-03 10:14:57</td>\n",
  1718.        "      <td>2016-03-03 10:32:51</td>\n",
  1719.        "      <td>1</td>\n",
  1720.        "      <td>-73.995880</td>\n",
  1721.        "      <td>40.759190</td>\n",
  1722.        "      <td>-73.979874</td>\n",
  1723.        "      <td>40.752781</td>\n",
  1724.        "      <td>N</td>\n",
  1725.        "      <td>1074</td>\n",
  1726.        "      <td>Weehawken</td>\n",
  1727.        "      <td>Long Island City</td>\n",
  1728.        "      <td>0.949190</td>\n",
  1729.        "    </tr>\n",
  1730.        "    <tr>\n",
  1731.        "      <th>...</th>\n",
  1732.        "      <td>...</td>\n",
  1733.        "      <td>...</td>\n",
  1734.        "      <td>...</td>\n",
  1735.        "      <td>...</td>\n",
  1736.        "      <td>...</td>\n",
  1737.        "      <td>...</td>\n",
  1738.        "      <td>...</td>\n",
  1739.        "      <td>...</td>\n",
  1740.        "      <td>...</td>\n",
  1741.        "      <td>...</td>\n",
  1742.        "      <td>...</td>\n",
  1743.        "      <td>...</td>\n",
  1744.        "      <td>...</td>\n",
  1745.        "      <td>...</td>\n",
  1746.        "    </tr>\n",
  1747.        "    <tr>\n",
  1748.        "      <th>118155</th>\n",
  1749.        "      <td>id2073065</td>\n",
  1750.        "      <td>2</td>\n",
  1751.        "      <td>2016-03-10 21:43:30</td>\n",
  1752.        "      <td>2016-03-10 21:50:55</td>\n",
  1753.        "      <td>1</td>\n",
  1754.        "      <td>-73.989738</td>\n",
  1755.        "      <td>40.756599</td>\n",
  1756.        "      <td>-74.005318</td>\n",
  1757.        "      <td>40.740231</td>\n",
  1758.        "      <td>N</td>\n",
  1759.        "      <td>445</td>\n",
  1760.        "      <td>Weehawken</td>\n",
  1761.        "      <td>New York City</td>\n",
  1762.        "      <td>1.394332</td>\n",
  1763.        "    </tr>\n",
  1764.        "    <tr>\n",
  1765.        "      <th>118156</th>\n",
  1766.        "      <td>id1042737</td>\n",
  1767.        "      <td>2</td>\n",
  1768.        "      <td>2016-03-10 06:10:29</td>\n",
  1769.        "      <td>2016-03-10 06:13:15</td>\n",
  1770.        "      <td>1</td>\n",
  1771.        "      <td>-73.985954</td>\n",
  1772.        "      <td>40.752129</td>\n",
  1773.        "      <td>-73.978592</td>\n",
  1774.        "      <td>40.752602</td>\n",
  1775.        "      <td>N</td>\n",
  1776.        "      <td>166</td>\n",
  1777.        "      <td>Manhattan</td>\n",
  1778.        "      <td>Long Island City</td>\n",
  1779.        "      <td>0.387712</td>\n",
  1780.        "    </tr>\n",
  1781.        "    <tr>\n",
  1782.        "      <th>118157</th>\n",
  1783.        "      <td>id0538386</td>\n",
  1784.        "      <td>1</td>\n",
  1785.        "      <td>2016-03-07 18:29:35</td>\n",
  1786.        "      <td>2016-03-07 18:36:43</td>\n",
  1787.        "      <td>1</td>\n",
  1788.        "      <td>-73.976997</td>\n",
  1789.        "      <td>40.755756</td>\n",
  1790.        "      <td>-73.990540</td>\n",
  1791.        "      <td>40.751163</td>\n",
  1792.        "      <td>N</td>\n",
  1793.        "      <td>428</td>\n",
  1794.        "      <td>Manhattan</td>\n",
  1795.        "      <td>Weehawken</td>\n",
  1796.        "      <td>0.778074</td>\n",
  1797.        "    </tr>\n",
  1798.        "    <tr>\n",
  1799.        "      <th>118158</th>\n",
  1800.        "      <td>id2824253</td>\n",
  1801.        "      <td>1</td>\n",
  1802.        "      <td>2016-03-03 08:09:29</td>\n",
  1803.        "      <td>2016-03-03 09:04:10</td>\n",
  1804.        "      <td>1</td>\n",
  1805.        "      <td>-73.961922</td>\n",
  1806.        "      <td>40.800533</td>\n",
  1807.        "      <td>-74.177269</td>\n",
  1808.        "      <td>40.691124</td>\n",
  1809.        "      <td>N</td>\n",
  1810.        "      <td>3281</td>\n",
  1811.        "      <td>Manhattan</td>\n",
  1812.        "      <td>Elizabeth</td>\n",
  1813.        "      <td>13.590991</td>\n",
  1814.        "    </tr>\n",
  1815.        "    <tr>\n",
  1816.        "      <th>118159</th>\n",
  1817.        "      <td>id1333654</td>\n",
  1818.        "      <td>1</td>\n",
  1819.        "      <td>2016-03-05 01:22:46</td>\n",
  1820.        "      <td>2016-03-05 01:34:27</td>\n",
  1821.        "      <td>1</td>\n",
  1822.        "      <td>-73.973228</td>\n",
  1823.        "      <td>40.792824</td>\n",
  1824.        "      <td>-73.945877</td>\n",
  1825.        "      <td>40.777721</td>\n",
  1826.        "      <td>N</td>\n",
  1827.        "      <td>701</td>\n",
  1828.        "      <td>Manhattan</td>\n",
  1829.        "      <td>Manhattan</td>\n",
  1830.        "      <td>1.773104</td>\n",
  1831.        "    </tr>\n",
  1832.        "    <tr>\n",
  1833.        "      <th>118160</th>\n",
  1834.        "      <td>id2731206</td>\n",
  1835.        "      <td>1</td>\n",
  1836.        "      <td>2016-03-13 20:14:32</td>\n",
  1837.        "      <td>2016-03-13 20:23:39</td>\n",
  1838.        "      <td>1</td>\n",
  1839.        "      <td>-73.981178</td>\n",
  1840.        "      <td>40.753674</td>\n",
  1841.        "      <td>-74.004509</td>\n",
  1842.        "      <td>40.747082</td>\n",
  1843.        "      <td>N</td>\n",
  1844.        "      <td>547</td>\n",
  1845.        "      <td>Manhattan</td>\n",
  1846.        "      <td>Weehawken</td>\n",
  1847.        "      <td>1.306062</td>\n",
  1848.        "    </tr>\n",
  1849.        "    <tr>\n",
  1850.        "      <th>118161</th>\n",
  1851.        "      <td>id2838932</td>\n",
  1852.        "      <td>1</td>\n",
  1853.        "      <td>2016-03-13 17:03:03</td>\n",
  1854.        "      <td>2016-03-13 17:11:10</td>\n",
  1855.        "      <td>1</td>\n",
  1856.        "      <td>-73.998634</td>\n",
  1857.        "      <td>40.726131</td>\n",
  1858.        "      <td>-73.985001</td>\n",
  1859.        "      <td>40.727985</td>\n",
  1860.        "      <td>N</td>\n",
  1861.        "      <td>487</td>\n",
  1862.        "      <td>New York City</td>\n",
  1863.        "      <td>New York City</td>\n",
  1864.        "      <td>0.727036</td>\n",
  1865.        "    </tr>\n",
  1866.        "    <tr>\n",
  1867.        "      <th>118162</th>\n",
  1868.        "      <td>id1486744</td>\n",
  1869.        "      <td>2</td>\n",
  1870.        "      <td>2016-03-09 10:45:19</td>\n",
  1871.        "      <td>2016-03-09 11:18:58</td>\n",
  1872.        "      <td>1</td>\n",
  1873.        "      <td>-73.982903</td>\n",
  1874.        "      <td>40.765659</td>\n",
  1875.        "      <td>-73.872917</td>\n",
  1876.        "      <td>40.774441</td>\n",
  1877.        "      <td>N</td>\n",
  1878.        "      <td>2019</td>\n",
  1879.        "      <td>Manhattan</td>\n",
  1880.        "      <td>The Bronx</td>\n",
  1881.        "      <td>5.801611</td>\n",
  1882.        "    </tr>\n",
  1883.        "    <tr>\n",
  1884.        "      <th>118163</th>\n",
  1885.        "      <td>id0042357</td>\n",
  1886.        "      <td>2</td>\n",
  1887.        "      <td>2016-03-10 20:56:32</td>\n",
  1888.        "      <td>2016-03-10 21:09:55</td>\n",
  1889.        "      <td>1</td>\n",
  1890.        "      <td>-73.993996</td>\n",
  1891.        "      <td>40.741283</td>\n",
  1892.        "      <td>-73.973114</td>\n",
  1893.        "      <td>40.757057</td>\n",
  1894.        "      <td>N</td>\n",
  1895.        "      <td>803</td>\n",
  1896.        "      <td>New York City</td>\n",
  1897.        "      <td>Manhattan</td>\n",
  1898.        "      <td>1.544497</td>\n",
  1899.        "    </tr>\n",
  1900.        "    <tr>\n",
  1901.        "      <th>118164</th>\n",
  1902.        "      <td>id3542490</td>\n",
  1903.        "      <td>2</td>\n",
  1904.        "      <td>2016-03-07 21:35:25</td>\n",
  1905.        "      <td>2016-03-07 21:47:42</td>\n",
  1906.        "      <td>1</td>\n",
  1907.        "      <td>-73.996368</td>\n",
  1908.        "      <td>40.723660</td>\n",
  1909.        "      <td>-73.975166</td>\n",
  1910.        "      <td>40.689621</td>\n",
  1911.        "      <td>N</td>\n",
  1912.        "      <td>737</td>\n",
  1913.        "      <td>New York City</td>\n",
  1914.        "      <td>New York City</td>\n",
  1915.        "      <td>2.599240</td>\n",
  1916.        "    </tr>\n",
  1917.        "    <tr>\n",
  1918.        "      <th>118165</th>\n",
  1919.        "      <td>id0998702</td>\n",
  1920.        "      <td>2</td>\n",
  1921.        "      <td>2016-03-06 02:15:18</td>\n",
  1922.        "      <td>2016-03-06 02:24:16</td>\n",
  1923.        "      <td>1</td>\n",
  1924.        "      <td>-73.963203</td>\n",
  1925.        "      <td>40.671833</td>\n",
  1926.        "      <td>-73.960808</td>\n",
  1927.        "      <td>40.648785</td>\n",
  1928.        "      <td>N</td>\n",
  1929.        "      <td>538</td>\n",
  1930.        "      <td>Brooklyn</td>\n",
  1931.        "      <td>Brooklyn</td>\n",
  1932.        "      <td>1.595351</td>\n",
  1933.        "    </tr>\n",
  1934.        "    <tr>\n",
  1935.        "      <th>118166</th>\n",
  1936.        "      <td>id0480063</td>\n",
  1937.        "      <td>1</td>\n",
  1938.        "      <td>2016-03-05 12:53:30</td>\n",
  1939.        "      <td>2016-03-05 12:57:32</td>\n",
  1940.        "      <td>1</td>\n",
  1941.        "      <td>-73.976250</td>\n",
  1942.        "      <td>40.728737</td>\n",
  1943.        "      <td>-73.989166</td>\n",
  1944.        "      <td>40.734058</td>\n",
  1945.        "      <td>N</td>\n",
  1946.        "      <td>242</td>\n",
  1947.        "      <td>Long Island City</td>\n",
  1948.        "      <td>New York City</td>\n",
  1949.        "      <td>0.771051</td>\n",
  1950.        "    </tr>\n",
  1951.        "    <tr>\n",
  1952.        "      <th>118167</th>\n",
  1953.        "      <td>id2034624</td>\n",
  1954.        "      <td>2</td>\n",
  1955.        "      <td>2016-03-12 20:01:27</td>\n",
  1956.        "      <td>2016-03-12 20:36:01</td>\n",
  1957.        "      <td>5</td>\n",
  1958.        "      <td>-73.781212</td>\n",
  1959.        "      <td>40.644951</td>\n",
  1960.        "      <td>-73.977303</td>\n",
  1961.        "      <td>40.750721</td>\n",
  1962.        "      <td>N</td>\n",
  1963.        "      <td>2074</td>\n",
  1964.        "      <td>Inwood</td>\n",
  1965.        "      <td>Long Island City</td>\n",
  1966.        "      <td>12.622102</td>\n",
  1967.        "    </tr>\n",
  1968.        "    <tr>\n",
  1969.        "      <th>118168</th>\n",
  1970.        "      <td>id1203726</td>\n",
  1971.        "      <td>2</td>\n",
  1972.        "      <td>2016-03-03 17:19:23</td>\n",
  1973.        "      <td>2016-03-03 17:27:35</td>\n",
  1974.        "      <td>2</td>\n",
  1975.        "      <td>-73.991798</td>\n",
  1976.        "      <td>40.749840</td>\n",
  1977.        "      <td>-73.993942</td>\n",
  1978.        "      <td>40.735722</td>\n",
  1979.        "      <td>N</td>\n",
  1980.        "      <td>492</td>\n",
  1981.        "      <td>Weehawken</td>\n",
  1982.        "      <td>New York City</td>\n",
  1983.        "      <td>0.980668</td>\n",
  1984.        "    </tr>\n",
  1985.        "    <tr>\n",
  1986.        "      <th>118169</th>\n",
  1987.        "      <td>id3860980</td>\n",
  1988.        "      <td>2</td>\n",
  1989.        "      <td>2016-03-11 23:59:25</td>\n",
  1990.        "      <td>2016-03-12 00:10:12</td>\n",
  1991.        "      <td>1</td>\n",
  1992.        "      <td>-73.971542</td>\n",
  1993.        "      <td>40.757721</td>\n",
  1994.        "      <td>-73.991043</td>\n",
  1995.        "      <td>40.750568</td>\n",
  1996.        "      <td>N</td>\n",
  1997.        "      <td>647</td>\n",
  1998.        "      <td>Long Island City</td>\n",
  1999.        "      <td>Weehawken</td>\n",
  2000.        "      <td>1.136068</td>\n",
  2001.        "    </tr>\n",
  2002.        "    <tr>\n",
  2003.        "      <th>118170</th>\n",
  2004.        "      <td>id2924763</td>\n",
  2005.        "      <td>2</td>\n",
  2006.        "      <td>2016-03-04 23:24:33</td>\n",
  2007.        "      <td>2016-03-04 23:31:02</td>\n",
  2008.        "      <td>1</td>\n",
  2009.        "      <td>-73.997643</td>\n",
  2010.        "      <td>40.756622</td>\n",
  2011.        "      <td>-73.984688</td>\n",
  2012.        "      <td>40.761581</td>\n",
  2013.        "      <td>N</td>\n",
  2014.        "      <td>389</td>\n",
  2015.        "      <td>Weehawken</td>\n",
  2016.        "      <td>Manhattan</td>\n",
  2017.        "      <td>0.760997</td>\n",
  2018.        "    </tr>\n",
  2019.        "    <tr>\n",
  2020.        "      <th>118171</th>\n",
  2021.        "      <td>id0873910</td>\n",
  2022.        "      <td>1</td>\n",
  2023.        "      <td>2016-03-10 12:12:01</td>\n",
  2024.        "      <td>2016-03-10 12:25:52</td>\n",
  2025.        "      <td>2</td>\n",
  2026.        "      <td>-73.973885</td>\n",
  2027.        "      <td>40.764061</td>\n",
  2028.        "      <td>-73.990173</td>\n",
  2029.        "      <td>40.741711</td>\n",
  2030.        "      <td>N</td>\n",
  2031.        "      <td>831</td>\n",
  2032.        "      <td>Manhattan</td>\n",
  2033.        "      <td>New York City</td>\n",
  2034.        "      <td>1.763251</td>\n",
  2035.        "    </tr>\n",
  2036.        "    <tr>\n",
  2037.        "      <th>118172</th>\n",
  2038.        "      <td>id1250471</td>\n",
  2039.        "      <td>1</td>\n",
  2040.        "      <td>2016-03-04 12:21:19</td>\n",
  2041.        "      <td>2016-03-04 12:37:49</td>\n",
  2042.        "      <td>1</td>\n",
  2043.        "      <td>-73.972527</td>\n",
  2044.        "      <td>40.758957</td>\n",
  2045.        "      <td>-73.956093</td>\n",
  2046.        "      <td>40.785572</td>\n",
  2047.        "      <td>N</td>\n",
  2048.        "      <td>990</td>\n",
  2049.        "      <td>Manhattan</td>\n",
  2050.        "      <td>Manhattan</td>\n",
  2051.        "      <td>2.028798</td>\n",
  2052.        "    </tr>\n",
  2053.        "    <tr>\n",
  2054.        "      <th>118173</th>\n",
  2055.        "      <td>id1192201</td>\n",
  2056.        "      <td>1</td>\n",
  2057.        "      <td>2016-03-05 03:56:36</td>\n",
  2058.        "      <td>2016-03-05 04:05:39</td>\n",
  2059.        "      <td>1</td>\n",
  2060.        "      <td>-73.988785</td>\n",
  2061.        "      <td>40.727390</td>\n",
  2062.        "      <td>-73.999474</td>\n",
  2063.        "      <td>40.744106</td>\n",
  2064.        "      <td>N</td>\n",
  2065.        "      <td>543</td>\n",
  2066.        "      <td>New York City</td>\n",
  2067.        "      <td>New York City</td>\n",
  2068.        "      <td>1.282649</td>\n",
  2069.        "    </tr>\n",
  2070.        "    <tr>\n",
  2071.        "      <th>118174</th>\n",
  2072.        "      <td>id3453691</td>\n",
  2073.        "      <td>2</td>\n",
  2074.        "      <td>2016-03-07 18:11:54</td>\n",
  2075.        "      <td>2016-03-07 18:29:09</td>\n",
  2076.        "      <td>1</td>\n",
  2077.        "      <td>-74.006531</td>\n",
  2078.        "      <td>40.738232</td>\n",
  2079.        "      <td>-73.985970</td>\n",
  2080.        "      <td>40.726978</td>\n",
  2081.        "      <td>N</td>\n",
  2082.        "      <td>1035</td>\n",
  2083.        "      <td>New York City</td>\n",
  2084.        "      <td>New York City</td>\n",
  2085.        "      <td>1.329572</td>\n",
  2086.        "    </tr>\n",
  2087.        "    <tr>\n",
  2088.        "      <th>118175</th>\n",
  2089.        "      <td>id2086152</td>\n",
  2090.        "      <td>1</td>\n",
  2091.        "      <td>2016-03-11 00:22:18</td>\n",
  2092.        "      <td>2016-03-11 00:29:14</td>\n",
  2093.        "      <td>2</td>\n",
  2094.        "      <td>-73.986481</td>\n",
  2095.        "      <td>40.725826</td>\n",
  2096.        "      <td>-73.987297</td>\n",
  2097.        "      <td>40.736004</td>\n",
  2098.        "      <td>N</td>\n",
  2099.        "      <td>416</td>\n",
  2100.        "      <td>New York City</td>\n",
  2101.        "      <td>New York City</td>\n",
  2102.        "      <td>0.703586</td>\n",
  2103.        "    </tr>\n",
  2104.        "    <tr>\n",
  2105.        "      <th>118176</th>\n",
  2106.        "      <td>id2525150</td>\n",
  2107.        "      <td>1</td>\n",
  2108.        "      <td>2016-03-08 12:56:58</td>\n",
  2109.        "      <td>2016-03-08 13:20:07</td>\n",
  2110.        "      <td>1</td>\n",
  2111.        "      <td>-73.978241</td>\n",
  2112.        "      <td>40.744911</td>\n",
  2113.        "      <td>-73.870483</td>\n",
  2114.        "      <td>40.773777</td>\n",
  2115.        "      <td>N</td>\n",
  2116.        "      <td>1389</td>\n",
  2117.        "      <td>Long Island City</td>\n",
  2118.        "      <td>The Bronx</td>\n",
  2119.        "      <td>5.994509</td>\n",
  2120.        "    </tr>\n",
  2121.        "    <tr>\n",
  2122.        "      <th>118177</th>\n",
  2123.        "      <td>id3780824</td>\n",
  2124.        "      <td>2</td>\n",
  2125.        "      <td>2016-03-12 01:08:45</td>\n",
  2126.        "      <td>2016-03-12 01:23:02</td>\n",
  2127.        "      <td>5</td>\n",
  2128.        "      <td>-73.991463</td>\n",
  2129.        "      <td>40.719189</td>\n",
  2130.        "      <td>-73.949112</td>\n",
  2131.        "      <td>40.711090</td>\n",
  2132.        "      <td>N</td>\n",
  2133.        "      <td>857</td>\n",
  2134.        "      <td>New York City</td>\n",
  2135.        "      <td>Long Island City</td>\n",
  2136.        "      <td>2.292714</td>\n",
  2137.        "    </tr>\n",
  2138.        "    <tr>\n",
  2139.        "      <th>118178</th>\n",
  2140.        "      <td>id2669138</td>\n",
  2141.        "      <td>2</td>\n",
  2142.        "      <td>2016-03-05 09:41:26</td>\n",
  2143.        "      <td>2016-03-05 09:52:15</td>\n",
  2144.        "      <td>6</td>\n",
  2145.        "      <td>-73.968597</td>\n",
  2146.        "      <td>40.786320</td>\n",
  2147.        "      <td>-73.981667</td>\n",
  2148.        "      <td>40.754440</td>\n",
  2149.        "      <td>N</td>\n",
  2150.        "      <td>649</td>\n",
  2151.        "      <td>Manhattan</td>\n",
  2152.        "      <td>Manhattan</td>\n",
  2153.        "      <td>2.304141</td>\n",
  2154.        "    </tr>\n",
  2155.        "    <tr>\n",
  2156.        "      <th>118179</th>\n",
  2157.        "      <td>id3087596</td>\n",
  2158.        "      <td>2</td>\n",
  2159.        "      <td>2016-03-13 15:25:46</td>\n",
  2160.        "      <td>2016-03-13 15:34:52</td>\n",
  2161.        "      <td>2</td>\n",
  2162.        "      <td>-73.998871</td>\n",
  2163.        "      <td>40.724781</td>\n",
  2164.        "      <td>-73.983299</td>\n",
  2165.        "      <td>40.743511</td>\n",
  2166.        "      <td>N</td>\n",
  2167.        "      <td>546</td>\n",
  2168.        "      <td>New York City</td>\n",
  2169.        "      <td>Long Island City</td>\n",
  2170.        "      <td>1.529184</td>\n",
  2171.        "    </tr>\n",
  2172.        "    <tr>\n",
  2173.        "      <th>118180</th>\n",
  2174.        "      <td>id3274818</td>\n",
  2175.        "      <td>2</td>\n",
  2176.        "      <td>2016-03-11 21:04:31</td>\n",
  2177.        "      <td>2016-03-11 21:08:41</td>\n",
  2178.        "      <td>2</td>\n",
  2179.        "      <td>-73.978233</td>\n",
  2180.        "      <td>40.763203</td>\n",
  2181.        "      <td>-73.982498</td>\n",
  2182.        "      <td>40.766701</td>\n",
  2183.        "      <td>N</td>\n",
  2184.        "      <td>250</td>\n",
  2185.        "      <td>Manhattan</td>\n",
  2186.        "      <td>Manhattan</td>\n",
  2187.        "      <td>0.329132</td>\n",
  2188.        "    </tr>\n",
  2189.        "    <tr>\n",
  2190.        "      <th>118181</th>\n",
  2191.        "      <td>id2224211</td>\n",
  2192.        "      <td>1</td>\n",
  2193.        "      <td>2016-03-06 10:42:32</td>\n",
  2194.        "      <td>2016-03-06 10:46:57</td>\n",
  2195.        "      <td>1</td>\n",
  2196.        "      <td>-73.987488</td>\n",
  2197.        "      <td>40.768585</td>\n",
  2198.        "      <td>-73.979660</td>\n",
  2199.        "      <td>40.759151</td>\n",
  2200.        "      <td>N</td>\n",
  2201.        "      <td>265</td>\n",
  2202.        "      <td>Manhattan</td>\n",
  2203.        "      <td>Manhattan</td>\n",
  2204.        "      <td>0.769679</td>\n",
  2205.        "    </tr>\n",
  2206.        "    <tr>\n",
  2207.        "      <th>118182</th>\n",
  2208.        "      <td>id3537077</td>\n",
  2209.        "      <td>2</td>\n",
  2210.        "      <td>2016-03-11 23:48:13</td>\n",
  2211.        "      <td>2016-03-12 00:01:36</td>\n",
  2212.        "      <td>1</td>\n",
  2213.        "      <td>-73.992729</td>\n",
  2214.        "      <td>40.752811</td>\n",
  2215.        "      <td>-73.987862</td>\n",
  2216.        "      <td>40.731930</td>\n",
  2217.        "      <td>N</td>\n",
  2218.        "      <td>803</td>\n",
  2219.        "      <td>Weehawken</td>\n",
  2220.        "      <td>New York City</td>\n",
  2221.        "      <td>1.463359</td>\n",
  2222.        "    </tr>\n",
  2223.        "    <tr>\n",
  2224.        "      <th>118183</th>\n",
  2225.        "      <td>id3482902</td>\n",
  2226.        "      <td>1</td>\n",
  2227.        "      <td>2016-03-01 07:21:04</td>\n",
  2228.        "      <td>2016-03-01 07:23:36</td>\n",
  2229.        "      <td>1</td>\n",
  2230.        "      <td>-73.974693</td>\n",
  2231.        "      <td>40.756088</td>\n",
  2232.        "      <td>-73.969971</td>\n",
  2233.        "      <td>40.762115</td>\n",
  2234.        "      <td>N</td>\n",
  2235.        "      <td>152</td>\n",
  2236.        "      <td>Long Island City</td>\n",
  2237.        "      <td>Manhattan</td>\n",
  2238.        "      <td>0.484116</td>\n",
  2239.        "    </tr>\n",
  2240.        "    <tr>\n",
  2241.        "      <th>118184</th>\n",
  2242.        "      <td>id0469946</td>\n",
  2243.        "      <td>2</td>\n",
  2244.        "      <td>2016-03-06 11:04:48</td>\n",
  2245.        "      <td>2016-03-06 11:17:45</td>\n",
  2246.        "      <td>2</td>\n",
  2247.        "      <td>-74.015572</td>\n",
  2248.        "      <td>40.710892</td>\n",
  2249.        "      <td>-73.996620</td>\n",
  2250.        "      <td>40.743633</td>\n",
  2251.        "      <td>N</td>\n",
  2252.        "      <td>777</td>\n",
  2253.        "      <td>New York City</td>\n",
  2254.        "      <td>New York City</td>\n",
  2255.        "      <td>2.468581</td>\n",
  2256.        "    </tr>\n",
  2257.        "  </tbody>\n",
  2258.        "</table>\n",
  2259.        "<p>118185 rows × 14 columns</p>\n",
  2260.        "</div>"
  2261.       ],
  2262.       "text/plain": [
  2263.        "               id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
  2264.        "0       id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
  2265.        "1       id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
  2266.        "2       id3361153          1  2016-03-11 07:11:23  2016-03-11 07:20:09   \n",
  2267.        "3       id2129090          1  2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
  2268.        "4       id0256505          1  2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
  2269.        "5       id0970832          1  2016-03-12 20:39:39  2016-03-12 21:05:40   \n",
  2270.        "6       id2049424          2  2016-03-02 20:15:07  2016-03-02 20:37:43   \n",
  2271.        "7       id0038484          2  2016-03-09 13:41:11  2016-03-09 13:53:27   \n",
  2272.        "8       id3092788          2  2016-03-03 22:01:32  2016-03-03 22:17:44   \n",
  2273.        "9       id3863815          2  2016-03-14 04:24:36  2016-03-14 04:37:11   \n",
  2274.        "10      id1832737          2  2016-03-06 10:53:26  2016-03-06 10:59:30   \n",
  2275.        "11      id2718231          1  2016-03-08 02:44:19  2016-03-08 03:04:35   \n",
  2276.        "12      id3956459          2  2016-03-05 10:23:45  2016-03-05 10:45:52   \n",
  2277.        "13      id2393811          1  2016-03-10 18:52:40  2016-03-10 19:08:43   \n",
  2278.        "14      id2808378          1  2016-03-09 17:11:16  2016-03-09 17:28:43   \n",
  2279.        "15      id1295254          1  2016-03-06 11:01:27  2016-03-06 11:08:29   \n",
  2280.        "16      id1660823          2  2016-03-01 06:40:18  2016-03-01 07:01:37   \n",
  2281.        "17      id0802391          1  2016-03-06 17:44:45  2016-03-06 17:52:14   \n",
  2282.        "18      id2268459          1  2016-03-02 07:02:21  2016-03-02 07:24:57   \n",
  2283.        "19      id2797773          1  2016-03-08 08:33:35  2016-03-08 08:36:35   \n",
  2284.        "20      id3817493          2  2016-03-14 14:57:56  2016-03-14 15:15:26   \n",
  2285.        "21      id1971518          1  2016-03-12 13:04:28  2016-03-12 13:14:33   \n",
  2286.        "22      id3911487          1  2016-03-03 17:56:45  2016-03-03 18:05:28   \n",
  2287.        "23      id3276198          2  2016-03-14 20:31:12  2016-03-14 20:36:18   \n",
  2288.        "24      id1527676          1  2016-03-07 19:38:25  2016-03-07 19:54:35   \n",
  2289.        "25      id1146853          2  2016-03-05 02:59:30  2016-03-05 03:20:50   \n",
  2290.        "26      id3714906          1  2016-03-01 08:33:57  2016-03-01 08:40:44   \n",
  2291.        "27      id1937745          2  2016-03-07 18:51:46  2016-03-07 18:58:30   \n",
  2292.        "28      id2672200          1  2016-03-08 10:59:46  2016-03-08 11:21:50   \n",
  2293.        "29      id3200728          2  2016-03-03 10:14:57  2016-03-03 10:32:51   \n",
  2294.        "...           ...        ...                  ...                  ...   \n",
  2295.        "118155  id2073065          2  2016-03-10 21:43:30  2016-03-10 21:50:55   \n",
  2296.        "118156  id1042737          2  2016-03-10 06:10:29  2016-03-10 06:13:15   \n",
  2297.        "118157  id0538386          1  2016-03-07 18:29:35  2016-03-07 18:36:43   \n",
  2298.        "118158  id2824253          1  2016-03-03 08:09:29  2016-03-03 09:04:10   \n",
  2299.        "118159  id1333654          1  2016-03-05 01:22:46  2016-03-05 01:34:27   \n",
  2300.        "118160  id2731206          1  2016-03-13 20:14:32  2016-03-13 20:23:39   \n",
  2301.        "118161  id2838932          1  2016-03-13 17:03:03  2016-03-13 17:11:10   \n",
  2302.        "118162  id1486744          2  2016-03-09 10:45:19  2016-03-09 11:18:58   \n",
  2303.        "118163  id0042357          2  2016-03-10 20:56:32  2016-03-10 21:09:55   \n",
  2304.        "118164  id3542490          2  2016-03-07 21:35:25  2016-03-07 21:47:42   \n",
  2305.        "118165  id0998702          2  2016-03-06 02:15:18  2016-03-06 02:24:16   \n",
  2306.        "118166  id0480063          1  2016-03-05 12:53:30  2016-03-05 12:57:32   \n",
  2307.        "118167  id2034624          2  2016-03-12 20:01:27  2016-03-12 20:36:01   \n",
  2308.        "118168  id1203726          2  2016-03-03 17:19:23  2016-03-03 17:27:35   \n",
  2309.        "118169  id3860980          2  2016-03-11 23:59:25  2016-03-12 00:10:12   \n",
  2310.        "118170  id2924763          2  2016-03-04 23:24:33  2016-03-04 23:31:02   \n",
  2311.        "118171  id0873910          1  2016-03-10 12:12:01  2016-03-10 12:25:52   \n",
  2312.        "118172  id1250471          1  2016-03-04 12:21:19  2016-03-04 12:37:49   \n",
  2313.        "118173  id1192201          1  2016-03-05 03:56:36  2016-03-05 04:05:39   \n",
  2314.        "118174  id3453691          2  2016-03-07 18:11:54  2016-03-07 18:29:09   \n",
  2315.        "118175  id2086152          1  2016-03-11 00:22:18  2016-03-11 00:29:14   \n",
  2316.        "118176  id2525150          1  2016-03-08 12:56:58  2016-03-08 13:20:07   \n",
  2317.        "118177  id3780824          2  2016-03-12 01:08:45  2016-03-12 01:23:02   \n",
  2318.        "118178  id2669138          2  2016-03-05 09:41:26  2016-03-05 09:52:15   \n",
  2319.        "118179  id3087596          2  2016-03-13 15:25:46  2016-03-13 15:34:52   \n",
  2320.        "118180  id3274818          2  2016-03-11 21:04:31  2016-03-11 21:08:41   \n",
  2321.        "118181  id2224211          1  2016-03-06 10:42:32  2016-03-06 10:46:57   \n",
  2322.        "118182  id3537077          2  2016-03-11 23:48:13  2016-03-12 00:01:36   \n",
  2323.        "118183  id3482902          1  2016-03-01 07:21:04  2016-03-01 07:23:36   \n",
  2324.        "118184  id0469946          2  2016-03-06 11:04:48  2016-03-06 11:17:45   \n",
  2325.        "\n",
  2326.        "        passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
  2327.        "0                     1        -73.982155        40.767937         -73.964630   \n",
  2328.        "1                     1        -73.981049        40.744339         -73.973000   \n",
  2329.        "2                     1        -73.994560        40.750526         -73.978500   \n",
  2330.        "3                     1        -73.975090        40.758766         -73.953201   \n",
  2331.        "4                     1        -73.994484        40.745087         -73.998993   \n",
  2332.        "5                     1        -74.008247        40.747353         -73.979446   \n",
  2333.        "6                     1        -73.963890        40.773651         -74.005112   \n",
  2334.        "7                     2        -73.972855        40.764400         -73.971809   \n",
  2335.        "8                     2        -73.984772        40.710571         -73.989410   \n",
  2336.        "9                     3        -73.944359        40.714489         -73.910530   \n",
  2337.        "10                    1        -73.984711        40.760181         -73.979561   \n",
  2338.        "11                    1        -73.992500        40.740444         -73.840111   \n",
  2339.        "12                    1        -73.986908        40.761608         -74.008408   \n",
  2340.        "13                    1        -73.970581        40.799046         -73.989815   \n",
  2341.        "14                    1        -73.978645        40.740932         -74.012695   \n",
  2342.        "15                    1        -73.975983        40.757748         -73.982162   \n",
  2343.        "16                    5        -73.982140        40.775326         -74.009850   \n",
  2344.        "17                    1        -73.997208        40.724072         -74.000618   \n",
  2345.        "18                    1        -73.985359        40.738411         -73.870422   \n",
  2346.        "19                    1        -73.967133        40.793465         -73.970390   \n",
  2347.        "20                    1        -73.952881        40.766468         -73.978630   \n",
  2348.        "21                    1        -73.988976        40.759205         -73.973991   \n",
  2349.        "22                    1        -73.962112        40.776100         -73.968521   \n",
  2350.        "23                    1        -73.981911        40.766880         -73.982597   \n",
  2351.        "24                    2        -73.986130        40.759720         -74.001488   \n",
  2352.        "25                    4        -74.005394        40.740810         -73.950630   \n",
  2353.        "26                    1        -73.989494        40.753677         -73.988335   \n",
  2354.        "27                    2        -73.990974        40.760632         -73.994720   \n",
  2355.        "28                    1        -73.964325        40.773594         -73.989769   \n",
  2356.        "29                    1        -73.995880        40.759190         -73.979874   \n",
  2357.        "...                 ...               ...              ...                ...   \n",
  2358.        "118155                1        -73.989738        40.756599         -74.005318   \n",
  2359.        "118156                1        -73.985954        40.752129         -73.978592   \n",
  2360.        "118157                1        -73.976997        40.755756         -73.990540   \n",
  2361.        "118158                1        -73.961922        40.800533         -74.177269   \n",
  2362.        "118159                1        -73.973228        40.792824         -73.945877   \n",
  2363.        "118160                1        -73.981178        40.753674         -74.004509   \n",
  2364.        "118161                1        -73.998634        40.726131         -73.985001   \n",
  2365.        "118162                1        -73.982903        40.765659         -73.872917   \n",
  2366.        "118163                1        -73.993996        40.741283         -73.973114   \n",
  2367.        "118164                1        -73.996368        40.723660         -73.975166   \n",
  2368.        "118165                1        -73.963203        40.671833         -73.960808   \n",
  2369.        "118166                1        -73.976250        40.728737         -73.989166   \n",
  2370.        "118167                5        -73.781212        40.644951         -73.977303   \n",
  2371.        "118168                2        -73.991798        40.749840         -73.993942   \n",
  2372.        "118169                1        -73.971542        40.757721         -73.991043   \n",
  2373.        "118170                1        -73.997643        40.756622         -73.984688   \n",
  2374.        "118171                2        -73.973885        40.764061         -73.990173   \n",
  2375.        "118172                1        -73.972527        40.758957         -73.956093   \n",
  2376.        "118173                1        -73.988785        40.727390         -73.999474   \n",
  2377.        "118174                1        -74.006531        40.738232         -73.985970   \n",
  2378.        "118175                2        -73.986481        40.725826         -73.987297   \n",
  2379.        "118176                1        -73.978241        40.744911         -73.870483   \n",
  2380.        "118177                5        -73.991463        40.719189         -73.949112   \n",
  2381.        "118178                6        -73.968597        40.786320         -73.981667   \n",
  2382.        "118179                2        -73.998871        40.724781         -73.983299   \n",
  2383.        "118180                2        -73.978233        40.763203         -73.982498   \n",
  2384.        "118181                1        -73.987488        40.768585         -73.979660   \n",
  2385.        "118182                1        -73.992729        40.752811         -73.987862   \n",
  2386.        "118183                1        -73.974693        40.756088         -73.969971   \n",
  2387.        "118184                2        -74.015572        40.710892         -73.996620   \n",
  2388.        "\n",
  2389.        "        dropoff_latitude store_and_fwd_flag  trip_duration   pickup_district  \\\n",
  2390.        "0              40.765602                  N            455         Manhattan   \n",
  2391.        "1              40.789989                  N           1225  Long Island City   \n",
  2392.        "2              40.756191                  N            526         Weehawken   \n",
  2393.        "3              40.765068                  N           1346         Manhattan   \n",
  2394.        "4              40.722710                  N            695     New York City   \n",
  2395.        "5              40.718750                  N           1561           Hoboken   \n",
  2396.        "6              40.751492                  N           1356         Manhattan   \n",
  2397.        "7              40.757889                  N            736         Manhattan   \n",
  2398.        "8              40.730148                  N            972     New York City   \n",
  2399.        "9              40.709492                  N            755  Long Island City   \n",
  2400.        "10             40.752705                  N            364         Manhattan   \n",
  2401.        "11             40.719517                  N           1216     New York City   \n",
  2402.        "12             40.711620                  N           1327         Manhattan   \n",
  2403.        "13             40.767246                  N            963         Manhattan   \n",
  2404.        "14             40.701588                  N           1047  Long Island City   \n",
  2405.        "15             40.740749                  N            422         Manhattan   \n",
  2406.        "16             40.721699                  N           1279         Manhattan   \n",
  2407.        "17             40.732155                  N            449     New York City   \n",
  2408.        "18             40.773682                  N           1356     New York City   \n",
  2409.        "19             40.795750                  N            180         Manhattan   \n",
  2410.        "20             40.761921                  N           1050         Manhattan   \n",
  2411.        "21             40.760590                  N            605         Weehawken   \n",
  2412.        "22             40.764408                  N            523         Manhattan   \n",
  2413.        "23             40.777180                  N            306         Manhattan   \n",
  2414.        "24             40.736065                  N            970         Manhattan   \n",
  2415.        "25             40.821037                  N           1280     New York City   \n",
  2416.        "26             40.745949                  N            407         Weehawken   \n",
  2417.        "27             40.750450                  N            404         Weehawken   \n",
  2418.        "28             40.738483                  N           1324         Manhattan   \n",
  2419.        "29             40.752781                  N           1074         Weehawken   \n",
  2420.        "...                  ...                ...            ...               ...   \n",
  2421.        "118155         40.740231                  N            445         Weehawken   \n",
  2422.        "118156         40.752602                  N            166         Manhattan   \n",
  2423.        "118157         40.751163                  N            428         Manhattan   \n",
  2424.        "118158         40.691124                  N           3281         Manhattan   \n",
  2425.        "118159         40.777721                  N            701         Manhattan   \n",
  2426.        "118160         40.747082                  N            547         Manhattan   \n",
  2427.        "118161         40.727985                  N            487     New York City   \n",
  2428.        "118162         40.774441                  N           2019         Manhattan   \n",
  2429.        "118163         40.757057                  N            803     New York City   \n",
  2430.        "118164         40.689621                  N            737     New York City   \n",
  2431.        "118165         40.648785                  N            538          Brooklyn   \n",
  2432.        "118166         40.734058                  N            242  Long Island City   \n",
  2433.        "118167         40.750721                  N           2074            Inwood   \n",
  2434.        "118168         40.735722                  N            492         Weehawken   \n",
  2435.        "118169         40.750568                  N            647  Long Island City   \n",
  2436.        "118170         40.761581                  N            389         Weehawken   \n",
  2437.        "118171         40.741711                  N            831         Manhattan   \n",
  2438.        "118172         40.785572                  N            990         Manhattan   \n",
  2439.        "118173         40.744106                  N            543     New York City   \n",
  2440.        "118174         40.726978                  N           1035     New York City   \n",
  2441.        "118175         40.736004                  N            416     New York City   \n",
  2442.        "118176         40.773777                  N           1389  Long Island City   \n",
  2443.        "118177         40.711090                  N            857     New York City   \n",
  2444.        "118178         40.754440                  N            649         Manhattan   \n",
  2445.        "118179         40.743511                  N            546     New York City   \n",
  2446.        "118180         40.766701                  N            250         Manhattan   \n",
  2447.        "118181         40.759151                  N            265         Manhattan   \n",
  2448.        "118182         40.731930                  N            803         Weehawken   \n",
  2449.        "118183         40.762115                  N            152  Long Island City   \n",
  2450.        "118184         40.743633                  N            777     New York City   \n",
  2451.        "\n",
  2452.        "         dropoff_district   distance  \n",
  2453.        "0               Manhattan   0.933406  \n",
  2454.        "1               Manhattan   3.178194  \n",
  2455.        "2               Manhattan   0.928961  \n",
  2456.        "3        Long Island City   1.228003  \n",
  2457.        "4           New York City   1.562103  \n",
  2458.        "5           New York City   2.486098  \n",
  2459.        "6               Weehawken   2.648687  \n",
  2460.        "7               Manhattan   0.452659  \n",
  2461.        "8           New York City   1.372636  \n",
  2462.        "9           East New York   1.809375  \n",
  2463.        "10       Long Island City   0.582402  \n",
  2464.        "11      Borough of Queens   8.128519  \n",
  2465.        "12          New York City   3.629181  \n",
  2466.        "13             Guttenberg   2.415044  \n",
  2467.        "14          New York City   3.250545  \n",
  2468.        "15       Long Island City   1.216934  \n",
  2469.        "16          New York City   3.975872  \n",
  2470.        "17          New York City   0.585795  \n",
  2471.        "18              The Bronx   6.503464  \n",
  2472.        "19              Manhattan   0.232480  \n",
  2473.        "20              Manhattan   1.386892  \n",
  2474.        "21              Manhattan   0.791979  \n",
  2475.        "22              Manhattan   0.874034  \n",
  2476.        "23              Manhattan   0.711621  \n",
  2477.        "24          New York City   1.820388  \n",
  2478.        "25              Edgewater   6.236767  \n",
  2479.        "26          New York City   0.536754  \n",
  2480.        "27              Weehawken   0.729526  \n",
  2481.        "28          New York City   2.766229  \n",
  2482.        "29       Long Island City   0.949190  \n",
  2483.        "...                   ...        ...  \n",
  2484.        "118155      New York City   1.394332  \n",
  2485.        "118156   Long Island City   0.387712  \n",
  2486.        "118157          Weehawken   0.778074  \n",
  2487.        "118158          Elizabeth  13.590991  \n",
  2488.        "118159          Manhattan   1.773104  \n",
  2489.        "118160          Weehawken   1.306062  \n",
  2490.        "118161      New York City   0.727036  \n",
  2491.        "118162          The Bronx   5.801611  \n",
  2492.        "118163          Manhattan   1.544497  \n",
  2493.        "118164      New York City   2.599240  \n",
  2494.        "118165           Brooklyn   1.595351  \n",
  2495.        "118166      New York City   0.771051  \n",
  2496.        "118167   Long Island City  12.622102  \n",
  2497.        "118168      New York City   0.980668  \n",
  2498.        "118169          Weehawken   1.136068  \n",
  2499.        "118170          Manhattan   0.760997  \n",
  2500.        "118171      New York City   1.763251  \n",
  2501.        "118172          Manhattan   2.028798  \n",
  2502.        "118173      New York City   1.282649  \n",
  2503.        "118174      New York City   1.329572  \n",
  2504.        "118175      New York City   0.703586  \n",
  2505.        "118176          The Bronx   5.994509  \n",
  2506.        "118177   Long Island City   2.292714  \n",
  2507.        "118178          Manhattan   2.304141  \n",
  2508.        "118179   Long Island City   1.529184  \n",
  2509.        "118180          Manhattan   0.329132  \n",
  2510.        "118181          Manhattan   0.769679  \n",
  2511.        "118182      New York City   1.463359  \n",
  2512.        "118183          Manhattan   0.484116  \n",
  2513.        "118184      New York City   2.468581  \n",
  2514.        "\n",
  2515.        "[118185 rows x 14 columns]"
  2516.       ]
  2517.      },
  2518.      "execution_count": 10,
  2519.      "metadata": {},
  2520.      "output_type": "execute_result"
  2521.     }
  2522.    ],
  2523.    "source": [
  2524.     "distance=[]\n",
  2525.     "for i in range(len(df.index)):\n",
  2526.     "    distance.append(geodesic(pick_coordinates[i],drop_coordinates[i]).miles)\n",
  2527.     "df[\"distance\"] = distance\n",
  2528.     "df"
  2529.    ]
  2530.   },
  2531.   {
  2532.    "cell_type": "code",
  2533.    "execution_count": 11,
  2534.    "metadata": {},
  2535.    "outputs": [
  2536.     {
  2537.      "data": {
  2538.       "text/html": [
  2539.        "<div>\n",
  2540.        "<style scoped>\n",
  2541.        "    .dataframe tbody tr th:only-of-type {\n",
  2542.        "        vertical-align: middle;\n",
  2543.        "    }\n",
  2544.        "\n",
  2545.        "    .dataframe tbody tr th {\n",
  2546.        "        vertical-align: top;\n",
  2547.        "    }\n",
  2548.        "\n",
  2549.        "    .dataframe thead th {\n",
  2550.        "        text-align: right;\n",
  2551.        "    }\n",
  2552.        "</style>\n",
  2553.        "<table border=\"1\" class=\"dataframe\">\n",
  2554.        "  <thead>\n",
  2555.        "    <tr style=\"text-align: right;\">\n",
  2556.        "      <th></th>\n",
  2557.        "      <th>id</th>\n",
  2558.        "      <th>vendor_id</th>\n",
  2559.        "      <th>pickup_datetime</th>\n",
  2560.        "      <th>dropoff_datetime</th>\n",
  2561.        "      <th>passenger_count</th>\n",
  2562.        "      <th>pickup_longitude</th>\n",
  2563.        "      <th>pickup_latitude</th>\n",
  2564.        "      <th>dropoff_longitude</th>\n",
  2565.        "      <th>dropoff_latitude</th>\n",
  2566.        "      <th>store_and_fwd_flag</th>\n",
  2567.        "      <th>trip_duration</th>\n",
  2568.        "      <th>pickup_district</th>\n",
  2569.        "      <th>dropoff_district</th>\n",
  2570.        "      <th>distance</th>\n",
  2571.        "      <th>time_of_day</th>\n",
  2572.        "    </tr>\n",
  2573.        "  </thead>\n",
  2574.        "  <tbody>\n",
  2575.        "    <tr>\n",
  2576.        "      <th>0</th>\n",
  2577.        "      <td>id2875421</td>\n",
  2578.        "      <td>2</td>\n",
  2579.        "      <td>2016-03-14 17:24:55</td>\n",
  2580.        "      <td>2016-03-14 17:32:30</td>\n",
  2581.        "      <td>1</td>\n",
  2582.        "      <td>-73.982155</td>\n",
  2583.        "      <td>40.767937</td>\n",
  2584.        "      <td>-73.964630</td>\n",
  2585.        "      <td>40.765602</td>\n",
  2586.        "      <td>N</td>\n",
  2587.        "      <td>455</td>\n",
  2588.        "      <td>Manhattan</td>\n",
  2589.        "      <td>Manhattan</td>\n",
  2590.        "      <td>0.933406</td>\n",
  2591.        "      <td>rush_hour_evening</td>\n",
  2592.        "    </tr>\n",
  2593.        "    <tr>\n",
  2594.        "      <th>1</th>\n",
  2595.        "      <td>id0012891</td>\n",
  2596.        "      <td>2</td>\n",
  2597.        "      <td>2016-03-10 21:45:01</td>\n",
  2598.        "      <td>2016-03-10 22:05:26</td>\n",
  2599.        "      <td>1</td>\n",
  2600.        "      <td>-73.981049</td>\n",
  2601.        "      <td>40.744339</td>\n",
  2602.        "      <td>-73.973000</td>\n",
  2603.        "      <td>40.789989</td>\n",
  2604.        "      <td>N</td>\n",
  2605.        "      <td>1225</td>\n",
  2606.        "      <td>Long Island City</td>\n",
  2607.        "      <td>Manhattan</td>\n",
  2608.        "      <td>3.178194</td>\n",
  2609.        "      <td>evening</td>\n",
  2610.        "    </tr>\n",
  2611.        "    <tr>\n",
  2612.        "      <th>2</th>\n",
  2613.        "      <td>id3361153</td>\n",
  2614.        "      <td>1</td>\n",
  2615.        "      <td>2016-03-11 07:11:23</td>\n",
  2616.        "      <td>2016-03-11 07:20:09</td>\n",
  2617.        "      <td>1</td>\n",
  2618.        "      <td>-73.994560</td>\n",
  2619.        "      <td>40.750526</td>\n",
  2620.        "      <td>-73.978500</td>\n",
  2621.        "      <td>40.756191</td>\n",
  2622.        "      <td>N</td>\n",
  2623.        "      <td>526</td>\n",
  2624.        "      <td>Weehawken</td>\n",
  2625.        "      <td>Manhattan</td>\n",
  2626.        "      <td>0.928961</td>\n",
  2627.        "      <td>rush_hour_morning</td>\n",
  2628.        "    </tr>\n",
  2629.        "    <tr>\n",
  2630.        "      <th>3</th>\n",
  2631.        "      <td>id2129090</td>\n",
  2632.        "      <td>1</td>\n",
  2633.        "      <td>2016-03-14 14:05:39</td>\n",
  2634.        "      <td>2016-03-14 14:28:05</td>\n",
  2635.        "      <td>1</td>\n",
  2636.        "      <td>-73.975090</td>\n",
  2637.        "      <td>40.758766</td>\n",
  2638.        "      <td>-73.953201</td>\n",
  2639.        "      <td>40.765068</td>\n",
  2640.        "      <td>N</td>\n",
  2641.        "      <td>1346</td>\n",
  2642.        "      <td>Manhattan</td>\n",
  2643.        "      <td>Long Island City</td>\n",
  2644.        "      <td>1.228003</td>\n",
  2645.        "      <td>afternoon</td>\n",
  2646.        "    </tr>\n",
  2647.        "    <tr>\n",
  2648.        "      <th>4</th>\n",
  2649.        "      <td>id0256505</td>\n",
  2650.        "      <td>1</td>\n",
  2651.        "      <td>2016-03-14 15:04:38</td>\n",
  2652.        "      <td>2016-03-14 15:16:13</td>\n",
  2653.        "      <td>1</td>\n",
  2654.        "      <td>-73.994484</td>\n",
  2655.        "      <td>40.745087</td>\n",
  2656.        "      <td>-73.998993</td>\n",
  2657.        "      <td>40.722710</td>\n",
  2658.        "      <td>N</td>\n",
  2659.        "      <td>695</td>\n",
  2660.        "      <td>New York City</td>\n",
  2661.        "      <td>New York City</td>\n",
  2662.        "      <td>1.562103</td>\n",
  2663.        "      <td>afternoon</td>\n",
  2664.        "    </tr>\n",
  2665.        "    <tr>\n",
  2666.        "      <th>5</th>\n",
  2667.        "      <td>id0970832</td>\n",
  2668.        "      <td>1</td>\n",
  2669.        "      <td>2016-03-12 20:39:39</td>\n",
  2670.        "      <td>2016-03-12 21:05:40</td>\n",
  2671.        "      <td>1</td>\n",
  2672.        "      <td>-74.008247</td>\n",
  2673.        "      <td>40.747353</td>\n",
  2674.        "      <td>-73.979446</td>\n",
  2675.        "      <td>40.718750</td>\n",
  2676.        "      <td>N</td>\n",
  2677.        "      <td>1561</td>\n",
  2678.        "      <td>Hoboken</td>\n",
  2679.        "      <td>New York City</td>\n",
  2680.        "      <td>2.486098</td>\n",
  2681.        "      <td>evening</td>\n",
  2682.        "    </tr>\n",
  2683.        "  </tbody>\n",
  2684.        "</table>\n",
  2685.        "</div>"
  2686.       ],
  2687.       "text/plain": [
  2688.        "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
  2689.        "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
  2690.        "1  id0012891          2  2016-03-10 21:45:01  2016-03-10 22:05:26   \n",
  2691.        "2  id3361153          1  2016-03-11 07:11:23  2016-03-11 07:20:09   \n",
  2692.        "3  id2129090          1  2016-03-14 14:05:39  2016-03-14 14:28:05   \n",
  2693.        "4  id0256505          1  2016-03-14 15:04:38  2016-03-14 15:16:13   \n",
  2694.        "5  id0970832          1  2016-03-12 20:39:39  2016-03-12 21:05:40   \n",
  2695.        "\n",
  2696.        "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
  2697.        "0                1        -73.982155        40.767937         -73.964630   \n",
  2698.        "1                1        -73.981049        40.744339         -73.973000   \n",
  2699.        "2                1        -73.994560        40.750526         -73.978500   \n",
  2700.        "3                1        -73.975090        40.758766         -73.953201   \n",
  2701.        "4                1        -73.994484        40.745087         -73.998993   \n",
  2702.        "5                1        -74.008247        40.747353         -73.979446   \n",
  2703.        "\n",
  2704.        "   dropoff_latitude store_and_fwd_flag  trip_duration   pickup_district  \\\n",
  2705.        "0         40.765602                  N            455         Manhattan   \n",
  2706.        "1         40.789989                  N           1225  Long Island City   \n",
  2707.        "2         40.756191                  N            526         Weehawken   \n",
  2708.        "3         40.765068                  N           1346         Manhattan   \n",
  2709.        "4         40.722710                  N            695     New York City   \n",
  2710.        "5         40.718750                  N           1561           Hoboken   \n",
  2711.        "\n",
  2712.        "   dropoff_district  distance        time_of_day  \n",
  2713.        "0         Manhattan  0.933406  rush_hour_evening  \n",
  2714.        "1         Manhattan  3.178194            evening  \n",
  2715.        "2         Manhattan  0.928961  rush_hour_morning  \n",
  2716.        "3  Long Island City  1.228003          afternoon  \n",
  2717.        "4     New York City  1.562103          afternoon  \n",
  2718.        "5     New York City  2.486098            evening  "
  2719.       ]
  2720.      },
  2721.      "execution_count": 11,
  2722.      "metadata": {},
  2723.      "output_type": "execute_result"
  2724.     }
  2725.    ],
  2726.    "source": [
  2727.     "a=[]\n",
  2728.     "for i in range(len(df.index)):\n",
  2729.     "    hour = int(df[\"pickup_datetime\"][i][11:13])\n",
  2730.     "    if(hour>=7 and hour<9):\n",
  2731.     "        a.append(\"rush_hour_morning\")\n",
  2732.     "    elif hour>=9 and hour < 16:\n",
  2733.     "        a.append(\"afternoon\")\n",
  2734.     "    elif(hour>=16 and hour < 18):\n",
  2735.     "        a.append(\"rush_hour_evening\")\n",
  2736.     "    elif(hour>=18 and hour<23):\n",
  2737.     "        a.append(\"evening\")\n",
  2738.     "    else:\n",
  2739.     "        a.append(\"late_night\")\n",
  2740.     "df[\"time_of_day\"]=a\n",
  2741.     "df.head(6)"
  2742.    ]
  2743.   },
  2744.   {
  2745.    "cell_type": "markdown",
  2746.    "metadata": {},
  2747.    "source": [
  2748.     "Most Frequent Dropoff districts"
  2749.    ]
  2750.   },
  2751.   {
  2752.    "cell_type": "code",
  2753.    "execution_count": 13,
  2754.    "metadata": {},
  2755.    "outputs": [
  2756.     {
  2757.      "data": {
  2758.       "text/plain": [
  2759.        "Manhattan           44478\n",
  2760.        "New York City       31082\n",
  2761.        "Long Island City    19919\n",
  2762.        "Weehawken           10621\n",
  2763.        "Brooklyn             2059\n",
  2764.        "Name: dropoff_district, dtype: int64"
  2765.       ]
  2766.      },
  2767.      "execution_count": 13,
  2768.      "metadata": {},
  2769.      "output_type": "execute_result"
  2770.     }
  2771.    ],
  2772.    "source": [
  2773.     "df[\"dropoff_district\"].value_counts()[:5]\n"
  2774.    ]
  2775.   },
  2776.   {
  2777.    "cell_type": "markdown",
  2778.    "metadata": {},
  2779.    "source": [
  2780.     "Most Frequent Pickup districts"
  2781.    ]
  2782.   },
  2783.   {
  2784.    "cell_type": "code",
  2785.    "execution_count": 14,
  2786.    "metadata": {},
  2787.    "outputs": [
  2788.     {
  2789.      "data": {
  2790.       "text/plain": [
  2791.        "Manhattan           45329\n",
  2792.        "New York City       34625\n",
  2793.        "Long Island City    17787\n",
  2794.        "Weehawken           11334\n",
  2795.        "The Bronx            2777\n",
  2796.        "Name: pickup_district, dtype: int64"
  2797.       ]
  2798.      },
  2799.      "execution_count": 14,
  2800.      "metadata": {},
  2801.      "output_type": "execute_result"
  2802.     }
  2803.    ],
  2804.    "source": [
  2805.     "df[\"pickup_district\"].value_counts()[:5]"
  2806.    ]
  2807.   },
  2808.   {
  2809.    "cell_type": "code",
  2810.    "execution_count": 60,
  2811.    "metadata": {},
  2812.    "outputs": [
  2813.     {
  2814.      "data": {
  2815.       "text/html": [
  2816.        "<div>\n",
  2817.        "<style scoped>\n",
  2818.        "    .dataframe tbody tr th:only-of-type {\n",
  2819.        "        vertical-align: middle;\n",
  2820.        "    }\n",
  2821.        "\n",
  2822.        "    .dataframe tbody tr th {\n",
  2823.        "        vertical-align: top;\n",
  2824.        "    }\n",
  2825.        "\n",
  2826.        "    .dataframe thead th {\n",
  2827.        "        text-align: right;\n",
  2828.        "    }\n",
  2829.        "</style>\n",
  2830.        "<table border=\"1\" class=\"dataframe\">\n",
  2831.        "  <thead>\n",
  2832.        "    <tr style=\"text-align: right;\">\n",
  2833.        "      <th></th>\n",
  2834.        "      <th>Time Of Day</th>\n",
  2835.        "      <th>Average Distance</th>\n",
  2836.        "    </tr>\n",
  2837.        "  </thead>\n",
  2838.        "  <tbody>\n",
  2839.        "    <tr>\n",
  2840.        "      <th>0</th>\n",
  2841.        "      <td>rush_hour_morning</td>\n",
  2842.        "      <td>1.943823</td>\n",
  2843.        "    </tr>\n",
  2844.        "    <tr>\n",
  2845.        "      <th>1</th>\n",
  2846.        "      <td>afternoon</td>\n",
  2847.        "      <td>1.932456</td>\n",
  2848.        "    </tr>\n",
  2849.        "    <tr>\n",
  2850.        "      <th>2</th>\n",
  2851.        "      <td>rush_hour_evening</td>\n",
  2852.        "      <td>2.077128</td>\n",
  2853.        "    </tr>\n",
  2854.        "    <tr>\n",
  2855.        "      <th>3</th>\n",
  2856.        "      <td>evening</td>\n",
  2857.        "      <td>2.099076</td>\n",
  2858.        "    </tr>\n",
  2859.        "    <tr>\n",
  2860.        "      <th>4</th>\n",
  2861.        "      <td>late_night</td>\n",
  2862.        "      <td>2.558580</td>\n",
  2863.        "    </tr>\n",
  2864.        "  </tbody>\n",
  2865.        "</table>\n",
  2866.        "</div>"
  2867.       ],
  2868.       "text/plain": [
  2869.        "         Time Of Day  Average Distance\n",
  2870.        "0  rush_hour_morning          1.943823\n",
  2871.        "1          afternoon          1.932456\n",
  2872.        "2  rush_hour_evening          2.077128\n",
  2873.        "3            evening          2.099076\n",
  2874.        "4         late_night          2.558580"
  2875.       ]
  2876.      },
  2877.      "execution_count": 60,
  2878.      "metadata": {},
  2879.      "output_type": "execute_result"
  2880.     }
  2881.    ],
  2882.    "source": [
  2883.     "templist = {\"Time Of Day\":[],\"Average Distance\":[]}\n",
  2884.     "templist[\"Average Distance\"].append(df[df['time_of_day']=='rush_hour_morning']['distance'].mean())\n",
  2885.     "templist[\"Time Of Day\"].append(\"rush_hour_morning\")\n",
  2886.     "templist[\"Average Distance\"].append(df[df['time_of_day']=='afternoon']['distance'].mean())\n",
  2887.     "templist[\"Time Of Day\"].append(\"afternoon\")\n",
  2888.     "templist[\"Average Distance\"].append(df[df['time_of_day']=='rush_hour_evening']['distance'].mean())\n",
  2889.     "templist[\"Time Of Day\"].append(\"rush_hour_evening\")\n",
  2890.     "templist[\"Average Distance\"].append(df[df['time_of_day']=='evening']['distance'].mean())\n",
  2891.     "templist[\"Time Of Day\"].append(\"evening\")\n",
  2892.     "templist[\"Average Distance\"].append(df[df['time_of_day']=='late_night']['distance'].mean())\n",
  2893.     "templist[\"Time Of Day\"].append(\"late_night\")\n",
  2894.     "df_timevsdistance = pd.DataFrame(templist)\n",
  2895.     "df_timevsdistance"
  2896.    ]
  2897.   },
  2898.   {
  2899.    "cell_type": "code",
  2900.    "execution_count": 65,
  2901.    "metadata": {},
  2902.    "outputs": [
  2903.     {
  2904.      "data": {
  2905.       "image/png": "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\n",
  2906.       "text/plain": [
  2907.        "<Figure size 432x288 with 1 Axes>"
  2908.       ]
  2909.      },
  2910.      "metadata": {
  2911.       "needs_background": "light"
  2912.      },
  2913.      "output_type": "display_data"
  2914.     }
  2915.    ],
  2916.    "source": [
  2917.     "import matplotlib.pyplot as plt\n",
  2918.     "df_timevsdistance.plot(kind='bar',x=\"Time Of Day\",y=\"Average Distance\",color='blue')\n",
  2919.     "plt.show()"
  2920.    ]
  2921.   },
  2922.   {
  2923.    "cell_type": "code",
  2924.    "execution_count": 62,
  2925.    "metadata": {},
  2926.    "outputs": [
  2927.     {
  2928.      "data": {
  2929.       "text/html": [
  2930.        "<div>\n",
  2931.        "<style scoped>\n",
  2932.        "    .dataframe tbody tr th:only-of-type {\n",
  2933.        "        vertical-align: middle;\n",
  2934.        "    }\n",
  2935.        "\n",
  2936.        "    .dataframe tbody tr th {\n",
  2937.        "        vertical-align: top;\n",
  2938.        "    }\n",
  2939.        "\n",
  2940.        "    .dataframe thead th {\n",
  2941.        "        text-align: right;\n",
  2942.        "    }\n",
  2943.        "</style>\n",
  2944.        "<table border=\"1\" class=\"dataframe\">\n",
  2945.        "  <thead>\n",
  2946.        "    <tr style=\"text-align: right;\">\n",
  2947.        "      <th></th>\n",
  2948.        "      <th>Time Of Day</th>\n",
  2949.        "      <th>Average Trip Duration</th>\n",
  2950.        "    </tr>\n",
  2951.        "  </thead>\n",
  2952.        "  <tbody>\n",
  2953.        "    <tr>\n",
  2954.        "      <th>0</th>\n",
  2955.        "      <td>rush_hour_morning</td>\n",
  2956.        "      <td>918.488716</td>\n",
  2957.        "    </tr>\n",
  2958.        "    <tr>\n",
  2959.        "      <th>1</th>\n",
  2960.        "      <td>afternoon</td>\n",
  2961.        "      <td>963.922746</td>\n",
  2962.        "    </tr>\n",
  2963.        "    <tr>\n",
  2964.        "      <th>2</th>\n",
  2965.        "      <td>rush_hour_evening</td>\n",
  2966.        "      <td>1024.494552</td>\n",
  2967.        "    </tr>\n",
  2968.        "    <tr>\n",
  2969.        "      <th>3</th>\n",
  2970.        "      <td>evening</td>\n",
  2971.        "      <td>893.595329</td>\n",
  2972.        "    </tr>\n",
  2973.        "    <tr>\n",
  2974.        "      <th>4</th>\n",
  2975.        "      <td>late_night</td>\n",
  2976.        "      <td>866.095457</td>\n",
  2977.        "    </tr>\n",
  2978.        "  </tbody>\n",
  2979.        "</table>\n",
  2980.        "</div>"
  2981.       ],
  2982.       "text/plain": [
  2983.        "         Time Of Day  Average Trip Duration\n",
  2984.        "0  rush_hour_morning             918.488716\n",
  2985.        "1          afternoon             963.922746\n",
  2986.        "2  rush_hour_evening            1024.494552\n",
  2987.        "3            evening             893.595329\n",
  2988.        "4         late_night             866.095457"
  2989.       ]
  2990.      },
  2991.      "execution_count": 62,
  2992.      "metadata": {},
  2993.      "output_type": "execute_result"
  2994.     }
  2995.    ],
  2996.    "source": [
  2997.     "templist = {\"Time Of Day\":[],\"Average Trip Duration\":[]}\n",
  2998.     "templist[\"Average Trip Duration\"].append(df[df['time_of_day']=='rush_hour_morning']['trip_duration'].mean())\n",
  2999.     "templist[\"Time Of Day\"].append(\"rush_hour_morning\")\n",
  3000.     "templist[\"Average Trip Duration\"].append(df[df['time_of_day']=='afternoon']['trip_duration'].mean())\n",
  3001.     "templist[\"Time Of Day\"].append(\"afternoon\")\n",
  3002.     "templist[\"Average Trip Duration\"].append(df[df['time_of_day']=='rush_hour_evening']['trip_duration'].mean())\n",
  3003.     "templist[\"Time Of Day\"].append(\"rush_hour_evening\")\n",
  3004.     "templist[\"Average Trip Duration\"].append(df[df['time_of_day']=='evening']['trip_duration'].mean())\n",
  3005.     "templist[\"Time Of Day\"].append(\"evening\")\n",
  3006.     "templist[\"Average Trip Duration\"].append(df[df['time_of_day']=='late_night']['trip_duration'].mean())\n",
  3007.     "templist[\"Time Of Day\"].append(\"late_night\")\n",
  3008.     "df_timevstripduration = pd.DataFrame(templist)\n",
  3009.     "df_timevstripduration"
  3010.    ]
  3011.   },
  3012.   {
  3013.    "cell_type": "code",
  3014.    "execution_count": 67,
  3015.    "metadata": {},
  3016.    "outputs": [
  3017.     {
  3018.      "data": {
  3019.       "image/png": "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\n",
  3020.       "text/plain": [
  3021.        "<Figure size 432x288 with 1 Axes>"
  3022.       ]
  3023.      },
  3024.      "metadata": {
  3025.       "needs_background": "light"
  3026.      },
  3027.      "output_type": "display_data"
  3028.     }
  3029.    ],
  3030.    "source": [
  3031.     "df_timevstripduration.plot(kind='bar',x=\"Time Of Day\",y=\"Average Trip Duration\",color='orange')\n",
  3032.     "plt.show()"
  3033.    ]
  3034.   },
  3035.   {
  3036.    "cell_type": "markdown",
  3037.    "metadata": {},
  3038.    "source": [
  3039.     "Null hypothesis: passenger group size has no effect on the distance."
  3040.    ]
  3041.   },
  3042.   {
  3043.    "cell_type": "code",
  3044.    "execution_count": 108,
  3045.    "metadata": {},
  3046.    "outputs": [],
  3047.    "source": [
  3048.     "BiggerThan1 = df[df['passenger_count']>1]['distance']\n",
  3049.     "NotBiggerThan1 = df[df['passenger_count']==1]['distance']\n"
  3050.    ]
  3051.   },
  3052.   {
  3053.    "cell_type": "code",
  3054.    "execution_count": 102,
  3055.    "metadata": {},
  3056.    "outputs": [
  3057.     {
  3058.      "data": {
  3059.       "image/png": "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\n",
  3060.       "text/plain": [
  3061.        "<Figure size 1080x432 with 2 Axes>"
  3062.       ]
  3063.      },
  3064.      "metadata": {
  3065.       "needs_background": "light"
  3066.      },
  3067.      "output_type": "display_data"
  3068.     }
  3069.    ],
  3070.    "source": [
  3071.     "\n",
  3072.     "fig, ax = plt.subplots(1, 2, figsize=(15,6))\n",
  3073.     "BiggerThan1.plot(kind=\"hist\",ax=ax[0],bins=50,label=\"aa\",color=\"c\")\n",
  3074.     "ax[0].set_title(\"Passenger Count bigger than 1\")\n",
  3075.     "NotBiggerThan1.plot(kind=\"hist\", ax=ax[1],bins=50,label=\"bb\",color=\"m\")\n",
  3076.     "ax[1].set_title(\"Passenger Count equal to 1\")\n",
  3077.     "\n",
  3078.     "\n",
  3079.     "plt.suptitle(\"Trip Durations\")\n",
  3080.     "plt.show()"
  3081.    ]
  3082.   },
  3083.   {
  3084.    "cell_type": "markdown",
  3085.    "metadata": {},
  3086.    "source": [
  3087.     "If we were to choose the significance level as 0.05, the null hypothesis can be rejected.\n",
  3088.     "\n",
  3089.     "Alternative Hypothesis: Passengers that do not travel alone, tend to travel for longer distances."
  3090.    ]
  3091.   },
  3092.   {
  3093.    "cell_type": "code",
  3094.    "execution_count": 142,
  3095.    "metadata": {},
  3096.    "outputs": [
  3097.     {
  3098.      "data": {
  3099.       "text/plain": [
  3100.        "Ttest_indResult(statistic=4.903362110492197, pvalue=9.445117080447406e-07)"
  3101.       ]
  3102.      },
  3103.      "execution_count": 142,
  3104.      "metadata": {},
  3105.      "output_type": "execute_result"
  3106.     }
  3107.    ],
  3108.    "source": [
  3109.     "from scipy import stats\n",
  3110.     "stats.ttest_ind(BiggerThan1,NotBiggerThan1, equal_var=False)"
  3111.    ]
  3112.   },
  3113.   {
  3114.    "cell_type": "markdown",
  3115.    "metadata": {},
  3116.    "source": [
  3117.     "Null hypothesis: The day of the week has no effect on the distance."
  3118.    ]
  3119.   },
  3120.   {
  3121.    "cell_type": "code",
  3122.    "execution_count": 138,
  3123.    "metadata": {},
  3124.    "outputs": [],
  3125.    "source": [
  3126.     "templist2={\"Day of Week\":[], \"Distance\":[]}\n",
  3127.     "for i in range(len(df.index)):\n",
  3128.     "    time = df[\"pickup_datetime\"][i][0:10]\n",
  3129.     "    datetime_object = datetime.strptime(time, '%Y-%m-%d')\n",
  3130.     "    if(datetime_object.weekday()>5):\n",
  3131.     "        templist2[\"Day of Week\"].append(\"WeekDay\")\n",
  3132.     "        templist2[\"Distance\"].append(df[\"distance\"][i])\n",
  3133.     "    else:\n",
  3134.     "        templist2[\"Day of Week\"].append(\"WeekEnd\")\n",
  3135.     "        templist2[\"Distance\"].append(df[\"distance\"][i])\n",
  3136.     "df_temp = pd.DataFrame(templist2)"
  3137.    ]
  3138.   },
  3139.   {
  3140.    "cell_type": "code",
  3141.    "execution_count": 129,
  3142.    "metadata": {},
  3143.    "outputs": [],
  3144.    "source": [
  3145.     "WeekDay = df[df_temp['Day of Week']==\"WeekDay\"]['distance']\n",
  3146.     "WeekEnd = df[df_temp['Day of Week']==\"WeekEnd\"]['distance']"
  3147.    ]
  3148.   },
  3149.   {
  3150.    "cell_type": "code",
  3151.    "execution_count": 133,
  3152.    "metadata": {},
  3153.    "outputs": [
  3154.     {
  3155.      "data": {
  3156.       "image/png": "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\n",
  3157.       "text/plain": [
  3158.        "<Figure size 1080x432 with 2 Axes>"
  3159.       ]
  3160.      },
  3161.      "metadata": {
  3162.       "needs_background": "light"
  3163.      },
  3164.      "output_type": "display_data"
  3165.     }
  3166.    ],
  3167.    "source": [
  3168.     "fig, ax = plt.subplots(1, 2, figsize=(15,6))\n",
  3169.     "WeekDay.plot(kind=\"hist\",ax=ax[0],bins=50,label=\"aa\",color=\"c\")\n",
  3170.     "ax[0].set_title(\"Distances Traveled on WeekDays\")\n",
  3171.     "WeekEnd.plot(kind=\"hist\", ax=ax[1],bins=50,label=\"bb\",color=\"m\")\n",
  3172.     "ax[1].set_title(\"Distances Traveled on WeekEnds\")\n",
  3173.     "\n",
  3174.     "\n",
  3175.     "plt.suptitle(\"Trip Distances depending on the Day of the Week\")\n",
  3176.     "plt.show()"
  3177.    ]
  3178.   },
  3179.   {
  3180.    "cell_type": "code",
  3181.    "execution_count": 134,
  3182.    "metadata": {},
  3183.    "outputs": [
  3184.     {
  3185.      "data": {
  3186.       "text/html": [
  3187.        "<div>\n",
  3188.        "<style scoped>\n",
  3189.        "    .dataframe tbody tr th:only-of-type {\n",
  3190.        "        vertical-align: middle;\n",
  3191.        "    }\n",
  3192.        "\n",
  3193.        "    .dataframe tbody tr th {\n",
  3194.        "        vertical-align: top;\n",
  3195.        "    }\n",
  3196.        "\n",
  3197.        "    .dataframe thead th {\n",
  3198.        "        text-align: right;\n",
  3199.        "    }\n",
  3200.        "</style>\n",
  3201.        "<table border=\"1\" class=\"dataframe\">\n",
  3202.        "  <thead>\n",
  3203.        "    <tr style=\"text-align: right;\">\n",
  3204.        "      <th></th>\n",
  3205.        "      <th>Time Of Week</th>\n",
  3206.        "      <th>Average Distance</th>\n",
  3207.        "    </tr>\n",
  3208.        "  </thead>\n",
  3209.        "  <tbody>\n",
  3210.        "    <tr>\n",
  3211.        "      <th>0</th>\n",
  3212.        "      <td>WeekEnd</td>\n",
  3213.        "      <td>2.078082</td>\n",
  3214.        "    </tr>\n",
  3215.        "    <tr>\n",
  3216.        "      <th>1</th>\n",
  3217.        "      <td>WeekDay</td>\n",
  3218.        "      <td>2.325042</td>\n",
  3219.        "    </tr>\n",
  3220.        "  </tbody>\n",
  3221.        "</table>\n",
  3222.        "</div>"
  3223.       ],
  3224.       "text/plain": [
  3225.        "  Time Of Week  Average Distance\n",
  3226.        "0      WeekEnd          2.078082\n",
  3227.        "1      WeekDay          2.325042"
  3228.       ]
  3229.      },
  3230.      "execution_count": 134,
  3231.      "metadata": {},
  3232.      "output_type": "execute_result"
  3233.     }
  3234.    ],
  3235.    "source": [
  3236.     "templist3 = {\"Time Of Week\":[],\"Average Distance\":[]}\n",
  3237.     "templist3[\"Average Distance\"].append(df[df_temp['Day of Week']=='WeekEnd']['distance'].mean())\n",
  3238.     "templist3[\"Time Of Week\"].append(\"WeekEnd\")\n",
  3239.     "templist3[\"Average Distance\"].append(df[df_temp['Day of Week']=='WeekDay']['distance'].mean())\n",
  3240.     "templist3[\"Time Of Week\"].append(\"WeekDay\")\n",
  3241.     "df_weekvsdistance = pd.DataFrame(templist3)\n",
  3242.     "df_weekvsdistance"
  3243.    ]
  3244.   },
  3245.   {
  3246.    "cell_type": "code",
  3247.    "execution_count": 135,
  3248.    "metadata": {},
  3249.    "outputs": [
  3250.     {
  3251.      "data": {
  3252.       "image/png": "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\n",
  3253.       "text/plain": [
  3254.        "<Figure size 432x288 with 1 Axes>"
  3255.       ]
  3256.      },
  3257.      "metadata": {
  3258.       "needs_background": "light"
  3259.      },
  3260.      "output_type": "display_data"
  3261.     }
  3262.    ],
  3263.    "source": [
  3264.     "df_weekvsdistance.plot(kind='bar',x=\"Time Of Week\",y=\"Average Distance\",color='pink')\n",
  3265.     "plt.show()"
  3266.    ]
  3267.   },
  3268.   {
  3269.    "cell_type": "code",
  3270.    "execution_count": 140,
  3271.    "metadata": {},
  3272.    "outputs": [
  3273.     {
  3274.      "data": {
  3275.       "text/plain": [
  3276.        "Ttest_indResult(statistic=11.49810369974188, pvalue=1.673193071277966e-30)"
  3277.       ]
  3278.      },
  3279.      "execution_count": 140,
  3280.      "metadata": {},
  3281.      "output_type": "execute_result"
  3282.     }
  3283.    ],
  3284.    "source": [
  3285.     "stats.ttest_ind(WeekDay,WeekEnd, equal_var=False)"
  3286.    ]
  3287.   },
  3288.   {
  3289.    "cell_type": "markdown",
  3290.    "metadata": {},
  3291.    "source": [
  3292.     "Once again if we choose the significance level as 0.05, the null hypothesis can be rejected. \n",
  3293.     "\n",
  3294.     "Alternative Hypothesis: People tend to travel for longer distances during the weekdays compared to weekends."
  3295.    ]
  3296.   }
  3297.  ],
  3298.  "metadata": {
  3299.   "kernelspec": {
  3300.    "display_name": "Python 3",
  3301.    "language": "python",
  3302.    "name": "python3"
  3303.   },
  3304.   "language_info": {
  3305.    "codemirror_mode": {
  3306.     "name": "ipython",
  3307.     "version": 3
  3308.    },
  3309.    "file_extension": ".py",
  3310.    "mimetype": "text/x-python",
  3311.    "name": "python",
  3312.    "nbconvert_exporter": "python",
  3313.    "pygments_lexer": "ipython3",
  3314.    "version": "3.7.1"
  3315.   }
  3316.  },
  3317.  "nbformat": 4,
  3318.  "nbformat_minor": 2
  3319. }
RAW Paste Data
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. OK, I Understand
 
Top