Driver behavior.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import tempfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "path_train = 'ignored_assets/public_data/training_data/order_data'\n",
    "trainfiles = [f for f in listdir(path_train) if isfile(join(path_train, f)) and f != '.DS_Store']\n",
    "\n",
    "path_test1 = 'ignored_assets/public_data/test_set_1/order_data'\n",
    "testfiles1 = [f for f in listdir(path_test1) if isfile(join(path_test1, f)) and f != '.DS_Store']\n",
    "\n",
    "path_test2 = 'ignored_assets/public_data/test_set_2/order_data'\n",
    "testfiles2 = [f for f in listdir(path_test2) if isfile(join(path_test2, f)) and f != '.DS_Store']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf = []\n",
    "for f in trainfiles:\n",
    "    orderdf.append(pd.read_csv(join(path_train, f), delimiter='\\t', header = None))\n",
    "    \n",
    "for f in testfiles1:\n",
    "    orderdf.append(pd.read_csv(join(path_test1, f), delimiter='\\t', header = None))\n",
    "    \n",
    "for f in testfiles2:\n",
    "    orderdf.append(pd.read_csv(join(path_test2, f), delimiter='\\t', header = None))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf = pd.concat(orderdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Clean up\n",
    "orderdf[6] = pd.to_datetime(orderdf[6])\n",
    "orderdf.columns = ['order_id', 'driver_id', 'passenger_id', 'start_district_hash', 'dest_district_hash', 'price', 'timestamp']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create map of driver IDs\n",
    "driver_ids=orderdf['driver_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 77.8 ms, sys: 39.4 ms, total: 117 ms\n",
      "Wall time: 117 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>driver_id</th>\n",
       "      <th>passenger_id</th>\n",
       "      <th>start_district_hash</th>\n",
       "      <th>dest_district_hash</th>\n",
       "      <th>price</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 09:22:58</th>\n",
       "      <td>236c00d175a2295ade03ab6ea4b9689a</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>ce27cf85aaa48764f650414d71f3674e</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-01-01 09:22:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 09:35:26</th>\n",
       "      <td>a741062b69f6f1828d69a3f771aab6d5</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>1dc3e244fdef29a3299722f9aa26720f</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-01-01 09:35:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 09:43:39</th>\n",
       "      <td>c88282a71e988c5fb7eb11e33d5ca512</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>adbd59353c4651fc77405876d9e92638</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2016-01-01 09:43:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 10:02:40</th>\n",
       "      <td>8fd177882b51161c854dbd1745a54c0c</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>2cf11c2f409e7c4503e0b47c6f7d2ba9</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>4f4041f7db0c7f69892d9b74c1a7efa1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2016-01-01 10:02:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 10:04:50</th>\n",
       "      <td>a174cc46d34b2b73bbb9b94a6d022b80</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>5d8886092bbb717e0ccf37cb2d7a9661</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>9.7</td>\n",
       "      <td>2016-01-01 10:04:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 10:28:18</th>\n",
       "      <td>80559050428f704e024376a9d7306db9</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>4c5946a01d3764886040ba18fac717d6</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>929ec6c160e6f52c20a4217c7978f681</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2016-01-01 10:28:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 10:30:13</th>\n",
       "      <td>5e03c747494657155b21320fa49144a9</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>da8ccb4ff0f510e46bb4643b8a785242</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>929ec6c160e6f52c20a4217c7978f681</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2016-01-01 10:30:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 10:31:14</th>\n",
       "      <td>ea13ec3fe3b639eaefc36e4b54ebfd8e</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>729164967a31e6befeecc44548220cab</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>82a0ceee5481d552b2bab973b5ed85cb</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2016-01-01 10:31:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 13:37:23</th>\n",
       "      <td>97ebd0c6680f7c0535dbfdead6e51b4b</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>ed180d7daf639d936f1aeae4f7fb482f</td>\n",
       "      <td>4725c39a5e5f4c188d382da3910b3f3f</td>\n",
       "      <td>3e12208dd0be281c92a6ab57d9a6fb32</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2016-01-01 13:37:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 14:10:12</th>\n",
       "      <td>219e2dc27791817db73e877d0451ac7b</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>3084402739dc8b47e820c24b8ab06195</td>\n",
       "      <td>3e12208dd0be281c92a6ab57d9a6fb32</td>\n",
       "      <td>82a0ceee5481d552b2bab973b5ed85cb</td>\n",
       "      <td>20.4</td>\n",
       "      <td>2016-01-01 14:10:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 14:10:40</th>\n",
       "      <td>d48bb6f68f10e71c358347f8d4014705</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>88505970b8a7039e07575480c221386d</td>\n",
       "      <td>3e12208dd0be281c92a6ab57d9a6fb32</td>\n",
       "      <td>38d5ad2d22b61109fd8e7b43cd0e8901</td>\n",
       "      <td>20.1</td>\n",
       "      <td>2016-01-01 14:10:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 09:05:01</th>\n",
       "      <td>239794c3391315ae796f48a81cd3f9f6</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>dc04e2cfdbac1a8e8a708e6500b224bb</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>d1e80a5dc54e5dc6568e0ca56b286f5c</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2016-01-02 09:05:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 10:57:40</th>\n",
       "      <td>0324b89ad8110089bcd1032280ba9cb3</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>0385c382b1436fa5d3b0874e5c9be200</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2016-01-02 10:57:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 11:02:19</th>\n",
       "      <td>cbf1a749fcec0f88ea24c71835fbaf89</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>f545fbb1de11c2db267e59ab6d9c7e58</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>2301bc920194c95cf0c7486e5675243c</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2016-01-02 11:02:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 12:47:45</th>\n",
       "      <td>46699e0ca7670201c3b341499f92724a</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>7de2f1bc90eee499b183ef0a4e9dcd05</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2016-01-02 12:47:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 13:16:27</th>\n",
       "      <td>ac7f7885a2ecbd2394f1a3c0e8c9c502</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>0dc8a8f09c6b080d0d07629693e1e947</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2016-01-02 13:16:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-02 18:07:34</th>\n",
       "      <td>6a598a958c28a872f8cad4aadd40342a</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>0771e90cf0d8c4d7bcb75aa1a67820df</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>d1e80a5dc54e5dc6568e0ca56b286f5c</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2016-01-02 18:07:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 05:26:29</th>\n",
       "      <td>12c2f111489a06d3cddf6faa839e0dec</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>e64a8bec6b6a0b7fca9c88bbdbb88817</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2016-01-03 05:26:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 05:38:28</th>\n",
       "      <td>7cbe93771a7c268e61bdebc03e6c3197</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>e5784a5e29b85597fd433a945550b103</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2016-01-03 05:38:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 05:52:14</th>\n",
       "      <td>da290dc3a3f0347bcbb63ad6b1303a2a</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>913a8dfa1bfef70d158da1c43cdbd581</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2016-01-03 05:52:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 06:09:52</th>\n",
       "      <td>6705b1beddb2196f66abcf06e3596cd3</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>717d28f48491af824597dd1af265e99b</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2016-01-03 06:09:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 06:10:41</th>\n",
       "      <td>970d64ebb3bc6a8529d40040a32f9a30</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>c71253aedf39f69cabe5719fcf889fa4</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>1afd7afbc81ecc1b13886a569d869e8a</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2016-01-03 06:10:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 07:21:36</th>\n",
       "      <td>f72713211a118fee4007005e15fe517f</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>20d7086f53f955577530442c809d1bcb</td>\n",
       "      <td>1afd7afbc81ecc1b13886a569d869e8a</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2016-01-03 07:21:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 08:06:17</th>\n",
       "      <td>349cbbb0a9a226fb9696cecbc4f86805</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>816d249293b7edf8dfc85bc7a4c5df7a</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2016-01-03 08:06:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 08:31:55</th>\n",
       "      <td>5263a0956fb5fdd2fbf74d37de9f0db5</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>2191a136cbb00f663ff14e70b95a1401</td>\n",
       "      <td>4725c39a5e5f4c188d382da3910b3f3f</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2016-01-03 08:31:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 08:55:54</th>\n",
       "      <td>272ee3a50821b5050071559ac1b9af77</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>83bd91f5b3b118d80c032661bb7a547e</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-01-03 08:55:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 09:49:34</th>\n",
       "      <td>6b7c4c83cdd91d6b7c58bd61505371fd</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>a1358875e79442067c619f3ade732902</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2016-01-03 09:49:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 10:06:56</th>\n",
       "      <td>faa5b813dedc7e4576c08489f76f35eb</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>239bb26279c627fc247d7f295464654b</td>\n",
       "      <td>62afaf3288e236b389af9cfdc5206415</td>\n",
       "      <td>2920ece99323b4c111d6f9affc7ea034</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2016-01-03 10:06:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 10:32:45</th>\n",
       "      <td>1853c7f2088cc7e0c300f10227c0d93c</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>5602f9465ebda4134f3e3833df4678b1</td>\n",
       "      <td>1afd7afbc81ecc1b13886a569d869e8a</td>\n",
       "      <td>74ec84f1cf75cf89ae176c8c6ceec5ba</td>\n",
       "      <td>20.1</td>\n",
       "      <td>2016-01-03 10:32:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-03 11:36:12</th>\n",
       "      <td>ac667342c337a13464765605fc749389</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>bf668829d5621ac535e0bf95c522a4ca</td>\n",
       "      <td>f9280c5dab6910ed44e518248048b9fe</td>\n",
       "      <td>f9280c5dab6910ed44e518248048b9fe</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-01-03 11:36:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 09:46:16</th>\n",
       "      <td>2a32f9ab50848f83985bf73eb8c77584</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>10290113be0d2e93c4cbdb32fa23294e</td>\n",
       "      <td>2301bc920194c95cf0c7486e5675243c</td>\n",
       "      <td>87285a66236346350541b8815c5fae94</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2016-01-19 09:46:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 09:59:58</th>\n",
       "      <td>f720d3008ee5b2a2c2bcbb79b214c260</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>68cab11f2da7fcf5d39305178581678d</td>\n",
       "      <td>87285a66236346350541b8815c5fae94</td>\n",
       "      <td>87285a66236346350541b8815c5fae94</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2016-01-19 09:59:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 10:24:09</th>\n",
       "      <td>250445517bf2f36aa10a174539cfa382</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>cc656892ae9c09becc1024f113b2a4db</td>\n",
       "      <td>87285a66236346350541b8815c5fae94</td>\n",
       "      <td>a01d1425bf21f16edc6055817badd907</td>\n",
       "      <td>13.9</td>\n",
       "      <td>2016-01-19 10:24:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 10:52:03</th>\n",
       "      <td>c8a90c9d748c1749a4124a3fa33ce042</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>e1de09f34ff84afed14217c8df6b320b</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-01-19 10:52:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 13:05:28</th>\n",
       "      <td>dcc787726b4dc914da84d40501269b89</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>f731cbe7b3990e2ea8fa79f44f2a108b</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>9.9</td>\n",
       "      <td>2016-01-19 13:05:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 13:28:41</th>\n",
       "      <td>7e67429d3fb0d8dd51ee39faad776094</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>e3ea51eb44c95841a56716b018accbad</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2016-01-19 13:28:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 13:41:24</th>\n",
       "      <td>cce63701e76a5c89a50d414fb21826fe</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>516c3265b11f13d42b3647ce4615e100</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2016-01-19 13:41:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 13:58:37</th>\n",
       "      <td>549f7ca594da9ff391a2206a2243b995</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>410142489fbb21648819dcef2086c0b0</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>2016-01-19 13:58:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 14:09:40</th>\n",
       "      <td>daad3d2a69dec763c1160a7bf7729041</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>7292ef1bcc53a83fe6d311c69983bed5</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>34.1</td>\n",
       "      <td>2016-01-19 14:09:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 18:15:40</th>\n",
       "      <td>b8a71a0786c062da36abe84cd2dcc6a6</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>28cf9fc5441bd26d5909088ae13b6ec7</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>11.8</td>\n",
       "      <td>2016-01-19 18:15:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 18:22:32</th>\n",
       "      <td>aa4422b5be6a81c4c68fe876845c3811</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>28cf9fc5441bd26d5909088ae13b6ec7</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>11.8</td>\n",
       "      <td>2016-01-19 18:22:32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 19:06:41</th>\n",
       "      <td>b9e419088a22d09da5cd0bb8c2a75d3d</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>4f1ed81217fb962bfc8e875bbf3e991b</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2016-01-19 19:06:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 19:15:52</th>\n",
       "      <td>c93eba5e3ea6ce2dbf5a198147d3ce02</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>0c0d50cc51b732ccf70382c74bdd099d</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>58c7a4888306d8ff3a641d1c0feccbe3</td>\n",
       "      <td>36.5</td>\n",
       "      <td>2016-01-19 19:15:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-19 19:58:04</th>\n",
       "      <td>282872e4002b4949bf00a78e1fb20155</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>7f3ae2d072fe1fc41b2ae06fd096adbc</td>\n",
       "      <td>2301bc920194c95cf0c7486e5675243c</td>\n",
       "      <td>2301bc920194c95cf0c7486e5675243c</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-01-19 19:58:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 12:27:22</th>\n",
       "      <td>341d66824da4dc99343e0147e1d462c4</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>1bd51083762182d88eb38ae7c5e3663d</td>\n",
       "      <td>82cc4851f9e4faa4e54309f8bb73fd7c</td>\n",
       "      <td>4725c39a5e5f4c188d382da3910b3f3f</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2016-01-21 12:27:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 12:54:43</th>\n",
       "      <td>9c3fc4aecb9c4253a1d2d025daccbade</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>4c9650ed7b532191434bc2a8f0a05484</td>\n",
       "      <td>4725c39a5e5f4c188d382da3910b3f3f</td>\n",
       "      <td>9868717243779473e6699d8a7bfce66f</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2016-01-21 12:54:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 16:42:31</th>\n",
       "      <td>ade19a9d28a4e6ec08df1557fa220251</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>ae4781e3199e515b13b27e12d1224e54</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2016-01-21 16:42:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 17:05:24</th>\n",
       "      <td>6b73abe54b8979c0d75078cf548a3bc9</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>f9dd2c0ec5868866d8761337ab29291a</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-01-21 17:05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 17:32:27</th>\n",
       "      <td>9edfb2d9ae7f0cb70576c2bf3f980068</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>ec4c50d27aa6f380a5ed398a5bb73150</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>d1e80a5dc54e5dc6568e0ca56b286f5c</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2016-01-21 17:32:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 18:18:35</th>\n",
       "      <td>c8b0bc760ae22ac760caabae25e24fe1</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>95597f013eb0983c52e0bd35476b3c79</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2016-01-21 18:18:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 18:31:05</th>\n",
       "      <td>2b0cc151f6be67e2c18155f18d2c4b1b</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>56018323b921dd2c5444f98fb45509de</td>\n",
       "      <td>52a4e8aaa12f70020e889aed8fd5ddbc</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2016-01-21 18:31:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-21 18:55:12</th>\n",
       "      <td>de0f7787c0709e9a1e66282e31f94d65</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>301dcfff4e0f4799a76bd3b16406ab7a</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>a01d1425bf21f16edc6055817badd907</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2016-01-21 18:55:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-22 13:05:55</th>\n",
       "      <td>2ced666aa7c3e59a3fe4b736eedbe9ab</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>3ac033f44b40bf8d268bbe8122fe2c83</td>\n",
       "      <td>82cc4851f9e4faa4e54309f8bb73fd7c</td>\n",
       "      <td>a5609739c6b5c2719a3752327c5e33a7</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2016-01-22 13:05:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27 07:24:06</th>\n",
       "      <td>bdf12fc4824679ae07d79157b3669d44</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>20264244d8473f30f7d5ad703a43d54f</td>\n",
       "      <td>2407d482f0ffa22a947068f2551fe62c</td>\n",
       "      <td>929ec6c160e6f52c20a4217c7978f681</td>\n",
       "      <td>29.3</td>\n",
       "      <td>2016-01-27 07:24:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27 09:03:16</th>\n",
       "      <td>648e102cadb0697ba078abcf86ae360f</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>79dfc5dcd263feb6dee10e5e389f1521</td>\n",
       "      <td>38d5ad2d22b61109fd8e7b43cd0e8901</td>\n",
       "      <td>2498d22093591889d753dc151de1add9</td>\n",
       "      <td>103.4</td>\n",
       "      <td>2016-01-27 09:03:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-27 09:04:37</th>\n",
       "      <td>824d824fb7bcdece0f5eeefff7ffc388</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>b051ff0582a2d87f6bcc24346480af10</td>\n",
       "      <td>38d5ad2d22b61109fd8e7b43cd0e8901</td>\n",
       "      <td>b26a240205c852804ff8758628c0a86a</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2016-01-27 09:04:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-28 09:00:11</th>\n",
       "      <td>0fa8e3e2b9a95f33d9f98a03d9c70f2f</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>4c04255a8ad47798d67c79bf17186a35</td>\n",
       "      <td>d4ec2125aff74eded207d2d915ef682f</td>\n",
       "      <td>9868717243779473e6699d8a7bfce66f</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2016-01-28 09:00:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-29 15:18:49</th>\n",
       "      <td>13ac5ba7c2239db2120baf7307a2d416</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>67d31282bc20181b1562688e9b95f0c3</td>\n",
       "      <td>82cc4851f9e4faa4e54309f8bb73fd7c</td>\n",
       "      <td>905ac1f4c0f46a8d31ac4dc68cef54ca</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2016-01-29 15:18:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-30 11:29:07</th>\n",
       "      <td>f491e50961553a3f3b3713a31c3de36b</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>56018323b921dd2c5444f98fb45509de</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>905ac1f4c0f46a8d31ac4dc68cef54ca</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2016-01-30 11:29:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31 17:19:32</th>\n",
       "      <td>e29beef406e2596d5455540a4ea5786d</td>\n",
       "      <td>dd65fa250fca2833a3a8c16d2cf0457c</td>\n",
       "      <td>56018323b921dd2c5444f98fb45509de</td>\n",
       "      <td>ca064c2682ca48c6a21de012e87c0df5</td>\n",
       "      <td>d1e80a5dc54e5dc6568e0ca56b286f5c</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2016-01-31 17:19:32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>301 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             order_id  \\\n",
       "timestamp                                               \n",
       "2016-01-01 09:22:58  236c00d175a2295ade03ab6ea4b9689a   \n",
       "2016-01-01 09:35:26  a741062b69f6f1828d69a3f771aab6d5   \n",
       "2016-01-01 09:43:39  c88282a71e988c5fb7eb11e33d5ca512   \n",
       "2016-01-01 10:02:40  8fd177882b51161c854dbd1745a54c0c   \n",
       "2016-01-01 10:04:50  a174cc46d34b2b73bbb9b94a6d022b80   \n",
       "2016-01-01 10:28:18  80559050428f704e024376a9d7306db9   \n",
       "2016-01-01 10:30:13  5e03c747494657155b21320fa49144a9   \n",
       "2016-01-01 10:31:14  ea13ec3fe3b639eaefc36e4b54ebfd8e   \n",
       "2016-01-01 13:37:23  97ebd0c6680f7c0535dbfdead6e51b4b   \n",
       "2016-01-01 14:10:12  219e2dc27791817db73e877d0451ac7b   \n",
       "2016-01-01 14:10:40  d48bb6f68f10e71c358347f8d4014705   \n",
       "2016-01-02 09:05:01  239794c3391315ae796f48a81cd3f9f6   \n",
       "2016-01-02 10:57:40  0324b89ad8110089bcd1032280ba9cb3   \n",
       "2016-01-02 11:02:19  cbf1a749fcec0f88ea24c71835fbaf89   \n",
       "2016-01-02 12:47:45  46699e0ca7670201c3b341499f92724a   \n",
       "2016-01-02 13:16:27  ac7f7885a2ecbd2394f1a3c0e8c9c502   \n",
       "2016-01-02 18:07:34  6a598a958c28a872f8cad4aadd40342a   \n",
       "2016-01-03 05:26:29  12c2f111489a06d3cddf6faa839e0dec   \n",
       "2016-01-03 05:38:28  7cbe93771a7c268e61bdebc03e6c3197   \n",
       "2016-01-03 05:52:14  da290dc3a3f0347bcbb63ad6b1303a2a   \n",
       "2016-01-03 06:09:52  6705b1beddb2196f66abcf06e3596cd3   \n",
       "2016-01-03 06:10:41  970d64ebb3bc6a8529d40040a32f9a30   \n",
       "2016-01-03 07:21:36  f72713211a118fee4007005e15fe517f   \n",
       "2016-01-03 08:06:17  349cbbb0a9a226fb9696cecbc4f86805   \n",
       "2016-01-03 08:31:55  5263a0956fb5fdd2fbf74d37de9f0db5   \n",
       "2016-01-03 08:55:54  272ee3a50821b5050071559ac1b9af77   \n",
       "2016-01-03 09:49:34  6b7c4c83cdd91d6b7c58bd61505371fd   \n",
       "2016-01-03 10:06:56  faa5b813dedc7e4576c08489f76f35eb   \n",
       "2016-01-03 10:32:45  1853c7f2088cc7e0c300f10227c0d93c   \n",
       "2016-01-03 11:36:12  ac667342c337a13464765605fc749389   \n",
       "...                                               ...   \n",
       "2016-01-19 09:46:16  2a32f9ab50848f83985bf73eb8c77584   \n",
       "2016-01-19 09:59:58  f720d3008ee5b2a2c2bcbb79b214c260   \n",
       "2016-01-19 10:24:09  250445517bf2f36aa10a174539cfa382   \n",
       "2016-01-19 10:52:03  c8a90c9d748c1749a4124a3fa33ce042   \n",
       "2016-01-19 13:05:28  dcc787726b4dc914da84d40501269b89   \n",
       "2016-01-19 13:28:41  7e67429d3fb0d8dd51ee39faad776094   \n",
       "2016-01-19 13:41:24  cce63701e76a5c89a50d414fb21826fe   \n",
       "2016-01-19 13:58:37  549f7ca594da9ff391a2206a2243b995   \n",
       "2016-01-19 14:09:40  daad3d2a69dec763c1160a7bf7729041   \n",
       "2016-01-19 18:15:40  b8a71a0786c062da36abe84cd2dcc6a6   \n",
       "2016-01-19 18:22:32  aa4422b5be6a81c4c68fe876845c3811   \n",
       "2016-01-19 19:06:41  b9e419088a22d09da5cd0bb8c2a75d3d   \n",
       "2016-01-19 19:15:52  c93eba5e3ea6ce2dbf5a198147d3ce02   \n",
       "2016-01-19 19:58:04  282872e4002b4949bf00a78e1fb20155   \n",
       "2016-01-21 12:27:22  341d66824da4dc99343e0147e1d462c4   \n",
       "2016-01-21 12:54:43  9c3fc4aecb9c4253a1d2d025daccbade   \n",
       "2016-01-21 16:42:31  ade19a9d28a4e6ec08df1557fa220251   \n",
       "2016-01-21 17:05:24  6b73abe54b8979c0d75078cf548a3bc9   \n",
       "2016-01-21 17:32:27  9edfb2d9ae7f0cb70576c2bf3f980068   \n",
       "2016-01-21 18:18:35  c8b0bc760ae22ac760caabae25e24fe1   \n",
       "2016-01-21 18:31:05  2b0cc151f6be67e2c18155f18d2c4b1b   \n",
       "2016-01-21 18:55:12  de0f7787c0709e9a1e66282e31f94d65   \n",
       "2016-01-22 13:05:55  2ced666aa7c3e59a3fe4b736eedbe9ab   \n",
       "2016-01-27 07:24:06  bdf12fc4824679ae07d79157b3669d44   \n",
       "2016-01-27 09:03:16  648e102cadb0697ba078abcf86ae360f   \n",
       "2016-01-27 09:04:37  824d824fb7bcdece0f5eeefff7ffc388   \n",
       "2016-01-28 09:00:11  0fa8e3e2b9a95f33d9f98a03d9c70f2f   \n",
       "2016-01-29 15:18:49  13ac5ba7c2239db2120baf7307a2d416   \n",
       "2016-01-30 11:29:07  f491e50961553a3f3b3713a31c3de36b   \n",
       "2016-01-31 17:19:32  e29beef406e2596d5455540a4ea5786d   \n",
       "\n",
       "                                            driver_id  \\\n",
       "timestamp                                               \n",
       "2016-01-01 09:22:58  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 09:35:26  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 09:43:39  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 10:02:40  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 10:04:50  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 10:28:18  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 10:30:13  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 10:31:14  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 13:37:23  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 14:10:12  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-01 14:10:40  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-02 09:05:01  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-02 10:57:40  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-02 11:02:19  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-02 12:47:45  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-02 13:16:27  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-02 18:07:34  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 05:26:29  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 05:38:28  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 05:52:14  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 06:09:52  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 06:10:41  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 07:21:36  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 08:06:17  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 08:31:55  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 08:55:54  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 09:49:34  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 10:06:56  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 10:32:45  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-03 11:36:12  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "...                                               ...   \n",
       "2016-01-19 09:46:16  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 09:59:58  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 10:24:09  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 10:52:03  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 13:05:28  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 13:28:41  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 13:41:24  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 13:58:37  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 14:09:40  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 18:15:40  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 18:22:32  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 19:06:41  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 19:15:52  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-19 19:58:04  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 12:27:22  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 12:54:43  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 16:42:31  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 17:05:24  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 17:32:27  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 18:18:35  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 18:31:05  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-21 18:55:12  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-22 13:05:55  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-27 07:24:06  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-27 09:03:16  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-27 09:04:37  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-28 09:00:11  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-29 15:18:49  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-30 11:29:07  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "2016-01-31 17:19:32  dd65fa250fca2833a3a8c16d2cf0457c   \n",
       "\n",
       "                                         passenger_id  \\\n",
       "timestamp                                               \n",
       "2016-01-01 09:22:58  ce27cf85aaa48764f650414d71f3674e   \n",
       "2016-01-01 09:35:26  1dc3e244fdef29a3299722f9aa26720f   \n",
       "2016-01-01 09:43:39  adbd59353c4651fc77405876d9e92638   \n",
       "2016-01-01 10:02:40  2cf11c2f409e7c4503e0b47c6f7d2ba9   \n",
       "2016-01-01 10:04:50  5d8886092bbb717e0ccf37cb2d7a9661   \n",
       "2016-01-01 10:28:18  4c5946a01d3764886040ba18fac717d6   \n",
       "2016-01-01 10:30:13  da8ccb4ff0f510e46bb4643b8a785242   \n",
       "2016-01-01 10:31:14  729164967a31e6befeecc44548220cab   \n",
       "2016-01-01 13:37:23  ed180d7daf639d936f1aeae4f7fb482f   \n",
       "2016-01-01 14:10:12  3084402739dc8b47e820c24b8ab06195   \n",
       "2016-01-01 14:10:40  88505970b8a7039e07575480c221386d   \n",
       "2016-01-02 09:05:01  dc04e2cfdbac1a8e8a708e6500b224bb   \n",
       "2016-01-02 10:57:40  0385c382b1436fa5d3b0874e5c9be200   \n",
       "2016-01-02 11:02:19  f545fbb1de11c2db267e59ab6d9c7e58   \n",
       "2016-01-02 12:47:45  7de2f1bc90eee499b183ef0a4e9dcd05   \n",
       "2016-01-02 13:16:27  0dc8a8f09c6b080d0d07629693e1e947   \n",
       "2016-01-02 18:07:34  0771e90cf0d8c4d7bcb75aa1a67820df   \n",
       "2016-01-03 05:26:29  e64a8bec6b6a0b7fca9c88bbdbb88817   \n",
       "2016-01-03 05:38:28  e5784a5e29b85597fd433a945550b103   \n",
       "2016-01-03 05:52:14  913a8dfa1bfef70d158da1c43cdbd581   \n",
       "2016-01-03 06:09:52  717d28f48491af824597dd1af265e99b   \n",
       "2016-01-03 06:10:41  c71253aedf39f69cabe5719fcf889fa4   \n",
       "2016-01-03 07:21:36  20d7086f53f955577530442c809d1bcb   \n",
       "2016-01-03 08:06:17  816d249293b7edf8dfc85bc7a4c5df7a   \n",
       "2016-01-03 08:31:55  2191a136cbb00f663ff14e70b95a1401   \n",
       "2016-01-03 08:55:54  83bd91f5b3b118d80c032661bb7a547e   \n",
       "2016-01-03 09:49:34  a1358875e79442067c619f3ade732902   \n",
       "2016-01-03 10:06:56  239bb26279c627fc247d7f295464654b   \n",
       "2016-01-03 10:32:45  5602f9465ebda4134f3e3833df4678b1   \n",
       "2016-01-03 11:36:12  bf668829d5621ac535e0bf95c522a4ca   \n",
       "...                                               ...   \n",
       "2016-01-19 09:46:16  10290113be0d2e93c4cbdb32fa23294e   \n",
       "2016-01-19 09:59:58  68cab11f2da7fcf5d39305178581678d   \n",
       "2016-01-19 10:24:09  cc656892ae9c09becc1024f113b2a4db   \n",
       "2016-01-19 10:52:03  e1de09f34ff84afed14217c8df6b320b   \n",
       "2016-01-19 13:05:28  f731cbe7b3990e2ea8fa79f44f2a108b   \n",
       "2016-01-19 13:28:41  e3ea51eb44c95841a56716b018accbad   \n",
       "2016-01-19 13:41:24  516c3265b11f13d42b3647ce4615e100   \n",
       "2016-01-19 13:58:37  410142489fbb21648819dcef2086c0b0   \n",
       "2016-01-19 14:09:40  7292ef1bcc53a83fe6d311c69983bed5   \n",
       "2016-01-19 18:15:40  28cf9fc5441bd26d5909088ae13b6ec7   \n",
       "2016-01-19 18:22:32  28cf9fc5441bd26d5909088ae13b6ec7   \n",
       "2016-01-19 19:06:41  4f1ed81217fb962bfc8e875bbf3e991b   \n",
       "2016-01-19 19:15:52  0c0d50cc51b732ccf70382c74bdd099d   \n",
       "2016-01-19 19:58:04  7f3ae2d072fe1fc41b2ae06fd096adbc   \n",
       "2016-01-21 12:27:22  1bd51083762182d88eb38ae7c5e3663d   \n",
       "2016-01-21 12:54:43  4c9650ed7b532191434bc2a8f0a05484   \n",
       "2016-01-21 16:42:31  ae4781e3199e515b13b27e12d1224e54   \n",
       "2016-01-21 17:05:24  f9dd2c0ec5868866d8761337ab29291a   \n",
       "2016-01-21 17:32:27  ec4c50d27aa6f380a5ed398a5bb73150   \n",
       "2016-01-21 18:18:35  95597f013eb0983c52e0bd35476b3c79   \n",
       "2016-01-21 18:31:05  56018323b921dd2c5444f98fb45509de   \n",
       "2016-01-21 18:55:12  301dcfff4e0f4799a76bd3b16406ab7a   \n",
       "2016-01-22 13:05:55  3ac033f44b40bf8d268bbe8122fe2c83   \n",
       "2016-01-27 07:24:06  20264244d8473f30f7d5ad703a43d54f   \n",
       "2016-01-27 09:03:16  79dfc5dcd263feb6dee10e5e389f1521   \n",
       "2016-01-27 09:04:37  b051ff0582a2d87f6bcc24346480af10   \n",
       "2016-01-28 09:00:11  4c04255a8ad47798d67c79bf17186a35   \n",
       "2016-01-29 15:18:49  67d31282bc20181b1562688e9b95f0c3   \n",
       "2016-01-30 11:29:07  56018323b921dd2c5444f98fb45509de   \n",
       "2016-01-31 17:19:32  56018323b921dd2c5444f98fb45509de   \n",
       "\n",
       "                                  start_district_hash  \\\n",
       "timestamp                                               \n",
       "2016-01-01 09:22:58  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-01 09:35:26  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-01 09:43:39  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-01 10:02:40  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-01 10:04:50  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-01 10:28:18  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-01 10:30:13  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-01 10:31:14  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-01 13:37:23  4725c39a5e5f4c188d382da3910b3f3f   \n",
       "2016-01-01 14:10:12  3e12208dd0be281c92a6ab57d9a6fb32   \n",
       "2016-01-01 14:10:40  3e12208dd0be281c92a6ab57d9a6fb32   \n",
       "2016-01-02 09:05:01  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-02 10:57:40  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-02 11:02:19  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-02 12:47:45  52a4e8aaa12f70020e889aed8fd5ddbc   \n",
       "2016-01-02 13:16:27  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-02 18:07:34  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-03 05:26:29  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-03 05:38:28  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-03 05:52:14  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-03 06:09:52  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-03 06:10:41  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-03 07:21:36  1afd7afbc81ecc1b13886a569d869e8a   \n",
       "2016-01-03 08:06:17  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-03 08:31:55  4725c39a5e5f4c188d382da3910b3f3f   \n",
       "2016-01-03 08:55:54  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-03 09:49:34  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-03 10:06:56  62afaf3288e236b389af9cfdc5206415   \n",
       "2016-01-03 10:32:45  1afd7afbc81ecc1b13886a569d869e8a   \n",
       "2016-01-03 11:36:12  f9280c5dab6910ed44e518248048b9fe   \n",
       "...                                               ...   \n",
       "2016-01-19 09:46:16  2301bc920194c95cf0c7486e5675243c   \n",
       "2016-01-19 09:59:58  87285a66236346350541b8815c5fae94   \n",
       "2016-01-19 10:24:09  87285a66236346350541b8815c5fae94   \n",
       "2016-01-19 10:52:03  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 13:05:28  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 13:28:41  52a4e8aaa12f70020e889aed8fd5ddbc   \n",
       "2016-01-19 13:41:24  52a4e8aaa12f70020e889aed8fd5ddbc   \n",
       "2016-01-19 13:58:37  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 14:09:40  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 18:15:40  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 18:22:32  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 19:06:41  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 19:15:52  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-19 19:58:04  2301bc920194c95cf0c7486e5675243c   \n",
       "2016-01-21 12:27:22  82cc4851f9e4faa4e54309f8bb73fd7c   \n",
       "2016-01-21 12:54:43  4725c39a5e5f4c188d382da3910b3f3f   \n",
       "2016-01-21 16:42:31  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-21 17:05:24  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-21 17:32:27  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-21 18:18:35  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-21 18:31:05  52a4e8aaa12f70020e889aed8fd5ddbc   \n",
       "2016-01-21 18:55:12  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-22 13:05:55  82cc4851f9e4faa4e54309f8bb73fd7c   \n",
       "2016-01-27 07:24:06  2407d482f0ffa22a947068f2551fe62c   \n",
       "2016-01-27 09:03:16  38d5ad2d22b61109fd8e7b43cd0e8901   \n",
       "2016-01-27 09:04:37  38d5ad2d22b61109fd8e7b43cd0e8901   \n",
       "2016-01-28 09:00:11  d4ec2125aff74eded207d2d915ef682f   \n",
       "2016-01-29 15:18:49  82cc4851f9e4faa4e54309f8bb73fd7c   \n",
       "2016-01-30 11:29:07  ca064c2682ca48c6a21de012e87c0df5   \n",
       "2016-01-31 17:19:32  ca064c2682ca48c6a21de012e87c0df5   \n",
       "\n",
       "                                   dest_district_hash  price  \\\n",
       "timestamp                                                      \n",
       "2016-01-01 09:22:58  62afaf3288e236b389af9cfdc5206415    6.0   \n",
       "2016-01-01 09:35:26  62afaf3288e236b389af9cfdc5206415    6.0   \n",
       "2016-01-01 09:43:39  d4ec2125aff74eded207d2d915ef682f   11.0   \n",
       "2016-01-01 10:02:40  4f4041f7db0c7f69892d9b74c1a7efa1   41.0   \n",
       "2016-01-01 10:04:50  d4ec2125aff74eded207d2d915ef682f    9.7   \n",
       "2016-01-01 10:28:18  929ec6c160e6f52c20a4217c7978f681   11.0   \n",
       "2016-01-01 10:30:13  929ec6c160e6f52c20a4217c7978f681   26.0   \n",
       "2016-01-01 10:31:14  82a0ceee5481d552b2bab973b5ed85cb   22.0   \n",
       "2016-01-01 13:37:23  3e12208dd0be281c92a6ab57d9a6fb32   24.0   \n",
       "2016-01-01 14:10:12  82a0ceee5481d552b2bab973b5ed85cb   20.4   \n",
       "2016-01-01 14:10:40  38d5ad2d22b61109fd8e7b43cd0e8901   20.1   \n",
       "2016-01-02 09:05:01  d1e80a5dc54e5dc6568e0ca56b286f5c   32.0   \n",
       "2016-01-02 10:57:40  52a4e8aaa12f70020e889aed8fd5ddbc   10.0   \n",
       "2016-01-02 11:02:19  2301bc920194c95cf0c7486e5675243c   20.2   \n",
       "2016-01-02 12:47:45  ca064c2682ca48c6a21de012e87c0df5   18.0   \n",
       "2016-01-02 13:16:27  ca064c2682ca48c6a21de012e87c0df5    7.0   \n",
       "2016-01-02 18:07:34  d1e80a5dc54e5dc6568e0ca56b286f5c    8.9   \n",
       "2016-01-03 05:26:29  62afaf3288e236b389af9cfdc5206415   11.0   \n",
       "2016-01-03 05:38:28  62afaf3288e236b389af9cfdc5206415    9.0   \n",
       "2016-01-03 05:52:14  d4ec2125aff74eded207d2d915ef682f    9.0   \n",
       "2016-01-03 06:09:52  d4ec2125aff74eded207d2d915ef682f   11.0   \n",
       "2016-01-03 06:10:41  1afd7afbc81ecc1b13886a569d869e8a   30.0   \n",
       "2016-01-03 07:21:36  d4ec2125aff74eded207d2d915ef682f   21.0   \n",
       "2016-01-03 08:06:17  d4ec2125aff74eded207d2d915ef682f   22.0   \n",
       "2016-01-03 08:31:55  d4ec2125aff74eded207d2d915ef682f   16.0   \n",
       "2016-01-03 08:55:54  62afaf3288e236b389af9cfdc5206415    6.0   \n",
       "2016-01-03 09:49:34  62afaf3288e236b389af9cfdc5206415    7.0   \n",
       "2016-01-03 10:06:56  2920ece99323b4c111d6f9affc7ea034   30.0   \n",
       "2016-01-03 10:32:45  74ec84f1cf75cf89ae176c8c6ceec5ba   20.1   \n",
       "2016-01-03 11:36:12  f9280c5dab6910ed44e518248048b9fe    3.0   \n",
       "...                                               ...    ...   \n",
       "2016-01-19 09:46:16  87285a66236346350541b8815c5fae94    8.0   \n",
       "2016-01-19 09:59:58  87285a66236346350541b8815c5fae94   10.0   \n",
       "2016-01-19 10:24:09  a01d1425bf21f16edc6055817badd907   13.9   \n",
       "2016-01-19 10:52:03  ca064c2682ca48c6a21de012e87c0df5    4.0   \n",
       "2016-01-19 13:05:28  52a4e8aaa12f70020e889aed8fd5ddbc    9.9   \n",
       "2016-01-19 13:28:41  52a4e8aaa12f70020e889aed8fd5ddbc   14.0   \n",
       "2016-01-19 13:41:24  ca064c2682ca48c6a21de012e87c0df5    9.0   \n",
       "2016-01-19 13:58:37  ca064c2682ca48c6a21de012e87c0df5   15.6   \n",
       "2016-01-19 14:09:40  d4ec2125aff74eded207d2d915ef682f   34.1   \n",
       "2016-01-19 18:15:40  52a4e8aaa12f70020e889aed8fd5ddbc   11.8   \n",
       "2016-01-19 18:22:32  52a4e8aaa12f70020e889aed8fd5ddbc   11.8   \n",
       "2016-01-19 19:06:41  ca064c2682ca48c6a21de012e87c0df5    8.0   \n",
       "2016-01-19 19:15:52  58c7a4888306d8ff3a641d1c0feccbe3   36.5   \n",
       "2016-01-19 19:58:04  2301bc920194c95cf0c7486e5675243c    4.0   \n",
       "2016-01-21 12:27:22  4725c39a5e5f4c188d382da3910b3f3f   21.0   \n",
       "2016-01-21 12:54:43  9868717243779473e6699d8a7bfce66f    7.0   \n",
       "2016-01-21 16:42:31  ca064c2682ca48c6a21de012e87c0df5   10.0   \n",
       "2016-01-21 17:05:24  ca064c2682ca48c6a21de012e87c0df5    3.0   \n",
       "2016-01-21 17:32:27  d1e80a5dc54e5dc6568e0ca56b286f5c   18.0   \n",
       "2016-01-21 18:18:35  52a4e8aaa12f70020e889aed8fd5ddbc   21.0   \n",
       "2016-01-21 18:31:05  ca064c2682ca48c6a21de012e87c0df5   11.0   \n",
       "2016-01-21 18:55:12  a01d1425bf21f16edc6055817badd907   12.0   \n",
       "2016-01-22 13:05:55  a5609739c6b5c2719a3752327c5e33a7   67.0   \n",
       "2016-01-27 07:24:06  929ec6c160e6f52c20a4217c7978f681   29.3   \n",
       "2016-01-27 09:03:16  2498d22093591889d753dc151de1add9  103.4   \n",
       "2016-01-27 09:04:37  b26a240205c852804ff8758628c0a86a   38.0   \n",
       "2016-01-28 09:00:11  9868717243779473e6699d8a7bfce66f   25.0   \n",
       "2016-01-29 15:18:49  905ac1f4c0f46a8d31ac4dc68cef54ca   35.0   \n",
       "2016-01-30 11:29:07  905ac1f4c0f46a8d31ac4dc68cef54ca   15.0   \n",
       "2016-01-31 17:19:32  d1e80a5dc54e5dc6568e0ca56b286f5c    7.0   \n",
       "\n",
       "                              timestamp  \n",
       "timestamp                                \n",
       "2016-01-01 09:22:58 2016-01-01 09:22:58  \n",
       "2016-01-01 09:35:26 2016-01-01 09:35:26  \n",
       "2016-01-01 09:43:39 2016-01-01 09:43:39  \n",
       "2016-01-01 10:02:40 2016-01-01 10:02:40  \n",
       "2016-01-01 10:04:50 2016-01-01 10:04:50  \n",
       "2016-01-01 10:28:18 2016-01-01 10:28:18  \n",
       "2016-01-01 10:30:13 2016-01-01 10:30:13  \n",
       "2016-01-01 10:31:14 2016-01-01 10:31:14  \n",
       "2016-01-01 13:37:23 2016-01-01 13:37:23  \n",
       "2016-01-01 14:10:12 2016-01-01 14:10:12  \n",
       "2016-01-01 14:10:40 2016-01-01 14:10:40  \n",
       "2016-01-02 09:05:01 2016-01-02 09:05:01  \n",
       "2016-01-02 10:57:40 2016-01-02 10:57:40  \n",
       "2016-01-02 11:02:19 2016-01-02 11:02:19  \n",
       "2016-01-02 12:47:45 2016-01-02 12:47:45  \n",
       "2016-01-02 13:16:27 2016-01-02 13:16:27  \n",
       "2016-01-02 18:07:34 2016-01-02 18:07:34  \n",
       "2016-01-03 05:26:29 2016-01-03 05:26:29  \n",
       "2016-01-03 05:38:28 2016-01-03 05:38:28  \n",
       "2016-01-03 05:52:14 2016-01-03 05:52:14  \n",
       "2016-01-03 06:09:52 2016-01-03 06:09:52  \n",
       "2016-01-03 06:10:41 2016-01-03 06:10:41  \n",
       "2016-01-03 07:21:36 2016-01-03 07:21:36  \n",
       "2016-01-03 08:06:17 2016-01-03 08:06:17  \n",
       "2016-01-03 08:31:55 2016-01-03 08:31:55  \n",
       "2016-01-03 08:55:54 2016-01-03 08:55:54  \n",
       "2016-01-03 09:49:34 2016-01-03 09:49:34  \n",
       "2016-01-03 10:06:56 2016-01-03 10:06:56  \n",
       "2016-01-03 10:32:45 2016-01-03 10:32:45  \n",
       "2016-01-03 11:36:12 2016-01-03 11:36:12  \n",
       "...                                 ...  \n",
       "2016-01-19 09:46:16 2016-01-19 09:46:16  \n",
       "2016-01-19 09:59:58 2016-01-19 09:59:58  \n",
       "2016-01-19 10:24:09 2016-01-19 10:24:09  \n",
       "2016-01-19 10:52:03 2016-01-19 10:52:03  \n",
       "2016-01-19 13:05:28 2016-01-19 13:05:28  \n",
       "2016-01-19 13:28:41 2016-01-19 13:28:41  \n",
       "2016-01-19 13:41:24 2016-01-19 13:41:24  \n",
       "2016-01-19 13:58:37 2016-01-19 13:58:37  \n",
       "2016-01-19 14:09:40 2016-01-19 14:09:40  \n",
       "2016-01-19 18:15:40 2016-01-19 18:15:40  \n",
       "2016-01-19 18:22:32 2016-01-19 18:22:32  \n",
       "2016-01-19 19:06:41 2016-01-19 19:06:41  \n",
       "2016-01-19 19:15:52 2016-01-19 19:15:52  \n",
       "2016-01-19 19:58:04 2016-01-19 19:58:04  \n",
       "2016-01-21 12:27:22 2016-01-21 12:27:22  \n",
       "2016-01-21 12:54:43 2016-01-21 12:54:43  \n",
       "2016-01-21 16:42:31 2016-01-21 16:42:31  \n",
       "2016-01-21 17:05:24 2016-01-21 17:05:24  \n",
       "2016-01-21 17:32:27 2016-01-21 17:32:27  \n",
       "2016-01-21 18:18:35 2016-01-21 18:18:35  \n",
       "2016-01-21 18:31:05 2016-01-21 18:31:05  \n",
       "2016-01-21 18:55:12 2016-01-21 18:55:12  \n",
       "2016-01-22 13:05:55 2016-01-22 13:05:55  \n",
       "2016-01-27 07:24:06 2016-01-27 07:24:06  \n",
       "2016-01-27 09:03:16 2016-01-27 09:03:16  \n",
       "2016-01-27 09:04:37 2016-01-27 09:04:37  \n",
       "2016-01-28 09:00:11 2016-01-28 09:00:11  \n",
       "2016-01-29 15:18:49 2016-01-29 15:18:49  \n",
       "2016-01-30 11:29:07 2016-01-30 11:29:07  \n",
       "2016-01-31 17:19:32 2016-01-31 17:19:32  \n",
       "\n",
       "[301 rows x 7 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Index stuff\n",
    "orderdf_f=orderdf[~pd.isnull(orderdf['driver_id'])]\n",
    "myindex=pd.MultiIndex.from_arrays([orderdf_f['driver_id'],orderdf_f['timestamp']],names=('driver_id','timestamp'))\n",
    "\n",
    "orderdf2=orderdf_f.set_index(myindex)\n",
    "orderdf_s=orderdf2.sort_index()\n",
    "\n",
    "%time orderdf_s.loc[driver_ids[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Index empty trips\n",
    "orderdf_s['next_timestamp']=orderdf_s['timestamp'].shift(-1)\n",
    "orderdf_s['next_start_district_hash']=orderdf_s['start_district_hash'].shift(-1)\n",
    "orderdf_s['next_driver_id']=orderdf_s['driver_id'].shift(-1)\n",
    "\n",
    "break_threshold=pd.Timedelta('1 hour')\n",
    "\n",
    "orderdf_s['break']=(orderdf_s['next_timestamp']-orderdf_s['timestamp'])>break_threshold\n",
    "orderdf_s['emptytrip']=(orderdf_s['dest_district_hash']!=orderdf_s['next_start_district_hash'])\n",
    "orderdf_s['last_ride']=(orderdf_s['next_driver_id']!=orderdf_s['driver_id'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rebalancing trips:  0.278313998535 of total\n"
     ]
    }
   ],
   "source": [
    "#Compute number of rebalancing trips\n",
    "num_reb_trips=sum(orderdf_s['emptytrip'] & ~orderdf_s['break'] & ~orderdf_s['last_ride'])\n",
    "frac_reb_trips=float(num_reb_trips)/float(len(orderdf_s))\n",
    "print 'Rebalancing trips: ', frac_reb_trips, 'of total'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Output actual reb trips\n",
    "\n",
    "reb_trips=orderdf_s[orderdf_s['emptytrip'] & ~orderdf_s['break'] & ~orderdf_s['last_ride']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Group rebalancing trips\n",
    "reb_trips.set_index('order_id')\n",
    "reb_trips['quarter_hour'] = reb_trips['timestamp'].dt.round('15min')  \n",
    "group_reb_trips=reb_trips.groupby(['quarter_hour','dest_district_hash']).count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>driver_id</th>\n",
       "      <th>passenger_id</th>\n",
       "      <th>start_district_hash</th>\n",
       "      <th>price</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>next_timestamp</th>\n",
       "      <th>next_start_district_hash</th>\n",
       "      <th>next_driver_id</th>\n",
       "      <th>break</th>\n",
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       "      <th>quarter_hour</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
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       "      <td>5</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               order_id  driver_id  \\\n",
       "quarter_hour dest_district_hash                                      \n",
       "2016-01-01   0423d2cd9db4272d92b1fe1dbf874352         5          5   \n",
       "             08232402614a9b48895cc3d0aeb0e9f2         1          1   \n",
       "             1afd7afbc81ecc1b13886a569d869e8a        23         23   \n",
       "             1c7245a5224902fb3d4feca21b7b5808         1          1   \n",
       "             1cbfbdd079ef93e74405c53fcfff8567         7          7   \n",
       "\n",
       "                                               passenger_id  \\\n",
       "quarter_hour dest_district_hash                               \n",
       "2016-01-01   0423d2cd9db4272d92b1fe1dbf874352             5   \n",
       "             08232402614a9b48895cc3d0aeb0e9f2             1   \n",
       "             1afd7afbc81ecc1b13886a569d869e8a            23   \n",
       "             1c7245a5224902fb3d4feca21b7b5808             1   \n",
       "             1cbfbdd079ef93e74405c53fcfff8567             7   \n",
       "\n",
       "                                               start_district_hash  price  \\\n",
       "quarter_hour dest_district_hash                                             \n",
       "2016-01-01   0423d2cd9db4272d92b1fe1dbf874352                    5      5   \n",
       "             08232402614a9b48895cc3d0aeb0e9f2                    1      1   \n",
       "             1afd7afbc81ecc1b13886a569d869e8a                   23     23   \n",
       "             1c7245a5224902fb3d4feca21b7b5808                    1      1   \n",
       "             1cbfbdd079ef93e74405c53fcfff8567                    7      7   \n",
       "\n",
       "                                               timestamp  next_timestamp  \\\n",
       "quarter_hour dest_district_hash                                            \n",
       "2016-01-01   0423d2cd9db4272d92b1fe1dbf874352          5               5   \n",
       "             08232402614a9b48895cc3d0aeb0e9f2          1               1   \n",
       "             1afd7afbc81ecc1b13886a569d869e8a         23              23   \n",
       "             1c7245a5224902fb3d4feca21b7b5808          1               1   \n",
       "             1cbfbdd079ef93e74405c53fcfff8567          7               7   \n",
       "\n",
       "                                               next_start_district_hash  \\\n",
       "quarter_hour dest_district_hash                                           \n",
       "2016-01-01   0423d2cd9db4272d92b1fe1dbf874352                         5   \n",
       "             08232402614a9b48895cc3d0aeb0e9f2                         1   \n",
       "             1afd7afbc81ecc1b13886a569d869e8a                        23   \n",
       "             1c7245a5224902fb3d4feca21b7b5808                         1   \n",
       "             1cbfbdd079ef93e74405c53fcfff8567                         7   \n",
       "\n",
       "                                               next_driver_id  break  \\\n",
       "quarter_hour dest_district_hash                                        \n",
       "2016-01-01   0423d2cd9db4272d92b1fe1dbf874352               5      5   \n",
       "             08232402614a9b48895cc3d0aeb0e9f2               1      1   \n",
       "             1afd7afbc81ecc1b13886a569d869e8a              23     23   \n",
       "             1c7245a5224902fb3d4feca21b7b5808               1      1   \n",
       "             1cbfbdd079ef93e74405c53fcfff8567               7      7   \n",
       "\n",
       "                                               emptytrip  last_ride  \n",
       "quarter_hour dest_district_hash                                      \n",
       "2016-01-01   0423d2cd9db4272d92b1fe1dbf874352          5          5  \n",
       "             08232402614a9b48895cc3d0aeb0e9f2          1          1  \n",
       "             1afd7afbc81ecc1b13886a569d869e8a         23         23  \n",
       "             1c7245a5224902fb3d4feca21b7b5808          1          1  \n",
       "             1cbfbdd079ef93e74405c53fcfff8567          7          7  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_reb_trips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#TODO: compare trips with outstanding pax requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>driver_id</th>\n",
       "      <th>passenger_id</th>\n",
       "      <th>dest_district_hash</th>\n",
       "      <th>price</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quarter_hour</th>\n",
       "      <th>start_district_hash</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2016-01-01</th>\n",
       "      <th>2920ece99323b4c111d6f9affc7ea034</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364bf755f9b270f0f9141d1a61de43ee</th>\n",
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       "    <tr>\n",
       "      <th>38d5ad2d22b61109fd8e7b43cd0e8901</th>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3a43dcdff3c0b66b1acb1644ff055f9d</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4725c39a5e5f4c188d382da3910b3f3f</th>\n",
       "      <td>5</td>\n",
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               order_id  driver_id  \\\n",
       "quarter_hour start_district_hash                                     \n",
       "2016-01-01   2920ece99323b4c111d6f9affc7ea034         5          0   \n",
       "             364bf755f9b270f0f9141d1a61de43ee         1          0   \n",
       "             38d5ad2d22b61109fd8e7b43cd0e8901         2          0   \n",
       "             3a43dcdff3c0b66b1acb1644ff055f9d         1          0   \n",
       "             4725c39a5e5f4c188d382da3910b3f3f         5          0   \n",
       "\n",
       "                                               passenger_id  \\\n",
       "quarter_hour start_district_hash                              \n",
       "2016-01-01   2920ece99323b4c111d6f9affc7ea034             5   \n",
       "             364bf755f9b270f0f9141d1a61de43ee             1   \n",
       "             38d5ad2d22b61109fd8e7b43cd0e8901             2   \n",
       "             3a43dcdff3c0b66b1acb1644ff055f9d             1   \n",
       "             4725c39a5e5f4c188d382da3910b3f3f             5   \n",
       "\n",
       "                                               dest_district_hash  price  \\\n",
       "quarter_hour start_district_hash                                           \n",
       "2016-01-01   2920ece99323b4c111d6f9affc7ea034                   5      5   \n",
       "             364bf755f9b270f0f9141d1a61de43ee                   1      1   \n",
       "             38d5ad2d22b61109fd8e7b43cd0e8901                   2      2   \n",
       "             3a43dcdff3c0b66b1acb1644ff055f9d                   1      1   \n",
       "             4725c39a5e5f4c188d382da3910b3f3f                   5      5   \n",
       "\n",
       "                                               timestamp  \n",
       "quarter_hour start_district_hash                          \n",
       "2016-01-01   2920ece99323b4c111d6f9affc7ea034          5  \n",
       "             364bf755f9b270f0f9141d1a61de43ee          1  \n",
       "             38d5ad2d22b61109fd8e7b43cd0e8901          2  \n",
       "             3a43dcdff3c0b66b1acb1644ff055f9d          1  \n",
       "             4725c39a5e5f4c188d382da3910b3f3f          5  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Find unfulfilled pax requests\n",
    "orderdf_uf=orderdf[pd.isnull(orderdf['driver_id'])].copy()\n",
    "orderdf_uf['quarter_hour'] = orderdf_uf['timestamp'].dt.round('15min')  \n",
    "group_order_uf=orderdf_uf.groupby(['quarter_hour','start_district_hash']).count()\n",
    "group_order_uf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "merging with both multi-indexes is not implemented",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-92-0a1b26a0204a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgroup_order_uf\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mgroup_reb_trips\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/ops.pyc\u001b[0m in \u001b[0;36mf\u001b[0;34m(self, other, axis, level, fill_value)\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1220\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Another DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1222\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_combine_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_combine_frame\u001b[0;34m(self, other, func, fill_value, level)\u001b[0m\n\u001b[1;32m   3530\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3531\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_combine_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3532\u001b[0;31m         \u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjoin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'outer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3533\u001b[0m         \u001b[0mnew_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_columns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36malign\u001b[0;34m(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)\u001b[0m\n\u001b[1;32m   2814\u001b[0m                                             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2815\u001b[0m                                             \u001b[0mfill_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_axis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2816\u001b[0;31m                                             broadcast_axis=broadcast_axis)\n\u001b[0m\u001b[1;32m   2817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2818\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reindex'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36malign\u001b[0;34m(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)\u001b[0m\n\u001b[1;32m   4405\u001b[0m                                      \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4406\u001b[0m                                      \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4407\u001b[0;31m                                      fill_axis=fill_axis)\n\u001b[0m\u001b[1;32m   4408\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4409\u001b[0m             return self._align_series(other, join=join, axis=axis, level=level,\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_align_frame\u001b[0;34m(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)\u001b[0m\n\u001b[1;32m   4427\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4428\u001b[0m                 join_index, ilidx, iridx = self.index.join(\n\u001b[0;32m-> 4429\u001b[0;31m                     other.index, how=join, level=level, return_indexers=True)\n\u001b[0m\u001b[1;32m   4430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4431\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36mjoin\u001b[0;34m(self, other, how, level, return_indexers)\u001b[0m\n\u001b[1;32m   2644\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2645\u001b[0m                 return self._join_multi(other, how=how,\n\u001b[0;32m-> 2646\u001b[0;31m                                         return_indexers=return_indexers)\n\u001b[0m\u001b[1;32m   2647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2648\u001b[0m         \u001b[0;31m# join on the level\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/indexes/base.pyc\u001b[0m in \u001b[0;36m_join_multi\u001b[0;34m(self, other, how, return_indexers)\u001b[0m\n\u001b[1;32m   2764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2765\u001b[0m         \u001b[0;31m# 2 multi-indexes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2766\u001b[0;31m         raise NotImplementedError(\"merging with both multi-indexes is not \"\n\u001b[0m\u001b[1;32m   2767\u001b[0m                                   \"implemented\")\n\u001b[1;32m   2768\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: merging with both multi-indexes is not implemented"
     ]
    }
   ],
   "source": [
    "# Do unfulfilled pax request correspond to rebalancing trips?\n",
    "# That is, in what fraction of the instances where pax are bounced are there vehicles rebalancing there?\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiments of the failed kind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def count_empty_trips(driver_hash):\n",
    "    #Copy out driver info\n",
    "    driver_orders=orderdf2.loc[driver_hash]\n",
    "    driver_orders=driver_orders.sort_values('timestamp')\n",
    "    \n",
    "    #Auxiliary stuff\n",
    "    driver_orders['next_timestamp']=driver_orders['timestamp'].shift(-1)\n",
    "    driver_orders['next_start_district_hash']=driver_orders['start_district_hash'].shift(-1)\n",
    "    break_threshold=pd.Timedelta('1 hour')\n",
    "    \n",
    "    #Did the driver go on break after the ride? Did the driver rebalance?\n",
    "    driver_orders['break']=(driver_orders['next_timestamp']-driver_orders['timestamp'])>break_threshold\n",
    "    driver_orders['emptytrip']=(driver_orders['dest_district_hash']!=driver_orders['next_start_district_hash'])\n",
    "    \n",
    "    if len(driver_orders)>0:\n",
    "        return float(sum(driver_orders['emptytrip'] & ~driver_orders['break']))/float(len(driver_orders))\n",
    "    else:\n",
    "        return np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "driver_pd=pd.DataFrame(driver_ids[~pd.isnull(driver_ids)],columns=['driver_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "driver_pd['frac_reb']=driver_pd.apply(lambda x: count_empty_trips(x['driver_id']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orderdf2=orderdf.sort_values('driver_id')\n",
    "\n",
    "myindex=pd.MultiIndex.from_tuples(list(zip(orderdf['driver_id'],orderdf['order_id'])))\n",
    "\n",
    "orderdf3=orderdf2.set_index('driver_id')\n",
    "\n",
    "\n",
    "#driver_orders=orderdf[orderdf['driver_id']==driver_hash].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "driver_ids[pd.isnull(driver_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list(zip(orderdf['driver_id'],orderdf['order_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_empty_trips(driver_ids[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create a timeline of rebalancing trips.\n",
    "# For each, record two timestamps (last pickup, next pickup), start location, and end location.\n",
    "# Sketch: for each driver, return a list of reb trips by masking. Then append.\n",
    "# Can we do this in a non-sequential way?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_empty_trips(driver_hash):\n",
    "    #Copy out driver info\n",
    "    driver_orders=orderdf2.loc[driver_hash]\n",
    "    driver_orders=driver_orders.sort_values('timestamp')\n",
    "    \n",
    "    #Auxiliary stuff\n",
    "    driver_orders['next_timestamp']=driver_orders['timestamp'].shift(-1)\n",
    "    driver_orders['next_start_district_hash']=driver_orders['start_district_hash'].shift(-1)\n",
    "    break_threshold=pd.Timedelta('1 hour')\n",
    "    \n",
    "    #Did the driver go on break after the ride? Did the driver rebalance?\n",
    "    driver_orders['break']=(driver_orders['next_timestamp']-driver_orders['timestamp'])>break_threshold\n",
    "    driver_orders['emptytrip']=(driver_orders['dest_district_hash']!=driver_orders['next_start_district_hash'])\n",
    "    \n",
    "    return driver_orders[driver_orders['emptytrip'] & ~driver_orders['break']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "empty_rides_list=driver_pd.apply(lambda x: find_empty_trips(x['driver_id']),axis=1)"
   ]
  }
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