missed_riders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import training_order_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "order_info_read = pd.read_csv('data/training_order_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "order_info = order_info_read.sort_values('time_stamp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "order_info.columns = ['initial_row', 'order_id', 'driver_id', 'passenger_id', \\\n",
    "                      'start_district_hash', 'dest_district_hash', 'price', 'time_stamp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>initial_row</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>time_stamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>262258</th>\n",
       "      <td>262258</td>\n",
       "      <td>9a864e958859b506f5f8bee9d8dfff17</td>\n",
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       "      <td>52d7b69796362a8ed1691a6cc02ddde4</td>\n",
       "      <td>45</td>\n",
       "      <td>2016-01-01 00:00:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231406</th>\n",
       "      <td>231406</td>\n",
       "      <td>6732f74a5a59353cddb421ceb39f7c26</td>\n",
       "      <td>a57d4acc407006cbb4086f0cc60d2575</td>\n",
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       "      <td>929ec6c160e6f52c20a4217c7978f681</td>\n",
       "      <td>18</td>\n",
       "      <td>2016-01-01 00:00:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210341</th>\n",
       "      <td>210341</td>\n",
       "      <td>f4beb0739bcee0f040385d69b9809b92</td>\n",
       "      <td>0b41511d4c63ed4bb030bd9d5ed5399e</td>\n",
       "      <td>b684445572045febd51a63a4e478b356</td>\n",
       "      <td>b05379ac3f9b7d99370d443cfd5dcc28</td>\n",
       "      <td>4725c39a5e5f4c188d382da3910b3f3f</td>\n",
       "      <td>31</td>\n",
       "      <td>2016-01-01 00:00:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268247</th>\n",
       "      <td>268247</td>\n",
       "      <td>1f5672197a352594fb546d7164a41f28</td>\n",
       "      <td>90f993e801c394a351321377de6820b3</td>\n",
       "      <td>7d61be2772673f4fb728c867fba64833</td>\n",
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       "      <td>82cc4851f9e4faa4e54309f8bb73fd7c</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-01-01 00:00:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188962</th>\n",
       "      <td>188962</td>\n",
       "      <td>7bd83a5dd61f5242f82c52075ad43cbf</td>\n",
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       "      <td>1cbfbdd079ef93e74405c53fcfff8567</td>\n",
       "      <td>24</td>\n",
       "      <td>2016-01-01 00:00:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        initial_row                          order_id  \\\n",
       "262258       262258  9a864e958859b506f5f8bee9d8dfff17   \n",
       "231406       231406  6732f74a5a59353cddb421ceb39f7c26   \n",
       "210341       210341  f4beb0739bcee0f040385d69b9809b92   \n",
       "268247       268247  1f5672197a352594fb546d7164a41f28   \n",
       "188962       188962  7bd83a5dd61f5242f82c52075ad43cbf   \n",
       "\n",
       "                               driver_id                      passenger_id  \\\n",
       "262258  a323121d71cd5247f38a4848c2039cb1  b9bd961ee676441d64c8748aa18efcda   \n",
       "231406  a57d4acc407006cbb4086f0cc60d2575  37e17d1ec5ca26d52be702ce74db64a6   \n",
       "210341  0b41511d4c63ed4bb030bd9d5ed5399e  b684445572045febd51a63a4e478b356   \n",
       "268247  90f993e801c394a351321377de6820b3  7d61be2772673f4fb728c867fba64833   \n",
       "188962  f5307b86a4d3da55e3626b9048d9b54a  d6c7bb502d01aed1c5031e17957e44ec   \n",
       "\n",
       "                     start_district_hash                dest_district_hash  \\\n",
       "262258  b05379ac3f9b7d99370d443cfd5dcc28  52d7b69796362a8ed1691a6cc02ddde4   \n",
       "231406  4725c39a5e5f4c188d382da3910b3f3f  929ec6c160e6f52c20a4217c7978f681   \n",
       "210341  b05379ac3f9b7d99370d443cfd5dcc28  4725c39a5e5f4c188d382da3910b3f3f   \n",
       "268247  82cc4851f9e4faa4e54309f8bb73fd7c  82cc4851f9e4faa4e54309f8bb73fd7c   \n",
       "188962  f47f35242ed40655814bc086d7514046  1cbfbdd079ef93e74405c53fcfff8567   \n",
       "\n",
       "        price           time_stamp  \n",
       "262258     45  2016-01-01 00:00:03  \n",
       "231406     18  2016-01-01 00:00:04  \n",
       "210341     31  2016-01-01 00:00:04  \n",
       "268247     10  2016-01-01 00:00:05  \n",
       "188962     24  2016-01-01 00:00:05  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "order_id = order_info['order_id']\n",
    "driver_id = order_info['driver_id']\n",
    "passenger_id = order_info['passenger_id']\n",
    "start_district_hash = order_info['start_district_hash']\n",
    "dest_district_hash = order_info['dest_district_hash']\n",
    "price = order_info['price']\n",
    "time_stamp = pd.to_datetime(order_info['time_stamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total order_id: 8540614\n",
      "unique order_id: 8518049\n"
     ]
    }
   ],
   "source": [
    "order_id_num = len(order_id)\n",
    "order_id_num_unique = len(order_id.unique())\n",
    "print 'total order_id:', order_id_num\n",
    "print 'unique order_id:', order_id_num_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total driver_id: 8540614\n",
      "unique driver_id: 93090\n",
      "null driver_id: 1516460\n",
      "fraction of missed orders: 0.177558662644\n",
      "fraction of received orders: 0.822441337356\n"
     ]
    }
   ],
   "source": [
    "driver_id_null = pd.isnull(driver_id)\n",
    "driver_id_num = len(driver_id)\n",
    "driver_id_num_unique = len(driver_id.unique())\n",
    "driver_id_num_null = len(driver_id[driver_id_null])\n",
    "print 'total driver_id:', driver_id_num\n",
    "print 'unique driver_id:', driver_id_num_unique\n",
    "print 'null driver_id:', driver_id_num_null\n",
    "print 'fraction of missed orders:', float(driver_id_num_null) / float(driver_id_num)\n",
    "print 'fraction of received orders:', 1.0 - float(driver_id_num_null) / float(driver_id_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total passenger_id: 8540614\n",
      "unique passenger_id: 1368071\n",
      "orders per passenger: 6.24281488315\n"
     ]
    }
   ],
   "source": [
    "passenger_id = order_info['passenger_id']\n",
    "passenger_id_num = len(passenger_id)\n",
    "passenger_id_num_unique = len(passenger_id.unique())\n",
    "print 'total passenger_id:', passenger_id_num\n",
    "print 'unique passenger_id:', passenger_id_num_unique\n",
    "print 'orders per passenger:', float(passenger_id_num) / float(passenger_id_num_unique)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Track missed riders (ignoring null orders before the first accepted order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "20000\n",
      "40000\n",
      "60000\n",
      "80000\n",
      "100000\n",
      "120000\n",
      "140000\n",
      "160000\n",
      "180000\n",
      "200000\n",
      "220000\n",
      "240000\n",
      "260000\n",
      "280000\n",
      "300000\n",
      "320000\n",
      "340000\n",
      "360000\n",
      "380000\n",
      "400000\n",
      "420000\n",
      "440000\n",
      "460000\n",
      "480000\n",
      "500000\n",
      "520000\n",
      "540000\n",
      "560000\n",
      "580000\n",
      "600000\n",
      "620000\n",
      "640000\n",
      "660000\n",
      "680000\n",
      "700000\n",
      "720000\n",
      "740000\n",
      "760000\n",
      "780000\n",
      "800000\n",
      "820000\n",
      "840000\n"
     ]
    }
   ],
   "source": [
    "minute = 10\n",
    "t = np.arange(60*minute) # track the first 10 minutes after an null order\n",
    "missed_rider_being_picked_up_num = np.zeros(t.shape)\n",
    "\n",
    "missed_rider_timestamp = {}\n",
    "\n",
    "### key: missed rider's hash value\n",
    "### value: the first time_stamp of an null order \n",
    "\n",
    "delta_time = None\n",
    "\n",
    "for i in range(order_id_num / 10): # only exploring tenth of data takes about half an hour on my laptop\n",
    "    dri_id = driver_id.iloc[i]\n",
    "    rid_id = passenger_id.iloc[i]\n",
    "    tim_stp = time_stamp.iloc[i]\n",
    "    if type(dri_id) == float and math.isnan(dri_id):\n",
    "        if rid_id not in missed_rider_timestamp.keys():\n",
    "            missed_rider_timestamp[rid_id] = time_stamp.iloc[i]\n",
    "    elif rid_id in missed_rider_timestamp.keys():\n",
    "        delta_time = time_stamp.iloc[i] - missed_rider_timestamp[rid_id]\n",
    "        delta_second = int(delta_time.total_seconds())\n",
    "        if delta_second < 60 * minute and delta_second > 0:\n",
    "            missed_rider_being_picked_up_num[delta_second] += 1\n",
    "        missed_rider_timestamp.pop(rid_id, None)\n",
    "    if i % 20000 == 0:\n",
    "        print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GBuAGYCFQANwTrL9eRN7OcLlqpV07+2Luu290vep8lnSGkerAA+1+1Cg4/fTo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\nLhYPGM4552LxgOGccy4WDxjOOedi+f9/zecwvtCxJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d4954d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,missed_rider_being_picked_up_num)\n",
    "plt.xlabel('time / s')\n",
    "plt.ylabel('#missed riders being picked up')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Track missed riders (update to the latest null order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "20000\n",
      "40000\n",
      "60000\n",
      "80000\n",
      "100000\n",
      "120000\n",
      "140000\n",
      "160000\n",
      "180000\n",
      "200000\n",
      "220000\n",
      "240000\n",
      "260000\n",
      "280000\n",
      "300000\n",
      "320000\n",
      "340000\n",
      "360000\n",
      "380000\n",
      "400000\n",
      "420000\n",
      "440000\n",
      "460000\n",
      "480000\n",
      "500000\n",
      "520000\n",
      "540000\n",
      "560000\n",
      "580000\n",
      "600000\n",
      "620000\n",
      "640000\n",
      "660000\n",
      "680000\n",
      "700000\n",
      "720000\n",
      "740000\n",
      "760000\n",
      "780000\n",
      "800000\n",
      "820000\n",
      "840000\n"
     ]
    }
   ],
   "source": [
    "minute = 10\n",
    "t = np.arange(60*minute) # track the first 10 minutes after an null order\n",
    "missed_rider_being_picked_up_num = np.zeros(t.shape)\n",
    "\n",
    "missed_rider_timestamp = {}\n",
    "\n",
    "### key: missed rider's hash value\n",
    "### value: the first time_stamp of an null order \n",
    "\n",
    "for i in range(order_id_num / 10):\n",
    "    dri_id = driver_id.iloc[i]\n",
    "    rid_id = passenger_id.iloc[i]\n",
    "    tim_stp = time_stamp.iloc[i]\n",
    "    if type(dri_id) == float and math.isnan(dri_id):\n",
    "        #if rid_id not in missed_rider_timestamp.keys():\n",
    "        missed_rider_timestamp[rid_id] = time_stamp.iloc[i]   ### notice the difference here!!\n",
    "    elif rid_id in missed_rider_timestamp.keys():\n",
    "        delta_time = time_stamp.iloc[i] - missed_rider_timestamp[rid_id]\n",
    "        delta_second = int(delta_time.total_seconds())\n",
    "        if delta_second < 60 * minute and delta_second > 0:\n",
    "            missed_rider_being_picked_up_num[delta_second] += 1\n",
    "        missed_rider_timestamp.pop(rid_id, None)\n",
    "    if i % 20000 == 0:\n",
    "        print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MqRJaszQD98aZ2ctmdgPwfrKw0vAmfGZvM6sEMLNFQO8kvS+Q27t/QZKWyvbb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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c501990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(t,missed_rider_being_picked_up_num)\n",
    "plt.xlabel('time / s')\n",
    "plt.ylabel('#missed riders being picked up')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}