segmentation+py .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ec4c725f62</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-01-01 00:11:54.000</td>\n",
       "      <td>508.0</td>\n",
       "      <td>3613.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8ad53f1e52</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-01 00:24:39.000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>f39c8f74a7</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-01-01 00:25:06.000</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>5449.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3a9852ced0</td>\n",
       "      <td>7a50190eaf</td>\n",
       "      <td>2017-01-01 01:02:33.000</td>\n",
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       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0f44dc5848</td>\n",
       "      <td>19dc69f7f3</td>\n",
       "      <td>2017-01-01 01:36:27.000</td>\n",
       "      <td>659.0</td>\n",
       "      <td>6856.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_id     user_id             completed_at  duration  distance  price  \\\n",
       "0  ec4c725f62  b14de8a940  2017-01-01 00:11:54.000     508.0    3613.0  498.0   \n",
       "1  8ad53f1e52  9f563f3b47  2017-01-01 00:24:39.000       0.0       0.0    0.0   \n",
       "2  f39c8f74a7  e43cc45c19  2017-01-01 00:25:06.000    1119.0    5449.0  726.0   \n",
       "3  3a9852ced0  7a50190eaf  2017-01-01 01:02:33.000       0.0       0.0    0.0   \n",
       "4  0f44dc5848  19dc69f7f3  2017-01-01 01:36:27.000     659.0    6856.0    0.0   \n",
       "\n",
       "   credit  to_pay   vendor_id         dropoff          pickup     status  \\\n",
       "0     0.0     0.0  d2371067ea             NaN  street_address       done   \n",
       "1     0.0     0.0  e2a2d88896             NaN             NaN  cancelled   \n",
       "2     0.0     0.0  d2371067ea             NaN  street_address       done   \n",
       "3     0.0     0.0  e2a2d88896             NaN  street_address  cancelled   \n",
       "4     0.0     0.0  e2a2d88896  street_address  street_address  cancelled   \n",
       "\n",
       "  payment_type  \n",
       "0         card  \n",
       "1         card  \n",
       "2         card  \n",
       "3         card  \n",
       "4         card  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders = pd.read_csv(\"orders.csv\")\n",
    "users = pd.read_csv(\"users.csv\")\n",
    "vendors = pd.read_csv(\"vendors.csv\")\n",
    "orders=orders.rename(columns = {'_id':'order_id'})\n",
    "orders.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>de974bbd30</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6e38b78ef6</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5655d8485c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ab0c976846</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69be64a10f</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id utm_medium   app_os\n",
       "0  de974bbd30    organic      iOS\n",
       "1  6e38b78ef6     invite      iOS\n",
       "2  5655d8485c        NaN  android\n",
       "3  ab0c976846     invite  android\n",
       "4  69be64a10f     invite      iOS"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users=users.rename(columns = {'_id':'user_id','app__os':'app_os'})\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    vendor_id name\n",
       "0  25ba3f0e07    C\n",
       "1  e2a2d88896    B\n",
       "2  d2371067ea    A"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendors=vendors.rename(columns = {'_id':'vendor_id'})\n",
    "vendors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's merge dataframes into one file. To identify appropriate join keys we first need to know which field(s) are shared between the files - have the same name and the same data. In our case it is user_id in first joint and vendor_id in second joint. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b42fce964b</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-01-06 00:16:06.000</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>21219.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fa8266307b</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-01-14 08:07:01.000</td>\n",
       "      <td>2179.0</td>\n",
       "      <td>20665.0</td>\n",
       "      <td>450.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cb9c59f681</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-17 14:24:44.000</td>\n",
       "      <td>1252.0</td>\n",
       "      <td>4807.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0707a069be</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-10 13:59:18.000</td>\n",
       "      <td>1789.0</td>\n",
       "      <td>5957.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b4076919fa</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-17 17:42:53.000</td>\n",
       "      <td>613.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5d9cf153a4</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-13 23:08:38.000</td>\n",
       "      <td>960.0</td>\n",
       "      <td>7285.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>railway_hub</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>932483985d</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-07 14:03:10.000</td>\n",
       "      <td>893.0</td>\n",
       "      <td>4627.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>07d7f107ef</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-07 20:15:57.000</td>\n",
       "      <td>692.0</td>\n",
       "      <td>3678.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60d26c3c99</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-07 19:19:34.000</td>\n",
       "      <td>464.0</td>\n",
       "      <td>3563.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>f5117d60da</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-23 23:11:56.000</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>20858.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>f510b8bc9a</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-07 14:06:57.000</td>\n",
       "      <td>803.0</td>\n",
       "      <td>3896.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>69de8acd9f</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-07 18:59:04.000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>3939.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6f05c1c544</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-10 05:16:50.000</td>\n",
       "      <td>2043.0</td>\n",
       "      <td>16254.0</td>\n",
       "      <td>452.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>16785b0076</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-11 13:03:52.000</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>6529.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>651b55afd6</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-21 16:17:27.000</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>4929.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fe30989576</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-15 16:15:51.000</td>\n",
       "      <td>755.0</td>\n",
       "      <td>4443.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>bb39732a82</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-15 10:31:31.000</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>5078.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>46e827a1b5</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-19 11:01:59.000</td>\n",
       "      <td>846.0</td>\n",
       "      <td>5787.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6c4593cb16</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-21 14:08:50.000</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>5451.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6f65042c36</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-05-21 10:26:34.000</td>\n",
       "      <td>974.0</td>\n",
       "      <td>4431.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>bus_station</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cbe0ecfb2c</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-05-13 12:06:10.000</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>5032.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ed6966861c</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-05-13 16:23:31.000</td>\n",
       "      <td>678.0</td>\n",
       "      <td>4169.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fb3e51ea64</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-06-29 12:35:57.000</td>\n",
       "      <td>1583.0</td>\n",
       "      <td>4352.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>f7e9c49854</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-07-02 15:54:13.000</td>\n",
       "      <td>914.0</td>\n",
       "      <td>3442.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8ad53f1e52</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-01 00:24:39.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>99a98cfe1a</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-01 15:42:02.781</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>08cf59621e</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-04 05:44:11.000</td>\n",
       "      <td>909.0</td>\n",
       "      <td>7687.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>e290c8e369</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-04 11:26:15.000</td>\n",
       "      <td>1273.0</td>\n",
       "      <td>7507.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8b490b401f</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-10 04:28:06.000</td>\n",
       "      <td>1166.0</td>\n",
       "      <td>8330.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>50164c0ccb</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-01 20:36:52.000</td>\n",
       "      <td>762.0</td>\n",
       "      <td>5949.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>554bd39ea9</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-01-01 19:47:17.000</td>\n",
       "      <td>889.0</td>\n",
       "      <td>5874.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0a86505ced</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-04-01 14:44:54.000</td>\n",
       "      <td>469.0</td>\n",
       "      <td>2826.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>b094573a23</td>\n",
       "      <td>9f563f3b47</td>\n",
       "      <td>2017-05-19 22:23:29.000</td>\n",
       "      <td>286.0</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>f39c8f74a7</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-01-01 00:25:06.000</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>5449.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2ef1b17085</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-01-13 19:21:54.000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1ddddef1b8</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-02-15 10:59:45.000</td>\n",
       "      <td>860.0</td>\n",
       "      <td>4453.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_number</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>053470d6b1</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-02-15 17:44:16.000</td>\n",
       "      <td>893.0</td>\n",
       "      <td>4981.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>cf54e20488</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-02-22 13:39:27.000</td>\n",
       "      <td>792.0</td>\n",
       "      <td>3446.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4cc2cfacdc</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-03 18:48:35.000</td>\n",
       "      <td>312.0</td>\n",
       "      <td>1543.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>35856c62b0</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-15 00:19:26.499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ac9b9d5d43</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-16 18:04:45.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>4179.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>8e59823809</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-17 18:24:25.000</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2ed9d3860f</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-23 01:29:04.000</td>\n",
       "      <td>743.0</td>\n",
       "      <td>5095.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>cdf55672f5</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-15 01:37:51.000</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>5054.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>dab35ffa00</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-16 12:12:15.000</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>5604.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>22284254ba</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-17 13:37:41.000</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>5192229722</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-23 17:07:53.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>4362.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>ec57091c4e</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-04-06 06:58:22.000</td>\n",
       "      <td>835.0</td>\n",
       "      <td>5480.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>538abf2a24</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-04-08 15:41:42.000</td>\n",
       "      <td>601.0</td>\n",
       "      <td>2912.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id     user_id             completed_at  duration  distance  \\\n",
       "1   b42fce964b  b14de8a940  2017-01-06 00:16:06.000    2079.0   21219.0   \n",
       "2   fa8266307b  b14de8a940  2017-01-14 08:07:01.000    2179.0   20665.0   \n",
       "3   cb9c59f681  b14de8a940  2017-02-17 14:24:44.000    1252.0    4807.0   \n",
       "4   0707a069be  b14de8a940  2017-02-10 13:59:18.000    1789.0    5957.0   \n",
       "5   b4076919fa  b14de8a940  2017-02-17 17:42:53.000     613.0    4367.0   \n",
       "6   5d9cf153a4  b14de8a940  2017-03-13 23:08:38.000     960.0    7285.0   \n",
       "7   932483985d  b14de8a940  2017-03-07 14:03:10.000     893.0    4627.0   \n",
       "8   07d7f107ef  b14de8a940  2017-03-07 20:15:57.000     692.0    3678.0   \n",
       "9   60d26c3c99  b14de8a940  2017-03-07 19:19:34.000     464.0    3563.0   \n",
       "10  f5117d60da  b14de8a940  2017-03-23 23:11:56.000    1456.0   20858.0   \n",
       "11  f510b8bc9a  b14de8a940  2017-04-07 14:06:57.000     803.0    3896.0   \n",
       "12  69de8acd9f  b14de8a940  2017-04-07 18:59:04.000     419.0    3939.0   \n",
       "13  6f05c1c544  b14de8a940  2017-04-10 05:16:50.000    2043.0   16254.0   \n",
       "14  16785b0076  b14de8a940  2017-04-11 13:03:52.000    1073.0    6529.0   \n",
       "15  651b55afd6  b14de8a940  2017-04-21 16:17:27.000    1339.0    4929.0   \n",
       "16  fe30989576  b14de8a940  2017-04-15 16:15:51.000     755.0    4443.0   \n",
       "17  bb39732a82  b14de8a940  2017-04-15 10:31:31.000    1546.0    5078.0   \n",
       "18  46e827a1b5  b14de8a940  2017-04-19 11:01:59.000     846.0    5787.0   \n",
       "19  6c4593cb16  b14de8a940  2017-04-21 14:08:50.000    1070.0    5451.0   \n",
       "20  6f65042c36  b14de8a940  2017-05-21 10:26:34.000     974.0    4431.0   \n",
       "21  cbe0ecfb2c  b14de8a940  2017-05-13 12:06:10.000    1009.0    5032.0   \n",
       "22  ed6966861c  b14de8a940  2017-05-13 16:23:31.000     678.0    4169.0   \n",
       "23  fb3e51ea64  b14de8a940  2017-06-29 12:35:57.000    1583.0    4352.0   \n",
       "24  f7e9c49854  b14de8a940  2017-07-02 15:54:13.000     914.0    3442.0   \n",
       "25  8ad53f1e52  9f563f3b47  2017-01-01 00:24:39.000       0.0       0.0   \n",
       "26  99a98cfe1a  9f563f3b47  2017-01-01 15:42:02.781       0.0       0.0   \n",
       "27  08cf59621e  9f563f3b47  2017-01-04 05:44:11.000     909.0    7687.0   \n",
       "28  e290c8e369  9f563f3b47  2017-01-04 11:26:15.000    1273.0    7507.0   \n",
       "29  8b490b401f  9f563f3b47  2017-01-10 04:28:06.000    1166.0    8330.0   \n",
       "30  50164c0ccb  9f563f3b47  2017-01-01 20:36:52.000     762.0    5949.0   \n",
       "31  554bd39ea9  9f563f3b47  2017-01-01 19:47:17.000     889.0    5874.0   \n",
       "32  0a86505ced  9f563f3b47  2017-04-01 14:44:54.000     469.0    2826.0   \n",
       "33  b094573a23  9f563f3b47  2017-05-19 22:23:29.000     286.0    1470.0   \n",
       "34  f39c8f74a7  e43cc45c19  2017-01-01 00:25:06.000    1119.0    5449.0   \n",
       "35  2ef1b17085  e43cc45c19  2017-01-13 19:21:54.000     365.0    1489.0   \n",
       "36  1ddddef1b8  e43cc45c19  2017-02-15 10:59:45.000     860.0    4453.0   \n",
       "37  053470d6b1  e43cc45c19  2017-02-15 17:44:16.000     893.0    4981.0   \n",
       "38  cf54e20488  e43cc45c19  2017-02-22 13:39:27.000     792.0    3446.0   \n",
       "39  4cc2cfacdc  e43cc45c19  2017-03-03 18:48:35.000     312.0    1543.0   \n",
       "40  35856c62b0  e43cc45c19  2017-03-15 00:19:26.499       0.0       0.0   \n",
       "41  ac9b9d5d43  e43cc45c19  2017-03-16 18:04:45.000     687.0    4179.0   \n",
       "42  8e59823809  e43cc45c19  2017-03-17 18:24:25.000     876.0    3482.0   \n",
       "43  2ed9d3860f  e43cc45c19  2017-03-23 01:29:04.000     743.0    5095.0   \n",
       "44  cdf55672f5  e43cc45c19  2017-03-15 01:37:51.000    1130.0    5054.0   \n",
       "45  dab35ffa00  e43cc45c19  2017-03-16 12:12:15.000    1174.0    5604.0   \n",
       "46  22284254ba  e43cc45c19  2017-03-17 13:37:41.000    1107.0    3699.0   \n",
       "47  5192229722  e43cc45c19  2017-03-23 17:07:53.000     687.0    4362.0   \n",
       "48  ec57091c4e  e43cc45c19  2017-04-06 06:58:22.000     835.0    5480.0   \n",
       "49  538abf2a24  e43cc45c19  2017-04-08 15:41:42.000     601.0    2912.0   \n",
       "\n",
       "    price  credit  to_pay   vendor_id         dropoff          pickup  \\\n",
       "1   900.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "2   450.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "3   366.0     0.0     0.0  d2371067ea             poi  street_address   \n",
       "4   461.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "5   277.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "6   383.0     0.0     0.0  d2371067ea     railway_hub  street_address   \n",
       "7   194.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "8   271.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "9   249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "10  900.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "11  292.0     0.0     0.0  d2371067ea             poi  street_address   \n",
       "12  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "13  452.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "14  236.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "15  384.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "16  294.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "17  403.0     0.0     0.0  d2371067ea             poi  street_address   \n",
       "18  211.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "19  355.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "20  197.0     0.0     0.0  e2a2d88896     bus_station  street_address   \n",
       "21  210.0     0.0     0.0  e2a2d88896             poi  street_address   \n",
       "22  281.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "23  250.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "24  298.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "25    0.0     0.0     0.0  e2a2d88896             NaN             NaN   \n",
       "26    0.0     0.0     0.0  e2a2d88896             NaN             NaN   \n",
       "27  242.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "28  270.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "29  271.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "30  203.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "31    0.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "32  134.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "33  100.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "34  726.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "35  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "36  311.0     0.0     0.0  d2371067ea             NaN   street_number   \n",
       "37  313.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "38  173.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "39  106.0     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "40    0.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "41  273.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "42  291.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "43  308.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "44  355.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "45  374.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "46  328.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "47  285.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "48  323.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "49  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "\n",
       "       status payment_type utm_medium app_os  \n",
       "1        done         card    blogger    iOS  \n",
       "2        done         card    blogger    iOS  \n",
       "3        done         card    blogger    iOS  \n",
       "4        done         card    blogger    iOS  \n",
       "5        done         card    blogger    iOS  \n",
       "6        done         card    blogger    iOS  \n",
       "7        done         card    blogger    iOS  \n",
       "8        done         card    blogger    iOS  \n",
       "9        done         card    blogger    iOS  \n",
       "10       done         card    blogger    iOS  \n",
       "11       done         card    blogger    iOS  \n",
       "12       done         card    blogger    iOS  \n",
       "13       done         card    blogger    iOS  \n",
       "14       done         card    blogger    iOS  \n",
       "15       done         card    blogger    iOS  \n",
       "16       done         card    blogger    iOS  \n",
       "17       done         card    blogger    iOS  \n",
       "18       done         card    blogger    iOS  \n",
       "19       done         card    blogger    iOS  \n",
       "20       done         card    blogger    iOS  \n",
       "21       done         card    blogger    iOS  \n",
       "22       done         card    blogger    iOS  \n",
       "23       done         card    blogger    iOS  \n",
       "24       done         card    blogger    iOS  \n",
       "25  cancelled         card     invite    iOS  \n",
       "26  cancelled         card     invite    iOS  \n",
       "27       done         card     invite    iOS  \n",
       "28       done         card     invite    iOS  \n",
       "29       done         card     invite    iOS  \n",
       "30       done         card     invite    iOS  \n",
       "31  cancelled         card     invite    iOS  \n",
       "32       done         card     invite    iOS  \n",
       "33       done         card     invite    iOS  \n",
       "34       done         card     invite    iOS  \n",
       "35       done         card     invite    iOS  \n",
       "36       done         card     invite    iOS  \n",
       "37       done         card     invite    iOS  \n",
       "38       done         card     invite    iOS  \n",
       "39       done         card     invite    iOS  \n",
       "40  cancelled         card     invite    iOS  \n",
       "41       done         card     invite    iOS  \n",
       "42       done         card     invite    iOS  \n",
       "43       done         card     invite    iOS  \n",
       "44       done         card     invite    iOS  \n",
       "45       done         card     invite    iOS  \n",
       "46       done         card     invite    iOS  \n",
       "47       done         card     invite    iOS  \n",
       "48       done         card     invite    iOS  \n",
       "49       done         card     invite    iOS  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged1 = pd.merge(left=orders,right=users, left_on='user_id', right_on='user_id')\n",
    "merged1[1:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b42fce964b</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-01-06 00:16:06.000</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>21219.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cb9c59f681</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-17 14:24:44.000</td>\n",
       "      <td>1252.0</td>\n",
       "      <td>4807.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0707a069be</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-10 13:59:18.000</td>\n",
       "      <td>1789.0</td>\n",
       "      <td>5957.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b4076919fa</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-17 17:42:53.000</td>\n",
       "      <td>613.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5d9cf153a4</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-13 23:08:38.000</td>\n",
       "      <td>960.0</td>\n",
       "      <td>7285.0</td>\n",
       "      <td>383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>railway_hub</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07d7f107ef</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-07 20:15:57.000</td>\n",
       "      <td>692.0</td>\n",
       "      <td>3678.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60d26c3c99</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-07 19:19:34.000</td>\n",
       "      <td>464.0</td>\n",
       "      <td>3563.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>f5117d60da</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-03-23 23:11:56.000</td>\n",
       "      <td>1456.0</td>\n",
       "      <td>20858.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>f510b8bc9a</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-07 14:06:57.000</td>\n",
       "      <td>803.0</td>\n",
       "      <td>3896.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>69de8acd9f</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-07 18:59:04.000</td>\n",
       "      <td>419.0</td>\n",
       "      <td>3939.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>651b55afd6</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-21 16:17:27.000</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>4929.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fe30989576</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-15 16:15:51.000</td>\n",
       "      <td>755.0</td>\n",
       "      <td>4443.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bb39732a82</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-15 10:31:31.000</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>5078.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>6c4593cb16</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-04-21 14:08:50.000</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>5451.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ed6966861c</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-05-13 16:23:31.000</td>\n",
       "      <td>678.0</td>\n",
       "      <td>4169.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>f7e9c49854</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-07-02 15:54:13.000</td>\n",
       "      <td>914.0</td>\n",
       "      <td>3442.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>f39c8f74a7</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-01-01 00:25:06.000</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>5449.0</td>\n",
       "      <td>726.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2ef1b17085</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-01-13 19:21:54.000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1489.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1ddddef1b8</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-02-15 10:59:45.000</td>\n",
       "      <td>860.0</td>\n",
       "      <td>4453.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_number</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>053470d6b1</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-02-15 17:44:16.000</td>\n",
       "      <td>893.0</td>\n",
       "      <td>4981.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>35856c62b0</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-15 00:19:26.499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ac9b9d5d43</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-16 18:04:45.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>4179.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8e59823809</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-17 18:24:25.000</td>\n",
       "      <td>876.0</td>\n",
       "      <td>3482.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2ed9d3860f</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-23 01:29:04.000</td>\n",
       "      <td>743.0</td>\n",
       "      <td>5095.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>cdf55672f5</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-15 01:37:51.000</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>5054.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>dab35ffa00</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-16 12:12:15.000</td>\n",
       "      <td>1174.0</td>\n",
       "      <td>5604.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>22284254ba</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-17 13:37:41.000</td>\n",
       "      <td>1107.0</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>328.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5192229722</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-03-23 17:07:53.000</td>\n",
       "      <td>687.0</td>\n",
       "      <td>4362.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ec57091c4e</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-04-06 06:58:22.000</td>\n",
       "      <td>835.0</td>\n",
       "      <td>5480.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>538abf2a24</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-04-08 15:41:42.000</td>\n",
       "      <td>601.0</td>\n",
       "      <td>2912.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>fb9909af14</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-04-08 23:04:02.000</td>\n",
       "      <td>607.0</td>\n",
       "      <td>2971.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>a84c01d73e</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-04-29 12:41:47.000</td>\n",
       "      <td>864.0</td>\n",
       "      <td>4706.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>8a400cba60</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-04-29 18:36:47.000</td>\n",
       "      <td>994.0</td>\n",
       "      <td>4654.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>b41c58f860</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-05-04 15:29:13.000</td>\n",
       "      <td>935.0</td>\n",
       "      <td>5533.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2f3397ba9c</td>\n",
       "      <td>e43cc45c19</td>\n",
       "      <td>2017-06-28 19:25:09.000</td>\n",
       "      <td>980.0</td>\n",
       "      <td>6483.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>6a69100904</td>\n",
       "      <td>19dc69f7f3</td>\n",
       "      <td>2017-01-01 02:01:20.000</td>\n",
       "      <td>894.0</td>\n",
       "      <td>7529.0</td>\n",
       "      <td>745.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>e52ffb6ef7</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-01 02:18:25.000</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>7041.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>13ecfe0645</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-01 22:33:01.000</td>\n",
       "      <td>647.0</td>\n",
       "      <td>4043.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>15074b3291</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-02 16:11:15.000</td>\n",
       "      <td>1348.0</td>\n",
       "      <td>3769.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0718870b06</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-02 20:55:15.000</td>\n",
       "      <td>352.0</td>\n",
       "      <td>2107.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>e4284618a6</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-05 17:01:45.000</td>\n",
       "      <td>3391.0</td>\n",
       "      <td>12887.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>10694d7418</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-07 15:47:23.000</td>\n",
       "      <td>924.0</td>\n",
       "      <td>6784.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7a74cc4917</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-15 00:26:36.000</td>\n",
       "      <td>646.0</td>\n",
       "      <td>2416.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>124ee8bff3</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-25 16:01:53.000</td>\n",
       "      <td>2206.0</td>\n",
       "      <td>18900.0</td>\n",
       "      <td>776.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>fbfba2ebcc</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-01 17:17:24.000</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>5798.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>80349537b7</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-02 19:52:00.000</td>\n",
       "      <td>475.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>b36478fdba</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-07 17:03:15.000</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>7214.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>9707929ebd</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-15 04:57:01.000</td>\n",
       "      <td>744.0</td>\n",
       "      <td>2816.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>9e7174c735</td>\n",
       "      <td>c8bc33bafd</td>\n",
       "      <td>2017-01-30 15:46:58.000</td>\n",
       "      <td>1029.0</td>\n",
       "      <td>9613.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id     user_id             completed_at  duration  distance  \\\n",
       "1   b42fce964b  b14de8a940  2017-01-06 00:16:06.000    2079.0   21219.0   \n",
       "2   cb9c59f681  b14de8a940  2017-02-17 14:24:44.000    1252.0    4807.0   \n",
       "3   0707a069be  b14de8a940  2017-02-10 13:59:18.000    1789.0    5957.0   \n",
       "4   b4076919fa  b14de8a940  2017-02-17 17:42:53.000     613.0    4367.0   \n",
       "5   5d9cf153a4  b14de8a940  2017-03-13 23:08:38.000     960.0    7285.0   \n",
       "6   07d7f107ef  b14de8a940  2017-03-07 20:15:57.000     692.0    3678.0   \n",
       "7   60d26c3c99  b14de8a940  2017-03-07 19:19:34.000     464.0    3563.0   \n",
       "8   f5117d60da  b14de8a940  2017-03-23 23:11:56.000    1456.0   20858.0   \n",
       "9   f510b8bc9a  b14de8a940  2017-04-07 14:06:57.000     803.0    3896.0   \n",
       "10  69de8acd9f  b14de8a940  2017-04-07 18:59:04.000     419.0    3939.0   \n",
       "11  651b55afd6  b14de8a940  2017-04-21 16:17:27.000    1339.0    4929.0   \n",
       "12  fe30989576  b14de8a940  2017-04-15 16:15:51.000     755.0    4443.0   \n",
       "13  bb39732a82  b14de8a940  2017-04-15 10:31:31.000    1546.0    5078.0   \n",
       "14  6c4593cb16  b14de8a940  2017-04-21 14:08:50.000    1070.0    5451.0   \n",
       "15  ed6966861c  b14de8a940  2017-05-13 16:23:31.000     678.0    4169.0   \n",
       "16  f7e9c49854  b14de8a940  2017-07-02 15:54:13.000     914.0    3442.0   \n",
       "17  f39c8f74a7  e43cc45c19  2017-01-01 00:25:06.000    1119.0    5449.0   \n",
       "18  2ef1b17085  e43cc45c19  2017-01-13 19:21:54.000     365.0    1489.0   \n",
       "19  1ddddef1b8  e43cc45c19  2017-02-15 10:59:45.000     860.0    4453.0   \n",
       "20  053470d6b1  e43cc45c19  2017-02-15 17:44:16.000     893.0    4981.0   \n",
       "21  35856c62b0  e43cc45c19  2017-03-15 00:19:26.499       0.0       0.0   \n",
       "22  ac9b9d5d43  e43cc45c19  2017-03-16 18:04:45.000     687.0    4179.0   \n",
       "23  8e59823809  e43cc45c19  2017-03-17 18:24:25.000     876.0    3482.0   \n",
       "24  2ed9d3860f  e43cc45c19  2017-03-23 01:29:04.000     743.0    5095.0   \n",
       "25  cdf55672f5  e43cc45c19  2017-03-15 01:37:51.000    1130.0    5054.0   \n",
       "26  dab35ffa00  e43cc45c19  2017-03-16 12:12:15.000    1174.0    5604.0   \n",
       "27  22284254ba  e43cc45c19  2017-03-17 13:37:41.000    1107.0    3699.0   \n",
       "28  5192229722  e43cc45c19  2017-03-23 17:07:53.000     687.0    4362.0   \n",
       "29  ec57091c4e  e43cc45c19  2017-04-06 06:58:22.000     835.0    5480.0   \n",
       "30  538abf2a24  e43cc45c19  2017-04-08 15:41:42.000     601.0    2912.0   \n",
       "31  fb9909af14  e43cc45c19  2017-04-08 23:04:02.000     607.0    2971.0   \n",
       "32  a84c01d73e  e43cc45c19  2017-04-29 12:41:47.000     864.0    4706.0   \n",
       "33  8a400cba60  e43cc45c19  2017-04-29 18:36:47.000     994.0    4654.0   \n",
       "34  b41c58f860  e43cc45c19  2017-05-04 15:29:13.000     935.0    5533.0   \n",
       "35  2f3397ba9c  e43cc45c19  2017-06-28 19:25:09.000     980.0    6483.0   \n",
       "36  6a69100904  19dc69f7f3  2017-01-01 02:01:20.000     894.0    7529.0   \n",
       "37  e52ffb6ef7  c8bc33bafd  2017-01-01 02:18:25.000    1027.0    7041.0   \n",
       "38  13ecfe0645  c8bc33bafd  2017-01-01 22:33:01.000     647.0    4043.0   \n",
       "39  15074b3291  c8bc33bafd  2017-01-02 16:11:15.000    1348.0    3769.0   \n",
       "40  0718870b06  c8bc33bafd  2017-01-02 20:55:15.000     352.0    2107.0   \n",
       "41  e4284618a6  c8bc33bafd  2017-01-05 17:01:45.000    3391.0   12887.0   \n",
       "42  10694d7418  c8bc33bafd  2017-01-07 15:47:23.000     924.0    6784.0   \n",
       "43  7a74cc4917  c8bc33bafd  2017-01-15 00:26:36.000     646.0    2416.0   \n",
       "44  124ee8bff3  c8bc33bafd  2017-01-25 16:01:53.000    2206.0   18900.0   \n",
       "45  fbfba2ebcc  c8bc33bafd  2017-01-01 17:17:24.000    1493.0    5798.0   \n",
       "46  80349537b7  c8bc33bafd  2017-01-02 19:52:00.000     475.0    2075.0   \n",
       "47  b36478fdba  c8bc33bafd  2017-01-07 17:03:15.000    1132.0    7214.0   \n",
       "48  9707929ebd  c8bc33bafd  2017-01-15 04:57:01.000     744.0    2816.0   \n",
       "49  9e7174c735  c8bc33bafd  2017-01-30 15:46:58.000    1029.0    9613.0   \n",
       "\n",
       "    price  credit  to_pay   vendor_id         dropoff          pickup  \\\n",
       "1   900.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "2   366.0     0.0     0.0  d2371067ea             poi  street_address   \n",
       "3   461.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "4   277.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "5   383.0     0.0     0.0  d2371067ea     railway_hub  street_address   \n",
       "6   271.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "7   249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "8   900.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "9   292.0     0.0     0.0  d2371067ea             poi  street_address   \n",
       "10  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "11  384.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "12  294.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "13  403.0     0.0     0.0  d2371067ea             poi  street_address   \n",
       "14  355.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "15  281.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "16  298.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "17  726.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "18  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "19  311.0     0.0     0.0  d2371067ea             NaN   street_number   \n",
       "20  313.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "21    0.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "22  273.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "23  291.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "24  308.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "25  355.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "26  374.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "27  328.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "28  285.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "29  323.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "30  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "31  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "32  316.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "33  331.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "34  340.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "35  367.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "36  745.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "37  888.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "38  270.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "39  361.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "40  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "41  815.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "42  365.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "43  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "44  776.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "45  418.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "46  249.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "47  398.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "48  262.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "49  438.0     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "\n",
       "       status payment_type utm_medium app_os name  \n",
       "1        done         card    blogger    iOS    A  \n",
       "2        done         card    blogger    iOS    A  \n",
       "3        done         card    blogger    iOS    A  \n",
       "4        done         card    blogger    iOS    A  \n",
       "5        done         card    blogger    iOS    A  \n",
       "6        done         card    blogger    iOS    A  \n",
       "7        done         card    blogger    iOS    A  \n",
       "8        done         card    blogger    iOS    A  \n",
       "9        done         card    blogger    iOS    A  \n",
       "10       done         card    blogger    iOS    A  \n",
       "11       done         card    blogger    iOS    A  \n",
       "12       done         card    blogger    iOS    A  \n",
       "13       done         card    blogger    iOS    A  \n",
       "14       done         card    blogger    iOS    A  \n",
       "15       done         card    blogger    iOS    A  \n",
       "16       done         card    blogger    iOS    A  \n",
       "17       done         card     invite    iOS    A  \n",
       "18       done         card     invite    iOS    A  \n",
       "19       done         card     invite    iOS    A  \n",
       "20       done         card     invite    iOS    A  \n",
       "21  cancelled         card     invite    iOS    A  \n",
       "22       done         card     invite    iOS    A  \n",
       "23       done         card     invite    iOS    A  \n",
       "24       done         card     invite    iOS    A  \n",
       "25       done         card     invite    iOS    A  \n",
       "26       done         card     invite    iOS    A  \n",
       "27       done         card     invite    iOS    A  \n",
       "28       done         card     invite    iOS    A  \n",
       "29       done         card     invite    iOS    A  \n",
       "30       done         card     invite    iOS    A  \n",
       "31       done         card     invite    iOS    A  \n",
       "32       done         card     invite    iOS    A  \n",
       "33       done         card     invite    iOS    A  \n",
       "34       done         card     invite    iOS    A  \n",
       "35       done         card     invite    iOS    A  \n",
       "36       done         card     invite    iOS    A  \n",
       "37       done         card      email    iOS    A  \n",
       "38       done         card      email    iOS    A  \n",
       "39       done         card      email    iOS    A  \n",
       "40       done         card      email    iOS    A  \n",
       "41       done         card      email    iOS    A  \n",
       "42       done         card      email    iOS    A  \n",
       "43       done         card      email    iOS    A  \n",
       "44       done         card      email    iOS    A  \n",
       "45       done         card      email    iOS    A  \n",
       "46       done         card      email    iOS    A  \n",
       "47       done         card      email    iOS    A  \n",
       "48       done         card      email    iOS    A  \n",
       "49       done         card      email    iOS    A  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df = pd.merge(left=merged1,right=vendors, left_on='vendor_id', right_on='vendor_id')\n",
    "orders_df[1:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id        False\n",
       "user_id         False\n",
       "completed_at    False\n",
       "duration         True\n",
       "distance         True\n",
       "price            True\n",
       "credit          False\n",
       "to_pay          False\n",
       "vendor_id       False\n",
       "dropoff          True\n",
       "pickup           True\n",
       "status          False\n",
       "payment_type    False\n",
       "utm_medium       True\n",
       "app_os           True\n",
       "name            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now, let's prepare the data for analysis. Let's check data for missing values \n",
    "orders_df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df['price'].isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We can set 0 values instead of NaN when status of order is \"Fail\"\n",
    "orders_df.loc[(orders_df['status']=='failed') & (orders_df['duration'].isnull()), 'duration']=0\n",
    "orders_df.loc[(orders_df['status']=='failed') & (orders_df['distance'].isnull()), 'distance']=0\n",
    "orders_df.loc[(orders_df['status']=='failed') & (orders_df['price'].isnull()), 'price']=0\n",
    "orders_df.loc[(orders_df['status']=='failed') & (orders_df['credit'].isnull()), 'credit']=0\n",
    "orders_df.loc[(orders_df['status']=='failed') & (orders_df['to_pay'].isnull()), 'to_pay']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df['price'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2407</th>\n",
       "      <td>8c396dee64</td>\n",
       "      <td>887d4f0504</td>\n",
       "      <td>2017-05-10 13:24:59.347</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>35e0d4a6f8</td>\n",
       "      <td>887d4f0504</td>\n",
       "      <td>2017-05-10 13:38:30.535</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5578</th>\n",
       "      <td>a77c01ff07</td>\n",
       "      <td>912b3a40bb</td>\n",
       "      <td>2017-05-10 13:20:51.411</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5579</th>\n",
       "      <td>f0b1d08588</td>\n",
       "      <td>912b3a40bb</td>\n",
       "      <td>2017-05-10 13:13:26.992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6101</th>\n",
       "      <td>b63fa82596</td>\n",
       "      <td>fb8c582e43</td>\n",
       "      <td>2017-05-11 14:04:06.659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10978</th>\n",
       "      <td>1d8b961854</td>\n",
       "      <td>b2426d7096</td>\n",
       "      <td>2017-07-05 03:57:36.862</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10979</th>\n",
       "      <td>0d88d84ec6</td>\n",
       "      <td>b2426d7096</td>\n",
       "      <td>2017-07-05 03:58:59.092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10980</th>\n",
       "      <td>56c79c1bab</td>\n",
       "      <td>b2426d7096</td>\n",
       "      <td>2017-07-05 04:00:13.532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12382</th>\n",
       "      <td>d96231911b</td>\n",
       "      <td>a2bfc1d1fa</td>\n",
       "      <td>2017-07-05 04:03:57.291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12969</th>\n",
       "      <td>20ba5c1e48</td>\n",
       "      <td>e8c9134a30</td>\n",
       "      <td>2017-07-05 04:41:07.071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18509</th>\n",
       "      <td>2483d8d6c3</td>\n",
       "      <td>adddc0da2a</td>\n",
       "      <td>2017-05-10 15:53:28.121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20304</th>\n",
       "      <td>02516a21b6</td>\n",
       "      <td>9317073208</td>\n",
       "      <td>2017-05-10 13:28:47.531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_number</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20952</th>\n",
       "      <td>b2663cc473</td>\n",
       "      <td>a0360760a6</td>\n",
       "      <td>2017-07-05 04:03:34.325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22377</th>\n",
       "      <td>f51a99b60a</td>\n",
       "      <td>6b1416912b</td>\n",
       "      <td>2017-07-05 01:33:30.974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22708</th>\n",
       "      <td>581430d547</td>\n",
       "      <td>f5a2e65b0d</td>\n",
       "      <td>2017-07-05 04:31:05.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24486</th>\n",
       "      <td>871b3be5fa</td>\n",
       "      <td>23228d3655</td>\n",
       "      <td>2017-07-05 04:35:59.399</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27837</th>\n",
       "      <td>9e98332ad7</td>\n",
       "      <td>887d4f0504</td>\n",
       "      <td>2017-05-10 13:33:08.522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30924</th>\n",
       "      <td>464893910f</td>\n",
       "      <td>239a139663</td>\n",
       "      <td>2017-01-08 18:51:40.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>others</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34696</th>\n",
       "      <td>6ace857392</td>\n",
       "      <td>912b3a40bb</td>\n",
       "      <td>2017-05-10 13:13:39.886</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34697</th>\n",
       "      <td>aefe3a7519</td>\n",
       "      <td>912b3a40bb</td>\n",
       "      <td>2017-05-10 13:13:33.747</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34698</th>\n",
       "      <td>0a1a5f5227</td>\n",
       "      <td>912b3a40bb</td>\n",
       "      <td>2017-05-10 13:13:17.057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35385</th>\n",
       "      <td>629f98fab7</td>\n",
       "      <td>fb8c582e43</td>\n",
       "      <td>2017-01-31 13:26:34.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35386</th>\n",
       "      <td>1611b09c98</td>\n",
       "      <td>fb8c582e43</td>\n",
       "      <td>2017-01-31 13:30:29.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36718</th>\n",
       "      <td>334cfd376e</td>\n",
       "      <td>0855f3dea9</td>\n",
       "      <td>2017-07-05 04:48:15.233</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40607</th>\n",
       "      <td>4abac1c06a</td>\n",
       "      <td>50c9f8eb53</td>\n",
       "      <td>2017-07-05 04:00:31.821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_number</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40649</th>\n",
       "      <td>6eb8dfc622</td>\n",
       "      <td>ecfc51bb5f</td>\n",
       "      <td>2017-02-22 16:54:55.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43522</th>\n",
       "      <td>f494147129</td>\n",
       "      <td>b2426d7096</td>\n",
       "      <td>2017-07-05 03:58:07.856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43532</th>\n",
       "      <td>3b5bb89e02</td>\n",
       "      <td>b2426d7096</td>\n",
       "      <td>2017-07-05 04:00:02.381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46072</th>\n",
       "      <td>597e12a34d</td>\n",
       "      <td>a2bfc1d1fa</td>\n",
       "      <td>2017-07-05 04:04:42.053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46076</th>\n",
       "      <td>d1b84b0c57</td>\n",
       "      <td>a2bfc1d1fa</td>\n",
       "      <td>2017-07-05 04:02:56.273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51914</th>\n",
       "      <td>3e0d27d37b</td>\n",
       "      <td>75ed4b0967</td>\n",
       "      <td>2017-05-11 13:49:20.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_number</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54114</th>\n",
       "      <td>d6443fe743</td>\n",
       "      <td>8840eae8cb</td>\n",
       "      <td>2017-07-05 07:16:38.376</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>others</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56340</th>\n",
       "      <td>f5e640fded</td>\n",
       "      <td>9317073208</td>\n",
       "      <td>2017-05-10 13:31:49.202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_number</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56341</th>\n",
       "      <td>85f5863169</td>\n",
       "      <td>9317073208</td>\n",
       "      <td>2017-05-10 13:32:51.844</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_number</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63629</th>\n",
       "      <td>b8e9a21255</td>\n",
       "      <td>f5a2e65b0d</td>\n",
       "      <td>2017-07-05 04:34:20.852</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64091</th>\n",
       "      <td>ff32437a94</td>\n",
       "      <td>02c9a1d9b6</td>\n",
       "      <td>2017-07-05 02:53:53.801</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66057</th>\n",
       "      <td>2c1de4d635</td>\n",
       "      <td>3360c409aa</td>\n",
       "      <td>2017-05-10 13:28:22.211</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66058</th>\n",
       "      <td>4a7e843c91</td>\n",
       "      <td>3360c409aa</td>\n",
       "      <td>2017-05-10 13:24:32.102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67152</th>\n",
       "      <td>4d8a8efc17</td>\n",
       "      <td>cdff81c9ed</td>\n",
       "      <td>2017-05-10 14:03:45.751</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68189</th>\n",
       "      <td>c41a07382b</td>\n",
       "      <td>852986bc32</td>\n",
       "      <td>2017-07-05 05:17:44.885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68990</th>\n",
       "      <td>83c02f6a17</td>\n",
       "      <td>38d94d6449</td>\n",
       "      <td>2017-05-10 13:54:12.538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70089</th>\n",
       "      <td>9f706ba5b7</td>\n",
       "      <td>23228d3655</td>\n",
       "      <td>2017-07-05 04:36:25.666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id     user_id             completed_at  duration  distance  \\\n",
       "2407   8c396dee64  887d4f0504  2017-05-10 13:24:59.347       NaN       NaN   \n",
       "2435   35e0d4a6f8  887d4f0504  2017-05-10 13:38:30.535       NaN       NaN   \n",
       "5578   a77c01ff07  912b3a40bb  2017-05-10 13:20:51.411       NaN       NaN   \n",
       "5579   f0b1d08588  912b3a40bb  2017-05-10 13:13:26.992       NaN       NaN   \n",
       "6101   b63fa82596  fb8c582e43  2017-05-11 14:04:06.659       NaN       NaN   \n",
       "10978  1d8b961854  b2426d7096  2017-07-05 03:57:36.862       NaN       NaN   \n",
       "10979  0d88d84ec6  b2426d7096  2017-07-05 03:58:59.092       NaN       NaN   \n",
       "10980  56c79c1bab  b2426d7096  2017-07-05 04:00:13.532       NaN       NaN   \n",
       "12382  d96231911b  a2bfc1d1fa  2017-07-05 04:03:57.291       NaN       NaN   \n",
       "12969  20ba5c1e48  e8c9134a30  2017-07-05 04:41:07.071       NaN       NaN   \n",
       "18509  2483d8d6c3  adddc0da2a  2017-05-10 15:53:28.121       NaN       NaN   \n",
       "20304  02516a21b6  9317073208  2017-05-10 13:28:47.531       NaN       NaN   \n",
       "20952  b2663cc473  a0360760a6  2017-07-05 04:03:34.325       NaN       NaN   \n",
       "22377  f51a99b60a  6b1416912b  2017-07-05 01:33:30.974       NaN       NaN   \n",
       "22708  581430d547  f5a2e65b0d  2017-07-05 04:31:05.997       NaN       NaN   \n",
       "24486  871b3be5fa  23228d3655  2017-07-05 04:35:59.399       NaN       NaN   \n",
       "27837  9e98332ad7  887d4f0504  2017-05-10 13:33:08.522       NaN       NaN   \n",
       "30924  464893910f  239a139663  2017-01-08 18:51:40.000       NaN       NaN   \n",
       "34696  6ace857392  912b3a40bb  2017-05-10 13:13:39.886       NaN       NaN   \n",
       "34697  aefe3a7519  912b3a40bb  2017-05-10 13:13:33.747       NaN       NaN   \n",
       "34698  0a1a5f5227  912b3a40bb  2017-05-10 13:13:17.057       NaN       NaN   \n",
       "35385  629f98fab7  fb8c582e43  2017-01-31 13:26:34.000       NaN       NaN   \n",
       "35386  1611b09c98  fb8c582e43  2017-01-31 13:30:29.000       NaN       NaN   \n",
       "36718  334cfd376e  0855f3dea9  2017-07-05 04:48:15.233       NaN       NaN   \n",
       "40607  4abac1c06a  50c9f8eb53  2017-07-05 04:00:31.821       NaN       NaN   \n",
       "40649  6eb8dfc622  ecfc51bb5f  2017-02-22 16:54:55.000       NaN       NaN   \n",
       "43522  f494147129  b2426d7096  2017-07-05 03:58:07.856       NaN       NaN   \n",
       "43532  3b5bb89e02  b2426d7096  2017-07-05 04:00:02.381       NaN       NaN   \n",
       "46072  597e12a34d  a2bfc1d1fa  2017-07-05 04:04:42.053       NaN       NaN   \n",
       "46076  d1b84b0c57  a2bfc1d1fa  2017-07-05 04:02:56.273       NaN       NaN   \n",
       "51914  3e0d27d37b  75ed4b0967  2017-05-11 13:49:20.000       NaN       NaN   \n",
       "54114  d6443fe743  8840eae8cb  2017-07-05 07:16:38.376       NaN       NaN   \n",
       "56340  f5e640fded  9317073208  2017-05-10 13:31:49.202       NaN       NaN   \n",
       "56341  85f5863169  9317073208  2017-05-10 13:32:51.844       NaN       NaN   \n",
       "63629  b8e9a21255  f5a2e65b0d  2017-07-05 04:34:20.852       NaN       NaN   \n",
       "64091  ff32437a94  02c9a1d9b6  2017-07-05 02:53:53.801       NaN       NaN   \n",
       "66057  2c1de4d635  3360c409aa  2017-05-10 13:28:22.211       NaN       NaN   \n",
       "66058  4a7e843c91  3360c409aa  2017-05-10 13:24:32.102       NaN       NaN   \n",
       "67152  4d8a8efc17  cdff81c9ed  2017-05-10 14:03:45.751       NaN       NaN   \n",
       "68189  c41a07382b  852986bc32  2017-07-05 05:17:44.885       NaN       NaN   \n",
       "68990  83c02f6a17  38d94d6449  2017-05-10 13:54:12.538       NaN       NaN   \n",
       "70089  9f706ba5b7  23228d3655  2017-07-05 04:36:25.666       NaN       NaN   \n",
       "\n",
       "       price  credit  to_pay   vendor_id         dropoff          pickup  \\\n",
       "2407     NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "2435     NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "5578     NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "5579     NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "6101     NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "10978    NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "10979    NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "10980    NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "12382    NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "12969    NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "18509    NaN     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "20304    NaN     0.0     0.0  d2371067ea   street_number  street_address   \n",
       "20952    NaN     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "22377    NaN     0.0     0.0  d2371067ea         airport  street_address   \n",
       "22708    NaN     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "24486    NaN     0.0     0.0  d2371067ea             NaN  street_address   \n",
       "27837    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "30924    NaN     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "34696    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "34697    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "34698    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "35385    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "35386    NaN     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "36718    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "40607    NaN     0.0     0.0  e2a2d88896   street_number  street_address   \n",
       "40649    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "43522    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "43532    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "46072    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "46076    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "51914    NaN     0.0     0.0  e2a2d88896             NaN   street_number   \n",
       "54114    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "56340    NaN     0.0     0.0  e2a2d88896   street_number  street_address   \n",
       "56341    NaN     0.0     0.0  e2a2d88896   street_number  street_address   \n",
       "63629    NaN     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "64091    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "66057    NaN     0.0     0.0  e2a2d88896         airport  street_address   \n",
       "66058    NaN     0.0     0.0  e2a2d88896         airport  street_address   \n",
       "67152    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "68189    NaN     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "68990    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "70089    NaN     0.0     0.0  e2a2d88896             NaN  street_address   \n",
       "\n",
       "          status payment_type       utm_medium   app_os name  \n",
       "2407   cancelled      invoice          organic      iOS    A  \n",
       "2435   cancelled      invoice          organic      iOS    A  \n",
       "5578   cancelled         card           invite  android    A  \n",
       "5579   cancelled         card           invite  android    A  \n",
       "6101   cancelled         card           invite  android    A  \n",
       "10978  cancelled         card          organic  android    A  \n",
       "10979  cancelled         card          organic  android    A  \n",
       "10980  cancelled         card          organic  android    A  \n",
       "12382  cancelled         card          organic      iOS    A  \n",
       "12969  cancelled         card          organic      iOS    A  \n",
       "18509  cancelled         card          organic      iOS    A  \n",
       "20304  cancelled         card  cross-promotion      iOS    A  \n",
       "20952  cancelled         card           invite      iOS    A  \n",
       "22377  cancelled         card          organic      iOS    A  \n",
       "22708  cancelled         card          organic      iOS    A  \n",
       "24486  cancelled         card           invite  android    A  \n",
       "27837  cancelled      invoice          organic      iOS    B  \n",
       "30924  cancelled         card           others  android    B  \n",
       "34696  cancelled         card           invite  android    B  \n",
       "34697  cancelled         card           invite  android    B  \n",
       "34698  cancelled         card           invite  android    B  \n",
       "35385  cancelled      invoice           invite  android    B  \n",
       "35386  cancelled      invoice           invite  android    B  \n",
       "36718  cancelled      invoice          organic      iOS    B  \n",
       "40607  cancelled      invoice          organic      iOS    B  \n",
       "40649       done         card           invite      iOS    B  \n",
       "43522  cancelled         card          organic  android    B  \n",
       "43532  cancelled         card          organic  android    B  \n",
       "46072  cancelled         card          organic      iOS    B  \n",
       "46076  cancelled         card          organic      iOS    B  \n",
       "51914  cancelled         card          organic      iOS    B  \n",
       "54114  cancelled         card           others      iOS    B  \n",
       "56340  cancelled         card  cross-promotion      iOS    B  \n",
       "56341  cancelled         card  cross-promotion      iOS    B  \n",
       "63629  cancelled         card          organic      iOS    B  \n",
       "64091  cancelled      invoice              NaN      NaN    B  \n",
       "66057  cancelled         card           invite      iOS    B  \n",
       "66058  cancelled         card           invite      iOS    B  \n",
       "67152  cancelled      invoice              NaN  android    B  \n",
       "68189  cancelled      invoice           invite  android    B  \n",
       "68990  cancelled         card           invite  android    B  \n",
       "70089  cancelled         card           invite  android    B  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.loc[(orders_df['duration'].isnull()) |\n",
    "              (orders_df['distance'].isnull()) |\n",
    "              (orders_df['price'].isnull()) |\n",
    "              (orders_df['credit'].isnull())]\n",
    "#We can see that most of these orders with NaN were canceled orders. Therefore we replace NaN with 0.\n",
    "#However, in real cases, it is better to ask people who provided the data for more detailes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "orders_df.loc[(orders_df['status']=='cancelled') & (orders_df['duration'].isnull()), 'duration']=0\n",
    "orders_df.loc[(orders_df['status']=='cancelled') & (orders_df['distance'].isnull()), 'distance']=0\n",
    "orders_df.loc[(orders_df['status']=='cancelled') & (orders_df['price'].isnull()), 'price']=0\n",
    "orders_df.loc[(orders_df['status']=='cancelled') & (orders_df['credit'].isnull()), 'credit']=0\n",
    "orders_df.loc[(orders_df['status']=='cancelled') & (orders_df['to_pay'].isnull()), 'to_pay']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df['duration'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>248f4612d2</td>\n",
       "      <td>a55eca3174</td>\n",
       "      <td>2017-04-08 19:46:11.000</td>\n",
       "      <td>649.0</td>\n",
       "      <td>1279.0</td>\n",
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       "    <tr>\n",
       "      <th>22327</th>\n",
       "      <td>7c4d14bc64</td>\n",
       "      <td>d2a77cceb4</td>\n",
       "      <td>2017-04-14 19:35:30.000</td>\n",
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       "    <tr>\n",
       "      <th>22328</th>\n",
       "      <td>36723180aa</td>\n",
       "      <td>d2a77cceb4</td>\n",
       "      <td>2017-06-08 19:41:47.000</td>\n",
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       "      <td>2442.0</td>\n",
       "      <td>19323.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2017-03-21 14:41:41.000</td>\n",
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       "      <th>22885</th>\n",
       "      <td>043c5406f6</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-03-31 23:53:37.000</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>19971.0</td>\n",
       "      <td>932.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22886</th>\n",
       "      <td>9666c0139f</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-04-24 13:59:02.000</td>\n",
       "      <td>3470.0</td>\n",
       "      <td>24505.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22887</th>\n",
       "      <td>bbf57a69d0</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-04-23 14:41:34.000</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>18782.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22888</th>\n",
       "      <td>b0d9114fd5</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-04-25 00:42:17.000</td>\n",
       "      <td>1459.0</td>\n",
       "      <td>20628.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>lodging</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22889</th>\n",
       "      <td>6f1849eadc</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-05-29 14:43:52.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>failed</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22890</th>\n",
       "      <td>e5bfd61319</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-05-29 15:23:43.000</td>\n",
       "      <td>1759.0</td>\n",
       "      <td>18988.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22891</th>\n",
       "      <td>9d4a2f242b</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-05-31 00:44:23.000</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>20961.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>lodging</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22892</th>\n",
       "      <td>d92aae4c86</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-07-03 09:11:39.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>failed</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22893</th>\n",
       "      <td>b1a28ca405</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-07-04 13:30:01.000</td>\n",
       "      <td>2475.0</td>\n",
       "      <td>19945.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22894</th>\n",
       "      <td>ebc3442893</td>\n",
       "      <td>f061ee662d</td>\n",
       "      <td>2017-07-03 10:22:06.000</td>\n",
       "      <td>2792.0</td>\n",
       "      <td>20663.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23289</th>\n",
       "      <td>5ade48758b</td>\n",
       "      <td>563229fed1</td>\n",
       "      <td>2017-06-17 20:01:28.000</td>\n",
       "      <td>2239.0</td>\n",
       "      <td>13972.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23635</th>\n",
       "      <td>09f8a45f1c</td>\n",
       "      <td>6e8c168f7b</td>\n",
       "      <td>2017-06-30 20:03:58.000</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>9574.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24663</th>\n",
       "      <td>d33f4d9ac1</td>\n",
       "      <td>5655d8485c</td>\n",
       "      <td>2017-01-01 06:40:31.000</td>\n",
       "      <td>1057.0</td>\n",
       "      <td>8005.0</td>\n",
       "      <td>514.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>NaN</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26288</th>\n",
       "      <td>ae47e06292</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-01 18:20:12.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>no_show</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26289</th>\n",
       "      <td>726e67a3f4</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-17 19:46:19.000</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>12202.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26290</th>\n",
       "      <td>3c1c0aba35</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-18 19:30:36.000</td>\n",
       "      <td>756.0</td>\n",
       "      <td>3635.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26291</th>\n",
       "      <td>f106bd88c2</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-20 19:46:36.000</td>\n",
       "      <td>1903.0</td>\n",
       "      <td>15955.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26292</th>\n",
       "      <td>43cbf92247</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-21 19:49:59.000</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>15503.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26293</th>\n",
       "      <td>ab44fb7f60</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-27 19:44:04.000</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>6414.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26294</th>\n",
       "      <td>6dafadaac4</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-19 19:32:46.000</td>\n",
       "      <td>572.0</td>\n",
       "      <td>3585.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26295</th>\n",
       "      <td>2b2106dea0</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-22 19:28:02.000</td>\n",
       "      <td>445.0</td>\n",
       "      <td>2075.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26296</th>\n",
       "      <td>f9922e7f9c</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-23 19:35:19.000</td>\n",
       "      <td>620.0</td>\n",
       "      <td>3626.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26297</th>\n",
       "      <td>597d47acba</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-24 19:45:34.000</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>14494.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26298</th>\n",
       "      <td>2d6fb619df</td>\n",
       "      <td>c92cff2444</td>\n",
       "      <td>2017-01-30 19:22:46.000</td>\n",
       "      <td>456.0</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</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",
       "      <td>...</td>\n",
       "      <td>...</td>\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>69701</th>\n",
       "      <td>9c7124a36d</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-06 05:32:18.000</td>\n",
       "      <td>2289.0</td>\n",
       "      <td>16201.0</td>\n",
       "      <td>476.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69702</th>\n",
       "      <td>1d459e2937</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-07 12:04:45.000</td>\n",
       "      <td>1548.0</td>\n",
       "      <td>2161.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69703</th>\n",
       "      <td>f65530d4ca</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-08 04:48:53.000</td>\n",
       "      <td>2473.0</td>\n",
       "      <td>9406.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69704</th>\n",
       "      <td>e5d299dddc</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-21 06:49:09.000</td>\n",
       "      <td>2339.0</td>\n",
       "      <td>15099.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69705</th>\n",
       "      <td>8cef9abc5e</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-21 12:17:15.000</td>\n",
       "      <td>4680.0</td>\n",
       "      <td>24581.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69706</th>\n",
       "      <td>97baa19d8d</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-22 05:35:52.000</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>15101.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69707</th>\n",
       "      <td>048906b484</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-22 11:17:32.000</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>15015.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69708</th>\n",
       "      <td>d2cfdf61c8</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-26 05:48:10.000</td>\n",
       "      <td>4306.0</td>\n",
       "      <td>14395.0</td>\n",
       "      <td>616.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69709</th>\n",
       "      <td>8e050ae2fa</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-15 09:22:52.000</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>15250.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69710</th>\n",
       "      <td>ee48a1da9c</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-30 07:42:11.230</td>\n",
       "      <td>1176.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69711</th>\n",
       "      <td>35e10923b6</td>\n",
       "      <td>2490a7a085</td>\n",
       "      <td>2017-06-30 12:44:33.000</td>\n",
       "      <td>2432.0</td>\n",
       "      <td>17633.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69810</th>\n",
       "      <td>a3431b17ec</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-13 19:38:47.000</td>\n",
       "      <td>469.0</td>\n",
       "      <td>3359.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69811</th>\n",
       "      <td>68a3b15675</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-15 19:38:16.000</td>\n",
       "      <td>1066.0</td>\n",
       "      <td>8049.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69812</th>\n",
       "      <td>d015564854</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-16 19:26:25.000</td>\n",
       "      <td>321.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69813</th>\n",
       "      <td>42335e6e27</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-18 19:57:22.000</td>\n",
       "      <td>1263.0</td>\n",
       "      <td>10134.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69814</th>\n",
       "      <td>be13ed4e9c</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-19 19:32:45.000</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69815</th>\n",
       "      <td>f434678381</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-25 19:32:45.000</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>8154.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69816</th>\n",
       "      <td>9a6e4eb815</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-14 19:21:57.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69817</th>\n",
       "      <td>db91730cba</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-17 19:35:29.000</td>\n",
       "      <td>769.0</td>\n",
       "      <td>7248.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69818</th>\n",
       "      <td>b521d25c59</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-20 19:48:45.000</td>\n",
       "      <td>752.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69819</th>\n",
       "      <td>5bd174fb20</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-21 19:36:15.000</td>\n",
       "      <td>487.0</td>\n",
       "      <td>3426.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69820</th>\n",
       "      <td>b159d5b806</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-28 19:36:14.000</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>7098.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69821</th>\n",
       "      <td>097f40bb8b</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-29 20:02:28.000</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>12809.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69822</th>\n",
       "      <td>adf569e3f2</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-07-08 19:36:53.000</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>5526.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69823</th>\n",
       "      <td>94ddd8c2ba</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-24 19:50:26.243</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69824</th>\n",
       "      <td>6cbada65c0</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-06-24 20:11:11.000</td>\n",
       "      <td>1309.0</td>\n",
       "      <td>12368.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69825</th>\n",
       "      <td>92e825c6de</td>\n",
       "      <td>46856804aa</td>\n",
       "      <td>2017-07-04 19:43:30.000</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>7906.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70065</th>\n",
       "      <td>ea8ab35f3b</td>\n",
       "      <td>8780d0afc6</td>\n",
       "      <td>2017-06-29 05:52:32.000</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>3182.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70066</th>\n",
       "      <td>9ded2b2ff0</td>\n",
       "      <td>8780d0afc6</td>\n",
       "      <td>2017-07-05 06:53:38.000</td>\n",
       "      <td>2155.0</td>\n",
       "      <td>5550.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70103</th>\n",
       "      <td>23ae942c90</td>\n",
       "      <td>1cb22cfe60</td>\n",
       "      <td>2017-07-07 08:09:23.000</td>\n",
       "      <td>12564.0</td>\n",
       "      <td>25886.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5045 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id     user_id             completed_at  duration  distance  \\\n",
       "3097   248f4612d2  a55eca3174  2017-04-08 19:46:11.000     649.0    1279.0   \n",
       "22327  7c4d14bc64  d2a77cceb4  2017-04-14 19:35:30.000    1080.0    8587.0   \n",
       "22328  36723180aa  d2a77cceb4  2017-06-08 19:41:47.000    1538.0   16395.0   \n",
       "22882  bc7c1ce8bf  f061ee662d  2017-03-22 13:29:17.000    2442.0   19323.0   \n",
       "22883  47f786f8d8  f061ee662d  2017-03-21 14:41:41.000    2046.0   18318.0   \n",
       "22884  be331261f3  f061ee662d  2017-04-02 00:54:48.000    1610.0   20781.0   \n",
       "22885  043c5406f6  f061ee662d  2017-03-31 23:53:37.000    1660.0   19971.0   \n",
       "22886  9666c0139f  f061ee662d  2017-04-24 13:59:02.000    3470.0   24505.0   \n",
       "22887  bbf57a69d0  f061ee662d  2017-04-23 14:41:34.000    1647.0   18782.0   \n",
       "22888  b0d9114fd5  f061ee662d  2017-04-25 00:42:17.000    1459.0   20628.0   \n",
       "22889  6f1849eadc  f061ee662d  2017-05-29 14:43:52.000       0.0       0.0   \n",
       "22890  e5bfd61319  f061ee662d  2017-05-29 15:23:43.000    1759.0   18988.0   \n",
       "22891  9d4a2f242b  f061ee662d  2017-05-31 00:44:23.000    1554.0   20961.0   \n",
       "22892  d92aae4c86  f061ee662d  2017-07-03 09:11:39.000       0.0       0.0   \n",
       "22893  b1a28ca405  f061ee662d  2017-07-04 13:30:01.000    2475.0   19945.0   \n",
       "22894  ebc3442893  f061ee662d  2017-07-03 10:22:06.000    2792.0   20663.0   \n",
       "23289  5ade48758b  563229fed1  2017-06-17 20:01:28.000    2239.0   13972.0   \n",
       "23635  09f8a45f1c  6e8c168f7b  2017-06-30 20:03:58.000    1280.0    9574.0   \n",
       "24663  d33f4d9ac1  5655d8485c  2017-01-01 06:40:31.000    1057.0    8005.0   \n",
       "26288  ae47e06292  c92cff2444  2017-01-01 18:20:12.000       0.0       0.0   \n",
       "26289  726e67a3f4  c92cff2444  2017-01-17 19:46:19.000    1786.0   12202.0   \n",
       "26290  3c1c0aba35  c92cff2444  2017-01-18 19:30:36.000     756.0    3635.0   \n",
       "26291  f106bd88c2  c92cff2444  2017-01-20 19:46:36.000    1903.0   15955.0   \n",
       "26292  43cbf92247  c92cff2444  2017-01-21 19:49:59.000    2004.0   15503.0   \n",
       "26293  ab44fb7f60  c92cff2444  2017-01-27 19:44:04.000    1145.0    6414.0   \n",
       "26294  6dafadaac4  c92cff2444  2017-01-19 19:32:46.000     572.0    3585.0   \n",
       "26295  2b2106dea0  c92cff2444  2017-01-22 19:28:02.000     445.0    2075.0   \n",
       "26296  f9922e7f9c  c92cff2444  2017-01-23 19:35:19.000     620.0    3626.0   \n",
       "26297  597d47acba  c92cff2444  2017-01-24 19:45:34.000    1845.0   14494.0   \n",
       "26298  2d6fb619df  c92cff2444  2017-01-30 19:22:46.000     456.0    2477.0   \n",
       "...           ...         ...                      ...       ...       ...   \n",
       "69701  9c7124a36d  2490a7a085  2017-06-06 05:32:18.000    2289.0   16201.0   \n",
       "69702  1d459e2937  2490a7a085  2017-06-07 12:04:45.000    1548.0    2161.0   \n",
       "69703  f65530d4ca  2490a7a085  2017-06-08 04:48:53.000    2473.0    9406.0   \n",
       "69704  e5d299dddc  2490a7a085  2017-06-21 06:49:09.000    2339.0   15099.0   \n",
       "69705  8cef9abc5e  2490a7a085  2017-06-21 12:17:15.000    4680.0   24581.0   \n",
       "69706  97baa19d8d  2490a7a085  2017-06-22 05:35:52.000    1585.0   15101.0   \n",
       "69707  048906b484  2490a7a085  2017-06-22 11:17:32.000    1575.0   15015.0   \n",
       "69708  d2cfdf61c8  2490a7a085  2017-06-26 05:48:10.000    4306.0   14395.0   \n",
       "69709  8e050ae2fa  2490a7a085  2017-06-15 09:22:52.000    1893.0   15250.0   \n",
       "69710  ee48a1da9c  2490a7a085  2017-06-30 07:42:11.230    1176.0       0.0   \n",
       "69711  35e10923b6  2490a7a085  2017-06-30 12:44:33.000    2432.0   17633.0   \n",
       "69810  a3431b17ec  46856804aa  2017-06-13 19:38:47.000     469.0    3359.0   \n",
       "69811  68a3b15675  46856804aa  2017-06-15 19:38:16.000    1066.0    8049.0   \n",
       "69812  d015564854  46856804aa  2017-06-16 19:26:25.000     321.0     951.0   \n",
       "69813  42335e6e27  46856804aa  2017-06-18 19:57:22.000    1263.0   10134.0   \n",
       "69814  be13ed4e9c  46856804aa  2017-06-19 19:32:45.000    1024.0    5700.0   \n",
       "69815  f434678381  46856804aa  2017-06-25 19:32:45.000    1038.0    8154.0   \n",
       "69816  9a6e4eb815  46856804aa  2017-06-14 19:21:57.000       0.0       0.0   \n",
       "69817  db91730cba  46856804aa  2017-06-17 19:35:29.000     769.0    7248.0   \n",
       "69818  b521d25c59  46856804aa  2017-06-20 19:48:45.000     752.0    4605.0   \n",
       "69819  5bd174fb20  46856804aa  2017-06-21 19:36:15.000     487.0    3426.0   \n",
       "69820  b159d5b806  46856804aa  2017-06-28 19:36:14.000    1100.0    7098.0   \n",
       "69821  097f40bb8b  46856804aa  2017-06-29 20:02:28.000    1563.0   12809.0   \n",
       "69822  adf569e3f2  46856804aa  2017-07-08 19:36:53.000    1011.0    5526.0   \n",
       "69823  94ddd8c2ba  46856804aa  2017-06-24 19:50:26.243     900.0       0.0   \n",
       "69824  6cbada65c0  46856804aa  2017-06-24 20:11:11.000    1309.0   12368.0   \n",
       "69825  92e825c6de  46856804aa  2017-07-04 19:43:30.000    1110.0    7906.0   \n",
       "70065  ea8ab35f3b  8780d0afc6  2017-06-29 05:52:32.000    1972.0    3182.0   \n",
       "70066  9ded2b2ff0  8780d0afc6  2017-07-05 06:53:38.000    2155.0    5550.0   \n",
       "70103  23ae942c90  1cb22cfe60  2017-07-07 08:09:23.000   12564.0   25886.0   \n",
       "\n",
       "        price  credit  to_pay   vendor_id         dropoff          pickup  \\\n",
       "3097    249.0     0.0     0.0  d2371067ea  street_address           store   \n",
       "22327   425.0     0.0     0.0  d2371067ea  street_address           store   \n",
       "22328   638.0     0.0     0.0  d2371067ea  street_address           store   \n",
       "22882   900.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "22883   900.0     0.0     0.0  d2371067ea         lodging         airport   \n",
       "22884   900.0     0.0     0.0  d2371067ea         airport         lodging   \n",
       "22885   932.0     0.0     0.0  d2371067ea         lodging         airport   \n",
       "22886   900.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "22887   900.0     0.0     0.0  d2371067ea         lodging         airport   \n",
       "22888   900.0     0.0     0.0  d2371067ea         airport         lodging   \n",
       "22889     0.0     0.0     0.0  d2371067ea         lodging         airport   \n",
       "22890   900.0     0.0     0.0  d2371067ea         lodging         airport   \n",
       "22891   900.0     0.0     0.0  d2371067ea         airport         lodging   \n",
       "22892     0.0     0.0     0.0  d2371067ea         lodging         airport   \n",
       "22893   900.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "22894   900.0     0.0     0.0  d2371067ea         lodging         airport   \n",
       "23289   677.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "23635   299.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "24663   514.0   300.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26288   260.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26289   375.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26290   170.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26291   438.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26292   442.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26293   239.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26294   155.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26295   124.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26296   160.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26297   412.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "26298   129.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "...       ...     ...     ...         ...             ...             ...   \n",
       "69701   476.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69702   215.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69703   396.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69704   461.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69705   794.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69706   396.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69707   400.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69708   616.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69709   428.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69710     0.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69711   506.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69810   142.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69811   257.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69812    99.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69813   306.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69814   224.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69815   259.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69816     0.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69817   221.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69818   184.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69819   148.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69820   249.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69821   369.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69822   217.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69823     0.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69824   338.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "69825   260.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "70065   264.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "70066   312.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "70103  1467.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "\n",
       "          status payment_type utm_medium   app_os name  \n",
       "3097        done      invoice        NaN      NaN    A  \n",
       "22327       done      invoice        NaN      NaN    A  \n",
       "22328       done      invoice        NaN      NaN    A  \n",
       "22882       done      invoice        NaN      NaN    A  \n",
       "22883       done      invoice        NaN      NaN    A  \n",
       "22884       done      invoice        NaN      NaN    A  \n",
       "22885       done      invoice        NaN      NaN    A  \n",
       "22886       done      invoice        NaN      NaN    A  \n",
       "22887       done      invoice        NaN      NaN    A  \n",
       "22888       done      invoice        NaN      NaN    A  \n",
       "22889     failed      invoice        NaN      NaN    A  \n",
       "22890       done      invoice        NaN      NaN    A  \n",
       "22891       done      invoice        NaN      NaN    A  \n",
       "22892     failed      invoice        NaN      NaN    A  \n",
       "22893       done      invoice        NaN      NaN    A  \n",
       "22894       done      invoice        NaN      NaN    A  \n",
       "23289       done      invoice        NaN      NaN    A  \n",
       "23635       done      invoice        NaN      NaN    A  \n",
       "24663       done         card        NaN  android    B  \n",
       "26288    no_show      invoice        NaN      NaN    B  \n",
       "26289       done      invoice        NaN      NaN    B  \n",
       "26290       done      invoice        NaN      NaN    B  \n",
       "26291       done      invoice        NaN      NaN    B  \n",
       "26292       done      invoice        NaN      NaN    B  \n",
       "26293       done      invoice        NaN      NaN    B  \n",
       "26294       done      invoice        NaN      NaN    B  \n",
       "26295       done      invoice        NaN      NaN    B  \n",
       "26296       done      invoice        NaN      NaN    B  \n",
       "26297       done      invoice        NaN      NaN    B  \n",
       "26298       done      invoice        NaN      NaN    B  \n",
       "...          ...          ...        ...      ...  ...  \n",
       "69701       done      invoice        NaN      NaN    B  \n",
       "69702       done      invoice        NaN      NaN    B  \n",
       "69703       done      invoice        NaN      NaN    B  \n",
       "69704       done      invoice        NaN      NaN    B  \n",
       "69705       done      invoice        NaN      NaN    B  \n",
       "69706       done      invoice        NaN      NaN    B  \n",
       "69707       done      invoice        NaN      NaN    B  \n",
       "69708       done      invoice        NaN      NaN    B  \n",
       "69709       done      invoice        NaN      NaN    B  \n",
       "69710  cancelled      invoice        NaN      NaN    B  \n",
       "69711       done      invoice        NaN      NaN    B  \n",
       "69810       done      invoice        NaN      NaN    B  \n",
       "69811       done      invoice        NaN      NaN    B  \n",
       "69812       done      invoice        NaN      NaN    B  \n",
       "69813       done      invoice        NaN      NaN    B  \n",
       "69814       done      invoice        NaN      NaN    B  \n",
       "69815       done      invoice        NaN      NaN    B  \n",
       "69816  cancelled      invoice        NaN      NaN    B  \n",
       "69817       done      invoice        NaN      NaN    B  \n",
       "69818       done      invoice        NaN      NaN    B  \n",
       "69819       done      invoice        NaN      NaN    B  \n",
       "69820       done      invoice        NaN      NaN    B  \n",
       "69821       done      invoice        NaN      NaN    B  \n",
       "69822       done      invoice        NaN      NaN    B  \n",
       "69823  cancelled      invoice        NaN      NaN    B  \n",
       "69824       done      invoice        NaN      NaN    B  \n",
       "69825       done      invoice        NaN      NaN    B  \n",
       "70065       done      invoice        NaN      NaN    B  \n",
       "70066       done      invoice        NaN      NaN    B  \n",
       "70103       done      invoice        NaN  android    B  \n",
       "\n",
       "[5045 rows x 16 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There are 3 different names for street location: 'street_number', 'street_address' and 'street'. Lets use just one: \n",
    "# -'street_address'\n",
    "orders_df.loc[orders_df['dropoff'] == 'street_number', 'dropoff'] = 'street_address'\n",
    "orders_df.loc[orders_df['dropoff'] == 'street', 'dropoff'] = 'street_address'\n",
    "orders_df.loc[orders_df['pickup'] == 'street_number', 'pickup'] = 'street_address'\n",
    "orders_df.loc[orders_df['pickup'] == 'street', 'pickup'] = 'street_address'\n",
    "orders_df.loc[(orders_df['dropoff'].isnull()), 'dropoff']='street_address'\n",
    "orders_df.loc[(orders_df['pickup'].isnull()), 'pickup']='street_address'\n",
    "\n",
    "orders_df.loc[(orders_df['duration'].isnull()) |\n",
    "              (orders_df['distance'].isnull()) |\n",
    "              (orders_df['price'].isnull()) |\n",
    "              (orders_df['credit'].isnull()) |\n",
    "             (orders_df['to_pay'].isnull()) |\n",
    "              (orders_df['dropoff'].isnull()) |\n",
    "              (orders_df['pickup'].isnull()) |\n",
    "              (orders_df['utm_medium'].isnull()) |\n",
    "              (orders_df['credit'].isnull())]\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#There are still some missing values in utm_medium and app_os. Normaly one should discuss it with the client\n",
    "#or corresponding department. For now, since it is test-task, we will replace NaN here with the type 'other' in app_os\n",
    "#and 'unknown' in utm_medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40649</th>\n",
       "      <td>6eb8dfc622</td>\n",
       "      <td>ecfc51bb5f</td>\n",
       "      <td>2017-02-22 16:54:55.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id     user_id             completed_at  duration  distance  \\\n",
       "40649  6eb8dfc622  ecfc51bb5f  2017-02-22 16:54:55.000       NaN       NaN   \n",
       "\n",
       "       price  credit  to_pay   vendor_id         dropoff          pickup  \\\n",
       "40649    NaN     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "\n",
       "      status payment_type utm_medium app_os name  \n",
       "40649   done         card     invite    iOS    B  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_df.loc[(orders_df['utm_medium'].isnull()), 'utm_medium']='unknown'\n",
    "orders_df.loc[(orders_df['app_os'].isnull()), 'app_os']='other'\n",
    "\n",
    "#Lets check again if any missing values are left\n",
    "\n",
    "orders_df.loc[(orders_df['duration'].isnull()) |\n",
    "              (orders_df['distance'].isnull()) |\n",
    "              (orders_df['price'].isnull()) |\n",
    "              (orders_df['credit'].isnull()) |\n",
    "             (orders_df['to_pay'].isnull()) |\n",
    "              (orders_df['dropoff'].isnull()) |\n",
    "              (orders_df['pickup'].isnull()) |\n",
    "              (orders_df['utm_medium'].isnull()) |\n",
    "              (orders_df['credit'].isnull())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [order_id, user_id, completed_at, duration, distance, price, credit, to_pay, vendor_id, dropoff, pickup, status, payment_type, utm_medium, app_os, name]\n",
       "Index: []"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can delete this single row as it is a test-task, but in reality normally we would ask\n",
    "#responsible people for this missing details\n",
    "\n",
    "df_orders = orders_df.dropna(axis=0, how=\"any\")\n",
    "\n",
    "df_orders.loc[(orders_df['duration'].isnull()) |\n",
    "              (orders_df['distance'].isnull()) |\n",
    "              (orders_df['price'].isnull()) |\n",
    "              (orders_df['credit'].isnull()) |\n",
    "             (orders_df['to_pay'].isnull()) |\n",
    "              (orders_df['dropoff'].isnull()) |\n",
    "              (orders_df['pickup'].isnull()) |\n",
    "              (orders_df['utm_medium'].isnull()) |\n",
    "              (orders_df['credit'].isnull())]\n",
    "\n",
    "#Now there are no NaN values in the data, so we can move further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ec4c725f62</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-01-01 00:11:54.000</td>\n",
       "      <td>508.0</td>\n",
       "      <td>3613.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b42fce964b</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-01-06 00:16:06.000</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>21219.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cb9c59f681</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-17 14:24:44.000</td>\n",
       "      <td>1252.0</td>\n",
       "      <td>4807.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0707a069be</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-10 13:59:18.000</td>\n",
       "      <td>1789.0</td>\n",
       "      <td>5957.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b4076919fa</td>\n",
       "      <td>b14de8a940</td>\n",
       "      <td>2017-02-17 17:42:53.000</td>\n",
       "      <td>613.0</td>\n",
       "      <td>4367.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>card</td>\n",
       "      <td>blogger</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_id     user_id             completed_at  duration  distance  price  \\\n",
       "0  ec4c725f62  b14de8a940  2017-01-01 00:11:54.000     508.0    3613.0  498.0   \n",
       "1  b42fce964b  b14de8a940  2017-01-06 00:16:06.000    2079.0   21219.0  900.0   \n",
       "2  cb9c59f681  b14de8a940  2017-02-17 14:24:44.000    1252.0    4807.0  366.0   \n",
       "3  0707a069be  b14de8a940  2017-02-10 13:59:18.000    1789.0    5957.0  461.0   \n",
       "4  b4076919fa  b14de8a940  2017-02-17 17:42:53.000     613.0    4367.0  277.0   \n",
       "\n",
       "   credit  to_pay   vendor_id         dropoff          pickup status  \\\n",
       "0     0.0     0.0  d2371067ea  street_address  street_address   done   \n",
       "1     0.0     0.0  d2371067ea         airport  street_address   done   \n",
       "2     0.0     0.0  d2371067ea             poi  street_address   done   \n",
       "3     0.0     0.0  d2371067ea  street_address  street_address   done   \n",
       "4     0.0     0.0  d2371067ea  street_address  street_address   done   \n",
       "\n",
       "  payment_type utm_medium app_os name  \n",
       "0         card    blogger    iOS    A  \n",
       "1         card    blogger    iOS    A  \n",
       "2         card    blogger    iOS    A  \n",
       "3         card    blogger    iOS    A  \n",
       "4         card    blogger    iOS    A  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f899d679da0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H9H3+N6+xTo51KF/pMeYMDUWvtcmq9PePbchxs6WqKph3bVqpha9Btk6q15vZQn7PCrXt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\ne4YsLGPitm8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f899ce98da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets vusialize our data. This line tells the notebook to show plots inside of the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "sb.pairplot(df_orders, hue='utm_medium')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>completed_at</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>price</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>vendor_id</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>pickup</th>\n",
       "      <th>status</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>utm_medium</th>\n",
       "      <th>app_os</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42623</th>\n",
       "      <td>501a09b2c5</td>\n",
       "      <td>74c76aa748</td>\n",
       "      <td>2017-01-31 13:38:21.000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76021.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42805</th>\n",
       "      <td>cc7d5d1328</td>\n",
       "      <td>74c76aa748</td>\n",
       "      <td>2017-02-28 12:58:19.000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76021.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42826</th>\n",
       "      <td>4a5eba11e6</td>\n",
       "      <td>74c76aa748</td>\n",
       "      <td>2017-04-28 10:13:31.000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86401.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42836</th>\n",
       "      <td>53caf8e9ac</td>\n",
       "      <td>74c76aa748</td>\n",
       "      <td>2017-05-31 12:33:19.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98610.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98511.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42849</th>\n",
       "      <td>bac9768a8f</td>\n",
       "      <td>74c76aa748</td>\n",
       "      <td>2017-06-30 14:34:18.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>102070.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101971.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65855</th>\n",
       "      <td>69198cb354</td>\n",
       "      <td>62e46c54f8</td>\n",
       "      <td>2017-03-31 16:09:36.000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>77850.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77751.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>done</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>other</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id     user_id             completed_at  duration  distance  \\\n",
       "42623  501a09b2c5  74c76aa748  2017-01-31 13:38:21.000       6.0       0.0   \n",
       "42805  cc7d5d1328  74c76aa748  2017-02-28 12:58:19.000       6.0       0.0   \n",
       "42826  4a5eba11e6  74c76aa748  2017-04-28 10:13:31.000       5.0       0.0   \n",
       "42836  53caf8e9ac  74c76aa748  2017-05-31 12:33:19.000       4.0       0.0   \n",
       "42849  bac9768a8f  74c76aa748  2017-06-30 14:34:18.000       4.0       0.0   \n",
       "65855  69198cb354  62e46c54f8  2017-03-31 16:09:36.000       4.0     587.0   \n",
       "\n",
       "          price  credit    to_pay   vendor_id         dropoff          pickup  \\\n",
       "42623   76120.0     0.0   76021.0  e2a2d88896  street_address  street_address   \n",
       "42805   76120.0     0.0   76021.0  e2a2d88896  street_address  street_address   \n",
       "42826   86500.0     0.0   86401.0  e2a2d88896  street_address  street_address   \n",
       "42836   98610.0     0.0   98511.0  e2a2d88896  street_address  street_address   \n",
       "42849  102070.0     0.0  101971.0  e2a2d88896  street_address  street_address   \n",
       "65855   77850.0     0.0   77751.0  e2a2d88896  street_address  street_address   \n",
       "\n",
       "      status payment_type       utm_medium   app_os name  \n",
       "42623   done      invoice  cross-promotion  android    B  \n",
       "42805   done      invoice  cross-promotion  android    B  \n",
       "42826   done      invoice  cross-promotion  android    B  \n",
       "42836   done      invoice  cross-promotion  android    B  \n",
       "42849   done      invoice  cross-promotion  android    B  \n",
       "65855   done      invoice          organic    other    B  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After visualizing all classes, we can still see some irregularities, e.g. too high prices when distance \n",
    "#or duration are close to zero\n",
    "\n",
    "\n",
    "df_orders.loc[(df_orders['price'] > 8000) & \n",
    "             (df_orders['duration'] < 100)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#We delete these rows where duration and distance are too small with relation to high price. These are\n",
    "#probably errors, which should be discussed with the client or appropriate department who provided the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>order_id</th>\n",
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       "    <tr>\n",
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       "      <td>a9be1df2f6</td>\n",
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       "    <tr>\n",
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       "      <td>eac2fda4b5</td>\n",
       "      <td>8305d32c80</td>\n",
       "      <td>2017-05-20 16:51:00.725</td>\n",
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       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
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       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>34e56533ce</td>\n",
       "      <td>8305d32c80</td>\n",
       "      <td>2017-06-05 14:33:11.000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
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       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>fd9c4fddae</td>\n",
       "      <td>8305d32c80</td>\n",
       "      <td>2017-07-03 06:55:08.379</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>6bb7bb509a</td>\n",
       "      <td>8305d32c80</td>\n",
       "      <td>2017-07-05 07:01:22.822</td>\n",
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       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
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       "      <td>A</td>\n",
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       "    <tr>\n",
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       "      <td>f948653963</td>\n",
       "      <td>c633276c4f</td>\n",
       "      <td>2017-01-07 21:16:08.000</td>\n",
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       "      <td>A</td>\n",
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       "      <td>82492eb779</td>\n",
       "      <td>c633276c4f</td>\n",
       "      <td>2017-01-01 14:13:13.000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>bdb8db8f45</td>\n",
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       "      <td>2017-04-24 03:22:08.741</td>\n",
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       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>fa5658ed10</td>\n",
       "      <td>5f49e8c19d</td>\n",
       "      <td>2017-01-07 18:31:47.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>ae7a0a5b45</td>\n",
       "      <td>2165ad9b64</td>\n",
       "      <td>2017-06-10 15:00:34.180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>b7593bd700</td>\n",
       "      <td>2165ad9b64</td>\n",
       "      <td>2017-06-10 22:28:20.904</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>3b23519276</td>\n",
       "      <td>64480fa185</td>\n",
       "      <td>2017-01-01 17:10:06.098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168</th>\n",
       "      <td>557e971edb</td>\n",
       "      <td>64480fa185</td>\n",
       "      <td>2017-01-01 17:01:09.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196</th>\n",
       "      <td>0fc7727b4a</td>\n",
       "      <td>fd6f8a421f</td>\n",
       "      <td>2017-01-08 16:03:18.747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>others</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1244</th>\n",
       "      <td>09febac5b3</td>\n",
       "      <td>fd6f8a421f</td>\n",
       "      <td>2017-02-14 16:43:26.494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>concert_hall</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>others</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>2909f6bbf9</td>\n",
       "      <td>fd6f8a421f</td>\n",
       "      <td>2017-03-07 07:54:29.051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>others</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>766ad365d4</td>\n",
       "      <td>fd6f8a421f</td>\n",
       "      <td>2017-03-06 08:55:44.013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>others</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>eaf9c9b926</td>\n",
       "      <td>fd6f8a421f</td>\n",
       "      <td>2017-04-25 06:43:07.563</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>d2371067ea</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>others</td>\n",
       "      <td>iOS</td>\n",
       "      <td>A</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",
       "      <td>...</td>\n",
       "      <td>...</td>\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>70157</th>\n",
       "      <td>7d8f247b33</td>\n",
       "      <td>ef816428c5</td>\n",
       "      <td>2017-07-08 19:52:09.167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70175</th>\n",
       "      <td>581628b706</td>\n",
       "      <td>a4f88dee69</td>\n",
       "      <td>2017-07-07 18:13:47.570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>e2a2d88896</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70184</th>\n",
       "      <td>fb2b5e2054</td>\n",
       "      <td>8305d32c80</td>\n",
       "      <td>2017-02-03 10:29:34.986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>airport</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70186</th>\n",
       "      <td>17603f1dbe</td>\n",
       "      <td>8305d32c80</td>\n",
       "      <td>2017-01-30 15:45:12.799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70188</th>\n",
       "      <td>6af560c1e8</td>\n",
       "      <td>a76224cfa2</td>\n",
       "      <td>2017-01-01 18:29:59.619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70195</th>\n",
       "      <td>10433800c3</td>\n",
       "      <td>83a4991577</td>\n",
       "      <td>2017-05-01 06:03:24.724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>blogger</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70202</th>\n",
       "      <td>0b0cbba2cc</td>\n",
       "      <td>fdd144335a</td>\n",
       "      <td>2017-04-05 13:45:23.559</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70223</th>\n",
       "      <td>5a3347882f</td>\n",
       "      <td>2d78433bff</td>\n",
       "      <td>2017-05-31 04:29:52.729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>web</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70230</th>\n",
       "      <td>65f745dfcb</td>\n",
       "      <td>e00d4ad699</td>\n",
       "      <td>2017-02-08 10:16:29.728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>poi</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>others</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70238</th>\n",
       "      <td>39bd4c7a83</td>\n",
       "      <td>74c76aa748</td>\n",
       "      <td>2017-02-02 07:37:44.307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>cross-promotion</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70254</th>\n",
       "      <td>f15dff923d</td>\n",
       "      <td>4e16d478b2</td>\n",
       "      <td>2017-06-29 04:41:05.169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>web</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70269</th>\n",
       "      <td>cdf950ce05</td>\n",
       "      <td>231e6fdda3</td>\n",
       "      <td>2017-02-28 05:32:49.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70287</th>\n",
       "      <td>803a7a6cb1</td>\n",
       "      <td>d1ac082895</td>\n",
       "      <td>2017-04-22 06:16:55.280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70294</th>\n",
       "      <td>bedd166126</td>\n",
       "      <td>137f1f0b85</td>\n",
       "      <td>2017-04-04 18:02:40.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>poi</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70298</th>\n",
       "      <td>9a350b1aae</td>\n",
       "      <td>3e6059d0fb</td>\n",
       "      <td>2017-02-20 06:49:42.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70300</th>\n",
       "      <td>07b2514926</td>\n",
       "      <td>f30c998392</td>\n",
       "      <td>2017-03-21 07:17:29.600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70301</th>\n",
       "      <td>ada691101d</td>\n",
       "      <td>f30c998392</td>\n",
       "      <td>2017-03-22 12:56:35.293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>lodging</td>\n",
       "      <td>airport</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70312</th>\n",
       "      <td>76ebe1282b</td>\n",
       "      <td>d64fdec484</td>\n",
       "      <td>2017-04-18 10:25:25.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>offline</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70313</th>\n",
       "      <td>a23a0a143a</td>\n",
       "      <td>d64fdec484</td>\n",
       "      <td>2017-04-18 10:17:21.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>offline</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70320</th>\n",
       "      <td>8658be7960</td>\n",
       "      <td>feb6048c6f</td>\n",
       "      <td>2017-06-30 08:58:36.664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>organic</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70322</th>\n",
       "      <td>dc421db4b4</td>\n",
       "      <td>6192b63a3e</td>\n",
       "      <td>2017-03-05 09:11:08.150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70323</th>\n",
       "      <td>c9c8597ac6</td>\n",
       "      <td>6ee52dae21</td>\n",
       "      <td>2017-04-28 16:56:20.185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70329</th>\n",
       "      <td>bd8be0861c</td>\n",
       "      <td>07461d01cb</td>\n",
       "      <td>2017-03-23 13:34:54.070</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>others</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70336</th>\n",
       "      <td>dee6574d73</td>\n",
       "      <td>531b103917</td>\n",
       "      <td>2017-03-20 14:07:35.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>invite</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70337</th>\n",
       "      <td>99309c0a37</td>\n",
       "      <td>a6d20ab9a6</td>\n",
       "      <td>2017-04-05 18:31:39.188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>leisure</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70349</th>\n",
       "      <td>c4c530c0ba</td>\n",
       "      <td>4746f69ab9</td>\n",
       "      <td>2017-05-05 16:22:26.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>airport</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>organic</td>\n",
       "      <td>Win32NT</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70354</th>\n",
       "      <td>323bdf886c</td>\n",
       "      <td>852986bc32</td>\n",
       "      <td>2017-05-30 10:22:07.760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invite</td>\n",
       "      <td>android</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70365</th>\n",
       "      <td>1cd9ca9d49</td>\n",
       "      <td>badbbbfccb</td>\n",
       "      <td>2017-05-23 15:11:59.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>email</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70369</th>\n",
       "      <td>ed9ba599ab</td>\n",
       "      <td>8a1edd26b9</td>\n",
       "      <td>2017-06-15 19:37:43.870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>apple_pay</td>\n",
       "      <td>organic</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70371</th>\n",
       "      <td>6c66d90fce</td>\n",
       "      <td>e50b43f928</td>\n",
       "      <td>2017-06-29 20:38:30.956</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25ba3f0e07</td>\n",
       "      <td>street_address</td>\n",
       "      <td>street_address</td>\n",
       "      <td>cancelled</td>\n",
       "      <td>card</td>\n",
       "      <td>affiliate</td>\n",
       "      <td>iOS</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3204 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id     user_id             completed_at  duration  distance  \\\n",
       "125    0426bbab40  36e45fe6d8  2017-01-08 16:19:09.000       0.0       0.0   \n",
       "584    5aa3448c3d  5c67ce0d38  2017-03-24 12:27:07.175       0.0       0.0   \n",
       "622    944c6978b6  6d0e904305  2017-01-02 18:23:50.554       0.0       0.0   \n",
       "624    9b2e8e8223  6d0e904305  2017-01-01 11:04:44.896       0.0       0.0   \n",
       "761    a493121b08  e1c6404747  2017-02-03 03:02:50.662       0.0       0.0   \n",
       "836    0f0293d8f1  8305d32c80  2017-02-03 05:45:11.327       0.0       0.0   \n",
       "838    afc55bf732  8305d32c80  2017-02-04 13:12:02.000       0.0       0.0   \n",
       "868    961cbdccb3  8305d32c80  2017-02-05 13:03:31.416       0.0       0.0   \n",
       "874    08a60a1993  8305d32c80  2017-02-10 04:30:46.000       0.0       0.0   \n",
       "882    afffb3b45e  8305d32c80  2017-02-19 14:08:17.000       0.0       0.0   \n",
       "890    3df11ff493  8305d32c80  2017-03-17 10:40:40.779       0.0       0.0   \n",
       "893    a9be1df2f6  8305d32c80  2017-03-10 19:35:08.000       0.0       0.0   \n",
       "898    eac2fda4b5  8305d32c80  2017-05-20 16:51:00.725       0.0       0.0   \n",
       "904    34e56533ce  8305d32c80  2017-06-05 14:33:11.000       0.0       0.0   \n",
       "907    fd9c4fddae  8305d32c80  2017-07-03 06:55:08.379       0.0       0.0   \n",
       "910    6bb7bb509a  8305d32c80  2017-07-05 07:01:22.822       0.0       0.0   \n",
       "1016   f948653963  c633276c4f  2017-01-07 21:16:08.000       0.0       0.0   \n",
       "1018   82492eb779  c633276c4f  2017-01-01 14:13:13.000       0.0       0.0   \n",
       "1022   952dda0636  c633276c4f  2017-02-02 02:07:23.221       0.0       0.0   \n",
       "1024   bdb8db8f45  34fce009fd  2017-04-24 03:22:08.741       0.0       0.0   \n",
       "1041   fa5658ed10  5f49e8c19d  2017-01-07 18:31:47.000       0.0       0.0   \n",
       "1150   ae7a0a5b45  2165ad9b64  2017-06-10 15:00:34.180       0.0       0.0   \n",
       "1151   b7593bd700  2165ad9b64  2017-06-10 22:28:20.904       0.0       0.0   \n",
       "1167   3b23519276  64480fa185  2017-01-01 17:10:06.098       0.0       0.0   \n",
       "1168   557e971edb  64480fa185  2017-01-01 17:01:09.000       0.0       0.0   \n",
       "1196   0fc7727b4a  fd6f8a421f  2017-01-08 16:03:18.747       0.0       0.0   \n",
       "1244   09febac5b3  fd6f8a421f  2017-02-14 16:43:26.494       0.0       0.0   \n",
       "1264   2909f6bbf9  fd6f8a421f  2017-03-07 07:54:29.051       0.0       0.0   \n",
       "1273   766ad365d4  fd6f8a421f  2017-03-06 08:55:44.013       0.0       0.0   \n",
       "1295   eaf9c9b926  fd6f8a421f  2017-04-25 06:43:07.563       0.0       0.0   \n",
       "...           ...         ...                      ...       ...       ...   \n",
       "70157  7d8f247b33  ef816428c5  2017-07-08 19:52:09.167       0.0       0.0   \n",
       "70175  581628b706  a4f88dee69  2017-07-07 18:13:47.570       0.0       0.0   \n",
       "70184  fb2b5e2054  8305d32c80  2017-02-03 10:29:34.986       0.0       0.0   \n",
       "70186  17603f1dbe  8305d32c80  2017-01-30 15:45:12.799       0.0       0.0   \n",
       "70188  6af560c1e8  a76224cfa2  2017-01-01 18:29:59.619       0.0       0.0   \n",
       "70195  10433800c3  83a4991577  2017-05-01 06:03:24.724       0.0       0.0   \n",
       "70202  0b0cbba2cc  fdd144335a  2017-04-05 13:45:23.559       0.0       0.0   \n",
       "70223  5a3347882f  2d78433bff  2017-05-31 04:29:52.729       0.0       0.0   \n",
       "70230  65f745dfcb  e00d4ad699  2017-02-08 10:16:29.728       0.0       0.0   \n",
       "70238  39bd4c7a83  74c76aa748  2017-02-02 07:37:44.307       0.0       0.0   \n",
       "70254  f15dff923d  4e16d478b2  2017-06-29 04:41:05.169       0.0       0.0   \n",
       "70269  cdf950ce05  231e6fdda3  2017-02-28 05:32:49.000       0.0       0.0   \n",
       "70287  803a7a6cb1  d1ac082895  2017-04-22 06:16:55.280       0.0       0.0   \n",
       "70294  bedd166126  137f1f0b85  2017-04-04 18:02:40.000       0.0       0.0   \n",
       "70298  9a350b1aae  3e6059d0fb  2017-02-20 06:49:42.000       0.0       0.0   \n",
       "70300  07b2514926  f30c998392  2017-03-21 07:17:29.600       0.0       0.0   \n",
       "70301  ada691101d  f30c998392  2017-03-22 12:56:35.293       0.0       0.0   \n",
       "70312  76ebe1282b  d64fdec484  2017-04-18 10:25:25.000       0.0       0.0   \n",
       "70313  a23a0a143a  d64fdec484  2017-04-18 10:17:21.000       0.0       0.0   \n",
       "70320  8658be7960  feb6048c6f  2017-06-30 08:58:36.664       0.0       0.0   \n",
       "70322  dc421db4b4  6192b63a3e  2017-03-05 09:11:08.150       0.0       0.0   \n",
       "70323  c9c8597ac6  6ee52dae21  2017-04-28 16:56:20.185       0.0       0.0   \n",
       "70329  bd8be0861c  07461d01cb  2017-03-23 13:34:54.070       0.0       0.0   \n",
       "70336  dee6574d73  531b103917  2017-03-20 14:07:35.000       0.0       0.0   \n",
       "70337  99309c0a37  a6d20ab9a6  2017-04-05 18:31:39.188       0.0       0.0   \n",
       "70349  c4c530c0ba  4746f69ab9  2017-05-05 16:22:26.000       0.0       0.0   \n",
       "70354  323bdf886c  852986bc32  2017-05-30 10:22:07.760       0.0       0.0   \n",
       "70365  1cd9ca9d49  badbbbfccb  2017-05-23 15:11:59.908       0.0       0.0   \n",
       "70369  ed9ba599ab  8a1edd26b9  2017-06-15 19:37:43.870       0.0       0.0   \n",
       "70371  6c66d90fce  e50b43f928  2017-06-29 20:38:30.956       0.0       0.0   \n",
       "\n",
       "       price  credit  to_pay   vendor_id         dropoff          pickup  \\\n",
       "125      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "584      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "622      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "624      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "761      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "836      0.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "838      0.0     0.0     0.0  d2371067ea             poi  street_address   \n",
       "868      0.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "874      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "882      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "890      0.0     0.0     0.0  d2371067ea         airport  street_address   \n",
       "893      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "898      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "904      0.0     0.0     0.0  d2371067ea  street_address         airport   \n",
       "907      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "910      0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1016     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1018     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1022     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1024     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1041     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1150     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1151     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1167     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1168     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1196     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1244     0.0     0.0     0.0  d2371067ea  street_address    concert_hall   \n",
       "1264     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1273     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "1295     0.0     0.0     0.0  d2371067ea  street_address  street_address   \n",
       "...      ...     ...     ...         ...             ...             ...   \n",
       "70157    0.0     0.0     0.0  e2a2d88896  street_address  street_address   \n",
       "70175    0.0     0.0     0.0  e2a2d88896             poi  street_address   \n",
       "70184    0.0     0.0     0.0  25ba3f0e07  street_address         airport   \n",
       "70186    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70188    0.0     0.0     0.0  25ba3f0e07         airport  street_address   \n",
       "70195    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70202    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70223    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70230    0.0     0.0     0.0  25ba3f0e07             poi  street_address   \n",
       "70238    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70254    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70269    0.0     0.0     0.0  25ba3f0e07         airport  street_address   \n",
       "70287    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70294    0.0     0.0     0.0  25ba3f0e07  street_address             poi   \n",
       "70298    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70300    0.0     0.0     0.0  25ba3f0e07         lodging         airport   \n",
       "70301    0.0     0.0     0.0  25ba3f0e07         lodging         airport   \n",
       "70312    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70313    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70320    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70322    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70323    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70329    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70336    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70337    0.0     0.0     0.0  25ba3f0e07         leisure  street_address   \n",
       "70349    0.0     0.0     0.0  25ba3f0e07         airport  street_address   \n",
       "70354    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70365    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70369    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "70371    0.0     0.0     0.0  25ba3f0e07  street_address  street_address   \n",
       "\n",
       "          status payment_type       utm_medium   app_os name  \n",
       "125    cancelled         card          organic      iOS    A  \n",
       "584    cancelled         card  cross-promotion      iOS    A  \n",
       "622    cancelled         card          organic      iOS    A  \n",
       "624    cancelled         card          organic      iOS    A  \n",
       "761    cancelled         card           invite  android    A  \n",
       "836    cancelled      invoice          organic      iOS    A  \n",
       "838    cancelled      invoice          organic      iOS    A  \n",
       "868    cancelled      invoice          organic      iOS    A  \n",
       "874    cancelled      invoice          organic      iOS    A  \n",
       "882    cancelled      invoice          organic      iOS    A  \n",
       "890    cancelled      invoice          organic      iOS    A  \n",
       "893    cancelled      invoice          organic      iOS    A  \n",
       "898    cancelled      invoice          organic      iOS    A  \n",
       "904    cancelled      invoice          organic      iOS    A  \n",
       "907    cancelled      invoice          organic      iOS    A  \n",
       "910    cancelled      invoice          organic      iOS    A  \n",
       "1016   cancelled         card          organic      iOS    A  \n",
       "1018   cancelled         card          organic      iOS    A  \n",
       "1022   cancelled         card          organic      iOS    A  \n",
       "1024   cancelled         card           invite  android    A  \n",
       "1041   cancelled         card           invite      iOS    A  \n",
       "1150   cancelled         card           invite      iOS    A  \n",
       "1151   cancelled         card           invite      iOS    A  \n",
       "1167   cancelled         card           invite      iOS    A  \n",
       "1168   cancelled         card           invite      iOS    A  \n",
       "1196   cancelled         card           others      iOS    A  \n",
       "1244   cancelled         card           others      iOS    A  \n",
       "1264   cancelled         card           others      iOS    A  \n",
       "1273   cancelled         card           others      iOS    A  \n",
       "1295   cancelled         card           others      iOS    A  \n",
       "...          ...          ...              ...      ...  ...  \n",
       "70157  cancelled         card           invite  android    B  \n",
       "70175  cancelled         card           invite      iOS    B  \n",
       "70184  cancelled      invoice          organic      iOS    C  \n",
       "70186  cancelled      invoice          organic      iOS    C  \n",
       "70188  cancelled         card          organic      iOS    C  \n",
       "70195  cancelled      invoice          blogger  android    C  \n",
       "70202  cancelled         card          organic      iOS    C  \n",
       "70223  cancelled      invoice          organic      web    C  \n",
       "70230  cancelled      invoice           others  android    C  \n",
       "70238  cancelled      invoice  cross-promotion  android    C  \n",
       "70254  cancelled      invoice          organic      web    C  \n",
       "70269  cancelled         card          organic      iOS    C  \n",
       "70287  cancelled         card          organic      iOS    C  \n",
       "70294  cancelled         card           invite      iOS    C  \n",
       "70298  cancelled         card           invite      iOS    C  \n",
       "70300  cancelled         card          organic  android    C  \n",
       "70301  cancelled         card          organic  android    C  \n",
       "70312  cancelled      invoice          offline  android    C  \n",
       "70313  cancelled      invoice          offline  android    C  \n",
       "70320  cancelled      invoice          organic  android    C  \n",
       "70322  cancelled         card          organic      iOS    C  \n",
       "70323  cancelled         card          organic      iOS    C  \n",
       "70329  cancelled      invoice           others      iOS    C  \n",
       "70336  cancelled         card           invite      iOS    C  \n",
       "70337  cancelled         card          organic      iOS    C  \n",
       "70349  cancelled         card          organic  Win32NT    C  \n",
       "70354  cancelled      invoice           invite  android    C  \n",
       "70365  cancelled         card            email      iOS    C  \n",
       "70369  cancelled    apple_pay          organic      iOS    C  \n",
       "70371  cancelled         card        affiliate      iOS    C  \n",
       "\n",
       "[3204 rows x 16 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders = df_orders.drop(df_orders[df_orders.duration < 10].index)\n",
    "df_orders.loc[(df_orders['price'] > 8000) & \n",
    "             (df_orders['duration'] < 100)]\n",
    "\n",
    "#Also, due to inconsistency in duration and distance for cancelled orders, lets make duration and distance equal to 0\n",
    "#in tnis case. Though in reality it should be better clarified.  \n",
    "df_orders.loc[df_orders['status'] == 'cancelled', 'duration'] = 0\n",
    "df_orders.loc[df_orders['status'] == 'cancelled', 'distance'] = 0\n",
    "df_orders.loc[df_orders['status'] == 'cancelled', 'price'] = 0\n",
    "df_orders.loc[df_orders['status'] == 'cancelled', 'to_pay'] = 0\n",
    "\n",
    "df_orders.loc[(df_orders['status'] == 'cancelled')]\n",
    "#df_orders.head()\n",
    "#df_orders.loc[(df_orders['to_pay'] > 1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Lets remove time from data column as exact time is not relevant for our segmentation\n",
    "df_orders['completed_at'] = df_orders['completed_at'].map(lambda x: x.rstrip('1234567890:.'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['done', 'cancelled', 'no_show'], dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders['status'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_orders['cancelled']=df_orders['status']\n",
    "df_orders.loc[df_orders['cancelled'] == 'cancelled', 'cancelled'] = 1\n",
    "df_orders.loc[df_orders['cancelled'] == 'done', 'cancelled'] = 0\n",
    "df_orders.loc[df_orders['cancelled'] == 'no_show', 'cancelled'] = 0\n",
    "\n",
    "df_orders['no_show']=df_orders['status']\n",
    "df_orders.loc[df_orders['no_show'] == 'no_show', 'no_show'] = 1\n",
    "df_orders.loc[df_orders['no_show'] == 'done', 'no_show'] = 0\n",
    "df_orders.loc[df_orders['no_show'] == 'cancelled', 'no_show'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NOW = dt.datetime(2017,7,20)\n",
    "df_orders['completed_at'] = pd.to_datetime(df_orders['completed_at'])\n",
    "rfm = df_orders.groupby('user_id').agg({'completed_at': lambda x: (NOW - x.max()).days, # Recency\n",
    "                                        'order_id': lambda x: len(x),      # Frequency\n",
    "                                        'price': lambda x: x.sum(),  #monetary value\n",
    "                                       'duration': lambda x: x.sum(), #trip duration\n",
    "                                       'distance': lambda x: x.sum(), #trip distance\n",
    "                                       'credit': lambda x: x.sum(),#payed with bonuses\n",
    "                                       'to_pay': lambda x: x.sum(), #not payed yet\n",
    "                                       'cancelled': lambda x: x.sum(),\n",
    "                                       'no_show': lambda x: x.sum()})   \n",
    "\n",
    "rfm['completed_at'] = rfm['completed_at'].astype(int)\n",
    "rfm.rename(columns={'completed_at': 'recency', \n",
    "                         'order_id': 'frequency', \n",
    "                         'price': 'paid'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quantiles = rfm.quantile(q=[0.25,0.5,0.75])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quantiles = quantiles.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Recency class. For big value in recency, number 4- \"bad\" value will be assigned\n",
    "def RClass(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "# Frequency and payment class. For high values in frequency and payment, number 1 will be assigned\n",
    "def FMClass(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "#Additional classes- cancellation, no-show clients, debt-clients    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfmSegm=rfm\n",
    "rfmSegm['R_Quartile'] = rfmSegm['recency'].apply(RClass, args=('recency',quantiles,))\n",
    "rfmSegm['F_Quartile'] = rfmSegm['frequency'].apply(FMClass, args=('frequency',quantiles,))\n",
    "rfmSegm['M_Quartile'] = rfmSegm['paid'].apply(FMClass, args=('paid',quantiles,))\n",
    "rfmSegm['Canc_Quartile'] = rfmSegm['cancelled'].apply(RClass, args=('cancelled',quantiles,))\n",
    "rfmSegm['N_Quartile'] = rfmSegm['no_show'].apply(RClass, args=('no_show',quantiles,))\n",
    "rfmSegm['D_Quartile'] = rfmSegm['to_pay'].apply(RClass, args=('to_pay',quantiles,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfmSegm['RFMClass'] = rfmSegm.R_Quartile.map(str) \\\n",
    "                            + rfmSegm.F_Quartile.map(str) \\\n",
    "                            + rfmSegm.M_Quartile.map(str)\\\n",
    "                            + rfmSegm.Canc_Quartile.map(str) \\\n",
    "                            + rfmSegm.N_Quartile.map(str)\\\n",
    "                            + rfmSegm.D_Quartile.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>paid</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>no_show</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>Canc_Quartile</th>\n",
       "      <th>N_Quartile</th>\n",
       "      <th>D_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>00100f0f3b</th>\n",
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       "    <tr>\n",
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       "      <td>15</td>\n",
       "      <td>2538.0</td>\n",
       "      <td>10183.0</td>\n",
       "      <td>52071.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>176.0</td>\n",
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      ],
      "text/plain": [
       "            recency  frequency    paid  duration  distance  credit  to_pay  \\\n",
       "user_id                                                                      \n",
       "00100f0f3b      148          1   462.0    2786.0   12319.0   108.0   354.0   \n",
       "0015d74ca0      173          1   296.0     964.0   11186.0   296.0     0.0   \n",
       "0016e9f3f8       34          6   712.0    2874.0   21648.0   421.0     0.0   \n",
       "001b9d8e6f       28         15  2538.0   10183.0   52071.0     0.0     0.0   \n",
       "002bade505       95          1   176.0     649.0    4783.0   176.0     0.0   \n",
       "\n",
       "            cancelled  no_show  R_Quartile  F_Quartile  M_Quartile  \\\n",
       "user_id                                                              \n",
       "00100f0f3b          0        0           3           4           3   \n",
       "0015d74ca0          0        0           4           4           3   \n",
       "0016e9f3f8          3        0           1           2           2   \n",
       "001b9d8e6f          0        0           1           1           1   \n",
       "002bade505          0        0           2           4           4   \n",
       "\n",
       "            Canc_Quartile  N_Quartile  D_Quartile RFMClass  \n",
       "user_id                                                     \n",
       "00100f0f3b              1           1           4   343114  \n",
       "0015d74ca0              1           1           1   443111  \n",
       "0016e9f3f8              4           1           1   122411  \n",
       "001b9d8e6f              1           1           1   111111  \n",
       "002bade505              1           1           1   244111  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "rfmSegm.head()\n",
    "#In RFMClass column, there is 6-digit number. First 3 numbers are crucial. \n",
    "#They represent recency, frequency and monetary_value. \n",
    "#The next 3 are optional, they represent wheather\n",
    "#the order was canceled, or customer has debt, or there was a \"no-show\".\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>paid</th>\n",
       "      <th>duration</th>\n",
       "      <th>distance</th>\n",
       "      <th>credit</th>\n",
       "      <th>to_pay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>no_show</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>Canc_Quartile</th>\n",
       "      <th>N_Quartile</th>\n",
       "      <th>D_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>0b53623741</th>\n",
       "      <td>11</td>\n",
       "      <td>701</td>\n",
       "      <td>178952.0</td>\n",
       "      <td>780781.0</td>\n",
       "      <td>5228925.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8aaf552fa9</th>\n",
       "      <td>12</td>\n",
       "      <td>295</td>\n",
       "      <td>125717.0</td>\n",
       "      <td>565055.0</td>\n",
       "      <td>4040816.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92f8dca2fd</th>\n",
       "      <td>11</td>\n",
       "      <td>501</td>\n",
       "      <td>122555.0</td>\n",
       "      <td>539602.0</td>\n",
       "      <td>3517985.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912b3a40bb</th>\n",
       "      <td>20</td>\n",
       "      <td>410</td>\n",
       "      <td>103823.0</td>\n",
       "      <td>507965.0</td>\n",
       "      <td>2539387.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7a0ec8ea0b</th>\n",
       "      <td>12</td>\n",
       "      <td>382</td>\n",
       "      <td>101475.0</td>\n",
       "      <td>574726.0</td>\n",
       "      <td>2014855.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency      paid  duration   distance  credit  to_pay  \\\n",
       "user_id                                                                         \n",
       "0b53623741       11        701  178952.0  780781.0  5228925.0     0.0     0.0   \n",
       "8aaf552fa9       12        295  125717.0  565055.0  4040816.0     0.0     0.0   \n",
       "92f8dca2fd       11        501  122555.0  539602.0  3517985.0     0.0     0.0   \n",
       "912b3a40bb       20        410  103823.0  507965.0  2539387.0   300.0     0.0   \n",
       "7a0ec8ea0b       12        382  101475.0  574726.0  2014855.0     0.0     0.0   \n",
       "\n",
       "            cancelled  no_show  R_Quartile  F_Quartile  M_Quartile  \\\n",
       "user_id                                                              \n",
       "0b53623741          0        0           1           1           1   \n",
       "8aaf552fa9          0        0           1           1           1   \n",
       "92f8dca2fd          0        0           1           1           1   \n",
       "912b3a40bb          0        0           1           1           1   \n",
       "7a0ec8ea0b          0        0           1           1           1   \n",
       "\n",
       "            Canc_Quartile  N_Quartile  D_Quartile RFMClass  \n",
       "user_id                                                     \n",
       "0b53623741              1           1           1   111111  \n",
       "8aaf552fa9              1           1           1   111111  \n",
       "92f8dca2fd              1           1           1   111111  \n",
       "912b3a40bb              1           1           1   111111  \n",
       "7a0ec8ea0b              1           1           1   111111  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here are our best customers with all six 111111 scores, \n",
    "#sorted by money payed (monetary value) \n",
    "rfmSegm[rfmSegm['RFMClass']=='111111'].sort_values('paid', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfmSegm.loc[(rfmSegm['R_Quartile']==1) & (rfmSegm['F_Quartile']==1) & (rfmSegm['M_Quartile']==1), 'Segment']='Best customers'\n",
    "rfmSegm.loc[(rfmSegm['R_Quartile']!=1) & (rfmSegm['F_Quartile']==1) & (rfmSegm['M_Quartile']!=1), 'Segment']='Loyal customers'\n",
    "rfmSegm.loc[(rfmSegm['R_Quartile']!=1) & (rfmSegm['F_Quartile']!=1) & (rfmSegm['M_Quartile']==1), 'Segment']='Big spenders'\n",
    "rfmSegm.loc[(rfmSegm['R_Quartile']==3) & (rfmSegm['F_Quartile']==1) & (rfmSegm['M_Quartile']==1), 'Segment']='Almost lost'\n",
    "rfmSegm.loc[(rfmSegm['R_Quartile']==4) & (rfmSegm['F_Quartile']==1) & (rfmSegm['M_Quartile']==1), 'Segment']='Lost customers'\n",
    "rfmSegm.loc[(rfmSegm['R_Quartile']==4) & (rfmSegm['F_Quartile']==4) & (rfmSegm['M_Quartile']==4), 'Segment']='Lost cheap customers'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfmSegm.to_csv('rfm-table.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8987ae0fd0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f898d4b02b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfmSegm['Segment'].groupby(rfmSegm[\"Segment\"]).count().plot(kind=\"bar\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Classification performed in this file is based on RFM analysis. It is a customer segmentation technique that uses past purchase behavior to divide customers into groups:\n",
    "\n",
    "RECENCY (R): Time since last purchase\n",
    "FREQUENCY (F): Total number of purchases\n",
    "MONETARY VALUE (M): Total monetary value\n",
    "\n",
    "RFM analysis was first used by the direct mail industry more than four decades ago, yet it is still an effective way to optimize marketing.\n",
    "\n",
    "\n",
    "\n",
    "Depending on combination of indicators for each customer (RMF class), there might be different marketing strategies. We can either consider only three first numbers, or include all 6, together with cancelation, debt and no-show statistics. Example for 3 digits indicator:\n",
    "\n",
    "Best Customers (111) \n",
    "Bought most recently and most often, and spend the most.\n",
    "Marketing strategies: No price incentives, new products, and loyalty programs\n",
    "\n",
    "Loyal Customers\t(X1X)\n",
    "Buy most frequently.\n",
    "Marketing strategies: Use R and M to further segment\n",
    "\n",
    "Big Spenders (XX1)\n",
    "Spend the most\n",
    "Marketing strategies: Market your most expensive products\n",
    "\n",
    "Almost Lost\t(311)\n",
    "Haven’t purchased for some time, but purchased frequently and spend the most\n",
    "Marketing strategies: Aggressive price incentives\n",
    "\n",
    "Lost Customers(411)\n",
    "Haven’t purchased for some time, but purchased frequently and spend the most\n",
    "Marketing strategies: Aggressive price incentives\n",
    "\n",
    "Lost Cheap Customers (444)\n",
    "Last purchased long time go, purchased few, and spent little\n",
    "Marketing strategies: Don’t spend too much trying to re-acquire\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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