swiftly mnl v1.2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys, os, copy, itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as st\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "INDIR = r'Q:\\Model Research\\Swiftly\\processed\\v0.6'\n",
    "INFILE = r'choices_with_valid_rideshare.csv'\n",
    "choices = pd.read_csv(os.path.join(INDIR,INFILE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# drop duplicate listings within a search_id\n",
    "choices = choices.drop_duplicates(subset=['search_id','travel_provider','travel_product'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# filter to just records where a choice is made\n",
    "chosen_search_ids = choices.loc[choices['chosen']==1,'search_id'].tolist()\n",
    "choices = choices[choices['search_id'].isin(chosen_search_ids)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# clean data using 'drop' flag\n",
    "choices['drop'] = 0\n",
    "# flag records with invalid walk time or travel time\n",
    "choices.loc[(choices['travel_mode'] == 'transit') & ((choices['travel_initial_walk_time'] == -1) | (pd.isnull(choices['travel_initial_walk_time']))),'drop'] = 1\n",
    "choices.loc[choices['travel_time'] == 0,'drop'] = 1\n",
    "# flag records with both trip-ends outside of 9 counties\n",
    "choices.loc[(choices['county_start']=='Other') & (choices['county_end']=='Other'),'drop'] = 1\n",
    "# flag records with invalid cost\n",
    "choices.loc[(choices['travel_mode'] == 'transit') & (choices['travel_price']==-1),'drop'] = 1\n",
    "# drop flagged records\n",
    "choices = choices[choices['drop']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_is_near_home</th>\n",
       "      <th>end_is_near_home</th>\n",
       "      <th>start_is_near_work</th>\n",
       "      <th>end_is_near_work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2862</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7979</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8201</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9183</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11995</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32665</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42768</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116952</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136007</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251659</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        start_is_near_home  end_is_near_home  start_is_near_work  \\\n",
       "16                       1                 0                   0   \n",
       "51                       0                 0                   0   \n",
       "140                      0                 1                   0   \n",
       "160                      0                 0                   0   \n",
       "275                      1                 0                   0   \n",
       "331                      0                 0                  -1   \n",
       "409                     -1                -1                  -1   \n",
       "970                      0                 0                   1   \n",
       "1268                     1                 0                  -1   \n",
       "1399                     0                 1                   1   \n",
       "2862                     0                 1                  -1   \n",
       "7979                    -1                -1                   0   \n",
       "8201                    -1                -1                   1   \n",
       "9183                    -1                -1                   0   \n",
       "11995                    0                 1                   0   \n",
       "32665                    1                 1                   0   \n",
       "42768                    1                 0                   1   \n",
       "116952                   0                 0                   1   \n",
       "136007                   1                 1                  -1   \n",
       "251659                   1                 1                   1   \n",
       "\n",
       "        end_is_near_work  \n",
       "16                     0  \n",
       "51                     0  \n",
       "140                    0  \n",
       "160                    1  \n",
       "275                    1  \n",
       "331                   -1  \n",
       "409                   -1  \n",
       "970                    0  \n",
       "1268                  -1  \n",
       "1399                   0  \n",
       "2862                  -1  \n",
       "7979                   1  \n",
       "8201                   0  \n",
       "9183                   0  \n",
       "11995                  1  \n",
       "32665                  0  \n",
       "42768                  0  \n",
       "116952                 1  \n",
       "136007                -1  \n",
       "251659                 1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices.loc[:,['start_is_near_home','end_is_near_home','start_is_near_work','end_is_near_work']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#ucost = choices[(choices['travel_mode']=='rideshare') & (choices['chosen']==1)]['travel_distance']\n",
    "#ln_ucost = np.log(ucost)\n",
    "#fig = plt.figure()\n",
    "#ax = ucost.plot.hist(bins=25)\n",
    "#fig2 = plt.figure()\n",
    "#ax2 = ln_ucost.plot.hist(bins=50)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# flag 'travel_provider' if they're infrequently or never chosen.\n",
    "transit_options = choices[choices['travel_mode']=='transit']\n",
    "#transit_choice_freq = transit_options.groupby(['travel_provider'])['chosen'].sum()\n",
    "#unchosen = transit_choice_freq[transit_choice_freq == 0]\n",
    "#choices.loc[choices['travel_provider'].isin(unchosen.index.tolist()),'drop'] = 1\n",
    "#infrequent = transit_choice_freq[transit_choice_freq.between(1,100)]\n",
    "#infreq_search_ids = choices.loc[(choices['chosen']==1) & (choices['travel_provider'].isin(infrequent.index.tolist())),'search_id']\n",
    "#choices.loc[choices['travel_provider'].isin(infrequent.index.tolist()),'drop'] = 1\n",
    "# flag the entire search if the chosen provider is an infrequent is the chosen mode\n",
    "#choices.loc[choices['search_id'].isin(infreq_search_ids.tolist()),'drop'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#choices = choices[choices['drop']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# get walk and wait time in minutes to next 3 arrivals\n",
    "choices['search_time'] = pd.to_datetime(choices['timestamp'])\n",
    "\n",
    "choices['arr1'] = pd.to_datetime(choices['travel_arrival_time_1'])\n",
    "choices['arr2'] = pd.to_datetime(choices['travel_arrival_time_2'])\n",
    "choices['arr3'] = pd.to_datetime(choices['travel_arrival_time_3'])\n",
    "\n",
    "choices['wait1'] = choices['arr1'] - choices['search_time']\n",
    "choices['wait2'] = choices['arr2'] - choices['search_time']\n",
    "choices['wait3'] = choices['arr3'] - choices['search_time']\n",
    "\n",
    "choices['wait1'] = choices['wait1'].map(lambda x:x.total_seconds() / 60.0)\n",
    "choices['wait2'] = choices['wait2'].map(lambda x:x.total_seconds() / 60.0)\n",
    "choices['wait3'] = choices['wait3'].map(lambda x:x.total_seconds() / 60.0)\n",
    "\n",
    "choices['walk_time'] = choices['travel_initial_walk_time'] / 60.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>walk_time</th>\n",
       "      <th>wait1</th>\n",
       "      <th>wait2</th>\n",
       "      <th>wait3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>8.383333</td>\n",
       "      <td>6.566667</td>\n",
       "      <td>22.383333</td>\n",
       "      <td>27.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.716667</td>\n",
       "      <td>31.916667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>17.833333</td>\n",
       "      <td>39.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>3.950000</td>\n",
       "      <td>3.483333</td>\n",
       "      <td>9.016667</td>\n",
       "      <td>17.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>4.933333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>12.366667</td>\n",
       "      <td>17.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>7.516667</td>\n",
       "      <td>2.766667</td>\n",
       "      <td>12.033333</td>\n",
       "      <td>23.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>4.100000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>11.833333</td>\n",
       "      <td>22.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>3.466667</td>\n",
       "      <td>2.416667</td>\n",
       "      <td>13.616667</td>\n",
       "      <td>21.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>2.150000</td>\n",
       "      <td>-0.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626</th>\n",
       "      <td>8.483333</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>15.050000</td>\n",
       "      <td>24.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>1.566667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>7.816667</td>\n",
       "      <td>14.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>12.350000</td>\n",
       "      <td>8.066667</td>\n",
       "      <td>18.583333</td>\n",
       "      <td>22.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>8.483333</td>\n",
       "      <td>-0.016667</td>\n",
       "      <td>13.983333</td>\n",
       "      <td>28.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>3.783333</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>36.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>6.633333</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>15.733333</td>\n",
       "      <td>19.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>1.383333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>1.566667</td>\n",
       "      <td>13.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>5.766667</td>\n",
       "      <td>3.066667</td>\n",
       "      <td>5.366667</td>\n",
       "      <td>9.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1185</th>\n",
       "      <td>5.750000</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>6.383333</td>\n",
       "      <td>8.566667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>2.816667</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>19.550000</td>\n",
       "      <td>20.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1230</th>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.183333</td>\n",
       "      <td>6.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>7.483333</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>4.783333</td>\n",
       "      <td>5.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>9.100000</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>35.716667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.616667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>16.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>12.116667</td>\n",
       "      <td>18.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1480</th>\n",
       "      <td>1.116667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.950000</td>\n",
       "      <td>6.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>5.916667</td>\n",
       "      <td>5.533333</td>\n",
       "      <td>7.116667</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>9.850000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>1.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>8.616667</td>\n",
       "      <td>4.983333</td>\n",
       "      <td>13.983333</td>\n",
       "      <td>19.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>6.700000</td>\n",
       "      <td>1.566667</td>\n",
       "      <td>11.966667</td>\n",
       "      <td>25.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918948</th>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.283333</td>\n",
       "      <td>9.083333</td>\n",
       "      <td>19.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919047</th>\n",
       "      <td>3.866667</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919151</th>\n",
       "      <td>2.333333</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>10.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919155</th>\n",
       "      <td>9.050000</td>\n",
       "      <td>1.633333</td>\n",
       "      <td>9.233333</td>\n",
       "      <td>12.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919190</th>\n",
       "      <td>14.100000</td>\n",
       "      <td>1.116667</td>\n",
       "      <td>6.883333</td>\n",
       "      <td>17.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919200</th>\n",
       "      <td>5.283333</td>\n",
       "      <td>4.016667</td>\n",
       "      <td>9.116667</td>\n",
       "      <td>17.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919275</th>\n",
       "      <td>2.783333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>6.316667</td>\n",
       "      <td>6.633333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919810</th>\n",
       "      <td>1.766667</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>11.316667</td>\n",
       "      <td>20.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919965</th>\n",
       "      <td>8.050000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>6.633333</td>\n",
       "      <td>14.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919970</th>\n",
       "      <td>3.350000</td>\n",
       "      <td>2.216667</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>31.783333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920048</th>\n",
       "      <td>2.816667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>27.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920138</th>\n",
       "      <td>2.266667</td>\n",
       "      <td>1.783333</td>\n",
       "      <td>5.816667</td>\n",
       "      <td>12.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920227</th>\n",
       "      <td>9.533333</td>\n",
       "      <td>6.050000</td>\n",
       "      <td>13.033333</td>\n",
       "      <td>24.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920233</th>\n",
       "      <td>14.183333</td>\n",
       "      <td>-0.016667</td>\n",
       "      <td>15.983333</td>\n",
       "      <td>30.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920241</th>\n",
       "      <td>4.150000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>5.216667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920244</th>\n",
       "      <td>4.150000</td>\n",
       "      <td>1.783333</td>\n",
       "      <td>10.816667</td>\n",
       "      <td>24.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920329</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>-0.183333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920423</th>\n",
       "      <td>3.566667</td>\n",
       "      <td>3.550000</td>\n",
       "      <td>13.050000</td>\n",
       "      <td>25.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920464</th>\n",
       "      <td>6.733333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>20.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920488</th>\n",
       "      <td>7.516667</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>9.416667</td>\n",
       "      <td>20.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920497</th>\n",
       "      <td>7.516667</td>\n",
       "      <td>2.233333</td>\n",
       "      <td>6.283333</td>\n",
       "      <td>22.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920509</th>\n",
       "      <td>4.183333</td>\n",
       "      <td>1.916667</td>\n",
       "      <td>16.550000</td>\n",
       "      <td>32.516667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920584</th>\n",
       "      <td>4.366667</td>\n",
       "      <td>2.983333</td>\n",
       "      <td>21.983333</td>\n",
       "      <td>41.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920591</th>\n",
       "      <td>4.366667</td>\n",
       "      <td>2.983333</td>\n",
       "      <td>21.983333</td>\n",
       "      <td>41.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920603</th>\n",
       "      <td>2.866667</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>11.566667</td>\n",
       "      <td>18.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920604</th>\n",
       "      <td>12.183333</td>\n",
       "      <td>8.416667</td>\n",
       "      <td>23.416667</td>\n",
       "      <td>38.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920616</th>\n",
       "      <td>6.766667</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.933333</td>\n",
       "      <td>13.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920655</th>\n",
       "      <td>0.016667</td>\n",
       "      <td>-2.833333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920693</th>\n",
       "      <td>9.250000</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>10.983333</td>\n",
       "      <td>17.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920829</th>\n",
       "      <td>1.833333</td>\n",
       "      <td>1.216667</td>\n",
       "      <td>12.583333</td>\n",
       "      <td>22.433333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13557 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        walk_time     wait1      wait2      wait3\n",
       "141      8.383333  6.566667  22.383333  27.183333\n",
       "157      8.000000  5.716667  31.916667        NaN\n",
       "216      8.500000  0.483333  17.833333  39.100000\n",
       "282      3.950000  3.483333   9.016667  17.983333\n",
       "301      4.933333  2.333333  12.366667  17.866667\n",
       "340      7.516667  2.766667  12.033333  23.050000\n",
       "349      4.100000  2.300000  11.833333  22.566667\n",
       "441      3.466667  2.416667  13.616667  21.500000\n",
       "482      2.150000 -0.250000        NaN        NaN\n",
       "626      8.483333  8.100000  15.050000  24.133333\n",
       "627      1.566667  0.633333   7.816667  14.233333\n",
       "641     12.350000  8.066667  18.583333  22.483333\n",
       "820      8.483333 -0.016667  13.983333  28.983333\n",
       "886      3.783333  2.533333  13.600000  36.866667\n",
       "888      6.633333  5.600000  15.733333  19.883333\n",
       "945      1.383333  0.066667   1.566667  13.950000\n",
       "1124     5.766667  3.066667   5.366667   9.900000\n",
       "1185     5.750000  2.333333   6.383333   8.566667\n",
       "1186     2.816667  0.233333  19.550000  20.533333\n",
       "1230     0.733333  0.333333   1.183333   6.250000\n",
       "1232     7.483333  0.716667   4.783333   5.183333\n",
       "1320     9.100000  0.633333  35.716667        NaN\n",
       "1428     4.500000  2.616667   6.200000  16.866667\n",
       "1478     4.700000  0.083333  12.116667  18.900000\n",
       "1480     1.116667  0.800000   1.950000   6.783333\n",
       "1618     5.916667  5.533333   7.116667  14.300000\n",
       "1620     9.850000  4.000000  11.000000  20.000000\n",
       "1621     1.083333  0.083333        NaN        NaN\n",
       "1657     8.616667  4.983333  13.983333  19.983333\n",
       "1658     6.700000  1.566667  11.966667  25.183333\n",
       "...           ...       ...        ...        ...\n",
       "918948   1.050000  0.283333   9.083333  19.966667\n",
       "919047   3.866667  3.000000  18.000000  33.000000\n",
       "919151   2.333333  0.250000  10.450000  10.533333\n",
       "919155   9.050000  1.633333   9.233333  12.083333\n",
       "919190  14.100000  1.116667   6.883333  17.116667\n",
       "919200   5.283333  4.016667   9.116667  17.616667\n",
       "919275   2.783333  1.333333   6.316667   6.633333\n",
       "919810   1.766667  0.150000  11.316667  20.266667\n",
       "919965   8.050000  0.933333   6.633333  14.650000\n",
       "919970   3.350000  2.216667   5.700000  31.783333\n",
       "920048   2.816667  1.166667  13.400000  27.850000\n",
       "920138   2.266667  1.783333   5.816667  12.533333\n",
       "920227   9.533333  6.050000  13.033333  24.600000\n",
       "920233  14.183333 -0.016667  15.983333  30.983333\n",
       "920241   4.150000  3.400000   5.216667        NaN\n",
       "920244   4.150000  1.783333  10.816667  24.483333\n",
       "920329   0.016667 -0.183333        NaN        NaN\n",
       "920423   3.566667  3.550000  13.050000  25.116667\n",
       "920464   6.733333  0.083333   8.916667  20.116667\n",
       "920488   7.516667  6.200000   9.416667  20.900000\n",
       "920497   7.516667  2.233333   6.283333  22.683333\n",
       "920509   4.183333  1.916667  16.550000  32.516667\n",
       "920584   4.366667  2.983333  21.983333  41.983333\n",
       "920591   4.366667  2.983333  21.983333  41.983333\n",
       "920603   2.866667  1.016667  11.566667  18.416667\n",
       "920604  12.183333  8.416667  23.416667  38.416667\n",
       "920616   6.766667  2.000000   4.933333  13.433333\n",
       "920655   0.016667 -2.833333        NaN        NaN\n",
       "920693   9.250000  3.983333  10.983333  17.983333\n",
       "920829   1.833333  1.216667  12.583333  22.433333\n",
       "\n",
       "[13557 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check that wait times are set correctly\n",
    "choices.loc[choices['wait1']<choices['walk_time'],['walk_time','wait1','wait2','wait3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calc headways in minutes\n",
    "choices['h1_2'] = choices['wait2'] - choices['wait1']\n",
    "choices['h2_3'] = choices['wait3'] - choices['wait2']\n",
    "choices['h3_3'] = choices['wait3'] - choices['wait1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pick first wait time that the user has time to walk to\n",
    "choices['wait'] = choices['wait1']\n",
    "choices.loc[(choices['wait'] < choices['walk_time']) & (pd.notnull(choices['wait2'])),'wait'] = choices['wait2']\n",
    "choices.loc[(choices['wait'] < choices['walk_time']) & (pd.notnull(choices['wait3'])),'wait'] = choices['wait3']\n",
    "choices['wait'] = choices['wait'] - choices['walk_time']\n",
    "choices['headway'] = 99\n",
    "choices.loc[pd.notnull(choices['h1_2']),'headway'] = choices['h1_2']\n",
    "choices['n_arrivals'] = 0\n",
    "choices.loc[pd.notnull(choices['arr1']),'n_arrivals'] += 1\n",
    "choices.loc[pd.notnull(choices['arr2']),'n_arrivals'] += 1\n",
    "choices.loc[pd.notnull(choices['arr3']),'n_arrivals'] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "choices.loc[choices['travel_product']=='pool','travel_product'] = 'uberpool'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>travel_provider</th>\n",
       "      <th>travel_product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Uber</td>\n",
       "      <td>uberxl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Uber</td>\n",
       "      <td>ubertaxi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Uber</td>\n",
       "      <td>assist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Uber</td>\n",
       "      <td>uberselect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Uber</td>\n",
       "      <td>uberblack</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Uber</td>\n",
       "      <td>uberx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Uber</td>\n",
       "      <td>ubersuv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Uber</td>\n",
       "      <td>uberpool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Uber</td>\n",
       "      <td>uberwav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    travel_provider travel_product\n",
       "16             Uber         uberxl\n",
       "19             Uber       ubertaxi\n",
       "20             Uber         assist\n",
       "22             Uber     uberselect\n",
       "23             Uber      uberblack\n",
       "24             Uber          uberx\n",
       "26             Uber        ubersuv\n",
       "66             Uber       uberpool\n",
       "199            Uber        uberwav"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices.loc[choices['travel_provider']=='Uber',['travel_provider','travel_product']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# classify uber products into 4 types\n",
    "choices['uber_mode_class'] = np.nan\n",
    "choices.loc[choices['travel_product'].isin(['uberx','uberxl']),'uber_mode_class'] = 'basic'\n",
    "choices.loc[choices['travel_product'].isin(['uberselect','uberblack','ubersuv']),'uber_mode_class'] = 'premium'\n",
    "choices.loc[choices['travel_product']=='uberpool','uber_mode_class'] = 'pool'\n",
    "choices.loc[choices['travel_product']=='ubertaxi','uber_mode_class'] = 'taxi'\n",
    "choices.loc[(pd.isnull(choices['uber_mode_class'])) & (choices['travel_provider']=='Uber'),'uber_mode_class'] = 'other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# classify uber products into 2 types\n",
    "choices['uber_mode_class2'] = np.nan\n",
    "choices.loc[choices['travel_product'].isin(['uberx','uberxl','pool','taxi']),'uber_mode_class2'] = 'basic'\n",
    "choices.loc[choices['travel_product'].isin(['uberselect','uberblack','ubersuv']),'uber_mode_class2'] = 'premium'\n",
    "choices.loc[(pd.isnull(choices['uber_mode_class'])) & (choices['travel_provider']=='Uber'),'uber_mode_class2'] = 'basic'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27_64\\lib\\site-packages\\ipython-5.0.0b3-py2.7.egg\\IPython\\core\\interactiveshell.py:2765: DtypeWarning: Columns (0,46,47) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "user_info = pd.read_csv(r'Q:\\Model Research\\Swiftly\\processed\\v0.6\\user_data\\users_with_demographics.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "382018\n"
     ]
    }
   ],
   "source": [
    "print len(choices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "user_info = user_info.set_index('user_id')\n",
    "choices = choices.set_index('user_id')\n",
    "choices['master_user_id'] = user_info['master_user_id']\n",
    "choices['gender'] = user_info['gender']\n",
    "choices['age'] = user_info['age']\n",
    "choices['age_cat'] = user_info['age_cat']\n",
    "choices['hhinc_cat'] = user_info['hhinc_cat']\n",
    "choices['race'] = user_info['race']\n",
    "choices['employ'] = user_info['employ']\n",
    "choices['hhinc'] = user_info['hhinc']\n",
    "choices['hhsize'] = user_info['hhsize']\n",
    "choices['hhvehs'] = user_info['hhvehs']\n",
    "choices['female'] = 0\n",
    "choices.loc[choices['gender']==2,'female'] = 1\n",
    "choices = choices.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "382018\n"
     ]
    }
   ],
   "source": [
    "print len(choices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chosen_mode3\n",
       "biking       107\n",
       "scoot        348\n",
       "transit    19189\n",
       "uber         779\n",
       "walking     1571\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up estimation set.\n",
    "estimation_set = pd.DataFrame(index=choices['search_id'].drop_duplicates())\n",
    "chosen_mode = choices[choices['chosen']==1]\n",
    "chosen_mode = chosen_mode.set_index('search_id')\n",
    "estimation_set['chosen_mode'] = chosen_mode['travel_mode']\n",
    "estimation_set['chosen_mode2'] = chosen_mode['travel_mode']\n",
    "estimation_set['chosen_mode3'] = chosen_mode['travel_mode']\n",
    "estimation_set.loc[estimation_set['chosen_mode']=='rideshare','chosen_mode'] = chosen_mode['uber_mode_class']\n",
    "estimation_set.loc[estimation_set['chosen_mode2']=='rideshare','chosen_mode2'] = chosen_mode['uber_mode_class2']\n",
    "estimation_set.loc[estimation_set['chosen_mode3']=='rideshare','chosen_mode3'] = \"uber\"\n",
    "\n",
    "estimation_set = estimation_set.dropna()\n",
    "estimation_set.groupby(['chosen_mode3']).count().max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age_cat_18-24', 'age_cat_25-34', 'age_cat_35-44', 'age_cat_45-54', 'age_cat_55-64', 'age_cat_65+', 'age_cat_nan']\n",
      "['hhinc_cat_$25-$50', 'hhinc_cat_$50-$75', 'hhinc_cat_$75+', 'hhinc_cat_nan']\n",
      "['employ_2.0', 'employ_3.0', 'employ_4.0', 'employ_5.0', 'employ_nan']\n"
     ]
    }
   ],
   "source": [
    "estimation_set['start_is_near_home'] = chosen_mode['start_is_near_home'].replace(-1, np.nan)\n",
    "estimation_set['start_is_near_work'] = chosen_mode['start_is_near_work'].replace(-1, np.nan)\n",
    "estimation_set['end_is_near_home'] = chosen_mode['end_is_near_home'].replace(-1, np.nan)\n",
    "estimation_set['end_is_near_work'] = chosen_mode['end_is_near_work'].replace(-1, np.nan)\n",
    "\n",
    "# add demographcis to estimation set\n",
    "estimation_set['master_user_id'] = chosen_mode['master_user_id']\n",
    "estimation_set['age_cat'] = chosen_mode['age_cat']\n",
    "estimation_set['age'] = chosen_mode['age']\n",
    "estimation_set['hhinc_cat'] = chosen_mode['hhinc_cat']\n",
    "estimation_set['hhinc'] = chosen_mode['hhinc']\n",
    "estimation_set['employ'] = chosen_mode['employ']\n",
    "estimation_set['hhvehs'] = chosen_mode['hhvehs']\n",
    "estimation_set['female'] = chosen_mode['female']\n",
    "\n",
    "# convert to dummies\n",
    "estimation_set = pd.get_dummies(estimation_set,columns=['master_user_id','age_cat','hhinc_cat','employ'], dummy_na=True, drop_first=True)\n",
    "\n",
    "# get dummy columns\n",
    "user_dummy_cols = []\n",
    "age_dummy_cols = []\n",
    "inc_dummy_cols = []\n",
    "employ_dummy_cols = []\n",
    "for col in estimation_set.columns.tolist():\n",
    "    if col[:14] == 'master_user_id':\n",
    "        user_dummy_cols.append(col)\n",
    "    elif col[:7] == 'age_cat':\n",
    "        age_dummy_cols.append(col)\n",
    "    elif col[:9] == 'hhinc_cat':\n",
    "        inc_dummy_cols.append(col)\n",
    "    elif col[:6] == 'employ':\n",
    "        employ_dummy_cols.append(col)\n",
    "        \n",
    "estimation_set['master_user_id'] = chosen_mode['master_user_id']\n",
    "print age_dummy_cols\n",
    "print inc_dummy_cols\n",
    "print employ_dummy_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transit_options = choices[choices['travel_mode']=='transit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "travel_provider\n",
       "Alcatraz Hornblower Ferry                            0.0\n",
       "Altamont Commuter Express                            0.0\n",
       "Bay Area Rapid Transit                            1917.0\n",
       "Bear Transit - UC Berkeley Shuttle                   6.0\n",
       "Blue & Gold Fleet                                    1.0\n",
       "Caltrain                                           109.0\n",
       "Commute.org Shuttle                                  6.0\n",
       "County Connection                                   13.0\n",
       "Golden Gate Ferry                                    0.0\n",
       "Marin Transit                                        6.0\n",
       "Mountain View Community Shuttle                      3.0\n",
       "PresidiGo Shuttle                                   37.0\n",
       "SamTrans                                           151.0\n",
       "San Francisco Bay Ferry                              2.0\n",
       "San Francisco Municipal Transportation Agency    16428.0\n",
       "Santa Cruz Metro                                     5.0\n",
       "SolTrans                                             2.0\n",
       "VTA                                                500.0\n",
       "WestCat (Western Contra Costa)                       3.0\n",
       "Name: chosen, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transit_options.groupby(['travel_provider'])['chosen'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "trn_sch_group = transit_options.groupby(['search_id'])\n",
    "best_transit = transit_options.loc[trn_sch_group['travel_time'].idxmin(),:]\n",
    "best_transit = best_transit.set_index('search_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "estimation_set['transit_cost'] = best_transit['travel_price']\n",
    "estimation_set['transit_time'] = best_transit['travel_time'] / 60.0\n",
    "estimation_set['transit_dist'] = best_transit['travel_distance'] / 5280.0\n",
    "estimation_set['wait'] = best_transit['wait']\n",
    "estimation_set['headway'] = best_transit['headway']\n",
    "estimation_set['walk'] = best_transit['walk_time']\n",
    "estimation_set['n_arr'] = best_transit['n_arrivals']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_transit = transit_options.groupby(['search_id']).count().max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimation_set['n_transit'] = n_transit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "estimation_set = estimation_set.dropna(subset=['transit_cost','transit_time','transit_dist'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "estimation_set['wait'] = estimation_set['wait'].fillna(999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chosen_mode</th>\n",
       "      <th>chosen_mode2</th>\n",
       "      <th>chosen_mode3</th>\n",
       "      <th>start_is_near_home</th>\n",
       "      <th>start_is_near_work</th>\n",
       "      <th>end_is_near_home</th>\n",
       "      <th>end_is_near_work</th>\n",
       "      <th>age</th>\n",
       "      <th>hhinc</th>\n",
       "      <th>hhvehs</th>\n",
       "      <th>...</th>\n",
       "      <th>employ_nan</th>\n",
       "      <th>master_user_id</th>\n",
       "      <th>transit_cost</th>\n",
       "      <th>transit_time</th>\n",
       "      <th>transit_dist</th>\n",
       "      <th>wait</th>\n",
       "      <th>headway</th>\n",
       "      <th>walk</th>\n",
       "      <th>n_arr</th>\n",
       "      <th>n_transit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search_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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>372006</th>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>6.916667</td>\n",
       "      <td>0.249432</td>\n",
       "      <td>15.566667</td>\n",
       "      <td>11.600000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379254</th>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>16.650000</td>\n",
       "      <td>0.622159</td>\n",
       "      <td>23.916667</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349759</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>26.233333</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>21.533333</td>\n",
       "      <td>11.650000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350042</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>19.516667</td>\n",
       "      <td>0.862121</td>\n",
       "      <td>16.983333</td>\n",
       "      <td>19.250000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350052</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>0.123485</td>\n",
       "      <td>15.950000</td>\n",
       "      <td>19.666667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430969</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>42.016667</td>\n",
       "      <td>1.542424</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>3.416667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414984</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>44.183333</td>\n",
       "      <td>2.141098</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>3.316667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365085</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>21.533333</td>\n",
       "      <td>0.868939</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>20.033333</td>\n",
       "      <td>3.583333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365086</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>1.011553</td>\n",
       "      <td>9.566667</td>\n",
       "      <td>13.666667</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365103</th>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>9.150000</td>\n",
       "      <td>0.236174</td>\n",
       "      <td>9.633333</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405090</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>49.750000</td>\n",
       "      <td>1.989394</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2.383333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405134</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.55</td>\n",
       "      <td>84.933333</td>\n",
       "      <td>6.783523</td>\n",
       "      <td>53.466667</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350473</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.60</td>\n",
       "      <td>34.383333</td>\n",
       "      <td>4.521970</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>7.383333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350491</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.35</td>\n",
       "      <td>66.016667</td>\n",
       "      <td>9.502273</td>\n",
       "      <td>2.133333</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.850000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350989</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.00</td>\n",
       "      <td>16.983333</td>\n",
       "      <td>0.713258</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>2.150000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443741</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>46.350000</td>\n",
       "      <td>1.773864</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380596</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>10.466667</td>\n",
       "      <td>0.334848</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380668</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>0.539394</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359372</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>0.283523</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351187</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>25.050000</td>\n",
       "      <td>1.100947</td>\n",
       "      <td>3.033333</td>\n",
       "      <td>9.766667</td>\n",
       "      <td>1.316667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431047</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>37.266667</td>\n",
       "      <td>1.827462</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>7.183333</td>\n",
       "      <td>1.566667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399216</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.20</td>\n",
       "      <td>31.216667</td>\n",
       "      <td>1.880871</td>\n",
       "      <td>6.366667</td>\n",
       "      <td>20.183333</td>\n",
       "      <td>3.566667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353134</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>20.769</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>003f15e9d4a55b2031923b78f38027a5</td>\n",
       "      <td>4.50</td>\n",
       "      <td>33.483333</td>\n",
       "      <td>4.773674</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>8.483333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399878</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>11.650000</td>\n",
       "      <td>0.826326</td>\n",
       "      <td>14.566667</td>\n",
       "      <td>3.116667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418862</th>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>walking</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>0.070833</td>\n",
       "      <td>19.133333</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>2.016667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418907</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>21.750000</td>\n",
       "      <td>0.652083</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419058</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>25.783333</td>\n",
       "      <td>0.733144</td>\n",
       "      <td>1.983333</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>2.616667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403625</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.70</td>\n",
       "      <td>77.666667</td>\n",
       "      <td>4.416856</td>\n",
       "      <td>10.416667</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403658</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.70</td>\n",
       "      <td>79.050000</td>\n",
       "      <td>4.408144</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>5.216667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404595</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>16.816667</td>\n",
       "      <td>0.455303</td>\n",
       "      <td>16.833333</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428724</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>33.116667</td>\n",
       "      <td>1.125758</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>3.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431208</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.55</td>\n",
       "      <td>55.883333</td>\n",
       "      <td>5.525758</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>3.216667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432516</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>27.666667</td>\n",
       "      <td>1.037689</td>\n",
       "      <td>4.650000</td>\n",
       "      <td>6.916667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432543</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>1.033712</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>3.616667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432601</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>36.216667</td>\n",
       "      <td>1.233523</td>\n",
       "      <td>9.550000</td>\n",
       "      <td>11.166667</td>\n",
       "      <td>1.766667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380461</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>40.150000</td>\n",
       "      <td>0.938447</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>14.383333</td>\n",
       "      <td>4.083333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396830</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.95</td>\n",
       "      <td>25.650000</td>\n",
       "      <td>0.838258</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>5.016667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340161</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.05</td>\n",
       "      <td>31.466667</td>\n",
       "      <td>4.195833</td>\n",
       "      <td>6.983333</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420245</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>39.816667</td>\n",
       "      <td>1.644508</td>\n",
       "      <td>2.350000</td>\n",
       "      <td>3.483333</td>\n",
       "      <td>3.350000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421548</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>12.166667</td>\n",
       "      <td>0.257576</td>\n",
       "      <td>11.350000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>9.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421590</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>30.683333</td>\n",
       "      <td>1.162689</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>8.250000</td>\n",
       "      <td>3.033333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407265</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>12.866667</td>\n",
       "      <td>0.450379</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>3.766667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427983</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>25.033333</td>\n",
       "      <td>1.106061</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390390</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>54.266667</td>\n",
       "      <td>2.161932</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>11.350000</td>\n",
       "      <td>1.933333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441514</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>0.875189</td>\n",
       "      <td>2.383333</td>\n",
       "      <td>3.683333</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442620</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>0.860606</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>9.033333</td>\n",
       "      <td>4.150000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405757</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.85</td>\n",
       "      <td>54.316667</td>\n",
       "      <td>3.892614</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351207</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>0.391098</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>3.566667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435611</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>1.488258</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435791</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>30.316667</td>\n",
       "      <td>1.390341</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>7.516667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435925</th>\n",
       "      <td>scoot</td>\n",
       "      <td>scoot</td>\n",
       "      <td>scoot</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>39.216667</td>\n",
       "      <td>1.283523</td>\n",
       "      <td>12.366667</td>\n",
       "      <td>14.633333</td>\n",
       "      <td>4.183333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366435</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>76.268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>03e8f87ae52743575d8272a691e0929b</td>\n",
       "      <td>2.25</td>\n",
       "      <td>36.950000</td>\n",
       "      <td>0.889962</td>\n",
       "      <td>11.233333</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.183333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366959</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>69.219</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ffe03147adfa02ed432c830621c0f06c</td>\n",
       "      <td>2.25</td>\n",
       "      <td>22.350000</td>\n",
       "      <td>1.201705</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352384</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>6.966667</td>\n",
       "      <td>0.275947</td>\n",
       "      <td>-2.850000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354437</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>64.510</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ffeaefdda92157edb6cf88e27465ce9d</td>\n",
       "      <td>3.65</td>\n",
       "      <td>43.066667</td>\n",
       "      <td>3.374621</td>\n",
       "      <td>1.733333</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>9.250000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431898</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.40</td>\n",
       "      <td>83.233333</td>\n",
       "      <td>8.624242</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>8.066667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431906</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.75</td>\n",
       "      <td>22.816667</td>\n",
       "      <td>1.619886</td>\n",
       "      <td>19.233333</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407974</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>32.716667</td>\n",
       "      <td>1.714962</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>8.366667</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427445</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>33.083333</td>\n",
       "      <td>1.749053</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>3.850000</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416265</th>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.25</td>\n",
       "      <td>37.866667</td>\n",
       "      <td>1.563258</td>\n",
       "      <td>3.416667</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.683333</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20783 rows × 943 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          chosen_mode chosen_mode2 chosen_mode3  start_is_near_home  \\\n",
       "search_id                                                             \n",
       "372006        walking      walking      walking                 0.0   \n",
       "379254        walking      walking      walking                 0.0   \n",
       "349759        transit      transit      transit                 1.0   \n",
       "350042        transit      transit      transit                 0.0   \n",
       "350052        transit      transit      transit                 0.0   \n",
       "430969        transit      transit      transit                 1.0   \n",
       "414984        transit      transit      transit                 0.0   \n",
       "365085        transit      transit      transit                 0.0   \n",
       "365086        transit      transit      transit                 0.0   \n",
       "365103        walking      walking      walking                 0.0   \n",
       "405090        transit      transit      transit                 NaN   \n",
       "405134        transit      transit      transit                 NaN   \n",
       "350473        transit      transit      transit                 0.0   \n",
       "350491        transit      transit      transit                 0.0   \n",
       "350989        transit      transit      transit                 0.0   \n",
       "443741        transit      transit      transit                 1.0   \n",
       "380596        transit      transit      transit                 0.0   \n",
       "380668        transit      transit      transit                 0.0   \n",
       "359372        transit      transit      transit                 0.0   \n",
       "351187        transit      transit      transit                 1.0   \n",
       "431047        transit      transit      transit                 1.0   \n",
       "399216        transit      transit      transit                 1.0   \n",
       "353134        transit      transit      transit                 0.0   \n",
       "399878        transit      transit      transit                 0.0   \n",
       "418862        walking      walking      walking                 0.0   \n",
       "418907        transit      transit      transit                 0.0   \n",
       "419058        transit      transit      transit                 0.0   \n",
       "403625        transit      transit      transit                 1.0   \n",
       "403658        transit      transit      transit                 1.0   \n",
       "404595        transit      transit      transit                 0.0   \n",
       "...               ...          ...          ...                 ...   \n",
       "428724        transit      transit      transit                 1.0   \n",
       "431208        transit      transit      transit                 1.0   \n",
       "432516        transit      transit      transit                 0.0   \n",
       "432543        transit      transit      transit                 0.0   \n",
       "432601        transit      transit      transit                 0.0   \n",
       "380461        transit      transit      transit                 1.0   \n",
       "396830        transit      transit      transit                 1.0   \n",
       "340161        transit      transit      transit                 NaN   \n",
       "420245        transit      transit      transit                 1.0   \n",
       "421548        transit      transit      transit                 0.0   \n",
       "421590        transit      transit      transit                 0.0   \n",
       "407265        transit      transit      transit                 1.0   \n",
       "427983        transit      transit      transit                 0.0   \n",
       "390390        transit      transit      transit                 0.0   \n",
       "441514        transit      transit      transit                 1.0   \n",
       "442620        transit      transit      transit                 0.0   \n",
       "405757        transit      transit      transit                 0.0   \n",
       "351207        transit      transit      transit                 0.0   \n",
       "435611        transit      transit      transit                 1.0   \n",
       "435791        transit      transit      transit                 0.0   \n",
       "435925          scoot        scoot        scoot                 0.0   \n",
       "366435        transit      transit      transit                 1.0   \n",
       "366959        transit      transit      transit                 0.0   \n",
       "352384        transit      transit      transit                 0.0   \n",
       "354437        transit      transit      transit                 0.0   \n",
       "431898        transit      transit      transit                 1.0   \n",
       "431906        transit      transit      transit                 1.0   \n",
       "407974        transit      transit      transit                 NaN   \n",
       "427445        transit      transit      transit                 1.0   \n",
       "416265        transit      transit      transit                 NaN   \n",
       "\n",
       "           start_is_near_work  end_is_near_home  end_is_near_work   age  \\\n",
       "search_id                                                                 \n",
       "372006                    0.0               0.0               0.0   NaN   \n",
       "379254                    0.0               1.0               0.0   NaN   \n",
       "349759                    0.0               0.0               0.0   NaN   \n",
       "350042                    0.0               1.0               0.0   NaN   \n",
       "350052                    0.0               0.0               0.0   NaN   \n",
       "430969                    0.0               0.0               1.0   NaN   \n",
       "414984                    0.0               0.0               0.0   NaN   \n",
       "365085                    NaN               0.0               NaN   NaN   \n",
       "365086                    NaN               0.0               NaN   NaN   \n",
       "365103                    NaN               0.0               NaN   NaN   \n",
       "405090                    NaN               NaN               NaN   NaN   \n",
       "405134                    NaN               NaN               NaN   NaN   \n",
       "350473                    NaN               0.0               NaN   NaN   \n",
       "350491                    NaN               0.0               NaN   NaN   \n",
       "350989                    0.0               0.0               1.0   NaN   \n",
       "443741                    0.0               0.0               0.0   NaN   \n",
       "380596                    NaN               0.0               NaN   NaN   \n",
       "380668                    NaN               0.0               NaN   NaN   \n",
       "359372                    0.0               0.0               0.0   NaN   \n",
       "351187                    0.0               0.0               0.0   NaN   \n",
       "431047                    0.0               0.0               1.0   NaN   \n",
       "399216                    0.0               0.0               0.0   NaN   \n",
       "353134                    0.0               0.0               1.0  61.0   \n",
       "399878                    0.0               1.0               0.0   NaN   \n",
       "418862                    0.0               0.0               0.0   NaN   \n",
       "418907                    0.0               1.0               0.0   NaN   \n",
       "419058                    0.0               0.0               1.0   NaN   \n",
       "403625                    0.0               0.0               0.0   NaN   \n",
       "403658                    0.0               0.0               0.0   NaN   \n",
       "404595                    0.0               1.0               0.0   NaN   \n",
       "...                       ...               ...               ...   ...   \n",
       "428724                    0.0               0.0               0.0   NaN   \n",
       "431208                    0.0               0.0               0.0   NaN   \n",
       "432516                    1.0               0.0               0.0   NaN   \n",
       "432543                    1.0               0.0               0.0   NaN   \n",
       "432601                    1.0               1.0               0.0   NaN   \n",
       "380461                    NaN               0.0               NaN   NaN   \n",
       "396830                    0.0               0.0               0.0   NaN   \n",
       "340161                    NaN               NaN               NaN   NaN   \n",
       "420245                    0.0               0.0               0.0   NaN   \n",
       "421548                    0.0               0.0               0.0   NaN   \n",
       "421590                    0.0               1.0               0.0   NaN   \n",
       "407265                    0.0               0.0               0.0   NaN   \n",
       "427983                    0.0               1.0               0.0   NaN   \n",
       "390390                    0.0               0.0               0.0   NaN   \n",
       "441514                    0.0               0.0               1.0   NaN   \n",
       "442620                    1.0               1.0               0.0   NaN   \n",
       "405757                    0.0               0.0               0.0   NaN   \n",
       "351207                    0.0               0.0               0.0   NaN   \n",
       "435611                    0.0               0.0               0.0   NaN   \n",
       "435791                    0.0               1.0               0.0   NaN   \n",
       "435925                    0.0               0.0               1.0   NaN   \n",
       "366435                    0.0               0.0               1.0  33.0   \n",
       "366959                    1.0               1.0               0.0  23.0   \n",
       "352384                    0.0               1.0               0.0   NaN   \n",
       "354437                    0.0               1.0               0.0  31.0   \n",
       "431898                    0.0               0.0               1.0   NaN   \n",
       "431906                    0.0               0.0               0.0   NaN   \n",
       "407974                    0.0               NaN               1.0   NaN   \n",
       "427445                    0.0               0.0               0.0   NaN   \n",
       "416265                    NaN               NaN               NaN   NaN   \n",
       "\n",
       "            hhinc  hhvehs    ...      employ_nan  \\\n",
       "search_id                    ...                   \n",
       "372006        NaN     NaN    ...             1.0   \n",
       "379254        NaN     NaN    ...             1.0   \n",
       "349759        NaN     NaN    ...             1.0   \n",
       "350042        NaN     NaN    ...             1.0   \n",
       "350052        NaN     NaN    ...             1.0   \n",
       "430969        NaN     NaN    ...             1.0   \n",
       "414984        NaN     NaN    ...             1.0   \n",
       "365085        NaN     NaN    ...             1.0   \n",
       "365086        NaN     NaN    ...             1.0   \n",
       "365103        NaN     NaN    ...             1.0   \n",
       "405090        NaN     NaN    ...             1.0   \n",
       "405134        NaN     NaN    ...             1.0   \n",
       "350473        NaN     NaN    ...             1.0   \n",
       "350491        NaN     NaN    ...             1.0   \n",
       "350989        NaN     NaN    ...             1.0   \n",
       "443741        NaN     NaN    ...             1.0   \n",
       "380596        NaN     NaN    ...             1.0   \n",
       "380668        NaN     NaN    ...             1.0   \n",
       "359372        NaN     NaN    ...             1.0   \n",
       "351187        NaN     NaN    ...             1.0   \n",
       "431047        NaN     NaN    ...             1.0   \n",
       "399216        NaN     NaN    ...             1.0   \n",
       "353134     20.769     3.0    ...             0.0   \n",
       "399878        NaN     NaN    ...             1.0   \n",
       "418862        NaN     NaN    ...             1.0   \n",
       "418907        NaN     NaN    ...             1.0   \n",
       "419058        NaN     NaN    ...             1.0   \n",
       "403625        NaN     NaN    ...             1.0   \n",
       "403658        NaN     NaN    ...             1.0   \n",
       "404595        NaN     NaN    ...             1.0   \n",
       "...           ...     ...    ...             ...   \n",
       "428724        NaN     NaN    ...             1.0   \n",
       "431208        NaN     NaN    ...             1.0   \n",
       "432516        NaN     NaN    ...             1.0   \n",
       "432543        NaN     NaN    ...             1.0   \n",
       "432601        NaN     NaN    ...             1.0   \n",
       "380461        NaN     NaN    ...             1.0   \n",
       "396830        NaN     NaN    ...             1.0   \n",
       "340161        NaN     NaN    ...             1.0   \n",
       "420245        NaN     NaN    ...             1.0   \n",
       "421548        NaN     NaN    ...             1.0   \n",
       "421590        NaN     NaN    ...             1.0   \n",
       "407265        NaN     NaN    ...             1.0   \n",
       "427983        NaN     NaN    ...             1.0   \n",
       "390390        NaN     NaN    ...             1.0   \n",
       "441514        NaN     NaN    ...             1.0   \n",
       "442620        NaN     NaN    ...             1.0   \n",
       "405757        NaN     NaN    ...             1.0   \n",
       "351207        NaN     NaN    ...             1.0   \n",
       "435611        NaN     NaN    ...             1.0   \n",
       "435791        NaN     NaN    ...             1.0   \n",
       "435925        NaN     NaN    ...             1.0   \n",
       "366435     76.268     0.0    ...             0.0   \n",
       "366959     69.219     2.0    ...             0.0   \n",
       "352384        NaN     NaN    ...             1.0   \n",
       "354437     64.510     1.0    ...             0.0   \n",
       "431898        NaN     NaN    ...             1.0   \n",
       "431906        NaN     NaN    ...             1.0   \n",
       "407974        NaN     NaN    ...             1.0   \n",
       "427445        NaN     NaN    ...             1.0   \n",
       "416265        NaN     NaN    ...             1.0   \n",
       "\n",
       "                             master_user_id  transit_cost  transit_time  \\\n",
       "search_id                                                                 \n",
       "372006                                  NaN          2.25      6.916667   \n",
       "379254                                  NaN          2.25     16.650000   \n",
       "349759                                  NaN          2.25     26.233333   \n",
       "350042                                  NaN          2.25     19.516667   \n",
       "350052                                  NaN          2.25      4.900000   \n",
       "430969                                  NaN          2.25     42.016667   \n",
       "414984                                  NaN          2.25     44.183333   \n",
       "365085                                  NaN          2.25     21.533333   \n",
       "365086                                  NaN          2.25     26.333333   \n",
       "365103                                  NaN          2.25      9.150000   \n",
       "405090                                  NaN          2.25     49.750000   \n",
       "405134                                  NaN          6.55     84.933333   \n",
       "350473                                  NaN          4.60     34.383333   \n",
       "350491                                  NaN         10.35     66.016667   \n",
       "350989                                  NaN          2.00     16.983333   \n",
       "443741                                  NaN          2.25     46.350000   \n",
       "380596                                  NaN          2.25     10.466667   \n",
       "380668                                  NaN          2.25     12.800000   \n",
       "359372                                  NaN          6.00     10.750000   \n",
       "351187                                  NaN          2.25     25.050000   \n",
       "431047                                  NaN          2.25     37.266667   \n",
       "399216                                  NaN          4.20     31.216667   \n",
       "353134     003f15e9d4a55b2031923b78f38027a5          4.50     33.483333   \n",
       "399878                                  NaN          2.25     11.650000   \n",
       "418862                                  NaN          2.25      3.950000   \n",
       "418907                                  NaN          2.25     21.750000   \n",
       "419058                                  NaN          2.25     25.783333   \n",
       "403625                                  NaN          5.70     77.666667   \n",
       "403658                                  NaN          5.70     79.050000   \n",
       "404595                                  NaN          2.25     16.816667   \n",
       "...                                     ...           ...           ...   \n",
       "428724                                  NaN          2.25     33.116667   \n",
       "431208                                  NaN          6.55     55.883333   \n",
       "432516                                  NaN          2.25     27.666667   \n",
       "432543                                  NaN          2.25     29.400000   \n",
       "432601                                  NaN          2.25     36.216667   \n",
       "380461                                  NaN          2.25     40.150000   \n",
       "396830                                  NaN          1.95     25.650000   \n",
       "340161                                  NaN          3.05     31.466667   \n",
       "420245                                  NaN          2.25     39.816667   \n",
       "421548                                  NaN          2.25     12.166667   \n",
       "421590                                  NaN          2.25     30.683333   \n",
       "407265                                  NaN          2.25     12.866667   \n",
       "427983                                  NaN          2.25     25.033333   \n",
       "390390                                  NaN          4.00     54.266667   \n",
       "441514                                  NaN          2.25     18.400000   \n",
       "442620                                  NaN          2.25     17.900000   \n",
       "405757                                  NaN          9.85     54.316667   \n",
       "351207                                  NaN          2.25     13.166667   \n",
       "435611                                  NaN          2.25     23.100000   \n",
       "435791                                  NaN          2.25     30.316667   \n",
       "435925                                  NaN          2.25     39.216667   \n",
       "366435     03e8f87ae52743575d8272a691e0929b          2.25     36.950000   \n",
       "366959     ffe03147adfa02ed432c830621c0f06c          2.25     22.350000   \n",
       "352384                                  NaN          2.25      6.966667   \n",
       "354437     ffeaefdda92157edb6cf88e27465ce9d          3.65     43.066667   \n",
       "431898                                  NaN          8.40     83.233333   \n",
       "431906                                  NaN          5.75     22.816667   \n",
       "407974                                  NaN          2.25     32.716667   \n",
       "427445                                  NaN          2.25     33.083333   \n",
       "416265                                  NaN          2.25     37.866667   \n",
       "\n",
       "           transit_dist        wait    headway       walk  n_arr  n_transit  \n",
       "search_id                                                                    \n",
       "372006         0.249432   15.566667  11.600000   0.833333    3.0        1.0  \n",
       "379254         0.622159   23.916667  26.200000   8.000000    2.0        4.0  \n",
       "349759         0.969697   21.533333  11.650000   0.016667    3.0        1.0  \n",
       "350042         0.862121   16.983333  19.250000   0.016667    2.0        3.0  \n",
       "350052         0.123485   15.950000  19.666667   0.016667    2.0        3.0  \n",
       "430969         1.542424    2.400000   6.850000   3.416667    3.0        3.0  \n",
       "414984         2.141098  999.000000  99.000000   3.316667    0.0        2.0  \n",
       "365085         0.868939    2.550000  20.033333   3.583333    2.0        3.0  \n",
       "365086         1.011553    9.566667  13.666667   4.100000    3.0        3.0  \n",
       "365103         0.236174    9.633333  19.700000   1.833333    2.0        3.0  \n",
       "405090         1.989394  999.000000  99.000000   2.383333    0.0        3.0  \n",
       "405134         6.783523   53.466667  99.000000   1.800000    1.0        4.0  \n",
       "350473         4.521970  999.000000  99.000000   7.383333    0.0        2.0  \n",
       "350491         9.502273    2.133333  12.000000   2.850000    3.0        1.0  \n",
       "350989         0.713258  999.000000  99.000000   2.150000    0.0        2.0  \n",
       "443741         1.773864  999.000000  99.000000   0.016667    0.0        3.0  \n",
       "380596         0.334848  999.000000  99.000000   1.466667    0.0        3.0  \n",
       "380668         0.539394  999.000000  99.000000   3.350000    0.0        4.0  \n",
       "359372         0.283523  999.000000  99.000000   0.016667    0.0        2.0  \n",
       "351187         1.100947    3.033333   9.766667   1.316667    3.0        3.0  \n",
       "431047         1.827462    6.250000   7.183333   1.566667    3.0        2.0  \n",
       "399216         1.880871    6.366667  20.183333   3.566667    2.0        3.0  \n",
       "353134         4.773674    5.500000  14.000000   8.483333    3.0        1.0  \n",
       "399878         0.826326   14.566667   3.116667   0.016667    3.0        4.0  \n",
       "418862         0.070833   19.133333  17.650000   2.016667    2.0        1.0  \n",
       "418907         0.652083  999.000000  99.000000   1.750000    0.0        4.0  \n",
       "419058         0.733144    1.983333   9.800000   2.616667    3.0        3.0  \n",
       "403625         4.416856   10.416667   0.466667   0.916667    3.0        1.0  \n",
       "403658         4.408144    3.150000   5.216667   0.016667    3.0        1.0  \n",
       "404595         0.455303   16.833333  21.600000   4.666667    2.0        3.0  \n",
       "...                 ...         ...        ...        ...    ...        ...  \n",
       "428724         1.125758  999.000000  99.000000   3.050000    0.0        2.0  \n",
       "431208         5.525758  999.000000  99.000000   3.216667    0.0        1.0  \n",
       "432516         1.037689    4.650000   6.916667   0.016667    3.0        1.0  \n",
       "432543         1.033712    1.416667  99.000000   3.616667    1.0        1.0  \n",
       "432601         1.233523    9.550000  11.166667   1.766667    3.0        2.0  \n",
       "380461         0.938447    0.550000  14.383333   4.083333    3.0        3.0  \n",
       "396830         0.838258    2.950000  99.000000   5.016667    1.0        3.0  \n",
       "340161         4.195833    6.983333  15.000000   0.016667    3.0        1.0  \n",
       "420245         1.644508    2.350000   3.483333   3.350000    3.0        4.0  \n",
       "421548         0.257576   11.350000  99.000000   9.100000    1.0        3.0  \n",
       "421590         1.162689    5.666667   8.250000   3.033333    3.0        3.0  \n",
       "407265         0.450379  999.000000  99.000000   3.766667    0.0        3.0  \n",
       "427983         1.106061  999.000000  99.000000   0.016667    0.0        3.0  \n",
       "390390         2.161932   14.400000  11.350000   1.933333    3.0        2.0  \n",
       "441514         0.875189    2.383333   3.683333   0.650000    3.0        3.0  \n",
       "442620         0.860606    6.666667   9.033333   4.150000    3.0        3.0  \n",
       "405757         3.892614  999.000000  99.000000   0.016667    0.0        2.0  \n",
       "351207         0.391098    0.350000   2.900000   3.566667    3.0        3.0  \n",
       "435611         1.488258    9.900000  14.700000   1.666667    3.0        4.0  \n",
       "435791         1.390341  999.000000  99.000000   7.516667    0.0        4.0  \n",
       "435925         1.283523   12.366667  14.633333   4.183333    3.0        3.0  \n",
       "366435         0.889962   11.233333  15.000000  12.183333    3.0        4.0  \n",
       "366959         1.201705  999.000000  99.000000   1.466667    0.0        1.0  \n",
       "352384         0.275947   -2.850000  99.000000   0.016667    1.0        2.0  \n",
       "354437         3.374621    1.733333  12.000000   9.250000    3.0        2.0  \n",
       "431898         8.624242  999.000000  99.000000   8.066667    0.0        3.0  \n",
       "431906         1.619886   19.233333  99.000000   8.050000    1.0        2.0  \n",
       "407974         1.714962    2.466667   8.366667   0.016667    3.0        4.0  \n",
       "427445         1.749053    8.400000   3.850000   1.833333    3.0        4.0  \n",
       "416265         1.563258    3.416667   0.100000   1.683333    3.0        1.0  \n",
       "\n",
       "[20783 rows x 943 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "uber_mode_class\n",
       "basic      444.0\n",
       "other       43.0\n",
       "pool       363.0\n",
       "premium    335.0\n",
       "taxi        49.0\n",
       "Name: chosen, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices.groupby('uber_mode_class')['chosen'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "travel_provider  travel_product\n",
       "Uber             assist             37.0\n",
       "                 uberblack         154.0\n",
       "                 uberpool          363.0\n",
       "                 uberselect        103.0\n",
       "                 ubersuv            78.0\n",
       "                 ubertaxi           49.0\n",
       "                 uberwav             6.0\n",
       "                 uberx             373.0\n",
       "                 uberxl             71.0\n",
       "Name: chosen, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = choices.groupby(['travel_provider','travel_product'])\n",
    "s = g['chosen'].sum()\n",
    "s.loc[['Uber',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "uber_grouped = choices[choices['travel_provider']=='Uber'].groupby(['search_id','uber_mode_class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_uber = uber_grouped['travel_price'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>search_id</th>\n",
       "      <th>travel_provider</th>\n",
       "      <th>uber_mode_class</th>\n",
       "      <th>travel_product</th>\n",
       "      <th>travel_price</th>\n",
       "      <th>travel_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224920</th>\n",
       "      <td>309474</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>6.55</td>\n",
       "      <td>2144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224921</th>\n",
       "      <td>309474</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>13.49</td>\n",
       "      <td>2144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224925</th>\n",
       "      <td>309474</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>6.55</td>\n",
       "      <td>2144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100855</th>\n",
       "      <td>309475</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>7.48</td>\n",
       "      <td>3335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100856</th>\n",
       "      <td>309475</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>15.77</td>\n",
       "      <td>3335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78546</th>\n",
       "      <td>309477</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>38.95</td>\n",
       "      <td>39564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78547</th>\n",
       "      <td>309477</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>90.36</td>\n",
       "      <td>39564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59904</th>\n",
       "      <td>309481</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>22.74</td>\n",
       "      <td>20872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59907</th>\n",
       "      <td>309481</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>51.93</td>\n",
       "      <td>20872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310828</th>\n",
       "      <td>309487</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>15.29</td>\n",
       "      <td>10647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310830</th>\n",
       "      <td>309487</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>34.14</td>\n",
       "      <td>10647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310834</th>\n",
       "      <td>309487</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>15.29</td>\n",
       "      <td>10647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182706</th>\n",
       "      <td>309488</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>8.63</td>\n",
       "      <td>3521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182708</th>\n",
       "      <td>309488</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>18.39</td>\n",
       "      <td>3521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182704</th>\n",
       "      <td>309488</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>8.63</td>\n",
       "      <td>3521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310837</th>\n",
       "      <td>309490</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>15.30</td>\n",
       "      <td>10636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310840</th>\n",
       "      <td>309490</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>34.16</td>\n",
       "      <td>10636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164320</th>\n",
       "      <td>309505</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>8.07</td>\n",
       "      <td>3859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164323</th>\n",
       "      <td>309505</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>17.15</td>\n",
       "      <td>3859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264696</th>\n",
       "      <td>309512</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>12.17</td>\n",
       "      <td>7008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264699</th>\n",
       "      <td>309512</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>26.73</td>\n",
       "      <td>7008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264692</th>\n",
       "      <td>309512</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>12.17</td>\n",
       "      <td>7008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273201</th>\n",
       "      <td>309516</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273197</th>\n",
       "      <td>309516</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>12.58</td>\n",
       "      <td>1967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273202</th>\n",
       "      <td>309516</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238256</th>\n",
       "      <td>309524</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238257</th>\n",
       "      <td>309524</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>12.66</td>\n",
       "      <td>1926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238260</th>\n",
       "      <td>309524</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1926.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346699</th>\n",
       "      <td>309525</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>10.27</td>\n",
       "      <td>5554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346703</th>\n",
       "      <td>309525</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>22.28</td>\n",
       "      <td>5554.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283713</th>\n",
       "      <td>445780</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>7.94</td>\n",
       "      <td>2787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283718</th>\n",
       "      <td>445780</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>16.75</td>\n",
       "      <td>2787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283720</th>\n",
       "      <td>445780</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>7.94</td>\n",
       "      <td>2787.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97860</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97869</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97861</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97864</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>31.88</td>\n",
       "      <td>8173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97863</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196497</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196496</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196489</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196493</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>21.64</td>\n",
       "      <td>4426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196490</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154892</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>19.74</td>\n",
       "      <td>13352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154904</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>19.74</td>\n",
       "      <td>13352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154891</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>19.74</td>\n",
       "      <td>13352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154898</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>44.47</td>\n",
       "      <td>13352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129398</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129410</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129400</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129405</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>22.83</td>\n",
       "      <td>3931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129411</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143955</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143958</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143952</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143946</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>17.43</td>\n",
       "      <td>2627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143954</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245860</th>\n",
       "      <td>445816</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>24.91</td>\n",
       "      <td>19274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245854</th>\n",
       "      <td>445816</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>24.91</td>\n",
       "      <td>19274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245863</th>\n",
       "      <td>445816</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>56.73</td>\n",
       "      <td>19274.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123449 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        search_id travel_provider uber_mode_class travel_product  \\\n",
       "224920     309474            Uber           basic          uberx   \n",
       "224921     309474            Uber         premium     uberselect   \n",
       "224925     309474            Uber            taxi       ubertaxi   \n",
       "100855     309475            Uber           basic          uberx   \n",
       "100856     309475            Uber         premium     uberselect   \n",
       "78546      309477            Uber           basic          uberx   \n",
       "78547      309477            Uber         premium     uberselect   \n",
       "59904      309481            Uber           basic          uberx   \n",
       "59907      309481            Uber         premium     uberselect   \n",
       "310828     309487            Uber           basic          uberx   \n",
       "310830     309487            Uber         premium     uberselect   \n",
       "310834     309487            Uber            taxi       ubertaxi   \n",
       "182706     309488            Uber           basic          uberx   \n",
       "182708     309488            Uber         premium     uberselect   \n",
       "182704     309488            Uber            taxi       ubertaxi   \n",
       "310837     309490            Uber           basic          uberx   \n",
       "310840     309490            Uber         premium     uberselect   \n",
       "164320     309505            Uber           basic          uberx   \n",
       "164323     309505            Uber         premium     uberselect   \n",
       "264696     309512            Uber           basic          uberx   \n",
       "264699     309512            Uber         premium     uberselect   \n",
       "264692     309512            Uber            taxi       ubertaxi   \n",
       "273201     309516            Uber           basic          uberx   \n",
       "273197     309516            Uber         premium     uberselect   \n",
       "273202     309516            Uber            taxi       ubertaxi   \n",
       "238256     309524            Uber           basic          uberx   \n",
       "238257     309524            Uber         premium     uberselect   \n",
       "238260     309524            Uber            taxi       ubertaxi   \n",
       "346699     309525            Uber           basic          uberx   \n",
       "346703     309525            Uber         premium     uberselect   \n",
       "...           ...             ...             ...            ...   \n",
       "283713     445780            Uber            pool       uberpool   \n",
       "283718     445780            Uber         premium     uberselect   \n",
       "283720     445780            Uber            taxi       ubertaxi   \n",
       "97860      445781            Uber           basic          uberx   \n",
       "97869      445781            Uber           other         assist   \n",
       "97861      445781            Uber            pool       uberpool   \n",
       "97864      445781            Uber         premium     uberselect   \n",
       "97863      445781            Uber            taxi       ubertaxi   \n",
       "196497     445782            Uber           basic          uberx   \n",
       "196496     445782            Uber           other         assist   \n",
       "196489     445782            Uber            pool       uberpool   \n",
       "196493     445782            Uber         premium     uberselect   \n",
       "196490     445782            Uber            taxi       ubertaxi   \n",
       "154892     445790            Uber           basic          uberx   \n",
       "154904     445790            Uber           other         assist   \n",
       "154891     445790            Uber            pool       uberpool   \n",
       "154898     445790            Uber         premium     uberselect   \n",
       "129398     445791            Uber           basic          uberx   \n",
       "129410     445791            Uber           other         assist   \n",
       "129400     445791            Uber            pool       uberpool   \n",
       "129405     445791            Uber         premium     uberselect   \n",
       "129411     445791            Uber            taxi       ubertaxi   \n",
       "143955     445799            Uber           basic          uberx   \n",
       "143958     445799            Uber           other         assist   \n",
       "143952     445799            Uber            pool       uberpool   \n",
       "143946     445799            Uber         premium     uberselect   \n",
       "143954     445799            Uber            taxi       ubertaxi   \n",
       "245860     445816            Uber           basic          uberx   \n",
       "245854     445816            Uber            pool       uberpool   \n",
       "245863     445816            Uber         premium     uberselect   \n",
       "\n",
       "        travel_price  travel_distance  \n",
       "224920          6.55           2144.0  \n",
       "224921         13.49           2144.0  \n",
       "224925          6.55           2144.0  \n",
       "100855          7.48           3335.0  \n",
       "100856         15.77           3335.0  \n",
       "78546          38.95          39564.0  \n",
       "78547          90.36          39564.0  \n",
       "59904          22.74          20872.0  \n",
       "59907          51.93          20872.0  \n",
       "310828         15.29          10647.0  \n",
       "310830         34.14          10647.0  \n",
       "310834         15.29          10647.0  \n",
       "182706          8.63           3521.0  \n",
       "182708         18.39           3521.0  \n",
       "182704          8.63           3521.0  \n",
       "310837         15.30          10636.0  \n",
       "310840         34.16          10636.0  \n",
       "164320          8.07           3859.0  \n",
       "164323         17.15           3859.0  \n",
       "264696         12.17           7008.0  \n",
       "264699         26.73           7008.0  \n",
       "264692         12.17           7008.0  \n",
       "273201          6.55           1967.0  \n",
       "273197         12.58           1967.0  \n",
       "273202          6.55           1967.0  \n",
       "238256          6.55           1926.0  \n",
       "238257         12.66           1926.0  \n",
       "238260          6.55           1926.0  \n",
       "346699         10.27           5554.0  \n",
       "346703         22.28           5554.0  \n",
       "...              ...              ...  \n",
       "283713          7.94           2787.0  \n",
       "283718         16.75           2787.0  \n",
       "283720          7.94           2787.0  \n",
       "97860          14.39           8173.0  \n",
       "97869          14.39           8173.0  \n",
       "97861          14.39           8173.0  \n",
       "97864          31.88           8173.0  \n",
       "97863          14.39           8173.0  \n",
       "196497         10.02           4426.0  \n",
       "196496         10.02           4426.0  \n",
       "196489         10.02           4426.0  \n",
       "196493         21.64           4426.0  \n",
       "196490         10.02           4426.0  \n",
       "154892         19.74          13352.0  \n",
       "154904         19.74          13352.0  \n",
       "154891         19.74          13352.0  \n",
       "154898         44.47          13352.0  \n",
       "129398         10.56           3931.0  \n",
       "129410         10.56           3931.0  \n",
       "129400         10.56           3931.0  \n",
       "129405         22.83           3931.0  \n",
       "129411         10.56           3931.0  \n",
       "143955          8.24           2627.0  \n",
       "143958          8.24           2627.0  \n",
       "143952          8.24           2627.0  \n",
       "143946         17.43           2627.0  \n",
       "143954          8.24           2627.0  \n",
       "245860         24.91          19274.0  \n",
       "245854         24.91          19274.0  \n",
       "245863         56.73          19274.0  \n",
       "\n",
       "[123449 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choices.loc[best_uber,['search_id','travel_provider','uber_mode_class','travel_product','travel_price','travel_distance']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "best_uber_idx = copy.deepcopy(best_uber)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_uber = choices.loc[best_uber_idx,['search_id','travel_provider','uber_mode_class','travel_product','travel_price','travel_distance','travel_time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>search_id</th>\n",
       "      <th>travel_provider</th>\n",
       "      <th>uber_mode_class</th>\n",
       "      <th>travel_product</th>\n",
       "      <th>travel_price</th>\n",
       "      <th>travel_distance</th>\n",
       "      <th>travel_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224920</th>\n",
       "      <td>309474</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>6.55</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224921</th>\n",
       "      <td>309474</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>13.49</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224925</th>\n",
       "      <td>309474</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>6.55</td>\n",
       "      <td>2144.0</td>\n",
       "      <td>393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100855</th>\n",
       "      <td>309475</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>7.48</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100856</th>\n",
       "      <td>309475</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>15.77</td>\n",
       "      <td>3335.0</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78546</th>\n",
       "      <td>309477</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>38.95</td>\n",
       "      <td>39564.0</td>\n",
       "      <td>1945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78547</th>\n",
       "      <td>309477</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>90.36</td>\n",
       "      <td>39564.0</td>\n",
       "      <td>1945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59904</th>\n",
       "      <td>309481</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>22.74</td>\n",
       "      <td>20872.0</td>\n",
       "      <td>1166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59907</th>\n",
       "      <td>309481</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>51.93</td>\n",
       "      <td>20872.0</td>\n",
       "      <td>1166.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310828</th>\n",
       "      <td>309487</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>15.29</td>\n",
       "      <td>10647.0</td>\n",
       "      <td>1128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310830</th>\n",
       "      <td>309487</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>34.14</td>\n",
       "      <td>10647.0</td>\n",
       "      <td>1128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310834</th>\n",
       "      <td>309487</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>15.29</td>\n",
       "      <td>10647.0</td>\n",
       "      <td>1128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182706</th>\n",
       "      <td>309488</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>8.63</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182708</th>\n",
       "      <td>309488</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>18.39</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182704</th>\n",
       "      <td>309488</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>8.63</td>\n",
       "      <td>3521.0</td>\n",
       "      <td>699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310837</th>\n",
       "      <td>309490</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>15.30</td>\n",
       "      <td>10636.0</td>\n",
       "      <td>1132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310840</th>\n",
       "      <td>309490</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>34.16</td>\n",
       "      <td>10636.0</td>\n",
       "      <td>1132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164320</th>\n",
       "      <td>309505</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>8.07</td>\n",
       "      <td>3859.0</td>\n",
       "      <td>481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164323</th>\n",
       "      <td>309505</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>17.15</td>\n",
       "      <td>3859.0</td>\n",
       "      <td>481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264696</th>\n",
       "      <td>309512</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>12.17</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>984.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264699</th>\n",
       "      <td>309512</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>26.73</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>984.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264692</th>\n",
       "      <td>309512</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>12.17</td>\n",
       "      <td>7008.0</td>\n",
       "      <td>984.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273201</th>\n",
       "      <td>309516</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273197</th>\n",
       "      <td>309516</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>12.58</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273202</th>\n",
       "      <td>309516</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238256</th>\n",
       "      <td>309524</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238257</th>\n",
       "      <td>309524</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>12.66</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238260</th>\n",
       "      <td>309524</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>6.55</td>\n",
       "      <td>1926.0</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346699</th>\n",
       "      <td>309525</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>10.27</td>\n",
       "      <td>5554.0</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346703</th>\n",
       "      <td>309525</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>22.28</td>\n",
       "      <td>5554.0</td>\n",
       "      <td>749.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283713</th>\n",
       "      <td>445780</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>7.94</td>\n",
       "      <td>2787.0</td>\n",
       "      <td>653.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283718</th>\n",
       "      <td>445780</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>16.75</td>\n",
       "      <td>2787.0</td>\n",
       "      <td>653.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283720</th>\n",
       "      <td>445780</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>7.94</td>\n",
       "      <td>2787.0</td>\n",
       "      <td>653.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97860</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97869</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97861</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97864</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>31.88</td>\n",
       "      <td>8173.0</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97863</th>\n",
       "      <td>445781</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>14.39</td>\n",
       "      <td>8173.0</td>\n",
       "      <td>1364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196497</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "      <td>903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196496</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "      <td>903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196489</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "      <td>903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196493</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>21.64</td>\n",
       "      <td>4426.0</td>\n",
       "      <td>903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196490</th>\n",
       "      <td>445782</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>10.02</td>\n",
       "      <td>4426.0</td>\n",
       "      <td>903.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154892</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>19.74</td>\n",
       "      <td>13352.0</td>\n",
       "      <td>1813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154904</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>19.74</td>\n",
       "      <td>13352.0</td>\n",
       "      <td>1813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154891</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>19.74</td>\n",
       "      <td>13352.0</td>\n",
       "      <td>1813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154898</th>\n",
       "      <td>445790</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>44.47</td>\n",
       "      <td>13352.0</td>\n",
       "      <td>1813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129398</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129410</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129400</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129405</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>22.83</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129411</th>\n",
       "      <td>445791</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>10.56</td>\n",
       "      <td>3931.0</td>\n",
       "      <td>1147.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143955</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143958</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>other</td>\n",
       "      <td>assist</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143952</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143946</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>17.43</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143954</th>\n",
       "      <td>445799</td>\n",
       "      <td>Uber</td>\n",
       "      <td>taxi</td>\n",
       "      <td>ubertaxi</td>\n",
       "      <td>8.24</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245860</th>\n",
       "      <td>445816</td>\n",
       "      <td>Uber</td>\n",
       "      <td>basic</td>\n",
       "      <td>uberx</td>\n",
       "      <td>24.91</td>\n",
       "      <td>19274.0</td>\n",
       "      <td>2070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245854</th>\n",
       "      <td>445816</td>\n",
       "      <td>Uber</td>\n",
       "      <td>pool</td>\n",
       "      <td>uberpool</td>\n",
       "      <td>24.91</td>\n",
       "      <td>19274.0</td>\n",
       "      <td>2070.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245863</th>\n",
       "      <td>445816</td>\n",
       "      <td>Uber</td>\n",
       "      <td>premium</td>\n",
       "      <td>uberselect</td>\n",
       "      <td>56.73</td>\n",
       "      <td>19274.0</td>\n",
       "      <td>2070.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123449 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        search_id travel_provider uber_mode_class travel_product  \\\n",
       "224920     309474            Uber           basic          uberx   \n",
       "224921     309474            Uber         premium     uberselect   \n",
       "224925     309474            Uber            taxi       ubertaxi   \n",
       "100855     309475            Uber           basic          uberx   \n",
       "100856     309475            Uber         premium     uberselect   \n",
       "78546      309477            Uber           basic          uberx   \n",
       "78547      309477            Uber         premium     uberselect   \n",
       "59904      309481            Uber           basic          uberx   \n",
       "59907      309481            Uber         premium     uberselect   \n",
       "310828     309487            Uber           basic          uberx   \n",
       "310830     309487            Uber         premium     uberselect   \n",
       "310834     309487            Uber            taxi       ubertaxi   \n",
       "182706     309488            Uber           basic          uberx   \n",
       "182708     309488            Uber         premium     uberselect   \n",
       "182704     309488            Uber            taxi       ubertaxi   \n",
       "310837     309490            Uber           basic          uberx   \n",
       "310840     309490            Uber         premium     uberselect   \n",
       "164320     309505            Uber           basic          uberx   \n",
       "164323     309505            Uber         premium     uberselect   \n",
       "264696     309512            Uber           basic          uberx   \n",
       "264699     309512            Uber         premium     uberselect   \n",
       "264692     309512            Uber            taxi       ubertaxi   \n",
       "273201     309516            Uber           basic          uberx   \n",
       "273197     309516            Uber         premium     uberselect   \n",
       "273202     309516            Uber            taxi       ubertaxi   \n",
       "238256     309524            Uber           basic          uberx   \n",
       "238257     309524            Uber         premium     uberselect   \n",
       "238260     309524            Uber            taxi       ubertaxi   \n",
       "346699     309525            Uber           basic          uberx   \n",
       "346703     309525            Uber         premium     uberselect   \n",
       "...           ...             ...             ...            ...   \n",
       "283713     445780            Uber            pool       uberpool   \n",
       "283718     445780            Uber         premium     uberselect   \n",
       "283720     445780            Uber            taxi       ubertaxi   \n",
       "97860      445781            Uber           basic          uberx   \n",
       "97869      445781            Uber           other         assist   \n",
       "97861      445781            Uber            pool       uberpool   \n",
       "97864      445781            Uber         premium     uberselect   \n",
       "97863      445781            Uber            taxi       ubertaxi   \n",
       "196497     445782            Uber           basic          uberx   \n",
       "196496     445782            Uber           other         assist   \n",
       "196489     445782            Uber            pool       uberpool   \n",
       "196493     445782            Uber         premium     uberselect   \n",
       "196490     445782            Uber            taxi       ubertaxi   \n",
       "154892     445790            Uber           basic          uberx   \n",
       "154904     445790            Uber           other         assist   \n",
       "154891     445790            Uber            pool       uberpool   \n",
       "154898     445790            Uber         premium     uberselect   \n",
       "129398     445791            Uber           basic          uberx   \n",
       "129410     445791            Uber           other         assist   \n",
       "129400     445791            Uber            pool       uberpool   \n",
       "129405     445791            Uber         premium     uberselect   \n",
       "129411     445791            Uber            taxi       ubertaxi   \n",
       "143955     445799            Uber           basic          uberx   \n",
       "143958     445799            Uber           other         assist   \n",
       "143952     445799            Uber            pool       uberpool   \n",
       "143946     445799            Uber         premium     uberselect   \n",
       "143954     445799            Uber            taxi       ubertaxi   \n",
       "245860     445816            Uber           basic          uberx   \n",
       "245854     445816            Uber            pool       uberpool   \n",
       "245863     445816            Uber         premium     uberselect   \n",
       "\n",
       "        travel_price  travel_distance  travel_time  \n",
       "224920          6.55           2144.0        393.0  \n",
       "224921         13.49           2144.0        393.0  \n",
       "224925          6.55           2144.0        393.0  \n",
       "100855          7.48           3335.0        422.0  \n",
       "100856         15.77           3335.0        422.0  \n",
       "78546          38.95          39564.0       1945.0  \n",
       "78547          90.36          39564.0       1945.0  \n",
       "59904          22.74          20872.0       1166.0  \n",
       "59907          51.93          20872.0       1166.0  \n",
       "310828         15.29          10647.0       1128.0  \n",
       "310830         34.14          10647.0       1128.0  \n",
       "310834         15.29          10647.0       1128.0  \n",
       "182706          8.63           3521.0        699.0  \n",
       "182708         18.39           3521.0        699.0  \n",
       "182704          8.63           3521.0        699.0  \n",
       "310837         15.30          10636.0       1132.0  \n",
       "310840         34.16          10636.0       1132.0  \n",
       "164320          8.07           3859.0        481.0  \n",
       "164323         17.15           3859.0        481.0  \n",
       "264696         12.17           7008.0        984.0  \n",
       "264699         26.73           7008.0        984.0  \n",
       "264692         12.17           7008.0        984.0  \n",
       "273201          6.55           1967.0        320.0  \n",
       "273197         12.58           1967.0        320.0  \n",
       "273202          6.55           1967.0        320.0  \n",
       "238256          6.55           1926.0        338.0  \n",
       "238257         12.66           1926.0        338.0  \n",
       "238260          6.55           1926.0        338.0  \n",
       "346699         10.27           5554.0        749.0  \n",
       "346703         22.28           5554.0        749.0  \n",
       "...              ...              ...          ...  \n",
       "283713          7.94           2787.0        653.0  \n",
       "283718         16.75           2787.0        653.0  \n",
       "283720          7.94           2787.0        653.0  \n",
       "97860          14.39           8173.0       1364.0  \n",
       "97869          14.39           8173.0       1364.0  \n",
       "97861          14.39           8173.0       1364.0  \n",
       "97864          31.88           8173.0       1364.0  \n",
       "97863          14.39           8173.0       1364.0  \n",
       "196497         10.02           4426.0        903.0  \n",
       "196496         10.02           4426.0        903.0  \n",
       "196489         10.02           4426.0        903.0  \n",
       "196493         21.64           4426.0        903.0  \n",
       "196490         10.02           4426.0        903.0  \n",
       "154892         19.74          13352.0       1813.0  \n",
       "154904         19.74          13352.0       1813.0  \n",
       "154891         19.74          13352.0       1813.0  \n",
       "154898         44.47          13352.0       1813.0  \n",
       "129398         10.56           3931.0       1147.0  \n",
       "129410         10.56           3931.0       1147.0  \n",
       "129400         10.56           3931.0       1147.0  \n",
       "129405         22.83           3931.0       1147.0  \n",
       "129411         10.56           3931.0       1147.0  \n",
       "143955          8.24           2627.0        767.0  \n",
       "143958          8.24           2627.0        767.0  \n",
       "143952          8.24           2627.0        767.0  \n",
       "143946         17.43           2627.0        767.0  \n",
       "143954          8.24           2627.0        767.0  \n",
       "245860         24.91          19274.0       2070.0  \n",
       "245854         24.91          19274.0       2070.0  \n",
       "245863         56.73          19274.0       2070.0  \n",
       "\n",
       "[123449 rows x 7 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_uber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "price = best_uber.pivot(index='search_id',columns='uber_mode_class',values='travel_price')\n",
    "dist = best_uber.pivot(index='search_id',columns='uber_mode_class',values='travel_distance')\n",
    "time = best_uber.pivot(index='search_id',columns='uber_mode_class',values='travel_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "estimation_set['uber_basic_cost'] = price['basic']\n",
    "estimation_set['uber_basic_dist'] = dist['basic'] / 5280.0\n",
    "estimation_set['uber_basic_time'] = time['basic'] / 60.0\n",
    "\n",
    "estimation_set['uber_premium_cost'] = price['premium']\n",
    "estimation_set['uber_premium_dist'] = dist['premium'] / 5280.0\n",
    "estimation_set['uber_premium_time'] = time['premium'] / 60.0\n",
    "\n",
    "estimation_set['uber_taxi_cost'] = price['taxi']\n",
    "estimation_set['uber_taxi_dist'] = dist['taxi'] / 5280.0\n",
    "estimation_set['uber_taxi_time'] = time['taxi'] / 60.0\n",
    "\n",
    "estimation_set['uber_other_cost'] = price['other']\n",
    "estimation_set['uber_other_dist'] = dist['other'] / 5280.0\n",
    "estimation_set['uber_other_time'] = time['other'] / 60.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "estimation_set['timestamp'] = choices.groupby('search_id')['timestamp'].first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dti = pd.DatetimeIndex(estimation_set['timestamp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dti = dti.tz_localize('UTC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dti_uspac =dti.tz_convert('US/Pacific')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimation_set['dayofweek'] = dti_uspac.dayofweek\n",
    "estimation_set['time'] = dti_uspac.time\n",
    "estimation_set['date'] = dti_uspac.date\n",
    "estimation_set['hour'] = dti_uspac.hour\n",
    "estimation_set['late_night'] = 0\n",
    "estimation_set.loc[estimation_set['hour'].isin([22,23,0,1,2,3]),'late_night'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "search_id\n",
       "372006    walking\n",
       "349759    transit\n",
       "409169      basic\n",
       "385323      scoot\n",
       "388246    premium\n",
       "364796     biking\n",
       "Name: chosen_mode2, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_set['chosen_mode2'].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "estimation_subset = estimation_set[estimation_set['chosen_mode3'].isin(['transit','uber'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chosen_mode\n",
       "basic        255\n",
       "premium      202\n",
       "transit    19189\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_subset.groupby('chosen_mode').count().max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "chosen_mode3\n",
       "transit    19189\n",
       "uber         457\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_subset.groupby(['chosen_mode3']).count().max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'chosen_mode', u'chosen_mode2', u'chosen_mode3', u'start_is_near_home',\n",
       "       u'start_is_near_work', u'end_is_near_home', u'end_is_near_work', u'age',\n",
       "       u'hhinc', u'hhvehs',\n",
       "       ...\n",
       "       u'uber_taxi_time', u'uber_other_cost', u'uber_other_dist',\n",
       "       u'uber_other_time', u'timestamp', u'dayofweek', u'time', u'date',\n",
       "       u'hour', u'late_night'],\n",
       "      dtype='object', length=961)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation_set.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Python27_64\\lib\\site-packages\\pandas-0.18.1-py2.7-win-amd64.egg\\pandas\\core\\indexing.py:465: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "C:\\Python27_64\\lib\\site-packages\\ipykernel-4.3.1-py2.7.egg\\ipykernel\\__main__.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "subset2 = estimation_set[estimation_set['chosen_mode3'].isin(['transit','uber'])]\n",
    "subset2['walkwait'] = subset2['walk'] + subset2['wait']\n",
    "subset2['ln_wait'] = np.log(subset2['wait']+1)\n",
    "subset2['ln_walk'] = np.log(subset2['walk']+1)\n",
    "subset2['ln_walkwait'] = np.log(subset2['walkwait']+1)\n",
    "subset2['ln_trn_cost'] = np.log(subset2['transit_cost']+1)\n",
    "subset2['ln_trn_time'] = np.log(subset2['transit_time']+1)\n",
    "subset2['ln_ubr_time'] = np.log(subset2['uber_basic_time']+1)\n",
    "subset2['ln_headway'] = np.log(subset2['headway']+1)\n",
    "subset2['tot_trn_time'] = subset2['walk'] + subset2['wait'] + subset2['transit_time']\n",
    "subset2['uber_time_save'] = subset2['tot_trn_time'] - subset2['uber_basic_time']\n",
    "subset2['uber_pct_time_save'] = subset2['uber_time_save'] / subset2['tot_trn_time']\n",
    "subset2['start_or_end_near_home'] = np.nan\n",
    "subset2.loc[(subset2['start_is_near_home']==0) & (subset2['end_is_near_home']==0),'start_or_end_near_home'] = 0\n",
    "subset2.loc[(subset2['start_is_near_home']==1) | (subset2['end_is_near_home']==1),'start_or_end_near_home'] = 1\n",
    "subset2['start_or_end_near_work'] = np.nan\n",
    "subset2.loc[(subset2['start_is_near_work']==0) & (subset2['end_is_near_work']==0),'start_or_end_near_work'] = 0\n",
    "subset2.loc[(subset2['start_is_near_work']==1) | (subset2['end_is_near_work']==1),'start_or_end_near_work'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105802\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19637</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04249</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:10</td>     <th>  Log-Likelihood:    </th> <td> -2078.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>9.662e-40</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5829</td> <td>    0.052</td> <td>  -11.108</td> <td> 0.000</td> <td>   -0.686    -0.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walkwait</th>          <td>    0.0006</td> <td> 9.82e-05</td> <td>    6.470</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ln_ubr_time</th>       <td>    0.1644</td> <td>    0.079</td> <td>    2.075</td> <td> 0.038</td> <td>    0.009     0.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.9010</td> <td>    0.264</td> <td>  -10.997</td> <td> 0.000</td> <td>   -3.418    -2.384</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19637\n",
       "Method:                           MLE   Df Model:                            3\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04249\n",
       "Time:                        11:18:10   Log-Likelihood:                -2078.1\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 9.662e-40\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5829      0.052    -11.108      0.000        -0.686    -0.480\n",
       "walkwait              0.0006   9.82e-05      6.470      0.000         0.000     0.001\n",
       "ln_ubr_time           0.1644      0.079      2.075      0.038         0.009     0.320\n",
       "const                -2.9010      0.264    -10.997      0.000        -3.418    -2.384\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['n_transit','walkwait','ln_ubr_time']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit()\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105330\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19589</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19584</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04546</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:10</td>     <th>  Log-Likelihood:    </th> <td> -2063.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2161.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.105e-41</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5744</td> <td>    0.053</td> <td>  -10.888</td> <td> 0.000</td> <td>   -0.678    -0.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ln_walkwait</th>       <td>    0.1350</td> <td>    0.021</td> <td>    6.534</td> <td> 0.000</td> <td>    0.094     0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0046</td> <td>    0.002</td> <td>    2.176</td> <td> 0.030</td> <td>    0.000     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>late_night</th>        <td>    0.4363</td> <td>    0.167</td> <td>    2.606</td> <td> 0.009</td> <td>    0.108     0.764</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.9335</td> <td>    0.175</td> <td>  -16.788</td> <td> 0.000</td> <td>   -3.276    -2.591</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19589\n",
       "Model:                        MNLogit   Df Residuals:                    19584\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04546\n",
       "Time:                        11:18:10   Log-Likelihood:                -2063.3\n",
       "converged:                       True   LL-Null:                       -2161.6\n",
       "                                        LLR p-value:                 2.105e-41\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5744      0.053    -10.888      0.000        -0.678    -0.471\n",
       "ln_walkwait           0.1350      0.021      6.534      0.000         0.094     0.175\n",
       "transit_time          0.0046      0.002      2.176      0.030         0.000     0.009\n",
       "late_night            0.4363      0.167      2.606      0.009         0.108     0.764\n",
       "const                -2.9335      0.175    -16.788      0.000        -3.276    -2.591\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['n_transit','ln_walkwait','transit_time','late_night']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit()\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105403\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19584</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19577</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04499</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:10</td>     <th>  Log-Likelihood:    </th> <td> -2064.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2161.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.824e-39</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5868</td> <td>    0.054</td> <td>  -10.862</td> <td> 0.000</td> <td>   -0.693    -0.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ln_walkwait</th>       <td>    0.1352</td> <td>    0.021</td> <td>    6.548</td> <td> 0.000</td> <td>    0.095     0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0036</td> <td>    0.004</td> <td>    0.944</td> <td> 0.345</td> <td>   -0.004     0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>      <td>    0.0559</td> <td>    0.029</td> <td>    1.913</td> <td> 0.056</td> <td>   -0.001     0.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>   <td>    0.0106</td> <td>    0.009</td> <td>    1.181</td> <td> 0.238</td> <td>   -0.007     0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>   <td>   -0.0162</td> <td>    0.013</td> <td>   -1.283</td> <td> 0.200</td> <td>   -0.041     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.9744</td> <td>    0.180</td> <td>  -16.504</td> <td> 0.000</td> <td>   -3.328    -2.621</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19584\n",
       "Model:                        MNLogit   Df Residuals:                    19577\n",
       "Method:                           MLE   Df Model:                            6\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04499\n",
       "Time:                        11:18:10   Log-Likelihood:                -2064.2\n",
       "converged:                       True   LL-Null:                       -2161.5\n",
       "                                        LLR p-value:                 2.824e-39\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5868      0.054    -10.862      0.000        -0.693    -0.481\n",
       "ln_walkwait           0.1352      0.021      6.548      0.000         0.095     0.176\n",
       "transit_time          0.0036      0.004      0.944      0.345        -0.004     0.011\n",
       "transit_cost          0.0559      0.029      1.913      0.056        -0.001     0.113\n",
       "uber_basic_time       0.0106      0.009      1.181      0.238        -0.007     0.028\n",
       "uber_basic_cost      -0.0162      0.013     -1.283      0.200        -0.041     0.009\n",
       "const                -2.9744      0.180    -16.504      0.000        -3.328    -2.621\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['n_transit','ln_walkwait','transit_time','transit_cost','uber_basic_time','uber_basic_cost']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit()\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105985\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19643</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04064</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:10</td>     <th>  Log-Likelihood:    </th> <td> -2082.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>4.901e-39</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5897</td> <td>    0.052</td> <td>  -11.276</td> <td> 0.000</td> <td>   -0.692    -0.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_arr</th>             <td>   -0.2181</td> <td>    0.035</td> <td>   -6.244</td> <td> 0.000</td> <td>   -0.287    -0.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -1.8346</td> <td>    0.140</td> <td>  -13.150</td> <td> 0.000</td> <td>   -2.108    -1.561</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19643\n",
       "Method:                           MLE   Df Model:                            2\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04064\n",
       "Time:                        11:18:10   Log-Likelihood:                -2082.2\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                 4.901e-39\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5897      0.052    -11.276      0.000        -0.692    -0.487\n",
       "n_arr                -0.2181      0.035     -6.244      0.000        -0.287    -0.150\n",
       "const                -1.8346      0.140    -13.150      0.000        -2.108    -1.561\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['n_transit','n_arr']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit()\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106076\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19643</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.03982</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:11</td>     <th>  Log-Likelihood:    </th> <td> -2084.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.896e-38</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5894</td> <td>    0.052</td> <td>  -11.292</td> <td> 0.000</td> <td>   -0.692    -0.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>headway</th>           <td>    0.0064</td> <td>    0.001</td> <td>    5.936</td> <td> 0.000</td> <td>    0.004     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.5223</td> <td>    0.146</td> <td>  -17.265</td> <td> 0.000</td> <td>   -2.809    -2.236</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19643\n",
       "Method:                           MLE   Df Model:                            2\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.03982\n",
       "Time:                        11:18:11   Log-Likelihood:                -2084.0\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                 2.896e-38\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5894      0.052    -11.292      0.000        -0.692    -0.487\n",
       "headway               0.0064      0.001      5.936      0.000         0.004     0.009\n",
       "const                -2.5223      0.146    -17.265      0.000        -2.809    -2.236\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['n_transit','headway']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit()\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105762\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19636</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04285</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:11</td>     <th>  Log-Likelihood:    </th> <td> -2077.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>3.812e-39</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5733</td> <td>    0.053</td> <td>  -10.853</td> <td> 0.000</td> <td>   -0.677    -0.470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walkwait</th>          <td>    0.0006</td> <td> 9.86e-05</td> <td>    6.347</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0043</td> <td>    0.003</td> <td>    1.388</td> <td> 0.165</td> <td>   -0.002     0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>   <td>    0.0022</td> <td>    0.005</td> <td>    0.412</td> <td> 0.681</td> <td>   -0.008     0.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.6325</td> <td>    0.162</td> <td>  -16.251</td> <td> 0.000</td> <td>   -2.950    -2.315</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19636\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04285\n",
       "Time:                        11:18:11   Log-Likelihood:                -2077.3\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 3.812e-39\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5733      0.053    -10.853      0.000        -0.677    -0.470\n",
       "walkwait              0.0006   9.86e-05      6.347      0.000         0.000     0.001\n",
       "transit_time          0.0043      0.003      1.388      0.165        -0.002     0.010\n",
       "uber_basic_time       0.0022      0.005      0.412      0.681        -0.008     0.013\n",
       "const                -2.6325      0.162    -16.251      0.000        -2.950    -2.315\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['n_transit','walkwait','transit_time','uber_basic_time']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105776\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19636</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04273</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:11</td>     <th>  Log-Likelihood:    </th> <td> -2077.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>4.991e-39</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5737</td> <td>    0.053</td> <td>  -10.876</td> <td> 0.000</td> <td>   -0.677    -0.470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>headway</th>           <td>    0.0019</td> <td>    0.002</td> <td>    0.922</td> <td> 0.356</td> <td>   -0.002     0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tot_trn_time</th>      <td>    0.0005</td> <td>    0.000</td> <td>    2.671</td> <td> 0.008</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>   <td>    0.0068</td> <td>    0.004</td> <td>    1.903</td> <td> 0.057</td> <td>   -0.000     0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.6461</td> <td>    0.167</td> <td>  -15.876</td> <td> 0.000</td> <td>   -2.973    -2.319</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19636\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04273\n",
       "Time:                        11:18:11   Log-Likelihood:                -2077.5\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 4.991e-39\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5737      0.053    -10.876      0.000        -0.677    -0.470\n",
       "headway               0.0019      0.002      0.922      0.356        -0.002     0.006\n",
       "tot_trn_time          0.0005      0.000      2.671      0.008         0.000     0.001\n",
       "uber_basic_time       0.0068      0.004      1.903      0.057        -0.000     0.014\n",
       "const                -2.6461      0.167    -15.876      0.000        -2.973    -2.319\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['n_transit','headway','tot_trn_time','uber_basic_time']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105631\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19631</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     9</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04404</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:11</td>     <th>  Log-Likelihood:    </th> <td> -2074.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.363e-36</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5788</td> <td>    0.054</td> <td>  -10.705</td> <td> 0.000</td> <td>   -0.685    -0.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_arr</th>             <td>    0.0629</td> <td>    0.202</td> <td>    0.311</td> <td> 0.756</td> <td>   -0.333     0.459</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walk</th>              <td>   -0.0040</td> <td>    0.013</td> <td>   -0.316</td> <td> 0.752</td> <td>   -0.029     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>              <td>    0.0006</td> <td>    0.000</td> <td>    1.992</td> <td> 0.046</td> <td> 8.97e-06     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>headway</th>           <td>    0.0030</td> <td>    0.005</td> <td>    0.641</td> <td> 0.521</td> <td>   -0.006     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0044</td> <td>    0.004</td> <td>    1.101</td> <td> 0.271</td> <td>   -0.003     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>   <td>    0.0095</td> <td>    0.009</td> <td>    1.058</td> <td> 0.290</td> <td>   -0.008     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>      <td>    0.0585</td> <td>    0.029</td> <td>    2.012</td> <td> 0.044</td> <td>    0.002     0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>   <td>   -0.0161</td> <td>    0.013</td> <td>   -1.274</td> <td> 0.203</td> <td>   -0.041     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.9183</td> <td>    0.646</td> <td>   -4.515</td> <td> 0.000</td> <td>   -4.185    -1.651</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19631\n",
       "Method:                           MLE   Df Model:                            9\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04404\n",
       "Time:                        11:18:11   Log-Likelihood:                -2074.7\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 2.363e-36\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5788      0.054    -10.705      0.000        -0.685    -0.473\n",
       "n_arr                 0.0629      0.202      0.311      0.756        -0.333     0.459\n",
       "walk                 -0.0040      0.013     -0.316      0.752        -0.029     0.021\n",
       "wait                  0.0006      0.000      1.992      0.046      8.97e-06     0.001\n",
       "headway               0.0030      0.005      0.641      0.521        -0.006     0.012\n",
       "transit_time          0.0044      0.004      1.101      0.271        -0.003     0.012\n",
       "uber_basic_time       0.0095      0.009      1.058      0.290        -0.008     0.027\n",
       "transit_cost          0.0585      0.029      2.012      0.044         0.002     0.115\n",
       "uber_basic_cost      -0.0161      0.013     -1.274      0.203        -0.041     0.009\n",
       "const                -2.9183      0.646     -4.515      0.000        -4.185    -1.651\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try everything except late_night\n",
    "exog_cols = ['n_transit','n_arr','walk','wait','headway','transit_time','uber_basic_time','transit_cost','uber_basic_cost']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105634\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19632</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04402</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:11</td>     <th>  Log-Likelihood:    </th> <td> -2074.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>4.887e-37</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5794</td> <td>    0.054</td> <td>  -10.719</td> <td> 0.000</td> <td>   -0.685    -0.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walk</th>              <td>   -0.0038</td> <td>    0.013</td> <td>   -0.302</td> <td> 0.762</td> <td>   -0.029     0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>              <td>    0.0005</td> <td>    0.000</td> <td>    2.653</td> <td> 0.008</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>headway</th>           <td>    0.0017</td> <td>    0.002</td> <td>    0.830</td> <td> 0.406</td> <td>   -0.002     0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0044</td> <td>    0.004</td> <td>    1.099</td> <td> 0.272</td> <td>   -0.003     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>   <td>    0.0096</td> <td>    0.009</td> <td>    1.072</td> <td> 0.284</td> <td>   -0.008     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>      <td>    0.0584</td> <td>    0.029</td> <td>    2.010</td> <td> 0.044</td> <td>    0.001     0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>   <td>   -0.0161</td> <td>    0.013</td> <td>   -1.277</td> <td> 0.202</td> <td>   -0.041     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.7244</td> <td>    0.171</td> <td>  -15.967</td> <td> 0.000</td> <td>   -3.059    -2.390</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19632\n",
       "Method:                           MLE   Df Model:                            8\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04402\n",
       "Time:                        11:18:11   Log-Likelihood:                -2074.8\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 4.887e-37\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5794      0.054    -10.719      0.000        -0.685    -0.473\n",
       "walk                 -0.0038      0.013     -0.302      0.762        -0.029     0.021\n",
       "wait                  0.0005      0.000      2.653      0.008         0.000     0.001\n",
       "headway               0.0017      0.002      0.830      0.406        -0.002     0.006\n",
       "transit_time          0.0044      0.004      1.099      0.272        -0.003     0.012\n",
       "uber_basic_time       0.0096      0.009      1.072      0.284        -0.008     0.027\n",
       "transit_cost          0.0584      0.029      2.010      0.044         0.001     0.115\n",
       "uber_basic_cost      -0.0161      0.013     -1.277      0.202        -0.041     0.009\n",
       "const                -2.7244      0.171    -15.967      0.000        -3.059    -2.390\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop n_arr\n",
    "exog_cols = ['n_transit','walk','wait','headway','transit_time','uber_basic_time','transit_cost','uber_basic_cost']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105636\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19633</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     7</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04399</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:11</td>     <th>  Log-Likelihood:    </th> <td> -2074.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>9.391e-38</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5804</td> <td>    0.054</td> <td>  -10.763</td> <td> 0.000</td> <td>   -0.686    -0.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>              <td>    0.0005</td> <td>    0.000</td> <td>    2.666</td> <td> 0.008</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>headway</th>           <td>    0.0017</td> <td>    0.002</td> <td>    0.816</td> <td> 0.415</td> <td>   -0.002     0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0041</td> <td>    0.004</td> <td>    1.054</td> <td> 0.292</td> <td>   -0.003     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>   <td>    0.0097</td> <td>    0.009</td> <td>    1.076</td> <td> 0.282</td> <td>   -0.008     0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>      <td>    0.0589</td> <td>    0.029</td> <td>    2.030</td> <td> 0.042</td> <td>    0.002     0.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>   <td>   -0.0160</td> <td>    0.013</td> <td>   -1.265</td> <td> 0.206</td> <td>   -0.041     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.7270</td> <td>    0.170</td> <td>  -15.996</td> <td> 0.000</td> <td>   -3.061    -2.393</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19633\n",
       "Method:                           MLE   Df Model:                            7\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04399\n",
       "Time:                        11:18:11   Log-Likelihood:                -2074.8\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 9.391e-38\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5804      0.054    -10.763      0.000        -0.686    -0.475\n",
       "wait                  0.0005      0.000      2.666      0.008         0.000     0.001\n",
       "headway               0.0017      0.002      0.816      0.415        -0.002     0.006\n",
       "transit_time          0.0041      0.004      1.054      0.292        -0.003     0.012\n",
       "uber_basic_time       0.0097      0.009      1.076      0.282        -0.008     0.027\n",
       "transit_cost          0.0589      0.029      2.030      0.042         0.002     0.116\n",
       "uber_basic_cost      -0.0160      0.013     -1.265      0.206        -0.041     0.009\n",
       "const                -2.7270      0.170    -15.996      0.000        -3.061    -2.393\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop walk\n",
    "exog_cols = ['n_transit','wait','headway','transit_time','uber_basic_time','transit_cost','uber_basic_cost']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105652\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19634</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04385</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:12</td>     <th>  Log-Likelihood:    </th> <td> -2075.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.180e-38</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5801</td> <td>    0.054</td> <td>  -10.754</td> <td> 0.000</td> <td>   -0.686    -0.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>              <td>    0.0006</td> <td> 9.89e-05</td> <td>    6.241</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0041</td> <td>    0.004</td> <td>    1.050</td> <td> 0.294</td> <td>   -0.004     0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>   <td>    0.0090</td> <td>    0.009</td> <td>    1.003</td> <td> 0.316</td> <td>   -0.009     0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>      <td>    0.0606</td> <td>    0.029</td> <td>    2.088</td> <td> 0.037</td> <td>    0.004     0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>   <td>   -0.0153</td> <td>    0.013</td> <td>   -1.217</td> <td> 0.224</td> <td>   -0.040     0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.6928</td> <td>    0.165</td> <td>  -16.331</td> <td> 0.000</td> <td>   -3.016    -2.370</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19634\n",
       "Method:                           MLE   Df Model:                            6\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04385\n",
       "Time:                        11:18:12   Log-Likelihood:                -2075.1\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 2.180e-38\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5801      0.054    -10.754      0.000        -0.686    -0.474\n",
       "wait                  0.0006   9.89e-05      6.241      0.000         0.000     0.001\n",
       "transit_time          0.0041      0.004      1.050      0.294        -0.004     0.012\n",
       "uber_basic_time       0.0090      0.009      1.003      0.316        -0.009     0.026\n",
       "transit_cost          0.0606      0.029      2.088      0.037         0.004     0.118\n",
       "uber_basic_cost      -0.0153      0.013     -1.217      0.224        -0.040     0.009\n",
       "const                -2.6928      0.165    -16.331      0.000        -3.016    -2.370\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop headway\n",
    "exog_cols = ['n_transit','wait','transit_time','uber_basic_time','transit_cost','uber_basic_cost']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105678\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19641</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19635</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     5</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04362</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:12</td>     <th>  Log-Likelihood:    </th> <td> -2075.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>5.468e-39</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5738</td> <td>    0.053</td> <td>  -10.730</td> <td> 0.000</td> <td>   -0.679    -0.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>              <td>    0.0006</td> <td>  9.9e-05</td> <td>    6.221</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>      <td>    0.0039</td> <td>    0.004</td> <td>    1.021</td> <td> 0.307</td> <td>   -0.004     0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>      <td>    0.0556</td> <td>    0.029</td> <td>    1.933</td> <td> 0.053</td> <td>   -0.001     0.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>   <td>   -0.0054</td> <td>    0.008</td> <td>   -0.709</td> <td> 0.478</td> <td>   -0.020     0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.6671</td> <td>    0.162</td> <td>  -16.467</td> <td> 0.000</td> <td>   -2.985    -2.350</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19641\n",
       "Model:                        MNLogit   Df Residuals:                    19635\n",
       "Method:                           MLE   Df Model:                            5\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04362\n",
       "Time:                        11:18:12   Log-Likelihood:                -2075.6\n",
       "converged:                       True   LL-Null:                       -2170.3\n",
       "                                        LLR p-value:                 5.468e-39\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5738      0.053    -10.730      0.000        -0.679    -0.469\n",
       "wait                  0.0006    9.9e-05      6.221      0.000         0.000     0.001\n",
       "transit_time          0.0039      0.004      1.021      0.307        -0.004     0.011\n",
       "transit_cost          0.0556      0.029      1.933      0.053        -0.001     0.112\n",
       "uber_basic_cost      -0.0054      0.008     -0.709      0.478        -0.020     0.010\n",
       "const                -2.6671      0.162    -16.467      0.000        -2.985    -2.350\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop uber time\n",
    "exog_cols = ['n_transit','wait','transit_time','transit_cost','uber_basic_cost']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.105683\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19642</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04338</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:12</td>     <th>  Log-Likelihood:    </th> <td> -2076.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.420e-40</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>         <td>   -0.5690</td> <td>    0.053</td> <td>  -10.787</td> <td> 0.000</td> <td>   -0.672    -0.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>              <td>    0.0006</td> <td> 9.89e-05</td> <td>    6.234</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>      <td>    0.0610</td> <td>    0.020</td> <td>    3.053</td> <td> 0.002</td> <td>    0.022     0.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -2.6575</td> <td>    0.158</td> <td>  -16.807</td> <td> 0.000</td> <td>   -2.967    -2.348</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19642\n",
       "Method:                           MLE   Df Model:                            3\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04338\n",
       "Time:                        11:18:12   Log-Likelihood:                -2076.2\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                 1.420e-40\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "n_transit            -0.5690      0.053    -10.787      0.000        -0.672    -0.466\n",
       "wait                  0.0006   9.89e-05      6.234      0.000         0.000     0.001\n",
       "transit_cost          0.0610      0.020      3.053      0.002         0.022     0.100\n",
       "const                -2.6575      0.158    -16.807      0.000        -2.967    -2.348\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop transit_time\n",
    "exog_cols = ['n_transit','wait','transit_cost']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106765\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16031</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16026</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04976</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:12</td>     <th>  Log-Likelihood:    </th> <td> -1711.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1801.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.081e-37</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>              <td>   -0.5659</td> <td>    0.058</td> <td>   -9.758</td> <td> 0.000</td> <td>   -0.680    -0.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walkwait</th>               <td>    0.0007</td> <td>    0.000</td> <td>    6.769</td> <td> 0.000</td> <td>    0.001     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.5787</td> <td>    0.105</td> <td>    5.518</td> <td> 0.000</td> <td>    0.373     0.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_home</th> <td>    0.2435</td> <td>    0.109</td> <td>    2.229</td> <td> 0.026</td> <td>    0.029     0.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -2.8788</td> <td>    0.177</td> <td>  -16.262</td> <td> 0.000</td> <td>   -3.226    -2.532</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16031\n",
       "Model:                        MNLogit   Df Residuals:                    16026\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04976\n",
       "Time:                        11:18:12   Log-Likelihood:                -1711.6\n",
       "converged:                       True   LL-Null:                       -1801.2\n",
       "                                        LLR p-value:                 1.081e-37\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "n_transit                 -0.5659      0.058     -9.758      0.000        -0.680    -0.452\n",
       "walkwait                   0.0007      0.000      6.769      0.000         0.001     0.001\n",
       "start_or_end_near_work     0.5787      0.105      5.518      0.000         0.373     0.784\n",
       "start_or_end_near_home     0.2435      0.109      2.229      0.026         0.029     0.458\n",
       "const                     -2.8788      0.177    -16.262      0.000        -3.226    -2.532\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop uber cost\n",
    "exog_cols = ['n_transit','walkwait','start_or_end_near_work','start_or_end_near_home']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.111069\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16259</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16257</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th> <td>0.006561</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:12</td>     <th>  Log-Likelihood:    </th> <td> -1805.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1817.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.040e-06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.5078</td> <td>    0.103</td> <td>    4.912</td> <td> 0.000</td> <td>    0.305     0.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -3.9413</td> <td>    0.072</td> <td>  -54.370</td> <td> 0.000</td> <td>   -4.083    -3.799</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16259\n",
       "Model:                        MNLogit   Df Residuals:                    16257\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                0.006561\n",
       "Time:                        11:18:12   Log-Likelihood:                -1805.9\n",
       "converged:                       True   LL-Null:                       -1817.8\n",
       "                                        LLR p-value:                 1.040e-06\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "start_or_end_near_work     0.5078      0.103      4.912      0.000         0.305     0.710\n",
       "const                     -3.9413      0.072    -54.370      0.000        -4.083    -3.799\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['start_or_end_near_work']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106675\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16026</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16014</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>    11</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.05079</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:12</td>     <th>  Log-Likelihood:    </th> <td> -1709.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1801.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.514e-33</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>              <td>   -0.5445</td> <td>    0.060</td> <td>   -9.142</td> <td> 0.000</td> <td>   -0.661    -0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_arr</th>                  <td>   -0.0417</td> <td>    0.221</td> <td>   -0.188</td> <td> 0.851</td> <td>   -0.475     0.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walk</th>                   <td>   -0.0004</td> <td>    0.015</td> <td>   -0.024</td> <td> 0.981</td> <td>   -0.029     0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>                   <td>    0.0006</td> <td>    0.000</td> <td>    1.884</td> <td> 0.060</td> <td>-2.34e-05     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>headway</th>                <td>    0.0002</td> <td>    0.005</td> <td>    0.032</td> <td> 0.974</td> <td>   -0.010     0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>           <td>   -0.0029</td> <td>    0.005</td> <td>   -0.602</td> <td> 0.547</td> <td>   -0.013     0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>        <td>   -0.0124</td> <td>    0.011</td> <td>   -1.182</td> <td> 0.237</td> <td>   -0.033     0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>           <td>    0.0152</td> <td>    0.044</td> <td>    0.345</td> <td> 0.730</td> <td>   -0.071     0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>        <td>    0.0208</td> <td>    0.015</td> <td>    1.358</td> <td> 0.174</td> <td>   -0.009     0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_home</th> <td>    0.2658</td> <td>    0.111</td> <td>    2.387</td> <td> 0.017</td> <td>    0.048     0.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.6085</td> <td>    0.108</td> <td>    5.638</td> <td> 0.000</td> <td>    0.397     0.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -2.8679</td> <td>    0.713</td> <td>   -4.023</td> <td> 0.000</td> <td>   -4.265    -1.471</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16026\n",
       "Model:                        MNLogit   Df Residuals:                    16014\n",
       "Method:                           MLE   Df Model:                           11\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.05079\n",
       "Time:                        11:18:12   Log-Likelihood:                -1709.6\n",
       "converged:                       True   LL-Null:                       -1801.1\n",
       "                                        LLR p-value:                 2.514e-33\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "n_transit                 -0.5445      0.060     -9.142      0.000        -0.661    -0.428\n",
       "n_arr                     -0.0417      0.221     -0.188      0.851        -0.475     0.392\n",
       "walk                      -0.0004      0.015     -0.024      0.981        -0.029     0.028\n",
       "wait                       0.0006      0.000      1.884      0.060     -2.34e-05     0.001\n",
       "headway                    0.0002      0.005      0.032      0.974        -0.010     0.010\n",
       "transit_time              -0.0029      0.005     -0.602      0.547        -0.013     0.007\n",
       "uber_basic_time           -0.0124      0.011     -1.182      0.237        -0.033     0.008\n",
       "transit_cost               0.0152      0.044      0.345      0.730        -0.071     0.102\n",
       "uber_basic_cost            0.0208      0.015      1.358      0.174        -0.009     0.051\n",
       "start_or_end_near_home     0.2658      0.111      2.387      0.017         0.048     0.484\n",
       "start_or_end_near_work     0.6085      0.108      5.638      0.000         0.397     0.820\n",
       "const                     -2.8679      0.713     -4.023      0.000        -4.265    -1.471\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# try everything except late_night\n",
    "exog_cols = ['n_transit','n_arr','walk','wait','headway','transit_time','uber_basic_time','transit_cost','uber_basic_cost','start_or_end_near_home','start_or_end_near_work']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106683\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16026</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16017</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     8</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.05073</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:13</td>     <th>  Log-Likelihood:    </th> <td> -1709.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1801.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.754e-35</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>              <td>   -0.5440</td> <td>    0.059</td> <td>   -9.163</td> <td> 0.000</td> <td>   -0.660    -0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>                   <td>    0.0007</td> <td>    0.000</td> <td>    6.526</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_time</th>           <td>   -0.0030</td> <td>    0.005</td> <td>   -0.643</td> <td> 0.520</td> <td>   -0.012     0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>        <td>   -0.0129</td> <td>    0.010</td> <td>   -1.234</td> <td> 0.217</td> <td>   -0.033     0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>transit_cost</th>           <td>    0.0165</td> <td>    0.044</td> <td>    0.373</td> <td> 0.709</td> <td>   -0.070     0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>        <td>    0.0213</td> <td>    0.015</td> <td>    1.391</td> <td> 0.164</td> <td>   -0.009     0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_home</th> <td>    0.2676</td> <td>    0.111</td> <td>    2.406</td> <td> 0.016</td> <td>    0.050     0.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.6074</td> <td>    0.108</td> <td>    5.631</td> <td> 0.000</td> <td>    0.396     0.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -2.9769</td> <td>    0.204</td> <td>  -14.601</td> <td> 0.000</td> <td>   -3.377    -2.577</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16026\n",
       "Model:                        MNLogit   Df Residuals:                    16017\n",
       "Method:                           MLE   Df Model:                            8\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.05073\n",
       "Time:                        11:18:13   Log-Likelihood:                -1709.7\n",
       "converged:                       True   LL-Null:                       -1801.1\n",
       "                                        LLR p-value:                 2.754e-35\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "n_transit                 -0.5440      0.059     -9.163      0.000        -0.660    -0.428\n",
       "wait                       0.0007      0.000      6.526      0.000         0.000     0.001\n",
       "transit_time              -0.0030      0.005     -0.643      0.520        -0.012     0.006\n",
       "uber_basic_time           -0.0129      0.010     -1.234      0.217        -0.033     0.008\n",
       "transit_cost               0.0165      0.044      0.373      0.709        -0.070     0.103\n",
       "uber_basic_cost            0.0213      0.015      1.391      0.164        -0.009     0.051\n",
       "start_or_end_near_home     0.2676      0.111      2.406      0.016         0.050     0.486\n",
       "start_or_end_near_work     0.6074      0.108      5.631      0.000         0.396     0.819\n",
       "const                     -2.9769      0.204    -14.601      0.000        -3.377    -2.577\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop walk, headway, n_arr\n",
    "exog_cols = ['n_transit','wait','transit_time','uber_basic_time','transit_cost','uber_basic_cost','start_or_end_near_home','start_or_end_near_work']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106698\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16026</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.05059</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:13</td>     <th>  Log-Likelihood:    </th> <td> -1709.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1801.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.137e-36</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>              <td>   -0.5459</td> <td>    0.059</td> <td>   -9.240</td> <td> 0.000</td> <td>   -0.662    -0.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>                   <td>    0.0007</td> <td>    0.000</td> <td>    6.551</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>        <td>   -0.0135</td> <td>    0.010</td> <td>   -1.321</td> <td> 0.186</td> <td>   -0.033     0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>        <td>    0.0196</td> <td>    0.011</td> <td>    1.713</td> <td> 0.087</td> <td>   -0.003     0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_home</th> <td>    0.2541</td> <td>    0.109</td> <td>    2.322</td> <td> 0.020</td> <td>    0.040     0.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.6009</td> <td>    0.107</td> <td>    5.596</td> <td> 0.000</td> <td>    0.390     0.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -2.9656</td> <td>    0.197</td> <td>  -15.031</td> <td> 0.000</td> <td>   -3.352    -2.579</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16026\n",
       "Model:                        MNLogit   Df Residuals:                    16019\n",
       "Method:                           MLE   Df Model:                            6\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.05059\n",
       "Time:                        11:18:13   Log-Likelihood:                -1709.9\n",
       "converged:                       True   LL-Null:                       -1801.1\n",
       "                                        LLR p-value:                 1.137e-36\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "n_transit                 -0.5459      0.059     -9.240      0.000        -0.662    -0.430\n",
       "wait                       0.0007      0.000      6.551      0.000         0.000     0.001\n",
       "uber_basic_time           -0.0135      0.010     -1.321      0.186        -0.033     0.007\n",
       "uber_basic_cost            0.0196      0.011      1.713      0.087        -0.003     0.042\n",
       "start_or_end_near_home     0.2541      0.109      2.322      0.020         0.040     0.469\n",
       "start_or_end_near_work     0.6009      0.107      5.596      0.000         0.390     0.811\n",
       "const                     -2.9656      0.197    -15.031      0.000        -3.352    -2.579\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop transit_cost, transit_time\n",
    "exog_cols = ['n_transit','wait','uber_basic_time','uber_basic_cost','start_or_end_near_home','start_or_end_near_work']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106699\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16026</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     6</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.05059</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:13</td>     <th>  Log-Likelihood:    </th> <td> -1710.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1801.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.150e-36</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>              <td>   -0.5462</td> <td>    0.059</td> <td>   -9.245</td> <td> 0.000</td> <td>   -0.662    -0.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walkwait</th>               <td>    0.0007</td> <td>    0.000</td> <td>    6.549</td> <td> 0.000</td> <td>    0.000     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_time</th>        <td>   -0.0134</td> <td>    0.010</td> <td>   -1.319</td> <td> 0.187</td> <td>   -0.033     0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>uber_basic_cost</th>        <td>    0.0195</td> <td>    0.011</td> <td>    1.707</td> <td> 0.088</td> <td>   -0.003     0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_home</th> <td>    0.2537</td> <td>    0.109</td> <td>    2.318</td> <td> 0.020</td> <td>    0.039     0.468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.6007</td> <td>    0.107</td> <td>    5.594</td> <td> 0.000</td> <td>    0.390     0.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -2.9659</td> <td>    0.197</td> <td>  -15.030</td> <td> 0.000</td> <td>   -3.353    -2.579</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16026\n",
       "Model:                        MNLogit   Df Residuals:                    16019\n",
       "Method:                           MLE   Df Model:                            6\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.05059\n",
       "Time:                        11:18:13   Log-Likelihood:                -1710.0\n",
       "converged:                       True   LL-Null:                       -1801.1\n",
       "                                        LLR p-value:                 1.150e-36\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "n_transit                 -0.5462      0.059     -9.245      0.000        -0.662    -0.430\n",
       "walkwait                   0.0007      0.000      6.549      0.000         0.000     0.001\n",
       "uber_basic_time           -0.0134      0.010     -1.319      0.187        -0.033     0.007\n",
       "uber_basic_cost            0.0195      0.011      1.707      0.088        -0.003     0.042\n",
       "start_or_end_near_home     0.2537      0.109      2.318      0.020         0.039     0.468\n",
       "start_or_end_near_work     0.6007      0.107      5.594      0.000         0.390     0.811\n",
       "const                     -2.9659      0.197    -15.030      0.000        -3.353    -2.579\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# wait -> walkwait\n",
    "exog_cols = ['n_transit','walkwait','uber_basic_time','uber_basic_cost','start_or_end_near_home','start_or_end_near_work']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106564\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16031</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16025</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     5</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.05155</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:13</td>     <th>  Log-Likelihood:    </th> <td> -1708.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1801.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>3.235e-38</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>              <td>   -0.5596</td> <td>    0.058</td> <td>   -9.654</td> <td> 0.000</td> <td>   -0.673    -0.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walkwait</th>               <td>    0.0007</td> <td>    0.000</td> <td>    6.733</td> <td> 0.000</td> <td>    0.001     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.5979</td> <td>    0.105</td> <td>    5.678</td> <td> 0.000</td> <td>    0.392     0.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_home</th> <td>    0.2262</td> <td>    0.110</td> <td>    2.065</td> <td> 0.039</td> <td>    0.011     0.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>late_night</th>             <td>    0.5189</td> <td>    0.192</td> <td>    2.703</td> <td> 0.007</td> <td>    0.143     0.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -2.9255</td> <td>    0.178</td> <td>  -16.419</td> <td> 0.000</td> <td>   -3.275    -2.576</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16031\n",
       "Model:                        MNLogit   Df Residuals:                    16025\n",
       "Method:                           MLE   Df Model:                            5\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.05155\n",
       "Time:                        11:18:13   Log-Likelihood:                -1708.3\n",
       "converged:                       True   LL-Null:                       -1801.2\n",
       "                                        LLR p-value:                 3.235e-38\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "n_transit                 -0.5596      0.058     -9.654      0.000        -0.673    -0.446\n",
       "walkwait                   0.0007      0.000      6.733      0.000         0.001     0.001\n",
       "start_or_end_near_work     0.5979      0.105      5.678      0.000         0.392     0.804\n",
       "start_or_end_near_home     0.2262      0.110      2.065      0.039         0.011     0.441\n",
       "late_night                 0.5189      0.192      2.703      0.007         0.143     0.895\n",
       "const                     -2.9255      0.178    -16.419      0.000        -3.275    -2.576\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop uber_basic_time, uber_basic_cost\n",
    "exog_cols = ['n_transit','walkwait','start_or_end_near_work','start_or_end_near_home','late_night']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.106457\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 16259</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 16255</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     3</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.04781</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:13</td>     <th>  Log-Likelihood:    </th> <td> -1730.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -1817.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>1.904e-37</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <th>chosen_mode3=uber</th>      <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>n_transit</th>              <td>   -0.5616</td> <td>    0.058</td> <td>   -9.735</td> <td> 0.000</td> <td>   -0.675    -0.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wait</th>                   <td>    0.0007</td> <td>    0.000</td> <td>    6.799</td> <td> 0.000</td> <td>    0.001     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>start_or_end_near_work</th> <td>    0.5626</td> <td>    0.104</td> <td>    5.394</td> <td> 0.000</td> <td>    0.358     0.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                  <td>   -2.7365</td> <td>    0.162</td> <td>  -16.896</td> <td> 0.000</td> <td>   -3.054    -2.419</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                16259\n",
       "Model:                        MNLogit   Df Residuals:                    16255\n",
       "Method:                           MLE   Df Model:                            3\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.04781\n",
       "Time:                        11:18:13   Log-Likelihood:                -1730.9\n",
       "converged:                       True   LL-Null:                       -1817.8\n",
       "                                        LLR p-value:                 1.904e-37\n",
       "==========================================================================================\n",
       "     chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "------------------------------------------------------------------------------------------\n",
       "n_transit                 -0.5616      0.058     -9.735      0.000        -0.675    -0.449\n",
       "wait                       0.0007      0.000      6.799      0.000         0.001     0.001\n",
       "start_or_end_near_work     0.5626      0.104      5.394      0.000         0.358     0.767\n",
       "const                     -2.7365      0.162    -16.896      0.000        -3.054    -2.419\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop uber_basic_time, uber_basic_cost\n",
    "exog_cols = ['n_transit','wait','start_or_end_near_work']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.109119\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19644</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     1</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th>  <td>0.01227</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:14</td>     <th>  Log-Likelihood:    </th> <td> -2143.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th> <td>2.892e-13</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>walkwait</th>          <td>    0.0007</td> <td> 9.73e-05</td> <td>    7.514</td> <td> 0.000</td> <td>    0.001     0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -3.9842</td> <td>    0.062</td> <td>  -64.002</td> <td> 0.000</td> <td>   -4.106    -3.862</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19644\n",
       "Method:                           MLE   Df Model:                            1\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                 0.01227\n",
       "Time:                        11:18:14   Log-Likelihood:                -2143.8\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                 2.892e-13\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "walkwait              0.0007   9.73e-05      7.514      0.000         0.001     0.001\n",
       "const                -3.9842      0.062    -64.002      0.000        -4.106    -3.862\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['walkwait']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=100)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.110271\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19635</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>    10</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th> <td>0.001844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:14</td>     <th>  Log-Likelihood:    </th> <td> -2166.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.6285</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>female</th>            <td>    0.0948</td> <td>    0.325</td> <td>    0.292</td> <td> 0.770</td> <td>   -0.541     0.731</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_18-24</th>     <td>    0.0114</td> <td>    0.499</td> <td>    0.023</td> <td> 0.982</td> <td>   -0.967     0.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_25-34</th>     <td>    0.7462</td> <td>    0.392</td> <td>    1.904</td> <td> 0.057</td> <td>   -0.022     1.514</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_35-44</th>     <td>    0.6662</td> <td>    0.500</td> <td>    1.332</td> <td> 0.183</td> <td>   -0.314     1.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_45-54</th>     <td>    0.0199</td> <td>    0.751</td> <td>    0.026</td> <td> 0.979</td> <td>   -1.451     1.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_55-64</th>     <td>    1.2127</td> <td>    0.629</td> <td>    1.927</td> <td> 0.054</td> <td>   -0.021     2.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_65+</th>       <td>    1.0979</td> <td>    1.071</td> <td>    1.025</td> <td> 0.305</td> <td>   -1.002     3.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hhinc_cat_$25-$50</th> <td>   -0.4863</td> <td>    0.428</td> <td>   -1.136</td> <td> 0.256</td> <td>   -1.325     0.353</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hhinc_cat_$50-$75</th> <td>   -0.5806</td> <td>    0.488</td> <td>   -1.190</td> <td> 0.234</td> <td>   -1.537     0.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hhinc_cat_$75+</th>    <td>   -0.6991</td> <td>    0.432</td> <td>   -1.617</td> <td> 0.106</td> <td>   -1.546     0.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -3.7533</td> <td>    0.050</td> <td>  -75.670</td> <td> 0.000</td> <td>   -3.850    -3.656</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19635\n",
       "Method:                           MLE   Df Model:                           10\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                0.001844\n",
       "Time:                        11:18:14   Log-Likelihood:                -2166.4\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                    0.6285\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "female                0.0948      0.325      0.292      0.770        -0.541     0.731\n",
       "age_cat_18-24         0.0114      0.499      0.023      0.982        -0.967     0.990\n",
       "age_cat_25-34         0.7462      0.392      1.904      0.057        -0.022     1.514\n",
       "age_cat_35-44         0.6662      0.500      1.332      0.183        -0.314     1.647\n",
       "age_cat_45-54         0.0199      0.751      0.026      0.979        -1.451     1.491\n",
       "age_cat_55-64         1.2127      0.629      1.927      0.054        -0.021     2.446\n",
       "age_cat_65+           1.0979      1.071      1.025      0.305        -1.002     3.197\n",
       "hhinc_cat_$25-$50    -0.4863      0.428     -1.136      0.256        -1.325     0.353\n",
       "hhinc_cat_$50-$75    -0.5806      0.488     -1.190      0.234        -1.537     0.376\n",
       "hhinc_cat_$75+       -0.6991      0.432     -1.617      0.106        -1.546     0.148\n",
       "const                -3.7533      0.050    -75.670      0.000        -3.850    -3.656\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['female'] + age_dummy_cols[:-1] + inc_dummy_cols[:-1]\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=500)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.110354\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19641</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th> <td>0.001094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:14</td>     <th>  Log-Likelihood:    </th> <td> -2168.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.3142</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>female</th>            <td>    0.0706</td> <td>    0.276</td> <td>    0.255</td> <td> 0.798</td> <td>   -0.471     0.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_25-34</th>     <td>    0.3871</td> <td>    0.277</td> <td>    1.397</td> <td> 0.162</td> <td>   -0.156     0.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_55-64</th>     <td>    0.8686</td> <td>    0.558</td> <td>    1.557</td> <td> 0.120</td> <td>   -0.225     1.962</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hhinc_cat_$75+</th>    <td>   -0.3055</td> <td>    0.342</td> <td>   -0.893</td> <td> 0.372</td> <td>   -0.976     0.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -3.7535</td> <td>    0.049</td> <td>  -76.623</td> <td> 0.000</td> <td>   -3.850    -3.657</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19641\n",
       "Method:                           MLE   Df Model:                            4\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                0.001094\n",
       "Time:                        11:18:14   Log-Likelihood:                -2168.0\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                    0.3142\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "female                0.0706      0.276      0.255      0.798        -0.471     0.612\n",
       "age_cat_25-34         0.3871      0.277      1.397      0.162        -0.156     0.930\n",
       "age_cat_55-64         0.8686      0.558      1.557      0.120        -0.225     1.962\n",
       "hhinc_cat_$75+       -0.3055      0.342     -0.893      0.372        -0.976     0.365\n",
       "const                -3.7535      0.049    -76.623      0.000        -3.850    -3.657\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['female','age_cat_25-34','age_cat_55-64','hhinc_cat_$75+']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=500)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.110356\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19642</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th> <td>0.001079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:14</td>     <th>  Log-Likelihood:    </th> <td> -2168.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.1965</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_25-34</th>     <td>    0.4194</td> <td>    0.246</td> <td>    1.704</td> <td> 0.088</td> <td>   -0.063     0.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_55-64</th>     <td>    0.9167</td> <td>    0.525</td> <td>    1.747</td> <td> 0.081</td> <td>   -0.112     1.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>hhinc_cat_$75+</th>    <td>   -0.2939</td> <td>    0.339</td> <td>   -0.866</td> <td> 0.387</td> <td>   -0.959     0.371</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -3.7524</td> <td>    0.049</td> <td>  -76.947</td> <td> 0.000</td> <td>   -3.848    -3.657</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19642\n",
       "Method:                           MLE   Df Model:                            3\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:                0.001079\n",
       "Time:                        11:18:14   Log-Likelihood:                -2168.1\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                    0.1965\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "age_cat_25-34         0.4194      0.246      1.704      0.088        -0.063     0.902\n",
       "age_cat_55-64         0.9167      0.525      1.747      0.081        -0.112     1.945\n",
       "hhinc_cat_$75+       -0.2939      0.339     -0.866      0.387        -0.959     0.371\n",
       "const                -3.7524      0.049    -76.947      0.000        -3.848    -3.657\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['age_cat_25-34','age_cat_55-64','hhinc_cat_$75+']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=500)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.110376\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>MNLogit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>chosen_mode3</td>   <th>  No. Observations:  </th>  <td> 19646</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>              <td>MNLogit</td>     <th>  Df Residuals:      </th>  <td> 19643</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>MLE</td>       <th>  Df Model:          </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Jul 2016</td> <th>  Pseudo R-squ.:     </th> <td>0.0008952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>11:18:15</td>     <th>  Log-Likelihood:    </th> <td> -2168.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>           <td>True</td>       <th>  LL-Null:           </th> <td> -2170.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th> </th>                      <td> </td>        <th>  LLR p-value:       </th>  <td>0.1433</td>  \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>chosen_mode3=uber</th>    <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th> <th>[95.0% Conf. Int.]</th> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_25-34</th>     <td>    0.3291</td> <td>    0.227</td> <td>    1.450</td> <td> 0.147</td> <td>   -0.116     0.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age_cat_55-64</th>     <td>    0.8242</td> <td>    0.515</td> <td>    1.599</td> <td> 0.110</td> <td>   -0.186     1.834</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>             <td>   -3.7554</td> <td>    0.049</td> <td>  -77.157</td> <td> 0.000</td> <td>   -3.851    -3.660</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                          MNLogit Regression Results                          \n",
       "==============================================================================\n",
       "Dep. Variable:           chosen_mode3   No. Observations:                19646\n",
       "Model:                        MNLogit   Df Residuals:                    19643\n",
       "Method:                           MLE   Df Model:                            2\n",
       "Date:                Wed, 20 Jul 2016   Pseudo R-squ.:               0.0008952\n",
       "Time:                        11:18:15   Log-Likelihood:                -2168.5\n",
       "converged:                       True   LL-Null:                       -2170.4\n",
       "                                        LLR p-value:                    0.1433\n",
       "=====================================================================================\n",
       "chosen_mode3=uber       coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
       "-------------------------------------------------------------------------------------\n",
       "age_cat_25-34         0.3291      0.227      1.450      0.147        -0.116     0.774\n",
       "age_cat_55-64         0.8242      0.515      1.599      0.110        -0.186     1.834\n",
       "const                -3.7554      0.049    -77.157      0.000        -3.851    -3.660\n",
       "=====================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_cols = ['age_cat_25-34','age_cat_55-64']\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=500)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration limit exceeded    (Exit mode 9)\n",
      "            Current function value: 0.0296224941782\n",
      "            Iterations: 51\n",
      "            Function evaluations: 63\n",
      "            Gradient evaluations: 51\n"
     ]
    },
    {
     "ename": "LinAlgError",
     "evalue": "Singular matrix",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-83-397053cea649>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[0ms2_mdl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMNLogit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0ms2_fit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ms2_mdl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_regularized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0ms2_fit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27_64\\lib\\site-packages\\statsmodels-0.6.1-py2.7-win-amd64.egg\\statsmodels\\discrete\\discrete_model.pyc\u001b[0m in \u001b[0;36mfit_regularized\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, alpha, trim_mode, auto_trim_tol, size_trim_tol, qc_tol, **kwargs)\u001b[0m\n\u001b[0;32m    603\u001b[0m                 \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    604\u001b[0m                 \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrim_mode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrim_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mauto_trim_tol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mauto_trim_tol\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 605\u001b[1;33m                 size_trim_tol=size_trim_tol, qc_tol=qc_tol, **kwargs)\n\u001b[0m\u001b[0;32m    606\u001b[0m         \u001b[0mmnfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmnfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mK\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'F'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    607\u001b[0m         \u001b[0mmnfit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mL1MultinomialResults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmnfit\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27_64\\lib\\site-packages\\statsmodels-0.6.1-py2.7-win-amd64.egg\\statsmodels\\discrete\\discrete_model.pyc\u001b[0m in \u001b[0;36mfit_regularized\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, callback, alpha, trim_mode, auto_trim_tol, size_trim_tol, qc_tol, qc_verbose, **kwargs)\u001b[0m\n\u001b[0;32m    354\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmaxiter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfull_output\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    355\u001b[0m                 \u001b[0mdisp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdisp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcallback\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextra_fit_funcs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mextra_fit_funcs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 356\u001b[1;33m                 cov_params_func=cov_params_func, **kwargs)\n\u001b[0m\u001b[0;32m    357\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    358\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmlefit\u001b[0m \u001b[1;31m# up to subclasses to wrap results\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27_64\\lib\\site-packages\\statsmodels-0.6.1-py2.7-win-amd64.egg\\statsmodels\\base\\model.pyc\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)\u001b[0m\n\u001b[0;32m    430\u001b[0m             \u001b[0mxopt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mretvals\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcov_params_func\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m             \u001b[0mHinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcov_params_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxopt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mretvals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'newton'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfull_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m             \u001b[0mHinv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mretvals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Hessian'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27_64\\lib\\site-packages\\statsmodels-0.6.1-py2.7-win-amd64.egg\\statsmodels\\discrete\\discrete_model.pyc\u001b[0m in \u001b[0;36mcov_params_func_l1\u001b[1;34m(self, likelihood_model, xopt, retvals)\u001b[0m\n\u001b[0;32m    374\u001b[0m             \u001b[0mH_restricted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mH\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnz_idx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnz_idx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    375\u001b[0m             \u001b[1;31m# Covariance estimate for the nonzero params\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 376\u001b[1;33m             \u001b[0mH_restricted_inv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mH_restricted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    377\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    378\u001b[0m             \u001b[0mH_restricted_inv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27_64\\lib\\site-packages\\numpy\\linalg\\linalg.pyc\u001b[0m in \u001b[0;36minv\u001b[1;34m(a)\u001b[0m\n\u001b[0;32m    518\u001b[0m     \u001b[0msignature\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'D->D'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'd->d'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m     \u001b[0mextobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_linalg_error_extobj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 520\u001b[1;33m     \u001b[0mainv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_umath_linalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msignature\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mextobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    521\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mainv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult_t\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    522\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Python27_64\\lib\\site-packages\\numpy\\linalg\\linalg.pyc\u001b[0m in \u001b[0;36m_raise_linalgerror_singular\u001b[1;34m(err, flag)\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_raise_linalgerror_singular\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 90\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Singular matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     91\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     92\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_raise_linalgerror_nonposdef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mLinAlgError\u001b[0m: Singular matrix"
     ]
    }
   ],
   "source": [
    "exog_cols = ['n_transit','walkwait','transit_time'] + user_dummy_cols[1:]\n",
    "sub3 = subset2[pd.notnull(subset2['master_user_id'])]\n",
    "defn = sub3.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit_regularized(maxiter=50,)\n",
    "s2_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "exog_cols = user_dummy_cols[1:]\n",
    "defn = subset2.dropna(subset=exog_cols)\n",
    "y = defn['chosen_mode3']\n",
    "x = defn.loc[:,exog_cols]\n",
    "x['const'] = 1\n",
    "\n",
    "s2_mdl = st.MNLogit(y, x)\n",
    "s2_fit = s2_mdl.fit(maxiter=500)\n",
    "s2_fit.summary()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}