weeklyAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A NYC Taxi data cleaning and model building pipeline to forecast the trip time from A2B in NYC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os as os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats, integrate\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "from statsmodels.distributions.empirical_distribution import ECDF\n",
    "import datetime as dt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "\n",
    "plt.rcParams['image.cmap'] = 'blue'\n",
    "\n",
    "#sns.set_context('notebook',font_scale=2)\n",
    "sns.set_style(\"whitegrid\")\n",
    "% matplotlib inline\n",
    "labelsize = 22\n",
    "mpl.rcParams.update({'font.size': labelsize})\n",
    "mpl.rcParams.update({'figure.figsize': (20,10)})\n",
    "mpl.rcParams.update({'axes.titlesize': 'large'})\n",
    "mpl.rcParams.update({'axes.labelsize': 'large'})\n",
    "mpl.rcParams.update({'xtick.labelsize': labelsize})\n",
    "mpl.rcParams.update({'ytick.labelsize': labelsize})\n",
    "# mpl.rcParams.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-05 - Citi Bike trip data.csv\r\n",
      "bike_20140505_with_dist_and_avg_velo.csv\r\n",
      "bike_20140505_with_dist.csv\r\n",
      "bike_20140505_with_dist_velo.csv\r\n",
      "bike_oneweekfrom20140505.csv\r\n",
      "data_jan13.csv\r\n",
      "README.txt\r\n",
      "Taxi_from_2013-05-06_to_2013-05-13.csv\r\n",
      "Taxi_from_2013-05-06_to_2013-05-13_testset.csv\r\n",
      "Taxi_from_2013-05-06_to_2013-05-13testset.csv\r\n",
      "Taxi_from_2013-05-06_to_2013-05-13_xtrain.csv\r\n",
      "Taxi_from_2013-05-06_to_2013-05-13_ytrain.csv\r\n",
      "taxi_oneweekfrom20130107.csv\r\n",
      "taxi_oneweekfrom20130506.csv\r\n",
      "taxi_oneweekfrom20140505.csv\r\n",
      "taxi_tree_test_X_20130506-12.csv\r\n",
      "taxi_tree_test_X.csv\r\n",
      "taxi_tree_test_Xy_20130506-12.csv\r\n",
      "taxi_tree_test_Xy.csv\r\n",
      "taxi_tree_test_Xy_sample.csv\r\n",
      "taxi_tree_test_y_20130506-12.csv\r\n",
      "taxi_tree_test_y.csv\r\n",
      "_testset.csv\r\n",
      "yellow_tripdata_2013-05.csv\r\n",
      "yellow_tripdata_2014-05.csv\r\n"
     ]
    }
   ],
   "source": [
    "!cd data && ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the bash =)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/Taxi_from_2013-05-06_to_2013-05-13.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3534632 entries, 0 to 3534631\n",
      "Data columns (total 19 columns):\n",
      "vendor_id             object\n",
      "pickup_datetime       object\n",
      "dropoff_datetime      object\n",
      "passenger_count       int64\n",
      "trip_distance         float64\n",
      "pickup_longitude      float64\n",
      "pickup_latitude       float64\n",
      "rate_code             int64\n",
      "store_and_fwd_flag    object\n",
      "dropoff_longitude     float64\n",
      "dropoff_latitude      float64\n",
      "payment_type          object\n",
      "fare_amount           float64\n",
      "surcharge             float64\n",
      "mta_tax               float64\n",
      "tip_amount            float64\n",
      "tolls_amount          float64\n",
      "total_amount          float64\n",
      "trip_time             object\n",
      "dtypes: float64(11), int64(2), object(6)\n",
      "memory usage: 539.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So parsing does not work, do it manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['pickup_datetime'] =pd.to_datetime(data['pickup_datetime'], format = '%Y-%m-%d %H:%M:%S')\n",
    "data['dropoff_datetime'] =pd.to_datetime(data['dropoff_datetime'], format = '%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": 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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>1.708266</td>\n",
       "      <td>1.380270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_distance</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>2.875593</td>\n",
       "      <td>3.343748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_longitude</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>-72.621180</td>\n",
       "      <td>10.142067</td>\n",
       "      <td>-1625.033333</td>\n",
       "      <td>-73.992272</td>\n",
       "      <td>-73.981786</td>\n",
       "      <td>-73.966735</td>\n",
       "      <td>40.867882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_latitude</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>39.981485</td>\n",
       "      <td>6.522288</td>\n",
       "      <td>-3113.281477</td>\n",
       "      <td>40.734916</td>\n",
       "      <td>40.752702</td>\n",
       "      <td>40.767551</td>\n",
       "      <td>2047.454950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_code</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>1.032870</td>\n",
       "      <td>0.345708</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>210.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <td>3534618.0</td>\n",
       "      <td>-72.575137</td>\n",
       "      <td>10.450838</td>\n",
       "      <td>-2038.393545</td>\n",
       "      <td>-73.991547</td>\n",
       "      <td>-73.980110</td>\n",
       "      <td>-73.963510</td>\n",
       "      <td>2386.995232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <td>3534618.0</td>\n",
       "      <td>39.952355</td>\n",
       "      <td>7.665360</td>\n",
       "      <td>-3114.304505</td>\n",
       "      <td>40.733845</td>\n",
       "      <td>40.753077</td>\n",
       "      <td>40.768337</td>\n",
       "      <td>3210.392762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>12.491013</td>\n",
       "      <td>10.078369</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surcharge</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>0.320163</td>\n",
       "      <td>0.362772</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mta_tax</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>0.498208</td>\n",
       "      <td>0.029876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip_amount</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>1.366541</td>\n",
       "      <td>2.157769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>155.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tolls_amount</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>0.260422</td>\n",
       "      <td>1.221513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amount</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>14.936348</td>\n",
       "      <td>12.109785</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std          min        25%  \\\n",
       "passenger_count    3534632.0   1.708266   1.380270     0.000000   1.000000   \n",
       "trip_distance      3534632.0   2.875593   3.343748     0.000000   1.050000   \n",
       "pickup_longitude   3534632.0 -72.621180  10.142067 -1625.033333 -73.992272   \n",
       "pickup_latitude    3534632.0  39.981485   6.522288 -3113.281477  40.734916   \n",
       "rate_code          3534632.0   1.032870   0.345708     0.000000   1.000000   \n",
       "dropoff_longitude  3534618.0 -72.575137  10.450838 -2038.393545 -73.991547   \n",
       "dropoff_latitude   3534618.0  39.952355   7.665360 -3114.304505  40.733845   \n",
       "fare_amount        3534632.0  12.491013  10.078369     2.500000   6.500000   \n",
       "surcharge          3534632.0   0.320163   0.362772     0.000000   0.000000   \n",
       "mta_tax            3534632.0   0.498208   0.029876     0.000000   0.500000   \n",
       "tip_amount         3534632.0   1.366541   2.157769     0.000000   0.000000   \n",
       "tolls_amount       3534632.0   0.260422   1.221513     0.000000   0.000000   \n",
       "total_amount       3534632.0  14.936348  12.109785     2.500000   8.000000   \n",
       "\n",
       "                         50%        75%          max  \n",
       "passenger_count     1.000000   2.000000     6.000000  \n",
       "trip_distance       1.800000   3.200000   100.000000  \n",
       "pickup_longitude  -73.981786 -73.966735    40.867882  \n",
       "pickup_latitude    40.752702  40.767551  2047.454950  \n",
       "rate_code           1.000000   1.000000   210.000000  \n",
       "dropoff_longitude -73.980110 -73.963510  2386.995232  \n",
       "dropoff_latitude   40.753077  40.768337  3210.392762  \n",
       "fare_amount         9.500000  14.500000   500.000000  \n",
       "surcharge           0.000000   0.500000     9.500000  \n",
       "mta_tax             0.500000   0.500000     0.500000  \n",
       "tip_amount          1.000000   2.000000   155.500000  \n",
       "tolls_amount        0.000000   0.000000    20.000000  \n",
       "total_amount       11.300000  16.750000   500.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": 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>vendor_id</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>rate_code</th>\n",
       "      <th>store_and_fwd_flag</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>surcharge</th>\n",
       "      <th>mta_tax</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VTS</td>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:10:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2.65</td>\n",
       "      <td>-73.981070</td>\n",
       "      <td>40.766047</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.981362</td>\n",
       "      <td>40.738092</td>\n",
       "      <td>CRD</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.70</td>\n",
       "      <td>0 days 00:10:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VTS</td>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:06:00</td>\n",
       "      <td>5</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-73.986312</td>\n",
       "      <td>40.760487</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-74.005082</td>\n",
       "      <td>40.743372</td>\n",
       "      <td>CSH</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>0 days 00:06:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VTS</td>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:04:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-73.989388</td>\n",
       "      <td>40.731577</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.986102</td>\n",
       "      <td>40.740732</td>\n",
       "      <td>CSH</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.50</td>\n",
       "      <td>0 days 00:04:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VTS</td>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2.59</td>\n",
       "      <td>-73.994590</td>\n",
       "      <td>40.751857</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.963470</td>\n",
       "      <td>40.765912</td>\n",
       "      <td>CSH</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.50</td>\n",
       "      <td>0 days 00:08:00.000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VTS</td>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:29:00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.26</td>\n",
       "      <td>-73.783327</td>\n",
       "      <td>40.648510</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-73.974808</td>\n",
       "      <td>40.624810</td>\n",
       "      <td>CRD</td>\n",
       "      <td>51.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.25</td>\n",
       "      <td>0 days 00:29:00.000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  vendor_id pickup_datetime    dropoff_datetime  passenger_count  \\\n",
       "0       VTS      2013-05-06 2013-05-06 00:10:00                1   \n",
       "1       VTS      2013-05-06 2013-05-06 00:06:00                5   \n",
       "2       VTS      2013-05-06 2013-05-06 00:04:00                1   \n",
       "3       VTS      2013-05-06 2013-05-06 00:08:00                1   \n",
       "4       VTS      2013-05-06 2013-05-06 00:29:00                1   \n",
       "\n",
       "   trip_distance  pickup_longitude  pickup_latitude  rate_code  \\\n",
       "0           2.65        -73.981070        40.766047          1   \n",
       "1           1.86        -73.986312        40.760487          1   \n",
       "2           0.90        -73.989388        40.731577          1   \n",
       "3           2.59        -73.994590        40.751857          1   \n",
       "4          18.26        -73.783327        40.648510          1   \n",
       "\n",
       "  store_and_fwd_flag  dropoff_longitude  dropoff_latitude payment_type  \\\n",
       "0                NaN         -73.981362         40.738092          CRD   \n",
       "1                NaN         -74.005082         40.743372          CSH   \n",
       "2                NaN         -73.986102         40.740732          CSH   \n",
       "3                NaN         -73.963470         40.765912          CSH   \n",
       "4                NaN         -73.974808         40.624810          CRD   \n",
       "\n",
       "   fare_amount  surcharge  mta_tax  tip_amount  tolls_amount  total_amount  \\\n",
       "0         10.5        0.5      0.5        2.20           0.0         13.70   \n",
       "1          7.5        0.5      0.5        0.00           0.0          8.50   \n",
       "2          5.5        0.5      0.5        0.00           0.0          6.50   \n",
       "3          9.5        0.5      0.5        0.00           0.0         10.50   \n",
       "4         51.5        0.5      0.5        6.75           0.0         59.25   \n",
       "\n",
       "                   trip_time  \n",
       "0  0 days 00:10:00.000000000  \n",
       "1  0 days 00:06:00.000000000  \n",
       "2  0 days 00:04:00.000000000  \n",
       "3  0 days 00:08:00.000000000  \n",
       "4  0 days 00:29:00.000000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "payments = data.payment_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some statistics about the payment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRD    0.537525\n",
       "CSH    0.459272\n",
       "NOC    0.002171\n",
       "DIS    0.000694\n",
       "UNK    0.000339\n",
       "Name: payment_type, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "payments/len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So thats the statistic about payments. Remember, there are to tips recorded for cash payment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many trips are affected by tolls?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00     9.535745e-01\n",
       "5.33     4.322657e-02\n",
       "10.25    1.000670e-03\n",
       "2.44     4.175824e-04\n",
       "8.25     3.850472e-04\n",
       "10.66    2.648083e-04\n",
       "4.80     9.760563e-05\n",
       "7.50     8.345989e-05\n",
       "15.58    7.016289e-05\n",
       "15.99    6.591917e-05\n",
       "15.00    3.904225e-05\n",
       "15.25    3.762768e-05\n",
       "13.25    3.564728e-05\n",
       "14.25    3.281813e-05\n",
       "12.25    3.196938e-05\n",
       "13.00    2.998898e-05\n",
       "2.00     2.319902e-05\n",
       "13.58    2.291611e-05\n",
       "12.00    2.291611e-05\n",
       "14.00    1.867238e-05\n",
       "5.00     1.782364e-05\n",
       "16.25    1.612615e-05\n",
       "12.75    1.527740e-05\n",
       "5.30     1.471157e-05\n",
       "16.00    1.357991e-05\n",
       "14.75    1.273117e-05\n",
       "17.00    1.159951e-05\n",
       "14.55    1.103368e-05\n",
       "12.40    1.046785e-05\n",
       "16.50    1.018494e-05\n",
       "             ...     \n",
       "8.24     2.829149e-07\n",
       "5.74     2.829149e-07\n",
       "17.99    2.829149e-07\n",
       "12.58    2.829149e-07\n",
       "16.16    2.829149e-07\n",
       "5.31     2.829149e-07\n",
       "0.06     2.829149e-07\n",
       "13.06    2.829149e-07\n",
       "12.97    2.829149e-07\n",
       "6.80     2.829149e-07\n",
       "4.30     2.829149e-07\n",
       "4.55     2.829149e-07\n",
       "12.22    2.829149e-07\n",
       "17.80    2.829149e-07\n",
       "13.80    2.829149e-07\n",
       "16.58    2.829149e-07\n",
       "11.05    2.829149e-07\n",
       "8.55     2.829149e-07\n",
       "11.22    2.829149e-07\n",
       "13.30    2.829149e-07\n",
       "5.22     2.829149e-07\n",
       "14.80    2.829149e-07\n",
       "11.30    2.829149e-07\n",
       "17.14    2.829149e-07\n",
       "15.39    2.829149e-07\n",
       "12.89    2.829149e-07\n",
       "5.39     2.829149e-07\n",
       "4.14     2.829149e-07\n",
       "14.30    2.829149e-07\n",
       "12.42    2.829149e-07\n",
       "Name: tolls_amount, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tolls_amount.value_counts()/len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So 95% of the drives do not deal with tolls. We will drop the column then."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are not interested in the following features (they do not add any further information):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.drop(['vendor_id', 'rate_code', 'store_and_fwd_flag','payment_type','mta_tax', 'tolls_amount',\n",
    "               'surcharge'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": 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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>1.708266</td>\n",
       "      <td>1.380270</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_distance</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>2.875593</td>\n",
       "      <td>3.343748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_longitude</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>-72.621180</td>\n",
       "      <td>10.142067</td>\n",
       "      <td>-1625.033333</td>\n",
       "      <td>-73.992272</td>\n",
       "      <td>-73.981786</td>\n",
       "      <td>-73.966735</td>\n",
       "      <td>40.867882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_latitude</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>39.981485</td>\n",
       "      <td>6.522288</td>\n",
       "      <td>-3113.281477</td>\n",
       "      <td>40.734916</td>\n",
       "      <td>40.752702</td>\n",
       "      <td>40.767551</td>\n",
       "      <td>2047.454950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <td>3534618.0</td>\n",
       "      <td>-72.575137</td>\n",
       "      <td>10.450838</td>\n",
       "      <td>-2038.393545</td>\n",
       "      <td>-73.991547</td>\n",
       "      <td>-73.980110</td>\n",
       "      <td>-73.963510</td>\n",
       "      <td>2386.995232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <td>3534618.0</td>\n",
       "      <td>39.952355</td>\n",
       "      <td>7.665360</td>\n",
       "      <td>-3114.304505</td>\n",
       "      <td>40.733845</td>\n",
       "      <td>40.753077</td>\n",
       "      <td>40.768337</td>\n",
       "      <td>3210.392762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>12.491013</td>\n",
       "      <td>10.078369</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip_amount</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>1.366541</td>\n",
       "      <td>2.157769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>155.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amount</th>\n",
       "      <td>3534632.0</td>\n",
       "      <td>14.936348</td>\n",
       "      <td>12.109785</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>16.750000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count       mean        std          min        25%  \\\n",
       "passenger_count    3534632.0   1.708266   1.380270     0.000000   1.000000   \n",
       "trip_distance      3534632.0   2.875593   3.343748     0.000000   1.050000   \n",
       "pickup_longitude   3534632.0 -72.621180  10.142067 -1625.033333 -73.992272   \n",
       "pickup_latitude    3534632.0  39.981485   6.522288 -3113.281477  40.734916   \n",
       "dropoff_longitude  3534618.0 -72.575137  10.450838 -2038.393545 -73.991547   \n",
       "dropoff_latitude   3534618.0  39.952355   7.665360 -3114.304505  40.733845   \n",
       "fare_amount        3534632.0  12.491013  10.078369     2.500000   6.500000   \n",
       "tip_amount         3534632.0   1.366541   2.157769     0.000000   0.000000   \n",
       "total_amount       3534632.0  14.936348  12.109785     2.500000   8.000000   \n",
       "\n",
       "                         50%        75%          max  \n",
       "passenger_count     1.000000   2.000000     6.000000  \n",
       "trip_distance       1.800000   3.200000   100.000000  \n",
       "pickup_longitude  -73.981786 -73.966735    40.867882  \n",
       "pickup_latitude    40.752702  40.767551  2047.454950  \n",
       "dropoff_longitude -73.980110 -73.963510  2386.995232  \n",
       "dropoff_latitude   40.753077  40.768337  3210.392762  \n",
       "fare_amount         9.500000  14.500000   500.000000  \n",
       "tip_amount          1.000000   2.000000   155.500000  \n",
       "total_amount       11.300000  16.750000   500.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First, we want to generate the trip_time because this is our target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": 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>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:10:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2.65</td>\n",
       "      <td>-73.981070</td>\n",
       "      <td>40.766047</td>\n",
       "      <td>-73.981362</td>\n",
       "      <td>40.738092</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>13.70</td>\n",
       "      <td>00:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:06:00</td>\n",
       "      <td>5</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-73.986312</td>\n",
       "      <td>40.760487</td>\n",
       "      <td>-74.005082</td>\n",
       "      <td>40.743372</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.50</td>\n",
       "      <td>00:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:04:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-73.989388</td>\n",
       "      <td>40.731577</td>\n",
       "      <td>-73.986102</td>\n",
       "      <td>40.740732</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.50</td>\n",
       "      <td>00:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:08:00</td>\n",
       "      <td>1</td>\n",
       "      <td>2.59</td>\n",
       "      <td>-73.994590</td>\n",
       "      <td>40.751857</td>\n",
       "      <td>-73.963470</td>\n",
       "      <td>40.765912</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.50</td>\n",
       "      <td>00:08:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:29:00</td>\n",
       "      <td>1</td>\n",
       "      <td>18.26</td>\n",
       "      <td>-73.783327</td>\n",
       "      <td>40.648510</td>\n",
       "      <td>-73.974808</td>\n",
       "      <td>40.624810</td>\n",
       "      <td>51.5</td>\n",
       "      <td>6.75</td>\n",
       "      <td>59.25</td>\n",
       "      <td>00:29:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pickup_datetime    dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0      2013-05-06 2013-05-06 00:10:00                1           2.65   \n",
       "1      2013-05-06 2013-05-06 00:06:00                5           1.86   \n",
       "2      2013-05-06 2013-05-06 00:04:00                1           0.90   \n",
       "3      2013-05-06 2013-05-06 00:08:00                1           2.59   \n",
       "4      2013-05-06 2013-05-06 00:29:00                1          18.26   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.981070        40.766047         -73.981362         40.738092   \n",
       "1        -73.986312        40.760487         -74.005082         40.743372   \n",
       "2        -73.989388        40.731577         -73.986102         40.740732   \n",
       "3        -73.994590        40.751857         -73.963470         40.765912   \n",
       "4        -73.783327        40.648510         -73.974808         40.624810   \n",
       "\n",
       "   fare_amount  tip_amount  total_amount  trip_time  \n",
       "0         10.5        2.20         13.70   00:10:00  \n",
       "1          7.5        0.00          8.50   00:06:00  \n",
       "2          5.5        0.00          6.50   00:04:00  \n",
       "3          9.5        0.00         10.50   00:08:00  \n",
       "4         51.5        6.75         59.25   00:29:00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['trip_time']=data.dropoff_datetime-data.pickup_datetime\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3534632 entries, 0 to 3534631\n",
      "Data columns (total 12 columns):\n",
      "pickup_datetime      datetime64[ns]\n",
      "dropoff_datetime     datetime64[ns]\n",
      "passenger_count      int64\n",
      "trip_distance        float64\n",
      "pickup_longitude     float64\n",
      "pickup_latitude      float64\n",
      "dropoff_longitude    float64\n",
      "dropoff_latitude     float64\n",
      "fare_amount          float64\n",
      "tip_amount           float64\n",
      "total_amount         float64\n",
      "trip_time            timedelta64[ns]\n",
      "dtypes: datetime64[ns](2), float64(8), int64(1), timedelta64[ns](1)\n",
      "memory usage: 350.6 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check for missing and false data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_datetime       0\n",
       "dropoff_datetime      0\n",
       "passenger_count       0\n",
       "trip_distance         0\n",
       "pickup_longitude      0\n",
       "pickup_latitude       0\n",
       "dropoff_longitude    14\n",
       "dropoff_latitude     14\n",
       "fare_amount           0\n",
       "tip_amount            0\n",
       "total_amount          0\n",
       "trip_time             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there is not that much data missing. That's quite surprising, maybe it's wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_datetime            0\n",
       "dropoff_datetime           0\n",
       "passenger_count           31\n",
       "trip_distance          39596\n",
       "pickup_longitude       61811\n",
       "pickup_latitude        61403\n",
       "dropoff_longitude      63932\n",
       "dropoff_latitude       63565\n",
       "fare_amount                0\n",
       "tip_amount           1690455\n",
       "total_amount               0\n",
       "trip_time              10653\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data==0).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have many zeros in the data. How much percent?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_datetime      0.000000\n",
       "dropoff_datetime     0.000000\n",
       "passenger_count      0.000009\n",
       "trip_distance        0.011202\n",
       "pickup_longitude     0.017487\n",
       "pickup_latitude      0.017372\n",
       "dropoff_longitude    0.018087\n",
       "dropoff_latitude     0.017983\n",
       "fare_amount          0.000000\n",
       "tip_amount           0.478255\n",
       "total_amount         0.000000\n",
       "trip_time            0.003014\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data==0).sum()/len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = 'blue' > Most of the zeros are missing data. So flag them as NaN (means also NA) to be consistent! </font color>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.replace(np.float64(0), np.nan);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_datetime            0\n",
       "dropoff_datetime           0\n",
       "passenger_count           31\n",
       "trip_distance          39596\n",
       "pickup_longitude       61811\n",
       "pickup_latitude        61403\n",
       "dropoff_longitude      63946\n",
       "dropoff_latitude       63579\n",
       "fare_amount                0\n",
       "tip_amount           1690455\n",
       "total_amount               0\n",
       "trip_time              10653\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quick preview about the trip_times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A quick look at the trip time before preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.025     2.000000\n",
      "0.500    10.516667\n",
      "0.750    16.833333\n",
      "0.950    31.000000\n",
      "0.975    38.400000\n",
      "0.990    49.850000\n",
      "Name: trip_time, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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LdwKeBFwCvAP42x4FROOpU3+398pYbR6g3MP/AWwI7ADMoHwGu2TmiV3W0Ys6J9J2Uqrv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\nMUmSJEmSJPWd/x8aMTkl/SWoDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce8a88c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trip_times_in_minutes = data['trip_time'] / np.timedelta64(1, 'm')\n",
    "plt.hist(trip_times_in_minutes , bins=30, range=[0, 60], \n",
    "         weights=np.zeros_like(trip_times_in_minutes) + 1. / trip_times_in_minutes.size)\n",
    "#plt.yscale('log')\n",
    "print(trip_times_in_minutes.quantile(q=[0.025, 0.5, 0.75, 0.95, 0.975, 0.99]))\n",
    "plt.xlabel('Trip Time in Minutes')\n",
    "plt.ylabel('Relative Frequency')\n",
    "plt.title('Distribution of Trip Time')\n",
    "plt.savefig('figures/trip_time_distribution.eps', format='eps', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5840"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data.trip_time.value_counts().values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That many unique values do we have in trip_time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Identify the the cases without geo data and remove them from our data to be processed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anomaly = data.loc[(data['dropoff_longitude'].isnull()) | (data['dropoff_latitude'].isnull()) | \n",
    "                       (data['pickup_longitude'].isnull()) | (data['pickup_latitude'].isnull())]\n",
    "data = data.drop(anomaly.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anomaly['flag'] = 'geo_NA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_datetime            0\n",
       "dropoff_datetime           0\n",
       "passenger_count           12\n",
       "trip_distance          32873\n",
       "pickup_longitude           0\n",
       "pickup_latitude            0\n",
       "dropoff_longitude          0\n",
       "dropoff_latitude           0\n",
       "fare_amount                0\n",
       "tip_amount           1656902\n",
       "total_amount               0\n",
       "trip_time               3864\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So how many percent of data are left to be processed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.981279805082962"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)/(len(data)+len(anomaly))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": 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>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_time</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3534421</th>\n",
       "      <td>2013-05-12 23:58:56</td>\n",
       "      <td>2013-05-13 00:05:43</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>00:06:47</td>\n",
       "      <td>geo_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534518</th>\n",
       "      <td>2013-05-12 23:59:02</td>\n",
       "      <td>2013-05-13 00:16:03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>00:17:01</td>\n",
       "      <td>geo_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534615</th>\n",
       "      <td>2013-05-12 23:59:52</td>\n",
       "      <td>2013-05-13 00:23:15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.5</td>\n",
       "      <td>00:23:23</td>\n",
       "      <td>geo_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534618</th>\n",
       "      <td>2013-05-12 23:59:54</td>\n",
       "      <td>2013-05-13 00:07:45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>00:07:51</td>\n",
       "      <td>geo_NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534623</th>\n",
       "      <td>2013-05-12 23:59:56</td>\n",
       "      <td>2013-05-13 00:14:28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>00:14:32</td>\n",
       "      <td>geo_NA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pickup_datetime    dropoff_datetime  passenger_count  \\\n",
       "3534421 2013-05-12 23:58:56 2013-05-13 00:05:43              1.0   \n",
       "3534518 2013-05-12 23:59:02 2013-05-13 00:16:03              3.0   \n",
       "3534615 2013-05-12 23:59:52 2013-05-13 00:23:15              1.0   \n",
       "3534618 2013-05-12 23:59:54 2013-05-13 00:07:45              1.0   \n",
       "3534623 2013-05-12 23:59:56 2013-05-13 00:14:28              1.0   \n",
       "\n",
       "         trip_distance  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
       "3534421            2.0               NaN              NaN                NaN   \n",
       "3534518            6.1               NaN              NaN                NaN   \n",
       "3534615            5.4               NaN              NaN                NaN   \n",
       "3534618            1.8               NaN              NaN                NaN   \n",
       "3534623            7.6               NaN              NaN                NaN   \n",
       "\n",
       "         dropoff_latitude  fare_amount  tip_amount  total_amount  trip_time  \\\n",
       "3534421               NaN          8.0         1.8          10.8   00:06:47   \n",
       "3534518               NaN         15.0         NaN          16.0   00:17:01   \n",
       "3534615               NaN         20.5         NaN          21.5   00:23:23   \n",
       "3534618               NaN          8.0         NaN           9.0   00:07:51   \n",
       "3534623               NaN         23.0         NaN          24.0   00:14:32   \n",
       "\n",
       "           flag  \n",
       "3534421  geo_NA  \n",
       "3534518  geo_NA  \n",
       "3534615  geo_NA  \n",
       "3534618  geo_NA  \n",
       "3534623  geo_NA  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = 'black'> So we only dropped 2% of the data because of missing geo tags. Someone could search the 'anomaly'-data for patterns, e.g. for fraud detection. We are also going to drop all the unrecognized trip_distances because we cannot (exactly) generate them (an approximation would be possible). </font color>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": 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>dropoff_datetime</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>flag</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3534334</th>\n",
       "      <td>2013-05-13 00:03:12</td>\n",
       "      <td>40.749736</td>\n",
       "      <td>-73.978713</td>\n",
       "      <td>5.5</td>\n",
       "      <td>trip_dist_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:58:16</td>\n",
       "      <td>40.754459</td>\n",
       "      <td>-73.964803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:04:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534517</th>\n",
       "      <td>2013-05-13 00:13:53</td>\n",
       "      <td>40.690212</td>\n",
       "      <td>-73.978735</td>\n",
       "      <td>14.0</td>\n",
       "      <td>trip_dist_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:59:01</td>\n",
       "      <td>40.733297</td>\n",
       "      <td>-73.987381</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:14:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534607</th>\n",
       "      <td>2013-05-13 00:05:02</td>\n",
       "      <td>40.744680</td>\n",
       "      <td>-73.999020</td>\n",
       "      <td>6.0</td>\n",
       "      <td>trip_dist_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:59:50</td>\n",
       "      <td>40.733435</td>\n",
       "      <td>-74.002994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:05:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534612</th>\n",
       "      <td>2013-05-13 00:09:30</td>\n",
       "      <td>40.706187</td>\n",
       "      <td>-74.013075</td>\n",
       "      <td>9.5</td>\n",
       "      <td>trip_dist_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:59:52</td>\n",
       "      <td>40.729327</td>\n",
       "      <td>-73.992207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:09:38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534626</th>\n",
       "      <td>2013-05-13 00:00:28</td>\n",
       "      <td>40.757354</td>\n",
       "      <td>-73.967262</td>\n",
       "      <td>58.0</td>\n",
       "      <td>trip_dist_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:59:58</td>\n",
       "      <td>40.757354</td>\n",
       "      <td>-73.967262</td>\n",
       "      <td>11.6</td>\n",
       "      <td>69.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>00:00:30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dropoff_datetime  dropoff_latitude  dropoff_longitude  fare_amount  \\\n",
       "3534334 2013-05-13 00:03:12         40.749736         -73.978713          5.5   \n",
       "3534517 2013-05-13 00:13:53         40.690212         -73.978735         14.0   \n",
       "3534607 2013-05-13 00:05:02         40.744680         -73.999020          6.0   \n",
       "3534612 2013-05-13 00:09:30         40.706187         -74.013075          9.5   \n",
       "3534626 2013-05-13 00:00:28         40.757354         -73.967262         58.0   \n",
       "\n",
       "                 flag  passenger_count     pickup_datetime  pickup_latitude  \\\n",
       "3534334  trip_dist_NA              1.0 2013-05-12 23:58:16        40.754459   \n",
       "3534517  trip_dist_NA              1.0 2013-05-12 23:59:01        40.733297   \n",
       "3534607  trip_dist_NA              1.0 2013-05-12 23:59:50        40.733435   \n",
       "3534612  trip_dist_NA              1.0 2013-05-12 23:59:52        40.729327   \n",
       "3534626  trip_dist_NA              1.0 2013-05-12 23:59:58        40.757354   \n",
       "\n",
       "         pickup_longitude  tip_amount  total_amount  trip_distance  trip_time  \n",
       "3534334        -73.964803         NaN           6.5            NaN   00:04:56  \n",
       "3534517        -73.987381         NaN          15.0            NaN   00:14:52  \n",
       "3534607        -74.002994         NaN           7.0            NaN   00:05:12  \n",
       "3534612        -73.992207         NaN          10.5            NaN   00:09:38  \n",
       "3534626        -73.967262        11.6          69.6            NaN   00:00:30  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly = anomaly.append(data.loc[(data['trip_distance'].isnull())])\n",
    "anomaly.loc[data.loc[(data['trip_distance'].isnull())].index,'flag'] = 'trip_dist_NA'\n",
    "anomaly.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = data.drop(anomaly.index, errors='ignore') # ignore uncontained labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_datetime            0\n",
       "dropoff_datetime           0\n",
       "passenger_count            9\n",
       "trip_distance              0\n",
       "pickup_longitude           0\n",
       "pickup_latitude            0\n",
       "dropoff_longitude          0\n",
       "dropoff_latitude           0\n",
       "fare_amount                0\n",
       "tip_amount           1638285\n",
       "total_amount               0\n",
       "trip_time               2922\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028020455877726502"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-len(data)/(len(data)+len(anomaly))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drop all the columns with trip_time.isnull() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": 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>dropoff_datetime</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>flag</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3525186</th>\n",
       "      <td>2013-05-12 23:22:39</td>\n",
       "      <td>40.763210</td>\n",
       "      <td>-73.962524</td>\n",
       "      <td>7.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:22:39</td>\n",
       "      <td>40.759930</td>\n",
       "      <td>-73.975325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3529613</th>\n",
       "      <td>2013-05-12 23:38:46</td>\n",
       "      <td>40.734109</td>\n",
       "      <td>-73.983598</td>\n",
       "      <td>5.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:38:46</td>\n",
       "      <td>40.742107</td>\n",
       "      <td>-73.974624</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3530873</th>\n",
       "      <td>2013-05-12 23:43:59</td>\n",
       "      <td>40.643328</td>\n",
       "      <td>-73.950397</td>\n",
       "      <td>34.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:43:59</td>\n",
       "      <td>40.727880</td>\n",
       "      <td>-74.001728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531118</th>\n",
       "      <td>2013-05-12 23:45:00</td>\n",
       "      <td>40.755222</td>\n",
       "      <td>-73.928660</td>\n",
       "      <td>3.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:45:00</td>\n",
       "      <td>40.753865</td>\n",
       "      <td>-73.929723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531331</th>\n",
       "      <td>2013-05-12 23:45:56</td>\n",
       "      <td>40.773872</td>\n",
       "      <td>-73.948701</td>\n",
       "      <td>5.5</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:45:56</td>\n",
       "      <td>40.766625</td>\n",
       "      <td>-73.962938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dropoff_datetime  dropoff_latitude  dropoff_longitude  fare_amount  \\\n",
       "3525186 2013-05-12 23:22:39         40.763210         -73.962524          7.0   \n",
       "3529613 2013-05-12 23:38:46         40.734109         -73.983598          5.0   \n",
       "3530873 2013-05-12 23:43:59         40.643328         -73.950397         34.0   \n",
       "3531118 2013-05-12 23:45:00         40.755222         -73.928660          3.0   \n",
       "3531331 2013-05-12 23:45:56         40.773872         -73.948701          5.5   \n",
       "\n",
       "                 flag  passenger_count     pickup_datetime  pickup_latitude  \\\n",
       "3525186  trip_time_NA              1.0 2013-05-12 23:22:39        40.759930   \n",
       "3529613  trip_time_NA              1.0 2013-05-12 23:38:46        40.742107   \n",
       "3530873  trip_time_NA              1.0 2013-05-12 23:43:59        40.727880   \n",
       "3531118  trip_time_NA              1.0 2013-05-12 23:45:00        40.753865   \n",
       "3531331  trip_time_NA              1.0 2013-05-12 23:45:56        40.766625   \n",
       "\n",
       "         pickup_longitude  tip_amount  total_amount  trip_distance  trip_time  \n",
       "3525186        -73.975325         NaN           8.0           1.30        NaT  \n",
       "3529613        -73.974624         1.2           7.2           1.00        NaT  \n",
       "3530873        -74.001728         NaN          35.0           9.90        NaT  \n",
       "3531118        -73.929723         NaN           4.0           0.15        NaT  \n",
       "3531331        -73.962938         NaN           6.5           1.20        NaT  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly = anomaly.append(data.loc[(data['trip_time'].isnull())])\n",
    "anomaly.loc[data.loc[(data['trip_time'].isnull())].index,'flag'] = 'trip_time_NA'\n",
    "anomaly.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.drop(anomaly.index, errors='ignore') # ignore uncontained labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is quite unreasonable. We have dropoff_datetime = pickup_datetime and the geo-coords of pickup and dropoff do not match! trip_time equals NaT here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": 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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>passenger_count</th>\n",
       "      <td>3.43266e+06</td>\n",
       "      <td>1.71448</td>\n",
       "      <td>1.38599</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_distance</th>\n",
       "      <td>3.43267e+06</td>\n",
       "      <td>2.91113</td>\n",
       "      <td>3.34836</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.09</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.21</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_longitude</th>\n",
       "      <td>3.43267e+06</td>\n",
       "      <td>-73.9154</td>\n",
       "      <td>2.68755</td>\n",
       "      <td>-736.5</td>\n",
       "      <td>-73.9925</td>\n",
       "      <td>-73.9821</td>\n",
       "      <td>-73.968</td>\n",
       "      <td>40.8679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_latitude</th>\n",
       "      <td>3.43267e+06</td>\n",
       "      <td>40.6919</td>\n",
       "      <td>2.91217</td>\n",
       "      <td>-74.0378</td>\n",
       "      <td>40.7365</td>\n",
       "      <td>40.7534</td>\n",
       "      <td>40.7679</td>\n",
       "      <td>2047.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <td>3.43267e+06</td>\n",
       "      <td>-73.913</td>\n",
       "      <td>2.98107</td>\n",
       "      <td>-740.173</td>\n",
       "      <td>-73.9918</td>\n",
       "      <td>-73.9806</td>\n",
       "      <td>-73.9651</td>\n",
       "      <td>2387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <td>3.43267e+06</td>\n",
       "      <td>40.6899</td>\n",
       "      <td>3.43265</td>\n",
       "      <td>-3084.28</td>\n",
       "      <td>40.7353</td>\n",
       "      <td>40.7538</td>\n",
       "      <td>40.7686</td>\n",
       "      <td>473.982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare_amount</th>\n",
       "      <td>3.43267e+06</td>\n",
       "      <td>12.4022</td>\n",
       "      <td>9.71666</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>382.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip_amount</th>\n",
       "      <td>1.79648e+06</td>\n",
       "      <td>2.59194</td>\n",
       "      <td>2.29176</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>155.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_amount</th>\n",
       "      <td>3.43267e+06</td>\n",
       "      <td>14.834</td>\n",
       "      <td>11.7278</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8</td>\n",
       "      <td>11.3</td>\n",
       "      <td>16.7</td>\n",
       "      <td>382.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trip_time</th>\n",
       "      <td>3432668</td>\n",
       "      <td>0 days 00:13:02.243506</td>\n",
       "      <td>0 days 00:27:26.535018</td>\n",
       "      <td>0 days 00:00:01</td>\n",
       "      <td>0 days 00:06:24</td>\n",
       "      <td>0 days 00:10:34</td>\n",
       "      <td>0 days 00:16:50</td>\n",
       "      <td>11 days 00:45:28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         count                    mean  \\\n",
       "passenger_count    3.43266e+06                 1.71448   \n",
       "trip_distance      3.43267e+06                 2.91113   \n",
       "pickup_longitude   3.43267e+06                -73.9154   \n",
       "pickup_latitude    3.43267e+06                 40.6919   \n",
       "dropoff_longitude  3.43267e+06                 -73.913   \n",
       "dropoff_latitude   3.43267e+06                 40.6899   \n",
       "fare_amount        3.43267e+06                 12.4022   \n",
       "tip_amount         1.79648e+06                 2.59194   \n",
       "total_amount       3.43267e+06                  14.834   \n",
       "trip_time              3432668  0 days 00:13:02.243506   \n",
       "\n",
       "                                      std              min              25%  \\\n",
       "passenger_count                   1.38599                1                1   \n",
       "trip_distance                     3.34836             0.01             1.09   \n",
       "pickup_longitude                  2.68755           -736.5         -73.9925   \n",
       "pickup_latitude                   2.91217         -74.0378          40.7365   \n",
       "dropoff_longitude                 2.98107         -740.173         -73.9918   \n",
       "dropoff_latitude                  3.43265         -3084.28          40.7353   \n",
       "fare_amount                       9.71666              2.5              6.5   \n",
       "tip_amount                        2.29176             0.01              1.3   \n",
       "total_amount                      11.7278              2.5                8   \n",
       "trip_time          0 days 00:27:26.535018  0 days 00:00:01  0 days 00:06:24   \n",
       "\n",
       "                               50%              75%               max  \n",
       "passenger_count                  1                2                 6  \n",
       "trip_distance                  1.8             3.21               100  \n",
       "pickup_longitude          -73.9821          -73.968           40.8679  \n",
       "pickup_latitude            40.7534          40.7679           2047.45  \n",
       "dropoff_longitude         -73.9806         -73.9651              2387  \n",
       "dropoff_latitude           40.7538          40.7686           473.982  \n",
       "fare_amount                    9.5             14.5            382.75  \n",
       "tip_amount                       2                3             155.5  \n",
       "total_amount                  11.3             16.7            382.76  \n",
       "trip_time          0 days 00:10:34  0 days 00:16:50  11 days 00:45:28  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## After filtering regarding the trip_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.14860000e+04,   2.47244000e+05,   4.11413000e+05,\n",
       "          4.47479000e+05,   4.18108000e+05,   3.64416000e+05,\n",
       "          3.02291000e+05,   2.45960000e+05,   1.96811000e+05,\n",
       "          1.57134000e+05,   1.23172000e+05,   9.75690000e+04,\n",
       "          7.55190000e+04,   5.99140000e+04,   4.69780000e+04,\n",
       "          3.66970000e+04,   2.90370000e+04,   2.32160000e+04,\n",
       "          1.81990000e+04,   1.47690000e+04,   1.18490000e+04,\n",
       "          9.75400000e+03,   7.90100000e+03,   6.83100000e+03,\n",
       "          5.64600000e+03,   4.78400000e+03,   4.11200000e+03,\n",
       "          3.75100000e+03,   3.13700000e+03,   2.76200000e+03,\n",
       "          2.28500000e+03,   1.89500000e+03,   1.59500000e+03,\n",
       "          1.43000000e+03,   1.15200000e+03,   1.03000000e+03,\n",
       "          8.11000000e+02,   7.01000000e+02,   5.63000000e+02,\n",
       "          4.33000000e+02,   3.88000000e+02,   3.50000000e+02,\n",
       "          2.88000000e+02,   2.23000000e+02,   2.36000000e+02,\n",
       "          1.84000000e+02,   1.59000000e+02,   1.33000000e+02,\n",
       "          1.06000000e+02,   1.35000000e+02]),\n",
       " array([   0.,    2.,    4.,    6.,    8.,   10.,   12.,   14.,   16.,\n",
       "          18.,   20.,   22.,   24.,   26.,   28.,   30.,   32.,   34.,\n",
       "          36.,   38.,   40.,   42.,   44.,   46.,   48.,   50.,   52.,\n",
       "          54.,   56.,   58.,   60.,   62.,   64.,   66.,   68.,   70.,\n",
       "          72.,   74.,   76.,   78.,   80.,   82.,   84.,   86.,   88.,\n",
       "          90.,   92.,   94.,   96.,   98.,  100.]),\n",
       " <a list of 50 Patch objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l0pOYMWPGVh1XkiRJkqQ3opGUZouAJ2hKqffTPAvs8Ig4NjNXAhNKbn2X4wfL\ntQnVdmvl62MGr7HX/EjmJA3LhEm7sfPkPUd7GpIkSZIkqYthl2blOWKDzxL7ekR8GPgWcDPwbmBd\n2Te+yxA7le26aru18vUxg8VXr/mRzEmSJEmSJEl9ZCQrzf5AZv6PiJgPHBIRfwqsLrt26XLIrmW7\nurUdM8x81/EjYizNbZkbaW6j3EDzXLOJEbFDZr62pefokN+sgYGBXmKjbtWqVaM9BW2hZcuWsXbt\n2tGexuvG9vLbk/qNvz1pdPjbk0aPvz+pPw1ZmkXEjsDxwJsz84YusX8H3gbsBSyjeSPm22neMlnb\nl+YZZo+Xv5eV7du7jL1v2db56JLfG9gR+FFmri/X8BTNQ/v3AX7c4zmGcw2bNXv27F5io27ixIlw\n9y9GexraAjNnzvSZZsXAwMB289uT+om/PWl0+NuTRo+/P2l0/DHK6rFDBTLzd8DVwHUR8dZ6f1nZ\ntXf581ngAZqVY0d1yO4IvAf4HfBg+fp7NLdQHhERnUq8o2kKqvvKfNYAS4ApETGrSx7g3tZ3D5Rt\npznNAqYCSzJzdSs/nGuQJEmSJElSHxmyNCvupCmRrupQbJ1L8xbJf8vM5TRl1XJgTkQcWmXPoXmB\nwK2l/BoswRbR3Ap5Tjtcjj+a5hlq97R2fbXMZ34p7QbzuwGfpXlQ/42t/A00t2ieHRFTWvmxwCU0\npdw1rfywrkGSJEmSJEn9pddnml0AvAv4a+DxiLgPeAU4BDgSWAucBpCZGyLiFOB+4P6IuAVYBRwE\nHENze+S51fjnlLHmR8RBwKPANOAjwMvAyZm5cTCcmTdHxFxgLvBYRNwF7AycBOwBnFkKvMH88og4\nD7gCWBoRt5X5zwUOAG7PzFtb+ZFcgyRJkiRJkvpETyvNyoqqA4ELaW5LPJ2mNHobsAD4s8x8tJV/\nBDgY+C5NMTWP5pliVwCH1Cu0ym2RBwNfAfYv+WOAbwMHt8duOZ5mVdk44DzgVJoya05mXt/hGq4u\nc3mmzP88mtVqZ9GUbXV+WNcgSZIkSZKk/tHz2zMzcx1wUfn0kn8S+NAwxl8DnF0+veQ30Dxr7eph\nnONu4O5h5Id1DZIkSZIkSeoPvT7TTJIkSZIkSXrDsDSTJEmSJEmSKpZmkiRJkiRJUsXSTJIkSZIk\nSapYmkmSJEmSJEkVSzNJkiRJkiSpYmkmSZIkSZIkVSzNJEmSJEmSpIqlmSRJkiRJklSxNJMkSZIk\nSZIqlmYjuPLiAAAgAElEQVSSJEmSJElSxdJMkiRJkiRJqliaSZIkSZIkSRVLM0mSJEmSJKliaSZJ\nkiRJkiRVLM0kSZIkSZKkiqWZJEmSJEmSVLE0kyRJkiRJkiqWZpIkSZIkSVLF0kySJEmSJEmqWJpJ\nkiRJkiRJFUszSZIkSZIkqWJpJkmSJEmSJFUszSRJkiRJkqSKpZkkSZIkSZJUsTSTJEmSJEmSKpZm\nkiRJkiRJUsXSTJIkSZIkSapYmkmSJEmSJEkVSzNJkiRJkiSpYmkmSZIkSZIkVSzNJEmSJEmSpIql\nmSRJkiRJklSxNJMkSZIkSZIqlmaSJEmSJElSxdJMkiRJkiRJqliaSZIkSZIkSRVLM0mSJEmSJKli\naSZJkiRJkiRVLM0kSZIkSZKkiqWZJEmSJEmSVLE0kyRJkiRJkiqWZpIkSZIkSVLF0kySJEmSJEmq\nWJpJkiRJkiRJFUszSZIkSZIkqbJDL6GIGAOcDpwC7AfsCDwLPADMz8yfldyfACuHGG5xZr6vGn8q\n8DlgDrAnsBZ4GLgkMx/rMJ/xwGeAE4EZwAbgCeD6zFzU5Ro+CHwaOAB4E7AcWARcmZm/6ZCfBVwA\nHApMBp4D7gUuysxnh7hGSZIkSZIkbceGLM1KYXYX8AHgJ8CNwCvAe4HTgL+KiIMyc1XrsBXA17oM\n2c4REXsBj9CUZd8BvgFMBT4GzImIuZl5Xys/FrgHOBJ4DLgMGA+cANwWEe/IzC9U57gQmNea/0vA\n+4D5wHsj4r2ZubGVf3+Zy2vALeW4dwKnAh+IiD/PzJ9u/r+cJEmSJEmStle9rDT7OE1h9ijw7sxc\nX77/YkTcBHwU+DxNoTTop5l5ZY9zuIamMDsrM68d/DIiFgADwMKImJ6Zr5Zdn6IpzO7IzBNa+UuB\nJcD5EfGdzBwo3+9f5rcCmJ2ZL5VDLo6I24APA2cBV5X8eGAhMKZc79LWOR4CFgDXAnN7vD5JkiRJ\nkiRtZ3p5ptmBwMvApa3CbNCNNOXSu0Zy8ojYHTiW5tbH69r7MvNJ4HZgd+C41q4zgE00RVg7/wpw\nOc01ndHadXqZ45dbhdmgeWXfJ1rfHQvsAdzZLsyKb9KsOju6rJCTJEmSJElSHxqyNMvM0zLzzZl5\nV4fda8t2XKdjI2JcREyNiEldhj+sHPtgZm7qsH8xTal1RBlvCrAv8PPMfLpLnsF89e/FVZbMXEFz\nu+j0Vgl2JE0p1ym/CXiozOnwLtckSZIkSZKk7dyWvj3zA2X7z9X3u0bELcCLNC8M+FVEZER8vMrt\nV7bLu4z/TJWbubl8ea7aemBaROwUETsA+9C8KKDbCwrqc/QypzGtnCRJkiRJkvrMiEuziHgnzdsl\nX6Z5oH7bfsDewHk0D+j/ErAX8PWIuLiVm0yzquvFLqdZ08q1t93yg/vGAG8B3kyzku3lLivZRnKO\nOi9JkiRJkqQ+08uLAP6TiDgQ+C6wE/BXmfnzsutXNM8a+0VmLmwdckdE3AH8ADg3Im7KzGeACWV/\n/ay0Qb8t2wnVtlu+Pmbw+nrN93KOOi9JkiRJkqQ+M+yVZhFxHM1zvXYGPpiZ9wzuy8yXMvOSqjAb\n3LeE5sH+Y/mPB/uvK9vxXU63U5UbKl8fM9z8SOYkSZIkSZKkPjOslWYRcQFwEc3bLv86Mx8b5vmW\nAicC08rfq2lupdylS37XVq697ZiPiLE0t2VupLmNcgPwGjAxInbIzNe29Bwd8kMaGBjoNTqqVq1a\nNdpT0BZatmwZa9euHTr4BrG9/PakfuNvTxod/vak0ePvT+pPPZdmETGf5hlmjwNHZ+ZzIzjf4Fs0\nB1dpLSvbt3fJ71u2j1f56JLfG9gR+FFmri/zformGWv7AD/u8RyHlTk91CW/qZUf0uzZs3uNjqqJ\nEyfC3b8Y7WloC8ycOZMZM2aM9jReFwYGBrab357UT/ztSaPD3540evz9SaPjj1FW91SaRcRnaAqz\nh4E5mflyl9zfAccC52fm/+4QOYSmcFpa/v4ezbPDjuiyEuzokr8PIDPXRMQSYFZEzMrMpR3yAPe2\nvnuApjQ7iqo0i4hZwFRgIDNXt/KfLvmvVfkdgfcAvwMe7PTf4I9hw4YNrFixYquPu3JltxeMSpIk\nSZIkvbEMWZpFxAHAZcAzNCvMOhZmxQvAwcDlEXFEZv7+uV8R8RHgcOB54E74fQm2CPgYcA5waSt/\nKE0J9jTw++emAV8F/hGYHxHHZObGkt8N+CzNg/pvbOVvAM4Ezo6IWzPzhZIfC1xCU8pd08rfCywH\n5kTEoZn5/da+c4ApwMLMXMMoWbFiBR89/1tMmLTbVh3333/2Y3bZa9+hg5IkSZIkSX2ul5Vml9Dc\n8vgEcFpEtzsjuRG4CTge+AvgqYj4NvBL4ECaFWhrgRPbZRpNEXUITQl2EPAozTPPPgK8DJw8WIwB\nZObNETEXmAs8FhF30byU4CRgD+DMzFzeyi+PiPOAK4ClEXEb8Eo5/gDg9sy8tZXfEBGnAPcD90fE\nLcAq4CDgGJrVauf28N9tm5owaTd2nrznVh1z3a9/uVXH0x/Xpo0bt9lqwenTpzNu3LhtMrYkSZIk\nSa9HvZRmg8/w+pvy6eb2zPxJRMwBPkFTep1C8xbK54CvA5dn5h/cV5iZqyPiYOALNEXWUcCvgG8D\nF2XmUx3OdTzN6rFTgPNobvH8IfDxzLy/Dmfm1RGxnGYl2uk0JWACZwHXd8g/UuY0r8zpLcDPaIq3\nizPzxc38d5BGxatrX2DegtVMmLR1b91d9+vnueXSk3xWmiRJkiTpDWXI0iwz3zacAcuqsOvKp9dj\n1gBnl08v+Q3A1eXT6znuBu4eRv5J4EO95qXXg22xAlGSJEmSpDeisaM9AUmSJEmSJOn1xtJMkiRJ\nkiRJqliaSZIkSZIkSRVLM0mSJEmSJKliaSZJkiRJkiRVLM0kSZIkSZKkiqWZJEmSJEmSVLE0kyRJ\nkiRJkiqWZpIkSZIkSVLF0kySJEmSJEmqWJpJkiRJkiRJFUszSZIkSZIkqWJpJkmSJEmSJFUszSRJ\nkiRJkqSKpZkkSZIkSZJUsTSTJEmSJEmSKpZmkiRJkiRJUsXSTJIkSZIkSapYmkmSJEmSJEkVSzNJ\nkiRJkiSpYmkmSZIkSZIkVSzNJEmSJEmSpIqlmSRJkiRJklSxNJMkSZIkSZIqlmaSJEmSJElSxdJM\nkiRJkiRJqliaSZIkSZIkSRVLM0mSJEmSJKliaSZJkiRJkiRVLM0kSZIkSZKkiqWZJEmSJEmSVLE0\nkyRJkiRJkiqWZpIkSZIkSVLF0kySJEmSJEmqWJpJkiRJkiRJFUszSZIkSZIkqWJpJkmSJEmSJFUs\nzSRJkiRJkqSKpZkkSZIkSZJUsTSTJEmSJEmSKpZmkiRJkiRJUsXSTJIkSZIkSapYmkmSJEmSJEkV\nSzNJkiRJkiSpYmkmSZIkSZIkVSzNJEmSJEmSpMoOvYQiYgxwOnAKsB+wI/As8AAwPzN/VuVnARcA\nhwKTgeeAe4GLMvPZDuNPBT4HzAH2BNYCDwOXZOZjHfLjgc8AJwIzgA3AE8D1mbmoyzV8EPg0cADw\nJmA5sAi4MjN/0yE/rGuQJEmSJElS/xhypVkpzO4CrgemAjcCl9GUSKcBP4yIP2nl3w88AhwF3Al8\nEfgX4FTgXyPirdX4ewGPAp8EHgf+HvgWcDjwcEQcVeXHAvcAXwLWl7l8BdgduC0iLupwDRcC/wRM\nK/OfD6wp23vKmO38sK5BkiRJkiRJ/aWXlWYfBz5AU2y9OzPXl++/GBE3AR8FPg+cWlaALQTGlOzS\nwUEi4iFgAXAtMLc1/jU0q8vOysxrW/kFwACwMCKmZ+arZdengCOBOzLzhFb+UmAJcH5EfCczB8r3\n+5f5rQBmZ+ZL5ZCLI+I24MPAWcBVJT+Sa5AkSZIkSVIf6eWZZgcCLwOXtgqzQTfSlEvvKn/PBfYA\n7myXTcU3gZ8AR5fVZUTE7sCxNKvWrmuHM/NJ4HaaFWTHtXadAWyiKcLa+VeAy8s1ndHadXqZ45db\nhdmgeWXfJ1rfHTuca5AkSZIkSVL/GbI0y8zTMvPNmXlXh91ry3Zc2R5JU2gt7jDOJuAhmpLq8PL1\nYeXYB8v+2uKSPwIgIqYA+wI/z8ynu+QZzFf/7jSnFcAqYHqrBBvuNUiSJEmSJKnPbOnbMz9Qtv9c\ntu8o2+Vd8s/QFE77lb8Ht5vLt3MzN5fPzFU0zzmbFhE7RcQOwD40LwpY2eM5eplT+xokSZIkSZLU\nZ0ZcmkXEO2neLvkKMPjw/cll+2KXw9ZUuck0q7qGk9/c+IP7xgBvAd5Ms5Lt5S4r2UZyjjovSZIk\nSZKkPtPLiwD+k4g4EPgusBPwV5n5bNk1oWzrZ58N+m2V29r5+pjB6+s1P5I5SZIkSZIkqc8Me6VZ\nRBxH81yvnYEPZuY9rd3rynZ8l8N3qnJbO18fM9z8SOYkSZIkSZKkPjOslWYRcQHNrZjPAX+dmY9V\nkdVlu0uXIXatcqtpbqUcTr7r+BExlua2zI00t1FuAF4DJkbEDpn52paeo0N+SAMDA71Ge7Jq1aqt\nOp40lGXLlrF27dqhg68zW/u3J6k3/vak0eFvTxo9/v6k/tRzaRYR82meYfY4cHRmPtchtozmjZhv\np1mNVtuX5hlmj7fylHwn+5ZtnY8u+b2BHYEfZeb6Mu+naB7avw/w4x7PMZxrGNLs2bN7jfZk4sSJ\ncPcvtuqY0ubMnDmTGTNmjPY0hmVgYGCr//YkDc3fnjQ6/O1Jo8ffnzQ6/hhldU+3Z0bEZ2gKs4eB\nd3cpzAAeoFk5dlSHMXYE3gP8DniwfP09mmeHHVHedFk7mqagug8gM9cAS4ApETGrSx7g3mpOdJnT\nLGAqsCQzV7fyw7kGSZIkSZIk9ZkhV5pFxAHAZcAzNCvMXt5M/F5gOTAnIg7NzO+39p0DTAEWlvKL\nzFwTEYuAj5X9l7bOeyhNCfY00H5u2leBfwTmR8Qxmbmx5HcDPkvzoP4bW/kbgDOBsyPi1sx8oeTH\nApfQlHLXjPQapH63aeNGVq5cuc3Gnz59OuPGjdtm40uSJEmSNBK93J55Cc0tj08Ap0V0uzOSGzPz\n5Yg4BbgfuD8ibgFWAQcBx9DcHnluddw5wCE0JdhBwKPANOAjwMvAyYPFGEBm3hwRc4G5wGMRcRfN\nSwlOAvYAzszM5a388og4D7gCWBoRtwGvlOMPAG7PzFtb+Q0juAapb7269gXmLVjNhEkrtvrY6379\nPLdcetJ2d+unJEmSJKn/9VKaDT7D62/Kp5vbgZcz85GIOBiYR1NMvQX4GU1pdXFmvtg+KDNXl/wX\nSv4o4FfAt4GLMvOpDuc6nmb12CnAeTS3eP4Q+Hhm3l+HM/PqiFhOsxLtdJoSMIGzgOs75Id1DVK/\nmzBpN3aevOdoT0OSJEmSpD+aIUuzzHzbcAfNzCeBDw0jvwY4u3x6yW8Ari6fXs9xN3D3MPLDugZJ\nkiRJkiT1j55eBCBJkiRJkiS9kViaSZIkSZIkSRVLM0mSJEmSJKliaSZJkiRJkiRVLM0kSZIkSZKk\niqWZJEmSJEmSVLE0kyRJkiRJkiqWZpIkSZIkSVLF0kySJEmSJEmqWJpJkiRJkiRJFUszSZIkSZIk\nqfL/t3fvYXZX9b3H32EC4jQxpEqAE2uxI/OVi5acHIEjRS5SRS7mHOsVrdjjsajVB6lFrGJEuaTe\noSCVtEZp5KSn8YKWA4IpWMvBFhzQFpQvJp1GEZDkBMKEBJHMnD/Wb+rPn7Mne5K9M5nJ+/U88/zI\nXt+99trDs5KZz16/tQzNJEmSJEmSpAZDM0mSJEmSJKnB0EySJEmSJElqMDSTJEmSJEmSGgzNJEmS\nJEmSpAZDM0mSJEmSJKnB0EySJEmSJElqMDSTJEmSJEmSGgzNJEmSJEmSpAZDM0mSJEmSJKnB0EyS\nJEmSJElqMDSTJEmSJEmSGgzNJEmSJEmSpAZDM0mSJEmSJKnB0EySJEmSJElqMDSTJEmSJEmSGgzN\nJEmSJEmSpAZDM0mSJEmSJKnB0EySJEmSJElqMDSTJEmSJEmSGgzNJEmSJEmSpAZDM0mSJEmSJKnB\n0EySJEmSJElqMDSTJEmSJEmSGgzNJEmSJEmSpAZDM0mSJEmSJKnB0EySJEmSJElqMDSTJEmSJEmS\nGgzNJEmSJEmSpAZDM0mSJEmSJKnB0EySJEmSJElqMDSTJEmSJEmSGgzNJEmSJEmSpAZDM0mSJEmS\nJKlh5mQPQNLua2R4mMHBwa70vXXr1q70K0mSJEnaPUw4NIuII4C/BvqB8zPzw4323wS29Vvwqsx8\nSeN5+wPvB04G5gNDwC3AxZl5+xjj2At4F/C6aixbge8BV2TmihZjfyXwDuBw4CnAamAF8MnMfHyM\n+gXA+4BjgLnAA8D1wAWZef823qOkbdgytI7FS9fTO2dNR/vdvPEhzn398zniiCM62q8kSZIkaffR\ndmgWETOBDwLvBUaqr/GsAf6iRdvaRt/PBG6lhGXXAH8F7A+8ETg5IhZl5tdr9XsA1wEnALcDHwH2\nAl4NXB0Rh2TmBxqvcT6wGPgRcCXwKPAS4ELgxIg4MTOHa/UvrcbyJLC8et7zgLcAp0bECzPzx9v4\nHkjaht4585g1d/5kD0OSJEmSpF8ykZVm3wKOAC4DHgfO3Ub9jzPzk232fSklMDsrMy8ffTAilgID\nwLKI6MvMLVXTH1ECsy9m5qtr9UuAO4A/jYhrMnOgevz5wHmUIG9hZj5aPeWiiLgaeC1wFvCpqn4v\nYBkwA3hRZt5Ze42bgaXA5cCiNt+fJEmSJEmSppCJHAQwB3hZZp5NCc06IiL2A15OufXx0/W2zLwb\nWAnsB7yi1vRWykq38xr1jwEfpbyvt9aazqQEYB+rBWajFldtb6s99nLgAODL9cCs8lnKqrNTqhVy\nkiRJkiRJmmYmEpodlZnfmOgLRERPROwfEXNalBwL9AA3ZeZYt3yuooRax1f97QscDPwkM+9tUc9o\nfeO/VzVqycw1lNtF+2oh2AmUUG6s+hHg5mpMx7V4T5IkSZIkSZrC2g7NMnNogn0/IyKWA48A9wMP\nR0RGxJsbdYdW19Ut+vlho+6w8eozcy3wBHBgROxd7cV2EOWggFYHFDRfo50xzajVSZIkSZIkaRqZ\nyEqziToUeA5l77NXA38GPBP4y4i4qFY3l7Kq65EW/Wyo1dWvrepH22YA+wBPo6xk29RiJdv2vEaz\nXpIkSZIkSdPIRA4CaNfDlL3GHszMZbXHvxgRXwT+CXhPRHw+M38I9FbtT7To72fVtbdxbVXffM7o\ne2y3vp3XaNZLkiRJkiRpGul4aFZttH9xi7Y7ImIl5bTKVwAfATZXzXu16HLv6rq5cW1V33zOaPDV\nbn07r9GslyRJkiRJ0jTSjZVm23In8DrgwOrP6ym3Uj69Rf0zanX165j1EbEH5bbMYcptlFuBJ4HZ\nETEzM5/c0dcYo35cAwMD7ZS1be3atR3tT5quOj33JLXHuSdNDueeNHmcf9L0NBmh2egpmqOrtO6q\nrs9tUX9wdf1uoz5a1D8H2BP4fmY+ARAR91D2WDsI+EGbr3FsNaabW9SP1OrHtXDhwnbK2jZ79my4\n9sGO9ilNR52ee5K2bWBgwLknTQLnnjR5nH/S5NgZYXXHDwKIiPdGxK0RcWyLkqMpgdOd1Z+/SbmF\n8vjqpMumU6r6rwNk5gbgDmDfiFjQoh7g+tpjN1bXk8YY7wJgf+COzFxfq5/Ron5P4MXAz4GbxnyH\nkiRJkiRJmtJ2JDRrdRLlOuAo4KMR8Usb5UfEG4Djqpovw3+EYCsot0Ke06g/hhKC3QtcV2u6jBJq\nXVjdjjlaPw94N2Wj/itr9Z+h3KJ5dkTsW6vfg7L/2ghwaa3+emA1cHI1hrpzgH2BL1RjlyRJkiRJ\n0jTT1u2ZEXEI8LLaQy+khFZHR8S7a49fCXweeBXwu8A9EfEV4KfAEcDLgSHgdZlZ30T/HMoKtAsj\n4kjgNsqeZ28ANgFnZObwaHFmXhURi4BFwO0R8VVgFnA6cADwzsxcXatfHRHnAp8A7oyIq4HHqucf\nDqzMzC/U6rdGxJuAG4AbImI5sBY4EjiNcovne9r53kmSJEmSJGnqaXdPsxcAH208NgKcWH2NWpmZ\nP4qIk4G3UUKvN1FOoXwA+Evgo5m5pt5RZq6PiKOAD1CCrJOAh4GvABdk5j1jjOlVwDur/s+l3OL5\nHeDNmXlDszgzL4mI1ZSVaGdS9j1L4CzgijHqb63GtLga0z7AfZTg7aLMfGSMMUmSJEmSJGkaaCs0\ny8yrgKva7bRaFfbp6qvd52wAzq6+2qnfClxSfbX7GtcC106g/m7gNe3WS5IkSZIkaXro+EEAkiRJ\nkiRJ0lRnaCZJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJkiRJUoOhmSRJkiRJktRgaCZJkiRJkiQ1GJpJ\nkiRJkiRJDYZmkiRJkiRJUoOhmSRJkiRJktRgaCZJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJkiRJUoOh\nmSRJkiRJktRgaCZJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJkiRJUoOhmSRJkiRJktRgaCZJkiRJkiQ1\nGJpJkiRJkiRJDYZmkiRJkiRJUoOhmSRJkiRJktRgaCZJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJkiRJ\nUoOhmSRJkiRJktQwc7IHIEmdNjI8zP3338+9997b8b77+vro6enpeL+SJEmSpF2LoZmkaWfL0DqW\n3zyDL92xqqP9bt74EMuXnE5/f39H+5UkSZIk7XoMzSRNS71z5jFr7vzJHoYkSZIkaYpyTzNJkiRJ\nkiSpwdBMkiRJkiRJajA0kyRJkiRJkhoMzSRJkiRJkqQGQzNJkiRJkiSpwdBMkiRJkiRJajA0kyRJ\nkiRJkhoMzSRJkiRJkqQGQzNJkiRJkiSpwdBMkiRJkiRJajA0kyRJkiRJkhoMzSRJkiRJkqQGQzNJ\nkiRJkiSpwdBMkiRJkiRJajA0kyRJkiRJkhoMzSRJkiRJkqQGQzNJkiRJkiSpwdBMkiRJkiRJapg5\n0SdExBHAXwP9wPmZ+eEWdQuA9wHHAHOBB4DrgQsy8/4x6vcH3g+cDMwHhoBbgIsz8/Yx6vcC3gW8\nrhrLVuB7wBWZuaLFmF4JvAM4HHgKsBpYAXwyMx/f0fcgSZIkSZKk6aHtlWYRMTMiLgD+L/BbwMg4\ntS8FbgVOAr4MfLB63luAf46I32jUPxO4DXg78F3gQ8D/Ao4DbomIkxr1ewDXAX8GPAF8BPhzYD/g\n6mqczTGdD/wtcCBwJXAhsKG6Xlf1ud3vQZIkSZIkSdPHRFaafQs4ArgMeBw4d6yiagXYMmAG8KLM\nvLPWdjOwFLgcWFR72qWU1WVnZebltfqlwACwLCL6MnNL1fRHwAnAFzPz1bX6JcAdwJ9GxDWZOVA9\n/nzgPGANsDAzH62eclFEXA28FjgL+NQOvAdJkiRJkiRNExPZ02wO8LLMPJsSmrXycuAA4Mv1sKny\nWeBHwCnV6jIiYr/qOQ8An64XZ+bdwErKCrJX1JreSlnpdl6j/jHgo9X7emut6UxKAPaxWmA2anHV\n9rbtfQ+SJEmSJEmaXiYSmh2Vmd9oo+4ESqC1qtmQmSPAzZSQ6rjq4WOBHuCmqr1pVVV/PEBE7Asc\nDPwkM+9tUc9ofeO/xxrTGmAt0FcLwSb6HiRJkiRJkjSNtB2aZeZQm6WHVtfVLdp/SAmcRuvaqa/X\nHTZefWaupexzdmBE7B0RM4GDKAcFDLb5GhN9D5IkSZIkSZpGJrLSrF1zq+sjLdo3NOrmUlZ1TaR+\nvP5H22YA+wBPo6xk29RiJdv2vEazXpIkSZIkSdPIRA4CaFdvdX2iRfvPGnWdrm8+Z/Q9tlu/PWOS\nJEmSJEnSNNKN0Gxzdd2rRfvejbpO1zefMxp8tVu/PWOSJEmSJEnSNNKN0Gx9dX16i/ZnNOrWU26l\nnEh9y/4jYg/KbZnDlNsotwJPArMjYmZmPrmjrzFG/bgGBgbaKWvb2rVrO9qfpPbdddddDA21u8Wj\ntHvq9L97ktrj3JMmj/NPmp66EZrdRTkR87mUUyabDqbsYfbdWj1V/VgOrq7N+mhR/xxgT+D7mfkE\nQETcQ9m0/yDgB22+xkTew7gWLlzYTlnbZs+eDdc+2NE+JbXnsMMOo7+/f7KHIe2yBgYGOv7vnqRt\nc+5Jk8f5J02OnRFWdyM0uxF4B3AS8Bf1hojYE3gx8HPgpurhb1JuoTy+xUqwUygB1dcBMnNDRNwB\nLIiIBZl55xj1ANc3xnRoNaZfCs0iYgGwPzCQmetr9RN5D5J2AyPDwwwOtjqEd8f19fXR09PTtf4l\nSZIkSe3bkdCs1UmU1wOrgZMj4pjM/Mda2znAvsCyzNwA/xGCrQDeWLUvGS2OiGMoIdi9wHW1fi4D\nPgdcGBGnZeZwVT8PeDdlo/4ra/WfAd4JnB0RX8jMdVX9HsDF1Xu5dHvfg6Tdw5ahdSxeup7eOWs6\n3vfmjQ+xfMnprmKTJEmSpF1EW6FZRBwCvKz20Asp+5AdHRHvrj1+ZWZuiog3ATcAN0TEcmAtcCRw\nGmWl13saL3EOcDQlBDsSuA04EHgDsAk4YzQYA8jMqyJiEbAIuD0ivgrMAk4HDgDemZmra/WrI+Jc\n4BPAnRFxNfBY9fzDgZWZ+YVa/dbteA+SdgO9c+Yxa+78yR6GJEmSJKnL2l1p9gLgo43HRoATq69R\nK4FNmXlrRBwFLKYEU/sA91FCq4sy85F6R5m5vqr/QFV/EvAw8BXggsy8Z4wxvYqyeuxNwLmUWzy/\nA7w5M29oFmfmJRGxmrIS7UzKvmcJnAVcMUb9hN6DJEmSJEmSpo+2QrPMvAq4aiIdZ+bdwGsmUL8B\nOLv6aqd+K3BJ9dXua1wLXDuB+gm9B0mSJEmSJE0Pe0z2ACRJkiRJkqRdjaGZJEmSJEmS1GBoJkmS\nJPfoZRwAABd8SURBVEmSJDUYmkmSJEmSJEkNhmaSJEmSJElSg6GZJEmSJEmS1GBoJkmSJEmSJDUY\nmkmSJEmSJEkNhmaSJEmSJElSg6GZJEmSJEmS1GBoJkmSJEmSJDUYmkmSJEmSJEkNhmaSJEmSJElS\ng6GZJEmSJEmS1GBoJkmSJEmSJDUYmkmSJEmSJEkNhmaSJEmSJElSg6GZJEmSJEmS1GBoJkmSJEmS\nJDUYmkmSJEmSJEkNhmaSJEmSJElSg6GZJEmSJEmS1DBzsgcgSYKR4WEGBwe70ndfXx89PT1d6VuS\nJEmSpitDM0naBWwZWsfipevpnbOmo/1u3vgQy5ecTn9/f0f7lSRJkqTpztBMknYRvXPmMWvu/Mke\nhiRJkiQJ9zSTJEmSJEmSfoWhmSRJkiRJktRgaCZJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJkiRJUoOh\nmSRJkiRJktRgaCZJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJkiRJUoOhmSRJkiRJktRgaCZJkiRJkiQ1\nGJpJkiRJkiRJDYZmkiRJkiRJUoOhmSRJkiRJktRgaCZJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJkiRJ\nUsPMyR6AJKl7RoaHGRwc7ErffX199PT0dKVvSZIkSZpshmaSNI1tGVrH4qXr6Z2zpqP9bt74EMuX\nnE5/f39H+5UkSZKkXYWhmSRNc71z5jFr7vzJHoYkSZIkTSnuaSZJkiRJkiQ1dGWlWUScAXxuG2Xn\nZebFtecsAN4HHAPMBR4ArgcuyMz7x3iN/YH3AycD84Eh4Bbg4sy8fYz6vYB3Aa8D+oGtwPeAKzJz\nRYv38UrgHcDhwFOA1cAK4JOZ+fg23p8kSZIkSZKmqG7fnnlj9TWWW0b/IyJeClwDPAksB34EPA94\nC3BqRLwwM39cq38mcCslLLsG+Ctgf+CNwMkRsSgzv16r3wO4DjgBuB34CLAX8Grg6og4JDM/UB9c\nRJwPLK7GciXwKPAS4ELgxIg4MTOHt+N7IkmSJEmSpF1ct0OzWzPzk+MVVCvAlgEzgBdl5p21tpuB\npcDlwKLa0y6lBGZnZebltfqlwACwLCL6MnNL1fRHlMDsi5n56lr9EuAO4E8j4prMHKgefz5wHrAG\nWJiZj1ZPuSgirgZeC5wFfGpC3w1JkiRJkiRNCbvCnmYvBw4AvlwPzCqfpaz0OqVaXUZE7Fc95wHg\n0/XizLwbWAnsB7yi1vRWYIQShNXrHwM+Svk+vLXWdCYlxPtYLTAbtbhqe9uE3qUkSZIkSZKmjJ0S\nmkXE3hFxQEQ8dYzmEyiB1qpmQ2aOADdTQqrjqoePBXqAm6r2plVV/fHVa+8LHAz8JDPvbVHPaH3j\nv8ca0xpgLdA3GuRJkiRJkiRpeul2aLYwIm6ibNL/E2AoIv4hIn6nVnNodV3doo8fUkKw0bp26ut1\nh41Xn5lrgSeAA6twbyZwEOWggME2X0OSJEmSJEnTSLdDs1OA9cD/BF4P/A3ldMybIuKkqmZudX2k\nRR8bGnVzKSvTJlI/Xv+jbTOAfYCnUVaybWqxkm2s15AkSZIkSdI00q2DAO6g7B92W2bWb3FcERG3\nAZcAfxERzwF6q7YnWvT1s+ra27h2qr75nNHvSbv1kiRJkiRJmma6Eppl5r8C/9qi+TLgT4BnAS8A\nNleP79Wifu/qurlx7VR98zmjYVm79ZIkSZIkSZpmurXSrKXMHImIfwHmA79JuX0T4OktnvKM6rq+\ndp0xwfqW/UfEHpTbMocpt11uBZ4EZkfEzMx8so3XGNfAwEA7ZW1bu3ZtR/uTpO1x1113MTQ0NNnD\n0C6o0//uSWqPc0+aPM4/aXra6aFZZU513QzcRTkR87mUkzKbDqbsYfbd6s93Vdfntuj74OrarI8W\n9c8B9gS+n5lPAETEPZRN/g8CftDGa4xr4cKF7ZS1bfbs2XDtgx3tU5Im6rDDDqO/v3+yh6FdzMDA\nQMf/3ZO0bc49afI4/6TJsTPC6q4cBBARKyLitoiYNUbbbH5xouWdwI2UlWMnjVG7J/Bi4OfATdXD\n36TcQnl8ddJl0ymUkO3rAJm5gbLH2r4RsaBFPcD1tcdurK5jjWkBsD9wR2a2tdJMkiRJkiRJU0u3\nVpo9CfwXYAnwztEHq1shP05ZaXZdZt4XEQ8Aq4GTI+KYzPzHWj/nAPsCy6rwi8zcEBErgDdW7Utq\n/R9DCcHuBa6r9XMZ8Dngwog4LTOHq/p5wLspG/tfWav/TDXusyPiC5m5rjb+iymh3KU78P2RpClt\nZHiYwcHBrvXf19dHT09P1/qXJEmSpG3pVmj2XuAY4O0RcSTw98DjwKnAQkpI9ocAmbk1It4E3ADc\nEBHLgbXAkcBplNsj39Po/xzgaEoIdiRwG3Ag8AZgE3DGaDBWvcZVEbEIWATcHhFfBWYBpwMHAO/M\nzNW1+tURcS7wCeDOiLgaeKx6/uHAysz8Qge+T5I0JW0ZWsfipevpnbOm431v3vgQy5ec7q2fkiRJ\nkiZVt07P/El1G+N7KcHXWZSN9v8N+BDwqcx8tFZ/a0QcBSymBFP7APdRQquLMvORRv/rq/oPVPUn\nAQ8DXwEuyMx7xhjWqyirx94EnEu5xfM7wJsz84Yx3sMlEbGashLtTMq+Z1m9lyu249siSdNK75x5\nzJo7f7KHIUmSJEld0bWDADLzYUo4dW6b9XcDr5lA/xuAs6uvduq3ApdUX+2+xrXAte3WS5IkSZIk\naXroykEAkiRJkiRJ0lRmaCZJkiRJkiQ1GJpJkiRJkiRJDV3b00y/8D/++FMd7e+n9/8b9B7c0T4l\nSZIkSZL0C4ZmO8G6Gb/V0f42bN3E3h3tUZIkSZIkSXXenilJkiRJkiQ1GJpJkiRJkiRJDYZmkiRJ\nkiRJUoN7mkmSdikjw8MMDg52pe++vj56enq60rckSZKk6cXQTJK0S9kytI7FS9fTO2dNR/vdvPEh\nli85nf7+/o72K0mSJGl6MjSTJO1yeufMY9bc+ZM9DEmSJEm7Mfc0kyRJkiRJkhoMzSRJkiRJkqQG\nQzNJkiRJkiSpwdBMkiRJkiRJajA0kyRJkiRJkhoMzSRJkiRJkqQGQzNJkiRJkiSpYeZkD0CSpJ1h\nZHiYwcHBrvTd19dHT09PV/qWJEmSNDkMzSRJu4UtQ+tYvHQ9vXPWdLTfzRsfYvmS0+nv7+9ov5Ik\nSZIml6GZJGm30TtnHrPmzp/sYUiSJEmaAtzTTJIkSZIkSWowNJMkSZIkSZIaDM0kSZIkSZKkBkMz\nSZIkSZIkqcGDACRJ2gEjw8MMDg52pe++vj56enq60rckSZKk8RmaSZK0A7YMrWPx0vX0zlnT0X43\nb3yI5UtOp7+/v6P9SpIkSWqPoZkkSTuod848Zs2dP9nDkCRJktRB7mkmSZIkSZIkNRiaSZIkSZIk\nSQ2GZpIkSZIkSVKDe5pJkrQL6uapnODJnJIkSdK2GJpJkrQL6tapnODJnJIkSVI7DM0kSdpFeSqn\nJEmSNHnc00ySJEmSJElqcKWZJEm7mW7ul7Z169au9CtJkiTtbIZmkiTtZrq1X9rmjQ9x7uufzxFH\nHNHRfiVJkqTJYGgmSdJuyP3SJEmSpPG5p5kkSZIkSZLU4EozSZLUESPDw9x///3ce++9He+7r6+P\nnp6ejvcrSZIktWJoJkmSOmLL0DqW3zyDL92xqqP9bt74EMuXnE5/f39H+5UkSZLGY2gmSZI6xr3S\nJEmSNF0YmkmSpF3ayPAwg4ODXevfWz8lSZI0FkMzSZK0S9sytI7FS9fTO2dNx/v21k9JkiS1Ymgm\nSZJ2ed267bObq9hcwSZJkjS1GZpJkqTdVrdWsT32yINccObRPPvZz+5ov2AYJ0mStLMYmm1DRLwS\neAdwOPAUYDWwAvhkZj4+mWOTJEk7rhur2DZv/CmLl36742Gct5NKkiTtPIZm44iI84HFwI+AK4FH\ngZcAFwInRsSJmTk8eSOUJEm7qm6EcR6KIEmStPMYmrUQEc8HzgPWAAsz89Gq6aKIuBp4LXAW8KlJ\nGqIkSdrNdPNQBG8plSRJ+mWGZq2dCcwAPlYLzEYtBl4HvA1DM0mStBN161CEbt1S2q0wbuvWrQBd\nC+MM+iRJkqFZa8dX11XNhsxcExFrgb6IeGZm3rdzhyZJktR5U2l/t/933w946uyn0ztnXkf7hakZ\n9I32LUmSOsfQbAwRMRM4CNgKtNo45IfAs4BDAUMzSZKkFroVxk21VXfdCvoee+RB3njCPPbZZ5+O\n9gvdDfpczSdJ2tUZmo3taUAP8EhmjrSo2VBd5+6cIUmSJGlnmUpB3+aNP2X5zev50h2/coPEDutm\n0DdVV/MZIErS7sPQbGy91fWJcWp+1qiVJEmSJkU3V9152253+zZAnLr9jvY9ODjI7NmzO94vTK3v\nheGvpiNDs7Ftrq57jVOzd6O2pRkb797hAf1Sf4/9O5uHO79vxZahDZSzDzqvW31PtX672fdU67eb\nfU+1frvZ91Trt5t922/3+55q/Xaz76nWbzf7nmr9drPvqdZvN/veMrSBp85+esf7nYoe3/Qw53z8\nq+w969c72u/Gn/4bT/m1fTrebzf7nmr9/nLf3+1Sv1Pje/H4pg187E8WdeUEZmkyzRgZaXX34e4r\nInqALZSfEJ6amU+OUfP3wHHASzOz5Vr4gYEBv8GSJEmSJEkdtnDhwu58alRxpdkYMnNrRNxD2eT/\nIOAHY5QdXF3H/Uih2/8DJUmSJEmS1Hl7TPYAdmE3VteTmg0RsQDYH7gjM9fv1FFJkiRJkiSp6wzN\nWvsM8CRwdkTsO/pgROwBXAyMAJdO0tgkSZIkSZLURe5pNo6IeBfwCeAB4GrgMWARcDiwMjNfO4nD\nkyRJkiRJUpcYmm1DRJwKvBtYAOwJJLAMuCIzhydzbJIkSZIkSeoOQzNJkiRJkiSpwT3NJEmSJEmS\npAZDM0mSJEmSJKlh5mQPYDqKiFcC76AcGPAUYDWwAvhkZj4+mWOTprqImAGcCbwJOJSy1+D9wI3A\nhZl5X6N+AfA+4BhgLuVgj+uBCzLz/p03cml6iYhZwL8ABwLnZ+aHG+3OPalDIuLXgfOB04ADgA3A\ntyj/7t3VqHXuSR0QEb8G/DHwCuAgyu/ODwL/AHw8M/91jOc4/6TtEBFHAH8N9DPGz5W1ugnNsYjY\nH3g/cDIwHxgCbgEuzszb2xmbe5p1WEScDywGfgT8b+BR4CWU/6nfBE70AAFp+1SB2VeBUylz7IuU\nU21PBP4r8BBwZGaurepfClwDPAksr57zPOA1lL9gX5iZP97Jb0OaFiLis8AfACPAh+o/3Dj3pM6J\niPnAt4F9gb8B7gUOAV4LbAZelJnfq2qde1IHVB8MfZvyAe33KB/OPkw5HO73KHPs1MxcVXuO80+a\noIiYCXwQeC/lZ8oeGj9X1monNMci4pnArZSw7BrgO8D+wBuBXmBRZn59W2N0pVkHRcTzgfOANcDC\nzHy0arooIq6m/HBzFvCpSRqiNNW9mRKY3Ub5JeGJ6vEPRsTngd+nzMG3RMRelJNuZ1S1d452EhE3\nA0uBy4FFO2/40vQQES+nBGbfARY22px7UmctowRmJ2Tmt0cfjIgbgcuA04HvOfekjnoHJTC7PjNP\nqTdExBnA54CPUUI0/+2Ttt+3gCMo/549Dpw7VtF2zrFLKYHZWZl5ea1+KTAALIuIvszcMt4A3dOs\ns86k/E/8WC0wG7W4anvbTh+VNH0cAWwCltQCs1FXUubY71R/XkS5heXL9b9UK5+lfDJxSvUJhKQ2\nRcQzKD+Y3Al8hjLv6l6Oc0/qiIj4beB3gb+sB2YAmbk8M/fJzNFfMJx7Uuf0U1a9/N0YbV+rrgfV\nHnP+SdtnDvCyzDybEpq1MqE5FhH7Vc95APh0vTgz7wZWAvtRbr8el6FZZx1fXVc1GzJzDbAW6PMv\nS2n7ZOYfZubTMvOrYzQPVdee6noC5YedsebjCHAz5Zf947owVGk6+yvKDzhnAFvHaHfuSZ3z3ynz\naSWUbQoiYl6111KTc0/qnH+hzJfnjtHWV12/W3vM+Sdtn6My8xtt1E10jh1L+b3wpqq9aVVVf/wY\nbb/E2zM7pLoX9yDKLxCDLcp+CDyLstT3vhY1krbPqdX176vrIdV1dYv6H1L+ojy0m4OSppOI+APK\np3bnZuZdEbFwjLLROeXck3bc4dV1bUT8OSWsng2MRMQ/A+/JzFuqGuee1DlXAq8D3hYRG4EvUfYQ\nPBS4iPJh7Tm1eueftB0yc2jbVcDE51g79fW6llxp1jlPoySZm1okmVBOOoJyyoOkDomI51FOUXkM\nuKB6eHSePdLiac5HaQIi4kDgEsqJQx8fp9S5J3XOb1TXz1E+NX8fZY/czwNHAasi4tiqxrkndUi1\nx9GxlD2SzqOsKrsX+AqwhbI6pn7LtPNP6q6JzrG5lJVpOzwnXWnWOb3VtbnPUt3PGrWSdlB1PPHf\nAXsD/6121PC25qTzUWpTdXLtVZQP284Y58MhcO5JnTS7uj4V+M+ZOXpL9N9GxN2UAPtyyulhzj2p\nQyJiT8pqs98HrqPcIr0Z+G3KHtU3RMSi2t5Kzj+puyY6xzo2Jw3NOmdzdd1rnJq9G7WSdkBEvIJy\n3DDAKzPzulrztuak81Fq37sph2y8PTNbbUEwyrkndc6T1fXiWmA26jLgg8AhEfFbOPekTjqHEph9\nJjPfXnt8ZUR8CbgdWBERh2Xmkzj/pG6b6Bzr2Jz09szO2Uj5wWZ2tb/ZWJ5RXdfvnCFJ01dEvI/y\nqd/DwHGZ+bVGyeg8e3qLLpyPUhsi4lDKbc//JzOvbDQ3T84E557USaO3j/y02ZCZP+cXe7L8Bs49\nqZPeSrm165JmQ7W67J8o+1mP7u3p/JO6a6JzbD3l59QdnpOGZh1Sffp3D+V7elCLsoOr63dbtEtq\nQ0RcCFwIfA94QWbePkbZXdV1rFOPoMzHEZyP0rb8HvAUyjHew/UvYBllHp1fPbYM557USd+vrs9q\n0f7U6vo4zj2pk/avrr8SWFdGf9H+zerq/JO6a6JzrJ16aGNOGpp11o3V9aRmQ0QsoPzle0dm+gmD\ntJ0i4l2UjZBvAV6UmQ+0KL2R8unCWPNxT+DFwM+Bm7o0VGm6uJWyb9Inqmv963rKPLu19mfnntQ5\no/Pp5GZDRMwBnk05uf37OPekTnqwurZaDNHXqHP+Sd010Tn2Tcp+Zse3uBPwFErI9vVtvbChWWd9\nhnKL5tkRse/ogxGxB3Ax5X/KpZM0NmnKi4jDgY9Qbkc5JTM3jVN+PeWI4ZMj4phG2znAvsAXMnPD\nrzxT0n/IzFWZ+Z6xvoC/rcpurB5biXNP6qSvAf8OvH6M+XQhZU+WazJzCOee1Elfo/yC/oHmL9wR\ncRpwKCUwGz1B0/kndUarw6YmNMeq6wrK7Znn1Iur559CORH3OrZhxsjIeAdgaaKqVTCfAB4ArgYe\nAxYBhwMrM/O1kzg8aUqLiOsony58EfjncUqvzMxNEfFC4Aagh3JgwFrgSOA04AfAMf7wIm2/iDgD\n+BxwfmZ+uPa4c0/qkIg4mjKf9qDMt58AvwscS5lbR4+eHO3ckzojIn4d+BblFq4fANcAmygn1b4S\nGAZekZnX157j/JMmICIOAV5We+gllH/fvsEv7uKD7fzdLiKeQbkbog/4O+A24EDgDZRVaC/JzNu2\nNU5Dsy6IiFMpJ40tAPYEkrLvyxWZOTyZY5OmsogYpPW+LnXPzswfVc85FFhM+eViH+A+4CvARZn5\nSLfGKu0OqtBsGSU0u6DR5tyTOiQignJS5vHAXOB+4KuU+bS+UevckzogInqBsyh7e/ZTTuF7ELgZ\n+Hhm3j3Gc5x/UptqP0duy3b/blcF4B+gLGT6T5RD5G4CLsjMe9oZp6GZJEmSJEmS1OCeZpIkSZIk\nSVKDoZkkSZIkSZLUYGgmSZIkSZIkNRiaSZIkSZIkSQ2GZpIkSZIkSVKDoZkkSZIkSZLUYGgmSZIk\nSZIkNRiaSZIkSZIkSQ2GZpIkSZIkSVKDoZkkSZIkSZLU8P8B1aDqw1H1IMIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce75edd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data.trip_time.values / np.timedelta64(1, 'm'), bins=50, range=[0,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count                   3432668\n",
      "mean     0 days 00:13:02.243506\n",
      "std      0 days 00:27:26.535018\n",
      "min             0 days 00:00:01\n",
      "25%             0 days 00:06:24\n",
      "50%             0 days 00:10:34\n",
      "75%             0 days 00:16:50\n",
      "max            11 days 00:45:28\n",
      "Name: trip_time, dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  1.7945    ,   3.        ,   4.        ,   5.        ,\n",
       "         6.4       ,  10.56666667,  16.83333333,  21.        ,\n",
       "        31.        ,  49.08883333])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data.trip_time.describe())\n",
    "np.percentile(data.trip_time, [1,5,10,15,25,50,75,85,95,99]) / np.timedelta64(1,'m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We sometimes have some unreasonably small trip_times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": 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>dropoff_datetime</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>flag</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3525186</th>\n",
       "      <td>2013-05-12 23:22:39</td>\n",
       "      <td>40.763210</td>\n",
       "      <td>-73.962524</td>\n",
       "      <td>7.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:22:39</td>\n",
       "      <td>40.759930</td>\n",
       "      <td>-73.975325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.30</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3529613</th>\n",
       "      <td>2013-05-12 23:38:46</td>\n",
       "      <td>40.734109</td>\n",
       "      <td>-73.983598</td>\n",
       "      <td>5.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:38:46</td>\n",
       "      <td>40.742107</td>\n",
       "      <td>-73.974624</td>\n",
       "      <td>1.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3530873</th>\n",
       "      <td>2013-05-12 23:43:59</td>\n",
       "      <td>40.643328</td>\n",
       "      <td>-73.950397</td>\n",
       "      <td>34.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:43:59</td>\n",
       "      <td>40.727880</td>\n",
       "      <td>-74.001728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.90</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531118</th>\n",
       "      <td>2013-05-12 23:45:00</td>\n",
       "      <td>40.755222</td>\n",
       "      <td>-73.928660</td>\n",
       "      <td>3.0</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:45:00</td>\n",
       "      <td>40.753865</td>\n",
       "      <td>-73.929723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3531331</th>\n",
       "      <td>2013-05-12 23:45:56</td>\n",
       "      <td>40.773872</td>\n",
       "      <td>-73.948701</td>\n",
       "      <td>5.5</td>\n",
       "      <td>trip_time_NA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:45:56</td>\n",
       "      <td>40.766625</td>\n",
       "      <td>-73.962938</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.20</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           dropoff_datetime  dropoff_latitude  dropoff_longitude  fare_amount  \\\n",
       "3525186 2013-05-12 23:22:39         40.763210         -73.962524          7.0   \n",
       "3529613 2013-05-12 23:38:46         40.734109         -73.983598          5.0   \n",
       "3530873 2013-05-12 23:43:59         40.643328         -73.950397         34.0   \n",
       "3531118 2013-05-12 23:45:00         40.755222         -73.928660          3.0   \n",
       "3531331 2013-05-12 23:45:56         40.773872         -73.948701          5.5   \n",
       "\n",
       "                 flag  passenger_count     pickup_datetime  pickup_latitude  \\\n",
       "3525186  trip_time_NA              1.0 2013-05-12 23:22:39        40.759930   \n",
       "3529613  trip_time_NA              1.0 2013-05-12 23:38:46        40.742107   \n",
       "3530873  trip_time_NA              1.0 2013-05-12 23:43:59        40.727880   \n",
       "3531118  trip_time_NA              1.0 2013-05-12 23:45:00        40.753865   \n",
       "3531331  trip_time_NA              1.0 2013-05-12 23:45:56        40.766625   \n",
       "\n",
       "         pickup_longitude  tip_amount  total_amount  trip_distance  trip_time  \n",
       "3525186        -73.975325         NaN           8.0           1.30        NaT  \n",
       "3529613        -73.974624         1.2           7.2           1.00        NaT  \n",
       "3530873        -74.001728         NaN          35.0           9.90        NaT  \n",
       "3531118        -73.929723         NaN           4.0           0.15        NaT  \n",
       "3531331        -73.962938         NaN           6.5           1.20        NaT  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028847133166903904"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-len(data)/(len(data)+len(anomaly))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = 'blue'> So all in all, we dropped less than 3% of the data. </font color>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pickup_datetime            0\n",
       "dropoff_datetime           0\n",
       "passenger_count            9\n",
       "trip_distance              0\n",
       "pickup_longitude           0\n",
       "pickup_latitude            0\n",
       "dropoff_longitude          0\n",
       "dropoff_latitude           0\n",
       "fare_amount                0\n",
       "tip_amount           1636185\n",
       "total_amount               0\n",
       "trip_time                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We can deal with that. External investigation of the anomaly is recommended."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start validating the non-anomaly data: Valid trip_time, valid distance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correct the avg amount for the initial charge. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['avg_amount_per_minute'] = (data.fare_amount-2.5) / (data.trip_time / np.timedelta64(1,'m'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.432668e+06\n",
       "mean     7.865239e-01\n",
       "std      6.410160e+00\n",
       "min      0.000000e+00\n",
       "25%      5.769231e-01\n",
       "50%      6.629834e-01\n",
       "75%      7.857143e-01\n",
       "max      6.450000e+03\n",
       "Name: avg_amount_per_minute, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.avg_amount_per_minute.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of the avg_amount_per_minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min:0.0\n",
      "Max:6450.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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7yhlKfmGGsA8AAAAANjTLlt7HdPeZ04Z9a3X3SzOc3PvuWcfak6o6ZrSi8PlJ\nDkmyPPpMO94ZST6b5MVJfi7J7ZMcPhr72CSnJ3lekkur6nFTzvEzSX4pyQ+mrRMAAACAxTJ14Nfd\nr97KQrr7su5+8FaOuaKqHpLhgJHTshryLe25x4bjnZXhJOLbZDU4/FyGwPIdSXatuX9UkpdW1ZMn\nnOPmGcLE5SSvmrZWAAAAABbLVrzD7wZrtOX2eUnOHt1aTnJRklNmGPPkJC/LEJYuZQj3ntDdHxpr\nt2PU7qTRredU1cXd/b5NTvXHSY5J8rokH0jyK9PWDAAAAMDimGVL7w1aVR2T5KO5ftj3wiT3XnM9\njT/MEJQuJbksyf3Gw74k6e6dSU4dtVketX/BJmv/2SSPTPLlJE+ask4AAAAAFtDcBn4Z3qN3Qoaw\n7RtJzuzuJ3f396cdsKpOSXK/0eVykqd299f21L67v57kaRnCvqUkJ1TVutuWq+oWSV40Gv/Xu/sb\n09YLAAAAwOKZ58BvxQeT3K27374FY501+l7KsPru/E30OT/J5VldUXjWOm2TIew7Osnruvut0xQJ\nAAAAwOKa58DvuiTPSnL/7v7iFo152uh7Ocn7uvu6jTp097VJLsjqKr/T9tS2qn4hyc8n+b9Jfmvm\nagEAAABYOHN7aEd3X5ThgI4tUVU3S3JiVlfqXThB9wuTPGr095FVdUJ37xob/+gMB3UsJzm7u6+Y\nsWQAAAAAFtA8r/DbaidkdZVeknx6gr7jbU/cTZsXJblFktds0fZjAAAAABaQwG/zjhu7nmSb8Erb\nldWBx6/9sap+McnPJvlSbOUFAAAAYAYCv807euz66xP0HT9p94djVdUxSV6Q5AdJHt/dV05XHgAA\nAADM8Tv89oLDx66vmqDveNubrvn79AxbeZPkXVW17kBVtXJQyP/s7lMnqAEAAACABSDw27yD1150\n9w8m6Dvedu1Y30/yzU3Mfdjo75W2355gfgAAAAAWhMBvSlW11N3LG7dMsnrQxz/T3a9L8roN5npM\nkpeP2h+16SIBAAAAWDje4bd53xu7vvEEfQ8bux4fCwAAAAC2xNQr/Krqgq0sZJOWu/un98G8STJ+\nmMaNk1yzyb7j4eBGW3gBAAAAYCqzbOm9f5LNbmndCkvbPN+4r4xdH53Nn9R7yw3G2ozl7NvnBwAA\nAGA/sLS8PF2GtOa02LXGBxt/d91Gv29kubsPnLDPPzOqfaWWc7v7vE30uXuSj67UkeRB3f2eTc53\nRpI3r+mzJrbPAAAgAElEQVT7M939V5NVPZ2dO3cun/Pay7Zjqhx97Sfzm48+fVvmAgAAANiXduzY\nMWmutW1mWeH3iiSHZgjt7phkR64f4H0xydeSXJXkRkkOT3KrJGsPnVhO8skkX5qhju2yK8NpuyuB\n452SbCrwS1Jj1xdvVVE3JEfd/Kjs2LFj2+bbuXPnts633Tzf/s3z7b/m+dkSz7e/83z7r3l+tsTz\n7e883/5rnp8t8Xz7u0V4vhuyqQO/7v6VJKmqpyQ5c3T7TRmCwPd397d216+qbpPkAUken2Fb8PFJ\n/qS7XzxtLduhu79bVTuT3CtDUPlTSf5ok90fsObvy7r7C1tdHwAAAAAks63wS1U9KckfJLk6yRnd\n/d6N+nT3l5K8JslrqupRSV6W5E+q6sju/s+z1LMN3pEh8FtKcmpVHdLd/7Reh6o6Isl9s7qF+C17\nt0QAAAAAFtkB03asqhOSPG90+VubCfvGdfdrkzwlQ4D2H6rqx6atZ5u8OsnK+/+OTPKYTfQ5O8lh\nWd3u/Iq9UhkAAAAAZIbAL8mvjvpfkeRVM4zz4iTfzPBuvCfMMM5eN9qK+/oM4d1SkmdW1bF7al9V\nleTpWV3d957uvmivFwoAAADAwpol8Pu3GYKsj3b3dEf9Junua5PszBCgPXiGerbL0zKEnMlwAMkH\nqur+442q6vQkFyS5SYZnuybJk7epRgAAAAAW1Czv8Lvd6Hu3h3NM6MqxMWdWVa/bZNOHV9WJe/jt\n3O7etfZGd3+pqh6a5F0ZTh6+XZILqurzST6dIdy7c5Lj1nT7TpIzu/szkzwDAAAAAExqlsBvpe9t\ntqCOW42+b7QFY614xAa/r7xT7y6jz7jlJC9Ksmv8h+7+UFWdmuSlSU4a3T5+9FnbPxlCwCd29wc3\nWTcAAAAATG2WLb1fyRCa3aOqbjvtIFV1TJJ7ZgjIvjZDPeOWt+CzR929M8nJSR6Z4b1+lyS5avS5\nJMkbkpyV5K7CPgAAAAC2yywr/D6Z5EczhIavrKqHdvd3Jhmgqg5K8pJRHcsZVsNtie4+cKvGWmeO\n5STnjz4AAAAAsM/NssLvjWv+fkCSi6rql6vqZht1rKpDquphST6c6x/U8eYZ6gEAAACAhTfLCr9X\nJvmtJHfLsDrvx5K8PMlLqurTST6f5KsZDqxYTnLjJLfIcJjFXZIcnGFL8MrW2UszvBMPAAAAAJjS\n1IFfd19bVT+f5D1Jbp8huFsajXlSkh/fQ9elNX+v9Plykp/v7n+ath4AAAAAYLYtvenuS5PcK8mf\nJbl2zU/rHXgx/tt/S3Kv7v7ELLUAAAAAALNt6U2SdPfXk5xdVeckOTPJv8mwZff2uxl/Ock/ZDic\n438keVN3f27WGgAAAACAwcyB34ruvjzJn44+qaqlJIePPktJrk7y7e6+bqvmBAAAAACub8sCv3Hd\nvZzk26MPAAAAALANZnqHHwAAAABww7LlK/xGW3nvkOE9frdKctMkl3b3m7d6LgAAAADg+rYs8Kuq\neyY5O8lDkxw19vMbkrx5rP2dk/yHJL/X3Z/ZqjoAAAAAYJHNvKW3qm5cVa9O8r+SPCbJLTIc0pE1\n37vz4CQPS/KxqnrkrHUAAAAAADMGflV10yR/neRR+efh3nphX5I8aPR9aJJXVdWps9QCAAAAAMy+\nwu8/J7n3mutvJ3lJkrOS/MQGfV+R5PIkyxm2Fv9pVR04Yz0AAAAAsNCmfodfVZ2Y5IkZArtkCPp+\nt7u/vabNHvt39yur6oNJPpDklhkO+viZjL3rDwAAAADYvFlW+P1CVrft/kV3/9rasG8zuvuSJP9u\nza0zZqgHAAAAABbeLIHfaaPva5M8bYZxXpfkitHfG20DBgAAAADWMUvgd9sM23k/3t1fnnaQ7v5+\nks9mWC146xnqAQAAAICFN0vgd/To+x+3oI4vjb4P34KxAAAAAGBhzRL4fX/0faMtqOOmo++rtmAs\nAAAAAFhYswR+X8mwDfeusxRQVQeMxlhO8tVZxgIAAACARTdL4PeR0fePVtVPzzDOw7O6PfijM4wD\nAAAAAAtvlsDvPWv+/tOqOnqPLfegqu6c5IVrbr17hnoAAAAAYOHNEvi9Lsnlo7/vkORvq+pBm+lY\nVYdX1e8k+WCSHxnd/mKS189QDwAAAAAsvIOm7djd/1RVv53kDRnev3dckrdX1WVJ3p/ky2uaV1X9\nXpJjkvx4kvskOSTDOwAz6v+U7v7etPUAAAAAADMEfknS3edX1R2T/McMod1SkmOTPGpNs6Ukdx99\n1t5bXnP977v7jbPUAgAAAADMGPglSXc/q6o+meFdfLcb+3l5N11WLCX5xyS/1t3vWacd+4Hl667N\nN7725XzmM5/ZtjmvvfbabZsLAAAAYH8xc+CXJN399qp6V5JfTPKwJD+Z1XfzjbssyYczvAPwbd39\ng62ogX3r6isvz6euTJ747L/elvmuufIr+Xdn3TX3vOc9t2U+AAAAgP3FlgR+STIK7l49+qSqjkly\nVJIjk1yb5MokX+3uK7ZqTm5YDjvimBx+89vu6zIAAAAAFtqWBX7juvsrSb6yt8YHAAAAAP65qQO/\nqqoMJ+0mSXf3P21NSQAAAADAtGZZ4feRJIdlOJjj1km+uiUVAQAAAABTmyXwOzDDSbtJ8o0tqAUA\nAAAAmNEBM/S9dM3fTmoAAAAAgBuAWQK/V6/5+9dnLQQAAAAAmN0sgd9zk/xlhm29T62q39makgAA\nAACAaU39Dr/uvjbJw6vq55L8bpLnVNWvJ3lNkguTfCbJV7v7u1tSKQAAAACwoakDv6p6/5rLHyT5\nSpI7Jvn9sXaTDLvc3bMcJAIAAAAAC22WcO0nkyyP3VvO6sm9AAAAAMA2m3U1nXAPAAAAAG5AZgn8\nXrllVQAAAAAAW2KWQzset5WFAAAAAACzO2BfFwAAAAAAbJ09rvCrqnOTHJvk0u5+5m5+f9nozw93\n94v3Un0AAAAAwATW29L760lukeRte/j9sRlO5T00icAPAAAAAG4A1tvSe+To++DtKAQAAAAAmN16\ngd93kiwl+amquvM21QMAAAAAzGC9Lb1/n+THkxyW5G+r6h1JPp8hCFzrx6vq97eqoO4+b6vGAgAA\nAIBFs17g96YkJ2V4T99hSR6+mzZLSe4y+mwVgR8AAAAATGm9Lb3PSfLxDKHe8jbUsrQNcwAAAADA\nXNtj4NfdVye5T5JnJPlwkv+b5LoMwdzacG5piz4AAAAAwIzW29Kb7r4myX8afa6nqq7LsPLvDd39\nqL1T3uKpqoOSPDbJzye5W5JbJPlukn9M8v4kL+7uj+2zAgEAAAC4QVs38JtXVfWkJM9Ocujo1jnT\nHhZSVYcmeXSSByY5OcnRSW6U5KtJPpXkvUle3t1XbmKs2yX5qwzvRFzOcEDKpUl+JMkJSU5M8qtV\n9f9297OmqRcAAACA+bbeO/w2Y7/ailtVx1TVu5I8P8khGUK1qd9PWFVnJPlskhcn+bkkt09y+Gjs\nY5OcnuR5SS6tqsdtMNYBSd6WIey7MslZ3X14d5/Y3UcnuXuSj2T4P/uPVfVvpq0bAAAAgPm1x8Bv\ntLV0j7r7gNFn27bzblTTBn0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rlXePoJzqeyBwN8qpu9uBW4BvZmYOql5JkiRJkiRJTQNL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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce75efdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = data.avg_amount_per_minute\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.hist(h, normed=False, stacked=True, bins=40, range=[0 , 100], )\n",
    "    #, histtype='stepfilled')\n",
    "plt.yscale('log')\n",
    "plt.ylabel('log(freq x)', fontsize=40)\n",
    "plt.xlabel('x = avg_amount_per_minute', fontsize=40)\n",
    "print('Min:' +  str(min(h)) + '\\nMax:' +  str(max(h)))\n",
    "plt.yticks(fontsize=40)\n",
    "plt.xticks(fontsize=40)\n",
    "plt.locator_params(axis = 'x', nbins = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": 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>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_time</th>\n",
       "      <th>avg_amount_per_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:10:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>-73.981070</td>\n",
       "      <td>40.766047</td>\n",
       "      <td>-73.981362</td>\n",
       "      <td>40.738092</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.20</td>\n",
       "      <td>13.70</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:06:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-73.986312</td>\n",
       "      <td>40.760487</td>\n",
       "      <td>-74.005082</td>\n",
       "      <td>40.743372</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.50</td>\n",
       "      <td>00:06:00</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:04:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-73.989388</td>\n",
       "      <td>40.731577</td>\n",
       "      <td>-73.986102</td>\n",
       "      <td>40.740732</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.50</td>\n",
       "      <td>00:04:00</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:08:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.59</td>\n",
       "      <td>-73.994590</td>\n",
       "      <td>40.751857</td>\n",
       "      <td>-73.963470</td>\n",
       "      <td>40.765912</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.50</td>\n",
       "      <td>00:08:00</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:29:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.26</td>\n",
       "      <td>-73.783327</td>\n",
       "      <td>40.648510</td>\n",
       "      <td>-73.974808</td>\n",
       "      <td>40.624810</td>\n",
       "      <td>51.5</td>\n",
       "      <td>6.75</td>\n",
       "      <td>59.25</td>\n",
       "      <td>00:29:00</td>\n",
       "      <td>1.689655</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pickup_datetime    dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0      2013-05-06 2013-05-06 00:10:00              1.0           2.65   \n",
       "1      2013-05-06 2013-05-06 00:06:00              5.0           1.86   \n",
       "2      2013-05-06 2013-05-06 00:04:00              1.0           0.90   \n",
       "3      2013-05-06 2013-05-06 00:08:00              1.0           2.59   \n",
       "4      2013-05-06 2013-05-06 00:29:00              1.0          18.26   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.981070        40.766047         -73.981362         40.738092   \n",
       "1        -73.986312        40.760487         -74.005082         40.743372   \n",
       "2        -73.989388        40.731577         -73.986102         40.740732   \n",
       "3        -73.994590        40.751857         -73.963470         40.765912   \n",
       "4        -73.783327        40.648510         -73.974808         40.624810   \n",
       "\n",
       "   fare_amount  tip_amount  total_amount  trip_time  avg_amount_per_minute  \n",
       "0         10.5        2.20         13.70   00:10:00               0.800000  \n",
       "1          7.5         NaN          8.50   00:06:00               0.833333  \n",
       "2          5.5         NaN          6.50   00:04:00               0.750000  \n",
       "3          9.5         NaN         10.50   00:08:00               0.875000  \n",
       "4         51.5        6.75         59.25   00:29:00               1.689655  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0001    0.000000\n",
       "0.0100    0.437318\n",
       "0.5000    0.662983\n",
       "0.7500    0.785714\n",
       "0.9500    1.151163\n",
       "0.9750    1.344677\n",
       "0.9900    1.605263\n",
       "0.9950    1.847222\n",
       "Name: avg_amount_per_minute, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.avg_amount_per_minute.quantile([.0001,.01, .5, .75, .95, .975, .99, .995])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare to http://www.nyc.gov/html/tlc/html/passenger/taxicab_rate.shtml . We have a strict lower bound with .5 \\$ per minute (taxi waiting in congestion). 2.5 \\$ per minute match roughly     1 mile / minute (no static fares included!). So the taxi would drive 60 mp/h.  We take this as an upper bound."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06037092404527544\n"
     ]
    }
   ],
   "source": [
    "lb = 0.5 \n",
    "ub = 2.5\n",
    "anomaly = anomaly.append(data.loc[(data['avg_amount_per_minute'] > ub) | \n",
    "                                  (data['avg_amount_per_minute'] < lb)])\n",
    "\n",
    "anomaly.loc[data.loc[(data['avg_amount_per_minute'] > ub)].index,'flag'] = 'too fast'\n",
    "anomaly.loc[data.loc[(data['avg_amount_per_minute'] < lb)].index,'flag'] = 'too slow'\n",
    "data = data.drop(anomaly.index, errors='ignore') # ignore uncontained labels / indices\n",
    "print(1-len(data)/(len(data)+len(anomaly)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we dropped around 6% of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.321243e+06\n",
       "mean     7.244103e-01\n",
       "std      2.173654e-01\n",
       "min      5.000000e-01\n",
       "25%      5.833333e-01\n",
       "50%      6.666667e-01\n",
       "75%      7.916667e-01\n",
       "max      2.500000e+00\n",
       "Name: avg_amount_per_minute, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.avg_amount_per_minute.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": 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>avg_amount_per_minute</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>flag</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3534347</th>\n",
       "      <td>3.571429</td>\n",
       "      <td>2013-05-13 00:17:15</td>\n",
       "      <td>40.721621</td>\n",
       "      <td>-74.033262</td>\n",
       "      <td>70.0</td>\n",
       "      <td>too fast</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:58:21</td>\n",
       "      <td>40.725023</td>\n",
       "      <td>-74.007006</td>\n",
       "      <td>2.00</td>\n",
       "      <td>80.25</td>\n",
       "      <td>3.40</td>\n",
       "      <td>00:18:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534376</th>\n",
       "      <td>297.000000</td>\n",
       "      <td>2013-05-12 23:58:46</td>\n",
       "      <td>40.761277</td>\n",
       "      <td>-73.993425</td>\n",
       "      <td>52.0</td>\n",
       "      <td>too fast</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:58:36</td>\n",
       "      <td>40.761278</td>\n",
       "      <td>-73.993303</td>\n",
       "      <td>11.55</td>\n",
       "      <td>69.38</td>\n",
       "      <td>0.90</td>\n",
       "      <td>00:00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534378</th>\n",
       "      <td>0.410959</td>\n",
       "      <td>2013-05-13 00:07:08</td>\n",
       "      <td>40.724592</td>\n",
       "      <td>-73.981777</td>\n",
       "      <td>6.0</td>\n",
       "      <td>too slow</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:58:37</td>\n",
       "      <td>40.735385</td>\n",
       "      <td>-73.991185</td>\n",
       "      <td>1.40</td>\n",
       "      <td>8.40</td>\n",
       "      <td>1.10</td>\n",
       "      <td>00:08:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534514</th>\n",
       "      <td>2.605263</td>\n",
       "      <td>2013-05-13 00:18:00</td>\n",
       "      <td>40.792230</td>\n",
       "      <td>-73.946103</td>\n",
       "      <td>52.0</td>\n",
       "      <td>too fast</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:59:00</td>\n",
       "      <td>40.666372</td>\n",
       "      <td>-73.793397</td>\n",
       "      <td>10.40</td>\n",
       "      <td>68.23</td>\n",
       "      <td>16.11</td>\n",
       "      <td>00:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534548</th>\n",
       "      <td>9.110429</td>\n",
       "      <td>2013-05-13 00:04:42</td>\n",
       "      <td>40.755859</td>\n",
       "      <td>-73.972549</td>\n",
       "      <td>52.0</td>\n",
       "      <td>too fast</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-05-12 23:59:16</td>\n",
       "      <td>40.740766</td>\n",
       "      <td>-73.944841</td>\n",
       "      <td>11.56</td>\n",
       "      <td>69.39</td>\n",
       "      <td>2.50</td>\n",
       "      <td>00:05:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         avg_amount_per_minute    dropoff_datetime  dropoff_latitude  \\\n",
       "3534347               3.571429 2013-05-13 00:17:15         40.721621   \n",
       "3534376             297.000000 2013-05-12 23:58:46         40.761277   \n",
       "3534378               0.410959 2013-05-13 00:07:08         40.724592   \n",
       "3534514               2.605263 2013-05-13 00:18:00         40.792230   \n",
       "3534548               9.110429 2013-05-13 00:04:42         40.755859   \n",
       "\n",
       "         dropoff_longitude  fare_amount      flag  passenger_count  \\\n",
       "3534347         -74.033262         70.0  too fast              1.0   \n",
       "3534376         -73.993425         52.0  too fast              1.0   \n",
       "3534378         -73.981777          6.0  too slow              1.0   \n",
       "3534514         -73.946103         52.0  too fast              1.0   \n",
       "3534548         -73.972549         52.0  too fast              1.0   \n",
       "\n",
       "            pickup_datetime  pickup_latitude  pickup_longitude  tip_amount  \\\n",
       "3534347 2013-05-12 23:58:21        40.725023        -74.007006        2.00   \n",
       "3534376 2013-05-12 23:58:36        40.761278        -73.993303       11.55   \n",
       "3534378 2013-05-12 23:58:37        40.735385        -73.991185        1.40   \n",
       "3534514 2013-05-12 23:59:00        40.666372        -73.793397       10.40   \n",
       "3534548 2013-05-12 23:59:16        40.740766        -73.944841       11.56   \n",
       "\n",
       "         total_amount  trip_distance  trip_time  \n",
       "3534347         80.25           3.40   00:18:54  \n",
       "3534376         69.38           0.90   00:00:10  \n",
       "3534378          8.40           1.10   00:08:31  \n",
       "3534514         68.23          16.11   00:19:00  \n",
       "3534548         69.39           2.50   00:05:26  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anomaly.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Only look at trips in a given bounding box\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "jfk_geodata = (40.641547, -73.778118)\n",
    "ridgefield_geodata = (40.856406, -74.020642)\n",
    "data_in_box = data.loc[(data['dropoff_latitude'] > jfk_geodata[0]) & \n",
    "                       (data['dropoff_longitude'] < jfk_geodata[1]) &\n",
    "                       (data['dropoff_latitude'] < ridgefield_geodata[0]) & \n",
    "                       (data['dropoff_longitude'] > ridgefield_geodata[1]) & \n",
    "                       (data['pickup_latitude'] > jfk_geodata[0]) & \n",
    "                       (data['pickup_longitude'] < jfk_geodata[1]) &\n",
    "                       (data['pickup_latitude'] < ridgefield_geodata[0]) & \n",
    "                       (data['pickup_longitude'] > ridgefield_geodata[1])         \n",
    "                       ]\n",
    "# taxidata = taxidata.drop(anomaly.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": 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>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_time</th>\n",
       "      <th>avg_amount_per_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:10:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>-73.981070</td>\n",
       "      <td>40.766047</td>\n",
       "      <td>-73.981362</td>\n",
       "      <td>40.738092</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:06:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-73.986312</td>\n",
       "      <td>40.760487</td>\n",
       "      <td>-74.005082</td>\n",
       "      <td>40.743372</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>00:06:00</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:04:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-73.989388</td>\n",
       "      <td>40.731577</td>\n",
       "      <td>-73.986102</td>\n",
       "      <td>40.740732</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>00:04:00</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:08:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.59</td>\n",
       "      <td>-73.994590</td>\n",
       "      <td>40.751857</td>\n",
       "      <td>-73.963470</td>\n",
       "      <td>40.765912</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5</td>\n",
       "      <td>00:08:00</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:14:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.86</td>\n",
       "      <td>-73.965532</td>\n",
       "      <td>40.717052</td>\n",
       "      <td>-73.972442</td>\n",
       "      <td>40.743247</td>\n",
       "      <td>16.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.5</td>\n",
       "      <td>00:14:00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pickup_datetime    dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0      2013-05-06 2013-05-06 00:10:00              1.0           2.65   \n",
       "1      2013-05-06 2013-05-06 00:06:00              5.0           1.86   \n",
       "2      2013-05-06 2013-05-06 00:04:00              1.0           0.90   \n",
       "3      2013-05-06 2013-05-06 00:08:00              1.0           2.59   \n",
       "5      2013-05-06 2013-05-06 00:14:00              3.0           4.86   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.981070        40.766047         -73.981362         40.738092   \n",
       "1        -73.986312        40.760487         -74.005082         40.743372   \n",
       "2        -73.989388        40.731577         -73.986102         40.740732   \n",
       "3        -73.994590        40.751857         -73.963470         40.765912   \n",
       "5        -73.965532        40.717052         -73.972442         40.743247   \n",
       "\n",
       "   fare_amount  tip_amount  total_amount  trip_time  avg_amount_per_minute  \n",
       "0         10.5         2.2          13.7   00:10:00               0.800000  \n",
       "1          7.5         NaN           8.5   00:06:00               0.833333  \n",
       "2          5.5         NaN           6.5   00:04:00               0.750000  \n",
       "3          9.5         NaN          10.5   00:08:00               0.875000  \n",
       "5         16.5         NaN          17.5   00:14:00               1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_in_box.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True 0.9834670332763968\n"
     ]
    }
   ],
   "source": [
    "print(jfk_geodata < ridgefield_geodata,\n",
    "len(data_in_box)/len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we've omitted about 2% of the data because the trips do not start and end in the box"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect Manhattan only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce86c7a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NZkhOGyDFWxlECkR8PZIj70UnTqb0y5f65EviXo30wj0I4ONZ4OYi8qxLU81syUzF05Hn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JAAAg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\n3Eq+3Z7PxaNBFqzsuWx8H/ux8vX3SA8f2ejTWprvq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j/0NwoRiQMA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\nERERERERERFpAwriiIiIiIiIiIi0gf8HCHphPdaAQOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce86da470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data_in_box.pickup_longitude\n",
    "y = data_in_box.pickup_latitude\n",
    "\n",
    "plt.jet()\n",
    "H, xedges, yedges = np.histogram2d(x, y, bins=300)#, normed=False, weights=None)\n",
    "\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "plt.hist2d(x, y, bins=300, range=[[min(x.values),-73.95],[40.675,40.8]])\n",
    "plt.colorbar()\n",
    "plt.title('Pickup density (first full week in May 2013)')\n",
    "plt.ylabel('Latitude')\n",
    "plt.xlabel('Longitude')\n",
    "\n",
    "ax = fig.gca()\n",
    "ax.grid(False)\n",
    "# plt.savefig('figures/pickup_density_manhattan_13.png', format='png', dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Again, let's take a look at the distribution of the target variable we want to estimate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nFi5c+Bj3RpIkSZIkScOwwKjH3U033cSbjvk202du8Khzd95yHev/wzaPQ68k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\nkkb2/wD6kWE4SxpVDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce86cf4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = data_in_box.trip_time.values / np.timedelta64(1, 'm')\n",
    "plt.hist(h, normed=False,  bins=150)\n",
    "plt.yticks(fontsize=40)\n",
    "plt.xticks(fontsize=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": 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>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>total_amount</th>\n",
       "      <th>trip_time</th>\n",
       "      <th>avg_amount_per_minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:10:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>-73.981070</td>\n",
       "      <td>40.766047</td>\n",
       "      <td>-73.981362</td>\n",
       "      <td>40.738092</td>\n",
       "      <td>10.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>13.7</td>\n",
       "      <td>00:10:00</td>\n",
       "      <td>0.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:06:00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.86</td>\n",
       "      <td>-73.986312</td>\n",
       "      <td>40.760487</td>\n",
       "      <td>-74.005082</td>\n",
       "      <td>40.743372</td>\n",
       "      <td>7.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.5</td>\n",
       "      <td>00:06:00</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:04:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>-73.989388</td>\n",
       "      <td>40.731577</td>\n",
       "      <td>-73.986102</td>\n",
       "      <td>40.740732</td>\n",
       "      <td>5.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.5</td>\n",
       "      <td>00:04:00</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:08:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.59</td>\n",
       "      <td>-73.994590</td>\n",
       "      <td>40.751857</td>\n",
       "      <td>-73.963470</td>\n",
       "      <td>40.765912</td>\n",
       "      <td>9.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.5</td>\n",
       "      <td>00:08:00</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-05-06</td>\n",
       "      <td>2013-05-06 00:14:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.86</td>\n",
       "      <td>-73.965532</td>\n",
       "      <td>40.717052</td>\n",
       "      <td>-73.972442</td>\n",
       "      <td>40.743247</td>\n",
       "      <td>16.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.5</td>\n",
       "      <td>00:14:00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  pickup_datetime    dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0      2013-05-06 2013-05-06 00:10:00              1.0           2.65   \n",
       "1      2013-05-06 2013-05-06 00:06:00              5.0           1.86   \n",
       "2      2013-05-06 2013-05-06 00:04:00              1.0           0.90   \n",
       "3      2013-05-06 2013-05-06 00:08:00              1.0           2.59   \n",
       "5      2013-05-06 2013-05-06 00:14:00              3.0           4.86   \n",
       "\n",
       "   pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
       "0        -73.981070        40.766047         -73.981362         40.738092   \n",
       "1        -73.986312        40.760487         -74.005082         40.743372   \n",
       "2        -73.989388        40.731577         -73.986102         40.740732   \n",
       "3        -73.994590        40.751857         -73.963470         40.765912   \n",
       "5        -73.965532        40.717052         -73.972442         40.743247   \n",
       "\n",
       "   fare_amount  tip_amount  total_amount  trip_time  avg_amount_per_minute  \n",
       "0         10.5         2.2          13.7   00:10:00               0.800000  \n",
       "1          7.5         NaN           8.5   00:06:00               0.833333  \n",
       "2          5.5         NaN           6.5   00:04:00               0.750000  \n",
       "3          9.5         NaN          10.5   00:08:00               0.875000  \n",
       "5         16.5         NaN          17.5   00:14:00               1.000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_in_box.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Make a new dataframe with features and targets to train the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "time_regression_df = pd.DataFrame([#data_in_box['pickup_datetime'].dt.day,\n",
    "                          data_in_box['pickup_datetime'].dt.dayofweek,\n",
    "                          data_in_box['pickup_datetime'].dt.hour,\n",
    "                          data_in_box['pickup_latitude'],\n",
    "                          data_in_box['pickup_longitude'],\n",
    "                          data_in_box['dropoff_latitude'],\n",
    "                          data_in_box['dropoff_longitude'],\n",
    "                          np.ceil(data_in_box['trip_time']/np.timedelta64(1, 'm')),\n",
    "                         ]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_regression_df.columns = ['pickup_datetime_dayofweek', 'pickup_datetime_hour',\n",
    "                              'pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude',\n",
    "                              'trip_time']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use minutes for prediction instead of seconds (ceil the time). Definitley more robust than seconds!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": 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>pickup_datetime_dayofweek</th>\n",
       "      <th>pickup_datetime_hour</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3534627</th>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.744021</td>\n",
       "      <td>-73.999512</td>\n",
       "      <td>40.825470</td>\n",
       "      <td>-73.953479</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534628</th>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.741475</td>\n",
       "      <td>-73.981322</td>\n",
       "      <td>40.751637</td>\n",
       "      <td>-73.975838</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534629</th>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.770077</td>\n",
       "      <td>-73.863887</td>\n",
       "      <td>40.644504</td>\n",
       "      <td>-73.963537</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534630</th>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.736240</td>\n",
       "      <td>-73.990593</td>\n",
       "      <td>40.744194</td>\n",
       "      <td>-73.985401</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534631</th>\n",
       "      <td>6.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.772929</td>\n",
       "      <td>-73.949516</td>\n",
       "      <td>40.728967</td>\n",
       "      <td>-73.988787</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pickup_datetime_dayofweek  pickup_datetime_hour  pickup_latitude  \\\n",
       "3534627                        6.0                  23.0        40.744021   \n",
       "3534628                        6.0                  23.0        40.741475   \n",
       "3534629                        6.0                  23.0        40.770077   \n",
       "3534630                        6.0                  23.0        40.736240   \n",
       "3534631                        6.0                  23.0        40.772929   \n",
       "\n",
       "         pickup_longitude  dropoff_latitude  dropoff_longitude  trip_time  \n",
       "3534627        -73.999512         40.825470         -73.953479       15.0  \n",
       "3534628        -73.981322         40.751637         -73.975838        3.0  \n",
       "3534629        -73.863887         40.644504         -73.963537       22.0  \n",
       "3534630        -73.990593         40.744194         -73.985401        5.0  \n",
       "3534631        -73.949516         40.728967         -73.988787       15.0  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_regression_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": 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>pickup_datetime_dayofweek</th>\n",
       "      <th>pickup_datetime_hour</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.766047</td>\n",
       "      <td>-73.981070</td>\n",
       "      <td>40.738092</td>\n",
       "      <td>-73.981362</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.760487</td>\n",
       "      <td>-73.986312</td>\n",
       "      <td>40.743372</td>\n",
       "      <td>-74.005082</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.731577</td>\n",
       "      <td>-73.989388</td>\n",
       "      <td>40.740732</td>\n",
       "      <td>-73.986102</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.751857</td>\n",
       "      <td>-73.994590</td>\n",
       "      <td>40.765912</td>\n",
       "      <td>-73.963470</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.717052</td>\n",
       "      <td>-73.965532</td>\n",
       "      <td>40.743247</td>\n",
       "      <td>-73.972442</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pickup_datetime_dayofweek  pickup_datetime_hour  pickup_latitude  \\\n",
       "0                        0.0                   0.0        40.766047   \n",
       "1                        0.0                   0.0        40.760487   \n",
       "2                        0.0                   0.0        40.731577   \n",
       "3                        0.0                   0.0        40.751857   \n",
       "5                        0.0                   0.0        40.717052   \n",
       "\n",
       "   pickup_longitude  dropoff_latitude  dropoff_longitude  trip_time  \n",
       "0        -73.981070         40.738092         -73.981362       10.0  \n",
       "1        -73.986312         40.743372         -74.005082        6.0  \n",
       "2        -73.989388         40.740732         -73.986102        4.0  \n",
       "3        -73.994590         40.765912         -73.963470        8.0  \n",
       "5        -73.965532         40.743247         -73.972442       14.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_regression_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": 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>pickup_datetime_dayofweek</th>\n",
       "      <th>pickup_datetime_hour</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.266333e+06</td>\n",
       "      <td>3.266333e+06</td>\n",
       "      <td>3.266333e+06</td>\n",
       "      <td>3.266333e+06</td>\n",
       "      <td>3.266333e+06</td>\n",
       "      <td>3.266333e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.039328e+00</td>\n",
       "      <td>1.350386e+01</td>\n",
       "      <td>4.075136e+01</td>\n",
       "      <td>-7.397645e+01</td>\n",
       "      <td>4.075162e+01</td>\n",
       "      <td>-7.397513e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.961083e+00</td>\n",
       "      <td>6.552920e+00</td>\n",
       "      <td>2.582007e-02</td>\n",
       "      <td>3.135320e-02</td>\n",
       "      <td>2.840570e-02</td>\n",
       "      <td>3.012289e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.064155e+01</td>\n",
       "      <td>-7.402063e+01</td>\n",
       "      <td>4.064155e+01</td>\n",
       "      <td>-7.402057e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>4.073668e+01</td>\n",
       "      <td>-7.399259e+01</td>\n",
       "      <td>4.073577e+01</td>\n",
       "      <td>-7.399176e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>4.075343e+01</td>\n",
       "      <td>-7.398216e+01</td>\n",
       "      <td>4.075387e+01</td>\n",
       "      <td>-7.398066e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>4.076813e+01</td>\n",
       "      <td>-7.396825e+01</td>\n",
       "      <td>4.076884e+01</td>\n",
       "      <td>-7.396539e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>4.085636e+01</td>\n",
       "      <td>-7.377812e+01</td>\n",
       "      <td>4.085640e+01</td>\n",
       "      <td>-7.377812e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pickup_datetime_dayofweek  pickup_datetime_hour  pickup_latitude  \\\n",
       "count               3.266333e+06          3.266333e+06     3.266333e+06   \n",
       "mean                3.039328e+00          1.350386e+01     4.075136e+01   \n",
       "std                 1.961083e+00          6.552920e+00     2.582007e-02   \n",
       "min                 0.000000e+00          0.000000e+00     4.064155e+01   \n",
       "25%                 1.000000e+00          9.000000e+00     4.073668e+01   \n",
       "50%                 3.000000e+00          1.400000e+01     4.075343e+01   \n",
       "75%                 5.000000e+00          1.900000e+01     4.076813e+01   \n",
       "max                 6.000000e+00          2.300000e+01     4.085636e+01   \n",
       "\n",
       "       pickup_longitude  dropoff_latitude  dropoff_longitude  \n",
       "count      3.266333e+06      3.266333e+06       3.266333e+06  \n",
       "mean      -7.397645e+01      4.075162e+01      -7.397513e+01  \n",
       "std        3.135320e-02      2.840570e-02       3.012289e-02  \n",
       "min       -7.402063e+01      4.064155e+01      -7.402057e+01  \n",
       "25%       -7.399259e+01      4.073577e+01      -7.399176e+01  \n",
       "50%       -7.398216e+01      4.075387e+01      -7.398066e+01  \n",
       "75%       -7.396825e+01      4.076884e+01      -7.396539e+01  \n",
       "max       -7.377812e+01      4.085640e+01      -7.377812e+01  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_regression_df.ix[:,0:6].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.0      216659\n",
      "6.0      212922\n",
      "8.0      212253\n",
      "9.0      203203\n",
      "5.0      198847\n",
      "10.0     190577\n",
      "11.0     176963\n",
      "4.0      167658\n",
      "12.0     161870\n",
      "13.0     146481\n",
      "14.0     132243\n",
      "15.0     118781\n",
      "3.0      115848\n",
      "16.0     106682\n",
      "17.0      94945\n",
      "18.0      84644\n",
      "19.0      75749\n",
      "20.0      67035\n",
      "21.0      58914\n",
      "2.0       55534\n",
      "22.0      52250\n",
      "23.0      46464\n",
      "24.0      40736\n",
      "25.0      35394\n",
      "26.0      31199\n",
      "27.0      27649\n",
      "28.0      24524\n",
      "29.0      21292\n",
      "30.0      18618\n",
      "31.0      16680\n",
      "          ...  \n",
      "120.0         3\n",
      "123.0         2\n",
      "143.0         2\n",
      "145.0         2\n",
      "128.0         2\n",
      "138.0         2\n",
      "121.0         2\n",
      "119.0         2\n",
      "118.0         2\n",
      "157.0         1\n",
      "156.0         1\n",
      "154.0         1\n",
      "153.0         1\n",
      "148.0         1\n",
      "160.0         1\n",
      "164.0         1\n",
      "137.0         1\n",
      "178.0         1\n",
      "136.0         1\n",
      "134.0         1\n",
      "133.0         1\n",
      "132.0         1\n",
      "131.0         1\n",
      "130.0         1\n",
      "129.0         1\n",
      "125.0         1\n",
      "124.0         1\n",
      "117.0         1\n",
      "115.0         1\n",
      "279.0         1\n",
      "Name: trip_time, dtype: int64\n",
      "148\n"
     ]
    }
   ],
   "source": [
    "print(time_regression_df.trip_time.value_counts())\n",
    "print(len(time_regression_df.trip_time.value_counts()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So we hace 148 different times to predict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05     3.0\n",
       "0.95    30.0\n",
       "Name: trip_time, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_regression_df.trip_time.quantile([0.05, 0.95])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So 90% of the trip_times are between 3 and 30 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few stats about the avg. pickups per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "hour_stats = time_regression_df.groupby(time_regression_df.pickup_datetime_hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b9pIkSZIkSeoTM2JkWUQcCHwDWB94aWZe2jiWmfdk5iktQVnj2HWUBftn8/CC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\nSZIkqTIskyRJkiRJkqr/AVg+lEtg9JyaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ceba65ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(left = hour_stats.pickup_datetime_hour.count().keys(), height=hour_stats.pickup_datetime_hour.count().values/7, \n",
    "       tick_label=hour_stats.pickup_datetime_hour.count().keys(), align='center')\n",
    "plt.title('Avg. pickups per hour')\n",
    "plt.xlabel('datetime_hour')\n",
    "plt.ylabel('frequency')\n",
    "plt.savefig('avg_pickups_per_hour.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. pickups per half-hour (summarized over 1 week)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pickup_datetime_hour\n",
       "0.0      9498.000000\n",
       "1.0      6936.000000\n",
       "2.0      4986.357143\n",
       "3.0      3496.357143\n",
       "4.0      2482.285714\n",
       "5.0      2151.500000\n",
       "6.0      4909.357143\n",
       "7.0      9031.214286\n",
       "8.0     10726.285714\n",
       "9.0     11130.571429\n",
       "10.0    10944.857143\n",
       "11.0    11205.857143\n",
       "12.0    11487.642857\n",
       "13.0    10997.642857\n",
       "14.0    11269.785714\n",
       "15.0    10798.785714\n",
       "16.0     8841.285714\n",
       "17.0    10963.857143\n",
       "18.0    13860.857143\n",
       "19.0    14584.714286\n",
       "20.0    14010.142857\n",
       "21.0    13788.142857\n",
       "22.0    13446.642857\n",
       "23.0    11761.357143\n",
       "Name: pickup_datetime_hour, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Avg. pickups per half-hour (summarized over 1 week)')\n",
    "hour_stats.pickup_datetime_hour.count()/14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": 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>pickup_datetime_dayofweek</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>10954.357143</td>\n",
       "      <td>10954.357143</td>\n",
       "      <td>10954.357143</td>\n",
       "      <td>10954.357143</td>\n",
       "      <td>10954.357143</td>\n",
       "      <td>10954.357143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     pickup_datetime_dayofweek  pickup_latitude  pickup_longitude  \\\n",
       "0.5               10954.357143     10954.357143      10954.357143   \n",
       "\n",
       "     dropoff_latitude  dropoff_longitude     trip_time  \n",
       "0.5      10954.357143       10954.357143  10954.357143  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(hour_stats.count()/14).quantile([.5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the data into a training dataset and a test dataset. Evaluate the performance of the decision tree on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pickup_datetime_dayofweek', 'pickup_datetime_hour', 'pickup_latitude',\n",
       "       'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude',\n",
       "       'trip_time'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_regression_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import cross_validation as cv\n",
    "time_regression_df_train, time_regression_df_test = cv.train_test_split(time_regression_df, test_size=0.1, random_state=99)\n",
    "y_train = time_regression_df_train['trip_time']\n",
    "x_train = time_regression_df_train.ix[:, 0:6]\n",
    "y_test = time_regression_df_test['trip_time']\n",
    "x_test = time_regression_df_test.ix[:, 0:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": 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>pickup_datetime_dayofweek</th>\n",
       "      <th>pickup_datetime_hour</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022607</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.669259</td>\n",
       "      <td>-73.986446</td>\n",
       "      <td>40.658110</td>\n",
       "      <td>-73.996890</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912919</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>40.784262</td>\n",
       "      <td>-73.977685</td>\n",
       "      <td>40.852637</td>\n",
       "      <td>-73.932985</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3090961</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.728087</td>\n",
       "      <td>-73.988042</td>\n",
       "      <td>40.690582</td>\n",
       "      <td>-73.966325</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400751</th>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.769983</td>\n",
       "      <td>-73.863293</td>\n",
       "      <td>40.815013</td>\n",
       "      <td>-73.944113</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889034</th>\n",
       "      <td>3.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>40.760670</td>\n",
       "      <td>-73.969077</td>\n",
       "      <td>40.716767</td>\n",
       "      <td>-74.004000</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pickup_datetime_dayofweek  pickup_datetime_hour  pickup_latitude  \\\n",
       "2022607                        4.0                   1.0        40.669259   \n",
       "912919                         1.0                  22.0        40.784262   \n",
       "3090961                        6.0                   0.0        40.728087   \n",
       "1400751                        2.0                  21.0        40.769983   \n",
       "1889034                        3.0                  20.0        40.760670   \n",
       "\n",
       "         pickup_longitude  dropoff_latitude  dropoff_longitude  trip_time  \n",
       "2022607        -73.986446         40.658110         -73.996890        4.0  \n",
       "912919         -73.977685         40.852637         -73.932985       17.0  \n",
       "3090961        -73.988042         40.690582         -73.966325       17.0  \n",
       "1400751        -73.863293         40.815013         -73.944113       15.0  \n",
       "1889034        -73.969077         40.716767         -74.004000       17.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_regression_df_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2939699"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xy_test = pd.concat([x_test, y_test], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": 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>pickup_datetime_dayofweek</th>\n",
       "      <th>pickup_datetime_hour</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "      <th>trip_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3122858</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.778213</td>\n",
       "      <td>-73.952551</td>\n",
       "      <td>40.799014</td>\n",
       "      <td>-73.968229</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967458</th>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.759134</td>\n",
       "      <td>-73.995934</td>\n",
       "      <td>40.717645</td>\n",
       "      <td>-74.015911</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427989</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>40.759487</td>\n",
       "      <td>-73.970370</td>\n",
       "      <td>40.712392</td>\n",
       "      <td>-73.826657</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1852088</th>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>40.716318</td>\n",
       "      <td>-73.955408</td>\n",
       "      <td>40.694955</td>\n",
       "      <td>-73.940483</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629946</th>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.707655</td>\n",
       "      <td>-74.009205</td>\n",
       "      <td>40.750217</td>\n",
       "      <td>-73.994900</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pickup_datetime_dayofweek  pickup_datetime_hour  pickup_latitude  \\\n",
       "3122858                        6.0                   1.0        40.778213   \n",
       "1967458                        3.0                  23.0        40.759134   \n",
       "427989                         0.0                  22.0        40.759487   \n",
       "1852088                        3.0                  19.0        40.716318   \n",
       "1629946                        3.0                  10.0        40.707655   \n",
       "\n",
       "         pickup_longitude  dropoff_latitude  dropoff_longitude  trip_time  \n",
       "3122858        -73.952551         40.799014         -73.968229        8.0  \n",
       "1967458        -73.995934         40.717645         -74.015911       12.0  \n",
       "427989         -73.970370         40.712392         -73.826657       21.0  \n",
       "1852088        -73.955408         40.694955         -73.940483       16.0  \n",
       "1629946        -74.009205         40.750217         -73.994900       42.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# xy_test.to_csv('taxi_tree_test_Xy_20130506-12.csv')\n",
    "# x_test.to_csv('taxi_tree_test_X_20130506-12.csv')\n",
    "# y_test.to_csv('taxi_tree_test_y_20130506-12.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# xy_test_sample = Xy_test.sample(10000, random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# xy_test_sample.to_csv('taxi_tree_test_Xy_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# xy_test_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2939699, 6)\n",
      "17638194\n",
      "(326634, 6)\n",
      "(3266333, 7)\n",
      "3266333\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_train.size)\n",
    "print(x_test.shape)\n",
    "print(time_regression_df.shape)\n",
    "print(x_train.shape[0]+x_test.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import time\n",
    "# Import the necessary modules and libraries\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and compare a few decision trees with different parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 15, 20, 25, 30)\n",
      "[0.51337097515902541, 0.64409382777503155, 0.6957163207523871, 0.71429589738370614, 0.7159815227278703]\n",
      "[1547595.3414912082, 1284490.8114952976, 1143568.0997977962, 1084873.9820350427, 1077427.5321143884]\n",
      "[14.24124002456665, 20.28833317756653, 24.131511449813843, 25.42002558708191, 25.958088874816895]\n"
     ]
    }
   ],
   "source": [
    "#features = ['pickup_latitude', 'pickup_longitude', 'dropoff_latitude', 'dropoff_longitude','pickup_datetime']\n",
    "#print(\"* features:\", features, sep=\"\\n\")\n",
    "\n",
    "max_depth_list = (10,15,20,25,30)\n",
    "scores = [-1, -1, -1, -1, -1]\n",
    "sum_abs_devs = [-1, -1, -1, -1, -1]\n",
    "times = [-1, -1, -1, -1, -1]\n",
    "for i in range(0,len(max_depth_list)):\n",
    "    start = time.time()\n",
    "    regtree = DecisionTreeRegressor(min_samples_split=1000, random_state=10, max_depth=max_depth_list[i])# formerly 15. 15 is reasonable, \n",
    "    # 30 brings best results # random states: 99\n",
    "    regtree.fit(x_train, y_train)\n",
    "    scores[i]= regtree.score(x_test, y_test)\n",
    "    y_pred = regtree.predict(x_test)\n",
    "    sum_abs_devs[i] = sum(abs(y_pred-y_test))\n",
    "    times[i] = time.time() - start\n",
    "print(max_depth_list)\n",
    "print(scores)\n",
    "print(sum_abs_devs)\n",
    "print(times)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some more results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Sum of abs. deviation     | max_depth |  max_depth    |  max_depth    |  max_depth    |  max_depth    |\n",
    "|---------------------------|-----------|------|------|------|------|\n",
    "| min_samples_split         | 10        | 15   | 20   | 25   | 30   |\n",
    "| 3                         | 1543      | 1267 | 1127 | 1088 | 1139 |\n",
    "| 10                        | 1544      | 1266 | 1117 | 1062 | 1086 |\n",
    "| 20                        | 1544      | 1265 | 1108 | 1037 | 1034 |\n",
    "| 50                        | 1544      | 1263 | 1097 | 1011 | 994  |\n",
    "| 250                       | 1544      | 1266 | 1103 | 1019 | 1001 |\n",
    "| 1000                      | 1548      | 1284 | 1144 | 1085 | 1077 |\n",
    "| 2500                      | 1555      | 1307 | 1189 | 1150 | 1146 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min_samples_split = 3  \n",
    "(10, 15, 20, 25, 30)  \n",
    "[0.51550436937183575, 0.64824394212610637, 0.68105673170887715, 0.66935222696811203, 0.62953726391785103]  \n",
    "[1543779.4758261547, 1267630.6429649692, 1126951.2647852183, 1088342.055931434, 1139060.7870262777]  \n",
    "[14.802491903305054, 21.25719118118286, 27.497225046157837, 32.381808280944824, 35.0844943523407]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min_samples_split = 10  \n",
    "(10, 15, 20, 25, 30)  \n",
    "[0.51546967657630205, 0.65055440252664309, 0.69398351369676525, 0.69678113708751077, 0.67518497976746361]  \n",
    "[1543829.4000325042, 1266104.6486240581, 1117165.9640872395, 1061893.3390857978, 1086045.4846943137]  \n",
    "[14.141993999481201, 20.831212759017944, 25.626588821411133, 29.81039047241211, 32.23483180999756]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min_samples_split = 20  \n",
    "(10, 15, 20, 25, 30)  \n",
    "[0.51537943698967736, 0.65215078696481421, 0.70216115764491505, 0.71547757670696144, 0.70494598277965781]  \n",
    "[1543841.1100632891, 1264595.0251062319, 1108064.4596608584, 1036593.8033015681, 1039378.3133869285]  \n",
    "[14.048030376434326, 20.481205463409424, 25.652794361114502, 29.03341507911682, 31.56394076347351]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_samples_split=50  \n",
    "(10, 15, 20, 25, 30)  \n",
    "[0.51540742268899331, 0.65383862050244068, 0.71125658610588971, 0.73440457163892259, 0.73435595461521908]  \n",
    "[1543721.3435906437, 1262877.4227863667, 1097080.889761846, 1010511.305738725, 994244.46643680066]  \n",
    "[14.682952404022217, 21.243955373764038, 25.80405569076538, 28.731933116912842, 32.00149917602539]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_samples_split=250  \n",
    "(10, 15, 20, 25, 30)  \n",
    "[0.51532618474195502, 0.65304694576643452, 0.712453138233199, 0.73862283625684677, 0.74248829470934752]  \n",
    "[1544004.1103626473, 1266358.9437320188, 1102793.6462709717, 1018555.9754967012, 1000675.2014443219]  \n",
    "[14.215412378311157, 20.32301664352417, 25.39385199546814, 27.81620717048645, 28.74960231781006]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_samples_split=1000  \n",
    "(10, 15, 20, 25, 30)  \n",
    "[0.51337097515902541, 0.64409382777503155, 0.6957163207523871, 0.71429589738370614, 0.7159815227278703]  \n",
    "[1547595.3414912082, 1284490.8114952976, 1143568.0997977962, 1084873.9820350427, 1077427.5321143884]  \n",
    "[14.676559448242188, 20.211236476898193, 23.846965551376343, 26.270352125167847, 26.993313789367676]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_samples_split=2500  \n",
    "(10, 15, 20, 25, 30)  \n",
    "[0.50872112253965895, 0.63184888428446373, 0.67528344919996985, 0.68767132817144228, 0.68837707513473978]  \n",
    "[1554528.9746030923, 1306995.3609336747, 1188981.9585730932, 1149615.9326777055, 1146209.3017767756]  \n",
    "[14.31177806854248, 20.02240490913391, 23.825161457061768, 24.616609811782837, 25.06274127960205]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the most promising decision tree again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.976523399353027\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "regtree = DecisionTreeRegressor(min_samples_split=50, random_state=10, max_depth=25, splitter='best' )\n",
    "regtree.fit(x_train, y_train)\n",
    "regtree.score(x_test, y_test)\n",
    "y_pred = regtree.predict(x_test)\n",
    "sum_abs_devs = sum(abs(y_pred-y_test))\n",
    "elapsed = time.time() - start\n",
    "print(elapsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A tree with this depth is too big to dump. Graphviz works fine until around depth 12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from sklearn import tree\n",
    "# tree.export_graphviz(regtree, out_file='figures/tree_d10.dot', feature_names=time_regression_df.ix[:,0:6].columns, class_names=time_regression_df.columns[6])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  84.65740931,   67.47581069,   54.53424675, ...,   18.58832645,\n",
       "         76.5443787 ,  109.78512397])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regtree.tree_.impurity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.939699e+06\n",
       "mean     1.303100e+01\n",
       "std      9.200948e+00\n",
       "min      1.000000e+00\n",
       "25%      7.000000e+00\n",
       "50%      1.100000e+01\n",
       "75%      1.700000e+01\n",
       "max      1.780000e+02\n",
       "Name: trip_time, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R²:  0.734404571639\n"
     ]
    }
   ],
   "source": [
    "print('R²: ', regtree.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['treelib/regtree_depth_25_mss_50_rs_10.pkl',\n",
       " 'treelib/regtree_depth_25_mss_50_rs_10.pkl_01.npy',\n",
       " 'treelib/regtree_depth_25_mss_50_rs_10.pkl_02.npy',\n",
       " 'treelib/regtree_depth_25_mss_50_rs_10.pkl_03.npy']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "joblib.dump(regtree, 'treelib/regtree_depth_25_mss_50_rs_10.pkl', protocol=2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few stats about the trained tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.03798533  0.08706913  0.20192368  0.26153429  0.17102708  0.24046048] \n",
      " None \n",
      " 1 \n",
      " [2939699 2839702 2767340 ...,      44      13      11] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(regtree.feature_importances_ ,'\\n',\n",
    "      regtree.class_weight,'\\n',\n",
    "      regtree.min_samples_leaf,'\\n',\n",
    "      regtree.tree_.n_node_samples,'\\n'\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perzentile(%):  [1, 5, 10, 15, 25, 50, 75, 90, 95, 99] \n",
      " [-15.          -7.51219512  -4.86363636  -3.45399089  -1.80952381\n",
      "   0.44690265   2.35         4.32142857   5.92        11.05128205]\n",
      "Absolute time deviation (in 1k):  1010.51130574\n"
     ]
    },
    {
     "data": {
      "image/png": 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SpJYM1yRJkiRJkqSWDNckSZIkSZKklgzXJEmSJEmSpJYM1yRJkiRJ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\n/WbSdwuVJEmSJEmSpqu6u4W+YaIakJl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4JkmSJEmSJNVkuCZJkiRJkiTVZLgmSZIkSZIk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\nJEmSJEk1Ga5JkiRJkiRJNf1/rJNdqsr3HwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce670bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = regtree.predict(x_test)\n",
    "np.linalg.norm(np.ceil(y_pred)-y_test)\n",
    "diff = (y_pred-y_test)\n",
    "# plt.figure(figsize=(12,10)) # not needed. set values globally\n",
    "plt.hist(diff.values, bins=100, range=[-50, 50])\n",
    "print('Perzentile(%): ', [1,5,10,15,25,50,75,90,95,99], '\\n', np.percentile(diff.values, [1,5,10,15,25,50,75,90,95,99]))\n",
    "print('Absolute time deviation (in 1k): ', sum(abs(diff))/1000)\n",
    "plt.title('Error Distribution on the 2013 Test Set')\n",
    "plt.xlabel('Error in Minutes')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('figures/simple_tree_error_d25_msp_50.eps', format='eps', dpi=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    326634.000000\n",
       "mean          0.002958\n",
       "std           4.758333\n",
       "min        -258.090909\n",
       "25%          -1.809524\n",
       "50%           0.446903\n",
       "75%           2.350000\n",
       "max          82.000000\n",
       "Name: trip_time, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the leaves / predicted times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "leaves = regtree.tree_.children_left*regtree.tree_.children_right\n",
    "for idx, a in enumerate(leaves):\n",
    "    if a==1:\n",
    "        x=1# do nothing\n",
    "    else:\n",
    "        leaves[idx] = 0\n",
    "print(leaves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67260\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "67260"
      ]
     },
     "execution_count": 699,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(leaves[leaves==1].sum())\n",
    "len(leaves[leaves==1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we have 67260 leaves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5000037169470484"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(leaves[leaves==1])/regtree.tree_.node_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So 50% of the nodes are leaves. A little bit cross-checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "134519\n",
      "134519\n"
     ]
    }
   ],
   "source": [
    "print((leaves==1).sum()+(leaves==0).sum())\n",
    "print(len(leaves))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2939699, 2839702, 2767340, ...,      44,      13,      11])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_samples = regtree.tree_.n_node_samples\n",
    "node_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    0,     1,     2,     3,     4,     5,     6,     7,     8,\n",
       "            9,    10,    11,    12,    13,    14,    15,    16,    17,\n",
       "           18,    19,    20,    21,    22,    23,    24,    25,    26,\n",
       "           27,    28,    29,    30,    31,    32,    33,    34,    35,\n",
       "           36,    37,    38,    39,    40,    41,    42,    43,    44,\n",
       "           45,    46,    47,    48,    49,    50,    51,    52,    53,\n",
       "           54,    55,    56,    57,    58,    59,    60,    61,    62,\n",
       "           63,    64,    65,    66,    67,    68,    69,    70,    71,\n",
       "           72,    73,    74,    75,    76,    77,    78,    79,    80,\n",
       "           81,    82,    83,    84,    85,    86,    87,    88,    89,\n",
       "           90,    91,    92,    93,    94,    95,    96,    97,    98,\n",
       "           99,   100,   101,   102,   103,   104,   105,   106,   107,\n",
       "          108,   109,   110,   111,   112,   113,   114,   115,   116,\n",
       "          117,   118,   119,   120,   121,   122,   123,   124,   125,\n",
       "          126,   127,   128,   129,   130,   131,   132,   133,   134,\n",
       "          135,   136,   137,   138,   139,   140,   141,   142,   143,\n",
       "          144,   145,   146,   147,   148,   149,   150,   151,   152,\n",
       "          153,   154,   155,   156,   157,   158,   159,   160,   161,\n",
       "          162,   163,   164,   165,   166,   167,   168,   169,   170,\n",
       "          171,   172,   173,   174,   175,   176,   177,   178,   179,\n",
       "          180,   181,   182,   183,   184,   185,   186,   187,   188,\n",
       "          189,   190,   191,   192,   193,   194,   195,   196,   197,\n",
       "          198,   199,   200,   201,   202,   203,   204,   205,   206,\n",
       "          207,   208,   209,   210,   211,   212,   213,   214,   215,\n",
       "          216,   217,   218,   219,   220,   221,   222,   223,   224,\n",
       "          225,   226,   227,   228,   229,   230,   231,   232,   233,\n",
       "          234,   235,   236,   237,   238,   239,   240,   241,   242,\n",
       "          243,   244,   245,   246,   247,   248,   249,   250,   251,\n",
       "          252,   253,   254,   255,   256,   257,   258,   259,   260,\n",
       "          261,   262,   263,   264,   265,   266,   267,   268,   269,\n",
       "          270,   271,   272,   273,   274,   275,   276,   277,   278,\n",
       "          279,   280,   281,   282,   283,   284,   285,   286,   287,\n",
       "          288,   289,   290,   291,   292,   293,   294,   295,   296,\n",
       "          297,   298,   299,   300,   301,   302,   303,   304,   305,\n",
       "          306,   307,   308,   309,   310,   311,   312,   313,   314,\n",
       "          315,   316,   317,   318,   319,   320,   321,   322,   323,\n",
       "          324,   325,   326,   327,   328,   330,   331,   332,   333,\n",
       "          334,   335,   336,   337,   338,   339,   340,   341,   343,\n",
       "          344,   345,   346,   347,   348,   349,   350,   351,   352,\n",
       "          353,   354,   355,   356,   357,   358,   359,   360,   361,\n",
       "          362,   363,   364,   365,   366,   367,   368,   369,   370,\n",
       "          371,   372,   373,   374,   375,   376,   377,   378,   379,\n",
       "          380,   381,   382,   384,   385,   386,   387,   388,   389,\n",
       "          390,   391,   392,   393,   394,   395,   396,   397,   398,\n",
       "          399,   400,   401,   402,   403,   405,   406,   407,   408,\n",
       "          409,   410,   411,   412,   413,   414,   415,   416,   417,\n",
       "          418,   419,   420,   421,   422,   424,   425,   426,   427,\n",
       "          428,   429,   430,   431,   432,   433,   434,   435,   436,\n",
       "          437,   438,   439,   440,   441,   445,   446,   447,   450,\n",
       "          451,   452,   453,   454,   456,   457,   458,   459,   460,\n",
       "          461,   463,   464,   466,   467,   468,   469,   470,   471,\n",
       "          472,   473,   474,   475,   476,   477,   478,   480,   481,\n",
       "          483,   484,   485,   486,   487,   489,   493,   494,   495,\n",
       "          496,   497,   498,   499,   500,   501,   502,   503,   505,\n",
       "          506,   507,   508,   509,   510,   512,   514,   515,   516,\n",
       "          517,   519,   522,   524,   525,   526,   527,   528,   530,\n",
       "          531,   533,   534,   536,   537,   538,   541,   543,   544,\n",
       "          547,   549,   551,   552,   554,   555,   556,   557,   559,\n",
       "          560,   561,   562,   563,   564,   566,   567,   568,   572,\n",
       "          574,   577,   578,   579,   586,   587,   588,   589,   590,\n",
       "          591,   592,   593,   595,   596,   598,   600,   602,   604,\n",
       "          605,   608,   609,   610,   611,   613,   614,   615,   618,\n",
       "          620,   621,   623,   624,   626,   627,   628,   629,   632,\n",
       "          634,   637,   638,   640,   642,   645,   646,   647,   650,\n",
       "          654,   656,   658,   659,   660,   661,   663,   666,   670,\n",
       "          671,   672,   674,   675,   676,   683,   684,   686,   687,\n",
       "          694,   695,   696,   697,   698,   701,   703,   707,   708,\n",
       "          709,   710,   711,   712,   713,   715,   717,   718,   719,\n",
       "          721,   723,   725,   726,   729,   733,   734,   739,   742,\n",
       "          743,   744,   748,   750,   751,   755,   757,   759,   762,\n",
       "          763,   770,   771,   773,   775,   776,   777,   786,   789,\n",
       "          790,   791,   792,   795,   798,   802,   805,   807,   810,\n",
       "          811,   812,   814,   815,   816,   817,   820,   822,   823,\n",
       "          826,   827,   828,   829,   832,   833,   835,   836,   838,\n",
       "          848,   850,   851,   854,   859,   860,   864,   867,   870,\n",
       "          871,   880,   881,   883,   893,   895,   897,   898,   902,\n",
       "          909,   910,   918,   920,   927,   931,   933,   939,   940,\n",
       "          941,   942,   946,   949,   952,   965,   966,   968,   971,\n",
       "          976,   978,   980,   981,   985,   987,   992,   999,  1000,\n",
       "         1001,  1010,  1011,  1013,  1020,  1021,  1026,  1029,  1031,\n",
       "         1037,  1042,  1043,  1051,  1066,  1085,  1087,  1092,  1097,\n",
       "         1099,  1102,  1105,  1108,  1114,  1120,  1123,  1128,  1135,\n",
       "         1150,  1152,  1155,  1163,  1164,  1170,  1184,  1192,  1193,\n",
       "         1195,  1202,  1204,  1207,  1208,  1214,  1218,  1226,  1228,\n",
       "         1232,  1245,  1248,  1270,  1282,  1287,  1297,  1301,  1307,\n",
       "         1316,  1317,  1330,  1336,  1343,  1349,  1387,  1390,  1392,\n",
       "         1428,  1433,  1436,  1457,  1465,  1483,  1499,  1500,  1510,\n",
       "         1515,  1519,  1533,  1559,  1561,  1564,  1566,  1570,  1572,\n",
       "         1585,  1594,  1598,  1601,  1606,  1618,  1619,  1635,  1639,\n",
       "         1643,  1645,  1646,  1657,  1666,  1668,  1676,  1719,  1755,\n",
       "         1759,  1764,  1785,  1798,  1801,  1812,  1815,  1841,  1844,\n",
       "         1859,  1867,  1875,  1877,  1917,  1928,  1981,  2013,  2036,\n",
       "         2062,  2070,  2073,  2095,  2131,  2155,  2160,  2195,  2317,\n",
       "         2319,  2354,  2380,  2420,  2433,  2451,  2576,  2577,  2735,\n",
       "         2740,  2821,  2832,  2854,  2942,  2956,  2962,  2977,  3154,\n",
       "         3174,  3232,  3254,  3277,  3359,  3402,  3441,  3545,  3755,\n",
       "         3785,  3880,  3885,  4255,  4378,  4523,  4656,  5672,  5866,\n",
       "         5878,  5905,  5921,  6397,  7092,  7462,  7463,  7756,  8454,\n",
       "         8460,  8487,  9138,  9567, 11936, 20535, 23278]),\n",
       " array([67259,  4765,  2758,  1954,  1637,  1449,  1369,  1308,  1203,\n",
       "         1165,  1087,  1088,  1051,  1083,   982,  1059,  1007,   991,\n",
       "         1006,  1043,  1063,  1014,  1048,  1032,  1027,   967,  1009,\n",
       "          996,   985,  1005,  1030,  1033,  1074,   995,   941,  1023,\n",
       "          981,  1084,  1028,  1001,   965,  1083,   980,  1061,  1023,\n",
       "         1105,  1046,  1105,  1123,  1281,   156,   159,   151,   143,\n",
       "          154,   133,   124,   159,   121,   119,   111,   110,    98,\n",
       "          115,   113,   118,    96,    90,    96,    72,    87,    93,\n",
       "           79,    94,    90,    67,    95,    78,    87,    63,    53,\n",
       "           56,    72,    60,    66,    76,    55,    59,    50,    49,\n",
       "           59,    32,    48,    44,    49,    46,    43,    40,    45,\n",
       "           46,    36,    39,    35,    42,    42,    47,    34,    46,\n",
       "           43,    28,    31,    35,    34,    33,    35,    40,    22,\n",
       "           33,    37,    33,    30,    27,    22,    25,    27,    23,\n",
       "           26,    24,    35,    15,    23,    23,    25,    24,    22,\n",
       "           25,    24,    16,    24,    23,    24,    16,    18,    19,\n",
       "           27,    25,    30,    23,    26,    24,    18,    19,    22,\n",
       "           18,    15,    16,     8,    22,    18,    15,    15,    13,\n",
       "           16,    23,    11,    10,    14,    18,    14,     8,    16,\n",
       "           20,     8,    11,    15,    15,    11,    18,    11,    16,\n",
       "            8,    14,    12,    19,    17,    12,     9,    12,    12,\n",
       "           11,    12,    12,     9,     8,     6,    19,    13,    10,\n",
       "           17,    10,    14,     5,    17,     5,    10,     7,     7,\n",
       "           12,    16,    13,     8,     9,    10,     7,    11,    16,\n",
       "           10,    12,     5,     8,     4,    11,     8,     9,     7,\n",
       "            9,     7,     5,     9,     4,    11,     8,    10,     5,\n",
       "           10,     5,     8,    13,     9,     1,     5,     2,     3,\n",
       "            6,     6,     8,     2,     5,     9,     9,    12,     8,\n",
       "           11,     2,     8,     9,     7,     9,     6,     1,     5,\n",
       "            8,     9,    10,     5,     7,     7,    10,     2,     6,\n",
       "            5,     7,     5,     6,     5,     6,     5,     5,     3,\n",
       "            9,     6,     3,     7,     3,     5,     3,     7,     6,\n",
       "            4,     4,     7,     4,     8,     6,     6,     3,     6,\n",
       "            2,     8,     3,     3,     2,     2,     3,     5,     5,\n",
       "            3,     3,     2,     4,     7,     2,     3,     3,     2,\n",
       "            4,    10,     5,     1,     4,     2,     5,     5,     4,\n",
       "            2,     5,     1,     5,     6,     3,     2,     1,     3,\n",
       "            5,     4,     4,     7,     2,     5,     2,     3,     7,\n",
       "            4,     4,     1,     2,     2,     2,     4,     7,     2,\n",
       "            4,     1,     2,     2,     4,     2,     3,     2,     2,\n",
       "            3,     4,     5,     4,     4,     3,     2,     1,     2,\n",
       "            3,     3,     2,     3,     4,     2,     2,     1,     1,\n",
       "            4,     2,     2,     1,     1,     1,     4,     3,     2,\n",
       "            2,     1,     3,     2,     2,     3,     3,     1,     2,\n",
       "            2,     4,     1,     1,     2,     2,     6,     3,     2,\n",
       "            2,     1,     1,     4,     5,     3,     2,     2,     3,\n",
       "            1,     1,     2,     2,     2,     2,     1,     3,     5,\n",
       "            2,     3,     3,     1,     2,     1,     3,     2,     2,\n",
       "            3,     5,     3,     3,     1,     1,     1,     2,     1,\n",
       "            1,     3,     4,     1,     3,     4,     1,     2,     3,\n",
       "            1,     2,     1,     2,     5,     1,     3,     1,     1,\n",
       "            2,     2,     4,     1,     2,     1,     1,     1,     1,\n",
       "            1,     5,     2,     1,     2,     3,     1,     3,     1,\n",
       "            1,     3,     3,     2,     2,     1,     1,     2,     1,\n",
       "            4,     2,     1,     4,     2,     2,     1,     2,     2,\n",
       "            1,     1,     3,     1,     1,     1,     1,     2,     2,\n",
       "            2,     1,     2,     2,     2,     2,     1,     1,     2,\n",
       "            1,     2,     2,     1,     1,     1,     2,     2,     1,\n",
       "            1,     2,     1,     3,     1,     3,     1,     2,     1,\n",
       "            1,     2,     1,     1,     2,     2,     1,     1,     1,\n",
       "            1,     2,     1,     1,     1,     1,     2,     1,     1,\n",
       "            1,     3,     1,     2,     1,     2,     1,     1,     2,\n",
       "            1,     1,     2,     2,     1,     4,     1,     2,     1,\n",
       "            1,     3,     3,     1,     2,     1,     1,     1,     1,\n",
       "            2,     2,     1,     2,     3,     2,     3,     2,     1,\n",
       "            2,     2,     1,     1,     1,     2,     1,     1,     1,\n",
       "            2,     1,     1,     1,     1,     1,     2,     1,     1,\n",
       "            1,     1,     2,     1,     1,     1,     1,     1,     2,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     3,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     2,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     2,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            2,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     2,     1,     2,     1,\n",
       "            1,     1,     2,     1,     1,     1,     1,     2,     1,\n",
       "            1,     1,     1,     1,     1,     1,     2,     2,     1,\n",
       "            1,     2,     1,     1,     1,     1,     1,     2,     2,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            2,     1,     2,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     2,\n",
       "            1,     1,     1,     1,     1,     1,     1,     2,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     2,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     2,     1,     1,     2,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     2,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     2,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     2,\n",
       "            2,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1,     1,     1,\n",
       "            1,     1,     1,     1,     1,     1,     1]))"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leaf_samples = np.multiply(leaves, node_samples)\n",
    "stats = np.unique(leaf_samples, return_counts=True)\n",
    "stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To get a feeling for the generalization of the tree: Do some leaves represent the vast amount of trips? This is what we would expect. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z6KOGqgMAnCqCKgCADK+uam7+aY4cOTOrVj0wbBbVaHcPBABg+giqAAAysrpq\nUzo7b0j9LKq7716dtrYttZDKIHUAgFNBUAUAMEJ19tTwWVRtbVuOC6/q2wJ7evoFWQAAUySoAgAY\nYfjdAKuzqEYLr+qNF2QBADA+QRUAwAijzaJat+6h48KreuMFWQAAjE9QBQDMe6O17Y2shhotvKrf\nt2fP40mujTsCAgBMnqAKAJj3JtK2Vz9sfVD90PXkPVm5ckOamy9zR0AAgEkSVAEA895k2/aG71uW\n5ubL8sMfXncKTggAMD8smO0DAADMtpaW/UkGas+Gt+319PSntXVTVq16IK2tG9Pb2z+hfQAAnDwV\nVQDAvDfa/KlBJ2oLHNy3c2eyd++zeeqpIq2tG9PeviZNTUtn41sBAJjTBFUAwLw32vypQSdqCxzc\n19q6KY89tj7d3ZU8+ujoM64AABif1j8AgBMY2d7X3PzT41oBJzvjCgCA4VRUAQCcwMi2wCNHzjyu\nFbClZaD2+0rMqgIAmDxBFQDACYxsC1y16oGMrJ56+OH3ZqwZVwAATJzWPwCAkzBaK+C6dVtqIdX+\ntLevNkgdAGCSVFQBAJyE8VoBDx26J+ecc166uhanufknWbBgYXbvbqqFWO4GCABwIoIqAICTMF4r\n4COP9KWv76baaxuTrE39PCt3AwQAGJvWPwCASejp6U9r66bs2vVk6lsBkwsyFFwtibsBAgBMnIoq\nAIBJaGvbUmv5eyHJpixbdiTvfe8r+du//Zf09Q3eAXBfquGVuwECAEyEoAoAYBKq1VGVJEuT3JhL\nLnkg55xzIM8915akI8miNDfvzL/+1/fWZlS5GyAAwHgEVQAAJ6mnpz979jye5NrUV0tVw6tlqc6l\nSlaurOTP//y62TsoAMAcY0YVAMBJamvbku7uj6daOfXtrFy5Ie3tq9PSsj/186q0+gEAnBwVVQAA\nJ2lk5VRzcyVNTUvT3r4mSUe6uhZr9QMAmAQVVQAAJ2msyqmmpqXZvHlttmx5b5LkmmseSWvrxvT2\n9s/OQQEA5hgVVQAAJ2m8yqnqHQGvSfJwtm9fnG3b2vOXf3ljbr99W23P/rS3r0lT09JZOT8AQKMS\nVAEAnKTByqmxVFsDH05yQ5IX0t19IG95y9dz9OhtSSrZvn0gSccJrwEAMB9p/QMAmGbV1sBFqd4R\ncEuStTl69K2150lSqYVZw/X09Ke1dVNWrXpAyyAAMC+pqAIAmGbt7WuybVt7uruvS7I41YBqf5K+\nVCutFqWZWBkYAAAgAElEQVS7e0c+9KHe7N7dlPPPfzabN19caxmsVmFt3/5Qtm69L1dffZY2QQBg\n3hBUAQBMs6ampfm7v1uXdes6snVrd/r6rk2yJkl7kluTVLJnT1/uu++jqYZYA/nYx+7N7t1Nqa/C\n6uurpLNTmyAAMH9o/QMAOAUG51jt2vUf8yu/ck+WLftKzjjjwgy1/w2kvhXw+98fqLub4OKM1yYI\nAHA6UlEFAHAKNTUtzTnnnJe+vpuSbMpQ+9/uDIVVA0n2pr3915J05OGHd2Xfvv1JliTZlxUrDs7S\n6QEAZpagCgDgFKtWRFVSbf/7UpL1Sb6SanC1JMn+/MIv/Cxve9uX0tu7Ij/72cEkazMYYlUq987S\nyQEAZpbWPwCAU2yopW9pkrekGkD9L0kqOe+8vlx9dV/+8R9fTnf3+rz00kdz6NAvpr71rzq7CgDg\n9KeiCgDgFGtvX5OkI11di7NnzxPp7n5vBu/+19T0RG699V257rofZyicOpD6tsCWlgPp6elPW9uW\ndHUtTkvLfncCBABOS4IqAIBTbHCwepL09r43b3vbl9LdvT5JJd3d1+X3fu/uNDXty0svDYZTq3Pu\nuZ/J5Ze/My0tB9Levjrr1m1JZ+cNSSrZvt2dAAGA05OgCgBgBjU1LU1z82Xp7h6snnohf/M3+/P6\n1y9PT8+nk1ySV796T773vY/l4otff2zf0JyrxJ0AAYDTlRlVAAAzbGhmVZI8lP371+fxx38tBw9+\nLtddd1b++Z9vHRZSHb+n2g4IAHC6UVEFADDD6mdW7dp1JH1941dK1e9pbv5pjhw5M6tWPWBeFQBw\nWhFUAQDMsPqZVa2tG9PZOXxw+vh7NplXBQCclgRVAACzpKenP4cPv5QlSzbkzDNfk6uuqqS9/fpx\n95lXBQCcrgRVAACzpK1tS+6//2NJXkjyUL7//SNZt+6hYa18PT39aWvbkp07k717n8ny5W/K888/\nmeTajKzCGlzb1bVYSyAAMCcJqgAAZslQZdSWJGvT11dJZ2dftm37UpqbL0tLy/4cOPBCHnro/CTP\nJbm1drfAvqxcuaG25kDa21cnqQZfWgIBgLlMUAUAMEtaWvbXAqX6Vr6H0929Pt3d1bBp4cI7k3w8\nyYN1a5alufmy/PCH1w27npZAAGCuWzDbBwAAmK/a29fkIx/pyJIljycZqL26KPVh089+dkGqrYGD\na/qTfCWPPfb3aWr603zwg3+a3t7+JNXga+g6Yw9mBwBoVCqqAABOoRPNjRq8k99f/MX38uUvd6Sr\na3H27Hki3d3XZXD+1IUX/jTPPfdQqlVVHUl2J1mRo0dvS19fJfffP5B166otfu3ta5J01L7WUEsg\nAMBcIagCADiFJjI36vzzl2Tz5l9KkvT2vjfr1g2FTXfe+dG8851/mb6+ZUnWJnmgtuv4Fr/B4AsA\nYK4SVAEAnEInOzeqPmwarMZKnk+1pa+SZF9t5eBzLX4AwOlDUAUAcAoNDUw/+VBpqBrrhSSbsmzZ\nkbzvfS/n6NEj+e///Y4cPFjJokXNOXx4QXp7+4+1FE7EiVoSAQBmi6AKAOAUmsrcqKFqrKVJbswl\nlzyQ++6r3umvtXVTOjtvSH//8DlVEzWRlkQAgJkmqAIAOIWmMjfqRNVYJ9tSONJU9wMAnAqCKgCA\nBnWiaqyptBROx34AgFNBUAUA0IDGmyE1lZbC6dgPAHAqCKoAABrQeDOkmpqW5u67V6etbUt27kze\n9rb2LF/+plx66cFxB6MbpA4ANCpBFQBAA5rIDKmhMKsjya3p7q7k0UfHH4xukDoA0KgWzPYBAAA4\nXkvL/iQDtWejz5AaCrMWJ3khyaYkD2br1u709vaPee2hff1JOvLgg0fT2rrxhHsAAGaCiioAgAY0\nkRlSQwPR9yd5KMnaJJX09V2bdevGrpIa2rclyQ156aVKOjtVVgEAs09QBQDQgJqalo4bGrW3r8mh\nQ/fke997Lvv2/VzqWwV37kyuv/6reeSRviQX5H3vS+6554NpalqaO+64Mtu2bcju3T+XgYETtxcC\nAMwkrX8AAHNUU9PSnHPOedm377eTLEl9q+Devc/m298+N319t6Sv7z/k/vv/fdat25Ikuf32benu\nXp+BgXMyXnshAMBMUlEFADCHDc2bujLJhlQqP5fXvvYnWbJkRZIjqc6u+laSPensHMh3vnNPbefx\ne+6888bZ+BYAAI4RVAEAzGFD86a2JVmfgYFKursHcu65n07yplRnV52b5OeTrE1/fyXJN1KtpBq+\n57bbOrJ58+tn6TsBAND6BwAwp7W3r8mv/Mo9OeOM/Rm6k989OXhwRZJfTrWqakmqYdXgPKpfzqte\n9fm6PcloM6p6evrT2ropq1Y94K6AAMCMUFEFADCHDc6peuWVn6VaJbUl1VDqnCTnJzkryb4kP669\nX0lyfo4ePZpXXllU99rxM6ra2raks/OGJJVa1Za7AgIAp5agCgBgjujp6U9b25Z0dS1OS8v+3HHH\nlbn99m158MGjSX4lSUeSo0mWJXlvknuSPJfkYJLltfcXJzmQaivgL9deSxYufDJPPfX2XH/9V7Jg\nwcLs3t2UXbsO50QVVwAA001QBQAwR4yscNq2bUO6u9cn2ZRq9dTaVOdP7as9Py/Jb6caNn0jyQ0Z\nrJ569at/L93dg3s25ujRz+bRRyt59NGNtdfqZ1mNXnEFADDdBFUAAHPE0B3+kqSS3t4VtedrknTk\nvPOO5uqrj+To0SP567++K/v2NeWVV4bmUi1bdlcuuaRIS8uB3Hnnjbntto50dS3Orl1H0tc3uG5J\n6mdZ1e9pb189Y98rADA/CaoAAOaIoTv8VSucmpp256WXBpIsTXJDrr22I5s3f/TY+tbWjensHJpL\ndfXVr83mzdcde3/wDn/D1+1L/SyrkXsAAE4lQRUAwBzR3l6tnKrOqBpeFTVaxdPI9WNVRNWvW7Hi\nYCqVe7N7d5MqKgBgxgmqAADmiKampcfddW+wKmqi66eybuQw9/b2NWlqWjr+wQEAJkhQBQDAhIwc\n5p50TCjgAgCYKEEVAMA8MrIq6o47rsztt2+bUJXUyGHuXV2LR62yGhiIyisAYFIEVQAA88jIqqht\n2zaku3t9JlIlNXKYe0vLgVGrrJKovAIAJkVQBQAwj4ysiurtXZGRVVJjGW04+zXXPDLG/oldEwCg\n3oLZPgAAADNnxYqeJAO1ZwN51aueqT3vT7IxZfnjvO51X8gv/MKfp7V1Y3p7+9PT05/W1k21UGog\nGze+NUlyzTWPZM+ex4ddr6XlQFpa9h/3GgDARKioAgCYV15OsinJkiT7MzBwuPb8uSS3ZN++juzb\n15bu7koefXT0Vr76dsHkPVm5ckOamy87VmVVNbzyCgBgIgRVAADzyHPPXZjkumPP9++/N8mNSR5I\nNXg6fmD64O8HH4e3Cy5Lc/Nl+eEPh66ZxEwqAGBStP4BAMwjI9vympp2157vH/FYfX+0Vr6hPUnS\nlz17nsiqVQ8caxVMcqxd8B3v2HRcKyEAwFhUVAEAzCMjB6LfeeeNue22juzcWcnevRuydOnK9Pdv\nyPLlRS699NCorXyDe7q6FmfPnifS3b0+3d3D7/A3dDfAjiS3DmslVG0FAIxFUAUAMI80NS09Lija\nvPn14+4ba8+qVUl39/GtgkN3FxyrlRAA4HiCKgCA01xPT3/a2rZk585k795nsnz5m3LppQfT3r4m\nTU1Lj1vX1bU4zc0/yYIFC7N7d1NaWvbnjjuuzO23b6tVVQ0937WrO8m1qYZRQ3f4a2nZX6uwGmwb\nfCHJQ9m160iuv/4rw6498hwAwPwlqAIAOM1NtA1vaF0lycYkazPanf6GP38hyaYsW3YkV1999rFW\nwcEWw8GWwgMHzkh//2+mr6+Sb397+LW1AwIAgwRVAACnuYm24Q2tS5IlGftOf/XPlya5MZdc8kA2\nbx6689/IFsNVqx7I9u2jX1s7IAAwyF3/AABOI4N326u/C9/QXfuOv6Nf/b49ex6vvf9skv+R0e/0\n92ySL+Sll3485rVGM/zOgftOai8AMH+oqAIAOI3Ut+8NttWNbMM7/o5+1X3d3R9PtT3wySS31H6/\nKOee+7f53vc+lttu68if//mTOXr0sxls+Vu4cH8+9KFXDbvWaOrvNrhixcFUKvfWZlQdGHcvADB/\nCKoAAE4jw9v3qm11o93pb/R9y1KdHXVv3e+TSqU/F1/8+mze/PosWnRvjh4davlbuPDeCc2XmsgZ\nAAC0/gEAnEaGt9hNvK1u+L7ddb/vyyuvPJGmpi+mqelP87Of7Rz23sDArmFthvVGa0MEADgRFVUA\nAKeR+ha7k2mrq993wQWL8nd/94X096/MwMCuHDz4thw+XL1LX/JMzj33M6lULqm99zvZvn30u/eN\n1oaoqgoAOBFBFQDAaWSyLXZj7averS8Zaid8Qy6//J354Q+vG3Env/HuIujufgDA+LT+AQAwpmpL\n4Oh36RuvzXCybYgAwPylogoAgOP09PSnrW1Ldu6spLn5yRw8+MVUKstz1VWVtLdfn+TEbYY9Pf05\nfPilLFt2V5ILctVVldxxxy+ltXVTbf3+tLevSVPT0jG/9njrAIDTj6AKAIDj1M+XSgbykY8cP1/q\nRG2GbW1bcv/9Hzu2/6yzOnL77dsmNLPKbCsAmL8EVQAAHGeq86XG3j/+Nc22AoD5y4wqAACOM9X5\nUqPtn+g1zbYCgPlLRRUAAMc50fypej09/bnppm/lL/6iKwcO7EvyqiQtWb786axZ89U8//yFaWk5\nkDvueHd+8ze3HptZ9a53HcyRI+dl1aoHjptDNfJr33HHuyc02woAmPumJagqimJVknuTvDHJZ8uy\n/NwJ1n44ya8neXuSs5PsSrIpyR+WZXlolPUXJ/nTJFcmeUNZlj+ejjMDADC2E82fqtfWtiXf/va5\nSRbVft2apJLnnx/I3//9hvzzP/9akqS1ddOwmVV///cb0t398Yw2h2rk125t3WRmFQDME1Nq/SuK\n4syiKH43yV8l+fkM1WiPtf6zSTqTvCHJl5N8Pklv7fGhoigWjFj/oST/Pck/jXdtAABmXnV+1JIk\nr639Gpot1du7YsS6ke9NbA6VmVUAMH9MdUbVD5J8Ksn/meSuDP0EcZyiKN6a5LeTPJ3krWVZri/L\n8o6yLK9KtaLqqiSfGLHt/CTvSTXcAgCgwVTnSe1Lsrv2a2i2VFPTcyPW1b83fO2J5lCZWQUA88dU\nW//OT7K6LMutRVF8Zpy1N6caZP1BWZb7Rrz36SRrk6xL8keDL5Zl+adJUhTFm6Z4TgAATlJPT3/a\n2rYcmw11xx1X5vbbt2XnzuQnP3kyBw8uzs9+tigXXvhcXnzxlRw40Jfk9iSX5Oyz/yn33feRY9eq\nnzvV3PzTvPzy+dm3744cPFjJokXN2b//UD70oa9l9+6mUWdWHTp0Tx55pC/JBTl8OOnt7TenCgBO\nQ1MNqn6xLMv9E1z7/trjd0e+UZbl00VRPJvk4qIoVpZl2T3FcwEAMEVtbVuGzYbatm1DurvXJ+lI\nUqT694yV7Ns3kI98pCNJjq0/fHggv//7Hdm8+a1Jhs+dqs6cuql2nRvS31/Jli0bj11vtJlV55xz\nXvr6bkpSyf33D2TdOnOqAOB0NKWgaqIhVVEUZya5NMkrSbrGWPZUkouSXJZEUAUAMMvGniu1+Nhr\ng49Dc6PGnyU1dN366y854V5zqgBgfpjqjKqJelWSM5IcKMtyrKHovbXHZTNzJAAATmTsuVKDc6mG\nz42a6CypoXX164+/3onOYk4VAJyeptr6N1Hn1R6PnGDN4RFrAQCYRfVzpVpaDuTOO2/Mbbd1ZOfO\nSn7ykzIHD34xlcryXHVVJe3t19d2Da1vb199wuvu3FnJ3r0bsnx5kYsuOphK5d7ajKrj9448y1jX\nBgDmtpkKql6qPZ51gjXnjFhbr1L3CwCAGdDUtDR337362ED12277q2FDzkczODeqp6c/v/qr38oj\nj/TllVfOy+LFfbnwwrfkda/rycsvH81f//WLSS7I+973mtxzz/vT1LQ0PT39uemmb2Xr1r5ccsl9\ned/7knvu+WCampYOm3HV09Ofdeu2ZOfOZO/eZ7J8+Zty6aUHxz3b4N76AfET2QMAzJyZCqpeSPJy\nkiVFUZxZluXLo6y5oPa4d/CFoiieTHVu1YJUa72fLIpiIMkPyrK85hSfGQBg3hs5UL1+yPl4+779\n7XOTVIem79u3Ls89V8mjj26srfh4Rg5GH75n7KHpQ2fqSHJrursrefTRiZ1tst8PADAzZiSoKsvy\nlVrodFmqQ9X/cZRlb649Pla3701T+bo7duyYynaYE3zOmQ98zpkPGvVz/vjjSf0Q88cfn9hZq/sG\nB6SPHJo++jWH7xn76w2dafG4a6fr+2F6+GfNfOBzDlMzUxVVSfKdVIOqazIiqCqK4h1JXpNkR1mW\ne0fZOylXXHHFdF0KGtKOHTt8zjnt+ZwzHzTy5/zyy8v86EcDqYY7A7n88on9jFXdNzggfXAQeiXV\noempez50zeF7xv56Q2faP+7a6fp+mLpG/pzDdPE5Zz441WHsdAdVY93RL0m+lOQ3knyyKIqvl2X5\nfJIURbEgyZ21vX8yzecBAGAKJjvEvL19TQ4f/rP84Ad31WZUfSEXXvjmXHTRwRw9eiR//dd3Jblg\n2CD2+j0j3xvtTPXD2C+99NCEzmYoOwA0tkkHVUVRvCVJ/X/Z353qX01dWRTFLXWvf7ksywNlWe4q\nimJ9kruSPFoUxTeSvJjk+iRvT/LNsiy/PtnzAAAw/eqHmI+lfkB5c/NPsmDBwtrd+87Lrl0fPjas\nfHDdc89dmKuvrg4yHxhIfvVXv5XvfW9PDh6sZNGi5rzrXefl7LMr2b27KevWPZTf/M3L8+/+3UP5\n6U/PzssvP5+FC1dmwYI9ueSSt+Xd774o7e3vz8BAsm7d+EPSJ/L9AACzZyoVVe9M8vsjXhtI8oHa\nr0HfTHIgScqy/OOiKHYluSXJzUkWJimTfCLJ3VM4CwAAs6R+QHmyMcnajDasfLRB5klqA9R/Psna\n9PdXsmXL8Gs88MBncvDg7yTZkOSOHD7ckWRd/uEfKvmHfxi6jiHpADD3TTqoKsvya0m+Nol9DyZ5\ncLJfFwCAxtLVNXJY+tCw8up7o60b/t7ga6Nd49ChN9Ser8hoQ9SHrnOiawMAc8GC2T4AAABzW0vL\n4FDzpDosffD3A2lpOTDGuup71df2jdg3/BrnnPNPtee7M3w4+8jrjP51AYC5Yybv+gcAwGmofkD5\nihUHU6ncW5tRNXxY+ViDzA8f/rP85V/uycGDd2bRoua8+92HctZZQ9f4rd/6SD74wQ356U/Pyssv\n316bUfWZXHLJW/PmNx+t+xqGpAPAXCeoAgAgyfCh6CMHkvf09Oemm76VRx7pS3JB3ve+5ItffH9u\nv31bbf1AHn74vRkYqM6iqhp+Q+iBYU8H0tv7Qm655bt55JH+nHHG6/KBD7yUs88+I888syx79z6T\n5ctflUOHXsznP/9oli5dnp6eZ3LGGUWWLv2nvOMdF+X5589OciTJ8UPSe3r6c/31Xx123nvu+eCo\nA9YBgMYhqAIAIMnow87rB6FXh57flKSS++8fyI4dG9LdvT4jh6Of6Br1723btiHd3Rcdu+bQEPWO\nJLemu7uSRx8dfG1Dks8lqeTgwY3Zs2f0ge3138vI865bZ8A6ADQ6QRUAAElOPOx8tIHlvb0rxlg/\nsWHq1f31g9MHfz/acPYVo7x2/Nc40XkNWAeAxmeYOgAASUYfdj78veFDzpuadh+3fvxrjNw/2hD1\n0Yaz7x7lteO/xonOa8A6ADQ+FVUAACQZe9j54HuHD/9ZfvCDu5JckKuuquSLX7wxt9022vqxr1H/\n3p133phbbtl67JrvetdLOeuse/PMM2dl794NWb68yEUXVYez79q1PE8//ZkkF9dmVH31/2/v7qPr\nKu8D33+PLEsylm0h2xibNwujbAgsAtfTTAgFFOdlFdoMtDfNxQxNZzyZm7GmaxquuUBCE5K0wLhp\nmmk6t2pup0ya4UW45N5AeuNMSCauTVltqEsCGcxjB8sOlo1BFrJk610+94+9j7V19GK9HXsbfT9r\nae1z9n5efnuf52z7/M7ez+Gtt1aMO3H6WPE2Nd1auoMnSZJmhYkqSZIkAaMnJC/e9u1vf3LU+ief\nvGSMdeO3UbxtrDZnw3jxSpKkbPPWP0mSJEmSJGWCV1RJkiSppI4c6WDDhm+xY8fbDA2dQ3X126xY\n8W7q63t48MHr2bTpB+zY8TawjBtvhEceuY3a2pox22ls3EpLSzVLl+7jpZe6aG+vJZdr5bLL3kNd\nXQeDgwM899wRenpyLFy4kptuKjvZXrp+XV0XTU23jFi/eze0te2jpuYiOjpeZ/nyy6mv7zlZTpIk\nlZ6JKkmSJJVUY+NWnnlmAbABaKazcyMHD+Z48cU8zz+/mQMHLk625Xj66TwbNzaPeftgY+NWtmy5\nnfiX/B4GPgM0A5/i5ZdzvPzy40nJJcB6OjpGtpeu/8ILeaB4fTNwHwcOFJZxjIVykiSp9ExUSZIk\nqaRaWqqTRzmgOlnGz9vbVwGLRqwbLj9WO4VyF4zR3qJU6dHtjaw/1vri5cTxSJKk2eccVZIkSSqp\nurouoBPIA13JEiBPbW1ralu8rq7u2ATtFMq1jtFeZ+pvdHsj64+1vng5cTySJGn2eUWVJEmSSqqp\n6Rb6+p5i+/avJHNUPcyKFVdQX9/LQw/dwaZNz7J9+1eAZdx0U46mplvHbQeaaWmpZtmyhfz0pw/T\n3n4uudwDXHbZ1Vx6aQ8DA/3JHFUPsXDhShoa5p1sL12/ru4YTU03j1i/e3eOtrbN1NRcSEfHZpYv\nj6iv7z1ZTpIklZ6JKkmSJJVUbW0N3/72J8fdPtG24nZmMlfUePVn2q4kSZo93vonSZIkSZKkTPCK\nKkmSJM2qI0c6aGzcmtxi10VT0y3U1tZMuZ09e/azbt0TtLevora2lW3b7qCmZsnJtleuPMzg4ADP\nPXeE48f7KCurYsGCGqqrO1i8eDktLT9nYKCGefOW8qEPlfHoo785Io7ZiHO29lWSJMVMVEmSJGlW\nNTZuZcuW24EcL7yQB5qndWvdunVPcODAvUCO7u48DQ2bef/7LznZNjyelFwCwNDQegYGmunsbOTg\nwc3ANcB6TpzIsXVrno0bR8YxG3HO1r5KkqSYiSpJkiTNqpaWauJEEkAueT517e2rRrTT3r6qqO1F\nRTVyQGH7qmT7+HHMRpyzta+SJCnmHFWSJEmaVXV1XUA+eZanru7YtNqprW0d0U5t7cGitjuL/vJA\nYXtrat3YccxGnLO1r5IkKeYVVZIkSZpVTU23AM3JvE3HaGq6eVrtbNt2Bw0Nm5M5qg6ybdt6zj13\nycm2V63qYWCgPzVH1UMsWLCE6uqHWbx4KS0tLzEwsJ9585by4Q/Po6npY7Me52ztqyRJipmokiRJ\n0qyqra2ZlXma1qy5hNdfv2/U+tmaA2o24pytfZUkSTFv/ZMkSZIkSVImmKiSJEmSJElSJpiokiRJ\nkiRJUiY4R5UkSZJKZs+e/axb90QyIXor27bdwZo1l3DkSAeNjVtpaalm5crDlJXNp7W1lqVL9/HS\nS120t9eSy7WyevVldHUdZPnyy6mv7+HBB6/n/vufZ/duOHz4VY4fL6ezsw1YCFxAWVkrl1/+Hq66\naoimpluora0ZEcOSJa9x5ZXL+Pu/P8yxY31ADWVlC1m6tI2BgSXkcsu58UZ45JHbqK2tGRFnXV3X\nyTaLFcrt3g1tbfuoqbmIjo7XT8ZdqDfefk/UtiRJc4mJKkmSJJXMunVPcODAvUCO7u48DQ2bef31\n+2hs3MqWLbcDOeBxYH3y+GHgM0Az8Cl27WoG7uPAgRwvvpjn+ec3J+01A1HSSz/wJaCZEycaeeWV\nHK+8kgeaefLJ9UUxPM6hQwDHgHcD6zlxopm33jrvZAxPP51n48a4bjrOF14YbrPYcLlCvCPjLtQb\nb78naluSpLnERJUkSZJKpr19FXFSBiCXPIeWlurU+kWpxxckj6uLlun6hfUFdWOWjfsojmFRqp9F\njGxrdN2RcQ6vLzZcbuy4x25v0ZhlJEmay5yjSpIkSSVTW9sK5JNneWprDwJQV9eVWt+Zelwo31W0\nLNRPb+9M/vaOWbau7tgYMRTqtKb67WJkDMN1R8Y5vL7YcLmx4x67vbH7lCRpLvOKKkmSJJXMtm13\n0NCwOZmj6iDbtsW3tjU13QI009JSzapVPeRy36S1tZZlyxby058+THv7ueRyD7B69Rq6ujazfHlE\nfX0vDz10B5/9bDO7d+c4fDhw/Pg8Ojs7gfuBVZSV3Z/MUXWCpqabR8UQz1G1lL//+y6OHfsJsI+y\nsnOSOar+iFxuOTfdlKOp6dZRcdbVHTvZZrFCud27c7S1baam5kI6OobjLtQbb78naluSpLnERJUk\nSZJKZs2aS3j99ftGra+trZn2fExPPnnJrMQwGZONc7bLSZI0V3nrnyRJkiRJkjLBRJUkSZIkSZIy\nwUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywV/9kyRJ0hlx5EgHjY1baWmpZuXKw5SVzae1\ntZa6ui6amm6htrZmWu3++Mcv0dDwFL29q6mq2suOHR9n9eqLaWzcyu7dcPjwqxw/Xk5PTxkLF65k\n7dq32bWrj7a2BfT3HwDOAU5QVlZGPt8HVJDPr6SiYh8VFYvo65vHwoUrue66XiorF9LaWjth/EeO\ndLBhw7fYseNtYBnve1/3yXqFsvk8I8rceCM88shtUzoG6eM502MoSdKZYqJKkiRJZ0Rj41a2bLkd\nyAGPA+uBHC+8kAeaefLJ9dNqt6HhKXp6vgjk6OnJc8MND/DRj16R9NUMREnJ9XR05PjhDx8GPgNs\nBhhq73sAACAASURBVNaebOfECYD9wH1AM/3959LfP1xv69bhmCeKv7FxK888swDYAIysVygLjCjz\n9NN5Nm6c2jFIH8+ZHkNJks4UE1WSJEk6I1paqomTPACLUo9zybbp6e1dPaKt3t7Vqb7S7RbKXJA8\nXpXEkXbBBPUWjfN4ZPzDjyezr9M/BiOP58yOoSRJZ4pzVEmSJOmMqKvrAvLJs87U4zx1dcem3W5V\n1d4RbVVV7Uv11ZX0le6vNXncmtpW+GudoN54j0fGH/c9cdnRZaZ+DEYez5kdQ0mSzhSvqJIkSdIZ\n0dR0C9BMS0s1q1b1kMt9M5m36RhNTTdPu90dOz7ODTc8kMxRtY8dOz5GXd3FQDO7d+c4fDhw/Pg8\nenoeYuHClfyzfzafV155mLa2Svr7/wlYwMg5qj5HPn8+FRW/oKJiIX19cb33v7+Xioo45onib2q6\nhb6+p9i+/SvAMq67rvtkvXTZdJmbbsrR1HTrtI/nTI+hJElniokqSZIknRG1tTUlmUNp7dqr6e6+\netT6MzVfU21tDd/+9idPWW4yZU7Vj3NSSZLOdt76J0mSJEmSpEwwUSVJkiRJkqRMMFElSZIkSZKk\nTDBRJUmSJEmSpEwwUSVJkiRJkqRMMFElSZIkSZKkTDBRJUmSJEmSpEwwUSVJkiRJkqRMKD/TAUiS\nJEljOXKkg8bGrbS0VFNX10VT0y3U1taMW/bOOx/l2WcPMDS0iPLyZdx0Ux+53CA/+tGbDA2dAHqB\nxUAd559/kOee+wQnTsC6dU/Q1lZDLtfKZZe9hyuu6OfBB69n06YfsG3bIbq6Osnn80DNyXYHBo6x\nY8cx8vnLqKjYxXXXnc+LL/Zw/HgfJ070kc/3JpEtBlYwb94ecrkahoZWUFl5iPr6a6ir62BwcIDt\n21s5fhxyuUs5//xDPP30R/nyl//nqP3es2c/69Y9QXv7KmprW9m27Q7WrLlk1HG6++6r+I3f+C7t\n7auorn6FsrJqOjsvHlFnusdZkqRSM1ElSZKkTGps3MqWLbcDOV54IQ808+ST68ct+73vdQFXA+sZ\nHMzxwx8+DuxP1u1PSt4H5HjjjTwNDZsBOHDgXqAZ+BQvv5zj5ZfzPP/8Zg4cuBi4NKl7SVG7bwEP\nAjn6+x/nb/82Hc3+1OP7gGaGho6dfNzb25j083hSpgO4j3w+x8GDeW688QF6er44ar/XrXsiiTVH\nd3cc/+uv3zfqOH3nO8P1u7sfPrnP6TrTPc6SJJWaiSpJkiRlUktLNZBLnuWS5xOVvQBYlKqzKLXu\ngpPtFJbt7atS66rH2LYoeT5Wu3VFz9MuSD0utH3BGP2k2x/uu7d39Zj7Hcc0Ov7i4zSy/gVj1kmb\nynGWJKnUnKNKkiRJmVRX1wXkk2d56uqOnaJsK9CZqtOZWtea/A23V1t7kNrawrquom2Fep3jtLu3\n6HlnUfl0f11Fj4vrjYyrqqplzP0ejnU4/rGOU1VVOrax64w+dpM7zpIklZpXVEmSJCmTmppuAZqT\nuZOO0dR084Rlu7q+yfe//zJDQw9RXr6MhoY+YD4/+tHLDA0NAX3A54jnqDrEtm2/BUBDw2ba2paQ\nyz3AZZddzRVXDPDQQ3ewadOz/OhHhTmq2oF9J9vt769kx477kzmqXuW6684rmqOqL4nsc8B5zJt3\niFzuAYaGzqOy8vPU17+HSy/tYWCgn+3buzh+/HPJHFVv8Mwzv8kf/uHo/d627Q4aGjYnc1QdZNu2\n9WMep3vu+Ti33bY5maPqKGVlDyZzVA3Xme5xliSp1HLxxJDvPDt37syvXbv2TIchldTOnTtxnOud\nznGuucBxrrnAca65wHGuuSAZ57lTl5web/2TJEmSJElSJpiokiRJkiRJUiaYqJIkSZIkSVImmKiS\nJEmSJElSJpiokiRJkiRJUiaYqJIkSZIkSVImmKiSJEmSJElSJpiokiRJkiRJUiaYqJIkSZIkSVIm\nmKiSJEmSJElSJpiokiRJkiRJUiaYqJIkSZIkSVImmKiSJEmSJElSJpiokiRJkiRJUiaUn+kAJEmS\npGJHjnTQ2LiV3buhrW0fNTUX0dHxOsuXX059fQ9NTbeQz0Nj41ZaWqqpq+uiqekWamtrRrRx552P\n8sMfvsXg4HnMn7+PiopF9PQMAFBWVsWCBTVUV3ewePFy9u9/nf7+BQwNvQWcAIaABUAlcBTIAzXA\nYmA/MK8o6n5gYbK+Nyl3PpWVb3DppfUcObKLt9+uYGCgAjgGLAPaKC9fzuBgC8PfIS8GKoCfA0uB\n+UBn0nYtsICamla6usoZGqpIYh1I+qwEqsjlKqishN7ew0m7lcASysoWsnTpEfr6Kjl+vJ+ysioW\nLlzJTTeV8cgjt406funXYPnyy7nooiOUlc2ntbWWurou7r77Kn7jN75LW1sNuVwrq1dfRlfXQWpq\nLuLNN3+W7O9iysraKS+vZ+nSZ7jyymXs3NkHLOPGGxnVb3H/LS3VrFx5eES/hdc6XWasMVBoZ8OG\nb7Fjx9sj+jzV+DmTJrNfE9mzZz/r1j1Be/sqamtb2bbtDtasuaSEEUvS7DFRJUmSpMxpbNzKli23\nA83AfRw4UFjmePHFfLKepEyOF16I1z355PoRbXzve13AF4Bm+vvPpb9/uI+hofUMDDTT2dnIwYOb\ngS8BDwC/BOwCzgEKH+4Lz+8DNgMXjhF1N3AFcRKLpGwzfX3/nl27moElyboHgIeTdh5mcHAzcEGq\nnUKZC4GrkvYqk7bXA810dPSlYiPVZ7wun19Pb+8DqXYvAdZz4kQzb721YsQx6OjI8fTTeTZuHH38\nRr4GOV588fEkhviYf+c7D9DT88WkzKeS/Sy8XoX93cyJEw/S35/j0KHHOXSIk22M1e/o/nPAyH4L\nr3W6zFhjoNDOM88sADaM6BMmHj9n0mT2ayLr1j3BgQP3Ajm6u/M0NGzm9dfvK1W4kjSrTFRJkiQp\nc1paqokTFMVLgFyynXHWpdu4IFW/WLrdVclyNbAIqCP+r/KipGzheaHsWAaT8oXkUHH8hVhWF/VZ\n3F6hDKn2Cm2n21qUqnNBqny6jfLU+rGOw6mOX/GxXzSiTm/vasZ+ndL7u6qo/sT9ju5/dL+FOiPL\njN3WxGPl1HGcCZPZr4m0t6ePeS55LklnB+eokiRJUubU1XUR32pXvATIU1d3LFVmeN3oNlpT9TuL\n/tLtFsrtTbbtTdZ1Fj0vlB3rr1C3tajfwjLdR36C9gpl9qTaK7Sdbiu9L61F6wptpNePdRxOdfyK\nj/3IOlVVexn7dUrvb2tR/Yn7Hd3/6H4LdU41BobLjK4/mbpnykxjq61NH/M8tbUHZzE6SSqtXD6f\nP3Wps9DOnTvza9euPdNhSCW1c+dOHOd6p3Ocay5wnI/W3t7Bxo2F+ZH2U1NzIR0dB1i+PKK+vpem\nppsB2LixMI/PMZqabh4xj097ewd33vlNfvCDIwwOLmf+/F9QUbEwNUfVAhYsWEJ1dSeLFy9l//5W\n+vvnMzTUTjw/1QniOaoqiOeogniOqmrgAKO/8y3MUVUG9BHPNbWCysrDRXNUzQN6iOebaqe8fBmD\ng/sYOUfVPKCFeI6qcuLET2GOqirOPbeVzs75DA3NZ3iOqj7iWwQryeUqqao6QU/PW4yco+qcojmq\nFrBw4UoaGubxl39566jjl34Nli+PuPjiI+RyhbmijnHPPVdy223fpa1tCbncIVavXkNX1yFqai5M\nzVFVTVlZRzJH1T6uvHIpO3f2A8u46abcqH6L+29pqWbVqsMj+i281ukyY42BQjsbNjzF9u0dI/o8\n1fg5kyazXxN57bX9NDQU5qg6yLZt652j6jTxfK65IBnnuVOXnB4TVdJZzH8INRc4zjUXOM41FzjO\nNRc4zjUXlDpR5a1/kiRJkiRJygQTVZIkSZIkScoEE1WSJEmSJEnKBBNVkiRJkiRJygQTVZIkSZIk\nScoEE1WSJEmSJEnKBBNVkiRJkiRJygQTVZIkSZIkScoEE1WSJEmSJEnKBBNVkiRJkiRJygQTVZIk\nSZIkScoEE1WSJEmSJEnKBBNVkiRJkiRJygQTVZIkSZIkScoEE1WSJEmSJEnKBBNVkiRJkiRJygQT\nVZIkSZIkScoEE1WSJEmSJEnKBBNVkiRJkiRJygQTVZIkSZIkScqE8tloJIqi9wLfBN4FfCGE8KUJ\nyn4M+B3gGqAS+DnwBPDHIYTeorJVwJeBm4HlQAA2hxC+NRtxS5IkSZIkKTtmdEVVFEXlURT9PvB3\nwKVA/hTlvwBsAVYDXwf+AGhPlt+Noqg4nv8MfBD4VWAZ8EdAcxRF/3wmcUuSJEmSJCl7ZnpF1Xbg\nvcCfAr3AveMVjKLoauD3gNeAtSGEzmTTg1EUPQbcDvwu8NWkfA3wW8AnQgghKbsliqJPAv8B+Jcz\njF2SJEmSJEkZMtM5qpYAN4cQ7iJOVE3kU0AO+HIqSVXw+WTbxtS6tcSJtH8oKvtj4PppRyxJkiRJ\nkqRMmmmi6n0hhGcnWfYDyfIHxRtCCK8B+4E1URRdmKxenizbi4q3AedNNVBJkiRJkiRl24wSVSGE\nrsmUi6KoHKgHhoCWcYrtSZZXziQmSZIkSZIknZ1mekXVZC0G5gHHQgjjTbheuHLq3GR5OFkuLSq3\nDHhjdsOTJEmSJEnSmTbTydQn65xk2T9Bmb6isjuT8tcx8iqsXwZ2zGp0kiRJypQjRzpobNxKS0s1\ndXVdNDXdQm1tzbjrp9P27t1w+PCr9PRUk8st533v66ayciH79lXQ1raP5csvp76+hwcfvJ7773+e\nV17pZteuf2Ro6BygFsgxf/7bDAyUAfOBGuAQ8X9nT1BWVkY+30c+3wksJP7etpP4v+DdxNO9ziee\n6rU/VaYt2bYs2dZOfGPCCaCC4e+a24Gq5HkFMJiUWZS0X1XUxrxUnaPAglR7/UXtH0v2p5PhqWhr\nGP09d0XRumPJ8yWpfalK2u4l/k56PvGPhdck/e5JHp9IjkEh9vnAQeLvvAsfWyqTv6PEHx+WJcf7\nGLncuygr+zHl5cvo66tKYlkGtFFevpzBwZZkn48BA6nj1pHqYzGwgsrKw6xaVUtLS2tRPJ3Er30t\n0EZFxcXMm3eI1asvo6NjL0ePHqe7O5/E1JPUW0lV1SHq66/hiiv6ufvuq7j11v+HN944AcxnwYIq\nBgffZHDwXObNW8qHPlTG1772Ee6///mT4/zBB69n06YfsGPH2wwM5Ojvf4PBwXPJ5c6hunovnZ3V\n5POXUVW1l+ee+zhr114NwJ49+7nppv/Km29WAOdQW9tGX18lPT1lLFhQQ3V1BytWvJv6+h6amm4h\nn4cNG751sp/BwbfJ5epZuvQg27bdwZo1lwAj30OF98pFFx2hrGz+qPfPVN+j6ff40qX7+Md/PEBb\nWwWwmvPPP8hzz32CNWsuOeW5YKztR44cZd26J2hvX8WSJa9x7bUX8dZbK0bV//GPX6Kh4Sl6e1dT\nVbWXHTviY1rcZuHckO5jqmbjnHY6TTbes22/lC2nK1HVnSwrJihTlS4bQuiMougR4IEoin5C/GuB\ndxInrj5dqkAlSZJ05jU2bmXLltuBHC+8kAeaefLJ9eOun17bzUAErAdybN36ePK4GbiPAwdyvPhi\nnuef38yBA/cCm4kTH1ck5TYzMHAt8VSr9yXb157s58QJkm2VSZ3C43OALuCqZB3E/wUulClPtq0H\nHgAuZPi/04XvdAuP56ceF8pcAfxsjDbSdZYRJ2rmJeuHkmXheS1xsuloqr/5jHZO0fOupI30vhQS\nZ0Opfb4kFdvFSV+FY/CzVLlC0qvgkmS5K1l+KWnjYfL5HENDLQwNDa+LX5OHGRzcDFyQ1P9Zaj/P\nAapTfdwHNNPX9zu0tDxA/Hqm46lM+ozb7e9vBjaya1dhLO1K9gGGx0Uzvb2NvPxyjpdfzvOd7zxA\nT8/lSZn1dHdvBq4F1nPiRI6tW/N84AOFMReP83gMXgxsSPq+lsJYPXr0SBJTjt7ePDfc8ADd3XGi\nat26Jzh06F0ny7711nkn+x0YaKazs5GDB+NxHo97eOaZBal+fh/IceBAnoaGzbz++n1A8Xuo8F4Z\n+/0z1fdo+j0ev4Y1yXHM8cYbw3Gc6lww1vbnn99/8rh2dz/OoUPrx6zf0PAUPT1fBHL09Awf0+I2\nh88Nw23cc8+7Jr2v48U51XPa6TTZeM+2/VK2nK5E1VHir3gWRVFUHkIYHKPMsmTZllr3aeKz0w+J\n//X4n8BHQwg/mUynO3funH7E0lnCca65wHGuucBxPtLPfgbxB9V4+bOfxcdovPXTa7v6ZDuxRan1\nw320tZ2fPF9F8l/a1PNFxAmQ9PO0C1J1Co/Li9YxRplCH6tT5WHkf99XpB6nyywCLhujjeI6p1I+\nifLFHyfSMRTvb3p9OrZ0/OnYL0j6T/dROL51ybLQRi61Pr1uVWpZqH/ZGPtY6CP9+q8eI57Bonar\nGTmW6lIxXsBY46m3d3WqTHrcjDXm0s+Lx12h7TqK2y+8J0bWK8RYvJ/x8/h9ASP7GRlDod2R76GJ\n3z9TfY+OfI8X3h+j4zjVuWCs7SOP66Jx68ev0ehjWtxm8etUOIbT39/pndNOp8nGe7btl7LltCSq\nQghDURS9SjxRej3DX4GkFb56+Emq3gBwd/I3ZWvXrj11IekstnPnTse53vEc55oLHOejXXVV4JVX\n8sQfdPJcdVX8f7vx1k+v7S7iW9AK7XUmj7tS6/IsW3aIAwfyQCtwnPiqmsLzecky/TwtXafweGHS\nV2EdRWUK2/LA3qT88aTcwlTbRxn+73y6TCXx7XTFbaTrFG59G++KqkGGr6gqTDE71hVVC4uedyZt\npPdlcRLbUGr9vFRshb4Kx2BPqtxRRl5RVYhvb7IstFF4vfYWrUu/NoX6e1LtLCzqI/367x0jnuNF\n7XaNU4cxysTjqapqLz09lan+0sejeMyln88fo3xX0f7nqarax9q1GwBYtuz7qXqFOIr3c/i9BHle\neaWTkftYiOEN1q79V8BY76Hx3z9TfY+OfI8XXrfRcZzqXDDW9s7O9HHtZLw4q6r+X3p6Rh/T4jaL\nX6f4GM5kf6d3TjudJhvv2bZfmppSJx1z+fx4c5tPTRRFDxBfY/uFEMKXxtj+R8BdwN0hhK8WbbuW\neE6qnSGEX5qNeHbu3Jn3jaB3Oj/YaC5wnGsucJyP1t7ewcaNhflNjtHUdDO1tTXjrp9O28VzVF13\nXTcVFYU5qvazfHlEfX0vDz30fj772ed55ZXj7Nq1MzVHVZ75848yMJAjnuFiMfHvAS1g5BxVXcQJ\nkTLiD/HpOarKieda6k+VOcLwHFXHiRMpU52j6ngSR7qN0z1HVWFfiueoKiTXapJ1rzFyjqpC7OXE\nc34Vz1FVkcTfn+xfJdCTzFH1AuXlS+nrm088R1Qt0E55+TIGB/cl7XYlx2q8OarOo7LyTVatqqGl\n5VBRPF1JmzVAezJH1RusXr0mmaOqi+7uXFK+J1meT1XVYerr38MVVwxwzz1X8tGPfiuZo6qCc86p\nZGCgjcHBGubNW8qHPzyPr33tw3z2s8+fHOcPPfR+Nm16lu3bOxgYgP7+NxkcrCGXW5DMUbUomaOq\nheee+82Tc1S99tp+brjhEd58sxJYkJqjah4LFiyhurqTFSuuoL6+l6ammwHYsOGpk/0MDh4ll7uM\npUsPsW3b+pNzVKXfQ4X3ysUXHyGXmz/q/TPV92j6Pb5sWQsvvHCAtrZK4jmqDvHcc7/FmjWXnPJc\nMNb2t98+SkPDWHNUjay/c+dL3HBDYY6qfezY8THWrr16VJuFc0O6j5aW16Z0Pp+Nc9rpNNl4z7b9\n0tQk/2/Jnbrk9Mx2ourzwBfHSVRdRnzr3mFgbQjhrWR9GfD/AR8BfjuE8OhsxGOiSnOBH2w0FzjO\nNRc4zjUXOM41FzjONReUOlE17Vv/oih6N3BzatX7ia/ruz6Kok2p9V8PIRwLIfw8iqJ7ga8AL0ZR\n9Bjx1xO3AtcAfz1bSSpJkiRJkiSdfWYyR9UvAX9YtC4PfCj5K/hr4muACSH8pyiKfg5sAj5FfKN7\nAH4X+LMZxCJJkiRJkqSz3LQTVSGEvwL+ahr1/gb4m+n2K0mSJEmSpHem4tkQJUmSJEmSpDPCRJUk\nSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLB\nRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJ\nkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJ\nkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFE\nlSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmS\nMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmS\nJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSV\nJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIy\nwUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIk\nSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUk\nSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLB\nRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJ\nkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJ\nkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFE\nlSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmS\nMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmSJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIywUSVJEmS\nJEmSMsFElSRJkiRJkjLBRJUkSZIkSZIyoXyqFaIoei/wTeBdwBdCCF+aoOzHgN8BrgEqgZ8DTwB/\nHELonWR/a4BvANcDq0MIv5hqzJIkSZIkScq+SV9RFUVReRRFvw/8HXApkD9F+S8AW4DVwNeBPwDa\nk+V3oyg6Zd9RFP068Byw91T9SZIkSZIk6ew2lSuqtgPvBf4U6AXuHa9gFEVXA78HvAasDSF0Jpse\njKLoMeB24HeBr56izyXALwOXA3dOIVZJkiRJkiSdZaYyR9US4OYQwl3EiaqJfArIAV9OJakKPp9s\n23iqDkMI3wghvDaFGCVJkiRJknSWmkqi6n0hhGcnWfYDyfIHxRuSxNN+YE0URRdOoX9JkiRJkiS9\ng006URVC6JpMuSiKyoF6YAhoGafYnmR55WT7lyRJkiRJ0jvblH/1bxIWA/OAjhDCeBOgtyfLcwGi\nKLoT+AuGJ0z/tyGEx0oQmyRJkiRJkjKqFImqc5Jl/wRl+tJlQwiPAo+WIBZJkiRJkiSdJaYyR9Vk\ndSfLignKVBWVPZVc6k+SJEmSJEnvQKW4ouooMAgsiqKoPIQwOEaZZcmybaKGoih6FbiYOKGWB16N\noigPbA8h/MosxixJkiRJkqQzbNYTVSGEoSTBdCXxpOq7xih2RbL8ySnaunwmsezcuXMm1aWzguNc\nc4HjXHOB41xzgeNcc4HjXJqZUlxRBfB94kTVr1CUqIqi6FrgfGBnCGHCK6pmYu3atd4mKEmSJEmS\ndBaZyRxV4/2iH8CfE9/+d1cURcsLK6MoKgMeSur+yQz6liRJkiRJ0jtMLp+fKN8Ui6Lo3cDNqVUf\nAT4MPEt89VTB10MIx5I6nwa+AhwCHgOOA7cC1wB/HUK4fTZ2QJIkSZIkSe8Mk01U/TbwyCTaqwsh\n/CJV79eATcC1wHwgJO38WQjhxLQiliRJkiRJ0jvSpBJVkiRJkiRJUqnNZI4qSZIkSZIkadaYqJIk\nSZIkSVImlJ/pACYSRdF7gW8C7wK+EEL40gRlPwb8DvFk7ZXAz4EngD8OIfROsr81wDeA64HV6fm2\npNOhVGM+iqIq4MvEP4qwnHi+uM0hhG+VYj+kiZTy3O55XFlRwvN5LfAw8fm8BtgP/EkI4b+UYj+k\niZRwnL8beBD4ZWAh0AJ81XGuM+F0fCaNouhdwD8BW0IIG2YxfGlSSng+3wesAgaTVTkgD/x6COG/\nj9dHJq+oiqKoPIqi3wf+DriUeEcmKv8FYAuwGvg68AdAe7L8bhRFp9zPKIp+HXgO2Huq/qTZdhrG\n/H8GPgj8KrAM+COgOYqifz57eyFNrNTj3PO4suA0nM+/Dbyb+AN8DfAfga9HUfQrs7cX0sRKOc6j\nKFoA/C3wBrAGWET8Zdv/HUXRzUinyen6TJqs/yugf7ZilybrNIzzPPBvQgjnJH8LkuW4SSrI7hVV\n24H3An8K9AL3jlcwiqKrgd8DXgPWhhA6k00PRlH0GHA78LvAV0/R5xLi//RdDtw5o+ilqSvZmI+i\nqAb4LeATIYSQlN0SRdEngf8A/MvZ3x1pTKU+t3seVxaU8ny+CHiV+NeTC1cLPhZF0eeAjwDfT7lY\nCAAABdBJREFUm/3dkcZUyvP5wqS9LSGEY8m6b0RR9MfA/wJsneV9kcZzuj6Tfob4ApLvzF7o0qSd\njnGem2pQmbyiivjDxs0hhLuID9ZEPkW8419OHaiCzyfbNp6qwxDCN0IIr00nWGkWlHLMryVOSv9D\nUdkfE98eJZ0uJT23ex5XRpRsnIcQukII/3sI4SeFdVEULQRWEN8aJZ0upRznbSGERwpJqiiKFkVR\ntCkp55QFOp1K/pk0iqJrgLuBTwAnZhyxNHWnI/fy8SiKXo6iqCOKop9EUfSvTxVUVhNV7wshPDvJ\nsh9Ilj8o3pB8YNkPrImi6MLZCk4qgVKO+eXJsr2oeBtw3lQDlWbAc7vmgtM2zqMomkc8n8RbwF9O\nI1Zpuk7LOI+i6A2gA9gA3BJCeHWa8UrTUdJxHkXRfOJb/r6YuutBOt1KfT7/KfH8yB8AVgL/F/Bf\noij63ybqKJOJqhBC12TKRVFUDtQDQ4z/TeKeZHnlLIQmlYRjXnOB41xzweka51EULQW+D1wBfDCE\n0D31aKXpOV3jPIRwPnAu8Two/z2KopumHq00PadhnP8B0B5C+E/TDlKaoVKP8xDCbSGETcnVsj0h\nhL8gnm/z7on6y+ocVZO1GJgHdIQQxpv0q3AVybkAURTdCfwFw5OE/dsQwmMljVKaPVMe88DhZLkU\nSF+iuYx4olIpa6YzzqWzzbTHefKLaH8DvET8qznFl99LWTHj83kyvr8WRdEHgf+TeKJ1KUum85n0\neuLbqK4tfXjSrJjN/5//nHherHGd7Ymqc5LlRL+Q0JcuG0J4FHi0lEFJJTTlMQ/sTMpfx8js9y8D\nO2Y1Oml2TGecS2ebaY3zKIoi4H8A/zWE8JkSxSbNlimP8yiKfo341pB3hxCOp8pVpMpKWTKd8/m/\nASqBf4iiqDDRdDWcfA9cG0Jone1ApRmYzvn8UuD/AD4fQkhPQ/Me4mTVuM72RFXhMveKCcpUFZU9\nlVzqT8qaKY/5EEJnFEWPAA9EUfQT4l9puJM4cfXpUgUqzcBMz+2ex3U2mPI4Tz7M/Dfg2yapdJaY\nzvn8+aT8n0dRdBdwFPgY8EHgX5UgRmmmpjPO7yL+9bS0rxLf9XMX3vWg7JnOOH8D+BfAiiiK/j3x\n3T0bgA8nf+PK5BxVU3AUGAQWJfdMjmVZsmybqKEoil6NoqgbeIr4BPFqFEXdURT5U8/KkumO+U8T\n/+TtD4G3gX8HfDT9y1FShkxrnHse11lmOuP8/cS/5PrbURT1JOO723GuDJvyOE++dW8AFhF/udYG\n3AP8uxDC4yWNVpqe6YzzoyGEg+k/4g/33SGEQxPcWiWdKdMZ593EXzKUAS8DR4gTVb8aQvgfE3V2\nVl9RFUIYiqLoVeLJuuqBXWMUuyJZTviBPIRw+SyHJ8266Y75EMIA8YR1E05aJ2XBDMa553GdNaYz\nzkMIf0c8P4R0VpjB+TwAt5U+QmnmZuszaQjhX5cgPGlWzOB8vgf4X6fa39l+RRXEv3gD8CvFG6Io\nuhY4H/inEMKEV1RJZxHHvOYCx7nmAse55gLHueYCx7nmgtM2zs+WRNVElz7+OfElaHdFUbS8sDKK\nojLgoaTun5Q2PGnWOeY1FzjONRc4zjUXOM41FzjONRdkYpzn8vls3f6a/OTyzalVHyGeaOtZhjN4\nAF8PIRxL6nwa+ApwCHgMOA7cClwD/HUI4fbTELo0LY55zQWOc80FjnPNBY5zzQWOc80FWR7nWUxU\n/TbwyCSK1oUQfpGq92vAJuBaYD4Qknb+LIRwohSxSrPBMa+5wHGuucBxrrnAca65wHGuuSDL4zxz\niSpJkiRJkiTNTWfLHFWSJEmSJEl6hzNRJUmSJEmSpEwwUSVJkiRJkqRMMFElSZIkSZKkTDBRJUmS\nJEmSpEwwUSVJkiRJkqRMMFElSZIkSZKkTDBRJUmSJEmSpEwwUSVJkiRJkqRMMFElSZIkSZKkTDBR\nJUmSJEmSpEz4/wFwizB9v9hfEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce6af6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(stats[0][1:], stats[1][1:])\n",
    "plt.yscale('log')\n",
    "plt.xscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The above plot looks promising, but is not very useful. Nonetheless, you can represent this in a Lorenzcurve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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llM/FS6q2HzTcdo7CA5Tafj+kjDx5AeX65GzK87mwxTb9nY+/o4Stn6f0Kj2E\ncj5vQxmC+/rMbA59l1HO23+iTJyyE+V1fDElSP0p8JrM/MfRPU1Jmj46arWJ+h0xfBGxY33oSUR8\nkvLN9Kcyc6Dpmxu3/yalyOe7M3OLb9Ii4nTK1NDvzMzvNy+XJEmSNDNVQ/q7KaHV2zPzB21ukiRp\nGCZ1D68xqLNSH9vf35Cb2yjf8E50DQBJkiRJkiSNk0kdeI2BXarblf0sX960niRJkiRJkqa46R54\nbV/d9ldAcmPTepIkSZIkSZripnvg9XB1u3U/y7dtWk+SJEmSGk3eoseSpH7NaXcDxll9uvVH97N8\nt6b1+rVgwQJ/0UmSJEkzxA9+8EiN+tnAmQsWLOhvpl1J0igddthhHYOvNTzTPfBaSJmq9ym0mFqc\nMh1wDbhuKDs77LDDxq5lkoZkwYIFvvekNvC9J7WH7z2pfXz/SRPrjs6VfPjLF/Opt+09Lvuf7kMa\nL6TMwviq5gURsRXwl5Sphn8zwe2SJEmSJEmasWrjPI5uqgVe/R6OiNgnisYC9L8AbgdeExEvbNrk\nX4Ddge9n5nIkSZIkSZI0IfrGOfGatEMaI+Ig4NUNd/0FpbfW8yPiow33fysz1wLfA15E6c11IUBm\n9kbEUcAFwAUR8T3gHuA5wOuAPwEfH+enIkmSJEmSNO0tWrSI8847j7lz57LjjjsOuG5tpgZewLOA\n/2y6rwa8rPqp+ymwtlrW17yTzPxDRDwXOB54PbAz0Al8ETghM1eOfdMlSZIkSZJmhkWLFnHiiSfy\n7W9/m66uLvbaay+OOOKIAbcZ7yGNkzbwyszvAt8dxvqHD7DsJuDvxqJdkiRJkiRJ2jLoeuITn8i8\nefN43eteN+i2M3ZIoyRJkiRJkiafVkHXcccdx5FHHslWW201pH3M2B5ekiRJkiRJmjzGIuiqs4eX\nJEmSJEmS2mYsg666mVy0XpIkSZIkSW0yHkFXXW2LaQfHloGXJEmSJEmSHjGeQVddrz28JEmSJEmS\nNN4mIuiqc0ijJEmSJEmSxs1EBl11ztIoSZIkSZKkMdeOoKvOWRolSZIkSZI0Zjo7O5k/f35bgq66\nWp+BlyRJkiRJkkapnT26mo1z3mXgJUmSJEmSNJ21CrrmzZvH3LlzJzzoqrNovSRJkiRJkoZtMgZd\ndRatlyRJkiRJ0pBN5qCrzqL1kiRJkiRJGtRUCLrqHNIoSZIkSZKkfk2loKvOovWSJEmSJEnawlQM\nuur6xjm+bQQcAAAgAElEQVTxMvCSJEmSJEmaQqZy0FXnkEZJkiRJkiRNi6CrzsBLkiRJkiRpBptO\nQVddT6+BlyRJkiRJ0ozT2dnJ/Pnzp1XQVdfb2zeu+zfwkiRJkiRJmkSmY4+uZj0WrZckSZIkSZr+\nWgVdxx57LG9/+9unTdBV1+uQRkmSJEmSpOlrJvToatbb55BGSZIkSZKkaWcmBl11Fq2XJEmSJEma\nRmZy0FVnDy9JkiRJkqRpwKBrE2t4SZIkSZIkTWEGXVvq6bWHlyRJkiRJ0pRj0NW/3j57eEmSJEmS\nJE0ZBl2D67WHlyRJkiRJ0uRn0DV09vCSJEmSJEmaxAy6hs8aXpIkSZIkSZOQQdfIOUujJEmSJEnS\nJGLQNXrd9vCSJEmSJElqP4OusdPdbeAlSZIkSZLUNgZdY6+rp3dc92/gJUmSJEmS1IJB1/jp7rGH\nlyRJkiRJ0oQx6Bp/Xd29dNRmjdv+DbwkSZIkSZIw6JpIpYfX7HHbv4GXJEmSJEma0Qy6Jl53jz28\nJEmSJEmSxlyroOu4447jyCOPNOgaZ109fVCzh5ckSZIkSdKYMOhqv+7uPqiN37E28JIkSZIkSTOC\nQdfk0dXTS61vm3Hbv4GXJEmSJEma1gy6Jp/unj5qfQ5plCRJkiRJGhaDrsmpVqvR3dNHX69F6yVJ\nkiRJkobEoGty6+7pA6DWaw8vSZIkSZKkARl0TQ1dVeDV22MPL0mSJEmSpJYMuqaWru5eAPrs4SVJ\nkiRJkrS5zs5O5s+fb9A1xWzo6gGgp2s20Dcuj2HgJUmSJEmSphR7dE1t6zeUwKt74xyga1wew8BL\nkiRJkiRNCa2Crnnz5jF37lyDrilkQ1cZ0mjgJUmSJEmSZix7dE0v6zeWHl5dG8YvljLwkiRJkiRJ\nk5JB1/RUr+G1cYNF6yVJkiRJ0gxh0DW9bah6eG1Ybw8vSZIkSZI0zTnr4szwcBV4rV9n4CVJkiRJ\nkqapzs5OTjzxRE499VSDrhlgw8ZStL6ny8BLkiRJkiRNMwZdM1O9hlevgZckSZIkSZouDLpmtvos\njT3dFq2XJEmSJElTnEGXYNOQxt5ue3hJkiRJkqQpyqBLjR7p4eWQRkmSJEmSNNUYdKmV9V31IY0G\nXpIkSZIkaYpoFXTNmzePuXPnGnSJDVUPr76eWeP2GAZekiRJkiRpTNijS0OxYWMvHbU5QMe4PYaB\nlyRJkiRJGhWDLg3Hwxu7q8Br/Bh4SZIkSZKkETHo0kisW99NrWfbcX0MAy9JkiRJkjQsBl0aqVqt\nVgVeO47r4xh4SZIkSZKkITHo0mit39hDXw16usf3fDHwkiRJkiRJAzLo0lhZt77M0Niz0cBLkiRJ\nkiS1gUGXxtq6Dd0AdG0w8JIkSZIkSRPIoEvjZd36EnhtXG/gJUmSJEmSJoBBl8ZbPfBav9bAS5Ik\nSZIkjSODLk2UtVXg9bCBlyRJkiRJGg8GXZpo9R5e61YbeEmSJEmSpDFk0KV2qRet7944vpGUgZck\nSZIkSTOEQZfabe3D9cDLHl6SJEmSJGkUDLo0WaxetxGArvVbj+vjGHhJkiRJkjRNGXRpslm9rguA\nrvXbjOvjGHhJkiRJkjTNGHRpslq1rosOZtHbPXtcH8fAS5IkSZKkaaJV0DVv3jzmzp1r0KVJYfXa\njXT0bQN0jOvjGHhJkiRJkjTF2aNLU8WqdV3UenYY98cx8JIkSZIkaYoy6NJUsqGrh41dvdS6xrdg\nPRh4SZIkSZI05Rh0aSqaqIL1YOAlSZIkSdKUYdClqWz12hJ4bVhnDy9JkiRJkmY8gy5NB6vWbQRg\n3Rp7eEmSJEmSNGMZdGk6qQ9pXLvSHl6SJEmSJM04Bl2ajlZVQxpXrzDwkiRJkiRpxjDo0nS2uhrS\nuPFhhzRKkiRJkjTtGXRpJli5pgReXQZekiRJkiRNXwZdmkkeWr0BgA3rth33xzLwkiRJkiRpgrUK\nuubNm8fcuXMNujRtLV+1gY7aHHq7xz+OMvCSJEmSJGmC2KNLM9ny1Rvo6B3/3l1g4CVJkiRJ0rgz\n6NJM193Tx+p1XdC104Q8noGXJEmSJEnjxKBLKlasKfW7uh62h5ckSZIkSVOSQZe0ueVVwfqHVxt4\nSZIkSZI0pRh0Sa0tX1UCrzUrDLwkSZIkSZoSDLqkgdV7eC1fauAFQES8CTgGOBTYBrgd+CHwpczc\nMMR9vBM4CjgE2AFYAfwR+FZmnjsOzZYkSZIkzQAGXdLQ1AOvdasmJvCaNSGPMkIR8SngJ8B+wLeA\nzwLLq9vzI2LQ9kfEd4HTgQC+BxwH/BR4AfB/EXH8eLRdkiRJkjR9dXZ2cswxx7D//vtz0kknsffe\ne3P66adzyy23cNRRRxl2SU3qgdeGddtMyONN2h5eEXEwMA+4AzgsM1dXi06IiDOBtwAfAr48wD6e\nA7wduB94amaubFj2VeBG4NiIOCkzHxqfZyJJkiRJmi7s0SWNzLKV6wHYsNYeXu8FOoDPN4RddcdX\ny94/yD6iur2kMewCyMzbgKSEfk8YfXMlSZIkSdOVPbqk0Xlw+Xpm1bahr2di+l5N5sDr8Or2V80L\nMvMO4B5g/4jYe4B93FjdRvOCiNgGeBywAbh1dE2VJEmSJE1HrYKu0047zaBLGoa+vhpLVz5MrWu7\nCXvMSTmkMSLmAAcAvcBd/ax2G7Av8FSgs9UKmXltRJwEfCAifgR8BXgQ2Av4GLAr8JEWPcgkSZIk\nSTOYQxelsbNizQZ6emt0P7z9hD3mpAy8gJ2A2cDKzKz1s87y6naXgXaUmf8UEdcAXwXe3LDoAeAN\nztIoSZIkSaoz6JLG3oPLS/2udSsnLvCarEMa60ega4B1Njat21JEHEOZ4fEe4B+BvwU+CqwCfhIR\nc0fXVEmSJEnSVLdkyRJrdEnj5MEVDwOwcukMH9IIPFzdbj3AOvWy/g/3t0JEPJ/Ss+t64LmZ2dWw\n7BTgT8C3I+KKzLx9dE2WJEmSJE019R5dp5xyCt3d3fboksZBPfBadr9DGlcBPcCOETEnM3tarLNb\ndbtsgP28p7o9uTHsAsjMdRHx38C/AX8D/L/BGrVgwYJBGy5p7Pnek9rD957UHr73pImxZMkSzjjj\nDM4++2y6u7vZa6+9eNe73sWrX/1q5syZww033NDuJkrTxk23rgBg3aoZHnhlZm9E3EIpSH8ApSdW\nswOr2+sG2NVjq9sl/SxfBnQAjx9Kuw477LChrCZpDC1YsMD3ntQGvvek9vC9J42/VjW65s2bx0EH\nHcRznvOcdjdPmpbOWXA5sI71qyduSONkreEFcGF1+6rmBRHxdGBP4JrMHKiH1wPV7QH9LN+/aT1J\nkiRJ0jTU2dnZb42uo48+mjlzJmV/EGlaeHDFw8yqbUVP18QNE57MgdfJlGGNH4mI3et3RsQs4HNA\njVKfq37/PlE09o87h9KD65jGfVTr7wW8o9rP2eP2LCRJkiRJbTNQ0GUxemn81Wo1Hlyxnlr3xA1n\nhEk6pBEgM2+PiE8AXwSujYgzgXXA64FDgZ9m5vcbNvke8CJKj7ALq32cFRE/Av4OuKnaxwPA3sBb\ngB2AEzLTwdmSJEmSNI20GrpoMXpp4i1fvYGu7l661xl4PSIzvxIRtwMfBd4LbAUk8CHgm02r14C+\nFvt4W0T8HDgaeDuwI7ASuBL4ZmaeP37PQJIkSZI0kQy6pMll8bJ1AKx5aIcJfdxJHXgBZOZ5wHlD\nWO/wAZadCZw5lu2SJEmSJE0eBl3S5LR4aQm8li1+1IQ+7qQPvCRJkiRJ6o9BlzS53b9sLQDL7jfw\nkiRJkiRpQAZd0tRQH9K4bqVDGiVJkiRJasmgS5pa7l+2jlnMZuO6bSb0cQ28JEmSJEmTnkGXNPX0\n9dVKD6/uHYCOCX1sAy9JkiRJ0qS1aNEiTjzxRL797W8bdElTzIo1G+jq7qVn7cTW7wIDL0mSJEnS\nJNQq6Jo3bx5z58416JKmiPoMjauWGXhJkiRJkmYwe3RJ00fng2sAWHrfxBasBwMvSZIkSdIkYNAl\nTT/3PlAPvHac8Mc28JIkSZIktY1BlzR93bukBF5rlxt4SZIkSZJmAIMuafq794E1zO7bnt7uiY+f\nDLwkSZIkSRPGoEuaGVat3cjKtRupPbxHWx7fwEuSJEmSNO4MuqSZpT6ccfXSiR/OCAZekiRJkqRx\nZNAlzUz1gvUP3LtTWx7fwEuSJEmSNOYMuqSZ7d4HVgOwZJE9vCRJkiRJU1xnZyfz58836JJmuE0z\nNO7Qlsc38JIkSZIkjZo9uiTV1Wo17l68mtm9j6Kvpz3Rk4GXJEmSJGnEWgVd8+bNY+7cuQZd0gy1\ndMV61q7vpmf17m1rg4GXJEmSJGnY7NElqT933LcKgIcW/1nb2mDgJUmSJEkaMoMuSYO5476VACy6\nfee2tcHAS5IkSZI0KIMuSUN1Z9XDa7k9vCRJkiRJk5FBl6ThuvO+VcyubUvX+m3a1gYDL0mSJEnS\nFgy6JI3EyjUbeWjVBli3R1vbYeAlSZIkSXqEQZek0XhkOOP97RvOCAZekiRJkiQMuiSNjU0F6w28\nJEmSJEltYtAlaSzd0Vl6eC27r30zNIKBlyRJkiTNSAZdksZD3rOc2bVtWL96u7a2w8BLkiRJkmYQ\ngy5J4+WhVetZtmoDtbV7Ah1tbYuBlyRJkiTNAAZdksZb3rMCgKWLdmlzSwy8JEmSJGlaM+iSNFFu\nvbcEXnffYuAlSZIkSRoHBl2SJtotVQ+vh9pcsB4MvCRJkiRpWjHoktQOvb193N65ktm9O9Lb3f7P\nGgMvSZIkSZoGOjs7mT9/vkGXpLa454E1bOzqZcOyXdvdFMDAS5IkSZKmNHt0SZoMbrrzIQA6b2t/\n/S4w8JIkSZKkKalV0DVv3jzmzp1r0CVpwi28cxkA9+ZubW5JYeAlSZIkSVOIPbokTTa1Wo2b7nyI\n2bVteXjV9u1uDmDgJUmSJElTgkGXpMmq88G1rFrbRc/KvYGOdjcHMPCSJEmSpEnNoEvSZLfwjjKc\n8b7bH93mlmxi4CVJkiRJk5BBl6SpYuEdpWD9nQsNvCRJkiRJLRh0SZpKarUaC+9cxuzaNqxbsUO7\nm/MIAy9JkiRJmgScdVHSVHT/snUsX72R3lWPY7LU7wIDL0mSJElqK4MuSVPZDbdPvvpdYOAlSZIk\nSW1h0CVpOrj21gcBuOOGx7S5JZsz8JIkSZKkCdTZ2cn8+fMNuiRNeb29fVx/2zJm17Zn3cpHtbs5\nmzHwkiRJkqQJYI8uSdPNbYtWsm59NxuXTq76XWDgJUmSJEnjyqBL0nR1bVbDGW+cXMMZwcBLkiRJ\nksaFQZek6e7aW5cCcO8tu7e5JVsy8JIkSZKkMWTQJWkmWLu+m7x3BbO7dqFn4+T7bDPwkiRJkqQx\nYNAlaSa54bal9PXVWH7P5BvOCAZekiRJkjQqBl2SZqKrbn4AgD8tMPCSJEmSpGnDoEvSTNXbV+OP\nNy9hNtuwYvEu7W5OSwZekiRJkjQMBl2SZrq8Zzmr13XRtfTxQEe7m9OSgZckSZIkDYFBlyQVV91U\nhjPees2ebW5J/wy8JEmSJGkABl2StLmrbn6AWczm3ty93U3pl4GXJEmSJLVg0CVJW1q8dC2LlqyF\ntXvS1zO73c3pl4GXJEmSJDUw6JKk/tVnZ7z7psk7nBEMvCRJkiQJMOiSpKG4/Mb7Abj12j3a3JKB\nGXhJkiRJmtEMuiRpaB5atZ4/3b2cWRsfTdfD27a7OQMy8JIkSZI0Ixl0SdLwXHbDYmo16Lz5ce1u\nyqAMvCRJkiTNKAZdkjQyl163GICbrzTwkiRJkqRJwaBLkkZu2coynHH2xt3YOMmHM4KBlyRJkqRp\nzqBLkkbvshtK765Ff5r8vbvAwEuSJEnSNNUq6Dr22GN5+9vfbtAlScN06XX3AbDwise2uSVDY+Al\nSZIkaVqxR5ckja2lK9Zzyz0rmLVht0k/O2OdgZckSZKkacGgS5LGx6XXl95d99w0NYYzgoGXJEmS\npCnOoEuSxtdvrl5EBx1TYnbGOgMvSZIkSVOSQZckjb+7Fq/i7vtXw9o96d6wTbubM2QGXpIkSZKm\nFIMuSZo4v7l6EQD5x33a3JLhMfCSJEmSNCW0CrqOO+44jjzySIMuSRoHvb19/O6aTmazFbdfv2e7\nmzMsBl6SJEmSJjWDLklqj2tvXcrKNRvZuOQJ1Ppmtbs5w2LgJUmSJGlSMuiSpPaqD2e87vdTazgj\nGHhJkiRJmmQ6OzuZP3++QZcktdGah7u4YuH9zOnbgaWLdm53c4bNwEuSJEnSpGCPLkmaPH5z9SK6\ne/p48E+PBzra3ZxhM/CSJEmS1FbOuihJk0utVuMXf7ibWczi2oun3nBGMPCSJEmS1Cb26JKkyWnh\nnQ9x39K19K3am+4N27S7OSNi4CVJkiRpQhl0SdLk9ss/3A3A9Rfv185mjIqBlyRJkqQJYdAlSZPf\nyjUb+cONi9mqbwfuu33XdjdnxAy8JEmSJI0rgy5Jmjp+/cd76emtseTm/ZiKxerrDLwkSZIkjQuD\nLkmaWvr6alxwxT3MYhbXTdFi9XUGXpIkSZLGlEGXJE1NV/9pCfc/tI6uZfvSvXHrdjdnVAy8JEmS\nJI0Jgy5Jmtr+7/d3AHD1Rfu3uSWjZ+AlSZIkaVQMuiRp6rtr8SpuuH0ZszfuxvL7d2p3c0bNwEuS\nJEnSiHR2djJ//nyDLkmaBs695E4AFl469Xt3gYGXJEmSpGHq7OzkxBNP5NRTTzXokqRpYOWajfzu\nmk7m1B7FHdfv0e7mjAkDL0mSJElD0iromjdvHnPnzjXokqQp7BeX3013Tx8P3PhEoKPdzRkTBl6S\nJEmSBmSPLkmavjZ09fDzy+5kdsdWXHvxvu1uzpgx8JIkSZLUkkGXJE1/F115L6vWdrH67ifT2z19\nYqLp80wkSZIkjQmDLkmaGbp7+vjZ725ndsdsLj//ie1uzpgy8JIkSZIEGHRJ0kxz8TWLWLZyPRse\neCLdG7Zpd3PGlIGXJEmSNMMZdEnSzNPbV+Os39zGrI4OLjv3Se1uzpgz8JIkSZJmKIMuSZq5Lr9x\nMfctXUfP8n1Zv2a7djdnzBl4SZIkSTOMQZckzWy1Wo2f/uo2OoDLzzug3c0ZFwZekiRJ0gxh0CVJ\nArhi4f3cuXgVfav2YtWyHdrdnHFh4CVJkiRNcwZdkqS63r4a3//lLXQAfzg32t2ccWPgJUmSJE1T\nBl2SpGaXXncf9z6wht4V+7DywR3b3ZxxY+AlSZIkTTOtgq558+Yxd+5cgy5JmsF6e/v4wQW30NHR\nwWXnTN/eXWDgJUmSJE0b9uiSJA3ktwsWsXjZOrqX7sfqhx7V7uaMqzEJvCJiZ2A/YHtgLXBHZq4b\no32/CTgGOBTYBrgd+CHwpczcMMR9zAY+DLwDOAB4GLih2sd5Y9FOSZIkqV0MuiRJg+nu6eOHFyaz\nOmZxyf89ud3NGXezRrNxRLwtIq4FHgIWAJcA1wIrI+I3EfG8Ue7/U8BPKGHat4DPAsur2/MjYtD2\nR8Qc4HzgP4H7q23PBA4GzomIfxhNGyVJkqR26ezs5JhjjmH//ffnpJNOYu+99+b000/nlltu4aij\njjLskiQ94vw/3MWDK9az7r79eHj1du1uzrgbcQ+viPgQ8GVgA3AZ0Al0AzsBTwNeAvwuIl6Vmb8d\nwf4PBuYBdwCHZebqatEJEXEm8Bag3oaBfBx4OfCxzPxSw/6/AVwFzAW+M9z2SZIkSe1ijy5J0nCs\nebiLH12YzOnYit+fPb1rd9WNZkjjv1N6dL0hM1c0L4yI1wI/A/4DeOEI9v9eoAP4fEPYVXc88Fbg\n/QwQeDUMZVzYGHYBZOZtwC4jaJckSZLUFgZdkqSR+NFFydr13Tx0y1Pp3rB1u5szIQYcEhgRB/Rz\n/27A7sAprcIugMz8OXAF8IwRtu3w6vZXLfZ9B3APsH9E7D3APl4A7Ab8tH5HROwaEbuOsE2SJEnS\nhGs1dPG0005z6KIkaVCLl63l/MvuYiu258oLntDu5kyYwWpgXRcRH42Ijqb711e3ew6y/W4N6w5Z\nVXfrAKAXuKuf1W6rbp86wK6eDtSAmyPinyLiHmAZsCwi7oyIdw23bZIkSdJEGahG19FHH23QJUka\n1Bnn3UxPb41b/3AQfb2z292cCTPYkMb7KMXe3xQRR2fmLQCZuS4irgaOjYh1wAVAZ2Z2R8QOwEHA\nPwJPAX40gnbtBMwGVmZmrZ91lle3Aw1L3Ke6/QAlGPsGcCtwIPAx4JSIeExmfm4EbZQkSZLGhUMX\nJUljYeEdy7j8xvuZ3b0rN13xuHY3Z0INFngdDJxAKQ5/bUR8GvjPzOyjhEgXAifVV47YrPBZByVc\n+tgI2rV9dds1wDobm9ZtZceqHYcAT8/MRfUFEfFz4Erg+Ig4PTPvH0E7JUmSpDGzZMkSjjnmGIMu\nSdKo9fb2ccrZNwLwx/OfSolHZo4BhzRm5obM/CilFtZdlPDryoh4WmZeDQRwHKWH183AncCNwNnA\n+4BDRxgkPVzdDlRJbdumdVvpoQxpPKMx7AKo2n8hsBXwqhG0UZIkSRoT9aGLb3jDG7YYumiNLknS\nSJz/h7u5a/Fqepbvy+I7Zl4p8yHN0piZV0TEocCnKT22ro6IE4DPjdNwwFWUsGrHiJiTmT0t1tmt\nul02wH7qwx6X9LP8JuAVwL5DadSCBQuGspqkMeZ7T2oP33vS+FuyZAlnnHEGZ599Nt3d3ey11168\n613v4tWvfjVz5szhhhtuaHcTpRnF332aLtau7+W7P3+AWczmNz8+sN3NaYshBV4AmdkF/FtEnAWc\nTgm/3ljV9rpuLBuVmb0RcQul7tYBwJ9arFZ/xQZ67Jspffb6C7S2q243DKVdhx122FBWkzSGFixY\n4HtPagPfe9L46q9G14EHHshznvOcdjdPmpH83afp5Ms/vIaN3TUeXHgQXeu3HXyDaWiwWRq3kJkL\ngGcAn6EUp78yIj4TEWPdz/rC6naL4YYR8XTKDJHXZOZAPbx+DfQBr2wx0ySU5wHgV2eSJEkadwPN\nunjUUUcxZ86Qv4+WJKmlm+96iN9cvYitev+Mqy56Qrub0zbDDrwAMrMnMz8JPBtYCBwLXBMRzxzD\ntp1MGdb4kYjYvX5nRMwCPkepzfXVhvv3ieKRIvaZuQT4CbA/8PHGnUfEm4DnAIuBi8aw3ZIkSdJm\nBgu6rNElSRoLvb19nPyz0qfn6l8cDLWZVai+0ai+QsrM6yPi2cC/UorXXx4RXwQ+mZkbB9560H3f\nHhGfAL5ImSHyTGAd8HrgUOCnmfn9hk2+B7yI0iPswob7Pww8E/hc1darKcMh30YZynhUPzXCJEmS\npFHpb+iisy5KksbDOZfcWRWq34dFt868QvWNBg28IuJRwN8DfwnsB2wPrAUSOB/4YWaeEBH/S6nt\n9XHgiIj4+8y8YjSNy8yvRMTtwEeB91JmVEzgQ8A3m1avUYYvNu/jwarn2fHAG4DXAKuB/wFOyEyH\nM0qSJGlMGXRJkiba/cvW8f1f3sJWHVtzwY+e2u7mtN2AgVdE7Av8Fmg16PNQ4M3AP0fEyzPz5oh4\nHiWc+jRwSUR8DTg2M4dUFL6VzDwPOG8I6x0+wLLVlNklPzbSdkiSJEmDaRV0zZs3j7lz5xp0SZLG\nTa1W4xs/vY6u7l4WXXUo3Ru2aXeT2m6wGl6fB9YAbwT2AramhGS7AS8FzgUOAb4AkJl9mfl5Shh2\nJfARLAgvSZKkaW6gGl1HH320YZckaVxddNW93HD7Mmat34PrL92r3c2ZFAYb0vgy4HmZeWvT/cuB\n3wG/i4gEXtu4MDNvjYgXAh8EThijtkqSJEmTikMXJUnttnz1Bk47ZyFzZs3h1z86BJi5heobDRZ4\nbQ8sHWSdJcA+zXdmZg34akScM8K2SZIkSZOSQZckaTKo1Wqc/LMbWLehhwcXHsyaFdu1u0mTxmBD\nGm8E/i0iZrdaGBGvAp5TrddSZt418uZJkiRJk8dAQxePOuoowy5J0oS6+Nr7uPzG+5ndtStXXbhf\nu5szqQzWw+tE4CzgqIi4htLbqw/YCXga8ETK7IifHM9GSpIkSe1kjy5J0mSzdMV6Tv6f65k9azYX\n/+TpOJRxcwMGXpn5s4j4G0qg9YqmxTXgcuC4zPztOLVPkiRJahuDLknSZNTXV+OrP76GdRt6WHLD\nIax8cId2N2nSGayHF5n5v8D/RsSuwH7AdsA64I7MXDO+zZMkSZImnkGXJGkyO+/SO7n+tmV0PLwH\nf/zV49vdnElp0MCrLjOXU2ZnlCRJkqYlgy5J0mR37wOr+e7Pb2arWVvzi+8fikMZWxty4CVJkiRN\nVwZdkqSpoKu7ly+eeQ1dPX3cddkhbFi7bbubNGkZeEmSJGnGMuiSJE0l3zlnIXcuXkXX0sdz05WP\na3dzJjUDL0mS/j979x0d53kf+P6LIqpZxaoURVEUSemV5KZYcRzvbhKnO87em7O7yZ7NNb3rxLvx\n+to+STY35cS0k02jnG5n7dixLFm2ZNmyCiWxSOwVJEGCIEACxEMARG9EI3qZdv8YUKYZsAwIcGaA\n7+ccnJHeeWfwky1wyK+e93klLTjTha41a9awevVqQ5ckKSftq2hnY0kji1I3s/H5d2V7nJxn8JIk\nSdKC4YouSVI+6ugZ4UsvlFNcWMSe53+UZLwo2yPlPIOXJEmS5j1DlyQpX8XiCf7624cYHY/TfuRH\n6G67Kdsj5QWDlyRJkuYtQ5ckKd99c301da0DxPvu48jOZdkeJ28YvCRJkjTvGLokSfPB7vJWXttz\nimtSb2PTs+/O9jh5ZcbBK4qi4hBC/LxjtwA/DlwP7A4h9F3hfJIkSdJlM3RJkuaLhvYBvvi9oxQX\nFrP7uR8jEXfNUiYKM31BFEU/FUVRNfDR847/ElAPbAReAlqiKFo9K1NKkiRJF9Ha2sqnP/1pVq5c\nyb4uU54AACAASURBVJe//GWWLl3K008/TU1NDR/72MeMXZKkvDI0OslfPl3KZCxB3Z730tvhvl2Z\nyih4RVH0ALABiIBl5xy/G3gBuA2YBHpJr/J6Koqid8zatJIkSdI5DF2SpPkmkUzxN98+TFffKMNN\nEccP3pPtkfJSpiu8PgHcADwH/P05xz8J3AicAu4LIdwF/AHpSyY/NQtzSpIkSW8xdEmS5qtnN52g\n/GQ3BaN3s/OlKNvj5K1MLwD9JWAI+B8hhIlzjv8akAL+dwihZ+rY3wG/B3zwSoeUJEmSwD26JEnz\n286yFl7cXssibuS1px8HCrI9Ut7KdIXXEuDIubEriqKlwCNAHFh39ngIIQVUAffNwpySJElawFzR\nJUma76obevni945yTWExu7/7fuITfrZdiUxXeN0CjJx37INTjwdCCEPnPTcB+P+QJEmSZsQVXZKk\nhaCzd4S/fLqURDJFzdb30dPuJvVXKtPgNQAsPu/YL5G+nHHzNOffCfTNYC5JkiQtYIYuSdJCMTwW\n48++cYDBkUm6q95DbcVd2R5pXsj0ksYq4LEoih4CiKJoBfArU8+tP/fEKIreDrwHaL3SISVJkrQw\neOmiJGkhiSeSfOFbh2jpGma8YyUH31ye7ZHmjUxXeL0I/CSwN4qincAHgOuBkhBCxdmToii6Bfjm\n1PtvmJVJJUmSNG+5okuStNCkUim++nIlR092w8hitn73HdkeaV7JNHj9C/DrpEPXr04dGwF+57zz\nngV+mfQlkN+4kgElSZI0fxm6JEkL1XfeDLx5oInixC28/tTjkPKOjLMpo+AVQpiMouiDwH8FHicd\ntJ4OIZw879QqYDnwmyEEL2mUJEnSDzF0SZIWsg37GvjulsCighvY9NSPk4hluh5Jl5Lx/6IhhBjp\nVVsXW7n1ZyGEP5rxVJIkSZqXpgtda9asYfXq1YYuSdKCsK+ina+9UsmiomvZ9e0PMDZ0XbZHmpfm\nJCGGEEbn4n0lSZKUn1zRJUkSVNZ187fPlVFUUETpKz9Ob+fbsj3SvDWj4BVF0Z3AbwG/ADwC3BJC\nuPac5/8dMHTuRvaSJElaeAxdkiSl1bb08xdPlZJMwomt76e17tZsjzSvFWb6giiKfg6oAf4M+Ang\nDv51OPtdoCyKok9d8YSSJEnKO62trXz6059m5cqVfPnLX2bp0qU8/fTT1NTU8LGPfczYJUlaUBra\nB/j81/YzPhGnYf97qa24M9sjzXsZBa8oipYC64C3Aw3A3wKV05zaO/Xe/xhF0WNXOqQkSZLyQ0tL\nC5/61KcMXZIkTWnuHGTNV0sYGYvRXv5ejpXcm+2RFoRMV3j9L+AG4DtAFEL4A9Lh64eEEH4L+FOg\nCPjMFc4oSZKkHHc2dK1atYqvfOUrhi5JkoD27mHWfLWEwZFJTh9/D2U77sv2SAtGpnt4/SIwBHw6\nhJC4xLlPkI5dPzWTwSRJkpT7WlpaeOKJJ3jyySfdo0uSpHN09o7w2X/eR//QBL0n3sXBzcuzPdKC\nkukKr6VAdQjhzKVODCFMAseBJTMZTJIkSbnLFV2SJF3Y2djVMzDOmdpH2b9pRbZHWnAyXeG1CBjM\n4PwYkMrwe0iSJClHuaJLkqSLa+8e/kHsqnuEva8/mO2RFqRMg1cb8PDlnBhF0bXAjwIdmQ4lSZKk\n3NLa2sratWsNXZIkXcTZDer7hyboP/kO9q1fle2RFqxML2ncDSyNoug3LuPc/wPcAuzKeCpJkiTl\nhHPvuuili5IkXVhD+wB/fM6eXcau7Mp0hdeXgI8CX42i6EHgBeB6gCiKlgD3Au8GPgn8CJAEvjhr\n00qSJOmqmO7SxTVr1rB69WojlyRJ56lrPcPnv1bC0GiM7uPvcYP6HJBR8AohHI2i6PeAfwD+cOrr\nrJZz/rqA9N5dvxdCqLziKSVJknRVuEeXJEmZqTrVy59/4wAj43E6jz7G4e33Z3skkfkljYQQvgR8\nCDhEOmxN97UP+MWpcyVJkpTjvOuiJEmZK63q5PNfK2FsIkHLoceNXTkk00saAQghbAG2RFF0F/Ae\n4DbSK7p6gYoQQs/sjShJkqS54oouSZJmZmtpM//0/aMUUsjJHT9GzZG7sz2SzjGj4HVWCOE0sGWW\nZpEkSdJVYuiSJGnmXt5Ry9Prq1lUdA1HXv9xGmtuy/ZIOs8VBS9JkiTlF0OXJEkzl0qleHp9Na/s\nrGNR4XWUfO8DdDbfnO2xNI0LBq8oirbP0vdIhRB+dpbeS5IkSTNg6JIk6crE4gm+9MJRdpa1soi3\nse3pDzDQe0O2x9IFXGyF1wdJ78tVcIXfI3WFr5ckSdIMGbokSbpyQ6OT/OXTpVSd6qUo9nbWP/l+\nJseuzfZYuoiLBa9vYaySJEnKS4YuSZJmR0fPCP/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AqYiSDStJxPy8li61wusTpKPTXuCX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\nSZIkSeopBl6SJEmSJEnqKf8fqMNFfcrEkdwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ce90012b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_perc = np.cumsum(stats[1][1:]) # Cumulative sum of nodes\n",
    "samples_perc = np.cumsum(np.multiply(stats[0][1:],stats[1][1:]))\n",
    "node_perc = node_perc / node_perc[-1]\n",
    "samples_perc = samples_perc / samples_perc[-1]\n",
    "plt.plot(node_perc, samples_perc)\n",
    "plt.plot((np.array(range(0,100,1))/100), (np.array(range(0,100,1))/100), color='black')\n",
    "plt.ylim(0,1)\n",
    "plt.xlim(0,1)\n",
    "plt.title('Lorenz Curve Between Nodes And Samples')\n",
    "plt.xlabel('Leaves %')\n",
    "plt.ylabel('Samples %')\n",
    "plt.fill_between(node_perc, samples_perc , color='blue', alpha='1')\n",
    "plt.savefig('figures/lorenzcurve_d25_msp_50.eps', format='eps', dpi=1000)\n",
    "plt.savefig('figures/lorenzcurve_d25_msp_50.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About 5% of the leaves represent about 40% of the samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(leaf_samples)==regtree.tree_.node_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found out that all samples have been considered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspect an arbitrary leaf and extract the rule set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We are taking a look at the leaf that represents the most samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "So node no. 56501 is a leaf and has 23278 samples in it.\n",
      "[56501, 23278]\n"
     ]
    }
   ],
   "source": [
    "max_leaf = [np.argmax(leaf_samples), max(leaf_samples)]\n",
    "print('So node no.', max_leaf[0] ,'is a leaf and has', max_leaf[1] ,'samples in it.')\n",
    "print(max_leaf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieve the decision path that leads to the leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 680,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Inspired by: http://stackoverflow.com/questions/20224526/\n",
    "# how-to-extract-the-decision-rules-from-scikit-learn-decision-tree\n",
    "def get_rule(tree, feature_names, leaf):\n",
    "        left      = tree.tree_.children_left\n",
    "        right     = tree.tree_.children_right\n",
    "        threshold = tree.tree_.threshold\n",
    "        features  = [feature_names[i] for i in tree.tree_.feature]\n",
    "        value = tree.tree_.value\n",
    "        samples = tree.tree_.n_node_samples\n",
    "        global count\n",
    "        count = 0;\n",
    "        global result\n",
    "        result = {};\n",
    "\n",
    "        def recurse_up(left, right, threshold, features, node):\n",
    "            global count\n",
    "            global result\n",
    "            count = count+1;\n",
    "            #print(count)\n",
    "            if node != 0:\n",
    "                for i, j in enumerate(right):\n",
    "                    if j == node:\n",
    "                        print( 'Node:', node, 'is right of:',i, ' with ', features[i], '>', threshold[i])\n",
    "                        result[count] = [features[i], False, threshold[i]]\n",
    "                        return(recurse_up(left, right, threshold, features, i))\n",
    "                for i, j in enumerate(left):\n",
    "                    if j == node:\n",
    "                        print('Node:', node,  'is left of',i,' with ', features[i], '<= ', threshold[i])\n",
    "                        result[count] = [features[i], True, threshold[i]]\n",
    "                        return(recurse_up(left, right, threshold, features, i))\n",
    "            else :\n",
    "                return(result)\n",
    "        \n",
    "        print('Leaf:',leaf, ', value: ', value[leaf][0][0], ', samples: ', samples[leaf])\n",
    "        recurse_up(left, right, threshold, features, leaf)\n",
    "        return(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 681,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Leaf: 56501 , value:  6.54600910731 , samples:  23278\n",
      "Node: 56501 is right of: 56499  with  dropoff_longitude > -73.9948272705\n",
      "Node: 56499 is right of: 56495  with  pickup_longitude > -73.9967498779\n",
      "Node: 56495 is left of 56494  with  pickup_longitude <=  -73.9749832153\n",
      "Node: 56494 is right of: 56478  with  dropoff_latitude > 40.7267570496\n",
      "Node: 56478 is right of: 56448  with  pickup_latitude > 40.7214622498\n",
      "Node: 56448 is right of: 56386  with  dropoff_longitude > -74.0030441284\n",
      "Node: 56386 is right of: 56270  with  pickup_longitude > -74.0040206909\n",
      "Node: 56270 is left of 56269  with  pickup_latitude <=  40.7473983765\n",
      "Node: 56269 is left of 56268  with  dropoff_latitude <=  40.741607666\n",
      "Node: 56268 is left of 56267  with  dropoff_longitude <=  -73.9636077881\n",
      "Node: 56267 is left of 56266  with  pickup_latitude <=  40.75025177\n",
      "Node: 56266 is left of 56265  with  pickup_latitude <=  40.7558288574\n",
      "Node: 56265 is left of 56264  with  dropoff_latitude <=  40.7464828491\n",
      "Node: 56264 is left of 56263  with  pickup_longitude <=  -73.9626312256\n",
      "Node: 56263 is left of 56262  with  pickup_latitude <=  40.7738380432\n",
      "Node: 56262 is left of 56261  with  dropoff_latitude <=  40.7698669434\n",
      "Node: 56261 is left of 56260  with  pickup_datetime_dayofweek <=  4.5\n",
      "Node: 56260 is left of 56259  with  pickup_datetime_hour <=  18.5\n",
      "Node: 56259 is right of: 46185  with  dropoff_latitude > 40.7240486145\n",
      "Node: 46185 is right of: 26933  with  pickup_datetime_hour > 7.5\n",
      "Node: 26933 is left of 26932  with  dropoff_longitude <=  -73.9435119629\n",
      "Node: 26932 is right of: 15398  with  pickup_latitude > 40.717376709\n",
      "Node: 15398 is right of: 2  with  dropoff_latitude > 40.7119789124\n",
      "Node: 2 is left of 1  with  dropoff_longitude <=  -73.8870849609\n",
      "Node: 1 is left of 0  with  pickup_longitude <=  -73.8860931396\n"
     ]
    }
   ],
   "source": [
    "branch_to_leaf=get_rule(regtree, time_regression_df.ix[:,0:6].columns,max_leaf[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 682,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: ['dropoff_longitude', False, -73.994827270507812],\n",
       " 2: ['pickup_longitude', False, -73.996749877929688],\n",
       " 3: ['pickup_longitude', True, -73.974983215332031],\n",
       " 4: ['dropoff_latitude', False, 40.726757049560547],\n",
       " 5: ['pickup_latitude', False, 40.721462249755859],\n",
       " 6: ['dropoff_longitude', False, -74.003044128417969],\n",
       " 7: ['pickup_longitude', False, -74.004020690917969],\n",
       " 8: ['pickup_latitude', True, 40.747398376464844],\n",
       " 9: ['dropoff_latitude', True, 40.741607666015625],\n",
       " 10: ['dropoff_longitude', True, -73.963607788085938],\n",
       " 11: ['pickup_latitude', True, 40.750251770019531],\n",
       " 12: ['pickup_latitude', True, 40.755828857421875],\n",
       " 13: ['dropoff_latitude', True, 40.746482849121094],\n",
       " 14: ['pickup_longitude', True, -73.962631225585938],\n",
       " 15: ['pickup_latitude', True, 40.773838043212891],\n",
       " 16: ['dropoff_latitude', True, 40.769866943359375],\n",
       " 17: ['pickup_datetime_dayofweek', True, 4.5],\n",
       " 18: ['pickup_datetime_hour', True, 18.5],\n",
       " 19: ['dropoff_latitude', False, 40.724048614501953],\n",
       " 20: ['pickup_datetime_hour', False, 7.5],\n",
       " 21: ['dropoff_longitude', True, -73.943511962890625],\n",
       " 22: ['pickup_latitude', False, 40.717376708984375],\n",
       " 23: ['dropoff_latitude', False, 40.711978912353516],\n",
       " 24: ['dropoff_longitude', True, -73.8870849609375],\n",
       " 25: ['pickup_longitude', True, -73.886093139648438]}"
      ]
     },
     "execution_count": 682,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "branch_to_leaf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Be aware, read this branch bottom up!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Processing is nicer if the path is in a data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {
    "collapsed": 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>features</th>\n",
       "      <th>leq</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dropoff_longitude</td>\n",
       "      <td>False</td>\n",
       "      <td>-73.9948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pickup_longitude</td>\n",
       "      <td>False</td>\n",
       "      <td>-73.9967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pickup_longitude</td>\n",
       "      <td>True</td>\n",
       "      <td>-73.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dropoff_latitude</td>\n",
       "      <td>False</td>\n",
       "      <td>40.7268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pickup_latitude</td>\n",
       "      <td>False</td>\n",
       "      <td>40.7215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dropoff_longitude</td>\n",
       "      <td>False</td>\n",
       "      <td>-74.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pickup_longitude</td>\n",
       "      <td>False</td>\n",
       "      <td>-74.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pickup_latitude</td>\n",
       "      <td>True</td>\n",
       "      <td>40.7474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dropoff_latitude</td>\n",
       "      <td>True</td>\n",
       "      <td>40.7416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dropoff_longitude</td>\n",
       "      <td>True</td>\n",
       "      <td>-73.9636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pickup_latitude</td>\n",
       "      <td>True</td>\n",
       "      <td>40.7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pickup_latitude</td>\n",
       "      <td>True</td>\n",
       "      <td>40.7558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>dropoff_latitude</td>\n",
       "      <td>True</td>\n",
       "      <td>40.7465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pickup_longitude</td>\n",
       "      <td>True</td>\n",
       "      <td>-73.9626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pickup_latitude</td>\n",
       "      <td>True</td>\n",
       "      <td>40.7738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>dropoff_latitude</td>\n",
       "      <td>True</td>\n",
       "      <td>40.7699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>pickup_datetime_dayofweek</td>\n",
       "      <td>True</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>pickup_datetime_hour</td>\n",
       "      <td>True</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>dropoff_latitude</td>\n",
       "      <td>False</td>\n",
       "      <td>40.724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>pickup_datetime_hour</td>\n",
       "      <td>False</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>dropoff_longitude</td>\n",
       "      <td>True</td>\n",
       "      <td>-73.9435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>pickup_latitude</td>\n",
       "      <td>False</td>\n",
       "      <td>40.7174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>dropoff_latitude</td>\n",
       "      <td>False</td>\n",
       "      <td>40.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>dropoff_longitude</td>\n",
       "      <td>True</td>\n",
       "      <td>-73.8871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>pickup_longitude</td>\n",
       "      <td>True</td>\n",
       "      <td>-73.8861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     features    leq    value\n",
       "1           dropoff_longitude  False -73.9948\n",
       "2            pickup_longitude  False -73.9967\n",
       "3            pickup_longitude   True  -73.975\n",
       "4            dropoff_latitude  False  40.7268\n",
       "5             pickup_latitude  False  40.7215\n",
       "6           dropoff_longitude  False  -74.003\n",
       "7            pickup_longitude  False  -74.004\n",
       "8             pickup_latitude   True  40.7474\n",
       "9            dropoff_latitude   True  40.7416\n",
       "10          dropoff_longitude   True -73.9636\n",
       "11            pickup_latitude   True  40.7503\n",
       "12            pickup_latitude   True  40.7558\n",
       "13           dropoff_latitude   True  40.7465\n",
       "14           pickup_longitude   True -73.9626\n",
       "15            pickup_latitude   True  40.7738\n",
       "16           dropoff_latitude   True  40.7699\n",
       "17  pickup_datetime_dayofweek   True      4.5\n",
       "18       pickup_datetime_hour   True     18.5\n",
       "19           dropoff_latitude  False   40.724\n",
       "20       pickup_datetime_hour  False      7.5\n",
       "21          dropoff_longitude   True -73.9435\n",
       "22            pickup_latitude  False  40.7174\n",
       "23           dropoff_latitude  False   40.712\n",
       "24          dropoff_longitude   True -73.8871\n",
       "25           pickup_longitude   True -73.8861"
      ]
     },
     "execution_count": 683,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitsdf = pd.DataFrame(branch_to_leaf).transpose()\n",
    "splitsdf.columns = ['features', 'leq', 'value']\n",
    "splitsdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Via grouping, we can extract the relevant splits that are always the ones towards the end of the branch. Earlier splits become obsolete if the feature is splitted in the same manner again downwards the tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('dropoff_latitude', False): [4, 19, 23],\n",
       " ('dropoff_latitude', True): [9, 13, 16],\n",
       " ('dropoff_longitude', False): [1, 6],\n",
       " ('dropoff_longitude', True): [10, 21, 24],\n",
       " ('pickup_datetime_dayofweek', True): [17],\n",
       " ('pickup_datetime_hour', False): [20],\n",
       " ('pickup_datetime_hour', True): [18],\n",
       " ('pickup_latitude', False): [5, 22],\n",
       " ('pickup_latitude', True): [8, 11, 12, 15],\n",
       " ('pickup_longitude', False): [2, 7],\n",
       " ('pickup_longitude', True): [3, 14, 25]}"
      ]
     },
     "execution_count": 685,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitstats = splitsdf.groupby(['features','leq'])\n",
    "splitstats.groups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Groupby is very helpful here. Choose always the split with the first index. \"min()\" is used here for demonstration purposes only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "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></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>features</th>\n",
       "      <th>leq</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dropoff_latitude</th>\n",
       "      <th>False</th>\n",
       "      <td>40.711979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>40.741608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">dropoff_longitude</th>\n",
       "      <th>False</th>\n",
       "      <td>-74.003044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>-73.963608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_datetime_dayofweek</th>\n",
       "      <th>True</th>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pickup_datetime_hour</th>\n",
       "      <th>False</th>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>18.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pickup_latitude</th>\n",
       "      <th>False</th>\n",
       "      <td>40.717377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>40.747398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pickup_longitude</th>\n",
       "      <th>False</th>\n",
       "      <td>-74.004021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>-73.974983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     value\n",
       "features                  leq             \n",
       "dropoff_latitude          False  40.711979\n",
       "                          True   40.741608\n",
       "dropoff_longitude         False -74.003044\n",
       "                          True  -73.963608\n",
       "pickup_datetime_dayofweek True    4.500000\n",
       "pickup_datetime_hour      False   7.500000\n",
       "                          True   18.500000\n",
       "pickup_latitude           False  40.717377\n",
       "                          True   40.747398\n",
       "pickup_longitude          False -74.004021\n",
       "                          True  -73.974983"
      ]
     },
     "execution_count": 698,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitstats.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One might use an own get_group method. This will throw less exceptions if the key is not valid (e.g. there is no lower range on day_of_week). This can especially happen in trees with low depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_group(g, key):\n",
    "    if key in g.groups: return g.get_group(key)\n",
    "    return pd.DataFrame(list(key).append(np.nan))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract the pickup- and dropoff-area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 689,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dropoff_lower_right': [40.726757049560547, -73.963607788085938],\n",
       " 'dropoff_upper_left': [40.741607666015625, -73.994827270507812],\n",
       " 'pickup_lower_right': [40.721462249755859, -73.974983215332031],\n",
       " 'pickup_upper_left': [40.747398376464844, -73.996749877929688]}"
      ]
     },
     "execution_count": 689,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_coords = dict()\n",
    "area_coords['dropoff_upper_left'] = [splitstats.get_group(('dropoff_latitude', True)).iloc[0].value,\n",
    "                  splitstats.get_group(('dropoff_longitude', False)).iloc[0].value]\n",
    "area_coords['dropoff_lower_right']  = [splitstats.get_group(('dropoff_latitude',False)).iloc[0].value,\n",
    "                                      splitstats.get_group(('dropoff_longitude',True)).iloc[0].value]\n",
    "area_coords['pickup_upper_left']  = [splitstats.get_group(('pickup_latitude',True)).iloc[0].value, \n",
    "                  splitstats.get_group(('pickup_longitude',False)).iloc[0].value]\n",
    "area_coords['pickup_lower_right']  = [splitstats.get_group(('pickup_latitude',False)).iloc[0].value,\n",
    "                                     splitstats.get_group(('pickup_longitude',True)).iloc[0].value]\n",
    "\n",
    "area_coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.014850616455078125, -0.031219482421875],\n",
       " [0.025936126708984375, -0.02176666259765625])"
      ]
     },
     "execution_count": 690,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import operator \n",
    "dropoff_rect_len = list(map(operator.sub,area_coords['dropoff_upper_left'],\n",
    "                                      area_coords['dropoff_lower_right']))\n",
    "pickup_rect_len = list(map(operator.sub,area_coords['pickup_upper_left'],\n",
    "                                      area_coords['pickup_lower_right']))\n",
    "dropoff_rect_len, pickup_rect_len"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to draw the rectangle, we need the side lengths of the areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 691,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NZkhOGyDFWxlECkR8PZIj70UnTqb0y5f65EviXo30wj0I4ONZ4OYi8qxLU81syUzF05Hn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\nW2bVHuIGa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V3abGL8qJbR19sHvjB7bRlf5Oo34T/uXnH9R2WN89b6pokd7lpjamuqRyi1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\not1xTWIAy/pp/MuZ/pXMr/vM0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GdL2lvVB9dPJH1T0ok552vjV0sppc+p+kJ5Ts75sQOe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\nDVFkj/cK+LqvDeo4326fp+f8uS2o07WSEaOa5n3xeXpUS5RLqCQ3TZRny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YNy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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4bf6df2fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as patches\n",
    "x = data_in_box.pickup_longitude\n",
    "y = data_in_box.pickup_latitude\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "# Reduce the plot to Manhattan\n",
    "plt.hist2d(x, y, bins=300, range=[[min(x.values),-73.95],[40.675,40.8]])\n",
    "plt.colorbar()\n",
    "plt.title('Pickup density (first full week in May 2013)')\n",
    "plt.ylabel('Latitude')\n",
    "plt.xlabel('Longitude')\n",
    "plt.hold(True)\n",
    "ax = fig.gca()\n",
    "\n",
    "\n",
    "ax.add_patch(patches.Rectangle((area_coords['dropoff_upper_left'][1], area_coords['dropoff_lower_right'][0]),\n",
    "                               abs(dropoff_rect_len[1]), dropoff_rect_len[0], fill=False, edgecolor='red', linewidth=5))\n",
    "ax.add_patch(patches.Rectangle((area_coords['pickup_upper_left'][1], area_coords['pickup_lower_right'][0]),\n",
    "                               abs(pickup_rect_len[1]), pickup_rect_len[0], fill=False, edgecolor='white', linewidth=5))\n",
    "ax.grid(False)\n",
    "plt.hold(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### White is pickup- red is dropoff. But are this really the correct areas? Lets check it via filtering in the training set. Do we get the same amount of trips as the no. of samples in the leaf? (Time splits are hard coded in this case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 692,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trips_of_leaf = x_train.loc[(x_train['dropoff_latitude'] > area_coords['dropoff_lower_right'][0]) &\n",
    "                       (x_train['dropoff_longitude'] < area_coords['dropoff_lower_right'][1]) &\n",
    "                       (x_train['dropoff_latitude'] < area_coords['dropoff_upper_left'][0]) & \n",
    "                       (x_train['dropoff_longitude'] > area_coords['dropoff_upper_left'][1]) & \n",
    "                       (x_train['pickup_latitude'] > area_coords['pickup_lower_right'][0]) & \n",
    "                       (x_train['pickup_longitude'] < area_coords['pickup_lower_right'][1]) &\n",
    "                       (x_train['pickup_latitude'] < area_coords['pickup_upper_left'][0]) & \n",
    "                       (x_train['pickup_longitude'] > area_coords['pickup_upper_left'][1]) &\n",
    "                       (x_train['pickup_datetime_dayofweek'] < 4.5) & \n",
    "                       (x_train['pickup_datetime_hour'] < 18.5) & \n",
    "                       (x_train['pickup_datetime_hour'] > 7.5)\n",
    "                       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 693,
   "metadata": {
    "collapsed": 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>pickup_datetime_dayofweek</th>\n",
       "      <th>pickup_datetime_hour</th>\n",
       "      <th>pickup_latitude</th>\n",
       "      <th>pickup_longitude</th>\n",
       "      <th>dropoff_latitude</th>\n",
       "      <th>dropoff_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1184741</th>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.742112</td>\n",
       "      <td>-73.983052</td>\n",
       "      <td>40.738077</td>\n",
       "      <td>-73.992777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2206938</th>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>40.732137</td>\n",
       "      <td>-73.984951</td>\n",
       "      <td>40.729175</td>\n",
       "      <td>-73.993996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1636835</th>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>40.730505</td>\n",
       "      <td>-73.990823</td>\n",
       "      <td>40.730505</td>\n",
       "      <td>-73.990823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585212</th>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.744025</td>\n",
       "      <td>-73.981375</td>\n",
       "      <td>40.738517</td>\n",
       "      <td>-73.987492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316670</th>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>40.742370</td>\n",
       "      <td>-73.978047</td>\n",
       "      <td>40.741061</td>\n",
       "      <td>-73.980647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pickup_datetime_dayofweek  pickup_datetime_hour  pickup_latitude  \\\n",
       "1184741                        2.0                  13.0        40.742112   \n",
       "2206938                        4.0                  12.0        40.732137   \n",
       "1636835                        3.0                  10.0        40.730505   \n",
       "1585212                        3.0                   9.0        40.744025   \n",
       "2316670                        4.0                  17.0        40.742370   \n",
       "\n",
       "         pickup_longitude  dropoff_latitude  dropoff_longitude  \n",
       "1184741        -73.983052         40.738077         -73.992777  \n",
       "2206938        -73.984951         40.729175         -73.993996  \n",
       "1636835        -73.990823         40.730505         -73.990823  \n",
       "1585212        -73.981375         40.738517         -73.987492  \n",
       "2316670        -73.978047         40.741061         -73.980647  "
      ]
     },
     "execution_count": 693,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trips_of_leaf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 694,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtered trips:  23282\n",
      "Trips in leaf:  23278\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 694,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Filtered trips: ', len(trips_of_leaf))\n",
    "print('Trips in leaf: ', max_leaf[1])\n",
    "len(trips_of_leaf) == max_leaf[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So we've found the trips that belong to this branch! The discrepancy, that might be caused by numeric instability when comparing the geo coordinates, is not that big."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A little bit of gmaps...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gmaps\n",
    "import gmaps.datasets\n",
    "gmaps.configure(api_key='AI****') # Fill in your API-Code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trips_of_leaf_pickup_list = trips_of_leaf.iloc[:,[2,3]].as_matrix().tolist()\n",
    "trips_of_leaf_dropoff_list = trips_of_leaf.iloc[:,[4,5]].as_matrix().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = gmaps.datasets.load_dataset('taxi_rides')\n",
    "pickups_gmap = gmaps.Map()\n",
    "dropoffs_gmap = gmaps.Map()\n",
    "pickups_gmap.add_layer(gmaps.Heatmap(data=trips_of_leaf_pickup_list[0:1000]))\n",
    "dropoffs_gmap.add_layer(gmaps.Heatmap(data=trips_of_leaf_dropoff_list[0:1000]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see, that the intersection of the pickup- and dropoff-area is quite big. This corresponds to the predicted trip_time that is about 6.5 minutes. In this short period of time, a car cannot drive that far. It looks also quite similar to the picture above, that is based on a 2d-histogram of the pickups. We can qualitatively see, that the dropoff-area is smaller than the pickup-area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pickups_gmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dropoffs_gmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You reached the end. Thank you for your interest. - N. Haas"
   ]
  }
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