phase4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## load libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from datetime import datetime\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy import stats\n",
    "from scipy.stats import poisson, ttest_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695</td>\n",
       "      <td>2013-06-01 00:00:01</td>\n",
       "      <td>2013-06-01 00:11:36</td>\n",
       "      <td>444</td>\n",
       "      <td>Broadway &amp; W 24 St</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>434.0</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>19678</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>693</td>\n",
       "      <td>2013-06-01 00:00:08</td>\n",
       "      <td>2013-06-01 00:11:41</td>\n",
       "      <td>444</td>\n",
       "      <td>Broadway &amp; W 24 St</td>\n",
       "      <td>40.742354</td>\n",
       "      <td>-73.989151</td>\n",
       "      <td>434.0</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>16649</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2059</td>\n",
       "      <td>2013-06-01 00:00:44</td>\n",
       "      <td>2013-06-01 00:35:03</td>\n",
       "      <td>406</td>\n",
       "      <td>Hicks St &amp; Montague St</td>\n",
       "      <td>40.695128</td>\n",
       "      <td>-73.995951</td>\n",
       "      <td>406.0</td>\n",
       "      <td>Hicks St &amp; Montague St</td>\n",
       "      <td>40.695128</td>\n",
       "      <td>-73.995951</td>\n",
       "      <td>19599</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>123</td>\n",
       "      <td>2013-06-01 00:01:04</td>\n",
       "      <td>2013-06-01 00:03:07</td>\n",
       "      <td>475</td>\n",
       "      <td>E 15 St &amp; Irving Pl</td>\n",
       "      <td>40.735243</td>\n",
       "      <td>-73.987586</td>\n",
       "      <td>262.0</td>\n",
       "      <td>Washington Park</td>\n",
       "      <td>40.691782</td>\n",
       "      <td>-73.973730</td>\n",
       "      <td>16352</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1521</td>\n",
       "      <td>2013-06-01 00:01:22</td>\n",
       "      <td>2013-06-01 00:26:43</td>\n",
       "      <td>2008</td>\n",
       "      <td>Little West St &amp; 1 Pl</td>\n",
       "      <td>40.705693</td>\n",
       "      <td>-74.016777</td>\n",
       "      <td>310.0</td>\n",
       "      <td>State St &amp; Smith St</td>\n",
       "      <td>40.689269</td>\n",
       "      <td>-73.989129</td>\n",
       "      <td>15567</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration            starttime             stoptime  start station id  \\\n",
       "0           695  2013-06-01 00:00:01  2013-06-01 00:11:36               444   \n",
       "1           693  2013-06-01 00:00:08  2013-06-01 00:11:41               444   \n",
       "2          2059  2013-06-01 00:00:44  2013-06-01 00:35:03               406   \n",
       "3           123  2013-06-01 00:01:04  2013-06-01 00:03:07               475   \n",
       "4          1521  2013-06-01 00:01:22  2013-06-01 00:26:43              2008   \n",
       "\n",
       "       start station name  start station latitude  start station longitude  \\\n",
       "0      Broadway & W 24 St               40.742354               -73.989151   \n",
       "1      Broadway & W 24 St               40.742354               -73.989151   \n",
       "2  Hicks St & Montague St               40.695128               -73.995951   \n",
       "3     E 15 St & Irving Pl               40.735243               -73.987586   \n",
       "4   Little West St & 1 Pl               40.705693               -74.016777   \n",
       "\n",
       "   end station id        end station name  end station latitude  \\\n",
       "0           434.0         9 Ave & W 18 St             40.743174   \n",
       "1           434.0         9 Ave & W 18 St             40.743174   \n",
       "2           406.0  Hicks St & Montague St             40.695128   \n",
       "3           262.0         Washington Park             40.691782   \n",
       "4           310.0     State St & Smith St             40.689269   \n",
       "\n",
       "   end station longitude  bikeid    usertype  birthyear  gender  \n",
       "0             -74.003664   19678  Subscriber     1983.0       1  \n",
       "1             -74.003664   16649  Subscriber     1984.0       1  \n",
       "2             -73.995951   19599    Customer        NaN       0  \n",
       "3             -73.973730   16352  Subscriber     1960.0       1  \n",
       "4             -73.989129   15567  Subscriber     1983.0       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## load data on ghg data\n",
    "biking_2013 = pd.read_csv(\"201306-citibike-tripdata.csv\")\n",
    "biking_2013 = biking_2013.rename(columns={\"birth year\":\"birthyear\"})\n",
    "biking_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1470</td>\n",
       "      <td>6/1/2016 00:00:18</td>\n",
       "      <td>6/1/2016 00:24:48</td>\n",
       "      <td>380</td>\n",
       "      <td>W 4 St &amp; 7 Ave S</td>\n",
       "      <td>40.734011</td>\n",
       "      <td>-74.002939</td>\n",
       "      <td>3236</td>\n",
       "      <td>W 42 St &amp; Dyer Ave</td>\n",
       "      <td>40.758985</td>\n",
       "      <td>-73.993800</td>\n",
       "      <td>19859</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>229</td>\n",
       "      <td>6/1/2016 00:00:20</td>\n",
       "      <td>6/1/2016 00:04:09</td>\n",
       "      <td>3092</td>\n",
       "      <td>Berry St &amp; N 8 St</td>\n",
       "      <td>40.719009</td>\n",
       "      <td>-73.958525</td>\n",
       "      <td>3103</td>\n",
       "      <td>N 11 St &amp; Wythe Ave</td>\n",
       "      <td>40.721533</td>\n",
       "      <td>-73.957824</td>\n",
       "      <td>16233</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>344</td>\n",
       "      <td>6/1/2016 00:00:21</td>\n",
       "      <td>6/1/2016 00:06:06</td>\n",
       "      <td>449</td>\n",
       "      <td>W 52 St &amp; 9 Ave</td>\n",
       "      <td>40.764618</td>\n",
       "      <td>-73.987895</td>\n",
       "      <td>469</td>\n",
       "      <td>Broadway &amp; W 53 St</td>\n",
       "      <td>40.763441</td>\n",
       "      <td>-73.982681</td>\n",
       "      <td>22397</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1120</td>\n",
       "      <td>6/1/2016 00:00:28</td>\n",
       "      <td>6/1/2016 00:19:09</td>\n",
       "      <td>522</td>\n",
       "      <td>E 51 St &amp; Lexington Ave</td>\n",
       "      <td>40.757148</td>\n",
       "      <td>-73.972078</td>\n",
       "      <td>401</td>\n",
       "      <td>Allen St &amp; Rivington St</td>\n",
       "      <td>40.720196</td>\n",
       "      <td>-73.989978</td>\n",
       "      <td>16231</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>229</td>\n",
       "      <td>6/1/2016 00:00:53</td>\n",
       "      <td>6/1/2016 00:04:42</td>\n",
       "      <td>335</td>\n",
       "      <td>Washington Pl &amp; Broadway</td>\n",
       "      <td>40.729039</td>\n",
       "      <td>-73.994046</td>\n",
       "      <td>285</td>\n",
       "      <td>Broadway &amp; E 14 St</td>\n",
       "      <td>40.734546</td>\n",
       "      <td>-73.990741</td>\n",
       "      <td>15400</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration          starttime           stoptime  start station id  \\\n",
       "0          1470  6/1/2016 00:00:18  6/1/2016 00:24:48               380   \n",
       "1           229  6/1/2016 00:00:20  6/1/2016 00:04:09              3092   \n",
       "2           344  6/1/2016 00:00:21  6/1/2016 00:06:06               449   \n",
       "3          1120  6/1/2016 00:00:28  6/1/2016 00:19:09               522   \n",
       "4           229  6/1/2016 00:00:53  6/1/2016 00:04:42               335   \n",
       "\n",
       "         start station name  start station latitude  start station longitude  \\\n",
       "0          W 4 St & 7 Ave S               40.734011               -74.002939   \n",
       "1         Berry St & N 8 St               40.719009               -73.958525   \n",
       "2           W 52 St & 9 Ave               40.764618               -73.987895   \n",
       "3   E 51 St & Lexington Ave               40.757148               -73.972078   \n",
       "4  Washington Pl & Broadway               40.729039               -73.994046   \n",
       "\n",
       "   end station id         end station name  end station latitude  \\\n",
       "0            3236       W 42 St & Dyer Ave             40.758985   \n",
       "1            3103      N 11 St & Wythe Ave             40.721533   \n",
       "2             469       Broadway & W 53 St             40.763441   \n",
       "3             401  Allen St & Rivington St             40.720196   \n",
       "4             285       Broadway & E 14 St             40.734546   \n",
       "\n",
       "   end station longitude  bikeid    usertype  birthyear  gender  \n",
       "0             -73.993800   19859  Subscriber     1972.0       1  \n",
       "1             -73.957824   16233  Subscriber     1967.0       1  \n",
       "2             -73.982681   22397  Subscriber     1989.0       1  \n",
       "3             -73.989978   16231  Subscriber     1991.0       1  \n",
       "4             -73.990741   15400  Subscriber     1989.0       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2016 = pd.read_csv(\"201606-citibike-tripdata.csv\")\n",
    "biking_2016 = biking_2016.rename(columns={\"birth year\":\"birthyear\"})\n",
    "biking_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fromstreet</th>\n",
       "      <th>street</th>\n",
       "      <th>boro</th>\n",
       "      <th>the_geom</th>\n",
       "      <th>tostreet</th>\n",
       "      <th>onoffst</th>\n",
       "      <th>allclasses</th>\n",
       "      <th>instdate</th>\n",
       "      <th>moddate</th>\n",
       "      <th>comments</th>\n",
       "      <th>bikedir</th>\n",
       "      <th>lanecount</th>\n",
       "      <th>ft_facilit</th>\n",
       "      <th>tf_facilit</th>\n",
       "      <th>SegmentID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>W 16 ST</td>\n",
       "      <td>9 AV</td>\n",
       "      <td>1</td>\n",
       "      <td>MULTILINESTRING ((-74.00039850899998 40.747767...</td>\n",
       "      <td>W 31 ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>I</td>\n",
       "      <td>09/25/2008 12:00:00 AM +0000</td>\n",
       "      <td>09/25/2008 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Protected Path</td>\n",
       "      <td>33547.0</td>\n",
       "      <td>0.000782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEAN ST</td>\n",
       "      <td>3 AV</td>\n",
       "      <td>3</td>\n",
       "      <td>MULTILINESTRING ((-73.98747868599997 40.675707...</td>\n",
       "      <td>15 ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>07/01/1980 12:00:00 AM +0000</td>\n",
       "      <td>07/01/1980 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>Standard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22630.0</td>\n",
       "      <td>0.000820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TILLARY ST</td>\n",
       "      <td>ADAMS ST</td>\n",
       "      <td>3</td>\n",
       "      <td>MULTILINESTRING ((-73.98850195799997 40.697357...</td>\n",
       "      <td>SANDS ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>III</td>\n",
       "      <td>06/01/2006 12:00:00 AM +0000</td>\n",
       "      <td>07/01/2017 12:00:00 AM +0000</td>\n",
       "      <td>downgrade to shared</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sharrows</td>\n",
       "      <td>299123.0</td>\n",
       "      <td>0.001014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5 AV</td>\n",
       "      <td>W 39 ST</td>\n",
       "      <td>1</td>\n",
       "      <td>MULTILINESTRING ((-73.98227490399995 40.751577...</td>\n",
       "      <td>8 AV</td>\n",
       "      <td>ON</td>\n",
       "      <td>III</td>\n",
       "      <td>09/12/2012 12:00:00 AM +0000</td>\n",
       "      <td>09/12/2012 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>Sharrows</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34170.0</td>\n",
       "      <td>0.003488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FLUSHING AV</td>\n",
       "      <td>THROOP AV</td>\n",
       "      <td>3</td>\n",
       "      <td>MULTILINESTRING ((-73.94149491699994 40.685764...</td>\n",
       "      <td>FULTON ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>06/01/2003 12:00:00 AM +0000</td>\n",
       "      <td>06/01/2003 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>43043.0</td>\n",
       "      <td>0.000748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    fromstreet     street  boro  \\\n",
       "0      W 16 ST       9 AV     1   \n",
       "1      DEAN ST       3 AV     3   \n",
       "2   TILLARY ST   ADAMS ST     3   \n",
       "3         5 AV    W 39 ST     1   \n",
       "4  FLUSHING AV  THROOP AV     3   \n",
       "\n",
       "                                            the_geom   tostreet onoffst  \\\n",
       "0  MULTILINESTRING ((-74.00039850899998 40.747767...    W 31 ST      ON   \n",
       "1  MULTILINESTRING ((-73.98747868599997 40.675707...      15 ST      ON   \n",
       "2  MULTILINESTRING ((-73.98850195799997 40.697357...   SANDS ST      ON   \n",
       "3  MULTILINESTRING ((-73.98227490399995 40.751577...       8 AV      ON   \n",
       "4  MULTILINESTRING ((-73.94149491699994 40.685764...  FULTON ST      ON   \n",
       "\n",
       "  allclasses                      instdate                       moddate  \\\n",
       "0          I  09/25/2008 12:00:00 AM +0000  09/25/2008 12:00:00 AM +0000   \n",
       "1         II  07/01/1980 12:00:00 AM +0000  07/01/1980 12:00:00 AM +0000   \n",
       "2        III  06/01/2006 12:00:00 AM +0000  07/01/2017 12:00:00 AM +0000   \n",
       "3        III  09/12/2012 12:00:00 AM +0000  09/12/2012 12:00:00 AM +0000   \n",
       "4         II  06/01/2003 12:00:00 AM +0000  06/01/2003 12:00:00 AM +0000   \n",
       "\n",
       "              comments bikedir  lanecount ft_facilit      tf_facilit  \\\n",
       "0                  NaN       L          1        NaN  Protected Path   \n",
       "1                  NaN       R          1   Standard             NaN   \n",
       "2  downgrade to shared       L          1        NaN        Sharrows   \n",
       "3                  NaN       R          1   Sharrows             NaN   \n",
       "4                  NaN       L          1        NaN        Standard   \n",
       "\n",
       "   SegmentID  Shape_Leng  \n",
       "0    33547.0    0.000782  \n",
       "1    22630.0    0.000820  \n",
       "2   299123.0    0.001014  \n",
       "3    34170.0    0.003488  \n",
       "4    43043.0    0.000748  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes = pd.read_csv(\"bicycle-routes-1.csv\")\n",
    "routes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460318"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(biking_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "577703"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(biking_2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(biking_2013[\"tripduration\"])\n",
    "plt.xlabel(\"User id\")\n",
    "plt.ylabel(\"Trip duration in 2013\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZZhiWQOeFJNYp/UecqqEr837fjaDraK3GNEiUrpUqo66IIv1DnKqh6QVPPWdmzyYUT03U+lqp0S7WGi3eRqLPVmohTtXQPnkPdwLeC+ybWEQiGZafBxIpcKkUJxHiVA1NJ9h9jKBKaClwcZJBiWSZxpyTtMWpGjokjUCy4LdjF3HWUfsxZJCGahKR+lFqqsr/V+qN7v5Q9cNJ33OL1rB0zabyC/bR2tat/PyJBfx5ynImXKUOVxKtUdsI5q5o4ZBBe7DHrrEGNJY6U+pb+/fw/7cBJwOjw8enAWOBfpEIPn3blFS2k6ua3bKtM5XtSWPK7+bZKBPTtLV3cs5NE/jA4YO56/MnJLCFwMa2dqYvW8epR7wtsW1kVdGxhtz9Ine/iGDfebe7n+fu5wFHphadSBblckEVz9pJljTaO7sAmLFsXXIbAb7+wCwu/MPzNK3T/BvVFmcY6iHunj9g/uvA4QnFU1PqFy/1oEFrhxK3pDmYwKmtXaXqaotToTfWzEYB9xJco5wPjEk0qozRgS/9gebzaFxxeg1dbmb/BXwgfGq4uz+cbFgi2dRtrKEG6fSf9vAVyjfVF3fO4oeBik7+ZnYHcA6w2t2PinjdgBuAs4DNwIXuPqOSbUj6Xn1jM//y86BAqOMxGUmeWBv5ql3jJSUnThtBb90JnFHi9TOBw8KfS4BbE4ylbjTuYRiYtuyNWofQr1lCFYVpnEMbfd/OssQSgbuPA0qdNc4F7vLAZGBvM9svqXhqTdcyEkcjXvSmHXKtE86WbZ1sbGuvcRTVFSsRmNkuZnaMmR1dxWkr9wdezXvcFD4Xtf1LzGyamU1rbm6u0uZF6lsD1+Ikol5y5Km/HMPR33+y1mFUVdlEYGZnA4uBG4GbgUVmdmYVth31vUbu+u4+3N2HuvvQwYMHV2HT0ltJVV3IDkmWCpRb+u71DVtrHULVxWks/hVwmrsvAjCzQ4ERwON93HYTcGDe4wMIpsSsIR0mUltJ5YA0EnhaJRiVlKovTtXQ6lwSCC0BVldh248BF1jgRKCl4MY1qUONWIfdSBrx800r5kb8bBpFnBLBPDMbCTxAcMn8MeD53KB0xQafM7N7gVOBQWbWBHwPGBi+ZxgwkqDr6CKC7qMX9ekvaWDawSWKLnyjNcr9FY0kTiLYjWBYiQ+Gj5uBfQgGpXOKDD7n7p8stVIPOjRfFjvSfqKR+3FL8izhSpwkd7+kT9Bqn0pOnDuLM3ulXk26GUbiSGo3SXL30wm68ZWaj+B/3f3nZnYTEaVUd/9yopFJn7RsaefNbxpY6zBEqk6F6uorVSJ4Mfx/WhqBSPWMmL2Ky/48g0cvez/HHrh3Vdfdfbx8HZGyQ9K7gwrVySmaCNz9b2Y2ADjK3b+ZYkw101/OaxMWBTfdzVu5oeqJQNLTKIlWJ+ju5q1sYZcBO3HY2/eqdSixlaoa2tndO8zsvWkGJH2X5PlDx3yyzCzZQecSW3N66j0/nn3jBABeue7sGkcSX6mqoanAe4AXzOwx4C/A9sl9+8ucxf2ZrtQkTXV+fpYS4nQf3QdYC3yI4Ls2SnQbrXdJdnF7dOYKZje1MHivXbn0g4cW2X5PjdSjqIFCbUhxP15357FZK/nIkfuy28ABicYk/V+pRPA2M/s6MJcdCSCn4ZJ/0ifbrR2dfOW+mdsfFyaCNM+f9V50luK6T0xT3PiFa/jKfTP5/PsP4dp/f3ficdWTqIu5Ba9tZMTslXz9346oQUSBrR2dDTsYXalEMADYkwoGh8uyejz56uK9McX53jaEwyC/vqGtonUn2gCdeK+h4p/MR4dNZGNbB//9wUPZY9dY821V3drWbWzr6KrJtvuq1Ce2yt1/mFokUjVJVn/p5qH6Eve7TrJEXA/Vhe2dwQm4HmLJGTlnFWcd3RhTrJQadK6OPlLpjXo6KKS4eStbtv8e9yvLckIuVaip5edSGNb/3DOD5xatqUkslSqVCE5PLYo60Zfr6EpOuqtatiRahEy0+2h2zz+JyXU3hMIb9moRTf0qtevV62fVsqUxZjIrmgjcXZPTJmDLtk5O+ulorn5oduLbyvJVYyOrJNnW0wmwHkYF1YVK7yQ5eb1E2BqWBEa/VI0pHaIleTg2dNexfqS3J7z++p3Vw9/VKHeCR1EiSEnhgZvKPqOro4aT2OijBY9bt3Zw+HcfZ0yCFyTVVulnM33ZuoY+OadJiaBKVA0j1dC9xFX+JNbb89yi1a1s6+ji+qdf7t0KqhhLdTbe/eHYBas579aJ3DnxlZqE02gynQgm1rBFP8krFTUWN7ryH3KWv4aSvYbCD6Zp3RYAXn69NYWIGl+mE8GnbpsSe9mmdZsZ/dLrCUZTfVk+WTSsCjNtxQ20CV4kdHQ5J//0GV59Y3Mi6y/10RR+DtW6YPnT5GW89NqG6qysjmU6ERQqdaVx5vXj+fydxadmqHTHS7IUnWzvjcZOL8++3MyUJWtrHUZRse8jqPBrSKskt7Kljb9MezWdjeXJHbs9q2j7dixc88hczrh+fJ/W0QiUCGLauLWj4veMnLOK1QVDAGw/IFOoT22kwezS8rk7pvKJ4ZMTWfeGtvbtQz9Uhdo5I8W50MklhDTbLRq5XVqJICFbtnXyP/fM4NMVVD9VjdoIauKY7z/JMVUYdKzUZ9zZ5Yx7uXn740Y++VSqVIeMwo9B+2lllAgS0hUeoblGq0LqPVpbi5vrsxGx3Ans1rGLuOCOqTz7cu86OhReTff3PFLrRFnr7ceVaCIwszPMbIGZLTKzqyNeP9XMWsxsZvhzbZLxJKnw+C08oHNXM2mcnNVCUN5nalFSiyH/qjfqe1y6JmiIbd64tegy5dYbPE5O0ue+qJNrYS+8HTWwDXImrrHExmsN5zu+Bfgw0AQ8b2aPufv8gkXHu/s5ScVRa8V2xPwdN7GbiFI+a9/0zEJ233VnLj7lkHQ33AvtnfV7gth+Eos64YX70079JSNXoJL9WVVDlUmyRHACsMjdl7j7NuA+4NwEt1dXitVn9qcG3MIT1a+eepkf/b0wz9ePRhgAzKxMN0nfsVzO+s3bkg2qyr736FyGXD2iquvM7YpdBTtlX6pmGumu675KMhHsD+T3I2sKnyt0kpnNMrPHzezIqBWZ2SVmNs3MpjU3N0ctUreK7YiJdh9NsGKykRPZsT/Y0ZBbr39GpcNQu8O8lY3Vz/2Pk5b16f2l9u5vPzwHyPt8+rCdi+58vg/vbixJJoI4M5vNAA5292OBm4BHolbk7sPdfai7Dx08eHCVw+y2naqtK3ei6dGboY/r7ejsomldvBt26vVkJ72X20d32in/uUreX+WAUhRnGOqHZqwov7D0kGQiaAIOzHt8ALAyfwF33+DureHvI4GBZjYowZjqxtaOLoZcPYKHZjRV9L7/G/kSp/xszPbGwij13lj84qoNzHp1fRXW1HuNcJ6Ial/KPdO9UTlGv/oE/uCNbe10dUXEmHCycXdWb2hjwWsbYyybbCz9RZKJ4HngMDM7xMx2Ac4HHstfwMz2tbCuwcxOCOOp39s+e6PIjtgZHkC/GLWgotWNWxhUja2LUS9crwPhnXnDeM695blEt9GypZ0t2zqLvl6vpSWzMv3ld2SC3DN92t6mrR384bmlFZeGWza3c/T3n+TXT1Vv0LpSNm/r6PalnXTdaD5y/biiy9ei11AjJ53EEoG7dwCXA6OAF4EH3H2emV1qZpeGi30UmGtms4AbgfO9n4wbu6NqaPu975HWtlbW0Bfn/KVB54L2gNN/NbZq62tr72T1xsomiu+Ncsk71xi6U4mZzNyd9183umRpM/f2xc2b+MHf5lc8P0buQuSxWSvLLNl3Y15azbuvHdWtFNkZURLJ18htWbWQ6H0E7j7S3Q9390Pd/Sfhc8PcfVj4+83ufqS7H+vuJ7r7xCTjqYa1rcWrZPIV7zXU/fG2zt5NWRnnZN8fj4VX1mxi3aZ4yXNlS/ETd6WlpS/eNY0TfvJM0deXr01moLVCua89133UvWeZoL3TWbF+C1c92HMWvGL7TVt7ZfthmvvWhIJRgiu6zilYeNHqjbS1Fy8p9kUj37OgO4sr9KW7Z2z/fWNbO1s7Su9Uvbk6//Hf53Pto3MjX+tR0siYU385lg//5tk+r6fSE9n4haXv5P1rhW09RZWLq3QBM1yk576R1Hm7XvfDqCG9Nra186+/Hsc3/jKrFiF1c9VfZ/OtFKarjUuJIIaOvKv2/OqBo7//JP9xU+m67jiHSeHV6W0TlnJXkS52cQbTSrSxOKUrweaNW4vWW69p3cbDLzTx9ftnphNMHFWqj+s2MU3JcffjzFmQ3JdVaj+sh+QQ9fHk2oymLq39dOz3T3uVe6emP0prMUoEMXz9gR1XEIVVkwteL99zoZr6Y3VPoSXNrfzzT57m9glLiy7ztftn8dALK3q9jWp/jNU89cUZdz//ircwYabRymZ5VVNpq6y77I6Fc8dupXdl9+Uu7npIinEoEeQp9pXlN4hV+sUm1fZdskSQ5A1lKfREWh5ObPLsy41z82BaJ8SusHAaqzE0YpGiQ55U8YR1y5jF3UrRtRB1H0+uob3SfTgLDc+ZTQSTFveul2pXxP79xNxVzGqK1y++WieMeH3Hk92Bk0o4aRx41d5Gb0+khUMtlItre4nA8p/bYVXLlurOidBLD8coraV9wVLY0B5/Xf1fYoPO1btqTjt5aV4DcqEuDw72Z795alW2lTtR1KqNII2jotSga3HMr8GQC9U6p3WfvL74dnb0Guq+1Ek/Hc3uuwzosa5io+H2Os4i289JdlC/+OvODy9389sbm7exbtM23rLHLrHWsVPMi4ZG7vie2RJBb7+03l7FzFi+rncbLFBsl1zV0nPeg0a9kulrz6hlazfF3ka1VLWNIMZ2ljQX/xs3h42iSRastl+QFH29/DrSuN8lfxO57bW1d3H8j56qYGVVC6tuZTYRlLkfpervS/pq4aSfjs7bWPDfxMVrY/e5T4K7s6mCKT6HPbuYP01e1udpBqNOQhsLqktWb9jKhDJdQiuR1tVgbjvTlgUXFr79n3SVK7UZcMuYRanchFcof59v3tjGZffMYE3r1h4jkxYz7ZU3uu232zq6Erv3oF5kNhGUutr8wh+f58I/TI18Le7OVCi/51FfVNJb496py/lckb+j19vP+31DW0fRAfA6u5zrHn+JI783itUb2vjTpFdo2Vy67vq6x1/imkfmdvsbn5j7GvdNXd6HKAPjCmb02tbZxWdun1K1G8Gq1dha7kq6dWv8+v/IevJqVWGViXPuyhZ+MWoB3yix35cKZfXGtu2l78JNFf4Nf57Sff84/kdPba+amrzkDUbMWcVNzyyMdeyubd3KR4dN4iv3de+afMrPRhd5R15cZZeoX9lNBCW+tadfXM3YBdE9VpL4snsz4UbcE8/LCXdv/cIfp0U+/38jX+R345YAMHLOKq55dB5fe2Am1zwSfaNcvvxxYi69ezpXPzSn5PL3P7+ciXl3n1byef7qqQUsbm5lyNUjmLuiJf4bCxV8Hds6urj20bmx70TPMdtxR3TPbqHO5CU9+8CX2xfaO7tKtmMF664ozLLbzg0BUWq8p2LVrPNWtnDCT54p2s/+yoIbwnJDT3fffvdeHR1dzvoY81G0dQTvm7+y+76wpsKhYBpNZhuLo3ZCd7ijRN/1Yu/rq+sef6nk9rocBoStg3Ea+fIPzmp39yzs1bIlosg8d0VLt3sAcgdX3PFs4jSI57vqweBE8Mp1Zwfvj/e27dt45sWg48CjM1dw1P5vruDdeespePz43FXcNWkZG9s6+M0njuvVOgvdPbnnTYbuJapnwg8ifzyr3pZoe6y7TPXd6g1BAozb0Jpv0epgPumRc1Zxx3NLOXif3bu9/kqMUlzhfnrPlOXcM6XSkmVlGrkpIbslgiLP/7DMDFu9bSMot91ifvP0Qg799sgeV1b10kMhauf/8r0vdHtcLNbJS9ZGJt6+3qxUSdfQqMbEUoZcPSKyMTr/AuGsG8Zvr1ooNzhaoVKJuy830OWMKVLSrVSx+TZyngmTfumb40qbsGgNi1a3bl9XJXqTgPqqTg7JXsluIki511Bv5eo/c42d5Q5A6P63Vft4KAWCdJ4AAA9ASURBVFxd1Em3s7BKo0i05w+fHJl4n5z3esn3VRpjqXW5e8WzWUWVbPL/5Pmret99tdQVe/RwDqUbbAvlElNf94u4by93Ql7VsoVX39jMp34/udf39kQZkNkzW+9ktmpo7abK6m5z0s76O+Wd+EfMXsXspqDuslajdW/r6F73Wupkk1Mu1AkL1wTjzYfueG5prPcVemXNJoYM2qPik1yly0dd5Ffr27ht/I4SUl+/4lySzv/7cvvTU/Orcx9NuRh3KnFCdu/e223pmk1M+tbpVSntplEiWPj6Rg57+16JbycNmcybHZ1djJzzWq/eGzUjU5Jy+3OXO79+Kt4kNt1KBMDMV9dz69jFVYnnf+4paHSMGsagwo/oM7dP4ZI/Te+5nspWw5qwYTbqHBCrbSXmBsflDX3R1eUMuXpE0XGRKv0bcsNrwI77AXKKndt+NCK6OjNq8dwJ8oZnFnZ7vuK9evvKS7/zuUVri160FJbSqnny7s263n/daP5vxItAvM/jw7/pPjFOsXbHRpDJEkFHH07mTnAiWNzcWnK5sQui6zUrrTPO7dCdXc7ivJuI4q7FzPjPcDawf3jTznz6fQdXtP1ChfMnxCkR9Nb0ZZXdhLdTBQ3qOd3aCCI+1dvGL2F1wbSg+WMg9WVfipJfNfTbsYu44fzjSy7v7iwr03haeIfxiNmr+hJiuM74Dfqzm1o49sC9yy63Yn1wU2Q1uuK+vqH8/QsTFq6had1mzj/hoO3bz8WQlnkrWzjyH4t3UBizYDUbtrRz+rvezp67Jne6zmQi6EvPCXe44I7yffOjrnArNWnx2u0H8cg53Q/eyPpid8ysaB31dx6e2+dEUCiqjaDw5JhWNdbOOxlN6zZH3ttQ9OTiO/6GqNLej8MrxLTkf1S5ajh3Z/i4JZXfHBiRD9s7ncv+3LMrae47mrhoDYfvuxeD9tw1cpXzVraw7z/sFqutKmdrR/QAdEnuFnG+t8/cPgVgeyKohbNvnLC9t1uUi/7w/Pbfr//Ecfzn8fsnEkcmq4b6chFXre53cXzy95O3n6Ra2wrv0O0Zx8SwsS2/eiHpqtLc6vMb+moxLDIEXWxP+dkYrnl0XkXv2xD2L/9jkTkgouS6nJb7fCtNgvn7V65kNX3ZOn76+Euxuk0Wamvv7NF4HyW3yKdum8LHfzep6HJn3ziBs2+cUFFXySSPmSfm9q6Kt9Cagvs9VpWY3a5WkhyNN5OJoFjVRZwiabGrm6RsP9HEOKO3RNwws7FHAqnMlm2d3W7WKpQL65O/n7z9uTgnniQM6MXA8Y5Hfm7lXFzkRroo33l4Tuy7o/M/uVUtbUxcvKbkdKalZk4z4J3XPMGlMUqn+btXqXGMAF7b0La9mvKNTdtYsa50dUpud+jq8r7dtBfh0rv7XvIGGPrjp6uynnI6Ort6DLuR6xG4bO0mWksMyZLkRV3mEsHSNZt4ukiPif/9a/ep41as38Ix3x/V7blq1X/HlfvyC++UdIebChv8ehHaPVOW8Y0HZhW9A/Q7D8/hU7dNYUmRNpGo+vj1BUNJpPWJ7dybROC9v2Jt7+wqe2Xc0encM2V52bujd8SzI5Y5K1r41O+n9Co22FHlNaup/Mk3uDEt/ueQX2r40K+eLbns1Q/N5lsPzeGqB2dzzk0Tum2zWCyNYsjVI3h9QxtzV7REjrjqBPOZ5Kr5fvj3+T3mvs6V4D/4i7F8okRp7KEZK2je2LvejuVkro1g7ILmosNHNBVc2dw16RU29PGKulJHfPfxbo9zjcW3jOne68eBXz31csFz5Y+gj/9uEnd9/gR2GxgMVfydh4MhH+asWM+TX/tgt2Xzx8ov9jnEmaFt3srqXgUW83iJnmClGpCLJYLCuQIK/WnSMi44qbI2l/ELm7ffdRsdS8/nXlgeb66LQpWUdBxP7AS8bO1mlq3tWSKK2l9XrN9StXG50jLmpdVFE/0zL77OozNXcvlp/8SVHzmCZ17s2YkkP4HMyxtCfdarPb/3r97/Avd84cQqRN1d5hJBJSoZObNaCqueip2+envQTl36Bl+7fyYr1m/hsctP2f78y6+30t7ZxfiFzYx+aTU//s+ju72vy505Ra4s75r0SsltjppXvbkfCuXPW1yYGPMVS5KP96GOefO2jrJ3MT8xr/v6P3t76Y4GUSXOX4yK1224L9rau3p8QreOXcyi1a18fOgBvLFpG18q7DrcR3dOfKXHc1f9tX4mdI+rVGkvdwWfu29p4ICe+8u9U5ZzXEGvqqfmv84X7+pZ/djXqt5ilAhKuHtysmOTxFHsRHP1gz0PmA1b4u0kxU5+h31nR2nkK6cf3u01d+ffb55Q+BYArq2wcbaayg270NHZxc4DdqIj5kQpxZJdlFHzXudLp/5T7OUfmtEUe9m0feuhOdxb0I7xsyeCMbAeTCjux2au7PFc2lWvENw/EFelXW9zHTh2Du+si6o+Whkxl0hhL8GcpD4fS3S6OLMzgBuAAcBt7n5dwesWvn4WsBm40N1LXnYMHTrUp02L31CXc8EdU7vdCNQoDtpn9269gMp5x+A9yjb29WY9nxh6IPdPix4NUqQ33rrHLqyt4XwZlbr10++peqko59cfP3Z7ldgPzz2y5MVVqe6mpZjZdHcfGvlagvPODgBeBj4MNAHPA5909/l5y5wFXEGQCN4H3ODu7yu13t4mgiO++3jqPX5ERKotiUSQZK+hE4BF7r7E3bcB9wHnFixzLnCXByYDe5vZfkkEoyQgIhItyUSwP5Bfl9AUPlfpMpjZJWY2zcymNTf3rnpn7JWn9up9IiL14qR3vDWR9SbZWBw9GnDly+Duw4HhEFQN9SaYIYP26HWRSkSkP0uyRNAEHJj3+ACgsJtAnGVERCRBSSaC54HDzOwQM9sFOB94rGCZx4ALLHAi0OLufR8aUUREYkusasjdO8zscmAUQffRO9x9npldGr4+DBhJ0GNoEUH30YuSikdERKIlekOZu48kONnnPzcs73cHLksyBhERKS1zg86JiEh3SgQiIhmnRCAiknFKBCIiGZfooHNJMLNmIP6cgt0NAopP6VQ/FGf1NEKMoDirrRHiTDvGg919cNQLDZcI+sLMphUbdKmeKM7qaYQYQXFWWyPEWU8xqmpIRCTjlAhERDIua4lgeK0DiElxVk8jxAiKs9oaIc66iTFTbQQiItJT1koEIiJSQIlARCTj+mUiMLMzzGyBmS0ys6sjXjczuzF8fbaZvadO4/x0GN9sM5toZsfWW4x5y/2zmXWa2UfTjC9v+2XjNLNTzWymmc0zs2fTjjGModx3/mYz+5uZzQrjTH1EXjO7w8xWm9ncIq/Xy/FTLs56OH5Kxpi3XE2PH9y9X/0QDHm9GHgHsAswC3h3wTJnAY8TzJB2IjClTuM8GXhL+PuZaccZJ8a85UYTjDT70Tr9LPcG5gMHhY/fVqdxfhv4Wfj7YOANYJeU4/wA8B5gbpHXa378xIyzpsdPnBjz9ouaHT/u3i9LBCcAi9x9ibtvA+4Dzi1Y5lzgLg9MBvY2s/3qLU53n+ju68KHkwlmcKurGENXAA8Cq9MMLk+cOD8FPOTuywHcvRaxxonTgb3MzIA9CRJBR5pBuvu4cLvF1MPxUzbOOjh+4nyWUPvjp18mgv2BV/MeN4XPVbpM0iqN4WKCq7A0lY3RzPYH/gsYRu3E+SwPB95iZmPNbLqZXZBadDvEifNm4F0EU7bOAb7i7l3phBdbPRw/larF8VNWnRw/yU5MUyMW8VxhH9k4yyQtdgxmdhrBjnxKohFFbDriucIYrweucvfO4CK2JuLEuTPwXuB04E3AJDOb7O4vJx1cnjhxfgSYCXwIOBR4yszGu/uGpIOrQD0cP7HV8PiJox6On36ZCJqAA/MeH0BwdVXpMkmLFYOZHQPcBpzp7mtTii0nToxDgfvCnXgQcJaZdbj7I+mECMT/zte4+yZgk5mNA44F0kwEceK8CLjOg8rjRWa2FHgnMDWdEGOph+MnlhofP3HUw/HTLxuLdwaWAIewo0HuyIJlzqZ7Y9fUOo3zIIL5nE+u18+yYPk7qU1jcZzP8l3AM+GyuwNzgaPqMM5bge+Hv78dWAEMqsFnOoTijbA1P35ixlnT4ydOjAXL1eT4cff+VyJw9w4zuxwYRdAaf4e7zzOzS8PXhxG0zp9FsJNsJrgKq8c4rwXeCvw2vGLo8BRHK4wZY83FidPdXzSzJ4DZQBdwm7uX7NJXiziBHwF3mtkcghPtVe6e6nDKZnYvcCowyMyagO8BA/NirPnxEzPOmh4/MWOsCxpiQkQk4/pjryEREamAEoGISMYpEYiIZJwSgYhIxikRiIjUsbgD1+Ut/3Ezmx8OWvjnOO9RIhABzGxI4YFmZt83sysT2NbEIs/fWbPRJ6We3QmcEWdBMzsM+Bbwfnc/EvhqnPcpEYgkyMx63Kvj7ifXIhZpTB4xcJ2ZHWpmT4TjZo03s3eGL30RuMXDwfY85uCKSgQiMZjZl8Pi9mwzuy98bo+w2P68mb1gZueGz19oZn8xs78BT0asqzX838zs5nC9I4C3pfk3SUMbDlzh7u8FrgR+Gz5/OHC4mT1nZpPNLFZJot/dWSySkKuBQ9x9q5ntHT73HWC0u38+fG6qmT0dvnYScIy7lxqC+L+AI4CjCYaTmA/ckUz40l+Y2Z4Ecy38JW+gul3D/3cGDiO4m/kAYLyZHeXu60utU4lAJFDsFvvc87OBe8zsESA3INi/Af+R146wG8H4NgBPlUkCEExacq+7dwIrzWx070KXjNkJWO/ux0W81gRMdvd2YKmZLSBIDM+XW6GIwFrgLQXP7QPkxvk5G7iFYCjr6WHdvwHnuftx4c9B7v5iuPymmNvVGC9SEQ+GJF9qZh+D7VWMuWk4HwFOC58fRFBVtKTcOpUIRAB3bwVWmdnpAGa2D0FPjQlmthNwoLuPAf6XYNrLPQkGj7sinE0MMzu+ws2OA843swHhDF+nVeevkf4kHLhuEnCEmTWZ2cXAp4GLzWwWMI8dM92NAtaa2XxgDPBNjzH8tgadEwmZ2bsJrvpzJYNfuPs9ZjaQ4KB6M0Ep4G53v87M3kQwscjJ4fOvuPs5ZnYhMNTdLy+ynVZ33zNMIDcRTEKTmxfhbnf/a0J/okgkJQIRkYxT1ZCISMYpEYiIZJwSgYhIxikRiIhknBKBiEjGKRGIiGScEoGISMb9f3IkVZo1XmYlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(biking_2016[\"tripduration\"])\n",
    "plt.xlabel(\"User id\")\n",
    "plt.ylabel(\"Trip duration in 2016\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "337384"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_subscribers_2013 = biking_2013.query(\"usertype == 'Subscriber'\")\n",
    "len(num_subscribers_2013)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1303486"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_subscribers_2016 = biking_2016.query(\"usertype == 'Subscriber'\")\n",
    "len(num_subscribers_2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent of subscribers in 2013 is 58.40%\n"
     ]
    }
   ],
   "source": [
    "percent_subscribers_2013 = len(num_subscribers_2013) / len(biking_2013)\n",
    "print(\"The percent of subscribers in 2013 is \" + \"{:.2%}\".format(percent_subscribers_2013))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent of subscribers in 2016 is 89.26%\n"
     ]
    }
   ],
   "source": [
    "percent_subscribers_2016 = len(num_subscribers_2016) / len(biking_2016)\n",
    "print(\"The percent of subscribers in 2016 is \" + \"{:.2%}\".format(percent_subscribers_2016))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent of change of subscribers from 2013 to 2016 is 286.35%\n"
     ]
    }
   ],
   "source": [
    "##Percent change in subscribers from 2013 to 2019\n",
    "percent_change = (len(num_subscribers_2016) - len(num_subscribers_2013) ) / len(num_subscribers_2013)\n",
    "print(\"The percent of change of subscribers from 2013 to 2016 is \" + \"{:.2%}\".format(percent_change))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293204</td>\n",
       "      <td>936</td>\n",
       "      <td>2013-06-20 17:34:19</td>\n",
       "      <td>2013-06-20 17:49:55</td>\n",
       "      <td>151</td>\n",
       "      <td>Cleveland Pl &amp; Spring St</td>\n",
       "      <td>40.722104</td>\n",
       "      <td>-73.997249</td>\n",
       "      <td>402.0</td>\n",
       "      <td>Broadway &amp; E 22 St</td>\n",
       "      <td>40.740343</td>\n",
       "      <td>-73.989551</td>\n",
       "      <td>15610</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>299950</td>\n",
       "      <td>349</td>\n",
       "      <td>2013-06-20 20:06:31</td>\n",
       "      <td>2013-06-20 20:12:20</td>\n",
       "      <td>435</td>\n",
       "      <td>W 21 St &amp; 6 Ave</td>\n",
       "      <td>40.741740</td>\n",
       "      <td>-73.994156</td>\n",
       "      <td>459.0</td>\n",
       "      <td>W 20 St &amp; 11 Ave</td>\n",
       "      <td>40.746745</td>\n",
       "      <td>-74.007756</td>\n",
       "      <td>14611</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>449621</td>\n",
       "      <td>1002</td>\n",
       "      <td>2013-06-26 11:14:51</td>\n",
       "      <td>2013-06-26 11:31:33</td>\n",
       "      <td>330</td>\n",
       "      <td>Reade St &amp; Broadway</td>\n",
       "      <td>40.714505</td>\n",
       "      <td>-74.005628</td>\n",
       "      <td>330.0</td>\n",
       "      <td>Reade St &amp; Broadway</td>\n",
       "      <td>40.714505</td>\n",
       "      <td>-74.005628</td>\n",
       "      <td>15378</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>467913</td>\n",
       "      <td>1077</td>\n",
       "      <td>2013-06-26 21:03:11</td>\n",
       "      <td>2013-06-26 21:21:08</td>\n",
       "      <td>327</td>\n",
       "      <td>Vesey Pl &amp; River Terrace</td>\n",
       "      <td>40.715338</td>\n",
       "      <td>-74.016584</td>\n",
       "      <td>327.0</td>\n",
       "      <td>Vesey Pl &amp; River Terrace</td>\n",
       "      <td>40.715338</td>\n",
       "      <td>-74.016584</td>\n",
       "      <td>17497</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>371314</td>\n",
       "      <td>317</td>\n",
       "      <td>2013-06-23 13:34:14</td>\n",
       "      <td>2013-06-23 13:39:31</td>\n",
       "      <td>489</td>\n",
       "      <td>10 Ave &amp; W 28 St</td>\n",
       "      <td>40.750664</td>\n",
       "      <td>-74.001768</td>\n",
       "      <td>441.0</td>\n",
       "      <td>E 52 St &amp; 2 Ave</td>\n",
       "      <td>40.756014</td>\n",
       "      <td>-73.967416</td>\n",
       "      <td>17213</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  tripduration            starttime             stoptime  \\\n",
       "0  293204           936  2013-06-20 17:34:19  2013-06-20 17:49:55   \n",
       "1  299950           349  2013-06-20 20:06:31  2013-06-20 20:12:20   \n",
       "2  449621          1002  2013-06-26 11:14:51  2013-06-26 11:31:33   \n",
       "3  467913          1077  2013-06-26 21:03:11  2013-06-26 21:21:08   \n",
       "4  371314           317  2013-06-23 13:34:14  2013-06-23 13:39:31   \n",
       "\n",
       "   start station id        start station name  start station latitude  \\\n",
       "0               151  Cleveland Pl & Spring St               40.722104   \n",
       "1               435           W 21 St & 6 Ave               40.741740   \n",
       "2               330       Reade St & Broadway               40.714505   \n",
       "3               327  Vesey Pl & River Terrace               40.715338   \n",
       "4               489          10 Ave & W 28 St               40.750664   \n",
       "\n",
       "   start station longitude  end station id          end station name  \\\n",
       "0               -73.997249           402.0        Broadway & E 22 St   \n",
       "1               -73.994156           459.0          W 20 St & 11 Ave   \n",
       "2               -74.005628           330.0       Reade St & Broadway   \n",
       "3               -74.016584           327.0  Vesey Pl & River Terrace   \n",
       "4               -74.001768           441.0           E 52 St & 2 Ave   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  birthyear  \\\n",
       "0             40.740343             -73.989551   15610  Subscriber     1981.0   \n",
       "1             40.746745             -74.007756   14611  Subscriber     1962.0   \n",
       "2             40.714505             -74.005628   15378  Subscriber     1957.0   \n",
       "3             40.715338             -74.016584   17497  Subscriber     1982.0   \n",
       "4             40.756014             -73.967416   17213  Subscriber     1944.0   \n",
       "\n",
       "   gender  \n",
       "0       2  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2013_subset = biking_2013.query('birthyear > 1920 & gender > 0 & tripduration < 60000')\n",
    "biking_2013_subset = biking_2013_subset.sample(n=1000, random_state=1)\n",
    "biking_2013_subset = biking_2013_subset.reset_index(level=0)\n",
    "biking_2013_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1048327</td>\n",
       "      <td>1103</td>\n",
       "      <td>6/22/2016 18:53:53</td>\n",
       "      <td>6/22/2016 19:12:16</td>\n",
       "      <td>513</td>\n",
       "      <td>W 56 St &amp; 10 Ave</td>\n",
       "      <td>40.768254</td>\n",
       "      <td>-73.988639</td>\n",
       "      <td>494</td>\n",
       "      <td>W 26 St &amp; 8 Ave</td>\n",
       "      <td>40.747348</td>\n",
       "      <td>-73.997236</td>\n",
       "      <td>21245</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59908</td>\n",
       "      <td>451</td>\n",
       "      <td>6/2/2016 07:54:58</td>\n",
       "      <td>6/2/2016 08:02:30</td>\n",
       "      <td>3122</td>\n",
       "      <td>48 Ave &amp; 5 St</td>\n",
       "      <td>40.744363</td>\n",
       "      <td>-73.955873</td>\n",
       "      <td>3126</td>\n",
       "      <td>44 Dr &amp; Jackson Ave</td>\n",
       "      <td>40.747182</td>\n",
       "      <td>-73.943264</td>\n",
       "      <td>17648</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97765</td>\n",
       "      <td>559</td>\n",
       "      <td>6/2/2016 18:49:19</td>\n",
       "      <td>6/2/2016 18:58:39</td>\n",
       "      <td>434</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>345</td>\n",
       "      <td>W 13 St &amp; 6 Ave</td>\n",
       "      <td>40.736494</td>\n",
       "      <td>-73.997044</td>\n",
       "      <td>20177</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>947825</td>\n",
       "      <td>316</td>\n",
       "      <td>6/20/2016 21:10:31</td>\n",
       "      <td>6/20/2016 21:15:48</td>\n",
       "      <td>388</td>\n",
       "      <td>W 26 St &amp; 10 Ave</td>\n",
       "      <td>40.749718</td>\n",
       "      <td>-74.002950</td>\n",
       "      <td>434</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>16844</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>805364</td>\n",
       "      <td>521</td>\n",
       "      <td>6/18/2016 00:44:11</td>\n",
       "      <td>6/18/2016 00:52:52</td>\n",
       "      <td>2003</td>\n",
       "      <td>1 Ave &amp; E 18 St</td>\n",
       "      <td>40.733812</td>\n",
       "      <td>-73.980544</td>\n",
       "      <td>454</td>\n",
       "      <td>E 51 St &amp; 1 Ave</td>\n",
       "      <td>40.754557</td>\n",
       "      <td>-73.965930</td>\n",
       "      <td>19623</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  tripduration           starttime            stoptime  \\\n",
       "0  1048327          1103  6/22/2016 18:53:53  6/22/2016 19:12:16   \n",
       "1    59908           451   6/2/2016 07:54:58   6/2/2016 08:02:30   \n",
       "2    97765           559   6/2/2016 18:49:19   6/2/2016 18:58:39   \n",
       "3   947825           316  6/20/2016 21:10:31  6/20/2016 21:15:48   \n",
       "4   805364           521  6/18/2016 00:44:11  6/18/2016 00:52:52   \n",
       "\n",
       "   start station id start station name  start station latitude  \\\n",
       "0               513   W 56 St & 10 Ave               40.768254   \n",
       "1              3122      48 Ave & 5 St               40.744363   \n",
       "2               434    9 Ave & W 18 St               40.743174   \n",
       "3               388   W 26 St & 10 Ave               40.749718   \n",
       "4              2003    1 Ave & E 18 St               40.733812   \n",
       "\n",
       "   start station longitude  end station id     end station name  \\\n",
       "0               -73.988639             494      W 26 St & 8 Ave   \n",
       "1               -73.955873            3126  44 Dr & Jackson Ave   \n",
       "2               -74.003664             345      W 13 St & 6 Ave   \n",
       "3               -74.002950             434      9 Ave & W 18 St   \n",
       "4               -73.980544             454      E 51 St & 1 Ave   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  birthyear  \\\n",
       "0             40.747348             -73.997236   21245  Subscriber     1983.0   \n",
       "1             40.747182             -73.943264   17648  Subscriber     1983.0   \n",
       "2             40.736494             -73.997044   20177  Subscriber     1988.0   \n",
       "3             40.743174             -74.003664   16844  Subscriber     1968.0   \n",
       "4             40.754557             -73.965930   19623  Subscriber     1989.0   \n",
       "\n",
       "   gender  \n",
       "0       2  \n",
       "1       2  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2016_subset = biking_2016.query('birthyear > 1920 & gender > 0 & tripduration < 60000')\n",
    "biking_2016_subset = biking_2016_subset.sample(n=1000, random_state=1)\n",
    "biking_2016_subset = biking_2016_subset.reset_index(level=0)\n",
    "biking_2016_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>fromstreet</th>\n",
       "      <th>street</th>\n",
       "      <th>boro</th>\n",
       "      <th>the_geom</th>\n",
       "      <th>tostreet</th>\n",
       "      <th>onoffst</th>\n",
       "      <th>allclasses</th>\n",
       "      <th>instdate</th>\n",
       "      <th>moddate</th>\n",
       "      <th>comments</th>\n",
       "      <th>bikedir</th>\n",
       "      <th>lanecount</th>\n",
       "      <th>ft_facilit</th>\n",
       "      <th>tf_facilit</th>\n",
       "      <th>SegmentID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7356</td>\n",
       "      <td>BEACH 193 ST</td>\n",
       "      <td>STATE RD</td>\n",
       "      <td>4</td>\n",
       "      <td>MULTILINESTRING ((-73.90225363399998 40.562189...</td>\n",
       "      <td>BEACH 169 ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>05/01/1997 12:00:00 AM +0000</td>\n",
       "      <td>05/01/1997 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>Curbside</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149081.0</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9269</td>\n",
       "      <td>32 AVE</td>\n",
       "      <td>101 ST</td>\n",
       "      <td>4</td>\n",
       "      <td>MULTILINESTRING ((-73.86824140499994 40.761723...</td>\n",
       "      <td>ASTORIA BLVD</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>10/14/2016 12:00:00 AM +0000</td>\n",
       "      <td>10/14/2016 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>177415.0</td>\n",
       "      <td>0.000619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12243</td>\n",
       "      <td>E 125 ST</td>\n",
       "      <td>2 AV</td>\n",
       "      <td>1</td>\n",
       "      <td>MULTILINESTRING ((-73.93411419099994 40.802046...</td>\n",
       "      <td>E 104 ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>I</td>\n",
       "      <td>10/26/2012 12:00:00 AM +0000</td>\n",
       "      <td>10/26/2012 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Protected Path</td>\n",
       "      <td>110979.0</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9482</td>\n",
       "      <td>SUNNYSIDE AV</td>\n",
       "      <td>VERMONT ST</td>\n",
       "      <td>3</td>\n",
       "      <td>MULTILINESTRING ((-73.89597729299999 40.680112...</td>\n",
       "      <td>COZINE AV</td>\n",
       "      <td>ON</td>\n",
       "      <td>III</td>\n",
       "      <td>10/01/2009 12:00:00 AM +0000</td>\n",
       "      <td>10/01/2009 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sharrows</td>\n",
       "      <td>47593.0</td>\n",
       "      <td>0.001175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13586</td>\n",
       "      <td>GARRISON AVE</td>\n",
       "      <td>HUNTS PT AV</td>\n",
       "      <td>2</td>\n",
       "      <td>MULTILINESTRING ((-73.88838357299994 40.817305...</td>\n",
       "      <td>SPOFFORD AVE</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>11/01/2011 12:00:00 AM +0000</td>\n",
       "      <td>11/01/2011 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Standard</td>\n",
       "      <td>9002144.0</td>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    fromstreet       street  boro  \\\n",
       "0   7356  BEACH 193 ST     STATE RD     4   \n",
       "1   9269        32 AVE       101 ST     4   \n",
       "2  12243      E 125 ST         2 AV     1   \n",
       "3   9482  SUNNYSIDE AV   VERMONT ST     3   \n",
       "4  13586  GARRISON AVE  HUNTS PT AV     2   \n",
       "\n",
       "                                            the_geom      tostreet onoffst  \\\n",
       "0  MULTILINESTRING ((-73.90225363399998 40.562189...  BEACH 169 ST      ON   \n",
       "1  MULTILINESTRING ((-73.86824140499994 40.761723...  ASTORIA BLVD      ON   \n",
       "2  MULTILINESTRING ((-73.93411419099994 40.802046...      E 104 ST      ON   \n",
       "3  MULTILINESTRING ((-73.89597729299999 40.680112...     COZINE AV      ON   \n",
       "4  MULTILINESTRING ((-73.88838357299994 40.817305...  SPOFFORD AVE      ON   \n",
       "\n",
       "  allclasses                      instdate                       moddate  \\\n",
       "0         II  05/01/1997 12:00:00 AM +0000  05/01/1997 12:00:00 AM +0000   \n",
       "1         II  10/14/2016 12:00:00 AM +0000  10/14/2016 12:00:00 AM +0000   \n",
       "2          I  10/26/2012 12:00:00 AM +0000  10/26/2012 12:00:00 AM +0000   \n",
       "3        III  10/01/2009 12:00:00 AM +0000  10/01/2009 12:00:00 AM +0000   \n",
       "4         II  11/01/2011 12:00:00 AM +0000  11/01/2011 12:00:00 AM +0000   \n",
       "\n",
       "  comments bikedir  lanecount ft_facilit      tf_facilit  SegmentID  \\\n",
       "0      NaN       R          1   Curbside             NaN   149081.0   \n",
       "1      NaN       L          1        NaN        Standard   177415.0   \n",
       "2      NaN       L          1        NaN  Protected Path   110979.0   \n",
       "3      NaN       L          1        NaN        Sharrows    47593.0   \n",
       "4      NaN       2          2   Standard        Standard  9002144.0   \n",
       "\n",
       "   Shape_Leng  \n",
       "0    0.000068  \n",
       "1    0.000619  \n",
       "2    0.000060  \n",
       "3    0.001175  \n",
       "4    0.000059  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes_subset = routes.sample(n=1000, random_state=1)\n",
    "routes_subset = routes_subset.reset_index(level=0)\n",
    "routes_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"tripduration\", y=\"birthyear\", hue=\"gender\", data=biking_2013_subset)\n",
    "plt.xlabel(\"trip duration\")\n",
    "plt.ylabel(\"birth year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"birthyear\", y=\"tripduration\", hue=\"gender\", data=biking_2013_subset)\n",
    "plt.xlabel(\"birth year\")\n",
    "plt.ylabel(\"trip duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"tripduration\", y=\"birthyear\", hue=\"gender\", data=biking_2016_subset)\n",
    "plt.xlabel(\"trip duration\")\n",
    "plt.ylabel(\"birth year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 431.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"birthyear\", y=\"tripduration\", hue=\"gender\", data=biking_2016_subset)\n",
    "plt.xlabel(\"birth year\")\n",
    "plt.ylabel(\"trip duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-23-36810c0d1a17>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  biking_2016_subset[\"start station latitude\"][x] = round(biking_2016_subset[\"start station latitude\"][x],2)\n",
      "<ipython-input-23-36810c0d1a17>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  biking_2016_subset[\"start station longitude\"][x] = round(biking_2016_subset[\"start station longitude\"][x],2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1048327</td>\n",
       "      <td>1103</td>\n",
       "      <td>6/22/2016 18:53:53</td>\n",
       "      <td>6/22/2016 19:12:16</td>\n",
       "      <td>513</td>\n",
       "      <td>W 56 St &amp; 10 Ave</td>\n",
       "      <td>40.77</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>494</td>\n",
       "      <td>W 26 St &amp; 8 Ave</td>\n",
       "      <td>40.747348</td>\n",
       "      <td>-73.997236</td>\n",
       "      <td>21245</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59908</td>\n",
       "      <td>451</td>\n",
       "      <td>6/2/2016 07:54:58</td>\n",
       "      <td>6/2/2016 08:02:30</td>\n",
       "      <td>3122</td>\n",
       "      <td>48 Ave &amp; 5 St</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.96</td>\n",
       "      <td>3126</td>\n",
       "      <td>44 Dr &amp; Jackson Ave</td>\n",
       "      <td>40.747182</td>\n",
       "      <td>-73.943264</td>\n",
       "      <td>17648</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>97765</td>\n",
       "      <td>559</td>\n",
       "      <td>6/2/2016 18:49:19</td>\n",
       "      <td>6/2/2016 18:58:39</td>\n",
       "      <td>434</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>345</td>\n",
       "      <td>W 13 St &amp; 6 Ave</td>\n",
       "      <td>40.736494</td>\n",
       "      <td>-73.997044</td>\n",
       "      <td>20177</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>947825</td>\n",
       "      <td>316</td>\n",
       "      <td>6/20/2016 21:10:31</td>\n",
       "      <td>6/20/2016 21:15:48</td>\n",
       "      <td>388</td>\n",
       "      <td>W 26 St &amp; 10 Ave</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>434</td>\n",
       "      <td>9 Ave &amp; W 18 St</td>\n",
       "      <td>40.743174</td>\n",
       "      <td>-74.003664</td>\n",
       "      <td>16844</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>805364</td>\n",
       "      <td>521</td>\n",
       "      <td>6/18/2016 00:44:11</td>\n",
       "      <td>6/18/2016 00:52:52</td>\n",
       "      <td>2003</td>\n",
       "      <td>1 Ave &amp; E 18 St</td>\n",
       "      <td>40.73</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>454</td>\n",
       "      <td>E 51 St &amp; 1 Ave</td>\n",
       "      <td>40.754557</td>\n",
       "      <td>-73.965930</td>\n",
       "      <td>19623</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index  tripduration           starttime            stoptime  \\\n",
       "0  1048327          1103  6/22/2016 18:53:53  6/22/2016 19:12:16   \n",
       "1    59908           451   6/2/2016 07:54:58   6/2/2016 08:02:30   \n",
       "2    97765           559   6/2/2016 18:49:19   6/2/2016 18:58:39   \n",
       "3   947825           316  6/20/2016 21:10:31  6/20/2016 21:15:48   \n",
       "4   805364           521  6/18/2016 00:44:11  6/18/2016 00:52:52   \n",
       "\n",
       "   start station id start station name  start station latitude  \\\n",
       "0               513   W 56 St & 10 Ave                   40.77   \n",
       "1              3122      48 Ave & 5 St                   40.74   \n",
       "2               434    9 Ave & W 18 St                   40.74   \n",
       "3               388   W 26 St & 10 Ave                   40.75   \n",
       "4              2003    1 Ave & E 18 St                   40.73   \n",
       "\n",
       "   start station longitude  end station id     end station name  \\\n",
       "0                   -73.99             494      W 26 St & 8 Ave   \n",
       "1                   -73.96            3126  44 Dr & Jackson Ave   \n",
       "2                   -74.00             345      W 13 St & 6 Ave   \n",
       "3                   -74.00             434      9 Ave & W 18 St   \n",
       "4                   -73.98             454      E 51 St & 1 Ave   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  birthyear  \\\n",
       "0             40.747348             -73.997236   21245  Subscriber     1983.0   \n",
       "1             40.747182             -73.943264   17648  Subscriber     1983.0   \n",
       "2             40.736494             -73.997044   20177  Subscriber     1988.0   \n",
       "3             40.743174             -74.003664   16844  Subscriber     1968.0   \n",
       "4             40.754557             -73.965930   19623  Subscriber     1989.0   \n",
       "\n",
       "   gender  \n",
       "0       2  \n",
       "1       2  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Edit the longitude and latitude to two decimal places\n",
    "for x in range(len(biking_2016_subset)):\n",
    "    biking_2016_subset[\"start station latitude\"][x] = round(biking_2016_subset[\"start station latitude\"][x],2)\n",
    "    biking_2016_subset[\"start station longitude\"][x] = round(biking_2016_subset[\"start station longitude\"][x],2)\n",
    "biking_2016_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-a74393b0deba>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  biking_2013_subset[\"start station latitude\"][x] = round(biking_2013_subset[\"start station latitude\"][x],2)\n",
      "<ipython-input-24-a74393b0deba>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  biking_2013_subset[\"start station longitude\"][x] = round(biking_2013_subset[\"start station longitude\"][x],2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>293204</td>\n",
       "      <td>936</td>\n",
       "      <td>2013-06-20 17:34:19</td>\n",
       "      <td>2013-06-20 17:49:55</td>\n",
       "      <td>151</td>\n",
       "      <td>Cleveland Pl &amp; Spring St</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>402.0</td>\n",
       "      <td>Broadway &amp; E 22 St</td>\n",
       "      <td>40.740343</td>\n",
       "      <td>-73.989551</td>\n",
       "      <td>15610</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>299950</td>\n",
       "      <td>349</td>\n",
       "      <td>2013-06-20 20:06:31</td>\n",
       "      <td>2013-06-20 20:12:20</td>\n",
       "      <td>435</td>\n",
       "      <td>W 21 St &amp; 6 Ave</td>\n",
       "      <td>40.74</td>\n",
       "      <td>-73.99</td>\n",
       "      <td>459.0</td>\n",
       "      <td>W 20 St &amp; 11 Ave</td>\n",
       "      <td>40.746745</td>\n",
       "      <td>-74.007756</td>\n",
       "      <td>14611</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>449621</td>\n",
       "      <td>1002</td>\n",
       "      <td>2013-06-26 11:14:51</td>\n",
       "      <td>2013-06-26 11:31:33</td>\n",
       "      <td>330</td>\n",
       "      <td>Reade St &amp; Broadway</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-74.01</td>\n",
       "      <td>330.0</td>\n",
       "      <td>Reade St &amp; Broadway</td>\n",
       "      <td>40.714505</td>\n",
       "      <td>-74.005628</td>\n",
       "      <td>15378</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>467913</td>\n",
       "      <td>1077</td>\n",
       "      <td>2013-06-26 21:03:11</td>\n",
       "      <td>2013-06-26 21:21:08</td>\n",
       "      <td>327</td>\n",
       "      <td>Vesey Pl &amp; River Terrace</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-74.02</td>\n",
       "      <td>327.0</td>\n",
       "      <td>Vesey Pl &amp; River Terrace</td>\n",
       "      <td>40.715338</td>\n",
       "      <td>-74.016584</td>\n",
       "      <td>17497</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>371314</td>\n",
       "      <td>317</td>\n",
       "      <td>2013-06-23 13:34:14</td>\n",
       "      <td>2013-06-23 13:39:31</td>\n",
       "      <td>489</td>\n",
       "      <td>10 Ave &amp; W 28 St</td>\n",
       "      <td>40.75</td>\n",
       "      <td>-74.00</td>\n",
       "      <td>441.0</td>\n",
       "      <td>E 52 St &amp; 2 Ave</td>\n",
       "      <td>40.756014</td>\n",
       "      <td>-73.967416</td>\n",
       "      <td>17213</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  tripduration            starttime             stoptime  \\\n",
       "0  293204           936  2013-06-20 17:34:19  2013-06-20 17:49:55   \n",
       "1  299950           349  2013-06-20 20:06:31  2013-06-20 20:12:20   \n",
       "2  449621          1002  2013-06-26 11:14:51  2013-06-26 11:31:33   \n",
       "3  467913          1077  2013-06-26 21:03:11  2013-06-26 21:21:08   \n",
       "4  371314           317  2013-06-23 13:34:14  2013-06-23 13:39:31   \n",
       "\n",
       "   start station id        start station name  start station latitude  \\\n",
       "0               151  Cleveland Pl & Spring St                   40.72   \n",
       "1               435           W 21 St & 6 Ave                   40.74   \n",
       "2               330       Reade St & Broadway                   40.71   \n",
       "3               327  Vesey Pl & River Terrace                   40.72   \n",
       "4               489          10 Ave & W 28 St                   40.75   \n",
       "\n",
       "   start station longitude  end station id          end station name  \\\n",
       "0                   -74.00           402.0        Broadway & E 22 St   \n",
       "1                   -73.99           459.0          W 20 St & 11 Ave   \n",
       "2                   -74.01           330.0       Reade St & Broadway   \n",
       "3                   -74.02           327.0  Vesey Pl & River Terrace   \n",
       "4                   -74.00           441.0           E 52 St & 2 Ave   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  birthyear  \\\n",
       "0             40.740343             -73.989551   15610  Subscriber     1981.0   \n",
       "1             40.746745             -74.007756   14611  Subscriber     1962.0   \n",
       "2             40.714505             -74.005628   15378  Subscriber     1957.0   \n",
       "3             40.715338             -74.016584   17497  Subscriber     1982.0   \n",
       "4             40.756014             -73.967416   17213  Subscriber     1944.0   \n",
       "\n",
       "   gender  \n",
       "0       2  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       2  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for x in range(len(biking_2013_subset)):\n",
    "    biking_2013_subset[\"start station latitude\"][x] = round(biking_2013_subset[\"start station latitude\"][x],2)\n",
    "    biking_2013_subset[\"start station longitude\"][x] = round(biking_2013_subset[\"start station longitude\"][x],2)\n",
    "biking_2013_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MULTILINESTRING ((-73.90225363399998 40.56218937300008, -73.90231957199995 40.562170853000055))\n"
     ]
    }
   ],
   "source": [
    "print(routes_subset[\"the_geom\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "routes_subset[\"latitude\"] = pd.DataFrame(np.zeros(len(routes_subset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "routes_subset[\"longitude\"] = pd.DataFrame(np.zeros(len(routes_subset)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-28-cbb1a64bbb9c>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  routes_subset[\"longitude\"][x] = float(routes_subset[\"the_geom\"][x][18:24])\n",
      "<ipython-input-28-cbb1a64bbb9c>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  routes_subset[\"latitude\"][x] = float(routes_subset[\"the_geom\"][x][37:42])\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>fromstreet</th>\n",
       "      <th>street</th>\n",
       "      <th>boro</th>\n",
       "      <th>the_geom</th>\n",
       "      <th>tostreet</th>\n",
       "      <th>onoffst</th>\n",
       "      <th>allclasses</th>\n",
       "      <th>instdate</th>\n",
       "      <th>moddate</th>\n",
       "      <th>comments</th>\n",
       "      <th>bikedir</th>\n",
       "      <th>lanecount</th>\n",
       "      <th>ft_facilit</th>\n",
       "      <th>tf_facilit</th>\n",
       "      <th>SegmentID</th>\n",
       "      <th>Shape_Leng</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7356</td>\n",
       "      <td>BEACH 193 ST</td>\n",
       "      <td>STATE RD</td>\n",
       "      <td>4</td>\n",
       "      <td>MULTILINESTRING ((-73.90225363399998 40.562189...</td>\n",
       "      <td>BEACH 169 ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>05/01/1997 12:00:00 AM +0000</td>\n",
       "      <td>05/01/1997 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>1</td>\n",
       "      <td>Curbside</td>\n",
       "      <td>NaN</td>\n",
       "      <td>149081.0</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>40.56</td>\n",
       "      <td>-73.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9269</td>\n",
       "      <td>32 AVE</td>\n",
       "      <td>101 ST</td>\n",
       "      <td>4</td>\n",
       "      <td>MULTILINESTRING ((-73.86824140499994 40.761723...</td>\n",
       "      <td>ASTORIA BLVD</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>10/14/2016 12:00:00 AM +0000</td>\n",
       "      <td>10/14/2016 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Standard</td>\n",
       "      <td>177415.0</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12243</td>\n",
       "      <td>E 125 ST</td>\n",
       "      <td>2 AV</td>\n",
       "      <td>1</td>\n",
       "      <td>MULTILINESTRING ((-73.93411419099994 40.802046...</td>\n",
       "      <td>E 104 ST</td>\n",
       "      <td>ON</td>\n",
       "      <td>I</td>\n",
       "      <td>10/26/2012 12:00:00 AM +0000</td>\n",
       "      <td>10/26/2012 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Protected Path</td>\n",
       "      <td>110979.0</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>40.80</td>\n",
       "      <td>-73.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9482</td>\n",
       "      <td>SUNNYSIDE AV</td>\n",
       "      <td>VERMONT ST</td>\n",
       "      <td>3</td>\n",
       "      <td>MULTILINESTRING ((-73.89597729299999 40.680112...</td>\n",
       "      <td>COZINE AV</td>\n",
       "      <td>ON</td>\n",
       "      <td>III</td>\n",
       "      <td>10/01/2009 12:00:00 AM +0000</td>\n",
       "      <td>10/01/2009 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sharrows</td>\n",
       "      <td>47593.0</td>\n",
       "      <td>0.001175</td>\n",
       "      <td>40.68</td>\n",
       "      <td>-73.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13586</td>\n",
       "      <td>GARRISON AVE</td>\n",
       "      <td>HUNTS PT AV</td>\n",
       "      <td>2</td>\n",
       "      <td>MULTILINESTRING ((-73.88838357299994 40.817305...</td>\n",
       "      <td>SPOFFORD AVE</td>\n",
       "      <td>ON</td>\n",
       "      <td>II</td>\n",
       "      <td>11/01/2011 12:00:00 AM +0000</td>\n",
       "      <td>11/01/2011 12:00:00 AM +0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Standard</td>\n",
       "      <td>Standard</td>\n",
       "      <td>9002144.0</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>40.81</td>\n",
       "      <td>-73.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index    fromstreet       street  boro  \\\n",
       "0   7356  BEACH 193 ST     STATE RD     4   \n",
       "1   9269        32 AVE       101 ST     4   \n",
       "2  12243      E 125 ST         2 AV     1   \n",
       "3   9482  SUNNYSIDE AV   VERMONT ST     3   \n",
       "4  13586  GARRISON AVE  HUNTS PT AV     2   \n",
       "\n",
       "                                            the_geom      tostreet onoffst  \\\n",
       "0  MULTILINESTRING ((-73.90225363399998 40.562189...  BEACH 169 ST      ON   \n",
       "1  MULTILINESTRING ((-73.86824140499994 40.761723...  ASTORIA BLVD      ON   \n",
       "2  MULTILINESTRING ((-73.93411419099994 40.802046...      E 104 ST      ON   \n",
       "3  MULTILINESTRING ((-73.89597729299999 40.680112...     COZINE AV      ON   \n",
       "4  MULTILINESTRING ((-73.88838357299994 40.817305...  SPOFFORD AVE      ON   \n",
       "\n",
       "  allclasses                      instdate                       moddate  \\\n",
       "0         II  05/01/1997 12:00:00 AM +0000  05/01/1997 12:00:00 AM +0000   \n",
       "1         II  10/14/2016 12:00:00 AM +0000  10/14/2016 12:00:00 AM +0000   \n",
       "2          I  10/26/2012 12:00:00 AM +0000  10/26/2012 12:00:00 AM +0000   \n",
       "3        III  10/01/2009 12:00:00 AM +0000  10/01/2009 12:00:00 AM +0000   \n",
       "4         II  11/01/2011 12:00:00 AM +0000  11/01/2011 12:00:00 AM +0000   \n",
       "\n",
       "  comments bikedir  lanecount ft_facilit      tf_facilit  SegmentID  \\\n",
       "0      NaN       R          1   Curbside             NaN   149081.0   \n",
       "1      NaN       L          1        NaN        Standard   177415.0   \n",
       "2      NaN       L          1        NaN  Protected Path   110979.0   \n",
       "3      NaN       L          1        NaN        Sharrows    47593.0   \n",
       "4      NaN       2          2   Standard        Standard  9002144.0   \n",
       "\n",
       "   Shape_Leng  latitude  longitude  \n",
       "0    0.000068     40.56     -73.90  \n",
       "1    0.000619     40.76     -73.86  \n",
       "2    0.000060     40.80     -73.93  \n",
       "3    0.001175     40.68     -73.89  \n",
       "4    0.000059     40.81     -73.88  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for x in range(len(routes_subset)):\n",
    "    routes_subset[\"longitude\"][x] = float(routes_subset[\"the_geom\"][x][18:24])\n",
    "    routes_subset[\"latitude\"][x] = float(routes_subset[\"the_geom\"][x][37:42])\n",
    "routes_subset.head()                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>40.56</td>\n",
       "      <td>-73.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>40.80</td>\n",
       "      <td>-73.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>40.68</td>\n",
       "      <td>-73.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>40.81</td>\n",
       "      <td>-73.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boro  latitude  longitude\n",
       "0     4     40.56     -73.90\n",
       "1     4     40.76     -73.86\n",
       "2     1     40.80     -73.93\n",
       "3     3     40.68     -73.89\n",
       "4     2     40.81     -73.88"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes_subset = routes_subset[[\"boro\", \"latitude\", \"longitude\"]]\n",
    "routes_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>40.56</td>\n",
       "      <td>-73.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>40.76</td>\n",
       "      <td>-73.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>40.80</td>\n",
       "      <td>-73.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>40.68</td>\n",
       "      <td>-73.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>40.81</td>\n",
       "      <td>-73.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boro  start station latitude  start station longitude\n",
       "0     4                   40.56                   -73.90\n",
       "1     4                   40.76                   -73.86\n",
       "2     1                   40.80                   -73.93\n",
       "3     3                   40.68                   -73.89\n",
       "4     2                   40.81                   -73.88"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes_subset.rename(columns={'latitude':'start station latitude', 'longitude':'start station longitude'}, inplace=True)\n",
    "routes_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "biking_2013_routes = pd.merge(routes_subset, biking_2013_subset, how=\"right\", on=[\"start station latitude\",\"start station longitude\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>43818</td>\n",
       "      <td>6865</td>\n",
       "      <td>2013-06-04 18:46:26</td>\n",
       "      <td>2013-06-04 20:40:51</td>\n",
       "      <td>356</td>\n",
       "      <td>Bialystoker Pl &amp; Delancey St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18408</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>43818</td>\n",
       "      <td>6865</td>\n",
       "      <td>2013-06-04 18:46:26</td>\n",
       "      <td>2013-06-04 20:40:51</td>\n",
       "      <td>356</td>\n",
       "      <td>Bialystoker Pl &amp; Delancey St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18408</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>43818</td>\n",
       "      <td>6865</td>\n",
       "      <td>2013-06-04 18:46:26</td>\n",
       "      <td>2013-06-04 20:40:51</td>\n",
       "      <td>356</td>\n",
       "      <td>Bialystoker Pl &amp; Delancey St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18408</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>43818</td>\n",
       "      <td>6865</td>\n",
       "      <td>2013-06-04 18:46:26</td>\n",
       "      <td>2013-06-04 20:40:51</td>\n",
       "      <td>356</td>\n",
       "      <td>Bialystoker Pl &amp; Delancey St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18408</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>43818</td>\n",
       "      <td>6865</td>\n",
       "      <td>2013-06-04 18:46:26</td>\n",
       "      <td>2013-06-04 20:40:51</td>\n",
       "      <td>356</td>\n",
       "      <td>Bialystoker Pl &amp; Delancey St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18408</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boro  start station latitude  start station longitude  index  tripduration  \\\n",
       "0   1.0                   40.72                   -73.98  43818          6865   \n",
       "1   1.0                   40.72                   -73.98  43818          6865   \n",
       "2   1.0                   40.72                   -73.98  43818          6865   \n",
       "3   1.0                   40.72                   -73.98  43818          6865   \n",
       "4   1.0                   40.72                   -73.98  43818          6865   \n",
       "\n",
       "             starttime             stoptime  start station id  \\\n",
       "0  2013-06-04 18:46:26  2013-06-04 20:40:51               356   \n",
       "1  2013-06-04 18:46:26  2013-06-04 20:40:51               356   \n",
       "2  2013-06-04 18:46:26  2013-06-04 20:40:51               356   \n",
       "3  2013-06-04 18:46:26  2013-06-04 20:40:51               356   \n",
       "4  2013-06-04 18:46:26  2013-06-04 20:40:51               356   \n",
       "\n",
       "             start station name  end station id end station name  \\\n",
       "0  Bialystoker Pl & Delancey St             NaN              NaN   \n",
       "1  Bialystoker Pl & Delancey St             NaN              NaN   \n",
       "2  Bialystoker Pl & Delancey St             NaN              NaN   \n",
       "3  Bialystoker Pl & Delancey St             NaN              NaN   \n",
       "4  Bialystoker Pl & Delancey St             NaN              NaN   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  birthyear  \\\n",
       "0                   NaN                    NaN   18408  Subscriber     1980.0   \n",
       "1                   NaN                    NaN   18408  Subscriber     1980.0   \n",
       "2                   NaN                    NaN   18408  Subscriber     1980.0   \n",
       "3                   NaN                    NaN   18408  Subscriber     1980.0   \n",
       "4                   NaN                    NaN   18408  Subscriber     1980.0   \n",
       "\n",
       "   gender  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2013_routes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>boro</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>index</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>43818</td>\n",
       "      <td>6865</td>\n",
       "      <td>2013-06-04 18:46:26</td>\n",
       "      <td>2013-06-04 20:40:51</td>\n",
       "      <td>356</td>\n",
       "      <td>Bialystoker Pl &amp; Delancey St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18408</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>416821</td>\n",
       "      <td>663</td>\n",
       "      <td>2013-06-25 08:23:45</td>\n",
       "      <td>2013-06-25 08:34:48</td>\n",
       "      <td>393</td>\n",
       "      <td>E 5 St &amp; Avenue C</td>\n",
       "      <td>293.0</td>\n",
       "      <td>Lafayette St &amp; E 8 St</td>\n",
       "      <td>40.730207</td>\n",
       "      <td>-73.991026</td>\n",
       "      <td>16733</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>305402</td>\n",
       "      <td>1452</td>\n",
       "      <td>2013-06-21 07:00:30</td>\n",
       "      <td>2013-06-21 07:24:42</td>\n",
       "      <td>349</td>\n",
       "      <td>Rivington St &amp; Ridge St</td>\n",
       "      <td>522.0</td>\n",
       "      <td>E 51 St &amp; Lexington Ave</td>\n",
       "      <td>40.757148</td>\n",
       "      <td>-73.972078</td>\n",
       "      <td>19616</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>525696</td>\n",
       "      <td>719</td>\n",
       "      <td>2013-06-29 09:27:43</td>\n",
       "      <td>2013-06-29 09:39:42</td>\n",
       "      <td>349</td>\n",
       "      <td>Rivington St &amp; Ridge St</td>\n",
       "      <td>369.0</td>\n",
       "      <td>Washington Pl &amp; 6 Ave</td>\n",
       "      <td>40.732241</td>\n",
       "      <td>-74.000264</td>\n",
       "      <td>17302</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>290404</td>\n",
       "      <td>561</td>\n",
       "      <td>2013-06-20 16:08:52</td>\n",
       "      <td>2013-06-20 16:18:13</td>\n",
       "      <td>411</td>\n",
       "      <td>E 6 St &amp; Avenue D</td>\n",
       "      <td>502.0</td>\n",
       "      <td>Henry St &amp; Grand St</td>\n",
       "      <td>40.714215</td>\n",
       "      <td>-73.981346</td>\n",
       "      <td>17235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    boro  start station latitude  start station longitude   index  \\\n",
       "0    1.0                   40.72                   -73.98   43818   \n",
       "8    1.0                   40.72                   -73.98  416821   \n",
       "16   1.0                   40.72                   -73.98  305402   \n",
       "24   1.0                   40.72                   -73.98  525696   \n",
       "32   1.0                   40.72                   -73.98  290404   \n",
       "\n",
       "    tripduration            starttime             stoptime  start station id  \\\n",
       "0           6865  2013-06-04 18:46:26  2013-06-04 20:40:51               356   \n",
       "8            663  2013-06-25 08:23:45  2013-06-25 08:34:48               393   \n",
       "16          1452  2013-06-21 07:00:30  2013-06-21 07:24:42               349   \n",
       "24           719  2013-06-29 09:27:43  2013-06-29 09:39:42               349   \n",
       "32           561  2013-06-20 16:08:52  2013-06-20 16:18:13               411   \n",
       "\n",
       "              start station name  end station id         end station name  \\\n",
       "0   Bialystoker Pl & Delancey St             NaN                      NaN   \n",
       "8              E 5 St & Avenue C           293.0    Lafayette St & E 8 St   \n",
       "16       Rivington St & Ridge St           522.0  E 51 St & Lexington Ave   \n",
       "24       Rivington St & Ridge St           369.0    Washington Pl & 6 Ave   \n",
       "32             E 6 St & Avenue D           502.0      Henry St & Grand St   \n",
       "\n",
       "    end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0                    NaN                    NaN   18408  Subscriber   \n",
       "8              40.730207             -73.991026   16733  Subscriber   \n",
       "16             40.757148             -73.972078   19616  Subscriber   \n",
       "24             40.732241             -74.000264   17302  Subscriber   \n",
       "32             40.714215             -73.981346   17235  Subscriber   \n",
       "\n",
       "    birthyear  gender  \n",
       "0      1980.0       1  \n",
       "8      1957.0       2  \n",
       "16     1987.0       2  \n",
       "24     1982.0       1  \n",
       "32     1964.0       2  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2013_routes = biking_2013_routes.drop_duplicates()\n",
    "biking_2013_routes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1002"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2013_routes.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "biking_2013_routes = biking_2013_routes.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "797"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2013_routes.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Zone, start station latitude, start station longitude, index, tripduration, starttime, stoptime, start station id, start station name, end station id, end station name, end station latitude, end station longitude, bikeid, usertype, birthyear, gender]\n",
       "Index: []"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2013_routes.rename(columns={'boro':'Zone'}, inplace=True)\n",
    "biking_2013_routes.head(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "biking_2016_routes = pd.merge(routes_subset, biking_2016_subset, how=\"right\", on=[\"start station latitude\",\"start station longitude\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>boro</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>900435</td>\n",
       "      <td>160</td>\n",
       "      <td>6/20/2016 06:22:53</td>\n",
       "      <td>6/20/2016 06:25:33</td>\n",
       "      <td>3070</td>\n",
       "      <td>McKibbin St &amp; Manhattan Ave</td>\n",
       "      <td>3071</td>\n",
       "      <td>Boerum St &amp; Broadway</td>\n",
       "      <td>40.705381</td>\n",
       "      <td>-73.949765</td>\n",
       "      <td>17226</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>900435</td>\n",
       "      <td>160</td>\n",
       "      <td>6/20/2016 06:22:53</td>\n",
       "      <td>6/20/2016 06:25:33</td>\n",
       "      <td>3070</td>\n",
       "      <td>McKibbin St &amp; Manhattan Ave</td>\n",
       "      <td>3071</td>\n",
       "      <td>Boerum St &amp; Broadway</td>\n",
       "      <td>40.705381</td>\n",
       "      <td>-73.949765</td>\n",
       "      <td>17226</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>750686</td>\n",
       "      <td>1474</td>\n",
       "      <td>6/17/2016 00:03:24</td>\n",
       "      <td>6/17/2016 00:27:59</td>\n",
       "      <td>3074</td>\n",
       "      <td>Montrose Ave &amp; Bushwick Ave</td>\n",
       "      <td>3129</td>\n",
       "      <td>Queens Plaza North &amp; Crescent St</td>\n",
       "      <td>40.751102</td>\n",
       "      <td>-73.940737</td>\n",
       "      <td>25447</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>750686</td>\n",
       "      <td>1474</td>\n",
       "      <td>6/17/2016 00:03:24</td>\n",
       "      <td>6/17/2016 00:27:59</td>\n",
       "      <td>3074</td>\n",
       "      <td>Montrose Ave &amp; Bushwick Ave</td>\n",
       "      <td>3129</td>\n",
       "      <td>Queens Plaza North &amp; Crescent St</td>\n",
       "      <td>40.751102</td>\n",
       "      <td>-73.940737</td>\n",
       "      <td>25447</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>747914</td>\n",
       "      <td>634</td>\n",
       "      <td>6/16/2016 21:44:55</td>\n",
       "      <td>6/16/2016 21:55:30</td>\n",
       "      <td>3081</td>\n",
       "      <td>Graham Ave &amp; Grand St</td>\n",
       "      <td>3068</td>\n",
       "      <td>Humboldt St &amp; Varet St</td>\n",
       "      <td>40.703172</td>\n",
       "      <td>-73.940636</td>\n",
       "      <td>19282</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   boro  start station latitude  start station longitude   index  \\\n",
       "0   3.0                   40.71                   -73.94  900435   \n",
       "1   3.0                   40.71                   -73.94  900435   \n",
       "2   3.0                   40.71                   -73.94  750686   \n",
       "3   3.0                   40.71                   -73.94  750686   \n",
       "4   3.0                   40.71                   -73.94  747914   \n",
       "\n",
       "   tripduration           starttime            stoptime  start station id  \\\n",
       "0           160  6/20/2016 06:22:53  6/20/2016 06:25:33              3070   \n",
       "1           160  6/20/2016 06:22:53  6/20/2016 06:25:33              3070   \n",
       "2          1474  6/17/2016 00:03:24  6/17/2016 00:27:59              3074   \n",
       "3          1474  6/17/2016 00:03:24  6/17/2016 00:27:59              3074   \n",
       "4           634  6/16/2016 21:44:55  6/16/2016 21:55:30              3081   \n",
       "\n",
       "            start station name  end station id  \\\n",
       "0  McKibbin St & Manhattan Ave            3071   \n",
       "1  McKibbin St & Manhattan Ave            3071   \n",
       "2  Montrose Ave & Bushwick Ave            3129   \n",
       "3  Montrose Ave & Bushwick Ave            3129   \n",
       "4        Graham Ave & Grand St            3068   \n",
       "\n",
       "                   end station name  end station latitude  \\\n",
       "0              Boerum St & Broadway             40.705381   \n",
       "1              Boerum St & Broadway             40.705381   \n",
       "2  Queens Plaza North & Crescent St             40.751102   \n",
       "3  Queens Plaza North & Crescent St             40.751102   \n",
       "4            Humboldt St & Varet St             40.703172   \n",
       "\n",
       "   end station longitude  bikeid    usertype  birthyear  gender  \n",
       "0             -73.949765   17226  Subscriber     1952.0       1  \n",
       "1             -73.949765   17226  Subscriber     1952.0       1  \n",
       "2             -73.940737   25447  Subscriber     1991.0       1  \n",
       "3             -73.940737   25447  Subscriber     1991.0       1  \n",
       "4             -73.940636   19282  Subscriber     1981.0       1  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2016_routes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>start station latitude</th>\n",
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       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
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       "      <th>end station id</th>\n",
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       "      <th>bikeid</th>\n",
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       "      <th>gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>900435</td>\n",
       "      <td>160</td>\n",
       "      <td>6/20/2016 06:22:53</td>\n",
       "      <td>6/20/2016 06:25:33</td>\n",
       "      <td>3070</td>\n",
       "      <td>McKibbin St &amp; Manhattan Ave</td>\n",
       "      <td>3071</td>\n",
       "      <td>Boerum St &amp; Broadway</td>\n",
       "      <td>40.705381</td>\n",
       "      <td>-73.949765</td>\n",
       "      <td>17226</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1952.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>750686</td>\n",
       "      <td>1474</td>\n",
       "      <td>6/17/2016 00:03:24</td>\n",
       "      <td>6/17/2016 00:27:59</td>\n",
       "      <td>3074</td>\n",
       "      <td>Montrose Ave &amp; Bushwick Ave</td>\n",
       "      <td>3129</td>\n",
       "      <td>Queens Plaza North &amp; Crescent St</td>\n",
       "      <td>40.751102</td>\n",
       "      <td>-73.940737</td>\n",
       "      <td>25447</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>40.71</td>\n",
       "      <td>-73.94</td>\n",
       "      <td>747914</td>\n",
       "      <td>634</td>\n",
       "      <td>6/16/2016 21:44:55</td>\n",
       "      <td>6/16/2016 21:55:30</td>\n",
       "      <td>3081</td>\n",
       "      <td>Graham Ave &amp; Grand St</td>\n",
       "      <td>3068</td>\n",
       "      <td>Humboldt St &amp; Varet St</td>\n",
       "      <td>40.703172</td>\n",
       "      <td>-73.940636</td>\n",
       "      <td>19282</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>895781</td>\n",
       "      <td>1249</td>\n",
       "      <td>6/19/2016 21:02:22</td>\n",
       "      <td>6/19/2016 21:23:11</td>\n",
       "      <td>411</td>\n",
       "      <td>E 6 St &amp; Avenue D</td>\n",
       "      <td>446</td>\n",
       "      <td>W 24 St &amp; 7 Ave</td>\n",
       "      <td>40.744876</td>\n",
       "      <td>-73.995299</td>\n",
       "      <td>20052</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>40.72</td>\n",
       "      <td>-73.98</td>\n",
       "      <td>1022077</td>\n",
       "      <td>443</td>\n",
       "      <td>6/22/2016 11:02:31</td>\n",
       "      <td>6/22/2016 11:09:55</td>\n",
       "      <td>150</td>\n",
       "      <td>E 2 St &amp; Avenue C</td>\n",
       "      <td>293</td>\n",
       "      <td>Lafayette St &amp; E 8 St</td>\n",
       "      <td>40.730287</td>\n",
       "      <td>-73.990765</td>\n",
       "      <td>18622</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    boro  start station latitude  start station longitude    index  \\\n",
       "0    3.0                   40.71                   -73.94   900435   \n",
       "2    3.0                   40.71                   -73.94   750686   \n",
       "4    3.0                   40.71                   -73.94   747914   \n",
       "6    1.0                   40.72                   -73.98   895781   \n",
       "14   1.0                   40.72                   -73.98  1022077   \n",
       "\n",
       "    tripduration           starttime            stoptime  start station id  \\\n",
       "0            160  6/20/2016 06:22:53  6/20/2016 06:25:33              3070   \n",
       "2           1474  6/17/2016 00:03:24  6/17/2016 00:27:59              3074   \n",
       "4            634  6/16/2016 21:44:55  6/16/2016 21:55:30              3081   \n",
       "6           1249  6/19/2016 21:02:22  6/19/2016 21:23:11               411   \n",
       "14           443  6/22/2016 11:02:31  6/22/2016 11:09:55               150   \n",
       "\n",
       "             start station name  end station id  \\\n",
       "0   McKibbin St & Manhattan Ave            3071   \n",
       "2   Montrose Ave & Bushwick Ave            3129   \n",
       "4         Graham Ave & Grand St            3068   \n",
       "6             E 6 St & Avenue D             446   \n",
       "14            E 2 St & Avenue C             293   \n",
       "\n",
       "                    end station name  end station latitude  \\\n",
       "0               Boerum St & Broadway             40.705381   \n",
       "2   Queens Plaza North & Crescent St             40.751102   \n",
       "4             Humboldt St & Varet St             40.703172   \n",
       "6                    W 24 St & 7 Ave             40.744876   \n",
       "14             Lafayette St & E 8 St             40.730287   \n",
       "\n",
       "    end station longitude  bikeid    usertype  birthyear  gender  \n",
       "0              -73.949765   17226  Subscriber     1952.0       1  \n",
       "2              -73.940737   25447  Subscriber     1991.0       1  \n",
       "4              -73.940636   19282  Subscriber     1981.0       1  \n",
       "6              -73.995299   20052  Subscriber     1980.0       1  \n",
       "14             -73.990765   18622  Subscriber     1991.0       1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2016_routes = biking_2016_routes.drop_duplicates()\n",
    "biking_2016_routes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "biking_2016_routes = biking_2016_routes.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Zone</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>index</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Zone, start station latitude, start station longitude, index, tripduration, starttime, stoptime, start station id, start station name, end station id, end station name, end station latitude, end station longitude, bikeid, usertype, birthyear, gender]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biking_2016_routes.rename(columns={'boro':'Zone'}, inplace=True)\n",
    "biking_2016_routes.head(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(biking_2013_routes[\"Zone\"])\n",
    "plt.xlabel(\"Zone\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(biking_2016_routes[\"Zone\"])\n",
    "plt.xlabel(\"Zone\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 441.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"tripduration\", y=\"Zone\", hue=\"birthyear\", data=biking_2013_routes)\n",
    "plt.xlabel(\"zone\")\n",
    "plt.ylabel(\"trip duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/+muWLW6ktqGGb/ziC4TDYX7zzetYvWwNU2YdwMe/9GHu+X+3s3ZZM5VDqzjx0jP40+/uomlFM1f8+FLG7z+G4E6ETfu6Vh795d9oWrySeFUZx196OsMmjSIUGZwf0IV8gY4163n11kdIbWwjUV/NgR89iURddY96qdYkz9/2BEuefY1AKMjBZx3OpOOnESuPD1DLZW9XstODrqjdmw17k+tV7WzgZq/uHKDazIbv6ra8u3g5t113F1nvCOnJB/5JLp2j8fVlvPTPVzhw1gHcfs3dvP7SmwC0bWznV/95HeW1lYCjkMsz7/4XWL1kNenONPscOB6ADU0bue57NzL2uINpb9rIc9ffh8tmSbYkAVjx+nIe+/1DZNrThOMR7vzN35h2/MFcf82fOOWUY1l465N0rGsFoHXVehb+5UkSsSh3X3kHyZYOAJa/toyn/vw4n/zKR1j33Hw6m9YDkO3oZPmDsznngtMoFByzH3qOxuWriVmO1iXLCNbVctev/kY6mcI5x+tzXueZu57huYdfYOPaluL78voy/vrru1j+zirWvrmC0z5yArlsjntufoB331re4z1s3dDGT798NauXNwGw6t01/PoHNzDzg0fx9rzFtK1t4ek7ZrP45bcBSLYm+eN/3UJnW2e/fkatG9v45bevZdniRgDWrVnP6uVNXYEFcMSJh3HvlXewdllzcZ2mFh74xd2cc9HpNK9ax0+uuIrWDW07+NsB6Y4Uz/zhEZoWrwSgs6WDh352J+mO9A5vyy+yyRSv/PEBUhuL71eyeSOv3vYomfbNPy/nHO/Oe5O3n3kVV3DkMzle/Ots2ptbBqrZIqW9pmVmQTN7GWgCHnHOPderykig+6djo1e2S817Zn6P+eGjG1j++jLqxjbwxvzF1A6r4fWX3upRp5AvsHbVOqobhnSVrVi0jLaN7UyYMq6rLNneScHL4o7mFsqqynpsZ8WiZbiCo3HxCgCGjmvgxRcWUFlRRsoLpk1aVq7DFQoUcvke5csWLqWqpor2FU0925jJEglvPqpIdnSSXbuOcHmCVW+v6vM+LH5pMcNGD+1ZtmAJZZUJLBJi3MTRXeUvzl7Qo142k6V55doeZauXN5GoKp5SskCAJQve6bHcFRxre62zNflsnsWv9ly/srqiK7AA6kbU0vTOmh51ki0dxGLFI7nWDW2kU5l+vV6P187kWPVGz5DOZ3Ok2pI7vC2/KGRzPQIKoGPNelxh8+9eLpNj+fwlfdZd9dq7JW+fyNaUNLScc3nn3DRgFDDTzA7sVcW2tFrvAjP7jJnNNbO5zc3NO9yOg2f2fNk1K5oZud8o1jeuZZ/J49jY3NJ19NT1mgGjblgtG9ds/lY5fOIIyqvKWfZmY1dZLBElYMW3MVFT0efIYti+IyFgjJhQPIBc27iWA6cdQHtHJ9GKnqdYKhqGYMEA1uu6zMhJo2hraadsWF2P8kAoRDZf2NyWeJRwTQ25jiTDxjf0eR/GHTiOpl4hMn7yWDpak7hsjsZ3VnaVT501uUe9UDhMzdAhPcrqh9eS8va3UCgw5oAxPZabGTXDa/u0Y0sCoSDjJ/Vcv62lnfqRm/d5fdMGakfX96gTr4iTyWQBKKssIxqL9Ov1ugtGQtTv0/MAPxAKDupTYIFwiHBZz+uWibqqHr97oXCIEd2+oG3SMGl0nzKR3WW39B50zm0EngBO6bWoEej+FzAKWNmrDs65a51zM5xzM+rr63sv3q4J+4/lrI+eTCAYwMyYcfQ0ookodWPqOey4Q5j35Mucf/k5jNu/+KEZL4vx6W9fQPuGNlyh2GHhoOMPZvjEUUTjERY+vwiA8qoyLv6PT7Ds6QXEq8uZefFp5B1EEsVv/vXjGnj/p08lVhYnl8py2oWnMO/heVz6+Qu5997HOPC8Y4lVFo9UErWVHPSRY0lnspxx+VlEvW00jB/GcRecyC0//wu1R0wjOqTYsSMYizLypCO5648PADD96KmMGz+KTChK2ajhuJYWTr34FEKR4mXLCVMncMyHjuXgf5lKWWXxaHDYmAY+dPkHGTVuGLUTRvD3Wx8hEAxw4jnHMWHyuB7vYdWQCr72s8sZUl+85lHbMITPfeffeP7uZxkzZSxVQ6s57iPHMtr7QIvGo3z4ax8iUdG/D/6qIRV84UefpWFk8edbOaSC2mE1XPrDT1HjHe0+fd8czvzyB6kaWmxDWXUZJ33+LO6++QGqair52s8up6KqfKuvsTXRshjHXHwKQ0YVAzKSiPK+z59FpKxvZ5TBIpyIceBHTybiBXOsupwp553YozOGBYzxs/ZnzPSJAASCAQ46fSaVw4ZscZsiu4M51+fAZtds2KweyDrnNppZHHgY+Ilz7t5udU4HLqfYe3AW8Evn3MxtbXfGjBlu7ty5O9yeZHuSZEcKcMTiMazgij3ogFyhQCAUIJ/Lk8/lCQaDxGJhXAFyuVzx2C9gBAIBQqEAmXSOXC5HMBQkEg7hcvniEVI4RC6dxTmHyzuC4SAVtcWQSXWkyKQy5HPFEMzkspgZsVAYAywYIOf1VoxEwxTyeQr5AqFIGAdkMhliiRjRYMDrrWjknKOzM41zEAwGCbjiEUI8EcHl8+QKjkw6hys4wtEwZVVl5LI52ja2k81kCQQDhMMhYpEI+QB0tncW25SIkthCT7JCvkDrxjZSnWlCoSCRUJhCIY+ZUcAVew8WHNl0lkAoSKI8vkM9zbr3TgxHQlRWV2ABo21Dsb3hSIiyqjJSbSny2eL7XzBHqjNTrD+kYqc6YWzS2dJR7D0YChIrjxHcwZ6PflPIF8gmUxRyeYKhIOGyOBboe/Ij3ZEil86CQSQeJbwTR7N7mS2dQZJdpJShNRW4CQhSPKL7i3PuB2Z2CYBz7hqvy/uvKB6BJYGLnHPbTKSdDS0Rkd1EoVVCJfsq6ZxbAByyhfJruj13wGWlaoOIiAwuuiOGiIj4hkJLRER8Q6ElIiK+odASERHfUGiJiIhvKLRERMQ3FFoiIuIbCi0REfENhZaIiPiGQktERHxDoSUiIr6h0BIREd/Y7g1zzSwKnAOM617fOfeD0jVLRESkr/7c5f1vQAswD0iXtjkiIiJb15/QGuWc6z3isIiIyG7Xn2ta/zSzg0reEhERke3oz5HW0cCFZvYOxdODRnH8xqklbZmIiEgv/QmtU0veChERkX7Y7ulB59y7QDVwpjdVe2UiIiK71XZDy8y+CPwJGOpNt5jZ50vdMBERkd76c3rwYmCWc64DwMx+AjwLXF3KhomIiPTWn96DBuS7zee9MhERkd2qP6H1B+A5M/uemX0PmAP8vqStEhGR98zMxpnZwi2UX29mk7eyzhVmlug2317KNu6o7Z4edM5daWZPUOz6bsBFzrmXSt0wEREpDefcp7ZUbmZB4ArgFiC5WxtVfP2Qcy63rTpbPdIys0rvsQZYSnEn/gi865WJiMieL2RmN5nZAjP7q5klzOwJM5sBxSMpM/uBmT0HfBsYATxuZo9v2oCZ/dDM5pvZHDNrMLMKM3vHzMLe8kozW2pmYTPbx8weNLN5Zva0me3v1TnTzJ4zs5fM7FEza/DKv2dm15rZw8DN29uZbZ0e/LP3OA+Y223aNC8iInu+ScC13g0hWoHP9VpeBix0zs3yboS+EjjeOXd8t+VznHMHA08Bn3bOtQFPAKd7dc4D7nDOZYFrgc8756YDXwV+49WZDRzunDsEuA34erc2TAfOds59dHs7s9XTg865M7zH8dvbiIiI7LGWO+ee8Z7fAnyh1/I8cMc21s8A93rP5wEnes+vpxg8dwMXAZ82s3LgSOB2s67+elHvcRTwf2Y2HIgA73R7jXucc5392Zn+/J/WY/0pExGRPZLbznzKOZdn67LOuU3r5PEOdrwgHGdmxwJB59xCipmy0Tk3rdt0gLfu1cCvnHMHAZ8FYt1eo6O/O7Ota1ox79pVnZkNMbMabxpH8ZyniIjs+caY2RHe8/Mpnqbbljagop/bvhm4lWIvc5xzrcA7ZvYhACs62KtbBazwnn+yn9vvY1tHWp+leCi4v/e4afob8OudfUEREdmtFgGfNLMFQA3w2+3UvxZ4oHtHjG34EzCEYnBt8jHgYjObD7wKnO2Vf4/iacOngbX9b35PtvmobysVzD7vnNtj7n4xY8YMN3eu+oGIyB5rr7n5gpmdS7EDxSd212v25/+0rjazA4HJdDsH6ZzbbtdEEREZnMzsaoqjgJy2O193u6FlZt8FjqMYWvdTbORs+tGfXkREBifn3IDcOL0/t3E6F3gfsNo5dxFwMJu7MIqIiOw2/QmtTudcAch5d8loAiaUtlkiIiJ99WdokrlmVg1cR7H3YDvwfElbJSIisgXbDC0r/kvz/zjnNgLXmNmDQKVzbsFuaZ2IiEg32zw96P0X9N3d5pcqsERE/MHMbjCzpu7Dk5jZwWb2rJm9YmZ/33Rz9G7Lx3g30f1qt7LpXv3FZvZL63aPpt2tP9e05pjZYTu6YTMbbWaPm9kiM3vVzL64hTrHmVmLmb3sTd/Z0dcREZGtuhE4pVfZ9cA3vNsp3QV8rdfynwMP9Cr7LfAZYKI39d7mbtOfa1rHA581s3cp3h/KKB6ETd3OejngK865F82sAphnZo84517rVe/pTTfnFRHZW61fMPejwI+AMcAy4Fs1U2f8edtrbZtz7inv1nvdTaJ4t3aAR4CHgP8EMLN/BZbQ7V6A3g1uK51zz3rzNwP/St9g2y36E1qn7syGnXOrgFXe8zYzWwSMBHqHlojIXs0LrOuATSMGjwWuW79gLu81uLZgIXAWxVvyfQgYDWBmZcC/U7yL+1e71R8JNHabb/TKBkR/Tg+6rUz95iX9IcBzW1h8hDe42ANmNmUr63/GzOaa2dzm5uYdeWkRET/4EZsDa5OEV76r/RtwmZnNo3hj3IxX/n3g58659l71t3T9aocyYFfqz5HWfRQbaBRv4zQeeAPYYsD05o2vcgdwhXcH4O5eBMY659rN7DSKnT4m9t6Gc+5aijdxZMaMGQP2ZomIlMiYHSzfac6514GTAMxsPzYP5DgLONfMfgpUAwUzS1H8/B7VbROjKA4UOSD6c+/Bg7rPm9mhFO8Av13eUMx3AH9yzt25hW23dnt+v5n9xszqnHM7fQdgEREfWkbxlOCWyncpMxvqnGsyswDwH8A1AM65f+lW53tAu3PuV958m5kdTvFs2QUUx8YaEP05PdiDc+5FYLu9Cb0ukb8HFjnnrtxKnWGbuk6a2UyvPet2tE0iIj73LSDZqyzple80M7sVeBaYZGaNZnYxcL6ZvQm8TvGI6Q/92NSlFHsdLgbeZoA6YUD/hib5crfZAHAoUOucO3k76x0NPA28AhS84m/hHe46564xs8spvhk5oBP4snPun9varoYmEZE93E79D1Mpeg8ORv0Jre92m80BS4E7nHOpErZrqxRaIrKH22vG0xoI/bmm9f3d0RAREZHt2Wpomdnf2Ua3RufcWSVpkYiIyFZs60jrf73HDwLDgFu8+fMpniIUERHZrbYaWs65JwHM7L+cc8d0W/R3M3tqK6uJiIiUTH+6vNebWdegj2Y2HqgvXZNERES2rD+h9SXgCTN7wsyeAB4H+tyxXURE9iw7OjSJmU31lr3qLY955f4ZmsQ59yDFWyt90ZsmOeceLnXDRETkPbuRfg5NYmYhin0XLnHOTQGOA7LeOr4amgTnXBqYX+K2iIjstV78+c19/rn40C9dsDuHJjkJWOCcm++tuw72vKFJdvg2TiIismt5gXUdxfsPmvd4nVe+q20amgS6DU0C7Ac4M3vIzF40s6975b4bmkREREprTxiaJAQcDXzMe/yAmb0PHw5Ngpl9kOJOOGC2c+6ukrZKRGTvsicMTdIIPLlplA0zu5/ivWZvYQ8ammS7R1pm9hvgEoo3vl0IfNbMfl3qhomI7EW2NgRJSYYm8R57DE1C8drWVDNLeJ0yjgVe80ahbzOzw71egxdQHPV4QPTnSOtY4EDn3VnXzG6iGGAiIrJrfIviNa3upwh31dAkxwF1ZtYIfBcoN7PLvCp34g1N4pzbYGZXAi9QPKt2v3PuPq/epRR7IsYpdsDYo4cmuRP4knPuXW9+LPBj59AhiL0AABwFSURBVNz5u6F9fegu7yKyh9up/2EqRe/Bwag/ofUkxUEfn/eKDqM4qFgSdv+NcxVaIrKH09AkJdSf04PfKXkrRERE+qE/42k9uTsaIiIisj3bGk9rtnPuaDNro2effAOcc65yK6uKiIiUxLaGJjnae6zYfc0RERHZum3+n5aZBbrfHVhERGQgbTO0nHMFYL6Z7fL/yhYRkdLakaFJzCxsZjd55YvM7Jvd1vHP0CTAcOBVM3vMzO7ZNJW6YSIi8p7dSD+HJqF489yoVz6d4t2PxnnLfDU0yfdL3goRkb3cf5/5nT7/XPwff//B7hyaxAFl3i2c4hRvpNvqx6FJTnPOPdl9Ak4rdcNERPYWXmD1GZrEK9/VtjY0yV+BDmAVxdD8X+fcenw4NMmJWyg7dVc3RERkL7YnDE0yE8gDI4DxwFfMbAJ+GZrEzC4FPgdMMLMF3RZVAM+UumEiInuRPWFoko8CDzrnskCTmT0DzACexidDk/wZOBO4x3vcNE13zn18N7RNRGRvsScMTbIMOMGKyoDDgdf3tKFJthpazrkW59xS59z5zrl3u03rd2cDRUT2At/Cuwl5N7tqaJJngUlm1mhmFwPnm9mbwOsUj5j+4FX/NVBO8ZrXC8AfnHObzrJdSrHX4WLgbfbkoUn2NLrLu4js4Xbqf5hK0XtwMFJoiYjsWhqapIT603tQRERkj6DQEhER31BoiYiIbyi0RETENxRaIiLiGwotEZFByMxGm9nj3jAjr5rZF73yGjN7xMze8h6HdFvnm97wI2+Y2cndyn01NImIiPhPDviKc+4Aine3uMzMJgPfAB5zzk0EHvPm8ZadB0yhOPTIb8ws6G1rjxmapGShtbWU71XHvNRebGYLzOzQUrVHRGRPNnXssR+dOvbYpVPHHlvwHt/THd6dc6uccy96z9uARRTvzn42cJNX7SaKw4zgld/mnEs7596hePeLmd2HJnHFf+y9uds6u10pj7S2lvLdncrm5P4MxTQXEdmreAHVZ2iS9xpcm3hjah0CPAc0ePcTxHsc6lUbCSzvttqmIUh8NzTJTtlGynd3NnCzK5oDVHupLiKyNynZ0CRmVg7cAVzhnGvdVtUtlLltlA+I3XJNq1fKd7e1ZBcR2ZuUZGgSMwtTDKw/Oefu9IrXbDo48B6bvPJGNg8ICZuHIGnEJ0OT7BLbSfl+JbiZfcbM5prZ3Obm5lI0U0RkIO3yoUm8Hn6/BxY5567stuge4JPe80+yeZiRe4DzzCxqZuMpXrZ53jdDk+wKW0n57raW7D045651zs1wzs2or68vTWNFRAZOKYYmOQr4BMUxsl72ptOAHwMnmtlbFEem/zGAc+5V4C/Aa8CDwGXOuby3rcE/NImXyDcB651zV2ylzunA5cBpwCzgl865mdvaru7yLiJ7uJ36Hyav00WPoUkWvPukhibppZShdTTFYZpfAQpe8bfwztE6567xgu1XFPv8J4GLnHPbTCSFlojs4TQ0SQmFSrVh59xstvPD8/r8X1aqNoiIyOCiO2KIiIhvKLRERMQ3FFoiIuIbCi0REfENhZaIiPiGQktERHxDoSUiIr6h0BIREd9QaImIiG8otERExDcUWiIi4hsKLRER8Q2FloiI+IZCS0REfEOhJSIivqHQEhER31BoiYiIbyi0RETENxRaIiLiGwotERHxDYWWiIj4hkJLRER8Q6ElIiK+odASERHfUGiJiIhvKLRERMQ3FFoiIuIbCi0REfENhZaIiPiGQktERHxDoSUiIr6h0BIREd9QaImIiG8otERExDcUWiIi4hsKLRER8Q2FloiI+IZCS0REfKNkoWVmN5hZk5kt3Mry48ysxcxe9qbvlKotIiIyOIRKuO0bgV8BN2+jztPOuTNK2AYRERlESnak5Zx7Clhfqu2LiMjeZ6CvaR1hZvPN7AEzm7K1Smb2GTOba2Zzm5ubd2f7RERkDzKQofUiMNY5dzBwNXD31io65651zs1wzs2or6/fbQ0UEZE9y4CFlnOu1TnX7j2/HwibWd1AtUdERPZ8AxZaZjbMzMx7PtNry7qBao+IiOz5StZ70MxuBY4D6sysEfguEAZwzl0DnAtcamY5oBM4zznnStUeERHxv5KFlnPu/O0s/xXFLvEiIiL9MtC9B0VERPpNoSUiIr6h0BIREd9QaImIiG8otERExDcUWiIi4hsKLRER8Q2FloiI+IZCS0REfEOhJSIivqHQEhER31BoiYiIbyi0RETENxRaIiLiGwotERHxDYWWiIj4hkJLRER8Q6ElIiK+odASERHfUGiJiIhvKLRERMQ3FFoiIuIbCi0REfENhZaIiPiGQktERHxDoSUiIr6h0BIREd9QaImIiG8otERExDcUWiIi4hsKLRER8Q2FloiI+IZCS0REfEOhJSIivqHQEhER31BoiYiIbyi0RETEN0ID3YBSy+fztK1vJxqLYDgsGCCbyVMo5ImEQ1AoEAiHyKSzmBn5fIFgNEgukwcc4XCYfDqLhYMEAgECOJxBJp3DggEMoOCIJaJkszlCkTC5VIZAwDCMbDZPOB6hoy1JMBwkFAgQCAZJp9IEggFC4SCFXHGb5iAYMAr5Ai5ghMMhctkc5hx5g1AoRKozTTQSIVfI4/IFCoUCibI4+UyOQqEAwQAWNMJmYICDTL5AIVsgFAxgoQDZTA4LGJlMlmg0QsAMV3CEYxHS7Z1YKEA0HqGzPYWZEQgHcQ6i4SCFXJ68A+ccBuSdIxwK4jBwrvj+BYxAIEDeOSjkIRAgGAphBUcoGiaTzkKhQC6bJxQNke7MYGZEYmGyuTzZdJbyqjJyqSyFQoFYWQyXy+EKkMlmCYRCmBlmRqwsRrItSS6bIxwJY8EAhUKBQr5AeWVZ1+9Aur2TUCREPuewoJHN5oiXxQiFtvwnkE6lyefyFPIFguEQ8USMQqFAR2uSgBnRRJRgKEiyLUkoHCIaj3atWygUSCfT5PJ5zIxCoUBZRYJgMAhAe0sHoUiQWDzWr9/fjrYk0XiUaDTSZ3kukyOfzRGJR7GA7eBfx45xBUemM138PY6ES/paA62Qz5NOpglFw4QH+b76TclCy8xuAM4AmpxzB25huQFXAacBSeBC59yLu7INretbmf/kAjpa2jn69MPItbfSng2yZlkzE/cbwYqXX6d+5lQ6Vm1g4X3PUSg4pp55OLmAcfvv7uYjl36ANQveYeXCdxm63wgmv/9QNq5YQ1NLJw/95XFiiSinf/wkli54h32mTqBuZC359k5cvgCBAG8+sYB4fSWuIs69f3qYUDjEBy4+g0AwwB+uupWABTjvsx8g29LJqIkjKXRmWPzoXPLZPKOOnALxCJVDymme9xbRukpCtVUkqstJdnTSmUzxl9/9jfaWdk465zgmThnPG3fNpmHqPtRNGkU2l8URIA8sX7CUt59dRHldJTM/eDTRshi3XnMXy95q5NgzjmTi/uOoqK5g7dureO3heUTKohx81pHMffwlVi5ZzckXnkRtfSWtjc2888TLBIIB9nn/DFYsWkZ5Qw3pQoHahmqCgQDp1iQbVjQz8uAJdCxfSWrFKsKVFdRMPYCMM9o2tBKJRljw0DxWLFrGiMljGT9zP+Y98wpHnjyTu2+4jxXvrGT6sdM48qSZJFetJxYx1i9cTDARpWbq/ry5YCljJ4+l8Y1G6sc3cO8ND5DL5Xn/ecdT0zCEa392C6lkio986mz2nTSOVx9fwFvPvc7QCcOZ9YEjeezvs3lx9gIOPmIKJ517PFU1lV2/M4VCgeZV62h8ZyUb17Xw+N+fZkj9EC684jxefHo+/3zoeUaMGcYZHz+JsvIEt/3yDkLhEGd+6jRqG2pItidpfHsl+XyBeEWMO2+5j3eXrOCE047muFOOZOHcRTx4+z+obajh/Es/yNARdV1h1lvLhlb+cd9s/nH/bCZMGsvHP3su9cNqu5Z3rG/ltfueo3X1esbOPIDR0ycSLY/vyj+hLun2Tla8/DZLn32N8qHVTDnjcBI1FRT/jAeXZEsHrzw+n0XPvErDhOEc9eFjqKyt3P6KsluYc640GzY7BmgHbt5KaJ0GfJ5iaM0CrnLOzdredmfMmOHmzp273dfPprP847bH+cdtj/Pv13+JzNI3CI6eyNX/fi1f+elnePuvDzP0yEMI19XywH//GVfw3geDU7/9UV5bsJjCivUsn7e4a5v7HDWZukP24adfuKqrLBQO8e3ffoUbvnE9l1x5KdVVcdpWreele+aQSabY7wNH8cPP/7yrfjAY5Ju//TLf/dxPSLZ3EggYV//lx8SDQeb86s5i4HkOvugUWto72We/Ufzz57cz/uSZlO0zgoAZXzvvu6RTma66X/qfSxji4I375rDfmUfSMHUCmc4US19cwnO3PtFVL1YR54Pf+TgvzH6ZG3/+fwBc/oNPMX5UA4/87I7N7QyHeP/XPsQvvvgrahpquOQHF/D8r+/qWm6BADMv/yAP//T/OOyiU1j8+lJmvX8Gz/zidg679GzSjStpXfTm5vepLM6oU44n05Hm8Rse5t35S7qWjZ46nmMuPpkfXX4laxqbu8ov+Mp5TD9oHO8+OLurLBAOMer047nnmvs47XNn8l8X/phCt/fs0h9/mit/8DvWrGjmwxedxYh4JfMfmte1vG7sUKaePYv/9/VfAzDzhOlc8p8XUlaRAGDD2o389r9u4LBjD+U3/3UDAO87+xhqa4dw3y0PdW2ndlgNX/nRJaxbs4Frv3cjiYoE/3nTN5j3j5eoG1VHrpDnyu9fw+oVTQDMOGoaJ5x8FNf88MaubSTKE1x954+pqa+mt0w6w82//gu3Xr/5PR8xZhi/vOWHDKmrprO1g0f/51Y6N7R3LZ9y5hHsf/IMgqEth+DOKuQLvPnYiyy4c/PPIVZVxonf+ijxqrJd+loDLZvO8I+bH+WFe+Z0ldWOquMTP/o3yoeU93czgy/J9yAlu6blnHsKWL+NKmdTDDTnnJsDVJvZ8F31+u0t7Tz/4AuMmTSaQnsLkYpyXnn2NfabNpENr70NQMWY4Sx9/o3NgQXgYPHTr3DQzANofPHtHtsMl8V57I4ne5Tlsjlenfs6oyeN5oUHnyefLxCpSLBhWRPDDhjLk/c/26N+Pp/npacXMGX6/gAUCo5Fr7zFujeX9wgsgOb5b7N65VqSrR1Ujqqnef5i4tEwb8xf3COwAB6960mq9hkJwJqX3iKdTNOyro3Fs1/tUS/V1knb2hamTJ/UVRaJhFn06Es925nN0fRWI6MnjmLfafuw/Nme23GFAmsXLaV+4khWznuTtpYkHS3t1EwYQTaVIblsec/3qaOTQjpDLpvvEVgAyxe8Q9ACPQILIGiw7pW3epQVsjlS6zZQN7qeF598uUdgAcz7x0tMP/JgAGYdcwgL//Fyj+Vr322iduiQrvkXHn+RdCrdNb9mRTP7Td2XJ+9/pqts+lEH88yDc3psZ93q9aTSGepH1AGQbEuycslKMukM/3z0eUKRUFdgAcz8l0N49K6evzvJ9iRL31zGlrS1dnDf7Y/0KFu5bDXtbR0ApFuTPQILYMnsV8h0pLa4vfci3d7J20+90qMs1dJB58b2razhX+lkmvmP9Dzhs65xLZnO9FbWkN1tIDtijAS6f7I1emV9mNlnzGyumc1tbm7eUpU+AoEAZVVldHakIBimkMtRWVNBsr2TcHnxW3U+lydW0fd0SqK6gnQ6Qzje8xpCPpNlSH1Vn/oV1eV0tndSWVOJYVigOGU701Rv4bRC5ZAKOlqTm18vESNS1vf6RjgRIxAwgqEguc4MkbI4ztHjdNbmbVbicnkAIuXxrut38cpEn7rRRJRMKts1n81mt1yvIk5nRyedHZ1Et7A8UpEg25khUlFsFxi5zgzOOYKxaJ/6gVAQMwhFe14jCEXDW7wek8vlCSX6vi+hWJSOlg6qavv+LMqryujwPtg72pJ998uKR4mbxMtiPU5xlZUnSLZ3UlFd0VXW0Z6korrvt+xoLAJs/sJTVllGLpunvLLcW7ZZR1uSypoKequs7lsGEDCjqqbv/kW861q930OAaHm8JNe1AsHAFk87hmJ9r7H5nhmJPr8zRjA86C//+8ZAhtaW/rq2eK7SOXetc26Gc25GfX19vzZeVVfFWZecyZp315BxQSg4JkwezdqV6yifMJpQWZwN819n7PSJlHcLonh1GWNm7scDtz3GgWce3mOblXWVnHjucZRVbj4lMnLCCEaMHUayLcmhJ07H5XO0r2xm0vsOYcWCJfzLybN6hEzD6KFMnLoPi14unjobOqKOiZMnUDGynrKhm08TRcrjDD1kIpOnTSQAdK5vZfhRB5LOZGkYWc+EA8ZubnNZnA9efAbLnppPIBxk7PHTiFeWUT2kgmlnH0EwvPl00ZhpEwjFwjz6t6eA4unNuoZaDjrzcCKJzUFTM2Yo0apy1rzbxLpV6xl1+BQi3T64EnVVVIysp61pAyMOncjk6ftSVVdF58Z2ItEoFZP3h25hUDFuFFlX/GA9/Nyje7yvh559BBs3tHLMGUd2lQWCAUZMGMnQGVMIdPvASAyrpTNboLq+mv2m7Uv9qM2/D5U1FRx24nTmPFE8Hfjo/bN536dO7fGbdsiph/HSs5uPGj5xxYe7OmxA8QvBxnUtnPnRk4l5gfno357k3M+e3ePa0xEnHobL5nn5mYUATJq+HzXDath/+n4cddJMli9ewQmnb97PR+99io9ddi6Rbh/0B87Yn/oRm69RdVddW8Xn/+NiAsHNf6KnnvM+EmXFn0E4EWXMYft1LbNAgEM+fByxir5fLt6raHmcQz58bI+2jDxkX6Jb+KLld2WVCU7+7Bk9wn/GaYcRiQ/CgPapkl3TAjCzccC9W7mm9TvgCefcrd78G8BxzrlV29pmf69pAXR2dJJsTbJi8Qr2mzYBshlSOWN980aGj6wjtXYDwfIyLBxm3dI1GFAzdig551i8cAkjxw4jHo3Q/PYq6sYPI1YeI72hlUIixtI3lhGLx6gfUcfG1etpGNtAosLrxZfNkc/kyGZyJFvaSQyrYclrSwlHwoyaMAILBnjrlcUEgkHGTxpDpi1F+ZAKLJ8nub6VbCpDxYhaXCBAJBykdcVaEvXV5HC4vCse1QRg1bI1tG5oY9LB+xIGNi5rYsjYYRAKEMplCcbjdKYzFHIFVr+5gqqGIVTWVVIAVjc20bx6HfsdtA8BV+wtlShPsOatFUTLYlSPrOXdN5YTDAUZNm4YsbIoZPO0Lm8iEA6RqK+mZfUG4jUVFHBEIyFymTwBM9KtHURrKggZdDavI1pdSSAeI53KEQgFCDhIt6doXrqG+gkNdCRTtLV0MGxsAxvXtbJiyUomTduXYCBAam0r9WOH0rF6LaF4FBeJkErlAEfHxg5qRtayYslKUskM4yePgYCxdPFy0qkMkw7ch0QiTmdrkpVvNFI3up5wPEJbe5Jlbzey75QJVA6p6LqetUnL+lY2rm8hEomw5I13qa6tYsTYYeTSWd565W2GDq+jpr6aUCTM4leWUFVXxdCRdVQMqSDdmSbZ3kkmnaEzlSbZnmRVYxNTDplEZVU5nR0p3liwmJr6aoaPGbbFo+au399kipYNrSya/yajxo1g6PA6qoZsrp9u66RjXSvtzRup3Wc40fJ4yXr15TI50u1J1i1ZRXldFWW1lURLEJB7gkxnmmRrksbXl1M3up7Kuqq+R1/bpmtaJTSQoXU6cDmbO2L80jk3c3vb3JHQEhEZAAqtEipll/dbgeOAOjNrBL4LhAGcc9cA91MMrMUUu7xfVKq2iIjI4FCy0HLOnb+d5Q64rFSvLyIig49u4yQiIr6h0BIREd9QaImIiG8otERExDcUWiIi4hsKLRER8Q2FloiI+IZCS0REfKOkt3EqBTNrBt7tR9U6YG2Jm7Mn0f4OfnvbPvt1f9c6504Z6EYMVr4Lrf4ys7nOuRkD3Y7dRfs7+O1t+7y37a/0j04PioiIbyi0RETENwZzaF070A3YzbS/g9/ets972/5KPwzaa1oiIjL4DOYjLRERGWQUWiIi4huDLrTM7BQze8PMFpvZNwa6PTvLzG4wsyYzW9itrMbMHjGzt7zHId2WfdPb5zfM7ORu5dPN7BVv2S/NbI8cCtzMRpvZ42a2yMxeNbMveuWDeZ9jZva8mc339vn7Xvlg3uegmb1kZvd684N2X6VEnHODZgKCwNvABCACzAcmD3S7dnJfjgEOBRZ2K/sp8A3v+TeAn3jPJ3v7GgXGe+9B0Fv2PHAEYMADwKkDvW9b2d/hwKHe8wrgTW+/BvM+G1DuPQ8DzwGHD/J9/jLwZ+Dewf47rak002A70poJLHbOLXHOZYDbgLMHuE07xTn3FLC+V/HZwE3e85uAf+1WfptzLu2cewdYDMw0s+FApXPuWeecA27uts4exTm3yjn3ove8DVgEjGRw77NzzrV7s2FvcgzSfTazUcDpwPXdigflvkrpDLbQGgks7zbf6JUNFg3OuVVQ/JAHhnrlW9vvkd7z3uV7NDMbBxxC8chjUO+zd7rsZaAJeMQ5N5j3+RfA14FCt7LBuq9SIoMttLZ0bntv6NO/tf323fthZuXAHcAVzrnWbVXdQpnv9tk5l3fOTQNGUTySOHAb1X27z2Z2BtDknJvX31W2UOaLfZXSGmyh1QiM7jY/Clg5QG0phTXe6RG8xyavfGv73eg9712+RzKzMMXA+pNz7k6veFDv8ybOuY3AE8ApDM59Pgo4y8yWUjxtf4KZ3cLg3FcpocEWWi8AE81svJlFgPOAewa4TbvSPcAnveefBP7Wrfw8M4ua2XhgIvC8d7qlzcwO93pYXdBtnT2K177fA4ucc1d2WzSY97nezKq953Hg/cDrDMJ9ds590zk3yjk3juLf5T+ccx9nEO6rlNhA9wTZ1RNwGsWeZ28D3x7o9ryH/bgVWAVkKX67vBioBR4D3vIea7rV/7a3z2/QrTcVMANY6C37Fd5dUPa0CTia4mmeBcDL3nTaIN/nqcBL3j4vBL7jlQ/affbaehybew8O6n3VtOsn3cZJRER8Y7CdHhQRkUFMoSUiIr6h0BIREd9QaImIiG8otERExDcUWiIi4hsKLRER8Q2Flgw6ZnaJmb3sTe9443Sd743BtNDMftKtbruZ/dAb02qOmTV45fVmdoeZveBNRw3cHonIJgotGXScc9e44k1oD6N4N5EbgZ8AJwDTgMPMbNNwFmXAHOfcwcBTwKe98quAnzvnDgPOoedwGiIyQEID3QCREroK+AewEXjCOdcMYGZ/ojjI5t1ABrjXqz8PONF7/n5gcrdBcSvNrMIVx/oSkQGi0JJBycwuBMYClwNnbaNq1m2+l1mezX8TAeAI51xnyRopIjtMpwdl0DGz6cBXgY875woUB5M81szqzCwInA88uZ3NPEwx8DZtc1qp2isi/acjLRmMLgdqgMe903tzgW8Cj1McRPB+59z2hrP4AvBrM1tA8e/kKeD/t3PHNAAAIBDE7OJfARZYGC5pFbBd8gPzdjFw4ss7ABnmQQAyRAuADNECIEO0AMgQLQAyRAuADNECIGMBUYS1P/oGh3EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 441.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"tripduration\", y=\"Zone\", hue=\"birthyear\", data=biking_2016_routes)\n",
    "plt.xlabel(\"zone\")\n",
    "plt.ylabel(\"trip duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"birthyear\", y=\"tripduration\", hue=\"Zone\", data=biking_2013_routes)\n",
    "plt.xlabel(\"zone\")\n",
    "plt.ylabel(\"trip duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"birthyear\", y=\"tripduration\", hue=\"Zone\", data=biking_2016_routes)\n",
    "plt.xlabel(\"zone\")\n",
    "plt.ylabel(\"trip duration\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN01</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>29.3</td>\n",
       "      <td>54.4</td>\n",
       "      <td>70.2</td>\n",
       "      <td>58.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN02</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>26.5</td>\n",
       "      <td>44.3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN03</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN04</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.1</td>\n",
       "      <td>37.4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>MN05</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.8</td>\n",
       "      <td>41.1</td>\n",
       "      <td>66.6</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       Zone District  Fiscal Month Number  Fiscal Year  \\\n",
       "0           0  Manhattan     MN01                   11         2010   \n",
       "1           1  Manhattan     MN02                   11         2010   \n",
       "2           2  Manhattan     MN03                   11         2010   \n",
       "3           3  Manhattan     MN04                   11         2010   \n",
       "4           4  Manhattan     MN05                   11         2010   \n",
       "\n",
       "  Month Name  Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0        May            29.3                                          54.4   \n",
       "1        May            26.5                                          44.3   \n",
       "2        May            13.1                                          47.5   \n",
       "3        May            23.1                                          37.4   \n",
       "4        May            23.8                                          41.1   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  \n",
       "0                                    70.2          58.2  \n",
       "1                                    79.0          52.7  \n",
       "2                                    39.5          44.0  \n",
       "3                                    73.0          45.9  \n",
       "4                                    66.6          47.3  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recycling_2010 = pd.read_csv('recycling_2010.csv')\n",
    "recycling_2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>14.7</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>43.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN02</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>16.5</td>\n",
       "      <td>30.2</td>\n",
       "      <td>49.8</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN03</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>10.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN04</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>12.4</td>\n",
       "      <td>30.6</td>\n",
       "      <td>46.4</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>BKN05</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>9.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      Zone District  Fiscal Month Number  Fiscal Year Month Name  \\\n",
       "0          59  Brooklyn    BKN01                    1         2016       July   \n",
       "1          60  Brooklyn    BKN02                    1         2016       July   \n",
       "2          61  Brooklyn    BKN03                    1         2016       July   \n",
       "3          62  Brooklyn    BKN04                    1         2016       July   \n",
       "4          63  Brooklyn    BKN05                    1         2016       July   \n",
       "\n",
       "   Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0            14.7                                          44.0   \n",
       "1            16.5                                          30.2   \n",
       "2            10.1                                          29.0   \n",
       "3            12.4                                          30.6   \n",
       "4             9.1                                          23.5   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  \n",
       "0                                    43.2          43.6  \n",
       "1                                    49.8          36.0  \n",
       "2                                    32.8          30.8  \n",
       "3                                    46.4          37.8  \n",
       "4                                    33.0          27.9  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recycling_2016 = pd.read_csv('recycling_2016.csv')\n",
    "recycling_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>MN01</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>29.3</td>\n",
       "      <td>54.4</td>\n",
       "      <td>70.2</td>\n",
       "      <td>58.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MN02</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>26.5</td>\n",
       "      <td>44.3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>52.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MN03</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>MN04</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.1</td>\n",
       "      <td>37.4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>45.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>MN05</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.8</td>\n",
       "      <td>41.1</td>\n",
       "      <td>66.6</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Zone District  Fiscal Month Number  Fiscal Year Month Name  \\\n",
       "0           0     1     MN01                   11         2010        May   \n",
       "1           1     1     MN02                   11         2010        May   \n",
       "2           2     1     MN03                   11         2010        May   \n",
       "3           3     1     MN04                   11         2010        May   \n",
       "4           4     1     MN05                   11         2010        May   \n",
       "\n",
       "   Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0            29.3                                          54.4   \n",
       "1            26.5                                          44.3   \n",
       "2            13.1                                          47.5   \n",
       "3            23.1                                          37.4   \n",
       "4            23.8                                          41.1   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  \n",
       "0                                    70.2          58.2  \n",
       "1                                    79.0          52.7  \n",
       "2                                    39.5          44.0  \n",
       "3                                    73.0          45.9  \n",
       "4                                    66.6          47.3  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_map = {\"Manhattan\":1, \"Bronx\":2, \"Brooklyn\":3, \"Queens\":4 , \"Staten Island\":5}\n",
    "w = recycling_2010['Zone'].map(zone_map)\n",
    "recycling_2010['Zone'] = w\n",
    "recycling_2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>3</td>\n",
       "      <td>BKN01</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>14.7</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>43.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>3</td>\n",
       "      <td>BKN02</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>16.5</td>\n",
       "      <td>30.2</td>\n",
       "      <td>49.8</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>BKN03</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>10.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32.8</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>BKN04</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>12.4</td>\n",
       "      <td>30.6</td>\n",
       "      <td>46.4</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>3</td>\n",
       "      <td>BKN05</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>July</td>\n",
       "      <td>9.1</td>\n",
       "      <td>23.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Zone District  Fiscal Month Number  Fiscal Year Month Name  \\\n",
       "0          59     3    BKN01                    1         2016       July   \n",
       "1          60     3    BKN02                    1         2016       July   \n",
       "2          61     3    BKN03                    1         2016       July   \n",
       "3          62     3    BKN04                    1         2016       July   \n",
       "4          63     3    BKN05                    1         2016       July   \n",
       "\n",
       "   Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0            14.7                                          44.0   \n",
       "1            16.5                                          30.2   \n",
       "2            10.1                                          29.0   \n",
       "3            12.4                                          30.6   \n",
       "4             9.1                                          23.5   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  \n",
       "0                                    43.2          43.6  \n",
       "1                                    49.8          36.0  \n",
       "2                                    32.8          30.8  \n",
       "3                                    46.4          37.8  \n",
       "4                                    33.0          27.9  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_map = {\"Manhattan\":1, \"Bronx\":2, \"Brooklyn\":3, \"Queens\":4 , \"Staten Island\":5}\n",
    "t = recycling_2016['Zone'].map(zone_map)\n",
    "recycling_2016['Zone'] = t\n",
    "recycling_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'biking_2013_recycle' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-94a79904680b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbiking_2013_recycle\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tripduration\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbiking_2013_recycle\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Capture Rate\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"trip duration\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"capture rate\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'biking_2013_recycle' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(biking_2013_recycle[\"tripduration\"], biking_2013_recycle[\"Capture Rate\"] )\n",
    "plt.xlabel(\"trip duration\")\n",
    "plt.ylabel(\"capture rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'biking_2013_recycle' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-61-cacf39e052c5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"tripduration\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Capture Rate\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Zone\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbiking_2013_recycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"trip duration\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"capture rate\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'biking_2013_recycle' is not defined"
     ]
    }
   ],
   "source": [
    "sns.relplot(x=\"tripduration\", y=\"Capture Rate\", hue=\"Zone\", data=biking_2013_recycle)\n",
    "plt.xlabel(\"trip duration\")\n",
    "plt.ylabel(\"capture rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_bootstrap = 1000\n",
    "bootstrap_slopes = np.zeros(n_bootstrap)\n",
    "bootstrap_intercepts = np.zeros(n_bootstrap)\n",
    "bootstrap_corr = np.zeros(n_bootstrap)\n",
    "\n",
    "for i in range(n_bootstrap):\n",
    "    fake_data = biking_2013_routes.sample(n=len(recycling_2010), replace=True)\n",
    "    fake_model = LinearRegression().fit(fake_data[[\"tripduration\"]], recycling_2010[[\"Diversion Rate\"]])\n",
    "    bootstrap_slopes[i] = fake_model.coef_\n",
    "    bootstrap_intercepts[i] = fake_model.intercept_\n",
    "    bootstrap_corr[i] = fake_model.score(fake_data[[\"tripduration\"]],  recycling_2010[[\"Diversion Rate\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_slopes, bins=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.622688158965649e-05"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope_average = bootstrap_slopes.mean()\n",
    "slope_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_intercepts, bins=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.622688158965649e-05"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intercept_average = bootstrap_slopes.mean()\n",
    "intercept_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_corr, bins=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01697830002430887"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_average = bootstrap_corr.mean()\n",
    "corr_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "bootstrap_slopes_16 = np.zeros(n_bootstrap)\n",
    "bootstrap_intercepts_16 = np.zeros(n_bootstrap)\n",
    "bootstrap_corr_16 = np.zeros(n_bootstrap)\n",
    "\n",
    "for i in range(n_bootstrap):\n",
    "    fake_data_16 = biking_2016_routes.sample(n=len(recycling_2016), replace=True)\n",
    "    fake_model_16 = LinearRegression().fit(fake_data_16[[\"tripduration\"]], recycling_2016[[\"Diversion Rate\"]])\n",
    "    bootstrap_slopes_16[i] = fake_model_16.coef_\n",
    "    bootstrap_intercepts_16[i] = fake_model_16.intercept_\n",
    "    bootstrap_corr_16[i] = fake_model_16.score(fake_data_16[[\"tripduration\"]],  recycling_2016[[\"Diversion Rate\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_slopes_16, bins=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.666892258744117e-06"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slope_average_16 = bootstrap_slopes_16.mean()\n",
    "slope_average_16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_intercepts_16, bins=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.666892258744117e-06"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intercept_average_16 = bootstrap_slopes_16.mean()\n",
    "intercept_average_16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bootstrap_corr_16, bins=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.008266920991367319"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_average_16 = bootstrap_corr_16.mean()\n",
    "corr_average_16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(28.39515625, 0.5, 'Diversion Rate 2010')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"Capture Rate\", y=\"Diversion Rate\", hue=\"Zone\", data=recycling_2010);\n",
    "plt.xlabel(\"Capture Rate 2010\")\n",
    "plt.ylabel(\"Diversion Rate 2010\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(30.036085069444454, 0.5, 'Diversion Rate 2016')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 421.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"Capture Rate\", y=\"Diversion Rate\", hue=\"Zone\", data=recycling_2016);\n",
    "plt.xlabel(\"Capture Rate 2016\")\n",
    "plt.ylabel(\"Diversion Rate 2016\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[51.27272727, 16.16363636],\n",
       "       [28.08      ,  8.68      ],\n",
       "       [40.96111111, 13.7       ],\n",
       "       [59.32222222, 20.5       ],\n",
       "       [48.9       , 24.61666667]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recycling_2010_copy = recycling_2010.copy()\n",
    "\n",
    "## initialize a KMeans object\n",
    "clustering = KMeans(n_clusters = 5, random_state = 15)\n",
    "\n",
    "## perform the KMeans algorithm on the penguins_clean bill and flipper length data\n",
    "clustering.fit(recycling_2010_copy[[\"Capture Rate\", \"Diversion Rate\"]])\n",
    "\n",
    "##finding the cluster centers\n",
    "clustering.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.728814</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>15.030508</td>\n",
       "      <td>39.864407</td>\n",
       "      <td>50.272881</td>\n",
       "      <td>43.216949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.175564</td>\n",
       "      <td>1.186570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.440977</td>\n",
       "      <td>11.478928</td>\n",
       "      <td>15.095110</td>\n",
       "      <td>11.305341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>16.700000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>16.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10.950000</td>\n",
       "      <td>32.200000</td>\n",
       "      <td>39.200000</td>\n",
       "      <td>34.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>14.400000</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>51.300000</td>\n",
       "      <td>43.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>17.900000</td>\n",
       "      <td>47.650000</td>\n",
       "      <td>61.350000</td>\n",
       "      <td>52.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>29.300000</td>\n",
       "      <td>65.600000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>66.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0       Zone  Fiscal Month Number  Fiscal Year  \\\n",
       "count   59.000000  59.000000                 59.0         59.0   \n",
       "mean    29.000000   2.728814                 11.0       2010.0   \n",
       "std     17.175564   1.186570                  0.0          0.0   \n",
       "min      0.000000   1.000000                 11.0       2010.0   \n",
       "25%     14.500000   2.000000                 11.0       2010.0   \n",
       "50%     29.000000   3.000000                 11.0       2010.0   \n",
       "75%     43.500000   4.000000                 11.0       2010.0   \n",
       "max     58.000000   5.000000                 11.0       2010.0   \n",
       "\n",
       "       Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "count       59.000000                                     59.000000   \n",
       "mean        15.030508                                     39.864407   \n",
       "std          5.440977                                     11.478928   \n",
       "min          4.900000                                     16.700000   \n",
       "25%         10.950000                                     32.200000   \n",
       "50%         14.400000                                     41.100000   \n",
       "75%         17.900000                                     47.650000   \n",
       "max         29.300000                                     65.600000   \n",
       "\n",
       "       Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  \n",
       "count                               59.000000     59.000000  \n",
       "mean                                50.272881     43.216949  \n",
       "std                                 15.095110     11.305341  \n",
       "min                                 16.500000     16.600000  \n",
       "25%                                 39.200000     34.700000  \n",
       "50%                                 51.300000     43.600000  \n",
       "75%                                 61.350000     52.050000  \n",
       "max                                 79.000000     66.100000  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recycling_2010_copy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 2, 4, 4], dtype=int32)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_labels = clustering.labels_\n",
    "cluster_labels[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cluster_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "      <th>cluster_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>MN01</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>29.3</td>\n",
       "      <td>54.4</td>\n",
       "      <td>70.2</td>\n",
       "      <td>58.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>MN02</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>26.5</td>\n",
       "      <td>44.3</td>\n",
       "      <td>79.0</td>\n",
       "      <td>52.7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MN03</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>MN04</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.1</td>\n",
       "      <td>37.4</td>\n",
       "      <td>73.0</td>\n",
       "      <td>45.9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>MN05</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>23.8</td>\n",
       "      <td>41.1</td>\n",
       "      <td>66.6</td>\n",
       "      <td>47.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Zone District  Fiscal Month Number  Fiscal Year Month Name  \\\n",
       "0           0     1     MN01                   11         2010        May   \n",
       "1           1     1     MN02                   11         2010        May   \n",
       "2           2     1     MN03                   11         2010        May   \n",
       "3           3     1     MN04                   11         2010        May   \n",
       "4           4     1     MN05                   11         2010        May   \n",
       "\n",
       "   Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0            29.3                                          54.4   \n",
       "1            26.5                                          44.3   \n",
       "2            13.1                                          47.5   \n",
       "3            23.1                                          37.4   \n",
       "4            23.8                                          41.1   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  cluster_label  \n",
       "0                                    70.2          58.2              3  \n",
       "1                                    79.0          52.7              4  \n",
       "2                                    39.5          44.0              2  \n",
       "3                                    73.0          45.9              4  \n",
       "4                                    66.6          47.3              4  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recycling_2010_copy['cluster_label'] = cluster_labels\n",
    "recycling_2010_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(33.09765625, 0.5, 'Diversion Rate 2010')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 459.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"Capture Rate\", y=\"Diversion Rate\", hue=\"Zone\", style=\"cluster_label\", data=recycling_2010_copy);\n",
    "plt.xlabel(\"Capture Rate 2010\")\n",
    "plt.ylabel(\"Diversion Rate 2010\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>predicted_zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster_label  predicted_zone\n",
       "0              0               1\n",
       "1              1               2\n",
       "2              2               3\n",
       "3              3               4\n",
       "4              4               5"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_zone_lookup = pd.DataFrame({\"cluster_label\": [0, 1, 2, 3, 4],\n",
    "                                        \"predicted_zone\": [1, 2, 3, 4, 5]})\n",
    "predicted_zone_lookup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Zone</th>\n",
       "      <th>District</th>\n",
       "      <th>Fiscal Month Number</th>\n",
       "      <th>Fiscal Year</th>\n",
       "      <th>Month Name</th>\n",
       "      <th>Diversion Rate</th>\n",
       "      <th>Capture Rate-Paper (Total Paper / Max Paper)</th>\n",
       "      <th>Capture Rate-MGP (Total MGP / Max MGP)</th>\n",
       "      <th>Capture Rate</th>\n",
       "      <th>cluster_label</th>\n",
       "      <th>predicted_zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>MN01</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>29.3</td>\n",
       "      <td>54.4</td>\n",
       "      <td>70.2</td>\n",
       "      <td>58.2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>BKS06</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>24.4</td>\n",
       "      <td>48.5</td>\n",
       "      <td>76.5</td>\n",
       "      <td>56.8</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>BKS10</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>20.2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>61.9</td>\n",
       "      <td>61.7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>QW01</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>18.3</td>\n",
       "      <td>54.8</td>\n",
       "      <td>57.5</td>\n",
       "      <td>55.8</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>QW02</td>\n",
       "      <td>11</td>\n",
       "      <td>2010</td>\n",
       "      <td>May</td>\n",
       "      <td>18.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>59.7</td>\n",
       "      <td>56.8</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Zone District  Fiscal Month Number  Fiscal Year Month Name  \\\n",
       "0           0     1     MN01                   11         2010        May   \n",
       "1          33     3    BKS06                   11         2010        May   \n",
       "2          35     3    BKS10                   11         2010        May   \n",
       "3          42     4     QW01                   11         2010        May   \n",
       "4          43     4     QW02                   11         2010        May   \n",
       "\n",
       "   Diversion Rate  Capture Rate-Paper (Total Paper / Max Paper)  \\\n",
       "0            29.3                                          54.4   \n",
       "1            24.4                                          48.5   \n",
       "2            20.2                                          61.5   \n",
       "3            18.3                                          54.8   \n",
       "4            18.6                                          55.0   \n",
       "\n",
       "   Capture Rate-MGP (Total MGP / Max MGP)  Capture Rate  cluster_label  \\\n",
       "0                                    70.2          58.2              3   \n",
       "1                                    76.5          56.8              3   \n",
       "2                                    61.9          61.7              3   \n",
       "3                                    57.5          55.8              3   \n",
       "4                                    59.7          56.8              3   \n",
       "\n",
       "   predicted_zone  \n",
       "0               4  \n",
       "1               4  \n",
       "2               4  \n",
       "3               4  \n",
       "4               4  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_predict = pd.merge(recycling_2010_copy, predicted_zone_lookup, on=\"cluster_label\")\n",
    "zone_predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of rows in zone predict data set: 59\n"
     ]
    }
   ],
   "source": [
    "total_rows = zone_predict.shape[0]\n",
    "print(\"number of rows in zone predict data set: \" + str(total_rows))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_correct = 0\n",
    "for i in range(total_rows):\n",
    "    val = zone_predict.iloc[i]['Zone'] == zone_predict.iloc[i]['predicted_zone']\n",
    "    val = str(val)\n",
    "    if (val == 'True'):        \n",
    "        num_correct += num_correct\n",
    "num_correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zone_predict.iloc[3]['Zone'] == zone_predict.iloc[3]['predicted_zone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(zone_predict.iloc[3]['Zone'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(zone_predict.iloc[3]['predicted_zone'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Story:\n",
    " - Ideal vs. realistic capture rate probabilities (decrease)/ difference in recycling from 2010 - 2016 (90% confidence\n",
    " - Graphs by boro (bar graphs)\n",
    " - Pollution aggregate per year vs. biking counts vs. recycling\n",
    " - Pollution 2011 vs. 2018 (train-test split to see trends/linear regression)\n",
    " - K-means by industry\n",
    " - Top 5 cities/industries for pollutors over the years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "biking_2013_routes.to_csv('biking_2013.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "biking_2016_routes.to_csv('biking_2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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