DFord_HW-v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/gobike_logo.png\" />\n",
    "<h1>Analyzing Ford's GoBike Pilot Data</h1>\n",
    "<h2> By Rahul Bonnerjee (RBONNE11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>First, let's import all the python packages and read in the data into a pandas DataFrame</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  #for data digestion & wrangling\n",
    "import numpy  \n",
    "import geopandas     #for mapping\n",
    "import folium        #rendering maps\n",
    "import datetime\n",
    "import matplotlib    #for plotting to view trends\n",
    "matplotlib.use('agg')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</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>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80110</td>\n",
       "      <td>2017-12-31 16:57:39.654</td>\n",
       "      <td>2018-01-01 15:12:50.245</td>\n",
       "      <td>74</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.776435</td>\n",
       "      <td>-122.426244</td>\n",
       "      <td>43</td>\n",
       "      <td>San Francisco Public Library (Grove St at Hyde...</td>\n",
       "      <td>37.778768</td>\n",
       "      <td>-122.415929</td>\n",
       "      <td>96</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1335.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78800</td>\n",
       "      <td>2017-12-31 15:56:34.842</td>\n",
       "      <td>2018-01-01 13:49:55.617</td>\n",
       "      <td>284</td>\n",
       "      <td>Yerba Buena Center for the Arts (Howard St at ...</td>\n",
       "      <td>37.784872</td>\n",
       "      <td>-122.400876</td>\n",
       "      <td>96</td>\n",
       "      <td>Dolores St at 15th St</td>\n",
       "      <td>37.766210</td>\n",
       "      <td>-122.426614</td>\n",
       "      <td>88</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1313.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time                end_time  \\\n",
       "0         80110 2017-12-31 16:57:39.654 2018-01-01 15:12:50.245   \n",
       "1         78800 2017-12-31 15:56:34.842 2018-01-01 13:49:55.617   \n",
       "\n",
       "   start_station_id                                 start_station_name  \\\n",
       "0                74                              Laguna St at Hayes St   \n",
       "1               284  Yerba Buena Center for the Arts (Howard St at ...   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.776435              -122.426244              43   \n",
       "1               37.784872              -122.400876              96   \n",
       "\n",
       "                                    end_station_name  end_station_latitude  \\\n",
       "0  San Francisco Public Library (Grove St at Hyde...             37.778768   \n",
       "1                              Dolores St at 15th St             37.766210   \n",
       "\n",
       "   end_station_longitude  bike_id user_type  duration_min  \n",
       "0            -122.415929       96  Customer   1335.166667  \n",
       "1            -122.426614       88  Customer   1313.333333  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data = pd.read_csv('2017-fordgobike-tripdata.csv')\n",
    "CSV_data['start_time'] = pd.to_datetime(CSV_data['start_time'])   #at initial read start_time & end_time are considered strings, let's cast them to the proper type (datetime)\n",
    "CSV_data['end_time'] = pd.to_datetime(CSV_data['end_time'])\n",
    "CSV_data['duration_min'] = CSV_data['duration_sec']/60            #also creating a new column that gives trip duration in minutes (easier to understand)\n",
    "CSV_data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Warm up Questions:</h2>\n",
    "<h3>1:  How many trips were taken?  When was the earliest check-out in the data set?  WHen was the lst bike in the dataset checked in?</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(519700, 14)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The shape of the CSV data contains 519700 rows and 14 columns.  This translates to <b>519,700 total bike trips</b>, and for each trip we have 14 (13 from original dataset and 1 that I created) associated data points as labeled by the headers</p>\n",
    "<p>To find the <i>earliest check-out</i> in the data set we can sort the dataframe by 'start_time' and look at the first record</p>\n",
    "<p>The earliest check-out in this dataset occured on <b>June 28th, 2017 at approximately 9:47AM </b> (presumably in the Pacific Time Zone)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                                                            424\n",
       "start_time                                       2017-06-28 09:47:36.347000\n",
       "end_time                                         2017-06-28 09:54:41.187000\n",
       "start_station_id                                                         21\n",
       "start_station_name         Montgomery St BART Station (Market St at 2nd St)\n",
       "start_station_latitude                                            37.789625\n",
       "start_station_longitude                                         -122.400811\n",
       "end_station_id                                                           48\n",
       "end_station_name                                        2nd St at S Park St\n",
       "end_station_latitude                                              37.782411\n",
       "end_station_longitude                                           -122.392706\n",
       "bike_id                                                                 240\n",
       "user_type                                                        Subscriber\n",
       "duration_min                                                       7.066667\n",
       "Name: 519697, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data.sort_values(by='start_time').iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Similarly, to find the last bike checked-in we should sort the dataframe by 'end_time' and look at the <i>last</i> record</h3>\n",
    "<p>The last check-in in this dataset occured on <b>January 1st, 2017 at 15:12:50.245 </b> or approximately 3:12PM (almost 3:13PM)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                                                           80110\n",
       "start_time                                        2017-12-31 16:57:39.654000\n",
       "end_time                                          2018-01-01 15:12:50.245000\n",
       "start_station_id                                                          74\n",
       "start_station_name                                     Laguna St at Hayes St\n",
       "start_station_latitude                                             37.776435\n",
       "start_station_longitude                                          -122.426244\n",
       "end_station_id                                                            43\n",
       "end_station_name           San Francisco Public Library (Grove St at Hyde...\n",
       "end_station_latitude                                               37.778768\n",
       "end_station_longitude                                            -122.415929\n",
       "bike_id                                                                   96\n",
       "user_type                                                           Customer\n",
       "duration_min                                                     1335.166667\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data.sort_values(by='end_time').iloc[-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2:  How long was the longest trip (in duration)?  How long was the shortest trip (in duration)?</h3>\n",
    "<p>To find the shortest and longest trips in duration, we can sort the dataframe by the column labeled 'duration_sec' and observe the first and last records respectively.</p> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</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>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312919</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-25 07:51:47.854</td>\n",
       "      <td>2017-09-25 07:52:49.637</td>\n",
       "      <td>29</td>\n",
       "      <td>O'Farrell St at Divisadero St</td>\n",
       "      <td>37.782405</td>\n",
       "      <td>-122.439446</td>\n",
       "      <td>29</td>\n",
       "      <td>O'Farrell St at Divisadero St</td>\n",
       "      <td>37.782405</td>\n",
       "      <td>-122.439446</td>\n",
       "      <td>2939</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94610</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-29 08:14:22.712</td>\n",
       "      <td>2017-11-29 08:15:23.883</td>\n",
       "      <td>99</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>99</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>3129</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293667</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-29 20:48:20.216</td>\n",
       "      <td>2017-09-29 20:49:21.305</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>1368</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313189</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-25 00:21:24.078</td>\n",
       "      <td>2017-09-25 00:22:25.803</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>239</td>\n",
       "      <td>Bancroft Way at Telegraph Ave</td>\n",
       "      <td>37.868813</td>\n",
       "      <td>-122.258764</td>\n",
       "      <td>2806</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292732</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-30 12:48:57.809</td>\n",
       "      <td>2017-09-30 12:49:59.272</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>2327</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>120423</th>\n",
       "      <td>86252</td>\n",
       "      <td>2017-11-18 12:16:33.230</td>\n",
       "      <td>2017-11-19 12:14:05.244</td>\n",
       "      <td>245</td>\n",
       "      <td>Downtown Berkeley BART</td>\n",
       "      <td>37.870348</td>\n",
       "      <td>-122.267764</td>\n",
       "      <td>18</td>\n",
       "      <td>Telegraph Ave at Alcatraz Ave</td>\n",
       "      <td>37.850222</td>\n",
       "      <td>-122.260172</td>\n",
       "      <td>1214</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1437.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54889</th>\n",
       "      <td>86281</td>\n",
       "      <td>2017-12-09 10:46:42.231</td>\n",
       "      <td>2017-12-10 10:44:44.161</td>\n",
       "      <td>212</td>\n",
       "      <td>Mosswood Park</td>\n",
       "      <td>37.824931</td>\n",
       "      <td>-122.260479</td>\n",
       "      <td>212</td>\n",
       "      <td>Mosswood Park</td>\n",
       "      <td>37.824931</td>\n",
       "      <td>-122.260479</td>\n",
       "      <td>3418</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1438.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54888</th>\n",
       "      <td>86325</td>\n",
       "      <td>2017-12-09 10:46:05.919</td>\n",
       "      <td>2017-12-10 10:44:51.113</td>\n",
       "      <td>212</td>\n",
       "      <td>Mosswood Park</td>\n",
       "      <td>37.824931</td>\n",
       "      <td>-122.260479</td>\n",
       "      <td>212</td>\n",
       "      <td>Mosswood Park</td>\n",
       "      <td>37.824931</td>\n",
       "      <td>-122.260479</td>\n",
       "      <td>3482</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1438.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223508</th>\n",
       "      <td>86355</td>\n",
       "      <td>2017-10-19 11:24:01.807</td>\n",
       "      <td>2017-10-20 11:23:17.737</td>\n",
       "      <td>123</td>\n",
       "      <td>Folsom St at 19th St</td>\n",
       "      <td>37.760594</td>\n",
       "      <td>-122.414817</td>\n",
       "      <td>70</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>229</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1439.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138862</th>\n",
       "      <td>86369</td>\n",
       "      <td>2017-11-12 10:44:32.043</td>\n",
       "      <td>2017-11-13 10:44:01.820</td>\n",
       "      <td>308</td>\n",
       "      <td>San Pedro Square</td>\n",
       "      <td>37.336802</td>\n",
       "      <td>-121.894090</td>\n",
       "      <td>308</td>\n",
       "      <td>San Pedro Square</td>\n",
       "      <td>37.336802</td>\n",
       "      <td>-121.894090</td>\n",
       "      <td>2231</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1439.483333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>519700 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "312919            61 2017-09-25 07:51:47.854 2017-09-25 07:52:49.637   \n",
       "94610             61 2017-11-29 08:14:22.712 2017-11-29 08:15:23.883   \n",
       "293667            61 2017-09-29 20:48:20.216 2017-09-29 20:49:21.305   \n",
       "313189            61 2017-09-25 00:21:24.078 2017-09-25 00:22:25.803   \n",
       "292732            61 2017-09-30 12:48:57.809 2017-09-30 12:49:59.272   \n",
       "...              ...                     ...                     ...   \n",
       "120423         86252 2017-11-18 12:16:33.230 2017-11-19 12:14:05.244   \n",
       "54889          86281 2017-12-09 10:46:42.231 2017-12-10 10:44:44.161   \n",
       "54888          86325 2017-12-09 10:46:05.919 2017-12-10 10:44:51.113   \n",
       "223508         86355 2017-10-19 11:24:01.807 2017-10-20 11:23:17.737   \n",
       "138862         86369 2017-11-12 10:44:32.043 2017-11-13 10:44:01.820   \n",
       "\n",
       "        start_station_id             start_station_name  \\\n",
       "312919                29  O'Farrell St at Divisadero St   \n",
       "94610                 99           Folsom St at 15th St   \n",
       "293667               243    Bancroft Way at College Ave   \n",
       "313189               243    Bancroft Way at College Ave   \n",
       "292732                58           Market St at 10th St   \n",
       "...                  ...                            ...   \n",
       "120423               245         Downtown Berkeley BART   \n",
       "54889                212                  Mosswood Park   \n",
       "54888                212                  Mosswood Park   \n",
       "223508               123           Folsom St at 19th St   \n",
       "138862               308               San Pedro Square   \n",
       "\n",
       "        start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "312919               37.782405              -122.439446              29   \n",
       "94610                37.767037              -122.415442              99   \n",
       "293667               37.869360              -122.254337             243   \n",
       "313189               37.869360              -122.254337             239   \n",
       "292732               37.776619              -122.417385              58   \n",
       "...                        ...                      ...             ...   \n",
       "120423               37.870348              -122.267764              18   \n",
       "54889                37.824931              -122.260479             212   \n",
       "54888                37.824931              -122.260479             212   \n",
       "223508               37.760594              -122.414817              70   \n",
       "138862               37.336802              -121.894090             308   \n",
       "\n",
       "                     end_station_name  end_station_latitude  \\\n",
       "312919  O'Farrell St at Divisadero St             37.782405   \n",
       "94610            Folsom St at 15th St             37.767037   \n",
       "293667    Bancroft Way at College Ave             37.869360   \n",
       "313189  Bancroft Way at Telegraph Ave             37.868813   \n",
       "292732           Market St at 10th St             37.776619   \n",
       "...                               ...                   ...   \n",
       "120423  Telegraph Ave at Alcatraz Ave             37.850222   \n",
       "54889                   Mosswood Park             37.824931   \n",
       "54888                   Mosswood Park             37.824931   \n",
       "223508         Central Ave at Fell St             37.773311   \n",
       "138862               San Pedro Square             37.336802   \n",
       "\n",
       "        end_station_longitude  bike_id   user_type  duration_min  \n",
       "312919            -122.439446     2939  Subscriber      1.016667  \n",
       "94610             -122.415442     3129    Customer      1.016667  \n",
       "293667            -122.254337     1368  Subscriber      1.016667  \n",
       "313189            -122.258764     2806  Subscriber      1.016667  \n",
       "292732            -122.417385     2327  Subscriber      1.016667  \n",
       "...                       ...      ...         ...           ...  \n",
       "120423            -122.260172     1214    Customer   1437.533333  \n",
       "54889             -122.260479     3418    Customer   1438.016667  \n",
       "54888             -122.260479     3482    Customer   1438.750000  \n",
       "223508            -122.444293      229    Customer   1439.250000  \n",
       "138862            -121.894090     2231    Customer   1439.483333  \n",
       "\n",
       "[519700 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data.sort_values(by='duration_sec')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Here I notice that many of the first records in the sorted dataframe have the same duration of 61 seconds.  <br/>I will create a subset on this condition so that we can determine how many trips had this 61 second duration (slightly over 1 minute ride)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data[CSV_data['duration_sec'] == 61].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The shape of the subset shows us that there are a total of <b>58 trips that have the shortest duration of 61 seconds.</b> <br/> Let's take a look at this subset (just to see if we can observe anything):</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</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>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8162</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-27 08:40:13.217</td>\n",
       "      <td>2017-12-27 08:41:14.973</td>\n",
       "      <td>7</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>7</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>1261</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14236</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-22 07:20:37.132</td>\n",
       "      <td>2017-12-22 07:21:38.626</td>\n",
       "      <td>43</td>\n",
       "      <td>San Francisco Public Library (Grove St at Hyde...</td>\n",
       "      <td>37.778768</td>\n",
       "      <td>-122.415929</td>\n",
       "      <td>42</td>\n",
       "      <td>San Francisco City Hall (Polk St at Grove St)</td>\n",
       "      <td>37.778650</td>\n",
       "      <td>-122.418230</td>\n",
       "      <td>2580</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16309</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-21 10:23:30.027</td>\n",
       "      <td>2017-12-21 10:24:31.689</td>\n",
       "      <td>76</td>\n",
       "      <td>McCoppin St at Valencia St</td>\n",
       "      <td>37.771662</td>\n",
       "      <td>-122.422423</td>\n",
       "      <td>76</td>\n",
       "      <td>McCoppin St at Valencia St</td>\n",
       "      <td>37.771662</td>\n",
       "      <td>-122.422423</td>\n",
       "      <td>2378</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17025</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-21 08:09:41.482</td>\n",
       "      <td>2017-12-21 08:10:42.777</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>2079</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21866</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-19 17:31:47.623</td>\n",
       "      <td>2017-12-19 17:32:49.416</td>\n",
       "      <td>17</td>\n",
       "      <td>Embarcadero BART Station (Beale St at Market St)</td>\n",
       "      <td>37.792251</td>\n",
       "      <td>-122.397086</td>\n",
       "      <td>17</td>\n",
       "      <td>Embarcadero BART Station (Beale St at Market St)</td>\n",
       "      <td>37.792251</td>\n",
       "      <td>-122.397086</td>\n",
       "      <td>2183</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23470</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-19 09:35:32.404</td>\n",
       "      <td>2017-12-19 09:36:34.063</td>\n",
       "      <td>118</td>\n",
       "      <td>Eureka Valley Recreation Center</td>\n",
       "      <td>37.759177</td>\n",
       "      <td>-122.436943</td>\n",
       "      <td>118</td>\n",
       "      <td>Eureka Valley Recreation Center</td>\n",
       "      <td>37.759177</td>\n",
       "      <td>-122.436943</td>\n",
       "      <td>3302</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44994</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-12 20:25:24.009</td>\n",
       "      <td>2017-12-12 20:26:25.161</td>\n",
       "      <td>259</td>\n",
       "      <td>Addison St at Fourth St</td>\n",
       "      <td>37.866249</td>\n",
       "      <td>-122.299371</td>\n",
       "      <td>259</td>\n",
       "      <td>Addison St at Fourth St</td>\n",
       "      <td>37.866249</td>\n",
       "      <td>-122.299371</td>\n",
       "      <td>801</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64776</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-07 08:53:02.622</td>\n",
       "      <td>2017-12-07 08:54:04.367</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>239</td>\n",
       "      <td>Bancroft Way at Telegraph Ave</td>\n",
       "      <td>37.868813</td>\n",
       "      <td>-122.258764</td>\n",
       "      <td>330</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65819</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-06 21:18:33.941</td>\n",
       "      <td>2017-12-06 21:19:35.094</td>\n",
       "      <td>129</td>\n",
       "      <td>Harrison St at 20th St</td>\n",
       "      <td>37.758862</td>\n",
       "      <td>-122.412544</td>\n",
       "      <td>129</td>\n",
       "      <td>Harrison St at 20th St</td>\n",
       "      <td>37.758862</td>\n",
       "      <td>-122.412544</td>\n",
       "      <td>2121</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69333</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-06 08:37:23.879</td>\n",
       "      <td>2017-12-06 08:38:25.642</td>\n",
       "      <td>80</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>37.775306</td>\n",
       "      <td>-122.397380</td>\n",
       "      <td>80</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>37.775306</td>\n",
       "      <td>-122.397380</td>\n",
       "      <td>3303</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80408</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-02 22:14:25.662</td>\n",
       "      <td>2017-12-02 22:15:26.744</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>1883</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84393</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-01 14:24:58.790</td>\n",
       "      <td>2017-12-01 14:26:00.019</td>\n",
       "      <td>279</td>\n",
       "      <td>Santa Clara St at 7th St</td>\n",
       "      <td>37.339146</td>\n",
       "      <td>-121.884105</td>\n",
       "      <td>280</td>\n",
       "      <td>San Fernando at 7th St</td>\n",
       "      <td>37.337246</td>\n",
       "      <td>-121.883074</td>\n",
       "      <td>1875</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94610</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-29 08:14:22.712</td>\n",
       "      <td>2017-11-29 08:15:23.883</td>\n",
       "      <td>99</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>99</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>3129</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102421</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-27 08:44:18.641</td>\n",
       "      <td>2017-11-27 08:45:19.951</td>\n",
       "      <td>134</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>134</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>763</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114535</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-21 08:23:34.992</td>\n",
       "      <td>2017-11-21 08:24:36.175</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>80</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>37.775306</td>\n",
       "      <td>-122.397380</td>\n",
       "      <td>3066</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119395</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-19 18:15:27.232</td>\n",
       "      <td>2017-11-19 18:16:28.506</td>\n",
       "      <td>127</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>121</td>\n",
       "      <td>Mission Playground</td>\n",
       "      <td>37.759210</td>\n",
       "      <td>-122.421339</td>\n",
       "      <td>1166</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133870</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-14 17:11:43.330</td>\n",
       "      <td>2017-11-14 17:12:44.495</td>\n",
       "      <td>76</td>\n",
       "      <td>McCoppin St at Valencia St</td>\n",
       "      <td>37.771662</td>\n",
       "      <td>-122.422423</td>\n",
       "      <td>76</td>\n",
       "      <td>McCoppin St at Valencia St</td>\n",
       "      <td>37.771662</td>\n",
       "      <td>-122.422423</td>\n",
       "      <td>2127</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165924</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-05 20:13:06.285</td>\n",
       "      <td>2017-11-05 20:14:07.378</td>\n",
       "      <td>45</td>\n",
       "      <td>5th St at Howard St</td>\n",
       "      <td>37.781752</td>\n",
       "      <td>-122.405127</td>\n",
       "      <td>45</td>\n",
       "      <td>5th St at Howard St</td>\n",
       "      <td>37.781752</td>\n",
       "      <td>-122.405127</td>\n",
       "      <td>962</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186841</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-30 19:13:40.428</td>\n",
       "      <td>2017-10-30 19:14:41.662</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>1981</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195131</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-27 18:16:21.260</td>\n",
       "      <td>2017-10-27 18:17:22.616</td>\n",
       "      <td>88</td>\n",
       "      <td>11th St at Bryant St</td>\n",
       "      <td>37.770030</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>88</td>\n",
       "      <td>11th St at Bryant St</td>\n",
       "      <td>37.770030</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>2358</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198700</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-26 21:32:43.730</td>\n",
       "      <td>2017-10-26 21:33:45.045</td>\n",
       "      <td>36</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>36</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>166</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204284</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-25 18:11:38.851</td>\n",
       "      <td>2017-10-25 18:12:40.772</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>915</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219367</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-21 17:16:15.533</td>\n",
       "      <td>2017-10-21 17:17:16.575</td>\n",
       "      <td>179</td>\n",
       "      <td>Telegraph Ave at 27th St</td>\n",
       "      <td>37.816073</td>\n",
       "      <td>-122.267886</td>\n",
       "      <td>179</td>\n",
       "      <td>Telegraph Ave at 27th St</td>\n",
       "      <td>37.816073</td>\n",
       "      <td>-122.267886</td>\n",
       "      <td>1108</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219427</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-21 16:56:45.398</td>\n",
       "      <td>2017-10-21 16:57:47.258</td>\n",
       "      <td>59</td>\n",
       "      <td>S Van Ness Ave at Market St</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>3150</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219658</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-21 16:02:05.053</td>\n",
       "      <td>2017-10-21 16:03:06.646</td>\n",
       "      <td>129</td>\n",
       "      <td>Harrison St at 20th St</td>\n",
       "      <td>37.758862</td>\n",
       "      <td>-122.412544</td>\n",
       "      <td>129</td>\n",
       "      <td>Harrison St at 20th St</td>\n",
       "      <td>37.758862</td>\n",
       "      <td>-122.412544</td>\n",
       "      <td>240</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227339</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-19 09:48:20.593</td>\n",
       "      <td>2017-10-19 09:49:22.472</td>\n",
       "      <td>110</td>\n",
       "      <td>17th &amp; Folsom Street Park (17th St at Folsom St)</td>\n",
       "      <td>37.763708</td>\n",
       "      <td>-122.415204</td>\n",
       "      <td>112</td>\n",
       "      <td>Harrison St at 17th St</td>\n",
       "      <td>37.763847</td>\n",
       "      <td>-122.413004</td>\n",
       "      <td>1827</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246408</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-13 15:19:13.525</td>\n",
       "      <td>2017-10-13 15:20:15.470</td>\n",
       "      <td>211</td>\n",
       "      <td>Broadway at 40th St</td>\n",
       "      <td>37.827757</td>\n",
       "      <td>-122.256716</td>\n",
       "      <td>211</td>\n",
       "      <td>Broadway at 40th St</td>\n",
       "      <td>37.827757</td>\n",
       "      <td>-122.256716</td>\n",
       "      <td>401</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248348</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-12 21:57:40.850</td>\n",
       "      <td>2017-10-12 21:58:42.422</td>\n",
       "      <td>24</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>24</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>3032</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260599</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-09 17:54:17.827</td>\n",
       "      <td>2017-10-09 17:55:19.075</td>\n",
       "      <td>80</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>37.775306</td>\n",
       "      <td>-122.397380</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>24</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264159</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-08 15:21:55.046</td>\n",
       "      <td>2017-10-08 15:22:56.913</td>\n",
       "      <td>89</td>\n",
       "      <td>Division St at Potrero Ave</td>\n",
       "      <td>37.769218</td>\n",
       "      <td>-122.407646</td>\n",
       "      <td>89</td>\n",
       "      <td>Division St at Potrero Ave</td>\n",
       "      <td>37.769218</td>\n",
       "      <td>-122.407646</td>\n",
       "      <td>1717</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270182</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-06 12:51:19.316</td>\n",
       "      <td>2017-10-06 12:52:20.349</td>\n",
       "      <td>7</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>7</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>1385</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276445</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-04 20:05:18.014</td>\n",
       "      <td>2017-10-04 20:06:19.720</td>\n",
       "      <td>134</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>133</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>2303</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292732</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-30 12:48:57.809</td>\n",
       "      <td>2017-09-30 12:49:59.272</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>2327</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293667</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-29 20:48:20.216</td>\n",
       "      <td>2017-09-29 20:49:21.305</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>1368</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312919</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-25 07:51:47.854</td>\n",
       "      <td>2017-09-25 07:52:49.637</td>\n",
       "      <td>29</td>\n",
       "      <td>O'Farrell St at Divisadero St</td>\n",
       "      <td>37.782405</td>\n",
       "      <td>-122.439446</td>\n",
       "      <td>29</td>\n",
       "      <td>O'Farrell St at Divisadero St</td>\n",
       "      <td>37.782405</td>\n",
       "      <td>-122.439446</td>\n",
       "      <td>2939</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313189</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-25 00:21:24.078</td>\n",
       "      <td>2017-09-25 00:22:25.803</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>239</td>\n",
       "      <td>Bancroft Way at Telegraph Ave</td>\n",
       "      <td>37.868813</td>\n",
       "      <td>-122.258764</td>\n",
       "      <td>2806</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316476</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-23 15:22:17.585</td>\n",
       "      <td>2017-09-23 15:23:19.340</td>\n",
       "      <td>286</td>\n",
       "      <td>San Carlos St at 11th St</td>\n",
       "      <td>37.336466</td>\n",
       "      <td>-121.876613</td>\n",
       "      <td>286</td>\n",
       "      <td>San Carlos St at 11th St</td>\n",
       "      <td>37.336466</td>\n",
       "      <td>-121.876613</td>\n",
       "      <td>1499</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332554</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-19 09:21:32.307</td>\n",
       "      <td>2017-09-19 09:22:33.396</td>\n",
       "      <td>64</td>\n",
       "      <td>5th St at Brannan St</td>\n",
       "      <td>37.776754</td>\n",
       "      <td>-122.399018</td>\n",
       "      <td>64</td>\n",
       "      <td>5th St at Brannan St</td>\n",
       "      <td>37.776754</td>\n",
       "      <td>-122.399018</td>\n",
       "      <td>2883</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346102</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-14 21:27:39.850</td>\n",
       "      <td>2017-09-14 21:28:41.545</td>\n",
       "      <td>48</td>\n",
       "      <td>2nd St at S Park St</td>\n",
       "      <td>37.782411</td>\n",
       "      <td>-122.392706</td>\n",
       "      <td>49</td>\n",
       "      <td>S Park St at 3rd St</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.394989</td>\n",
       "      <td>872</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376309</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-06 08:34:43.267</td>\n",
       "      <td>2017-09-06 08:35:44.867</td>\n",
       "      <td>97</td>\n",
       "      <td>14th St at Mission St</td>\n",
       "      <td>37.768265</td>\n",
       "      <td>-122.420110</td>\n",
       "      <td>97</td>\n",
       "      <td>14th St at Mission St</td>\n",
       "      <td>37.768265</td>\n",
       "      <td>-122.420110</td>\n",
       "      <td>2564</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381108</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-04 17:56:04.293</td>\n",
       "      <td>2017-09-04 17:57:05.411</td>\n",
       "      <td>24</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>24</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>2204</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383732</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-03 14:08:28.953</td>\n",
       "      <td>2017-09-03 14:09:30.739</td>\n",
       "      <td>8</td>\n",
       "      <td>The Embarcadero at Vallejo St</td>\n",
       "      <td>37.799953</td>\n",
       "      <td>-122.398525</td>\n",
       "      <td>8</td>\n",
       "      <td>The Embarcadero at Vallejo St</td>\n",
       "      <td>37.799953</td>\n",
       "      <td>-122.398525</td>\n",
       "      <td>1383</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383812</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-03 13:39:22.801</td>\n",
       "      <td>2017-09-03 13:40:23.813</td>\n",
       "      <td>74</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.776435</td>\n",
       "      <td>-122.426244</td>\n",
       "      <td>74</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.776435</td>\n",
       "      <td>-122.426244</td>\n",
       "      <td>1437</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386883</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-01 19:38:27.327</td>\n",
       "      <td>2017-09-01 19:39:29.070</td>\n",
       "      <td>240</td>\n",
       "      <td>Haste St at Telegraph Ave</td>\n",
       "      <td>37.866043</td>\n",
       "      <td>-122.258804</td>\n",
       "      <td>240</td>\n",
       "      <td>Haste St at Telegraph Ave</td>\n",
       "      <td>37.866043</td>\n",
       "      <td>-122.258804</td>\n",
       "      <td>465</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395252</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-30 13:51:38.291</td>\n",
       "      <td>2017-08-30 13:52:39.351</td>\n",
       "      <td>181</td>\n",
       "      <td>Grand Ave at Webster St</td>\n",
       "      <td>37.811377</td>\n",
       "      <td>-122.265192</td>\n",
       "      <td>181</td>\n",
       "      <td>Grand Ave at Webster St</td>\n",
       "      <td>37.811377</td>\n",
       "      <td>-122.265192</td>\n",
       "      <td>1238</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395719</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-30 10:20:40.867</td>\n",
       "      <td>2017-08-30 10:21:42.118</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>125</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396097</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-30 09:04:16.374</td>\n",
       "      <td>2017-08-30 09:05:18.048</td>\n",
       "      <td>181</td>\n",
       "      <td>Grand Ave at Webster St</td>\n",
       "      <td>37.811377</td>\n",
       "      <td>-122.265192</td>\n",
       "      <td>181</td>\n",
       "      <td>Grand Ave at Webster St</td>\n",
       "      <td>37.811377</td>\n",
       "      <td>-122.265192</td>\n",
       "      <td>2083</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401675</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-28 15:38:18.446</td>\n",
       "      <td>2017-08-28 15:39:19.607</td>\n",
       "      <td>59</td>\n",
       "      <td>S Van Ness Ave at Market St</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>2622</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406132</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-26 16:50:40.178</td>\n",
       "      <td>2017-08-26 16:51:42.087</td>\n",
       "      <td>145</td>\n",
       "      <td>29th St at Church St</td>\n",
       "      <td>37.743684</td>\n",
       "      <td>-122.426806</td>\n",
       "      <td>145</td>\n",
       "      <td>29th St at Church St</td>\n",
       "      <td>37.743684</td>\n",
       "      <td>-122.426806</td>\n",
       "      <td>2136</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420504</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-22 00:40:36.275</td>\n",
       "      <td>2017-08-22 00:41:37.920</td>\n",
       "      <td>230</td>\n",
       "      <td>14th St at Mandela Pkwy</td>\n",
       "      <td>37.810743</td>\n",
       "      <td>-122.291415</td>\n",
       "      <td>230</td>\n",
       "      <td>14th St at Mandela Pkwy</td>\n",
       "      <td>37.810743</td>\n",
       "      <td>-122.291415</td>\n",
       "      <td>1833</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420942</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-21 18:52:53.635</td>\n",
       "      <td>2017-08-21 18:53:55.283</td>\n",
       "      <td>53</td>\n",
       "      <td>Grove St at Divisadero</td>\n",
       "      <td>37.775946</td>\n",
       "      <td>-122.437777</td>\n",
       "      <td>53</td>\n",
       "      <td>Grove St at Divisadero</td>\n",
       "      <td>37.775946</td>\n",
       "      <td>-122.437777</td>\n",
       "      <td>1937</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445176</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-12 14:49:18.154</td>\n",
       "      <td>2017-08-12 14:50:19.538</td>\n",
       "      <td>190</td>\n",
       "      <td>West St at 40th St</td>\n",
       "      <td>37.830223</td>\n",
       "      <td>-122.270950</td>\n",
       "      <td>190</td>\n",
       "      <td>West St at 40th St</td>\n",
       "      <td>37.830223</td>\n",
       "      <td>-122.270950</td>\n",
       "      <td>1345</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448634</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-11 08:07:59.039</td>\n",
       "      <td>2017-08-11 08:09:00.603</td>\n",
       "      <td>16</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>16</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>2126</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456381</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-08 08:56:49.874</td>\n",
       "      <td>2017-08-08 08:57:51.430</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>164</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488887</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-07-23 15:25:16.857</td>\n",
       "      <td>2017-07-23 15:26:18.302</td>\n",
       "      <td>159</td>\n",
       "      <td>24th St at Market St</td>\n",
       "      <td>37.816060</td>\n",
       "      <td>-122.278244</td>\n",
       "      <td>159</td>\n",
       "      <td>24th St at Market St</td>\n",
       "      <td>37.816060</td>\n",
       "      <td>-122.278244</td>\n",
       "      <td>1201</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497147</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-07-18 18:52:12.537</td>\n",
       "      <td>2017-07-18 18:53:13.844</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>265</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497676</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-07-18 16:31:47.835</td>\n",
       "      <td>2017-07-18 16:32:49.439</td>\n",
       "      <td>59</td>\n",
       "      <td>S Van Ness Ave at Market St</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>687</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501494</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-07-15 21:22:02.589</td>\n",
       "      <td>2017-07-15 21:23:04.360</td>\n",
       "      <td>109</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>109</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>201</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "8162              61 2017-12-27 08:40:13.217 2017-12-27 08:41:14.973   \n",
       "14236             61 2017-12-22 07:20:37.132 2017-12-22 07:21:38.626   \n",
       "16309             61 2017-12-21 10:23:30.027 2017-12-21 10:24:31.689   \n",
       "17025             61 2017-12-21 08:09:41.482 2017-12-21 08:10:42.777   \n",
       "21866             61 2017-12-19 17:31:47.623 2017-12-19 17:32:49.416   \n",
       "23470             61 2017-12-19 09:35:32.404 2017-12-19 09:36:34.063   \n",
       "44994             61 2017-12-12 20:25:24.009 2017-12-12 20:26:25.161   \n",
       "64776             61 2017-12-07 08:53:02.622 2017-12-07 08:54:04.367   \n",
       "65819             61 2017-12-06 21:18:33.941 2017-12-06 21:19:35.094   \n",
       "69333             61 2017-12-06 08:37:23.879 2017-12-06 08:38:25.642   \n",
       "80408             61 2017-12-02 22:14:25.662 2017-12-02 22:15:26.744   \n",
       "84393             61 2017-12-01 14:24:58.790 2017-12-01 14:26:00.019   \n",
       "94610             61 2017-11-29 08:14:22.712 2017-11-29 08:15:23.883   \n",
       "102421            61 2017-11-27 08:44:18.641 2017-11-27 08:45:19.951   \n",
       "114535            61 2017-11-21 08:23:34.992 2017-11-21 08:24:36.175   \n",
       "119395            61 2017-11-19 18:15:27.232 2017-11-19 18:16:28.506   \n",
       "133870            61 2017-11-14 17:11:43.330 2017-11-14 17:12:44.495   \n",
       "165924            61 2017-11-05 20:13:06.285 2017-11-05 20:14:07.378   \n",
       "186841            61 2017-10-30 19:13:40.428 2017-10-30 19:14:41.662   \n",
       "195131            61 2017-10-27 18:16:21.260 2017-10-27 18:17:22.616   \n",
       "198700            61 2017-10-26 21:32:43.730 2017-10-26 21:33:45.045   \n",
       "204284            61 2017-10-25 18:11:38.851 2017-10-25 18:12:40.772   \n",
       "219367            61 2017-10-21 17:16:15.533 2017-10-21 17:17:16.575   \n",
       "219427            61 2017-10-21 16:56:45.398 2017-10-21 16:57:47.258   \n",
       "219658            61 2017-10-21 16:02:05.053 2017-10-21 16:03:06.646   \n",
       "227339            61 2017-10-19 09:48:20.593 2017-10-19 09:49:22.472   \n",
       "246408            61 2017-10-13 15:19:13.525 2017-10-13 15:20:15.470   \n",
       "248348            61 2017-10-12 21:57:40.850 2017-10-12 21:58:42.422   \n",
       "260599            61 2017-10-09 17:54:17.827 2017-10-09 17:55:19.075   \n",
       "264159            61 2017-10-08 15:21:55.046 2017-10-08 15:22:56.913   \n",
       "270182            61 2017-10-06 12:51:19.316 2017-10-06 12:52:20.349   \n",
       "276445            61 2017-10-04 20:05:18.014 2017-10-04 20:06:19.720   \n",
       "292732            61 2017-09-30 12:48:57.809 2017-09-30 12:49:59.272   \n",
       "293667            61 2017-09-29 20:48:20.216 2017-09-29 20:49:21.305   \n",
       "312919            61 2017-09-25 07:51:47.854 2017-09-25 07:52:49.637   \n",
       "313189            61 2017-09-25 00:21:24.078 2017-09-25 00:22:25.803   \n",
       "316476            61 2017-09-23 15:22:17.585 2017-09-23 15:23:19.340   \n",
       "332554            61 2017-09-19 09:21:32.307 2017-09-19 09:22:33.396   \n",
       "346102            61 2017-09-14 21:27:39.850 2017-09-14 21:28:41.545   \n",
       "376309            61 2017-09-06 08:34:43.267 2017-09-06 08:35:44.867   \n",
       "381108            61 2017-09-04 17:56:04.293 2017-09-04 17:57:05.411   \n",
       "383732            61 2017-09-03 14:08:28.953 2017-09-03 14:09:30.739   \n",
       "383812            61 2017-09-03 13:39:22.801 2017-09-03 13:40:23.813   \n",
       "386883            61 2017-09-01 19:38:27.327 2017-09-01 19:39:29.070   \n",
       "395252            61 2017-08-30 13:51:38.291 2017-08-30 13:52:39.351   \n",
       "395719            61 2017-08-30 10:20:40.867 2017-08-30 10:21:42.118   \n",
       "396097            61 2017-08-30 09:04:16.374 2017-08-30 09:05:18.048   \n",
       "401675            61 2017-08-28 15:38:18.446 2017-08-28 15:39:19.607   \n",
       "406132            61 2017-08-26 16:50:40.178 2017-08-26 16:51:42.087   \n",
       "420504            61 2017-08-22 00:40:36.275 2017-08-22 00:41:37.920   \n",
       "420942            61 2017-08-21 18:52:53.635 2017-08-21 18:53:55.283   \n",
       "445176            61 2017-08-12 14:49:18.154 2017-08-12 14:50:19.538   \n",
       "448634            61 2017-08-11 08:07:59.039 2017-08-11 08:09:00.603   \n",
       "456381            61 2017-08-08 08:56:49.874 2017-08-08 08:57:51.430   \n",
       "488887            61 2017-07-23 15:25:16.857 2017-07-23 15:26:18.302   \n",
       "497147            61 2017-07-18 18:52:12.537 2017-07-18 18:53:13.844   \n",
       "497676            61 2017-07-18 16:31:47.835 2017-07-18 16:32:49.439   \n",
       "501494            61 2017-07-15 21:22:02.589 2017-07-15 21:23:04.360   \n",
       "\n",
       "        start_station_id                                 start_station_name  \\\n",
       "8162                   7                                Frank H Ogawa Plaza   \n",
       "14236                 43  San Francisco Public Library (Grove St at Hyde...   \n",
       "16309                 76                         McCoppin St at Valencia St   \n",
       "17025                 30     San Francisco Caltrain (Townsend St at 4th St)   \n",
       "21866                 17   Embarcadero BART Station (Beale St at Market St)   \n",
       "23470                118                    Eureka Valley Recreation Center   \n",
       "44994                259                            Addison St at Fourth St   \n",
       "64776                243                        Bancroft Way at College Ave   \n",
       "65819                129                             Harrison St at 20th St   \n",
       "69333                 80                              Townsend St at 5th St   \n",
       "80408                 58                               Market St at 10th St   \n",
       "84393                279                           Santa Clara St at 7th St   \n",
       "94610                 99                               Folsom St at 15th St   \n",
       "102421               134                             Valencia St at 24th St   \n",
       "114535                67  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "119395               127                             Valencia St at 21st St   \n",
       "133870                76                         McCoppin St at Valencia St   \n",
       "165924                45                                5th St at Howard St   \n",
       "186841                15  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "195131                88                               11th St at Bryant St   \n",
       "198700                36                                Folsom St at 3rd St   \n",
       "204284                30     San Francisco Caltrain (Townsend St at 4th St)   \n",
       "219367               179                           Telegraph Ave at 27th St   \n",
       "219427                59                        S Van Ness Ave at Market St   \n",
       "219658               129                             Harrison St at 20th St   \n",
       "227339               110   17th & Folsom Street Park (17th St at Folsom St)   \n",
       "246408               211                                Broadway at 40th St   \n",
       "248348                24                              Spear St at Folsom St   \n",
       "260599                80                              Townsend St at 5th St   \n",
       "264159                89                         Division St at Potrero Ave   \n",
       "270182                 7                                Frank H Ogawa Plaza   \n",
       "276445               134                             Valencia St at 24th St   \n",
       "292732                58                               Market St at 10th St   \n",
       "293667               243                        Bancroft Way at College Ave   \n",
       "312919                29                      O'Farrell St at Divisadero St   \n",
       "313189               243                        Bancroft Way at College Ave   \n",
       "316476               286                           San Carlos St at 11th St   \n",
       "332554                64                               5th St at Brannan St   \n",
       "346102                48                                2nd St at S Park St   \n",
       "376309                97                              14th St at Mission St   \n",
       "381108                24                              Spear St at Folsom St   \n",
       "383732                 8                      The Embarcadero at Vallejo St   \n",
       "383812                74                              Laguna St at Hayes St   \n",
       "386883               240                          Haste St at Telegraph Ave   \n",
       "395252               181                            Grand Ave at Webster St   \n",
       "395719               243                        Bancroft Way at College Ave   \n",
       "396097               181                            Grand Ave at Webster St   \n",
       "401675                59                        S Van Ness Ave at Market St   \n",
       "406132               145                               29th St at Church St   \n",
       "420504               230                            14th St at Mandela Pkwy   \n",
       "420942                53                             Grove St at Divisadero   \n",
       "445176               190                                 West St at 40th St   \n",
       "448634                16                            Steuart St at Market St   \n",
       "456381                67  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "488887               159                               24th St at Market St   \n",
       "497147                15  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "497676                59                        S Van Ness Ave at Market St   \n",
       "501494               109                             17th St at Valencia St   \n",
       "\n",
       "        start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "8162                 37.804562              -122.271738               7   \n",
       "14236                37.778768              -122.415929              42   \n",
       "16309                37.771662              -122.422423              76   \n",
       "17025                37.776598              -122.395282              30   \n",
       "21866                37.792251              -122.397086              17   \n",
       "23470                37.759177              -122.436943             118   \n",
       "44994                37.866249              -122.299371             259   \n",
       "64776                37.869360              -122.254337             239   \n",
       "65819                37.758862              -122.412544             129   \n",
       "69333                37.775306              -122.397380              80   \n",
       "80408                37.776619              -122.417385              58   \n",
       "84393                37.339146              -121.884105             280   \n",
       "94610                37.767037              -122.415442              99   \n",
       "102421               37.752428              -122.420628             134   \n",
       "114535               37.776639              -122.395526              80   \n",
       "119395               37.756708              -122.421025             121   \n",
       "133870               37.771662              -122.422423              76   \n",
       "165924               37.781752              -122.405127              45   \n",
       "186841               37.795392              -122.394203              15   \n",
       "195131               37.770030              -122.411726              88   \n",
       "198700               37.783830              -122.398870              36   \n",
       "204284               37.776598              -122.395282              30   \n",
       "219367               37.816073              -122.267886             179   \n",
       "219427               37.774814              -122.418954              58   \n",
       "219658               37.758862              -122.412544             129   \n",
       "227339               37.763708              -122.415204             112   \n",
       "246408               37.827757              -122.256716             211   \n",
       "248348               37.789677              -122.390428              24   \n",
       "260599               37.775306              -122.397380              67   \n",
       "264159               37.769218              -122.407646              89   \n",
       "270182               37.804562              -122.271738               7   \n",
       "276445               37.752428              -122.420628             133   \n",
       "292732               37.776619              -122.417385              58   \n",
       "293667               37.869360              -122.254337             243   \n",
       "312919               37.782405              -122.439446              29   \n",
       "313189               37.869360              -122.254337             239   \n",
       "316476               37.336466              -121.876613             286   \n",
       "332554               37.776754              -122.399018              64   \n",
       "346102               37.782411              -122.392706              49   \n",
       "376309               37.768265              -122.420110              97   \n",
       "381108               37.789677              -122.390428              24   \n",
       "383732               37.799953              -122.398525               8   \n",
       "383812               37.776435              -122.426244              74   \n",
       "386883               37.866043              -122.258804             240   \n",
       "395252               37.811377              -122.265192             181   \n",
       "395719               37.869360              -122.254337             243   \n",
       "396097               37.811377              -122.265192             181   \n",
       "401675               37.774814              -122.418954              58   \n",
       "406132               37.743684              -122.426806             145   \n",
       "420504               37.810743              -122.291415             230   \n",
       "420942               37.775946              -122.437777              53   \n",
       "445176               37.830223              -122.270950             190   \n",
       "448634               37.794130              -122.394430              16   \n",
       "456381               37.776639              -122.395526              67   \n",
       "488887               37.816060              -122.278244             159   \n",
       "497147               37.795392              -122.394203              15   \n",
       "497676               37.774814              -122.418954              58   \n",
       "501494               37.763316              -122.421904             109   \n",
       "\n",
       "                                         end_station_name  \\\n",
       "8162                                  Frank H Ogawa Plaza   \n",
       "14236       San Francisco City Hall (Polk St at Grove St)   \n",
       "16309                          McCoppin St at Valencia St   \n",
       "17025      San Francisco Caltrain (Townsend St at 4th St)   \n",
       "21866    Embarcadero BART Station (Beale St at Market St)   \n",
       "23470                     Eureka Valley Recreation Center   \n",
       "44994                             Addison St at Fourth St   \n",
       "64776                       Bancroft Way at Telegraph Ave   \n",
       "65819                              Harrison St at 20th St   \n",
       "69333                               Townsend St at 5th St   \n",
       "80408                                Market St at 10th St   \n",
       "84393                              San Fernando at 7th St   \n",
       "94610                                Folsom St at 15th St   \n",
       "102421                             Valencia St at 24th St   \n",
       "114535                              Townsend St at 5th St   \n",
       "119395                                 Mission Playground   \n",
       "133870                         McCoppin St at Valencia St   \n",
       "165924                                5th St at Howard St   \n",
       "186841  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "195131                               11th St at Bryant St   \n",
       "198700                                Folsom St at 3rd St   \n",
       "204284     San Francisco Caltrain (Townsend St at 4th St)   \n",
       "219367                           Telegraph Ave at 27th St   \n",
       "219427                               Market St at 10th St   \n",
       "219658                             Harrison St at 20th St   \n",
       "227339                             Harrison St at 17th St   \n",
       "246408                                Broadway at 40th St   \n",
       "248348                              Spear St at Folsom St   \n",
       "260599  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "264159                         Division St at Potrero Ave   \n",
       "270182                                Frank H Ogawa Plaza   \n",
       "276445                             Valencia St at 22nd St   \n",
       "292732                               Market St at 10th St   \n",
       "293667                        Bancroft Way at College Ave   \n",
       "312919                      O'Farrell St at Divisadero St   \n",
       "313189                      Bancroft Way at Telegraph Ave   \n",
       "316476                           San Carlos St at 11th St   \n",
       "332554                               5th St at Brannan St   \n",
       "346102                                S Park St at 3rd St   \n",
       "376309                              14th St at Mission St   \n",
       "381108                              Spear St at Folsom St   \n",
       "383732                      The Embarcadero at Vallejo St   \n",
       "383812                              Laguna St at Hayes St   \n",
       "386883                          Haste St at Telegraph Ave   \n",
       "395252                            Grand Ave at Webster St   \n",
       "395719                        Bancroft Way at College Ave   \n",
       "396097                            Grand Ave at Webster St   \n",
       "401675                               Market St at 10th St   \n",
       "406132                               29th St at Church St   \n",
       "420504                            14th St at Mandela Pkwy   \n",
       "420942                             Grove St at Divisadero   \n",
       "445176                                 West St at 40th St   \n",
       "448634                            Steuart St at Market St   \n",
       "456381  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "488887                               24th St at Market St   \n",
       "497147  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "497676                               Market St at 10th St   \n",
       "501494                             17th St at Valencia St   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "8162               37.804562            -122.271738     1261  Subscriber   \n",
       "14236              37.778650            -122.418230     2580  Subscriber   \n",
       "16309              37.771662            -122.422423     2378  Subscriber   \n",
       "17025              37.776598            -122.395282     2079  Subscriber   \n",
       "21866              37.792251            -122.397086     2183  Subscriber   \n",
       "23470              37.759177            -122.436943     3302  Subscriber   \n",
       "44994              37.866249            -122.299371      801  Subscriber   \n",
       "64776              37.868813            -122.258764      330  Subscriber   \n",
       "65819              37.758862            -122.412544     2121    Customer   \n",
       "69333              37.775306            -122.397380     3303  Subscriber   \n",
       "80408              37.776619            -122.417385     1883  Subscriber   \n",
       "84393              37.337246            -121.883074     1875  Subscriber   \n",
       "94610              37.767037            -122.415442     3129    Customer   \n",
       "102421             37.752428            -122.420628      763  Subscriber   \n",
       "114535             37.775306            -122.397380     3066  Subscriber   \n",
       "119395             37.759210            -122.421339     1166  Subscriber   \n",
       "133870             37.771662            -122.422423     2127  Subscriber   \n",
       "165924             37.781752            -122.405127      962    Customer   \n",
       "186841             37.795392            -122.394203     1981  Subscriber   \n",
       "195131             37.770030            -122.411726     2358  Subscriber   \n",
       "198700             37.783830            -122.398870      166  Subscriber   \n",
       "204284             37.776598            -122.395282      915  Subscriber   \n",
       "219367             37.816073            -122.267886     1108  Subscriber   \n",
       "219427             37.776619            -122.417385     3150  Subscriber   \n",
       "219658             37.758862            -122.412544      240  Subscriber   \n",
       "227339             37.763847            -122.413004     1827  Subscriber   \n",
       "246408             37.827757            -122.256716      401  Subscriber   \n",
       "248348             37.789677            -122.390428     3032  Subscriber   \n",
       "260599             37.776639            -122.395526       24  Subscriber   \n",
       "264159             37.769218            -122.407646     1717  Subscriber   \n",
       "270182             37.804562            -122.271738     1385  Subscriber   \n",
       "276445             37.755213            -122.420975     2303  Subscriber   \n",
       "292732             37.776619            -122.417385     2327  Subscriber   \n",
       "293667             37.869360            -122.254337     1368  Subscriber   \n",
       "312919             37.782405            -122.439446     2939  Subscriber   \n",
       "313189             37.868813            -122.258764     2806  Subscriber   \n",
       "316476             37.336466            -121.876613     1499  Subscriber   \n",
       "332554             37.776754            -122.399018     2883  Subscriber   \n",
       "346102             37.780760            -122.394989      872  Subscriber   \n",
       "376309             37.768265            -122.420110     2564  Subscriber   \n",
       "381108             37.789677            -122.390428     2204    Customer   \n",
       "383732             37.799953            -122.398525     1383  Subscriber   \n",
       "383812             37.776435            -122.426244     1437  Subscriber   \n",
       "386883             37.866043            -122.258804      465    Customer   \n",
       "395252             37.811377            -122.265192     1238  Subscriber   \n",
       "395719             37.869360            -122.254337      125  Subscriber   \n",
       "396097             37.811377            -122.265192     2083  Subscriber   \n",
       "401675             37.776619            -122.417385     2622  Subscriber   \n",
       "406132             37.743684            -122.426806     2136  Subscriber   \n",
       "420504             37.810743            -122.291415     1833  Subscriber   \n",
       "420942             37.775946            -122.437777     1937  Subscriber   \n",
       "445176             37.830223            -122.270950     1345    Customer   \n",
       "448634             37.794130            -122.394430     2126  Subscriber   \n",
       "456381             37.776639            -122.395526      164  Subscriber   \n",
       "488887             37.816060            -122.278244     1201    Customer   \n",
       "497147             37.795392            -122.394203      265    Customer   \n",
       "497676             37.776619            -122.417385      687  Subscriber   \n",
       "501494             37.763316            -122.421904      201  Subscriber   \n",
       "\n",
       "        duration_min  \n",
       "8162        1.016667  \n",
       "14236       1.016667  \n",
       "16309       1.016667  \n",
       "17025       1.016667  \n",
       "21866       1.016667  \n",
       "23470       1.016667  \n",
       "44994       1.016667  \n",
       "64776       1.016667  \n",
       "65819       1.016667  \n",
       "69333       1.016667  \n",
       "80408       1.016667  \n",
       "84393       1.016667  \n",
       "94610       1.016667  \n",
       "102421      1.016667  \n",
       "114535      1.016667  \n",
       "119395      1.016667  \n",
       "133870      1.016667  \n",
       "165924      1.016667  \n",
       "186841      1.016667  \n",
       "195131      1.016667  \n",
       "198700      1.016667  \n",
       "204284      1.016667  \n",
       "219367      1.016667  \n",
       "219427      1.016667  \n",
       "219658      1.016667  \n",
       "227339      1.016667  \n",
       "246408      1.016667  \n",
       "248348      1.016667  \n",
       "260599      1.016667  \n",
       "264159      1.016667  \n",
       "270182      1.016667  \n",
       "276445      1.016667  \n",
       "292732      1.016667  \n",
       "293667      1.016667  \n",
       "312919      1.016667  \n",
       "313189      1.016667  \n",
       "316476      1.016667  \n",
       "332554      1.016667  \n",
       "346102      1.016667  \n",
       "376309      1.016667  \n",
       "381108      1.016667  \n",
       "383732      1.016667  \n",
       "383812      1.016667  \n",
       "386883      1.016667  \n",
       "395252      1.016667  \n",
       "395719      1.016667  \n",
       "396097      1.016667  \n",
       "401675      1.016667  \n",
       "406132      1.016667  \n",
       "420504      1.016667  \n",
       "420942      1.016667  \n",
       "445176      1.016667  \n",
       "448634      1.016667  \n",
       "456381      1.016667  \n",
       "488887      1.016667  \n",
       "497147      1.016667  \n",
       "497676      1.016667  \n",
       "501494      1.016667  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_duration = CSV_data[CSV_data['duration_sec'] == 61]\n",
    "min_duration.head(58)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Nothing too noticeable - these short trips occur throughout date range of the pilot data (June - December).  Also they often seem to have the same start and end station, which makes sense for short bike trips (perhaps just around the block).</p>\n",
    "\n",
    "<p>I also observed that the last record in the dataframe sorted by 'duration_sec' will have the longest trip.  The largest value for 'duration_sec' is 86,369 seconds.  Which equates to almost 1440 minutes full days! <b>The longest trip (in duration) for this dataset was almost 24 hours or 1440 minutes, or more specifically 86,369 seconds!</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                                    86369\n",
       "start_time                 2017-11-12 10:44:32.043000\n",
       "end_time                   2017-11-13 10:44:01.820000\n",
       "start_station_id                                  308\n",
       "start_station_name                   San Pedro Square\n",
       "start_station_latitude                      37.336802\n",
       "start_station_longitude                    -121.89409\n",
       "end_station_id                                    308\n",
       "end_station_name                     San Pedro Square\n",
       "end_station_latitude                        37.336802\n",
       "end_station_longitude                      -121.89409\n",
       "bike_id                                          2231\n",
       "user_type                                    Customer\n",
       "duration_min                              1439.483333\n",
       "Name: 138862, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data.sort_values(by='duration_sec').iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1439.4833333333333"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data.sort_values(by='duration_sec').iloc[-1]['duration_min']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.99138888888889"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data.sort_values(by='duration_sec').iloc[-1]['duration_min']/60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>This trip is certainly worth noting!  A customer checked out a bike for almost a full day, which is certainly going to cost them!  <br/>The start and end station of this trip are also identical so it seems to have been a day-long round trip</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3:  How many stations are there?  How many stations were active in the first 30 days of service?  How many stations were active in the last month of service (December)?</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(CSV_data['start_station_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "272"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(CSV_data['end_station_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.array_equal(numpy.sort(pd.unique(CSV_data['start_station_id'])),numpy.sort(pd.unique(CSV_data['end_station_id'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> There are a total of <b>272 stations</b> in this dataset, which was determined by getting all the unique values of 'start_station_id' and 'end_station_id' then getting the length of that resulting array and ensuring those arrays have equivalent contents.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_30_days = CSV_data[CSV_data['start_time'] <= (CSV_data.sort_values(by='start_time').iloc[0]['start_time'] + datetime.timedelta(days=30) )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(first_30_days['start_station_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "146"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(first_30_days['end_station_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.array_equal(numpy.sort(pd.unique(first_30_days['start_station_id'])),numpy.sort(pd.unique(first_30_days['end_station_id'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> For the first 30 days of this pilot, a total of <b>146 stations</b> were active.  This was calculated similarly as described above, but with a subset of the total dataset.  The subset, labeled as first_30_days, was created with a filter that applied a timedelta of 30 days from the first recorded start_time</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "december_data = CSV_data[CSV_data['start_time'] >= datetime.datetime(2017,12,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "267"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(december_data['start_station_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "267"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.unique(december_data['end_station_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.array_equal(numpy.sort(pd.unique(december_data['start_station_id'])),numpy.sort(pd.unique(december_data['end_station_id'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p> Similarly, for the last month a total of <b>267 stations</b> were active.  This subset, labeled as december_data, was created with a filter on the condition that the start_time datetime was greater or equal to December 1st, 2017</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>4:  Between which two stations were there the most journeys (i.e., between a particular start and end station)? </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_data['trip_start2end'] = CSV_data['start_station_name'] + ' -> traveling to -> ' + CSV_data['end_station_name']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>First, I decided to construct another column labeled 'trip_start2end' that captured the full trip information by concatenating the 'start_station_name' with a string ' -> traveling to -> ' and the 'end_station_name'</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'San Francisco Ferry Building (Harry Bridges Plaza) -> traveling to -> The Embarcadero at Sansome St'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data['trip_start2end'].mode().iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>To find the most common journey, I simply calculate the mode of this newly created column.  As shown by the above cell's output, the most common journey was from <b>San Francisco Ferry Building (Harry Bridges Plaza) traveling to The Embarcadero at Sansome St.</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3344, 15)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV_data[CSV_data['trip_start2end']== CSV_data['trip_start2end'].mode().iloc[0]].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Here I created a subset of the total dataset based on the condition that the 'trip_start2end' is equal to the most common occuring value (as determined above).  Then I took the shape to determine how many trips were between these two stations - in this case there are 3344 records that traveled between these two stations </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>4b.  What about just Subscribers?  Just Customers?</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>To determine the most common trip per each user-type, I've created two subsets 'Sub_only' & 'Cust_only' that consist of only trips for Subscribers and Customers respectively</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'San Francisco Ferry Building (Harry Bridges Plaza) -> traveling to -> The Embarcadero at Sansome St'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sub_only = CSV_data[CSV_data['user_type'] =='Subscriber']\n",
    "Sub_only['trip_start2end'].mode().iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'San Francisco Ferry Building (Harry Bridges Plaza) -> traveling to -> The Embarcadero at Sansome St'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cust_only = CSV_data[CSV_data['user_type'] =='Customer']\n",
    "Cust_only['trip_start2end'].mode().iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1697, 15)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sub_only[Sub_only['trip_start2end']== Sub_only['trip_start2end'].mode().iloc[0]].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1647, 15)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cust_only[Cust_only['trip_start2end']== Cust_only['trip_start2end'].mode().iloc[0]].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Similarly to before, I calculated the mode of the 'trip_start2end' column.  For both of these subsets, Sub_only and Cust_only, the most common journey is identical to the total datasets most common journey: from <b>San Francisco Ferry Building (Harry Bridges Plaza) traveling to The Embarcadero at Sansome St.</b></p>\n",
    "<p>I've also determined that there were 1697 trips by Subscribers while there were 1647 bike trips by Customers between the Ferry Building and the Embarcadero.  Just for my own sanity check.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Open-ended Questions</h1>\n",
    "<h3>1.  What would you say about the status of the pilot to a stakeholder?</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>In order to comment on the status of the pilot, I want to evaluate the number of trips that occur per month throughout the dataset.  Perhaps I will find a trend that I could report on.</p>\n",
    "    <ul><li>Though we should note that there will be limited information from June 2017 since our earliest trips started on the 28th of the month</li></ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSV_data.index = CSV_data['start_time']\n",
    "monthly_Data = CSV_data.groupby(by=CSV_data.index.month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Actually, on second thought, it would be nice to see the breakdown of monthly trips per user-type.  And plotting these trips on a histogram would make it easier to spot any trends of note</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Trips per month by user type')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Sub_only.index = Sub_only['start_time'] #referring to subset created earlier in the notebook of only the subscriber trips\n",
    "Sub_monthly_Data = Sub_only.groupby(by=Sub_only.index.month) #grouping by month  of start_time\n",
    "Sub_count_series = Sub_monthly_Data.count()['bike_id']  #count the number of trips and 'bike_id' is an abritrary column to access single series from Groupby object (bit wonky)\n",
    "Sub_count_series = Sub_count_series.rename(\"Subscriber_trips\") #renaming to more understandable terms\n",
    "Sub_count_series = Sub_count_series.rename(index={6: 'June', 7:'July',8:'August',9:'September',10:'October',11:'November',12:'December'})\n",
    "Cust_only.index = Cust_only['start_time']  #doing same exact methods to customer only dataset\n",
    "Cust_only_monthly_Data = Cust_only.groupby(by=Cust_only.index.month)\n",
    "Cust_count_series = Cust_only_monthly_Data.count()['bike_id']\n",
    "Cust_count_series = Cust_count_series.rename(\"Customer_trips\")\n",
    "Cust_count_series = Cust_count_series.rename(index={6: 'June', 7:'July',8:'August',9:'September',10:'October',11:'November',12:'December'})\n",
    "total_count_df = pd.concat([Sub_count_series,Cust_count_series], axis=1)  #concating the two series into a DataFrame - that we can make a stacked barchart in next cell\n",
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(12,4))  #Making subplots so that we can plot the monthly trips side by side\n",
    "axe = ax.ravel()\n",
    "all_ax = total_count_df.plot.bar(stacked=True,ax=axe[0])\n",
    "all_ax.set_xlabel('Month of 2017')\n",
    "all_ax.legend(loc='lower right')\n",
    "all_ax.set_ylabel(\"# of trips\")\n",
    "Sub_count_series.plot.bar(color='blue',ax=axe[1]).set_xlabel('Month of 2017')\n",
    "Cust_count_series.plot.bar(color='#ff7b00',ax=axe[2]).set_xlabel('Month of 2017')\n",
    "fig.suptitle(\"Trips per month by user type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>As mentioned, we can expect low numbers for June since there isn't much data from earlier in the month.  Still, a few notable observations include: </p>\n",
    "    <ol><li>We see a promising upward trend in the number of trips for both subscribers and customers between July and October.</li>\n",
    "        <li>Then we start to see a decline in the number of trips (both for subscribers and customers) from October to November and November to December.</li>\n",
    "        <li>The decline in the number of trips is notably steeper for customers, whereas subscribers doesn't fall off as much and hold fairly steady near 70,000 trips per month towards the end of the year.</li>\n",
    "    <li>During this phase in the pilot (from late June until the end of 2017), our subscribers take many more bike trips since our dataset contains almost 4 times more subscribers trips (409,230 vs. 110470) than customers.</li>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(409230, 15)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sub_only.shape #how many trips were taken by subscribers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110470, 15)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cust_only.shape #how many trips were taken by customers only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>More questions arise from the above observations:  <ul><li>Is this decline due to seasonality of weather or tourism? <br/>(perhaps we can find data on the weather or tourism from this time - or we could conduct similar analysis on another year's data)</li><li>Or perhaps the number of trips correlates with the number of stations (we can create another variable for number of stations & conduct a similar analysis)?</li></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_time\n",
       "June          41\n",
       "July         153\n",
       "August       209\n",
       "September    235\n",
       "October      247\n",
       "November     254\n",
       "December     267\n",
       "Name: start_station_id, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Avg1nioiYMGS76Qw1SfsDe9t+T9k+GHiZ7fe1HHM4cHjZ3Bq4u4ORJgOLOvj6nZb8zUr+5vRzduh8/hfZnjLUjpU7+KajoSHalqpatmcBs7oSRppne0Y33qsTkr9Zyd+cfs4Ozebvte6jBcBmLdubAg82lCUiYsLptaLwS2C6pM0lvQCYCcxpOFNExITRU91HthdLeh9wOTAJONv27Q1G6ko3VQclf7OSvzn9nB0azN9TF5ojIqJZvdZ9FBERDUpRiIiIWorCIJJWl7R10zkiIpqQotBC0puBm4EflO0dJfXN6CdJp0jarukcE5GkSZK+1nSO0Sr5P9V0jtFK/rGTorC0E6mm2vgDgO2bgWmNpRm5u4BZkq6T9H8kvbDpQCMhaW47bb3I9nPAlDKUuu+U/H8vaagbSHte8o+dnhqS2gMW2/5jD/x3GRXbZwJnlu6vQ4FbJP0U+LLtK5tNt2ySVgPWACZLWo8ld7avA2zSWLCRux/4aTm7fGqg0fapjSUamZuAiyV9i6XzX9hcpBFJ/jGQorC02yT9L2CSpOnA0cDPGs40ImWm2W3K1yLgV8CHJB1he2aj4ZbtCOAYqgJwA0uKwhNUs+b2iwfL10rA2g1nGY31gUeBPVvaDPTLh2ryj4Hcp9BC0hrAx4DXU30wXQ580vafGw3WJkmnAm8B5gJn2b6+Zd/dtnv6Arqk99v+fNM5VpSkNW0/tfwjI3pPrim0sP207Y/Z3sX2jPK4LwpCcRuwve0jWgtCsWsTgUbo95LWBpD0cUkXStq56VDtkrSbpDuAO8v2DpJOazhW2yRtJWmupNvK9vaSPt50rnYl/xjlyJnCEpK2Av6F6uJy3bVme89lPacXLO+D0/aN3cqyIiTdYnt7Sa8A/hM4BTje9ssajtYWSdcB+wNzbO9U2m6z/ZJmk7VH0tXAR4Azkr/7eiV/riks7VvAl4AzgecazjISnx5mn1m6j7KXDfzO9wFOt32xpBMbzDNith8YNFChn/4drWH7+kH5FzcVZhSSfwykKCxtse3Tmw4xUrZf03SGMfI7SWcArwX+S9Kq9FcX5wOSdgdchqYeTelK6hOLJG1JWcOkLHr1ULORRiT5x0C6j1qUv0ofAS4Cnhlot/1YU5lGQtK7hmq3/ZVuZxmNcqF/b+BW2/dI2hh4qe0fNhytLZImA5+lKmorUQ1U+IDtRxsN1iZJW1DNzrk78DhwH3CQ7d80GqxNyT9GOVIUlpB03xDNtr1F18OMgqTWkTurAXsBN9rev6FIIyLpb4dqt/3bbmeZyCStCaxk+8mms4xG8q/g+6cojF/ljuav2n5L01naIelWqlNnURW1zYG7bffF1B3lL73PAi+n+jl+DnzQ9r2NBmuTpA2AE4BXUOW/FvhEH53pJP8YyDWFFv3e/TKEp4HpTYdol+2Xtm6XUVVHNBRnNL5BdbPdfmV7JnA+0Bejp4ALgGuAfyrbBwHfpOoO6wfJPwZyptBiHHS/XEK5SEW1ct22wGzbH20u1YqRdKPtvrhXQdJ1g4fPSvqF7Zc3lWkkJN1g++8HtTW2gPxIJf/YyJlCC9vvb90e6H5pKM5onMKSorAY+I3t3zWYZ0QkfahlcyVgZ2BhQ3HaJmn98vBKScdS/cVn4B3AZY0FG7krJc0EZpft/Un+buqJ/DlTGIakVYBbbP9d01mGI+lJlvTFtzLVKKpfAx+z3dMzjko6oWVzMdUEc9/p9bvKywCFoX7/0AcDFQb9+1kT+GvZtRLwP7bXaSpbO5J/jPOkKCwxTrtfJgEvAb7eL3d2RkRzUhRaSHoVfdz9MpwyS+oZTecYjoZe0OiPwDyqW/97/YxhEtXd2NNYepqUfpk6G0lvAV5ZNq+yfWmTeUZK0vY8//ffL7Ok9kT+FAXGT/dLv5P0WWAK1YgdqPrkfw+sDqxj++CmsrVD0veAPwO3sqQLANv/1lioEZB0MrAL8PXSdCBwg+1jm0vVPklnA9sDt7Pk92/b724uVft6JX+KwnKk+6V7JF1j+5VDtUm6vdfvVxiY0K/pHKMl6RZgR9t/LduTgJv65WeSdIftbZvOMVq9kr+f5pVphO3nbP8K6Pt5/vvAlNa7msvjyWXz2WYijcj3Jb2+6RAraN2Wx321nCvwc0mNf6iugJ7InyGpber1/vhx4sPAtZJ+TdWVtzlwZLnt/7xGk7XnF8BFklYC/kL1M7jXR7+0+E/gJklXUmV/JXB8s5FG5DyqD9bfU3X7Dvz+++JMhx7Jn+6j6CllZtRtqP6HuKvXLy63knQv8FaqCf368n+sMgnhLlS//+ts/77hSG2TNB/4EM+/ptMvE+L1RP4UhegZ/T7NiKTLgTcO9Mn3G0lzbe+1vLZeJemKXl8Qazi9kj/dR9FLdml5XE8zAvRFUaCa+/4qSd9n6anXe3pIqqTVgDWAyZLWY8kovHWATRoLNnJ3SfoGcAlL//77ZUhqT+RPUYieMQ6mGbmvfL2gfPWLI4BjqArADSwpCk9QTfDXL1an+jBtvdhvoF+KQk/kT/dR9KwyzcittrdpOstISFrT9lNN5xgpSe+3nVF2E1yGpEbPkHSJpDnl6zLgbuC7Dcdqm6TdJN1BWYJT0g6STms41kj8VdK6AxuS1pN0ZIN5RkTSVpLmSrqtbG8v6eNN52pXr+TPmUL0jDLNyIDFVN0YB9o+qqFIIyLpOqqZLefY3qm03dYvNz1Kutn2joPabhr4WXqdpKuBj1BNidKPv/+eyJ8zhegZtq+mmutoH6qpFj5Jfy18j+0HBjU910iQ0VlJUj3VS7mjuZ+ujaxh+/pBbYsbSTI6PZE/F5qjcZK2olql7EDgUarVpmT7NY0GG7kHJO0OWNILgKPpr6L2Q2C2pC9RXeD8Z+AHzUYakUWStqRMailpf6oRYf2iJ/Kn+ygaJ+mvwE+Aw2zPL2339vo6BINJmky1RvNrqbq+fggcbfuxRoO1SdLqwHuBf2BJ/rNs98XZjqo1smcBuwOPU40EO6iPbl4bKv87bd/fzRw5U4he8E9UZwpXSvoB1cplQy1Y0+u2tn1Qa4OkPYCfNpSnLZJWBv4DOBR4gOp3vxnVh9JK9E8X2G9sv7ZMi7KS7SebDjQStu8FGs+fawrRONsX2X4H1fQWVwEfBDaSdHqfTTA31HDOfhji+SlgfWAL2zuXi5ybU02Id0qjyUbmPkmzgJcD/9N0mJGS9B+S1rX9lO0ny+ivf+96jnQfRS8q6x4fALyjF279H46k3ahO+Y8BPtOyax1gP9s7NJGrXZLuAbYaPF9TudB8l+3pzSQbmdL99Waqs86dgUuBC2xf22iwNg010kvSjbZ37maOnClET7L9mO0zer0gFC8A1qLqjl275esJqiGqvc5DTeBXriX0zV+Ntv9ke7bttwE7URXlqxuONRKTyoSQQF3kVh3m+I7INYWIFVSG0l4t6Vzbv5G0TtXcN33ad0h61+CJByW9E7iroUyjUu51eQfwRuCXwNubTTQiXwPmSjqHqhi/mwamjE/3UcQYkTQDOIfqLAGqey7ebfuG5lItn6SpVPPr/Ilq7iNTTU64OlX3V1+sUy7pPuBmYDbVDYT9ONXI3rSMXrN9edczpChEjI2ynOVRtn9Stl8BnNYvi7xI2hPYjuoD6fZ+W5Nc0jq2n2g6x4qQ9CJguu0fS1oDmNTtM850H0WMnScHCgKA7Wsl9UsXEravAK5oOscKWEfSecAeVGc71wIfsL2g2VjtkfRe4HCqkWBbAlOBL1FNId81udAcMXaul3SGpFdLelWZDO8qSTtL6uoIkgnqHGAO1RTgU6nWJTin0UQjcxRVQXsCwPY9wIbdDpEzhYixs2P5fsKg9t2p/nLth5FU/WyK7dYicK6kY5oKMwrP2H52YPqpclNh1/v3UxQixkgfztU03iwqI6bOL9sDc2n1i6slHQ+sLul1wJFUZztdlQvNEWNE0kZU00VsYvuNkrYFdrN9VsPRJgRJfwt8AdiN6i/sn1FdU+iXuY9WAg6jWnlNwOXAmUPdQ9LRHCkKEWOjrM18DvAx2zuU0/+bbL+04WjRJyRNAbC9sKkM6T6KGDuTbc+WdByA7cWS+mUyub4l6f8Ns9u2P9m1MKNQ1rA4AXgf1RmCyr+bz9v+RLfzZPRRxNh5StIGLJkP/+VUN7BFZz01xBdUXTEfbSrUCBxDNepoF9sb2F4feBmwh6QPdjtMuo8ixkgZdvp54CXAbcAUYH/btzQabAKRtDbwAaqCMBv4tO1Hmk01PEk3Aa+zvWhQ+xSqu5q7uhxqzhQiVpCkXST9je0bgVcBxwPPUC1S0xc3TvU7SeuXaaZvoeoW39n2R3u9IBSrDC4IUF9XWKXbYVIUIlbcGcCz5fHuwMeAL1KtnjWrqVAThaRPUU1+9yTwUtsn2n684Vgj8ewo93VEuo8iVpCkXw2smSDpi8BC2yeW7Ztt79hgvHGvLOf6DNUi960faKK60LxOI8HaVC4qDzV5n4DVbHf1bCGjjyJW3CRJK9teTDVPzeEt+/L/WIfZ7useD9uTms7QKv9gI1bc+VR3oy6imn56YJbUF5PRR9Fn0n0UMQbK8NONqUaLPFXatgLWKhegI/pCikJERNT6ui8uIiLGVopCRETUUhRi3JBkSV9t2V5Z0kJJl47y9daVdGTL9qtH+1rl+dtIulnSTZK2bGlfQ9Jlku6SdLukk1v2rSrpm5LmS7pO0rSWfT+Q9IfBmST9pLzPzZIelPTd0WaOiSdFIcaTp4CXSFq9bL8OWJFF59elmtN+rLwVuNj2TrZ/PWjfKba3AXaimvPmjaX9MOBx2y8GPgP8V8tzPgUcPPhNbP+D7R3L/RE/By4cw58hxrkUhRhvvg/sUx4fyJIFVwamQviupFsk/ULS9qX9RElnS7pK0r2Sji5PORnYsvzF/anStpakb5e/6r+ugWWyWkjasbz+LZIukrSepDdRTXz2HklXth5v+2nbV5bHzwI3ApuW3fsC55XH3wb2GnhP23Op7uIdUpkHaE/gu8v9rUUUKQox3lwAzJS0GrA9cF3Lvn+jWt9ge6r5ib7Ssm8b4A3ArsAJklYBjgV+Xf7q/kg5bieqD/dtgS2oZrcc7CvAR8v73AqcYPt7VIuwf2a4FdokrQu8GZhbmqYCD0A1FTfVfQ8bLP/XAMB+wFzbT7R5fESKQowvZUbSaVRnCd8btPsVwFfLcVcAG0h6Ydl3me1nysRkjwAbLeMtrre9wPZfgZvLe9XK661r++rSdB7wynayl0V5zgc+Z/vegeahfsx2Xo9BZ0oR7UhRiPFoDnAKz/9AHO4D9pmWtudY9t3+7R43GrOAe2z/d0vbAmAzqIvGC4HHlvdCZV2HXYHLxjBfTAApCjEenQ18wvatg9qvAQ6CaiQRsGg5XStPAmuP5I1t/xF4XNI/lKaDgauHeQolz79TfeAfM2jXHOCQ8nh/4Io21+w9ALjU9p/byR0xIHMfxbhjewHw2SF2nQicI+kW4GmWfNgu63UelfRTSbdRXcBu96/uQ4AvSVoDuBc4dLiDJW1KNd32XcCN5TryF2yfCZwFfFXSfKozhJktz/sJ1bWQtSQtAA6zfXnZPZPqQnnEiGSai4iIqKX7KCIiaikKERFRS1GIiIhaikJERNRSFCIiopaiEBERtRSFiIiopShERETt/wOxQ0cLzkLT0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numStationsPerMonth = monthly_Data['start_station_id'].nunique()\n",
    "numStationsPerMonth = numStationsPerMonth.rename(index={6: 'June', 7:'July',8:'August',9:'September',10:'October',11:'November',12:'December'})\n",
    "station_hist = numStationsPerMonth.plot.bar()\n",
    "station_hist.set_xlabel('Month of 2017')\n",
    "station_hist.set_ylabel(\"# of stations\")\n",
    "numStationsPerMonth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>By calculating & plotting these numbers in a histogram, we can see that the number of stations in use rises month over month throughout the whole date-range of this pilot dataset.<br/>  Though the initial rise in the number of stations available per month may account for some of the increase in the number of trips taken per month, we do not find a strong correlation because the number of stations in use continues to rise from October to December while the number of trips taken per month starts its decline.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2. What users or people would you be interested in interviewing to learn more about their experience and why?</h3>\n",
    "<p>I was highly intrigued by the range of the trip durations that we calculated in the 2nd warm-up question.  So I wanted to learn more about the distribution of trip duration.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>90%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>2689.0</td>\n",
       "      <td>17.751419</td>\n",
       "      <td>46.535901</td>\n",
       "      <td>1.033333</td>\n",
       "      <td>4.516667</td>\n",
       "      <td>6.383333</td>\n",
       "      <td>9.366667</td>\n",
       "      <td>13.516667</td>\n",
       "      <td>22.636667</td>\n",
       "      <td>44.296667</td>\n",
       "      <td>229.512000</td>\n",
       "      <td>1176.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>44073.0</td>\n",
       "      <td>25.279279</td>\n",
       "      <td>75.297851</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.583333</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>10.433333</td>\n",
       "      <td>16.883333</td>\n",
       "      <td>32.216667</td>\n",
       "      <td>75.250000</td>\n",
       "      <td>344.906000</td>\n",
       "      <td>1417.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>83292.0</td>\n",
       "      <td>21.006761</td>\n",
       "      <td>67.058908</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>27.865000</td>\n",
       "      <td>46.315000</td>\n",
       "      <td>239.457500</td>\n",
       "      <td>1436.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>98558.0</td>\n",
       "      <td>19.358276</td>\n",
       "      <td>58.902067</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.316667</td>\n",
       "      <td>6.566667</td>\n",
       "      <td>10.266667</td>\n",
       "      <td>16.533333</td>\n",
       "      <td>26.533333</td>\n",
       "      <td>41.283333</td>\n",
       "      <td>199.905000</td>\n",
       "      <td>1432.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>108937.0</td>\n",
       "      <td>16.933229</td>\n",
       "      <td>52.057272</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.183333</td>\n",
       "      <td>6.283333</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>15.400000</td>\n",
       "      <td>23.933333</td>\n",
       "      <td>33.603333</td>\n",
       "      <td>150.110667</td>\n",
       "      <td>1439.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>95612.0</td>\n",
       "      <td>15.795706</td>\n",
       "      <td>49.646486</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.083333</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>9.633333</td>\n",
       "      <td>14.883333</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>30.433333</td>\n",
       "      <td>116.346333</td>\n",
       "      <td>1439.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>86539.0</td>\n",
       "      <td>15.540561</td>\n",
       "      <td>48.675905</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.016667</td>\n",
       "      <td>6.033333</td>\n",
       "      <td>9.416667</td>\n",
       "      <td>14.566667</td>\n",
       "      <td>22.366667</td>\n",
       "      <td>30.333333</td>\n",
       "      <td>120.485000</td>\n",
       "      <td>1438.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count       mean        std       min       10%       25%  \\\n",
       "start_time                                                                 \n",
       "June          2689.0  17.751419  46.535901  1.033333  4.516667  6.383333   \n",
       "July         44073.0  25.279279  75.297851  1.016667  4.583333  6.850000   \n",
       "August       83292.0  21.006761  67.058908  1.016667  4.400000  6.600000   \n",
       "September    98558.0  19.358276  58.902067  1.016667  4.316667  6.566667   \n",
       "October     108937.0  16.933229  52.057272  1.016667  4.183333  6.283333   \n",
       "November     95612.0  15.795706  49.646486  1.016667  4.083333  6.166667   \n",
       "December     86539.0  15.540561  48.675905  1.016667  4.016667  6.033333   \n",
       "\n",
       "                  50%        75%        90%        95%         99%  \\\n",
       "start_time                                                           \n",
       "June         9.366667  13.516667  22.636667  44.296667  229.512000   \n",
       "July        10.433333  16.883333  32.216667  75.250000  344.906000   \n",
       "August      10.400000  16.600000  27.865000  46.315000  239.457500   \n",
       "September   10.266667  16.533333  26.533333  41.283333  199.905000   \n",
       "October      9.800000  15.400000  23.933333  33.603333  150.110667   \n",
       "November     9.633333  14.883333  22.700000  30.433333  116.346333   \n",
       "December     9.416667  14.566667  22.366667  30.333333  120.485000   \n",
       "\n",
       "                    max  \n",
       "start_time               \n",
       "June        1176.400000  \n",
       "July        1417.233333  \n",
       "August      1436.650000  \n",
       "September   1432.866667  \n",
       "October     1439.250000  \n",
       "November    1439.483333  \n",
       "December    1438.750000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_duration_permonth = monthly_Data.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99])['duration_min']\n",
    "average_duration_permonth = average_duration_permonth.rename(index={6: 'June', 7:'July',8:'August',9:'September',10:'October',11:'November',12:'December'})\n",
    "average_duration_permonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <th>start_time</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>2239.0</td>\n",
       "      <td>10.996926</td>\n",
       "      <td>27.689277</td>\n",
       "      <td>1.033333</td>\n",
       "      <td>4.366667</td>\n",
       "      <td>6.066667</td>\n",
       "      <td>8.616667</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>15.933333</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>38.333000</td>\n",
       "      <td>1039.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>31095.0</td>\n",
       "      <td>12.395492</td>\n",
       "      <td>40.179336</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.133333</td>\n",
       "      <td>6.033333</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>12.383333</td>\n",
       "      <td>17.066667</td>\n",
       "      <td>21.483333</td>\n",
       "      <td>45.034333</td>\n",
       "      <td>1417.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>60781.0</td>\n",
       "      <td>12.291582</td>\n",
       "      <td>37.139807</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>8.866667</td>\n",
       "      <td>12.933333</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>43.190000</td>\n",
       "      <td>1434.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>74374.0</td>\n",
       "      <td>12.207233</td>\n",
       "      <td>34.008569</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>5.950000</td>\n",
       "      <td>8.900000</td>\n",
       "      <td>13.283333</td>\n",
       "      <td>19.183333</td>\n",
       "      <td>23.966667</td>\n",
       "      <td>45.237833</td>\n",
       "      <td>1428.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>87526.0</td>\n",
       "      <td>11.519524</td>\n",
       "      <td>27.526961</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>8.783333</td>\n",
       "      <td>13.116667</td>\n",
       "      <td>18.816667</td>\n",
       "      <td>23.283333</td>\n",
       "      <td>40.679167</td>\n",
       "      <td>1389.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>79636.0</td>\n",
       "      <td>11.275268</td>\n",
       "      <td>24.063178</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>3.883333</td>\n",
       "      <td>5.783333</td>\n",
       "      <td>8.833333</td>\n",
       "      <td>13.166667</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>23.016667</td>\n",
       "      <td>40.233333</td>\n",
       "      <td>1405.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>73579.0</td>\n",
       "      <td>11.407327</td>\n",
       "      <td>27.552987</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>3.816667</td>\n",
       "      <td>5.683333</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>18.650000</td>\n",
       "      <td>23.266667</td>\n",
       "      <td>42.533333</td>\n",
       "      <td>1417.266667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              count       mean        std       min       10%       25%  \\\n",
       "start_time                                                                \n",
       "June         2239.0  10.996926  27.689277  1.033333  4.366667  6.066667   \n",
       "July        31095.0  12.395492  40.179336  1.016667  4.133333  6.033333   \n",
       "August      60781.0  12.291582  37.139807  1.016667  4.000000  5.916667   \n",
       "September   74374.0  12.207233  34.008569  1.016667  3.983333  5.950000   \n",
       "October     87526.0  11.519524  27.526961  1.016667  3.900000  5.800000   \n",
       "November    79636.0  11.275268  24.063178  1.016667  3.883333  5.783333   \n",
       "December    73579.0  11.407327  27.552987  1.016667  3.816667  5.683333   \n",
       "\n",
       "                 50%        75%        90%        95%        99%          max  \n",
       "start_time                                                                     \n",
       "June        8.616667  12.000000  15.933333  18.800000  38.333000  1039.266667  \n",
       "July        8.783333  12.383333  17.066667  21.483333  45.034333  1417.233333  \n",
       "August      8.866667  12.933333  18.500000  23.500000  43.190000  1434.583333  \n",
       "September   8.900000  13.283333  19.183333  23.966667  45.237833  1428.050000  \n",
       "October     8.783333  13.116667  18.816667  23.283333  40.679167  1389.500000  \n",
       "November    8.833333  13.166667  18.750000  23.016667  40.233333  1405.716667  \n",
       "December    8.700000  13.000000  18.650000  23.266667  42.533333  1417.266667  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_duration_permon_subs = Sub_monthly_Data.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99])['duration_min']\n",
    "avg_duration_permon_subs = avg_duration_permon_subs.rename(index={6: 'June', 7:'July',8:'August',9:'September',10:'October',11:'November',12:'December'})\n",
    "avg_duration_permon_subs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>450.0</td>\n",
       "      <td>51.358778</td>\n",
       "      <td>88.224702</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>7.263333</td>\n",
       "      <td>11.070833</td>\n",
       "      <td>20.533333</td>\n",
       "      <td>47.212500</td>\n",
       "      <td>147.826667</td>\n",
       "      <td>227.582500</td>\n",
       "      <td>356.728833</td>\n",
       "      <td>1176.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>12978.0</td>\n",
       "      <td>56.148545</td>\n",
       "      <td>118.475858</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>12.283333</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>38.612500</td>\n",
       "      <td>133.813333</td>\n",
       "      <td>252.584167</td>\n",
       "      <td>561.852167</td>\n",
       "      <td>1393.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>22511.0</td>\n",
       "      <td>44.538246</td>\n",
       "      <td>110.254265</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>7.316667</td>\n",
       "      <td>11.550000</td>\n",
       "      <td>18.850000</td>\n",
       "      <td>30.666667</td>\n",
       "      <td>79.950000</td>\n",
       "      <td>160.716667</td>\n",
       "      <td>509.243333</td>\n",
       "      <td>1436.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>24184.0</td>\n",
       "      <td>41.350159</td>\n",
       "      <td>99.708189</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>18.433333</td>\n",
       "      <td>29.037500</td>\n",
       "      <td>76.345000</td>\n",
       "      <td>143.814167</td>\n",
       "      <td>475.422333</td>\n",
       "      <td>1432.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>21411.0</td>\n",
       "      <td>39.063907</td>\n",
       "      <td>100.405504</td>\n",
       "      <td>1.033333</td>\n",
       "      <td>7.016667</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>17.433333</td>\n",
       "      <td>27.766667</td>\n",
       "      <td>66.250000</td>\n",
       "      <td>130.900000</td>\n",
       "      <td>464.625000</td>\n",
       "      <td>1439.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>15976.0</td>\n",
       "      <td>38.328855</td>\n",
       "      <td>106.092612</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>6.633333</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>16.766667</td>\n",
       "      <td>27.133333</td>\n",
       "      <td>61.616667</td>\n",
       "      <td>116.954167</td>\n",
       "      <td>460.825000</td>\n",
       "      <td>1439.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>12960.0</td>\n",
       "      <td>39.006548</td>\n",
       "      <td>104.230604</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>6.948333</td>\n",
       "      <td>10.550000</td>\n",
       "      <td>16.816667</td>\n",
       "      <td>27.350000</td>\n",
       "      <td>68.693333</td>\n",
       "      <td>129.651667</td>\n",
       "      <td>431.051000</td>\n",
       "      <td>1438.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count       mean         std       min       10%        25%  \\\n",
       "start_time                                                                  \n",
       "June          450.0  51.358778   88.224702  1.333333  7.263333  11.070833   \n",
       "July        12978.0  56.148545  118.475858  1.016667  7.833333  12.283333   \n",
       "August      22511.0  44.538246  110.254265  1.016667  7.316667  11.550000   \n",
       "September   24184.0  41.350159   99.708189  1.016667  7.300000  11.400000   \n",
       "October     21411.0  39.063907  100.405504  1.033333  7.016667  10.700000   \n",
       "November    15976.0  38.328855  106.092612  1.016667  6.633333  10.300000   \n",
       "December    12960.0  39.006548  104.230604  1.016667  6.948333  10.550000   \n",
       "\n",
       "                  50%        75%         90%         95%         99%  \\\n",
       "start_time                                                             \n",
       "June        20.533333  47.212500  147.826667  227.582500  356.728833   \n",
       "July        20.750000  38.612500  133.813333  252.584167  561.852167   \n",
       "August      18.850000  30.666667   79.950000  160.716667  509.243333   \n",
       "September   18.433333  29.037500   76.345000  143.814167  475.422333   \n",
       "October     17.433333  27.766667   66.250000  130.900000  464.625000   \n",
       "November    16.766667  27.133333   61.616667  116.954167  460.825000   \n",
       "December    16.816667  27.350000   68.693333  129.651667  431.051000   \n",
       "\n",
       "                    max  \n",
       "start_time               \n",
       "June        1176.400000  \n",
       "July        1393.616667  \n",
       "August      1436.650000  \n",
       "September   1432.866667  \n",
       "October     1439.250000  \n",
       "November    1439.483333  \n",
       "December    1438.750000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_duration_permon_cust = Cust_only_monthly_Data.describe(percentiles=[.1,.25,.5,.75,.9,.95,.99])['duration_min']\n",
    "avg_duration_permon_cust = avg_duration_permon_cust.rename(index={6: 'June', 7:'July',8:'August',9:'September',10:'October',11:'November',12:'December'})\n",
    "avg_duration_permon_cust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>There is a clear separation between the user-types in the distribution of their trip durations.  <br/>Whereas 99% of subscriber trips fall under 45 minutes in duration, the top 10 percentile of customer trips (across all months) were greater than 60 minutes (or an hour)<br/>\n",
    "I was curious about the costs of Ford's GoBike so I researched pricing and found information here: <a href=\"https://bikeshare.cc/guide/howtos/how-much-does-ford-gobike-cost/\">How much does Ford GoBike Cost?</a><br/>Here I learned that there are: <ol><li>Single-Rides (what I will assume are considered Customers in this dataset), and </li><li>Access Pass or Monthly & Annual Membership plans (what I assume are considered Subscribers).</li></ol><br/>  I found it especially interesting given that Single-Rides are 2 dollars for the first 30 minutes and then 3 dollars per 15-minutes after that.  Therefore, a <i>two hours long single-ride</i> would cost: 2+3(6)= <b>20 dollars!</b></p>  I would be specifically interested in interviewing the Customers that fall within the top 5 percentile of trip duration since they for the most part (besides the month of November) rode for more than 120 minutes (2 hours)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>If 5 percent of the customers is too many people to interview, I certainly would like to interview the users who checked a bike our for the max duration for each month, which is consistently over 1000 minutes between June - December.  To calculate the price of a 1000-minute ride:  <br/>((1000min - 30min) / 15minutes )* 3 dollars)+ 2 dollars = <b>196 dollars!</b> for customers, or similarly <br/>((1000min - 45min) / 15minutes )* 3 dollars) = <b>191 dollars </b>for subscribers (on top of their associated subscription fee)\n",
    "<br/>Overall, I'm curious about their riding experience and also whether they thought the costs were justified.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Plotting percentiles of duration (in minutes) per user types')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots(nrows=1, ncols=3, figsize=(12,4))\n",
    "axe2 = ax2.ravel()\n",
    "average_duration_permonth.drop(['count','max','99%','std','min'], axis=1, inplace=True)\n",
    "all_users_duration_plot = average_duration_permonth.plot.bar(color=['black', 'red', 'orange', 'yellow', 'green', 'blue','purple'],ax=axe2[0])\n",
    "all_users_duration_plot.set_xlabel('All users duration statistics')\n",
    "all_users_duration_plot.set_ylabel('Trip Duration (in minutes)')\n",
    "avg_duration_permon_subs.drop(['count','max','99%','std','min'], axis=1, inplace=True)\n",
    "sub_dur_plot = avg_duration_permon_subs.plot.bar(color=['black', 'red', 'orange', 'yellow', 'green', 'blue','purple'],ax=axe2[1])\n",
    "sub_dur_plot.get_legend().remove()\n",
    "sub_dur_plot.set_xlabel('Subscribers trip duration')\n",
    "avg_duration_permon_cust.drop(['count','max','99%','std','min'], axis=1, inplace=True)\n",
    "cust_dur_plot = avg_duration_permon_cust.plot.bar(color=['black', 'red', 'orange', 'yellow', 'green', 'blue','purple'],ax=axe2[2])\n",
    "cust_dur_plot.get_legend().remove()\n",
    "cust_dur_plot.set_xlabel('Customers trip duration')\n",
    "fig2.suptitle(\"Plotting percentiles of duration (in minutes) per user types\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Although these charts are hard to read, if you note the scale of the y-axis for each graph you can tell that Customer trips were typically longer in duration.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Also as someone who loves maps, I thought it would be noteworthy to plot the longest trips & the shortest trips (between different stations) in this dataset.</h3><p>For this I used the package geopandas to create a GeoDataFrame, and the package folium to plot it on a map of the Bay Area.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = geopandas.GeoDataFrame(CSV_data, geometry=geopandas.points_from_xy(CSV_data.start_station_latitude, CSV_data.start_station_longitude))\n",
    "gdf['geometryB'] = geopandas.points_from_xy(CSV_data.end_station_latitude,CSV_data.end_station_longitude)\n",
    "gdf['same_station?'] = gdf['geometry'] == gdf['geometryB']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf['distance'] = gdf.geometry.distance(gdf.geometryB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</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>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>trip_start2end</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometryB</th>\n",
       "      <th>same_station?</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-10 14:02:06.731</th>\n",
       "      <td>27166</td>\n",
       "      <td>2017-11-10 14:02:06.731</td>\n",
       "      <td>2017-11-10 21:34:53.109</td>\n",
       "      <td>314</td>\n",
       "      <td>Santa Clara St at Almaden Blvd</td>\n",
       "      <td>37.333988</td>\n",
       "      <td>-121.894902</td>\n",
       "      <td>40</td>\n",
       "      <td>Laguna St at McAllister St</td>\n",
       "      <td>37.779208</td>\n",
       "      <td>-122.426829</td>\n",
       "      <td>1842</td>\n",
       "      <td>Customer</td>\n",
       "      <td>452.766667</td>\n",
       "      <td>Santa Clara St at Almaden Blvd -&gt; traveling to...</td>\n",
       "      <td>POINT (37.334 -121.895)</td>\n",
       "      <td>POINT (37.779 -122.427)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.693662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-10 14:02:14.719</th>\n",
       "      <td>9580</td>\n",
       "      <td>2017-11-10 14:02:14.719</td>\n",
       "      <td>2017-11-10 16:41:55.054</td>\n",
       "      <td>314</td>\n",
       "      <td>Santa Clara St at Almaden Blvd</td>\n",
       "      <td>37.333988</td>\n",
       "      <td>-121.894902</td>\n",
       "      <td>74</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.776435</td>\n",
       "      <td>-122.426244</td>\n",
       "      <td>2118</td>\n",
       "      <td>Customer</td>\n",
       "      <td>159.666667</td>\n",
       "      <td>Santa Clara St at Almaden Blvd -&gt; traveling to...</td>\n",
       "      <td>POINT (37.334 -121.895)</td>\n",
       "      <td>POINT (37.776 -122.426)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.691436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-07 07:07:21.130</th>\n",
       "      <td>5642</td>\n",
       "      <td>2017-09-07 07:07:21.130</td>\n",
       "      <td>2017-09-07 08:41:23.461</td>\n",
       "      <td>195</td>\n",
       "      <td>Bay Pl at Vernon St</td>\n",
       "      <td>37.812314</td>\n",
       "      <td>-122.260779</td>\n",
       "      <td>312</td>\n",
       "      <td>San Jose Diridon Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>465</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>94.033333</td>\n",
       "      <td>Bay Pl at Vernon St -&gt; traveling to -&gt; San Jos...</td>\n",
       "      <td>POINT (37.812 -122.261)</td>\n",
       "      <td>POINT (37.330 -121.902)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.601468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-31 07:57:18.251</th>\n",
       "      <td>5471</td>\n",
       "      <td>2017-10-31 07:57:18.251</td>\n",
       "      <td>2017-10-31 09:28:29.835</td>\n",
       "      <td>195</td>\n",
       "      <td>Bay Pl at Vernon St</td>\n",
       "      <td>37.812314</td>\n",
       "      <td>-122.260779</td>\n",
       "      <td>312</td>\n",
       "      <td>San Jose Diridon Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>1210</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>91.183333</td>\n",
       "      <td>Bay Pl at Vernon St -&gt; traveling to -&gt; San Jos...</td>\n",
       "      <td>POINT (37.812 -122.261)</td>\n",
       "      <td>POINT (37.330 -121.902)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.601468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 11:00:34.416</th>\n",
       "      <td>13593</td>\n",
       "      <td>2017-08-01 11:00:34.416</td>\n",
       "      <td>2017-08-01 14:47:07.779</td>\n",
       "      <td>59</td>\n",
       "      <td>S Van Ness Ave at Market St</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>227</td>\n",
       "      <td>Foothill Blvd at Fruitvale Ave</td>\n",
       "      <td>37.783757</td>\n",
       "      <td>-122.222603</td>\n",
       "      <td>2185</td>\n",
       "      <td>Customer</td>\n",
       "      <td>226.550000</td>\n",
       "      <td>S Van Ness Ave at Market St -&gt; traveling to -&gt;...</td>\n",
       "      <td>POINT (37.775 -122.419)</td>\n",
       "      <td>POINT (37.784 -122.223)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.196554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-24 18:22:57.159</th>\n",
       "      <td>23457</td>\n",
       "      <td>2017-09-24 18:22:57.159</td>\n",
       "      <td>2017-09-25 00:53:55.114</td>\n",
       "      <td>227</td>\n",
       "      <td>Foothill Blvd at Fruitvale Ave</td>\n",
       "      <td>37.783757</td>\n",
       "      <td>-122.222603</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>1349</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>390.950000</td>\n",
       "      <td>Foothill Blvd at Fruitvale Ave -&gt; traveling to...</td>\n",
       "      <td>POINT (37.784 -122.223)</td>\n",
       "      <td>POINT (37.777 -122.417)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.194912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-01 09:35:08.379</th>\n",
       "      <td>3397</td>\n",
       "      <td>2017-08-01 09:35:08.379</td>\n",
       "      <td>2017-08-01 10:31:45.723</td>\n",
       "      <td>227</td>\n",
       "      <td>Foothill Blvd at Fruitvale Ave</td>\n",
       "      <td>37.783757</td>\n",
       "      <td>-122.222603</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>780</td>\n",
       "      <td>Customer</td>\n",
       "      <td>56.616667</td>\n",
       "      <td>Foothill Blvd at Fruitvale Ave -&gt; traveling to...</td>\n",
       "      <td>POINT (37.784 -122.223)</td>\n",
       "      <td>POINT (37.777 -122.417)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.194912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-08 13:36:26.861</th>\n",
       "      <td>16338</td>\n",
       "      <td>2017-10-08 13:36:26.861</td>\n",
       "      <td>2017-10-08 18:08:45.021</td>\n",
       "      <td>71</td>\n",
       "      <td>Broderick St at Oak St</td>\n",
       "      <td>37.773063</td>\n",
       "      <td>-122.439078</td>\n",
       "      <td>197</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>3027</td>\n",
       "      <td>Customer</td>\n",
       "      <td>272.300000</td>\n",
       "      <td>Broderick St at Oak St -&gt; traveling to -&gt; El E...</td>\n",
       "      <td>POINT (37.773 -122.439)</td>\n",
       "      <td>POINT (37.809 -122.250)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.192749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-08 18:55:08.567</th>\n",
       "      <td>3503</td>\n",
       "      <td>2017-09-08 18:55:08.567</td>\n",
       "      <td>2017-09-08 19:53:32.396</td>\n",
       "      <td>240</td>\n",
       "      <td>Haste St at Telegraph Ave</td>\n",
       "      <td>37.866043</td>\n",
       "      <td>-122.258804</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>1940</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>58.383333</td>\n",
       "      <td>Haste St at Telegraph Ave -&gt; traveling to -&gt; M...</td>\n",
       "      <td>POINT (37.866 -122.259)</td>\n",
       "      <td>POINT (37.777 -122.417)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.182056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-22 10:23:08.306</th>\n",
       "      <td>7596</td>\n",
       "      <td>2017-07-22 10:23:08.306</td>\n",
       "      <td>2017-07-22 12:29:44.829</td>\n",
       "      <td>44</td>\n",
       "      <td>Civic Center/UN Plaza BART Station (Market St ...</td>\n",
       "      <td>37.781074</td>\n",
       "      <td>-122.411738</td>\n",
       "      <td>245</td>\n",
       "      <td>Downtown Berkeley BART</td>\n",
       "      <td>37.870348</td>\n",
       "      <td>-122.267764</td>\n",
       "      <td>27</td>\n",
       "      <td>Customer</td>\n",
       "      <td>126.600000</td>\n",
       "      <td>Civic Center/UN Plaza BART Station (Market St ...</td>\n",
       "      <td>POINT (37.781 -122.412)</td>\n",
       "      <td>POINT (37.870 -122.268)</td>\n",
       "      <td>False</td>\n",
       "      <td>0.169406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         duration_sec              start_time  \\\n",
       "start_time                                                      \n",
       "2017-11-10 14:02:06.731         27166 2017-11-10 14:02:06.731   \n",
       "2017-11-10 14:02:14.719          9580 2017-11-10 14:02:14.719   \n",
       "2017-09-07 07:07:21.130          5642 2017-09-07 07:07:21.130   \n",
       "2017-10-31 07:57:18.251          5471 2017-10-31 07:57:18.251   \n",
       "2017-08-01 11:00:34.416         13593 2017-08-01 11:00:34.416   \n",
       "2017-09-24 18:22:57.159         23457 2017-09-24 18:22:57.159   \n",
       "2017-08-01 09:35:08.379          3397 2017-08-01 09:35:08.379   \n",
       "2017-10-08 13:36:26.861         16338 2017-10-08 13:36:26.861   \n",
       "2017-09-08 18:55:08.567          3503 2017-09-08 18:55:08.567   \n",
       "2017-07-22 10:23:08.306          7596 2017-07-22 10:23:08.306   \n",
       "\n",
       "                                       end_time  start_station_id  \\\n",
       "start_time                                                          \n",
       "2017-11-10 14:02:06.731 2017-11-10 21:34:53.109               314   \n",
       "2017-11-10 14:02:14.719 2017-11-10 16:41:55.054               314   \n",
       "2017-09-07 07:07:21.130 2017-09-07 08:41:23.461               195   \n",
       "2017-10-31 07:57:18.251 2017-10-31 09:28:29.835               195   \n",
       "2017-08-01 11:00:34.416 2017-08-01 14:47:07.779                59   \n",
       "2017-09-24 18:22:57.159 2017-09-25 00:53:55.114               227   \n",
       "2017-08-01 09:35:08.379 2017-08-01 10:31:45.723               227   \n",
       "2017-10-08 13:36:26.861 2017-10-08 18:08:45.021                71   \n",
       "2017-09-08 18:55:08.567 2017-09-08 19:53:32.396               240   \n",
       "2017-07-22 10:23:08.306 2017-07-22 12:29:44.829                44   \n",
       "\n",
       "                                                        start_station_name  \\\n",
       "start_time                                                                   \n",
       "2017-11-10 14:02:06.731                     Santa Clara St at Almaden Blvd   \n",
       "2017-11-10 14:02:14.719                     Santa Clara St at Almaden Blvd   \n",
       "2017-09-07 07:07:21.130                                Bay Pl at Vernon St   \n",
       "2017-10-31 07:57:18.251                                Bay Pl at Vernon St   \n",
       "2017-08-01 11:00:34.416                        S Van Ness Ave at Market St   \n",
       "2017-09-24 18:22:57.159                     Foothill Blvd at Fruitvale Ave   \n",
       "2017-08-01 09:35:08.379                     Foothill Blvd at Fruitvale Ave   \n",
       "2017-10-08 13:36:26.861                             Broderick St at Oak St   \n",
       "2017-09-08 18:55:08.567                          Haste St at Telegraph Ave   \n",
       "2017-07-22 10:23:08.306  Civic Center/UN Plaza BART Station (Market St ...   \n",
       "\n",
       "                         start_station_latitude  start_station_longitude  \\\n",
       "start_time                                                                 \n",
       "2017-11-10 14:02:06.731               37.333988              -121.894902   \n",
       "2017-11-10 14:02:14.719               37.333988              -121.894902   \n",
       "2017-09-07 07:07:21.130               37.812314              -122.260779   \n",
       "2017-10-31 07:57:18.251               37.812314              -122.260779   \n",
       "2017-08-01 11:00:34.416               37.774814              -122.418954   \n",
       "2017-09-24 18:22:57.159               37.783757              -122.222603   \n",
       "2017-08-01 09:35:08.379               37.783757              -122.222603   \n",
       "2017-10-08 13:36:26.861               37.773063              -122.439078   \n",
       "2017-09-08 18:55:08.567               37.866043              -122.258804   \n",
       "2017-07-22 10:23:08.306               37.781074              -122.411738   \n",
       "\n",
       "                         end_station_id                end_station_name  \\\n",
       "start_time                                                                \n",
       "2017-11-10 14:02:06.731              40      Laguna St at McAllister St   \n",
       "2017-11-10 14:02:14.719              74           Laguna St at Hayes St   \n",
       "2017-09-07 07:07:21.130             312        San Jose Diridon Station   \n",
       "2017-10-31 07:57:18.251             312        San Jose Diridon Station   \n",
       "2017-08-01 11:00:34.416             227  Foothill Blvd at Fruitvale Ave   \n",
       "2017-09-24 18:22:57.159              58            Market St at 10th St   \n",
       "2017-08-01 09:35:08.379              58            Market St at 10th St   \n",
       "2017-10-08 13:36:26.861             197     El Embarcadero at Grand Ave   \n",
       "2017-09-08 18:55:08.567              58            Market St at 10th St   \n",
       "2017-07-22 10:23:08.306             245          Downtown Berkeley BART   \n",
       "\n",
       "                         end_station_latitude  end_station_longitude  bike_id  \\\n",
       "start_time                                                                      \n",
       "2017-11-10 14:02:06.731             37.779208            -122.426829     1842   \n",
       "2017-11-10 14:02:14.719             37.776435            -122.426244     2118   \n",
       "2017-09-07 07:07:21.130             37.329732            -121.901782      465   \n",
       "2017-10-31 07:57:18.251             37.329732            -121.901782     1210   \n",
       "2017-08-01 11:00:34.416             37.783757            -122.222603     2185   \n",
       "2017-09-24 18:22:57.159             37.776619            -122.417385     1349   \n",
       "2017-08-01 09:35:08.379             37.776619            -122.417385      780   \n",
       "2017-10-08 13:36:26.861             37.808848            -122.249680     3027   \n",
       "2017-09-08 18:55:08.567             37.776619            -122.417385     1940   \n",
       "2017-07-22 10:23:08.306             37.870348            -122.267764       27   \n",
       "\n",
       "                          user_type  duration_min  \\\n",
       "start_time                                          \n",
       "2017-11-10 14:02:06.731    Customer    452.766667   \n",
       "2017-11-10 14:02:14.719    Customer    159.666667   \n",
       "2017-09-07 07:07:21.130  Subscriber     94.033333   \n",
       "2017-10-31 07:57:18.251  Subscriber     91.183333   \n",
       "2017-08-01 11:00:34.416    Customer    226.550000   \n",
       "2017-09-24 18:22:57.159  Subscriber    390.950000   \n",
       "2017-08-01 09:35:08.379    Customer     56.616667   \n",
       "2017-10-08 13:36:26.861    Customer    272.300000   \n",
       "2017-09-08 18:55:08.567  Subscriber     58.383333   \n",
       "2017-07-22 10:23:08.306    Customer    126.600000   \n",
       "\n",
       "                                                            trip_start2end  \\\n",
       "start_time                                                                   \n",
       "2017-11-10 14:02:06.731  Santa Clara St at Almaden Blvd -> traveling to...   \n",
       "2017-11-10 14:02:14.719  Santa Clara St at Almaden Blvd -> traveling to...   \n",
       "2017-09-07 07:07:21.130  Bay Pl at Vernon St -> traveling to -> San Jos...   \n",
       "2017-10-31 07:57:18.251  Bay Pl at Vernon St -> traveling to -> San Jos...   \n",
       "2017-08-01 11:00:34.416  S Van Ness Ave at Market St -> traveling to ->...   \n",
       "2017-09-24 18:22:57.159  Foothill Blvd at Fruitvale Ave -> traveling to...   \n",
       "2017-08-01 09:35:08.379  Foothill Blvd at Fruitvale Ave -> traveling to...   \n",
       "2017-10-08 13:36:26.861  Broderick St at Oak St -> traveling to -> El E...   \n",
       "2017-09-08 18:55:08.567  Haste St at Telegraph Ave -> traveling to -> M...   \n",
       "2017-07-22 10:23:08.306  Civic Center/UN Plaza BART Station (Market St ...   \n",
       "\n",
       "                                        geometry                geometryB  \\\n",
       "start_time                                                                  \n",
       "2017-11-10 14:02:06.731  POINT (37.334 -121.895)  POINT (37.779 -122.427)   \n",
       "2017-11-10 14:02:14.719  POINT (37.334 -121.895)  POINT (37.776 -122.426)   \n",
       "2017-09-07 07:07:21.130  POINT (37.812 -122.261)  POINT (37.330 -121.902)   \n",
       "2017-10-31 07:57:18.251  POINT (37.812 -122.261)  POINT (37.330 -121.902)   \n",
       "2017-08-01 11:00:34.416  POINT (37.775 -122.419)  POINT (37.784 -122.223)   \n",
       "2017-09-24 18:22:57.159  POINT (37.784 -122.223)  POINT (37.777 -122.417)   \n",
       "2017-08-01 09:35:08.379  POINT (37.784 -122.223)  POINT (37.777 -122.417)   \n",
       "2017-10-08 13:36:26.861  POINT (37.773 -122.439)  POINT (37.809 -122.250)   \n",
       "2017-09-08 18:55:08.567  POINT (37.866 -122.259)  POINT (37.777 -122.417)   \n",
       "2017-07-22 10:23:08.306  POINT (37.781 -122.412)  POINT (37.870 -122.268)   \n",
       "\n",
       "                         same_station?  distance  \n",
       "start_time                                        \n",
       "2017-11-10 14:02:06.731          False  0.693662  \n",
       "2017-11-10 14:02:14.719          False  0.691436  \n",
       "2017-09-07 07:07:21.130          False  0.601468  \n",
       "2017-10-31 07:57:18.251          False  0.601468  \n",
       "2017-08-01 11:00:34.416          False  0.196554  \n",
       "2017-09-24 18:22:57.159          False  0.194912  \n",
       "2017-08-01 09:35:08.379          False  0.194912  \n",
       "2017-10-08 13:36:26.861          False  0.192749  \n",
       "2017-09-08 18:55:08.567          False  0.182056  \n",
       "2017-07-22 10:23:08.306          False  0.169406  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "longest_trips_distance = gdf.sort_values(by='distance',ascending=False).iloc[0:10]  #creating another GeoDataframe of just the 10 longest trips in the dataset\n",
    "longest_trips_distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Should note here that doing the similar calculation with shortest distance will often result in a 0 distance (meaning same station).  I could either:<ul><li>create another condition that it is the shortest but not yet zero, or</li><li>use the shortest duration trips (remember 61 seconds or just over 1 minute) that travelled from one station to different one.</li></ul>I chose the latter method because I was curious how these users were able to travel between stations so quickly!</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</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>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>geometry</th>\n",
       "      <th>geometryB</th>\n",
       "      <th>same_station?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14236</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-22 07:20:37.132</td>\n",
       "      <td>2017-12-22 07:21:38.626</td>\n",
       "      <td>43</td>\n",
       "      <td>San Francisco Public Library (Grove St at Hyde...</td>\n",
       "      <td>37.778768</td>\n",
       "      <td>-122.415929</td>\n",
       "      <td>42</td>\n",
       "      <td>San Francisco City Hall (Polk St at Grove St)</td>\n",
       "      <td>37.778650</td>\n",
       "      <td>-122.418230</td>\n",
       "      <td>2580</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.779 -122.416)</td>\n",
       "      <td>POINT (37.779 -122.418)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64776</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-07 08:53:02.622</td>\n",
       "      <td>2017-12-07 08:54:04.367</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>239</td>\n",
       "      <td>Bancroft Way at Telegraph Ave</td>\n",
       "      <td>37.868813</td>\n",
       "      <td>-122.258764</td>\n",
       "      <td>330</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.869 -122.254)</td>\n",
       "      <td>POINT (37.869 -122.259)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84393</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-12-01 14:24:58.790</td>\n",
       "      <td>2017-12-01 14:26:00.019</td>\n",
       "      <td>279</td>\n",
       "      <td>Santa Clara St at 7th St</td>\n",
       "      <td>37.339146</td>\n",
       "      <td>-121.884105</td>\n",
       "      <td>280</td>\n",
       "      <td>San Fernando at 7th St</td>\n",
       "      <td>37.337246</td>\n",
       "      <td>-121.883074</td>\n",
       "      <td>1875</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.339 -121.884)</td>\n",
       "      <td>POINT (37.337 -121.883)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114535</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-21 08:23:34.992</td>\n",
       "      <td>2017-11-21 08:24:36.175</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>80</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>37.775306</td>\n",
       "      <td>-122.397380</td>\n",
       "      <td>3066</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.777 -122.396)</td>\n",
       "      <td>POINT (37.775 -122.397)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119395</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-11-19 18:15:27.232</td>\n",
       "      <td>2017-11-19 18:16:28.506</td>\n",
       "      <td>127</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>121</td>\n",
       "      <td>Mission Playground</td>\n",
       "      <td>37.759210</td>\n",
       "      <td>-122.421339</td>\n",
       "      <td>1166</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.757 -122.421)</td>\n",
       "      <td>POINT (37.759 -122.421)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219427</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-21 16:56:45.398</td>\n",
       "      <td>2017-10-21 16:57:47.258</td>\n",
       "      <td>59</td>\n",
       "      <td>S Van Ness Ave at Market St</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>3150</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.775 -122.419)</td>\n",
       "      <td>POINT (37.777 -122.417)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227339</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-19 09:48:20.593</td>\n",
       "      <td>2017-10-19 09:49:22.472</td>\n",
       "      <td>110</td>\n",
       "      <td>17th &amp; Folsom Street Park (17th St at Folsom St)</td>\n",
       "      <td>37.763708</td>\n",
       "      <td>-122.415204</td>\n",
       "      <td>112</td>\n",
       "      <td>Harrison St at 17th St</td>\n",
       "      <td>37.763847</td>\n",
       "      <td>-122.413004</td>\n",
       "      <td>1827</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.764 -122.415)</td>\n",
       "      <td>POINT (37.764 -122.413)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260599</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-09 17:54:17.827</td>\n",
       "      <td>2017-10-09 17:55:19.075</td>\n",
       "      <td>80</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>37.775306</td>\n",
       "      <td>-122.397380</td>\n",
       "      <td>67</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>24</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.775 -122.397)</td>\n",
       "      <td>POINT (37.777 -122.396)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276445</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-10-04 20:05:18.014</td>\n",
       "      <td>2017-10-04 20:06:19.720</td>\n",
       "      <td>134</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>133</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>2303</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.752 -122.421)</td>\n",
       "      <td>POINT (37.755 -122.421)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313189</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-25 00:21:24.078</td>\n",
       "      <td>2017-09-25 00:22:25.803</td>\n",
       "      <td>243</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>239</td>\n",
       "      <td>Bancroft Way at Telegraph Ave</td>\n",
       "      <td>37.868813</td>\n",
       "      <td>-122.258764</td>\n",
       "      <td>2806</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.869 -122.254)</td>\n",
       "      <td>POINT (37.869 -122.259)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346102</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-09-14 21:27:39.850</td>\n",
       "      <td>2017-09-14 21:28:41.545</td>\n",
       "      <td>48</td>\n",
       "      <td>2nd St at S Park St</td>\n",
       "      <td>37.782411</td>\n",
       "      <td>-122.392706</td>\n",
       "      <td>49</td>\n",
       "      <td>S Park St at 3rd St</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.394989</td>\n",
       "      <td>872</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.782 -122.393)</td>\n",
       "      <td>POINT (37.781 -122.395)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401675</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-08-28 15:38:18.446</td>\n",
       "      <td>2017-08-28 15:39:19.607</td>\n",
       "      <td>59</td>\n",
       "      <td>S Van Ness Ave at Market St</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>2622</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.775 -122.419)</td>\n",
       "      <td>POINT (37.777 -122.417)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497676</th>\n",
       "      <td>61</td>\n",
       "      <td>2017-07-18 16:31:47.835</td>\n",
       "      <td>2017-07-18 16:32:49.439</td>\n",
       "      <td>59</td>\n",
       "      <td>S Van Ness Ave at Market St</td>\n",
       "      <td>37.774814</td>\n",
       "      <td>-122.418954</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>687</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>POINT (37.775 -122.419)</td>\n",
       "      <td>POINT (37.777 -122.417)</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "14236             61 2017-12-22 07:20:37.132 2017-12-22 07:21:38.626   \n",
       "64776             61 2017-12-07 08:53:02.622 2017-12-07 08:54:04.367   \n",
       "84393             61 2017-12-01 14:24:58.790 2017-12-01 14:26:00.019   \n",
       "114535            61 2017-11-21 08:23:34.992 2017-11-21 08:24:36.175   \n",
       "119395            61 2017-11-19 18:15:27.232 2017-11-19 18:16:28.506   \n",
       "219427            61 2017-10-21 16:56:45.398 2017-10-21 16:57:47.258   \n",
       "227339            61 2017-10-19 09:48:20.593 2017-10-19 09:49:22.472   \n",
       "260599            61 2017-10-09 17:54:17.827 2017-10-09 17:55:19.075   \n",
       "276445            61 2017-10-04 20:05:18.014 2017-10-04 20:06:19.720   \n",
       "313189            61 2017-09-25 00:21:24.078 2017-09-25 00:22:25.803   \n",
       "346102            61 2017-09-14 21:27:39.850 2017-09-14 21:28:41.545   \n",
       "401675            61 2017-08-28 15:38:18.446 2017-08-28 15:39:19.607   \n",
       "497676            61 2017-07-18 16:31:47.835 2017-07-18 16:32:49.439   \n",
       "\n",
       "        start_station_id                                 start_station_name  \\\n",
       "14236                 43  San Francisco Public Library (Grove St at Hyde...   \n",
       "64776                243                        Bancroft Way at College Ave   \n",
       "84393                279                           Santa Clara St at 7th St   \n",
       "114535                67  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "119395               127                             Valencia St at 21st St   \n",
       "219427                59                        S Van Ness Ave at Market St   \n",
       "227339               110   17th & Folsom Street Park (17th St at Folsom St)   \n",
       "260599                80                              Townsend St at 5th St   \n",
       "276445               134                             Valencia St at 24th St   \n",
       "313189               243                        Bancroft Way at College Ave   \n",
       "346102                48                                2nd St at S Park St   \n",
       "401675                59                        S Van Ness Ave at Market St   \n",
       "497676                59                        S Van Ness Ave at Market St   \n",
       "\n",
       "        start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "14236                37.778768              -122.415929              42   \n",
       "64776                37.869360              -122.254337             239   \n",
       "84393                37.339146              -121.884105             280   \n",
       "114535               37.776639              -122.395526              80   \n",
       "119395               37.756708              -122.421025             121   \n",
       "219427               37.774814              -122.418954              58   \n",
       "227339               37.763708              -122.415204             112   \n",
       "260599               37.775306              -122.397380              67   \n",
       "276445               37.752428              -122.420628             133   \n",
       "313189               37.869360              -122.254337             239   \n",
       "346102               37.782411              -122.392706              49   \n",
       "401675               37.774814              -122.418954              58   \n",
       "497676               37.774814              -122.418954              58   \n",
       "\n",
       "                                         end_station_name  \\\n",
       "14236       San Francisco City Hall (Polk St at Grove St)   \n",
       "64776                       Bancroft Way at Telegraph Ave   \n",
       "84393                              San Fernando at 7th St   \n",
       "114535                              Townsend St at 5th St   \n",
       "119395                                 Mission Playground   \n",
       "219427                               Market St at 10th St   \n",
       "227339                             Harrison St at 17th St   \n",
       "260599  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "276445                             Valencia St at 22nd St   \n",
       "313189                      Bancroft Way at Telegraph Ave   \n",
       "346102                                S Park St at 3rd St   \n",
       "401675                               Market St at 10th St   \n",
       "497676                               Market St at 10th St   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "14236              37.778650            -122.418230     2580  Subscriber   \n",
       "64776              37.868813            -122.258764      330  Subscriber   \n",
       "84393              37.337246            -121.883074     1875  Subscriber   \n",
       "114535             37.775306            -122.397380     3066  Subscriber   \n",
       "119395             37.759210            -122.421339     1166  Subscriber   \n",
       "219427             37.776619            -122.417385     3150  Subscriber   \n",
       "227339             37.763847            -122.413004     1827  Subscriber   \n",
       "260599             37.776639            -122.395526       24  Subscriber   \n",
       "276445             37.755213            -122.420975     2303  Subscriber   \n",
       "313189             37.868813            -122.258764     2806  Subscriber   \n",
       "346102             37.780760            -122.394989      872  Subscriber   \n",
       "401675             37.776619            -122.417385     2622  Subscriber   \n",
       "497676             37.776619            -122.417385      687  Subscriber   \n",
       "\n",
       "        duration_min                 geometry                geometryB  \\\n",
       "14236       1.016667  POINT (37.779 -122.416)  POINT (37.779 -122.418)   \n",
       "64776       1.016667  POINT (37.869 -122.254)  POINT (37.869 -122.259)   \n",
       "84393       1.016667  POINT (37.339 -121.884)  POINT (37.337 -121.883)   \n",
       "114535      1.016667  POINT (37.777 -122.396)  POINT (37.775 -122.397)   \n",
       "119395      1.016667  POINT (37.757 -122.421)  POINT (37.759 -122.421)   \n",
       "219427      1.016667  POINT (37.775 -122.419)  POINT (37.777 -122.417)   \n",
       "227339      1.016667  POINT (37.764 -122.415)  POINT (37.764 -122.413)   \n",
       "260599      1.016667  POINT (37.775 -122.397)  POINT (37.777 -122.396)   \n",
       "276445      1.016667  POINT (37.752 -122.421)  POINT (37.755 -122.421)   \n",
       "313189      1.016667  POINT (37.869 -122.254)  POINT (37.869 -122.259)   \n",
       "346102      1.016667  POINT (37.782 -122.393)  POINT (37.781 -122.395)   \n",
       "401675      1.016667  POINT (37.775 -122.419)  POINT (37.777 -122.417)   \n",
       "497676      1.016667  POINT (37.775 -122.419)  POINT (37.777 -122.417)   \n",
       "\n",
       "        same_station?  \n",
       "14236           False  \n",
       "64776           False  \n",
       "84393           False  \n",
       "114535          False  \n",
       "119395          False  \n",
       "219427          False  \n",
       "227339          False  \n",
       "260599          False  \n",
       "276445          False  \n",
       "313189          False  \n",
       "346102          False  \n",
       "401675          False  \n",
       "497676          False  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_duration_gdf = geopandas.GeoDataFrame(min_duration, geometry = geopandas.points_from_xy(min_duration.start_station_latitude, min_duration.start_station_longitude))\n",
    "min_duration_gdf['geometryB'] = geopandas.points_from_xy(min_duration.end_station_latitude,min_duration.end_station_longitude)\n",
    "min_duration_gdf['same_station?'] = min_duration_gdf['geometry'] == min_duration_gdf['geometryB']\n",
    "oneMinDiff = min_duration_gdf[min_duration_gdf['same_station?'] == False]\n",
    "oneMinDiff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>The above subset shows 13 trips that were 61 seconds in duration with different start / end stations.</p>\n",
    "<p>Below I plot those 13 trips.  If a line is more opaque, then multiple trips occured between these two stations (for example: starting at S Van Ness at Market St & ending at Market St at 10th St.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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onload=\"this.contentDocument.open();this.contentDocument.write(    decodeURIComponent(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fa8cee473a0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp2 = folium.Map(location=[37.75,-122.4], tiles='OpenStreetMap',zoom_start=12)\n",
    "for ind,row in oneMinDiff.iterrows():\n",
    "    tripGeo = [(row['start_station_latitude'],row['start_station_longitude']),(row['end_station_latitude'],row['end_station_longitude'])]\n",
    "    folium.PolyLine(tripGeo, color='red', weight=10, opacity=0.3).add_to(mp2)\n",
    "mp2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>I also ploted the 10 longest distance trips (calculated previously).  If a line is more opaque, then multiple trips occured between these two stations (for example: starting at Foothill Blvd at Fruitvale Ave & ending at Market St at 10th St.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fa8ceec3370>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp3 = folium.Map(location=[37.67,-122.5], tiles='OpenStreetMap',zoom_start=9)\n",
    "for ind,row in longest_trips_distance.iterrows():\n",
    "    tripGeo = [(row['start_station_latitude'],row['start_station_longitude']),(row['end_station_latitude'],row['end_station_longitude'])]\n",
    "    folium.PolyLine(tripGeo, color='green', weight=10, opacity=0.3).add_to(mp3)\n",
    "mp3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.  What other information would you like to collect, and why? </h3>\n",
    "<p><ul><li>As mentioned previously, I may want to consider weather data or even tourism data to determine why the number of bike trips started to decline towards the end of the year (from October until December), while the number of bike-sharing stations seems to increase throughout the pilot.\n",
    "<li>Another data point I'd like to capture would be the mileage of a trip with an odometer reading.  This data point would provide more insight into these longer trips (both in duration and distance).  The longest trip in duration (68,369 seconds) started and ended at the same station, and so we don't quite have as much information on how far the bike was actually ridden in that almost full-day.  Furthermore, perhaps these longest-distance trips plotted above were actually taken on the train, because to bike these distances (from South to East Bay or across the Bay Bridge) would take multiple hours and sometimes their trip duration seems to imply that they took the bike on transit with them.  If these users are taking the bike on the BART of CalTrain, then the odometer would not increase as much as physical distance between the start and end locations.  I would imagine that the mileage of a bike would also be beneficial for considering the routine maintence checks that occur on these shared bikes.</li>\n",
    "<li>And lastly, I think it would be nice if we could attach an ID record to each subscriber so that we could observe their behavior from trip to trip.  For example, maybe we could determine whether a certain subscriber uses the bike-sharing program for commuting each workday from one part of the bay area to another</p>"
   ]
  }
 ],
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