exploration_fordgobike.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration of FordGoBike Data, San Francisco, To Identify Growth Opportunies Among User Groups\n",
    "\n",
    "## A User Group Analysis by Stefan Cornelißen\n",
    "\n",
    "## Preliminary Wrangling\n",
    "\n",
    "This project aims to demonstrate exploring and explanation data analysis skills. The project has been executed using a dataset from FordGoBike San Francisco area. This files contains the exploration part of the project. The explanation, that means, visualizing and presenting results is covered in a separate notebook file. At the moment of analysis, the dataset contains bike sharing rides from June 2017 till January 2019. Around 2.57 million rides are described by 16 different dimensions and measures. The dataset contains \"ride-related\" and anonymized data only. That means, it contains no reference to the user or user ID. Therefore, the exploration is limited to the dimensions of the ride itself, which includes the following dimensions and metrics:\n",
    "<ul>\n",
    "<li>Trip Duration (seconds)\n",
    "<li>Start Time and Date\n",
    "<li>End Time and Date\n",
    "<li>Start Station ID\n",
    "<li>Start Station Name\n",
    "<li>Start Station Latitude\n",
    "<li>Start Station Longitude\n",
    "<li>End Station ID\n",
    "<li>End Station Name\n",
    "<li>End Station Latitude\n",
    "<li>End Station Longitude\n",
    "<li>Bike ID\n",
    "<li>User Type (Subscriber or Customer – “Subscriber” = Member or “Customer” = Casual)\n",
    "<li>Member Year of Birth\n",
    "<li>Member Gender\n",
    "</ul>\n",
    "Source and downloads to raw data files: https://www.fordgobike.com/system-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import requests\n",
    "from os import makedirs\n",
    "from os import listdir\n",
    "from io import BytesIO\n",
    "from zipfile import ZipFile\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I am going to download and unzip the raw data files from the source. There is one file covering all rides from 2017 and then one file per consecutive month. Then each file will be loaded into a combined dataframe to run analysis across the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a destination folder to download the raw files to\n",
    "destination_folder = \"fordgobike_raw_data\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the file for 2017 separately, because it formatted as a CSV-file, which in contrast \n",
    "# to all following filles does not need to unzipped.\n",
    "\n",
    "downloaded_response = requests.get(\"https://s3.amazonaws.com/fordgobike-data/2017-fordgobike-tripdata.csv\")\n",
    "\n",
    "# Storing the file to a raw-data folder\n",
    "with open('fordgobike_raw_data/2017-fordgobike-tripdata.csv', 'wb') as file:\n",
    "    file.write(downloaded_response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of the following file_urls to be downloaded for the analysis. \n",
    "# This is not elegantly solved, but for reasons of reliability ensures reproducibility.\n",
    "#All insights are limited to the period covered by the following\n",
    "# files and cannot be extended to any future months without further analysis.\n",
    "\n",
    "download_urls = [\"https://s3.amazonaws.com/fordgobike-data/201801-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201802-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201803-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201804-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201805-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201806-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201807-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201808-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201809-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201810-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201811-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201812-fordgobike-tripdata.csv.zip\",\n",
    "                 \"https://s3.amazonaws.com/fordgobike-data/201901-fordgobike-tripdata.csv.zip\"\n",
    "                 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Downloading, unzipping and storing the raw data files.\n",
    "for url in download_urls: \n",
    "    downloaded_response = requests.get(url)\n",
    "    path_file = destination_folder + url\n",
    "    zipped_file = ZipFile(BytesIO(downloaded_response.content))\n",
    "    zipped_file.extractall(destination_folder)\n",
    "    zipped_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a list with all file names in the destination folder to loop through\n",
    "files_raw_data_folder = []\n",
    "files_raw_data_folder = listdir(destination_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2017-fordgobike-tripdata.csv',\n",
       " '201801-fordgobike-tripdata.csv',\n",
       " '201802-fordgobike-tripdata.csv',\n",
       " '201803-fordgobike-tripdata.csv',\n",
       " '201804-fordgobike-tripdata.csv',\n",
       " '201805-fordgobike-tripdata.csv',\n",
       " '201806-fordgobike-tripdata.csv',\n",
       " '201807-fordgobike-tripdata.csv',\n",
       " '201808-fordgobike-tripdata.csv',\n",
       " '201809-fordgobike-tripdata.csv',\n",
       " '201810-fordgobike-tripdata.csv',\n",
       " '201811-fordgobike-tripdata.csv',\n",
       " '201812-fordgobike-tripdata.csv',\n",
       " '201901-fordgobike-tripdata.csv']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of the files to load into a a combined dataframe for the analysis.\n",
    "files_raw_data_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-fordgobike-tripdata.csv\n",
      "Shape first data frame (519700, 15)\n",
      "\n",
      "\n",
      "201801-fordgobike-tripdata.csv\n",
      "Shape appended data frame (94802, 16)\n",
      "Shape combined data frame  (614502, 16)\n",
      "\n",
      "\n",
      "201802-fordgobike-tripdata.csv\n",
      "Shape appended data frame (106718, 16)\n",
      "Shape combined data frame  (721220, 16)\n",
      "\n",
      "\n",
      "201803-fordgobike-tripdata.csv\n",
      "Shape appended data frame (111382, 16)\n",
      "Shape combined data frame  (832602, 16)\n",
      "\n",
      "\n",
      "201804-fordgobike-tripdata.csv\n",
      "Shape appended data frame (131169, 16)\n",
      "Shape combined data frame  (963771, 16)\n",
      "\n",
      "\n",
      "201805-fordgobike-tripdata.csv\n",
      "Shape appended data frame (179125, 16)\n",
      "Shape combined data frame  (1142896, 16)\n",
      "\n",
      "\n",
      "201806-fordgobike-tripdata.csv\n",
      "Shape appended data frame (195968, 16)\n",
      "Shape combined data frame  (1338864, 16)\n",
      "\n",
      "\n",
      "201807-fordgobike-tripdata.csv\n",
      "Shape appended data frame (199222, 16)\n",
      "Shape combined data frame  (1538086, 16)\n",
      "\n",
      "\n",
      "201808-fordgobike-tripdata.csv\n",
      "Shape appended data frame (192162, 16)\n",
      "Shape combined data frame  (1730248, 16)\n",
      "\n",
      "\n",
      "201809-fordgobike-tripdata.csv\n",
      "Shape appended data frame (186217, 16)\n",
      "Shape combined data frame  (1916465, 16)\n",
      "\n",
      "\n",
      "201810-fordgobike-tripdata.csv\n",
      "Shape appended data frame (201458, 16)\n",
      "Shape combined data frame  (2117923, 16)\n",
      "\n",
      "\n",
      "201811-fordgobike-tripdata.csv\n",
      "Shape appended data frame (134135, 16)\n",
      "Shape combined data frame  (2252058, 16)\n",
      "\n",
      "\n",
      "201812-fordgobike-tripdata.csv\n",
      "Shape appended data frame (131363, 16)\n",
      "Shape combined data frame  (2383421, 16)\n",
      "\n",
      "\n",
      "201901-fordgobike-tripdata.csv\n",
      "Shape appended data frame (192082, 16)\n",
      "Shape combined data frame  (2575503, 16)\n"
     ]
    }
   ],
   "source": [
    "# Loading the files into a combined list\n",
    "j = 1 #iterator\n",
    "\n",
    "for file in files_raw_data_folder:\n",
    "    if j != 1:\n",
    "        # Case for the second file and onwards\n",
    "        df_temp_gobike_raw = pd.read_csv(destination_folder + \"/\" + file)\n",
    "        df_gobike_raw = df_gobike_raw.append(df_temp_gobike_raw, sort=False, ignore_index=True)\n",
    "        # Skip header rows from the second file on.\n",
    "        # Creating some output to check on the progress. In case the kernel shuts down due to overload\n",
    "        # it is easier to find currupted files or anything.\n",
    "        print(\"\\n\")\n",
    "        print(file)\n",
    "        print(\"Shape appended data frame \" + str(df_temp_gobike_raw.shape))    \n",
    "        print(\"Shape combined data frame  \" + str(df_gobike_raw.shape))\n",
    "    else:\n",
    "        #Case for the first file.\n",
    "        df_gobike_raw = pd.read_csv(destination_folder + \"/\" + file) # Here, including the header row.\n",
    "        print(file)\n",
    "        print(\"Shape first data frame \" + str(df_gobike_raw.shape))\n",
    "    j = j + 1\n",
    "    \n",
    "    # Uncomment the following lines in order to interrupt the loading process after a few files for\n",
    "    #if j == 4:\n",
    "    #break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears from the column count, that the dataset for 2017 has one column less than the others. Overall, the process of appending respectively loading the contents into a combined data frame went well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking, which column has been added from 2018 onwards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 519700 entries, 0 to 519699\n",
      "Data columns (total 15 columns):\n",
      "duration_sec               519700 non-null int64\n",
      "start_time                 519700 non-null object\n",
      "end_time                   519700 non-null object\n",
      "start_station_id           519700 non-null int64\n",
      "start_station_name         519700 non-null object\n",
      "start_station_latitude     519700 non-null float64\n",
      "start_station_longitude    519700 non-null float64\n",
      "end_station_id             519700 non-null int64\n",
      "end_station_name           519700 non-null object\n",
      "end_station_latitude       519700 non-null float64\n",
      "end_station_longitude      519700 non-null float64\n",
      "bike_id                    519700 non-null int64\n",
      "user_type                  519700 non-null object\n",
      "member_birth_year          453159 non-null float64\n",
      "member_gender              453238 non-null object\n",
      "dtypes: float64(5), int64(4), object(6)\n",
      "memory usage: 59.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_2017 = pd.read_csv(destination_folder + \"/\" + \"2017-fordgobike-tripdata.csv\")\n",
    "df_2017.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 94802 entries, 0 to 94801\n",
      "Data columns (total 16 columns):\n",
      "duration_sec               94802 non-null int64\n",
      "start_time                 94802 non-null object\n",
      "end_time                   94802 non-null object\n",
      "start_station_id           94802 non-null int64\n",
      "start_station_name         94802 non-null object\n",
      "start_station_latitude     94802 non-null float64\n",
      "start_station_longitude    94802 non-null float64\n",
      "end_station_id             94802 non-null int64\n",
      "end_station_name           94802 non-null object\n",
      "end_station_latitude       94802 non-null float64\n",
      "end_station_longitude      94802 non-null float64\n",
      "bike_id                    94802 non-null int64\n",
      "user_type                  94802 non-null object\n",
      "member_birth_year          86963 non-null float64\n",
      "member_gender              87001 non-null object\n",
      "bike_share_for_all_trip    94802 non-null object\n",
      "dtypes: float64(5), int64(4), object(7)\n",
      "memory usage: 11.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_2018_01 = pd.read_csv(destination_folder + \"/\" + \"201801-fordgobike-tripdata.csv\")\n",
    "df_2018_01.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "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",
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       "        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>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>75284</td>\n",
       "      <td>2018-01-31 22:52:35.2390</td>\n",
       "      <td>2018-02-01 19:47:19.8240</td>\n",
       "      <td>120</td>\n",
       "      <td>Mission Dolores Park</td>\n",
       "      <td>37.761420</td>\n",
       "      <td>-122.426435</td>\n",
       "      <td>285</td>\n",
       "      <td>Webster St at O'Farrell St</td>\n",
       "      <td>37.783521</td>\n",
       "      <td>-122.431158</td>\n",
       "      <td>2765</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85422</td>\n",
       "      <td>2018-01-31 16:13:34.3510</td>\n",
       "      <td>2018-02-01 15:57:17.3100</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>2815</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71576</td>\n",
       "      <td>2018-01-31 14:23:55.8890</td>\n",
       "      <td>2018-02-01 10:16:52.1160</td>\n",
       "      <td>304</td>\n",
       "      <td>Jackson St at 5th St</td>\n",
       "      <td>37.348759</td>\n",
       "      <td>-121.894798</td>\n",
       "      <td>296</td>\n",
       "      <td>5th St at Virginia St</td>\n",
       "      <td>37.325998</td>\n",
       "      <td>-121.877120</td>\n",
       "      <td>3039</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61076</td>\n",
       "      <td>2018-01-31 14:53:23.5620</td>\n",
       "      <td>2018-02-01 07:51:20.5000</td>\n",
       "      <td>75</td>\n",
       "      <td>Market St at Franklin St</td>\n",
       "      <td>37.773793</td>\n",
       "      <td>-122.421239</td>\n",
       "      <td>47</td>\n",
       "      <td>4th St at Harrison St</td>\n",
       "      <td>37.780955</td>\n",
       "      <td>-122.399749</td>\n",
       "      <td>321</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39966</td>\n",
       "      <td>2018-01-31 19:52:24.6670</td>\n",
       "      <td>2018-02-01 06:58:31.0530</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>19</td>\n",
       "      <td>Post St at Kearny St</td>\n",
       "      <td>37.788975</td>\n",
       "      <td>-122.403452</td>\n",
       "      <td>617</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time                  end_time  \\\n",
       "0         75284  2018-01-31 22:52:35.2390  2018-02-01 19:47:19.8240   \n",
       "1         85422  2018-01-31 16:13:34.3510  2018-02-01 15:57:17.3100   \n",
       "2         71576  2018-01-31 14:23:55.8890  2018-02-01 10:16:52.1160   \n",
       "3         61076  2018-01-31 14:53:23.5620  2018-02-01 07:51:20.5000   \n",
       "4         39966  2018-01-31 19:52:24.6670  2018-02-01 06:58:31.0530   \n",
       "\n",
       "   start_station_id                                 start_station_name  \\\n",
       "0               120                               Mission Dolores Park   \n",
       "1                15  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "2               304                               Jackson St at 5th St   \n",
       "3                75                           Market St at Franklin St   \n",
       "4                74                              Laguna St at Hayes St   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.761420              -122.426435             285   \n",
       "1               37.795392              -122.394203              15   \n",
       "2               37.348759              -121.894798             296   \n",
       "3               37.773793              -122.421239              47   \n",
       "4               37.776435              -122.426244              19   \n",
       "\n",
       "                                    end_station_name  end_station_latitude  \\\n",
       "0                         Webster St at O'Farrell St             37.783521   \n",
       "1  San Francisco Ferry Building (Harry Bridges Pl...             37.795392   \n",
       "2                              5th St at Virginia St             37.325998   \n",
       "3                              4th St at Harrison St             37.780955   \n",
       "4                               Post St at Kearny St             37.788975   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.431158     2765  Subscriber             1986.0   \n",
       "1            -122.394203     2815    Customer                NaN   \n",
       "2            -121.877120     3039    Customer             1996.0   \n",
       "3            -122.399749      321    Customer                NaN   \n",
       "4            -122.403452      617  Subscriber             1991.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip  \n",
       "0          Male                      No  \n",
       "1           NaN                      No  \n",
       "2          Male                      No  \n",
       "3           NaN                      No  \n",
       "4          Male                      No  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018_01.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From 2018 on, the column \"bike_share_for_all_trip\" has been added to the raw-data files.\n",
    "Furthermore, it became visible, that the columns member_birth_year and member_gender do not necessarily contain values for each row.\n",
    "\n",
    "Quick check on the combined data set / data frame regarding the structure and the datatypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2575503 entries, 0 to 2575502\n",
      "Data columns (total 16 columns):\n",
      "duration_sec               int64\n",
      "start_time                 object\n",
      "end_time                   object\n",
      "start_station_id           float64\n",
      "start_station_name         object\n",
      "start_station_latitude     float64\n",
      "start_station_longitude    float64\n",
      "end_station_id             float64\n",
      "end_station_name           object\n",
      "end_station_latitude       float64\n",
      "end_station_longitude      float64\n",
      "bike_id                    int64\n",
      "user_type                  object\n",
      "member_birth_year          float64\n",
      "member_gender              object\n",
      "bike_share_for_all_trip    object\n",
      "dtypes: float64(7), int64(2), object(7)\n",
      "memory usage: 314.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_gobike_raw.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most data types are correctly assigned, except start_station_ID end_station_ID and bike_id columns. Those should rather be type “string” to enhance matching quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_gobike_raw.start_station_id = df_gobike_raw.start_station_id.astype(str)\n",
    "df_gobike_raw.end_station_id = df_gobike_raw.end_station_id.astype(str)\n",
    "df_gobike_raw.bike_id = df_gobike_raw.bike_id.astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check on duplicate rows and missing values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df_gobike_raw.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values in column: duration_sec\n",
      "0\n",
      "Missing values in column: start_time\n",
      "0\n",
      "Missing values in column: end_time\n",
      "0\n",
      "Missing values in column: start_station_id\n",
      "0\n",
      "Missing values in column: start_station_name\n",
      "12019\n",
      "Missing values in column: start_station_latitude\n",
      "0\n",
      "Missing values in column: start_station_longitude\n",
      "0\n",
      "Missing values in column: end_station_id\n",
      "0\n",
      "Missing values in column: end_station_name\n",
      "12019\n",
      "Missing values in column: end_station_latitude\n",
      "0\n",
      "Missing values in column: end_station_longitude\n",
      "0\n",
      "Missing values in column: bike_id\n",
      "0\n",
      "Missing values in column: user_type\n",
      "0\n",
      "Missing values in column: member_birth_year\n",
      "186979\n",
      "Missing values in column: member_gender\n",
      "186546\n",
      "Missing values in column: bike_share_for_all_trip\n",
      "519700\n"
     ]
    }
   ],
   "source": [
    "for column in df_gobike_raw:\n",
    "    print(\"Missing values in column: \" + str(column))\n",
    "    print(len(df_gobike_raw) - sum(df_gobike_raw[column].notnull()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detailed check on the missing values in the start and end columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_missing_rows_gobike_raw = df_gobike_raw[(df_gobike_raw.start_station_name.isna()) |\n",
    "                                          (df_gobike_raw.end_station_name.isna())\n",
    "                                          ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12019, 16)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_rows_gobike_raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1916224</th>\n",
       "      <td>1649</td>\n",
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       "      <td>Customer</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2092987</th>\n",
       "      <td>238</td>\n",
       "      <td>2018-10-04 09:56:30.8380</td>\n",
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       "    <tr>\n",
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       "      <td>Customer</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1395635</th>\n",
       "      <td>887</td>\n",
       "      <td>2018-07-23 16:32:06.4240</td>\n",
       "      <td>2018-07-23 16:46:53.7050</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4128</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1255819</th>\n",
       "      <td>4200</td>\n",
       "      <td>2018-06-13 21:29:45.6660</td>\n",
       "      <td>2018-06-13 22:39:46.1870</td>\n",
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       "      <th>2037961</th>\n",
       "      <td>509</td>\n",
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       "      <th>1190873</th>\n",
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       "    <tr>\n",
       "      <th>1753475</th>\n",
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       "      <td>No</td>\n",
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       "      <td>-121.92</td>\n",
       "      <td>4167</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>2325417</th>\n",
       "      <td>270</td>\n",
       "      <td>2018-12-13 08:47:20.4190</td>\n",
       "      <td>2018-12-13 08:51:50.6080</td>\n",
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       "      <td>Subscriber</td>\n",
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       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>1642917</th>\n",
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       "      <td>No</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-121.93</td>\n",
       "      <td>4242</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1998.0</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1691965</th>\n",
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       "      <td>2018-08-07 10:09:31.8430</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4148</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1357166</th>\n",
       "      <td>971</td>\n",
       "      <td>2018-07-28 22:15:53.1460</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4252</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730238</th>\n",
       "      <td>418</td>\n",
       "      <td>2018-08-01 00:26:40.5910</td>\n",
       "      <td>2018-08-01 00:33:39.1990</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-121.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>4128</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883077</th>\n",
       "      <td>1835</td>\n",
       "      <td>2018-09-07 08:06:47.6860</td>\n",
       "      <td>2018-09-07 08:37:23.4860</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-121.96</td>\n",
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       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1942922</th>\n",
       "      <td>619</td>\n",
       "      <td>2018-10-27 17:04:38.4600</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-121.94</td>\n",
       "      <td>4271</td>\n",
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       "      <td>1994.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1750970</th>\n",
       "      <td>893</td>\n",
       "      <td>2018-09-27 10:53:43.6090</td>\n",
       "      <td>2018-09-27 11:08:37.3290</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>-121.96</td>\n",
       "      <td>4284</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235085</th>\n",
       "      <td>1540</td>\n",
       "      <td>2018-06-17 14:46:40.8460</td>\n",
       "      <td>2018-06-17 15:12:21.7520</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.36</td>\n",
       "      <td>-121.91</td>\n",
       "      <td>4193</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2379400</th>\n",
       "      <td>457</td>\n",
       "      <td>2018-12-02 15:08:45.3860</td>\n",
       "      <td>2018-12-02 15:16:22.7380</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4181</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1588359</th>\n",
       "      <td>253</td>\n",
       "      <td>2018-08-23 17:24:28.2040</td>\n",
       "      <td>2018-08-23 17:28:41.9540</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4094</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2259845</th>\n",
       "      <td>1538</td>\n",
       "      <td>2018-12-29 12:22:05.3560</td>\n",
       "      <td>2018-12-29 12:47:43.4140</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4098</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1665921</th>\n",
       "      <td>566</td>\n",
       "      <td>2018-08-10 19:06:54.7130</td>\n",
       "      <td>2018-08-10 19:16:21.4640</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>4120</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485292</th>\n",
       "      <td>372</td>\n",
       "      <td>2018-07-10 09:42:13.7730</td>\n",
       "      <td>2018-07-10 09:48:26.4430</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4156</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452062</th>\n",
       "      <td>2725</td>\n",
       "      <td>2018-07-14 19:53:18.3890</td>\n",
       "      <td>2018-07-14 20:38:44.2360</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>3766</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404716</th>\n",
       "      <td>2145</td>\n",
       "      <td>2018-07-21 19:23:29.5470</td>\n",
       "      <td>2018-07-21 19:59:15.1240</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>4189</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1245035</th>\n",
       "      <td>1145</td>\n",
       "      <td>2018-06-15 11:47:00.6620</td>\n",
       "      <td>2018-06-15 12:06:05.8220</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4263</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1666386</th>\n",
       "      <td>130</td>\n",
       "      <td>2018-08-10 18:26:12.0850</td>\n",
       "      <td>2018-08-10 18:28:22.9800</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4125</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426650</th>\n",
       "      <td>671</td>\n",
       "      <td>2018-07-18 17:35:44.6380</td>\n",
       "      <td>2018-07-18 17:46:56.3670</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4286</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2317324</th>\n",
       "      <td>1799</td>\n",
       "      <td>2018-12-14 13:23:18.0580</td>\n",
       "      <td>2018-12-14 13:53:17.6140</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4168</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214967</th>\n",
       "      <td>71</td>\n",
       "      <td>2018-06-20 13:21:04.8910</td>\n",
       "      <td>2018-06-20 13:22:16.1510</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4127</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452056</th>\n",
       "      <td>2231</td>\n",
       "      <td>2018-07-14 20:04:43.8030</td>\n",
       "      <td>2018-07-14 20:41:55.1900</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4100</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1528200</th>\n",
       "      <td>168</td>\n",
       "      <td>2018-07-03 00:06:53.8700</td>\n",
       "      <td>2018-07-03 00:09:42.6490</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.42</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4284</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161418</th>\n",
       "      <td>844</td>\n",
       "      <td>2018-06-28 08:32:35.0030</td>\n",
       "      <td>2018-06-28 08:46:39.1500</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>4202</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1183108</th>\n",
       "      <td>561</td>\n",
       "      <td>2018-06-25 13:20:08.7130</td>\n",
       "      <td>2018-06-25 13:29:30.4040</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.42</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.42</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>4288</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1203596</th>\n",
       "      <td>746</td>\n",
       "      <td>2018-06-21 21:09:19.3050</td>\n",
       "      <td>2018-06-21 21:21:45.6950</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4152</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2244905</th>\n",
       "      <td>529</td>\n",
       "      <td>2018-11-01 21:14:23.6940</td>\n",
       "      <td>2018-11-01 21:23:12.9960</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4184</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175788</th>\n",
       "      <td>894</td>\n",
       "      <td>2018-06-26 10:43:03.3020</td>\n",
       "      <td>2018-06-26 10:57:58.1450</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>4105</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1738652</th>\n",
       "      <td>427</td>\n",
       "      <td>2018-09-29 08:22:38.9430</td>\n",
       "      <td>2018-09-29 08:29:46.7500</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4158</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339462</th>\n",
       "      <td>1974</td>\n",
       "      <td>2018-07-31 19:35:20.4040</td>\n",
       "      <td>2018-07-31 20:08:15.0030</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.42</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>4089</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1468387</th>\n",
       "      <td>191</td>\n",
       "      <td>2018-07-12 12:26:22.5920</td>\n",
       "      <td>2018-07-12 12:29:34.3140</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4118</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452532</th>\n",
       "      <td>4883</td>\n",
       "      <td>2018-07-14 17:31:57.5440</td>\n",
       "      <td>2018-07-14 18:53:20.9820</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4100</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1605927</th>\n",
       "      <td>57760</td>\n",
       "      <td>2018-08-20 17:22:49.3540</td>\n",
       "      <td>2018-08-21 09:25:29.3630</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.38</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4185</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890198</th>\n",
       "      <td>211</td>\n",
       "      <td>2018-09-06 08:51:47.0920</td>\n",
       "      <td>2018-09-06 08:55:18.9920</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>4129</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1199962</th>\n",
       "      <td>1619</td>\n",
       "      <td>2018-06-22 12:55:18.7260</td>\n",
       "      <td>2018-06-22 13:22:18.5850</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4092</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354947</th>\n",
       "      <td>558</td>\n",
       "      <td>2018-07-29 15:29:00.8640</td>\n",
       "      <td>2018-07-29 15:38:18.9840</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4087</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036786</th>\n",
       "      <td>630</td>\n",
       "      <td>2018-10-12 18:07:03.4490</td>\n",
       "      <td>2018-10-12 18:17:34.1020</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4259</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1580027</th>\n",
       "      <td>266</td>\n",
       "      <td>2018-08-24 19:09:20.8450</td>\n",
       "      <td>2018-08-24 19:13:46.8530</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>4200</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250030</th>\n",
       "      <td>178</td>\n",
       "      <td>2018-06-14 17:57:43.6530</td>\n",
       "      <td>2018-06-14 18:00:41.8330</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4160</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730951</th>\n",
       "      <td>7669</td>\n",
       "      <td>2018-09-30 16:50:03.4270</td>\n",
       "      <td>2018-09-30 18:57:52.6480</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>4141</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_sec                start_time                  end_time  \\\n",
       "1916224          1649  2018-09-01 08:14:23.2840  2018-09-01 08:41:53.1630   \n",
       "2092987           238  2018-10-04 09:56:30.8380  2018-10-04 10:00:29.1570   \n",
       "1849821          1545  2018-09-12 12:55:39.7570  2018-09-12 13:21:25.2160   \n",
       "1395635           887  2018-07-23 16:32:06.4240  2018-07-23 16:46:53.7050   \n",
       "1255819          4200  2018-06-13 21:29:45.6660  2018-06-13 22:39:46.1870   \n",
       "2037961           509  2018-10-12 16:39:58.1890  2018-10-12 16:48:27.4090   \n",
       "1190873            84  2018-06-24 01:36:51.9840  2018-06-24 01:38:16.1840   \n",
       "1753475           120  2018-09-27 07:26:34.8480  2018-09-27 07:28:35.7890   \n",
       "1722726          2427  2018-08-02 06:32:13.2380  2018-08-02 07:12:41.1790   \n",
       "2325417           270  2018-12-13 08:47:20.4190  2018-12-13 08:51:50.6080   \n",
       "1642917           251  2018-08-15 06:20:33.6720  2018-08-15 06:24:44.7270   \n",
       "1640349           434  2018-08-15 10:54:35.7850  2018-08-15 11:01:50.0890   \n",
       "1691965           530  2018-08-07 10:00:41.1680  2018-08-07 10:09:31.8430   \n",
       "1357166           971  2018-07-28 22:15:53.1460  2018-07-28 22:32:04.1460   \n",
       "1730238           418  2018-08-01 00:26:40.5910  2018-08-01 00:33:39.1990   \n",
       "1883077          1835  2018-09-07 08:06:47.6860  2018-09-07 08:37:23.4860   \n",
       "1942922           619  2018-10-27 17:04:38.4600  2018-10-27 17:14:58.3440   \n",
       "1750970           893  2018-09-27 10:53:43.6090  2018-09-27 11:08:37.3290   \n",
       "1235085          1540  2018-06-17 14:46:40.8460  2018-06-17 15:12:21.7520   \n",
       "2379400           457  2018-12-02 15:08:45.3860  2018-12-02 15:16:22.7380   \n",
       "1588359           253  2018-08-23 17:24:28.2040  2018-08-23 17:28:41.9540   \n",
       "2259845          1538  2018-12-29 12:22:05.3560  2018-12-29 12:47:43.4140   \n",
       "1665921           566  2018-08-10 19:06:54.7130  2018-08-10 19:16:21.4640   \n",
       "1485292           372  2018-07-10 09:42:13.7730  2018-07-10 09:48:26.4430   \n",
       "1452062          2725  2018-07-14 19:53:18.3890  2018-07-14 20:38:44.2360   \n",
       "1404716          2145  2018-07-21 19:23:29.5470  2018-07-21 19:59:15.1240   \n",
       "1245035          1145  2018-06-15 11:47:00.6620  2018-06-15 12:06:05.8220   \n",
       "1666386           130  2018-08-10 18:26:12.0850  2018-08-10 18:28:22.9800   \n",
       "1426650           671  2018-07-18 17:35:44.6380  2018-07-18 17:46:56.3670   \n",
       "2317324          1799  2018-12-14 13:23:18.0580  2018-12-14 13:53:17.6140   \n",
       "1214967            71  2018-06-20 13:21:04.8910  2018-06-20 13:22:16.1510   \n",
       "1452056          2231  2018-07-14 20:04:43.8030  2018-07-14 20:41:55.1900   \n",
       "1528200           168  2018-07-03 00:06:53.8700  2018-07-03 00:09:42.6490   \n",
       "1161418           844  2018-06-28 08:32:35.0030  2018-06-28 08:46:39.1500   \n",
       "1183108           561  2018-06-25 13:20:08.7130  2018-06-25 13:29:30.4040   \n",
       "1203596           746  2018-06-21 21:09:19.3050  2018-06-21 21:21:45.6950   \n",
       "2244905           529  2018-11-01 21:14:23.6940  2018-11-01 21:23:12.9960   \n",
       "1175788           894  2018-06-26 10:43:03.3020  2018-06-26 10:57:58.1450   \n",
       "1738652           427  2018-09-29 08:22:38.9430  2018-09-29 08:29:46.7500   \n",
       "1339462          1974  2018-07-31 19:35:20.4040  2018-07-31 20:08:15.0030   \n",
       "1468387           191  2018-07-12 12:26:22.5920  2018-07-12 12:29:34.3140   \n",
       "1452532          4883  2018-07-14 17:31:57.5440  2018-07-14 18:53:20.9820   \n",
       "1605927         57760  2018-08-20 17:22:49.3540  2018-08-21 09:25:29.3630   \n",
       "1890198           211  2018-09-06 08:51:47.0920  2018-09-06 08:55:18.9920   \n",
       "1199962          1619  2018-06-22 12:55:18.7260  2018-06-22 13:22:18.5850   \n",
       "1354947           558  2018-07-29 15:29:00.8640  2018-07-29 15:38:18.9840   \n",
       "2036786           630  2018-10-12 18:07:03.4490  2018-10-12 18:17:34.1020   \n",
       "1580027           266  2018-08-24 19:09:20.8450  2018-08-24 19:13:46.8530   \n",
       "1250030           178  2018-06-14 17:57:43.6530  2018-06-14 18:00:41.8330   \n",
       "1730951          7669  2018-09-30 16:50:03.4270  2018-09-30 18:57:52.6480   \n",
       "\n",
       "        start_station_id start_station_name  start_station_latitude  \\\n",
       "1916224              nan                NaN                   37.40   \n",
       "2092987              nan                NaN                   37.41   \n",
       "1849821              nan                NaN                   37.39   \n",
       "1395635              nan                NaN                   37.41   \n",
       "1255819              nan                NaN                   37.41   \n",
       "2037961              nan                NaN                   37.41   \n",
       "1190873              nan                NaN                   37.41   \n",
       "1753475              nan                NaN                   37.41   \n",
       "1722726              nan                NaN                   37.40   \n",
       "2325417              nan                NaN                   37.39   \n",
       "1642917              nan                NaN                   37.42   \n",
       "1640349              nan                NaN                   37.41   \n",
       "1691965              nan                NaN                   37.40   \n",
       "1357166              nan                NaN                   37.42   \n",
       "1730238              nan                NaN                   37.39   \n",
       "1883077              nan                NaN                   37.41   \n",
       "1942922              nan                NaN                   37.42   \n",
       "1750970              nan                NaN                   37.41   \n",
       "1235085              nan                NaN                   37.40   \n",
       "2379400              nan                NaN                   37.40   \n",
       "1588359              nan                NaN                   37.40   \n",
       "2259845              nan                NaN                   37.41   \n",
       "1665921              nan                NaN                   37.41   \n",
       "1485292              nan                NaN                   37.40   \n",
       "1452062              nan                NaN                   37.40   \n",
       "1404716              nan                NaN                   37.40   \n",
       "1245035              nan                NaN                   37.39   \n",
       "1666386              nan                NaN                   37.41   \n",
       "1426650              nan                NaN                   37.40   \n",
       "2317324              nan                NaN                   37.40   \n",
       "1214967              nan                NaN                   37.40   \n",
       "1452056              nan                NaN                   37.40   \n",
       "1528200              nan                NaN                   37.41   \n",
       "1161418              nan                NaN                   37.40   \n",
       "1183108              nan                NaN                   37.42   \n",
       "1203596              nan                NaN                   37.40   \n",
       "2244905              nan                NaN                   37.41   \n",
       "1175788              nan                NaN                   37.41   \n",
       "1738652              nan                NaN                   37.40   \n",
       "1339462              nan                NaN                   37.42   \n",
       "1468387              nan                NaN                   37.41   \n",
       "1452532              nan                NaN                   37.40   \n",
       "1605927              nan                NaN                   37.38   \n",
       "1890198              nan                NaN                   37.41   \n",
       "1199962              nan                NaN                   37.39   \n",
       "1354947              nan                NaN                   37.41   \n",
       "2036786              nan                NaN                   37.41   \n",
       "1580027              nan                NaN                   37.41   \n",
       "1250030              nan                NaN                   37.40   \n",
       "1730951              nan                NaN                   37.41   \n",
       "\n",
       "         start_station_longitude end_station_id end_station_name  \\\n",
       "1916224                  -121.94            nan              NaN   \n",
       "2092987                  -121.94            nan              NaN   \n",
       "1849821                  -121.93            nan              NaN   \n",
       "1395635                  -121.96            nan              NaN   \n",
       "1255819                  -121.96            nan              NaN   \n",
       "2037961                  -121.95            nan              NaN   \n",
       "1190873                  -121.94            nan              NaN   \n",
       "1753475                  -121.94            nan              NaN   \n",
       "1722726                  -121.92            nan              NaN   \n",
       "2325417                  -121.93            nan              NaN   \n",
       "1642917                  -121.95            nan              NaN   \n",
       "1640349                  -121.94            nan              NaN   \n",
       "1691965                  -121.92            nan              NaN   \n",
       "1357166                  -121.96            nan              NaN   \n",
       "1730238                  -121.95            nan              NaN   \n",
       "1883077                  -121.96            nan              NaN   \n",
       "1942922                  -121.96            nan              NaN   \n",
       "1750970                  -121.94            nan              NaN   \n",
       "1235085                  -121.94            nan              NaN   \n",
       "2379400                  -121.93            nan              NaN   \n",
       "1588359                  -121.94            nan              NaN   \n",
       "2259845                  -121.94            nan              NaN   \n",
       "1665921                  -121.95            nan              NaN   \n",
       "1485292                  -121.92            nan              NaN   \n",
       "1452062                  -121.93            nan              NaN   \n",
       "1404716                  -121.92            nan              NaN   \n",
       "1245035                  -121.93            nan              NaN   \n",
       "1666386                  -121.94            nan              NaN   \n",
       "1426650                  -121.92            nan              NaN   \n",
       "2317324                  -121.94            nan              NaN   \n",
       "1214967                  -121.94            nan              NaN   \n",
       "1452056                  -121.94            nan              NaN   \n",
       "1528200                  -121.94            nan              NaN   \n",
       "1161418                  -121.92            nan              NaN   \n",
       "1183108                  -121.96            nan              NaN   \n",
       "1203596                  -121.92            nan              NaN   \n",
       "2244905                  -121.95            nan              NaN   \n",
       "1175788                  -121.94            nan              NaN   \n",
       "1738652                  -121.93            nan              NaN   \n",
       "1339462                  -121.95            nan              NaN   \n",
       "1468387                  -121.93            nan              NaN   \n",
       "1452532                  -121.94            nan              NaN   \n",
       "1605927                  -121.94            nan              NaN   \n",
       "1890198                  -121.94            nan              NaN   \n",
       "1199962                  -121.93            nan              NaN   \n",
       "1354947                  -121.94            nan              NaN   \n",
       "2036786                  -121.94            nan              NaN   \n",
       "1580027                  -121.95            nan              NaN   \n",
       "1250030                  -121.93            nan              NaN   \n",
       "1730951                  -121.94            nan              NaN   \n",
       "\n",
       "         end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "1916224                 37.40                -121.94    4289    Customer   \n",
       "2092987                 37.41                -121.94    4287  Subscriber   \n",
       "1849821                 37.39                -121.93    4126    Customer   \n",
       "1395635                 37.40                -121.94    4128  Subscriber   \n",
       "1255819                 37.41                -121.96    4087    Customer   \n",
       "2037961                 37.42                -121.94    4126  Subscriber   \n",
       "1190873                 37.41                -121.94    4257  Subscriber   \n",
       "1753475                 37.41                -121.94    4130  Subscriber   \n",
       "1722726                 37.40                -121.92    4167  Subscriber   \n",
       "2325417                 37.38                -121.94    4147  Subscriber   \n",
       "1642917                 37.41                -121.95    4253  Subscriber   \n",
       "1640349                 37.41                -121.93    4242  Subscriber   \n",
       "1691965                 37.40                -121.93    4148  Subscriber   \n",
       "1357166                 37.41                -121.94    4252  Subscriber   \n",
       "1730238                 37.39                -121.95    4128  Subscriber   \n",
       "1883077                 37.41                -121.96    4164  Subscriber   \n",
       "1942922                 37.41                -121.94    4271  Subscriber   \n",
       "1750970                 37.41                -121.96    4284  Subscriber   \n",
       "1235085                 37.36                -121.91    4193    Customer   \n",
       "2379400                 37.40                -121.93    4181  Subscriber   \n",
       "1588359                 37.40                -121.93    4094  Subscriber   \n",
       "2259845                 37.40                -121.93    4098    Customer   \n",
       "1665921                 37.41                -121.96    4120  Subscriber   \n",
       "1485292                 37.40                -121.93    4156  Subscriber   \n",
       "1452062                 37.40                -121.93    3766  Subscriber   \n",
       "1404716                 37.40                -121.92    4189  Subscriber   \n",
       "1245035                 37.39                -121.93    4263  Subscriber   \n",
       "1666386                 37.41                -121.94    4125  Subscriber   \n",
       "1426650                 37.39                -121.93    4286  Subscriber   \n",
       "2317324                 37.40                -121.94    4168    Customer   \n",
       "1214967                 37.40                -121.94    4127  Subscriber   \n",
       "1452056                 37.40                -121.93    4100  Subscriber   \n",
       "1528200                 37.42                -121.94    4284  Subscriber   \n",
       "1161418                 37.40                -121.92    4202    Customer   \n",
       "1183108                 37.42                -121.96    4288    Customer   \n",
       "1203596                 37.40                -121.93    4152  Subscriber   \n",
       "2244905                 37.40                -121.94    4184    Customer   \n",
       "1175788                 37.41                -121.95    4105  Subscriber   \n",
       "1738652                 37.40                -121.94    4158  Subscriber   \n",
       "1339462                 37.41                -121.96    4089  Subscriber   \n",
       "1468387                 37.41                -121.93    4118  Subscriber   \n",
       "1452532                 37.40                -121.94    4100  Subscriber   \n",
       "1605927                 37.39                -121.93    4185    Customer   \n",
       "1890198                 37.40                -121.95    4129  Subscriber   \n",
       "1199962                 37.39                -121.93    4092    Customer   \n",
       "1354947                 37.41                -121.94    4087  Subscriber   \n",
       "2036786                 37.40                -121.94    4259  Subscriber   \n",
       "1580027                 37.41                -121.96    4200  Subscriber   \n",
       "1250030                 37.40                -121.93    4160  Subscriber   \n",
       "1730951                 37.41                -121.94    4141    Customer   \n",
       "\n",
       "         member_birth_year member_gender bike_share_for_all_trip  \n",
       "1916224             1965.0          Male                      No  \n",
       "2092987             1989.0          Male                      No  \n",
       "1849821             1982.0          Male                      No  \n",
       "1395635             1990.0        Female                      No  \n",
       "1255819             1997.0          Male                      No  \n",
       "2037961             2000.0        Female                     Yes  \n",
       "1190873             1991.0          Male                      No  \n",
       "1753475             1989.0          Male                      No  \n",
       "1722726             1983.0        Female                      No  \n",
       "2325417             1994.0          Male                     Yes  \n",
       "1642917             1983.0          Male                      No  \n",
       "1640349             1998.0          Male                      No  \n",
       "1691965             1988.0          Male                      No  \n",
       "1357166             1990.0          Male                      No  \n",
       "1730238             1982.0        Female                      No  \n",
       "1883077             1983.0          Male                      No  \n",
       "1942922             1994.0          Male                      No  \n",
       "1750970             1983.0          Male                      No  \n",
       "1235085             1980.0          Male                      No  \n",
       "2379400             1990.0          Male                      No  \n",
       "1588359             1987.0          Male                      No  \n",
       "2259845             1982.0        Female                      No  \n",
       "1665921             1993.0          Male                      No  \n",
       "1485292             1998.0          Male                      No  \n",
       "1452062             1990.0        Female                      No  \n",
       "1404716             1980.0          Male                      No  \n",
       "1245035             1986.0        Female                      No  \n",
       "1666386             1993.0          Male                      No  \n",
       "1426650             1990.0          Male                      No  \n",
       "2317324             1979.0          Male                      No  \n",
       "1214967             1999.0          Male                      No  \n",
       "1452056             1989.0          Male                      No  \n",
       "1528200             1990.0        Female                      No  \n",
       "1161418                NaN           NaN                      No  \n",
       "1183108             1990.0         Other                      No  \n",
       "1203596             1989.0        Female                      No  \n",
       "2244905             1996.0        Female                      No  \n",
       "1175788             1983.0          Male                      No  \n",
       "1738652             1990.0          Male                      No  \n",
       "1339462             1976.0        Female                      No  \n",
       "1468387             1987.0          Male                      No  \n",
       "1452532             1983.0        Female                      No  \n",
       "1605927             1979.0          Male                      No  \n",
       "1890198             1994.0          Male                      No  \n",
       "1199962                NaN           NaN                      No  \n",
       "1354947             1982.0        Female                      No  \n",
       "2036786             1995.0          Male                      No  \n",
       "1580027             1997.0          Male                      No  \n",
       "1250030             1985.0          Male                     Yes  \n",
       "1730951             1988.0          Male                      No  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_rows_gobike_raw.sample(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values in the columns start_station_id, start_station_name, end_station_id and end_station_name concern exactly the same rows for all cases. This means, the values are missing simultaneously per row. I checked some sample coordinates on Google and Bing Maps and searched on https://member.fordgobike.com/map/ for a station nearby. However, in the sample cases there are no stations nearby. As also the station names are missing on both start and end points, there must be another explanation. A quick search on the FordGoBike site and Google News revealed, that FordGoBike introduced dock less renting mid-2018, for dedicated bikes in a dedicated, smaller area of \"North San Jose\". Refer to: https://www.mercurynews.com/2018/06/14/ford-gobike-launches-dockless-program-in-north-san-jose/\n",
    "https://www.fordgobike.com/nsj\n",
    "https://d21xlh2maitm24.cloudfront.net/fgb/NSJ-service-area-map.png?mtime=20180531110105  \n",
    "\n",
    "The dates of start and end time corresponded well with the moment of introduction within the manual sample. Those rows with missing start stations shall therefore be kept in mind.\n",
    "\n",
    "Missing values in the columns member_birth_year and member_gender may be explained by their non-compulsory characteristic during the registration process. They will be left as they are. I prefer to limit validity of any age or gender analysis accordingly, instead of excluding them from the beginning on. The same goes for the column bike_share_for_all_trip which is missing in all 2017 data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The dataset can now be regarded as ready for the exploration phase.**\n",
    "\n",
    "The combined data set shall first be saved to a CSV-file in order to spare re-downloading and appending the files in case of failures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gobike_raw.to_csv(\"fordgobike_combined_2017-06-28_to_2019-01-31.csv\", header=True, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "The data set has around 2.57 million entries restively rows and 16 columns in total. Each row represents a bike ride / rental in the period of (end) June 2017 till January 2019. The dataset contains numerical and categorical data at the same time. Station names, the user type (subscriber or casual user), gender belong to the categorical type. To numerical data belongs the user’s year of birth and the trips duration, which is also implied by the moments start and end data-time-stamps. Furthermore, the start and end stations coordinates are also included as numeric latitude and longitude columns. In addition, bikes and stations are indicated by unique IDs.\n",
    "\n",
    "The value presence across the single columns is fragmented. The variable bike_share_for_all_trip has been introduced in January 2018. Furthermore, not all users have provided their year of birth or checked the value \"Other\" value for gender. Lastly FordGoBike introduced mid 2018 dock less bike renting as an additional service to the common station oriented renting. All of these fragmentations need to be taken into account, during the exploration phase.\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "I am most interested to look at the usage in terms of the number of overall rides, duration and duration per ride. I want to explore what segment of user (divided by user type, gender and age) is using the FordGoBike service most and least. This exploration shall provide guidance for potential growth / business opportunities.\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "The amount of rides, the metric duration and the dimension’s user type, gender and birth year will the features of most interest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: I create a copy of the data frame prior to exploration, to separate wrangling from exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gobike = df_gobike_raw.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "\n",
    "I will start by looking at the distribution of the user types \"customer and subscriber\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting some default parameters for visualization.\n",
    "sb.set_style('whitegrid')\n",
    "default_color = sb.color_palette()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    83.925975\n",
       "Customer      16.074025\n",
       "Name: user_type, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_gobike.user_type.value_counts(sort=True, normalize=True)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8, 6])\n",
    "(df_gobike.user_type.value_counts(sort=True, normalize=True)*100).plot.bar(color = default_color)\n",
    "plt.title(\"Percentage of Rides per User Type\")\n",
    "plt.ylabel(\"Percentage of all Rides\")\n",
    "plt.xlabel(\"User Type\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of rides has been done by users of the type subscribers. The subscription is payed either monthly or yearly with discount.\n",
    "Refer to: https://www.fordgobike.com/pricing\n",
    "<li>Customer: first 30min for 2 USD, every additional 15min for 3 USD or day /3 day pass.</li>\n",
    "<li>Subcriber: 15 USD per month or 149 USD per year - unlimited 45min periods - every additional 15min for 3 USD.</li>\n",
    "\n",
    "Next am I am going to look at the distribution of the variable \"gender\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      74.161234\n",
       "Female    24.292777\n",
       "Other      1.545988\n",
       "Name: member_gender, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_gobike.member_gender.value_counts(sort=True, normalize=True)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8, 6])\n",
    "(df_gobike.member_gender.value_counts(sort=True, normalize=True)*100).plot.bar(color = default_color);\n",
    "plt.title(\"Percentage of Rides per Gender\")\n",
    "plt.ylabel(\"Percentage of all Rides\")\n",
    "plt.xlabel(\"Gender\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of rides has been done by male users. Very few indicated their gender by the value \"Other\".\n",
    "\n",
    "Next I want to explore the age distribution. To plot the distribution of the column member_birth_year, it is useful to first calculate the age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gobike['member_age'] = 2019 - df_gobike.member_birth_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5z3+u2tpaxcfH64033vBEXQAAwAe4DQo1NTV64IEHZLPZFBMTo5CQEE/UBQAAfIDboBAcHKytW7fK4XDo008/VXBwsCfqAgAAPsBtUMjOztbq1atVWVmp3/3ud5oxY4YHygIAAL7A7cmM7du31/z58z1RCwAA8DHGoJCQkCBJqqur0/nz59WhQwedOHFCUVFR2rx5s8cKvFFdqKtXyxaBV3wuLi7Ow9UAAHB9jEGhpKREkvTqq69qwoQJzqAwe/ZsjxV3I2vZIlDdpmzwdhlX9dWcx7xdAgDAx7k9R+HIkSPOqzG2a9dOx48fb/aiAACAb3B7jsIdd9yhiRMnqmfPnvr000/Vu3dvT9QFAAB8gNugkJ2dreLiYh08eFCPPfaY+vfv74m6AACADzAeevjLX/4iSSosLNSJEycUERGhkydP6r333vNYcQAAwLuMMwpnzpyRJJ08edJjxQAAAN9iDApPPfWUKioqNHr0aLVq1UqSZFmWCgoKPFYcAADwLuOhh7ffflvJyckaMmSIPv74Yx07dkwpKSkqKiryZH0AAMCLjDMKGzZs0IYNG1RZWalXXnlFp06d0i9+8QslJSV5sj4AAOBFxqBw6623Kjg4WO3atdOJEye0YMEC3X333Z6sDQAAeJnx0IPNZnP+3KFDB0ICAAB+yDijcOLECb333nuyLEvffvuty9cik5OTPVIcAADwLmNQePzxx51fjfz+zwAAwH8Yg8JLL73kyToAAIAPcntTKAAA4L+MMwp///vfFR0d3aQbe/vtt7V582bV1dVp2LBh6tOnj6ZMmSKbzabY2FhNnz5dAQEBWrRokbZs2aKgoCClp6erZ8+eOnz4cKPHAgCAhjF+er722muSpDFjxjTJhkpLS7Vnzx4VFBQoLy9P33zzjWbPnq3x48crPz9flmWpqKhIZWVl2rlzpwoLC5WTk6OsrCxJavRYAADQcMYZhS5duuihhx7SP//5TyUkJLg8V1JS0uANlZSU6M4779SYMWNkt9s1adIkrVy5Un369JEk9e3bV9u2bVN0dLQSEhJks9nUsWNH1dfXq6KiQmVlZY0aO2DAgAbXDACAvzMGhTfffFOSlJWVpenTpzd6Q5WVlTp27JiWLFmiI0eO6IUXXpBlWc7rNYSGhqqqqkp2u12RkZHO111a3tix7tTU1Ki8vLzR+3lJXFxck63LnzXl7+RGcOHCBb/b56uhH5fRC1f0w1Vz9sMYFC555ZVX9Oabb+rQoUPq1q2bXnzxRZcP52sVGRmpmJgYBQcHKyYmRiEhIfrmm2+cz1dXVysiIkJhYWGqrq52WR4eHu5yjsH1jHUnJCSED3cf5G+/k/Lycr/b56uhH5fRC1f0w9X19ONag4XbM/ymTp2q9u3b6+WXX9ZPfvITTZkypUGFXNK7d29t3bpVlmXpxIkTOn/+vB544AGVlpZKkoqLixUfH69evXqppKREDodDx44dk8PhUFRUlHr06NGosQAAoOHczihUVlZqxIgRkr776+7Pf/7zdW3okUce0a5du5SUlCTLspSZmalOnTopIyNDOTk5iomJUWJiogIDAxUfH6/k5GQ5HA5lZmZKkiZPntyosQAAoOHcBoWamhqdPHlSbdq00alTp+RwOK57Y5MmTfrRsuXLl/9o2dixYzV27FiXZdHR0Y0eCwAAGsZtUPj1r3+tlJQUhYeHy263Kzs72xN1AQAAH+A2KDz00EMqKipSRUWFoqKiPFETAADwEdd8uUJCAgAA/ofrGgMAACO3QaGwsNDlcW5ubrMVAwAAfIvxHIX169dr8+bNKi0t1Y4dOyRJ9fX1OnjwoPPrkgAA4OZmDAoPP/yw2rRpozNnzig5OVmSFBAQoM6dO3usOAAA4F3GoHDrrbfqvvvu03333afTp0+rpqZG0nezCgAAwD+4/XpkVlaWPvroI7Vt29Z5s6V3333XE7UBAAAvcxsU9u7dq02bNrncaAkAAPgHt5/+Xbt2dR52AAAA/sXtjMLx48f1yCOPqGvXrpLEoQcAAPyI26Dwn//5n56oAwAA+CC3QeGDDz740bKXXnqpWYoBAAC+xW1QuP322yVJlmXps88+a9RtpgEAwI3FbVBISUlxefz88883WzEAAMC3uA0Kf//7350/nzx5UsePH2/WggAAgO9wGxQyMzOdP4eEhGjSpEnNWhAAAPAdboNCXl6eKisr9fXXX6tTp06KioryRF0AAMAHuL3g0p/+9CelpKRoyZIlSk5O1po1azxRFwAA8AFuZxSWLVum1atXKzQ0VHa7XSNHjtQTTzzhidoAAICXuZ1RsNlsCg0NlSSFhYUpJCSk2YsCAAC+we2MQpcuXTRnzhzFx8frk08+UZcuXTxRFwAA8AFuZxRmzZqlzp07a/v27ercubNmzpzpiboAAIAPcBsUzp8/r/bt2ys+Pl633XabPvzwQ0/UBQAAfIDbQw+jR49W9+7dFR4eLum7cxYGDx7c7IUBAADvcxsUwsPDNXv2bE/UAgAAfIzboJCQkKCCggJ1797duezee+9t1qIAAIBvcBsUPvnkE9XW1mrXrl2Svjv0QFAAAMA/uA0K586d07JlyzxQCgAA8DVug0JsbKzWr1+vHj16yGazSZKio6ObvTAAAOB9boPC559/rs8//1w2m02VlZX66quv9Ne//tUTtQEAAC9zex2FvLw8TZw4Ue3bt1dFRYWSkpI8URcAAPABxhmF2tpabdiwQfn5+WrRooXsdruKiorUsmVLT9YHAAC8yDij0L9/f33xxReaN2+e8vPz1bZtW0ICAAB+xjijMGLECK1fv15Hjx5VUlKSLMvyZF0AAMAHGGcU/uM//kNr165VWlqa1q9fr/3792vevHk6cOCAJ+sDAABe5PZkxj59+mjevHnauHGj2rdvr0mTJnmiLgAA4APcBoVLIiIilJaWpj/84Q/NWQ8AAPAh1xwUAACA/yEoAAAAI4ICAAAwIigAAAAjggIAADAiKAAAACOCAgAAMCIowGddqKv3dglu3Qg1AkBjGO/1AHhbyxaB6jZlg7fLuKqv5jzm7RIAoFl5fEbh9OnT6tevn7788ksdPnxYw4YNU2pqqqZPny6HwyFJWrRokZKSkpSSkqJ9+/ZJUpOMBQAADePRoFBXV6fMzEzn7apnz56t8ePHKz8/X5ZlqaioSGVlZdq5c6cKCwuVk5OjrKysJhkLAAAazqNBYe7cuUpJSVHbtm0lSWVlZerTp48kqW/fvtq+fbt2796thIQE2Ww2dezYUfX19aqoqGj0WAAA0HAeO0dh9erVioqK0sMPP6ylS5dKkizLks1mkySFhoaqqqpKdrtdkZGRztddWt7Yse7U1NSovLy8yfY3Li6uydYF39aU75sLFy406fpudPTjMnrhin64as5+eCworFq1SjabTR9//LHKy8s1efJkVVRUOJ+vrq5WRESEwsLCVF1d7bI8PDxcAQEBjRrrTkhICB/uuC5N+b4pLy/nffg99OMyeuGKfri6nn5ca7Dw2KGHFStWaPny5crLy1NcXJzmzp2rvn37qrS0VJJUXFys+Ph49erVSyUlJXI4HDp27JgcDoeioqLUo0ePRo0FAAAN59WvR06ePFkZGRnKyclRTEyMEhMTFRgYqPj4eCUnJ8vhcCgzM7NJxgIAgIbzSlDIy8tz/rx8+fIfPT927FiNHTvWZVl0dHSjxwIAgIbhyowAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwIigAAAAjAgKAADAiKAAAACMgjy1obq6OqWnp+vo0aOqra3VCy+8oO7du2vKlCmy2WyKjY3V9OnTFRAQoEWLFmnLli0KCgpSenq6evbsqcOHDzd6LAAAaBiPfXquXbtWkZGRys/P1zvvvKPs7GzNnj1b48ePV35+vizLUlFRkcrKyrRz504VFhYqJydHWVlZktTosQAAoOE8NqMwaNAgJSYmOh8HBgaqrKxMffr0kST17dtX27ZtU3R0tBISEmSz2dSxY0fV19eroqKi0WMHDBjgqV0FAOCm4bGgEBoaKkmy2+0aN26cxo8fr7lz58pmszmfr6qqkt1uV2RkpMvrqqqqZFlWo8a6U1NTo/Ly8ibb37i4uCZbF3xbU75vLly40KTru9HRj8vohSv64ao5++GxoCBJx48f15gxY5SamqrHH39c8+bNcz5XXV2tiIgIhYWFqbq62mV5eHi4yzkG1zPWnZCQED7ccV2a8n1TXl7O+/B76Mdl9MIV/XB1Pf241mDhsXMUTp06pdGjR2vixIlKSkqSJPXo0UOlpaWSpOLiYsXHx6tXr14qKSmRw+HQsWPH5HA4FBUV1eixAACg4Tw2o7BkyRKdPXtWixcv1uLFiyVJU6dO1cyZM5WTk6OYmBglJiYqMDBQ8fHxSk5OlsPhUGZmpiRp8uTJysjIuO6xAACg4TwWFKZNm6Zp06b9aPny5ct/tGzs2LEaO3asy7Lo6OhGjwUAAA3DxQUAAIARQQEAABgRFAAAgBFBAWiEC3X1Tbq+pv66V1PXB8D/ePQ6CsDNpmWLQHWbssHbZRh9Necxb5cA4AbHjAIAADAiKAAAACOCAgAAMCIoAAAAI4ICAAAwIigAAAAjggIAADAiKAAAACOCAgAAMCIoAAAAI4ICAAAwIigAAAAjggIAADAiKAAAACOCAgAAMCIoAAAAI4ICAAAwIigAAAAjggIAADAiKAAAACOCAgAAMCIoAAAAI4ICAAAwIigAAAAjggIAADAiKAAAACOCAgAAMCIoAAAAI4ICAAAwIigAAAAjggIAADAiKAAAACOCAgAAMCIoAAAAI4ICAAAwIigAAAAjggIAADAiKAAAACOCAgAAMCIoAAAAI4ICcBO7UFfv7RLcuhFqBPxZkLcLANB8WrYIVLcpG7xdxlV9Necxb5cA4CqYUQAAAEY37YyCw+HQjBkz9MUXXyg4OFgzZ85U165dvV0WAAA3lJt2RmHTpk2qra3Ve++9pwkTJmjOnDneLgkAgBvOTRsUdu/erYcffliSdM8992j//v1ergjAlVztZMa4uDgPVmLGCZeN19Q9bOr3Br9jM5tlWZa3i2gOU6dO1cCBA9WvXz9J0s9+9jNt2rRJQUFXPtry6aefKiQkxJMlAgDgNTU1Nbrnnnvcjrtpz1EICwtTdXW187HD4TCGBEnX1CwAAPzNTXvooVevXiouLpb03WzBnXfe6eWKAAC48dy0hx4ufevhwIEDsixLs2bN0h133OHtsgAAuKHctEEBAAA03k176AEAADQeQQEAABgRFJpZXV2dJk6cqNTUVCUlJamoqEiHDx/WsGHDlJqaqunTp8vhcHi7TI86ffq0+vXrpy+//NLve/H2228rOTlZQ4cOVWFhoV/3o66uThMmTFBKSopSU1P99v2xd+9epaWlSZJx/xctWqSkpCSlpKRo37593iy32X2/H+Xl5UpNTVVaWpqee+45nTp1SpK0cuVKDR06VM8884z+8pe/eLPcZvX9Xlyybt06JScnOx83Sy8sNKv333/fmjlzpmVZllVRUWH169fP+uUvf2nt2LHDsizLysjIsD788ENvluhRtbW11osvvmgNHDjQOnTokF/3YseOHdYvf/lLq76+3rLb7dZ//dd/+XU/Nm7caI0bN86yLMsqKSmxXnrpJb/rx9KlS60hQ4ZYTz/9tGVZ1hX3f//+/VZaWprlcDiso0ePWkOHDvVmyc3qh/0YPny49dlnn1mWZVkFBQXWrFmzrG+//dYaMmSIVVNTY509e9b5883mh72wLMv67LPPrBEjRjiXNVcvmFFoZoMGDdKvf/1r5+PAwECVlZWpT5+sXzqrAAAHR0lEQVQ+kqS+fftq+/bt3irP4+bOnauUlBS1bdtWkvy6FyUlJbrzzjs1ZswY/epXv9LPfvYzv+5HdHS06uvr5XA4ZLfbFRQU5Hf96NKlixYuXOh8fKX93717txISEmSz2dSxY0fV19eroqLCWyU3qx/2Iycnx3lFxvr6eoWEhGjfvn36t3/7NwUHBys8PFxdunTR559/7q2Sm80Pe1FZWam33npL6enpzmXN1QuCQjMLDQ1VWFiY7Ha7xo0bp/Hjx8uyLNlsNufzVVVVXq7SM1avXq2oqCjnpbUl+W0vpO/+oe/fv18LFixQVlaWXn31Vb/uR6tWrXT06FE9+uijysjIUFpamt/1IzEx0eXCcFfaf7vdrrCwMOeYm7kvP+zHpT8w/vd//1fLly/XqFGjZLfbFR4e7hwTGhoqu93u8Vqb2/d7UV9fr6lTpyo9PV2hoaHOMc3Vi5v2yoy+5Pjx4xozZoxSU1P1+OOPa968ec7nqqurFRER4cXqPGfVqlWy2Wz6+OOPVV5ersmTJ7v8JeRPvZCkyMhIxcTEKDg4WDExMQoJCdE333zjfN7f+rFs2TIlJCRowoQJOn78uEaOHKm6ujrn8/7WD0kKCLj8t9yl/f/hVWerq6tdPhxudn/84x/13//931q6dKmioqL8sh9lZWU6fPiwZsyYoZqaGh06dEhvvPGG7r///mbpBTMKzezUqVMaPXq0Jk6cqKSkJElSjx49VFpaKkkqLi5WfHy8N0v0mBUrVmj58uXKy8tTXFyc5s6dq759+/plLySpd+/e2rp1qyzL0okTJ3T+/Hk98MADftuPiIgI539qt956qy5evOi3/1YuudL+9+rVSyUlJXI4HDp27JgcDoeioqK8XKlnrFmzxvl/SOfOnSVJPXv21O7du1VTU6Oqqip9+eWXN/2VeHv27KkNGzYoLy9POTk56t69u6ZOndpsvWBGoZktWbJEZ8+e1eLFi7V48WJJ392waubMmcrJyVFMTIwSExO9XKX3TJ48WRkZGX7Zi0ceeUS7du1SUlKSLMtSZmamOnXq5Lf9GDVqlNLT05Wamqq6ujq9/PLL+ulPf+q3/ZCu/O8jMDBQ8fHxSk5OlsPhUGZmprfL9Ij6+nq98cYb6tChg8aOHStJuvfeezVu3DilpaUpNTVVlmXp5Zdf9tsb/LVp06ZZesGVGQEAgBGHHgAAgBFBAQAAGBEUAACAEUEBAAAYERQAAIARX48EcM2WLl2q3NxcFRUVNfprV8uXL9e6deucV5t78MEHNWbMmKYoE0ATYkYBwDVbt26dBg8erA0bNjRqPfn5+dqzZ49yc3O1YsUKLVu2TAcOHFBJSUkTVQqgqXAdBQDXpLS0VLm5uZowYYImTpyoVatWad++fcrKylJoaKhat26tkJAQzZkzR3l5eVq/fr1sNpsGDx6sESNGuKxryJAhys3Ndbmi4KX7Ghw5ckQvvPCCIiMj1bdvXz300EPKzs5WYGCgQkJClJ2dLYfDoVdeeUUrV66UJD3zzDPKycnRBx98oL/97W86ffq0zp49q2nTpvnd1RyBpsahBwDXpLCwUE8//bTz/hR79+7VjBkz9Oabbyo2Nlbz58/XiRMndOjQIf3xj39Ufn6+bDabRo0apYSEBMXExDjXdebMGWdI2Lhxo3Jzc3XhwgXFx8dr+PDhOnnypFatWqXg4GANHTpUb7zxhuLi4rRp0ybNmTNHkyZNMtbZsmVL5ebm6uDBg5owYYLWrl3b7L0BbmYcegDg1j//+U8VFxcrNzdXzz33nOx2u5YvX65vv/1WsbGxkr67d4UkHThwQMeOHdOoUaM0cuRInTlzRv/4xz9c1hcaGqozZ85IkgYMGKC8vDyNHTtWlZWVkqROnTopODhYkvTtt986by1877336uDBgz+q7/sTo/fff78kKTY2VqdOnWrKNgB+iaAAwK21a9fq5z//uX73u9/pt7/9rVauXKlt27YpJCREhw4dkiTt3btXkhQTE6Pu3bsrNzdXeXl5Gjp06I9uTDN8+HDNmjVLtbW1kr67jv/u3budt1T+/l0T27Ztq88//1yStGvXLnXr1k0hISE6ffq06uvrdfbsWR05csQ5vqysTNJ3gaVdu3bN1BHAf3DoAYBbhYWFevPNN52Pb7nlFg0cOFC333670tPT1apVK7Vo0ULt2rXTXXfdpQceeEDDhg1TbW2tevbs+aMP7BEjRqigoEDPPvusAgICZLfb1adPH02cOFHnzp1zGTtz5kxlZ2fLsiwFBgZq1qxZatOmjR566CElJSWpS5cu6tq1q3N8eXm5Ro4cqfPnzys7O7t5GwP4AU5mBHDdVqxYoUcffVRRUVGaP3++WrRooZdeeslr9SxcuFC33367hg0b5rUagJsNMwoArlvr1q01evRotWrVSuHh4ZozZ463SwLQxJhRAAAARpzMCAAAjAgKAADAiKAAAACMCAoAAMCIoAAAAIwICgAAwOj/AMyIQGM/FgVCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8, 6])\n",
    "df_gobike.member_age.hist(color = default_color)\n",
    "plt.title(\"Age Distribution\")\n",
    "plt.ylabel(\"Amount of Rides\")\n",
    "plt.xlabel(\"Age Group\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It makes sense, to reduce the bin size, to get more detail, especially for higher ages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binsize = 5\n",
    "bins = np.arange(10, df_gobike.member_age.max()+binsize, binsize)\n",
    "plt.figure(figsize=[8, 6])\n",
    "plt.hist(data=df_gobike, x='member_age', bins = bins, color = default_color)\n",
    "plt.title(\"Age Distribution\")\n",
    "plt.ylabel(\"Amount of Rides\")\n",
    "plt.xlabel(\"Age Group\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this plot does not reveal any potential outliers due to the fitted scale for the high amount of users aged around 30, I shall add a box plot to gain more insight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,2))\n",
    "sb.boxplot(x='member_age', data = df_gobike, color = default_color);\n",
    "plt.title(\"Age Distribution (given per Ride)\")\n",
    "plt.xlabel(\"Age\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot shows, like the histogram before, that the majority of ages around 30. Unlike the histogram, the boxplot reveals outliers, which need to be investigated. The outliers between 60 and 100 years seem reasonable. To use this service one needs to know how to operate with apps and needs to understand that the service is based on internet connectivity. Apart to the physical ability to ride bikes, this setting naturally induces these outliers. However, people aging above hundred years seem odd. I therefore want to go into detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating entries with invalid ages, to set those to 0\n",
    "df_gobike_outliers_age_100 = df_gobike[(df_gobike.member_age > 100)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119.0    840\n",
       "117.0    398\n",
       "108.0    274\n",
       "118.0     42\n",
       "131.0     23\n",
       "105.0     18\n",
       "115.0     12\n",
       "112.0      7\n",
       "138.0      5\n",
       "110.0      4\n",
       "130.0      3\n",
       "113.0      2\n",
       "133.0      2\n",
       "116.0      1\n",
       "Name: member_age, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting an overview of those unnatural outliers.\n",
    "df_gobike_outliers_age_100.member_age.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1631"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many outliers are existent?\n",
    "len(df_gobike_outliers_age_100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many people seem to have checked the birth year 1900 or earlier (which makes me think, that this might have been a default value in the registration process). It appears, these users did not want to provide their birth year from some reason or just missed it. I therefore shall insert 0 in the according rows. This solution does not compromise data quality too much in comparison to delete those rows instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating entries with valid ages, by filtering user with age above 100.\n",
    "df_go_bike_temp = df_gobike[(df_gobike.member_age <= 100)] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2386893, 17)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_go_bike_temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check to ensure correct handling.\n",
    "len(df_gobike) == len(df_gobike_outliers_age_100) + len(df_go_bike_temp) + 186979 \n",
    "# 186.979 is the amount where there is NaN-values in the column member_birth_year.\n",
    "# If True, the all entries in the dataset are taken into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\scorneli\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4405: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    },
    {
     "data": {
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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>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>795361</th>\n",
       "      <td>706</td>\n",
       "      <td>2018-03-12 07:44:44.4740</td>\n",
       "      <td>2018-03-12 07:56:31.2950</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Mechanics Monument Plaza (Market St at Bush St)</td>\n",
       "      <td>37.791300</td>\n",
       "      <td>-122.399051</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>2947</td>\n",
       "      <td>Customer</td>\n",
       "      <td>0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660186</th>\n",
       "      <td>712</td>\n",
       "      <td>2018-02-15 12:45:26.9700</td>\n",
       "      <td>2018-02-15 12:57:19.3720</td>\n",
       "      <td>173.0</td>\n",
       "      <td>Shattuck Ave at 55th Ave</td>\n",
       "      <td>37.840364</td>\n",
       "      <td>-122.264488</td>\n",
       "      <td>148.0</td>\n",
       "      <td>Horton St at 40th St</td>\n",
       "      <td>37.829705</td>\n",
       "      <td>-122.287610</td>\n",
       "      <td>92</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
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       "      <th>1140904</th>\n",
       "      <td>1526</td>\n",
       "      <td>2018-05-01 09:29:56.1040</td>\n",
       "      <td>2018-05-01 09:55:22.2580</td>\n",
       "      <td>145.0</td>\n",
       "      <td>29th St at Church St</td>\n",
       "      <td>37.743684</td>\n",
       "      <td>-122.426806</td>\n",
       "      <td>49.0</td>\n",
       "      <td>S Park St at 3rd St</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.394989</td>\n",
       "      <td>3555</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>0</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_sec                start_time                  end_time  \\\n",
       "795361            706  2018-03-12 07:44:44.4740  2018-03-12 07:56:31.2950   \n",
       "660186            712  2018-02-15 12:45:26.9700  2018-02-15 12:57:19.3720   \n",
       "1140904          1526  2018-05-01 09:29:56.1040  2018-05-01 09:55:22.2580   \n",
       "\n",
       "        start_station_id                               start_station_name  \\\n",
       "795361              20.0  Mechanics Monument Plaza (Market St at Bush St)   \n",
       "660186             173.0                         Shattuck Ave at 55th Ave   \n",
       "1140904            145.0                             29th St at Church St   \n",
       "\n",
       "         start_station_latitude  start_station_longitude end_station_id  \\\n",
       "795361                37.791300              -122.399051           58.0   \n",
       "660186                37.840364              -122.264488          148.0   \n",
       "1140904               37.743684              -122.426806           49.0   \n",
       "\n",
       "             end_station_name  end_station_latitude  end_station_longitude  \\\n",
       "795361   Market St at 10th St             37.776619            -122.417385   \n",
       "660186   Horton St at 40th St             37.829705            -122.287610   \n",
       "1140904   S Park St at 3rd St             37.780760            -122.394989   \n",
       "\n",
       "        bike_id   user_type  member_birth_year member_gender  \\\n",
       "795361     2947    Customer                  0        Female   \n",
       "660186       92  Subscriber                  0          Male   \n",
       "1140904    3555  Subscriber                  0         Other   \n",
       "\n",
       "        bike_share_for_all_trip  member_age  \n",
       "795361                       No           0  \n",
       "660186                      Yes           0  \n",
       "1140904                      No           0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting columns member_birth_year and member_age to 0 for those with invalid ages.\n",
    "\n",
    "df_gobike_outliers_age_100.member_birth_year = 0\n",
    "df_gobike_outliers_age_100.member_age = 0\n",
    "\n",
    "df_gobike_outliers_age_100.sample(3) # Check on the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Appending the dataframes, into a new dataframe, one with the corrected values for age and birth year.\n",
    "df_go_bike_temp = df_go_bike_temp.append(df_gobike_outliers_age_100, sort=False, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now appending the rows without provided birth year to the dataset in order to restore the orignal amount of entries. \n",
    "df_go_bike_temp_birth_nan = df_gobike[(df_gobike.member_birth_year.isna())]\n",
    "df_go_bike_temp_birth_nan.member_birth_year = 0\n",
    "df_go_bike_temp_birth_nan.member_age = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>end_station_latitude</th>\n",
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       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
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       "      <th>bike_share_for_all_trip</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2033519</th>\n",
       "      <td>2765</td>\n",
       "      <td>2018-10-13 13:05:21.1010</td>\n",
       "      <td>2018-10-13 13:51:26.8550</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>6.0</td>\n",
       "      <td>The Embarcadero at Sansome St</td>\n",
       "      <td>37.804770</td>\n",
       "      <td>-122.403234</td>\n",
       "      <td>3001</td>\n",
       "      <td>Customer</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1066731</th>\n",
       "      <td>328</td>\n",
       "      <td>2018-05-14 09:26:59.4190</td>\n",
       "      <td>2018-05-14 09:32:27.9140</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Division St at Potrero Ave</td>\n",
       "      <td>37.769218</td>\n",
       "      <td>-122.407646</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>1397</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
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       "      <th>2130391</th>\n",
       "      <td>641</td>\n",
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       "      <td>119.0</td>\n",
       "      <td>18th St at Noe St</td>\n",
       "      <td>37.761047</td>\n",
       "      <td>-122.432642</td>\n",
       "      <td>125.0</td>\n",
       "      <td>20th St at Bryant St</td>\n",
       "      <td>37.759200</td>\n",
       "      <td>-122.409851</td>\n",
       "      <td>1537</td>\n",
       "      <td>Customer</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "         duration_sec                start_time                  end_time  \\\n",
       "2033519          2765  2018-10-13 13:05:21.1010  2018-10-13 13:51:26.8550   \n",
       "1066731           328  2018-05-14 09:26:59.4190  2018-05-14 09:32:27.9140   \n",
       "2130391           641  2018-11-28 14:37:41.7690  2018-11-28 14:48:23.3750   \n",
       "\n",
       "        start_station_id          start_station_name  start_station_latitude  \\\n",
       "2033519             16.0     Steuart St at Market St               37.794130   \n",
       "1066731             89.0  Division St at Potrero Ave               37.769218   \n",
       "2130391            119.0           18th St at Noe St               37.761047   \n",
       "\n",
       "         start_station_longitude end_station_id  \\\n",
       "2033519              -122.394430            6.0   \n",
       "1066731              -122.407646           67.0   \n",
       "2130391              -122.432642          125.0   \n",
       "\n",
       "                                          end_station_name  \\\n",
       "2033519                      The Embarcadero at Sansome St   \n",
       "1066731  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "2130391                               20th St at Bryant St   \n",
       "\n",
       "         end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "2033519             37.804770            -122.403234    3001    Customer   \n",
       "1066731             37.776639            -122.395526    1397  Subscriber   \n",
       "2130391             37.759200            -122.409851    1537    Customer   \n",
       "\n",
       "         member_birth_year member_gender bike_share_for_all_trip  member_age  \n",
       "2033519                  0           NaN                      No           0  \n",
       "1066731                  0           NaN                      No           0  \n",
       "2130391                  0           NaN                      No           0  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_go_bike_temp_birth_nan.sample(3) # Check if everything went well? Yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_go_bike_temp = df_go_bike_temp.append(df_go_bike_temp_birth_nan, sort=False, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "2575503\n"
     ]
    }
   ],
   "source": [
    "# Checking if the total amount of entries is restored to its original amount of 2.575.503 .\n",
    "print(len(df_go_bike_temp) == len(df_gobike))\n",
    "print(len(df_gobike))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2575503 entries, 0 to 2575502\n",
      "Data columns (total 17 columns):\n",
      "duration_sec               int64\n",
      "start_time                 object\n",
      "end_time                   object\n",
      "start_station_id           object\n",
      "start_station_name         object\n",
      "start_station_latitude     float64\n",
      "start_station_longitude    float64\n",
      "end_station_id             object\n",
      "end_station_name           object\n",
      "end_station_latitude       float64\n",
      "end_station_longitude      float64\n",
      "bike_id                    object\n",
      "user_type                  object\n",
      "member_birth_year          float64\n",
      "member_gender              object\n",
      "bike_share_for_all_trip    object\n",
      "member_age                 float64\n",
      "dtypes: float64(6), int64(1), object(10)\n",
      "memory usage: 334.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking if datatypes are still correct after the appending action?\n",
    "df_go_bike_temp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The types for member_birth_year and member_age need to be converted back to type integer.\n",
    "df_go_bike_temp.member_birth_year = df_go_bike_temp.member_birth_year.astype(int)\n",
    "df_go_bike_temp.member_age = df_go_bike_temp.member_age.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copying the corrected data frame to a new version for further analysis\n",
    "df_gobike_v2 = df_go_bike_temp.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, that the values for age (and birth year) have been corrected to 0, as well as the NaNs, I want to segment the age values to into age groups, also to make further bivariate or multivariate analysis more readable. Segments of ten years should work. Starting at 15 to 24 years, 25 to 34 years, etc. should be sufficient. Furthermore, in the online marketing industry, these segments can often be used in campaign targeting settings. So potential insights can directly be translated to dedicated campaigns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gobike_v2['age_group'] = pd.cut(df_gobike_v2.member_age, [0,24,34,44,54,64,74,84,94,100], labels =['15-24', '25-34',\n",
    "                                                                                        '35-44', '45-54',\n",
    "                                                                                       '55-64', '65-74',\n",
    "                                                                                        '75-84', '85-94',\n",
    "                                                                                        '95-100'                      \n",
    "                                                                                       ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>Telegraph Ave at 19th St</td>\n",
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       "      <td>College Ave at Harwood Ave</td>\n",
       "      <td>37.848152</td>\n",
       "      <td>-122.252160</td>\n",
       "      <td>418</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1953</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>66</td>\n",
       "      <td>65-74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248479</th>\n",
       "      <td>269</td>\n",
       "      <td>2018-07-25 19:49:58.9870</td>\n",
       "      <td>2018-07-25 19:54:28.8090</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3rd St at Townsend St</td>\n",
       "      <td>37.778742</td>\n",
       "      <td>-122.392741</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4th St at Harrison St</td>\n",
       "      <td>37.780955</td>\n",
       "      <td>-122.399749</td>\n",
       "      <td>1150</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>36</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2252849</th>\n",
       "      <td>290</td>\n",
       "      <td>2019-01-25 16:55:16.2680</td>\n",
       "      <td>2019-01-25 17:00:06.6030</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4th St at Harrison St</td>\n",
       "      <td>37.780955</td>\n",
       "      <td>-122.399749</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>4802</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1968</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>51</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45712</th>\n",
       "      <td>778</td>\n",
       "      <td>2017-12-11 13:23:49.1410</td>\n",
       "      <td>2017-12-11 13:36:48.0100</td>\n",
       "      <td>97.0</td>\n",
       "      <td>14th St at Mission St</td>\n",
       "      <td>37.768265</td>\n",
       "      <td>-122.420110</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>458</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834466</th>\n",
       "      <td>762</td>\n",
       "      <td>2018-04-09 08:25:10.5720</td>\n",
       "      <td>2018-04-09 08:37:53.5180</td>\n",
       "      <td>30.0</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>15.0</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>1817</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1978</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>41</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948844</th>\n",
       "      <td>515</td>\n",
       "      <td>2018-05-15 18:17:21.4160</td>\n",
       "      <td>2018-05-15 18:25:57.1070</td>\n",
       "      <td>49.0</td>\n",
       "      <td>S Park St at 3rd St</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.394989</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Civic Center/UN Plaza BART Station (Market St ...</td>\n",
       "      <td>37.781074</td>\n",
       "      <td>-122.411738</td>\n",
       "      <td>1665</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>39</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517754</th>\n",
       "      <td>351</td>\n",
       "      <td>2018-01-10 21:15:44.8730</td>\n",
       "      <td>2018-01-10 21:21:36.4830</td>\n",
       "      <td>266.0</td>\n",
       "      <td>Parker St at Fulton St</td>\n",
       "      <td>37.862464</td>\n",
       "      <td>-122.264791</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Downtown Berkeley BART</td>\n",
       "      <td>37.870348</td>\n",
       "      <td>-122.267764</td>\n",
       "      <td>3410</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1994</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>25</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170920</th>\n",
       "      <td>478</td>\n",
       "      <td>2017-10-30 08:57:34.0190</td>\n",
       "      <td>2017-10-30 09:05:32.2020</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Beale St at Harrison St</td>\n",
       "      <td>37.788059</td>\n",
       "      <td>-122.391865</td>\n",
       "      <td>49.0</td>\n",
       "      <td>S Park St at 3rd St</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.394989</td>\n",
       "      <td>3008</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795860</th>\n",
       "      <td>416</td>\n",
       "      <td>2018-04-18 17:05:49.4480</td>\n",
       "      <td>2018-04-18 17:12:46.1870</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Beale St at Harrison St</td>\n",
       "      <td>37.788059</td>\n",
       "      <td>-122.391865</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>267</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>44</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2331380</th>\n",
       "      <td>320</td>\n",
       "      <td>2019-01-11 18:11:43.5420</td>\n",
       "      <td>2019-01-11 18:17:03.5970</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4th St at Harrison St</td>\n",
       "      <td>37.780955</td>\n",
       "      <td>-122.399749</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>37.789756</td>\n",
       "      <td>-122.394643</td>\n",
       "      <td>4984</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>39</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2046832</th>\n",
       "      <td>302</td>\n",
       "      <td>2018-11-06 12:29:47.9750</td>\n",
       "      <td>2018-11-06 12:34:50.0560</td>\n",
       "      <td>267.0</td>\n",
       "      <td>Derby St at College Ave</td>\n",
       "      <td>37.861804</td>\n",
       "      <td>-122.253569</td>\n",
       "      <td>243.0</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>2506</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1977</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>42</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991925</th>\n",
       "      <td>1234</td>\n",
       "      <td>2018-05-08 07:34:07.8860</td>\n",
       "      <td>2018-05-08 07:54:41.9290</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Grove St at Divisadero</td>\n",
       "      <td>37.775946</td>\n",
       "      <td>-122.437777</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>1303</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1994</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>25</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2333463</th>\n",
       "      <td>274</td>\n",
       "      <td>2019-01-11 10:23:50.0620</td>\n",
       "      <td>2019-01-11 10:28:24.8280</td>\n",
       "      <td>255.0</td>\n",
       "      <td>Virginia St at Shattuck Ave</td>\n",
       "      <td>37.876573</td>\n",
       "      <td>-122.269528</td>\n",
       "      <td>246.0</td>\n",
       "      <td>Berkeley Civic Center</td>\n",
       "      <td>37.869060</td>\n",
       "      <td>-122.270556</td>\n",
       "      <td>5514</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1968</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>51</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664570</th>\n",
       "      <td>642</td>\n",
       "      <td>2018-09-17 15:24:51.5530</td>\n",
       "      <td>2018-09-17 15:35:33.8520</td>\n",
       "      <td>52.0</td>\n",
       "      <td>McAllister St at Baker St</td>\n",
       "      <td>37.777416</td>\n",
       "      <td>-122.441838</td>\n",
       "      <td>109.0</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>4365</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>37</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825581</th>\n",
       "      <td>443</td>\n",
       "      <td>2018-10-20 12:25:27.3090</td>\n",
       "      <td>2018-10-20 12:32:50.4510</td>\n",
       "      <td>56.0</td>\n",
       "      <td>Koshland Park</td>\n",
       "      <td>37.773414</td>\n",
       "      <td>-122.427317</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Market St at Franklin St</td>\n",
       "      <td>37.773793</td>\n",
       "      <td>-122.421239</td>\n",
       "      <td>3116</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466490</th>\n",
       "      <td>137</td>\n",
       "      <td>2018-08-20 19:13:51.4710</td>\n",
       "      <td>2018-08-20 19:16:09.2030</td>\n",
       "      <td>336.0</td>\n",
       "      <td>Potrero Ave and Mariposa St</td>\n",
       "      <td>37.763281</td>\n",
       "      <td>-122.407377</td>\n",
       "      <td>124.0</td>\n",
       "      <td>19th St at Florida St</td>\n",
       "      <td>37.760447</td>\n",
       "      <td>-122.410807</td>\n",
       "      <td>2203</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>26</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559689</th>\n",
       "      <td>2386</td>\n",
       "      <td>2018-02-22 15:31:11.7470</td>\n",
       "      <td>2018-02-22 16:10:58.2150</td>\n",
       "      <td>243.0</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Franklin St at 9th St</td>\n",
       "      <td>37.800516</td>\n",
       "      <td>-122.272080</td>\n",
       "      <td>1364</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1992</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1585101</th>\n",
       "      <td>3561</td>\n",
       "      <td>2018-09-29 17:28:08.3430</td>\n",
       "      <td>2018-09-29 18:27:29.3640</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.400000</td>\n",
       "      <td>-121.920000</td>\n",
       "      <td>nan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.400000</td>\n",
       "      <td>-121.920000</td>\n",
       "      <td>4241</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1989</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656148</th>\n",
       "      <td>687</td>\n",
       "      <td>2018-09-18 17:27:12.3290</td>\n",
       "      <td>2018-09-18 17:38:39.9520</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>2436</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>36</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274539</th>\n",
       "      <td>293</td>\n",
       "      <td>2017-09-26 11:45:31.7300</td>\n",
       "      <td>2017-09-26 11:50:25.1040</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Howard St at 8th St</td>\n",
       "      <td>37.776513</td>\n",
       "      <td>-122.411306</td>\n",
       "      <td>2479</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503503</th>\n",
       "      <td>742</td>\n",
       "      <td>2018-08-14 08:56:51.4160</td>\n",
       "      <td>2018-08-14 09:09:14.0330</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Clay St at Battery St</td>\n",
       "      <td>37.795001</td>\n",
       "      <td>-122.399970</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2nd St at Townsend St</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>2397</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1979</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>40</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331720</th>\n",
       "      <td>727</td>\n",
       "      <td>2017-09-06 19:32:02.5880</td>\n",
       "      <td>2017-09-06 19:44:10.5560</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>2235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1986</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368019</th>\n",
       "      <td>2199</td>\n",
       "      <td>2018-07-06 20:26:55.2110</td>\n",
       "      <td>2018-07-06 21:03:35.1030</td>\n",
       "      <td>109.0</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>121.0</td>\n",
       "      <td>Mission Playground</td>\n",
       "      <td>37.759210</td>\n",
       "      <td>-122.421339</td>\n",
       "      <td>647</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1992</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479618</th>\n",
       "      <td>826</td>\n",
       "      <td>2018-01-24 00:53:59.5360</td>\n",
       "      <td>2018-01-24 01:07:46.5300</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Mechanics Monument Plaza (Market St at Bush St)</td>\n",
       "      <td>37.791300</td>\n",
       "      <td>-122.399051</td>\n",
       "      <td>79.0</td>\n",
       "      <td>7th St at Brannan St</td>\n",
       "      <td>37.773492</td>\n",
       "      <td>-122.403673</td>\n",
       "      <td>3591</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316495</th>\n",
       "      <td>301</td>\n",
       "      <td>2018-07-15 15:43:37.0470</td>\n",
       "      <td>2018-07-15 15:48:38.6170</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Powell St BART Station (Market St at 5th St)</td>\n",
       "      <td>37.783899</td>\n",
       "      <td>-122.408445</td>\n",
       "      <td>47.0</td>\n",
       "      <td>4th St at Harrison St</td>\n",
       "      <td>37.780955</td>\n",
       "      <td>-122.399749</td>\n",
       "      <td>3288</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1629549</th>\n",
       "      <td>412</td>\n",
       "      <td>2018-09-22 16:01:44.6920</td>\n",
       "      <td>2018-09-22 16:08:37.6900</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>52.0</td>\n",
       "      <td>McAllister St at Baker St</td>\n",
       "      <td>37.777416</td>\n",
       "      <td>-122.441838</td>\n",
       "      <td>3381</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1973</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>46</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670371</th>\n",
       "      <td>414</td>\n",
       "      <td>2018-03-23 09:45:01.0670</td>\n",
       "      <td>2018-03-23 09:51:56.0610</td>\n",
       "      <td>321.0</td>\n",
       "      <td>5th at Folsom</td>\n",
       "      <td>37.780146</td>\n",
       "      <td>-122.403071</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Post St at Kearny St</td>\n",
       "      <td>37.788975</td>\n",
       "      <td>-122.403452</td>\n",
       "      <td>2817</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531961</th>\n",
       "      <td>255</td>\n",
       "      <td>2018-08-09 08:13:19.7930</td>\n",
       "      <td>2018-08-09 08:17:34.9300</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Townsend St at 7th St</td>\n",
       "      <td>37.771058</td>\n",
       "      <td>-122.402717</td>\n",
       "      <td>1298</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1981</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>38</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169306</th>\n",
       "      <td>234</td>\n",
       "      <td>2017-10-30 17:22:19.4560</td>\n",
       "      <td>2017-10-30 17:26:14.4270</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2nd St at Folsom St</td>\n",
       "      <td>37.785000</td>\n",
       "      <td>-122.395936</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2nd St at Townsend St - Coming Soon</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>176</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215663</th>\n",
       "      <td>1401</td>\n",
       "      <td>2019-01-30 17:33:34.0750</td>\n",
       "      <td>2019-01-30 17:56:55.3170</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Beale St at Harrison St</td>\n",
       "      <td>37.788059</td>\n",
       "      <td>-122.391865</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Embarcadero BART Station (Beale St at Market St)</td>\n",
       "      <td>37.792251</td>\n",
       "      <td>-122.397086</td>\n",
       "      <td>4737</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1973</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>46</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898956</th>\n",
       "      <td>724</td>\n",
       "      <td>2018-05-24 17:14:44.4600</td>\n",
       "      <td>2018-05-24 17:26:48.7440</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.391198</td>\n",
       "      <td>15.0</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>467</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154212</th>\n",
       "      <td>318</td>\n",
       "      <td>2018-06-11 10:31:19.1460</td>\n",
       "      <td>2018-06-11 10:36:37.5260</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2nd Ave at E 18th St</td>\n",
       "      <td>37.800214</td>\n",
       "      <td>-122.253810</td>\n",
       "      <td>163.0</td>\n",
       "      <td>Lake Merritt BART Station</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.265320</td>\n",
       "      <td>3632</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998185</th>\n",
       "      <td>1306</td>\n",
       "      <td>2018-05-07 07:18:09.4650</td>\n",
       "      <td>2018-05-07 07:39:56.2410</td>\n",
       "      <td>133.0</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3rd St at Townsend St</td>\n",
       "      <td>37.778742</td>\n",
       "      <td>-122.392741</td>\n",
       "      <td>1952</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301695</th>\n",
       "      <td>72</td>\n",
       "      <td>2017-09-17 14:34:27.0530</td>\n",
       "      <td>2017-09-17 14:35:39.6290</td>\n",
       "      <td>243.0</td>\n",
       "      <td>Bancroft Way at College Ave</td>\n",
       "      <td>37.869360</td>\n",
       "      <td>-122.254337</td>\n",
       "      <td>239.0</td>\n",
       "      <td>Bancroft Way at Telegraph Ave</td>\n",
       "      <td>37.868813</td>\n",
       "      <td>-122.258764</td>\n",
       "      <td>1229</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1996</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>15-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119427</th>\n",
       "      <td>471</td>\n",
       "      <td>2017-11-14 22:20:21.1840</td>\n",
       "      <td>2017-11-14 22:28:12.3840</td>\n",
       "      <td>160.0</td>\n",
       "      <td>West Oakland BART Station</td>\n",
       "      <td>37.805318</td>\n",
       "      <td>-122.294837</td>\n",
       "      <td>230.0</td>\n",
       "      <td>14th St at Mandela Pkwy</td>\n",
       "      <td>37.810743</td>\n",
       "      <td>-122.291415</td>\n",
       "      <td>1222</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1997</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>15-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349151</th>\n",
       "      <td>642</td>\n",
       "      <td>2018-07-10 12:36:56.3040</td>\n",
       "      <td>2018-07-10 12:47:38.9960</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>284.0</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>3384</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2213589</th>\n",
       "      <td>217</td>\n",
       "      <td>2019-01-30 22:18:04.7650</td>\n",
       "      <td>2019-01-30 22:21:41.8380</td>\n",
       "      <td>356.0</td>\n",
       "      <td>Valencia St at Clinton Park</td>\n",
       "      <td>37.769188</td>\n",
       "      <td>-122.422285</td>\n",
       "      <td>109.0</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>5436</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>26</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148519</th>\n",
       "      <td>1210</td>\n",
       "      <td>2017-11-06 08:51:54.7410</td>\n",
       "      <td>2017-11-06 09:12:05.5080</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Golden Gate Ave at Polk St</td>\n",
       "      <td>37.781270</td>\n",
       "      <td>-122.418740</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Folsom St at 9th St</td>\n",
       "      <td>37.773717</td>\n",
       "      <td>-122.411647</td>\n",
       "      <td>2788</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2292955</th>\n",
       "      <td>351</td>\n",
       "      <td>2019-01-19 16:15:04.3310</td>\n",
       "      <td>2019-01-19 16:20:55.4490</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Raymond Kimbell Playground</td>\n",
       "      <td>37.783813</td>\n",
       "      <td>-122.434559</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.776435</td>\n",
       "      <td>-122.426244</td>\n",
       "      <td>4832</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1994</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>25</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516418</th>\n",
       "      <td>638</td>\n",
       "      <td>2018-01-11 09:34:58.9880</td>\n",
       "      <td>2018-01-11 09:45:37.9170</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>3303</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392501</th>\n",
       "      <td>498</td>\n",
       "      <td>2017-08-11 18:50:42.9670</td>\n",
       "      <td>2017-08-11 18:59:01.2040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>321.0</td>\n",
       "      <td>5th at Folsom</td>\n",
       "      <td>37.780146</td>\n",
       "      <td>-122.403071</td>\n",
       "      <td>2291</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484516</th>\n",
       "      <td>126</td>\n",
       "      <td>2018-08-17 07:22:57.2450</td>\n",
       "      <td>2018-08-17 07:25:04.1790</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Howard St at 8th St</td>\n",
       "      <td>37.776513</td>\n",
       "      <td>-122.411306</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8th St at Ringold St</td>\n",
       "      <td>37.774520</td>\n",
       "      <td>-122.409449</td>\n",
       "      <td>1092</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1664078</th>\n",
       "      <td>1071</td>\n",
       "      <td>2018-09-17 16:36:35.1210</td>\n",
       "      <td>2018-09-17 16:54:26.3640</td>\n",
       "      <td>265.0</td>\n",
       "      <td>Ninth St at Parker St</td>\n",
       "      <td>37.858868</td>\n",
       "      <td>-122.291209</td>\n",
       "      <td>273.0</td>\n",
       "      <td>Fulton St at Ashby Ave</td>\n",
       "      <td>37.855574</td>\n",
       "      <td>-122.263565</td>\n",
       "      <td>3417</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>26</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411722</th>\n",
       "      <td>524</td>\n",
       "      <td>2017-08-02 09:30:48.0380</td>\n",
       "      <td>2017-08-02 09:39:33.0030</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>2545</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1986</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2075935</th>\n",
       "      <td>763</td>\n",
       "      <td>2018-11-01 09:56:48.2340</td>\n",
       "      <td>2018-11-01 10:09:31.7660</td>\n",
       "      <td>310.0</td>\n",
       "      <td>San Fernando St at 4th St</td>\n",
       "      <td>37.335885</td>\n",
       "      <td>-121.885660</td>\n",
       "      <td>313.0</td>\n",
       "      <td>Almaden Blvd at San Fernando St</td>\n",
       "      <td>37.331415</td>\n",
       "      <td>-121.893200</td>\n",
       "      <td>132</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1968</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>51</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540824</th>\n",
       "      <td>1457</td>\n",
       "      <td>2018-08-07 18:39:14.3040</td>\n",
       "      <td>2018-08-07 19:03:31.5130</td>\n",
       "      <td>30.0</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>3396</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1026399</th>\n",
       "      <td>428</td>\n",
       "      <td>2018-05-01 13:51:59.0180</td>\n",
       "      <td>2018-05-01 13:59:07.7770</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>639</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1969</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862347</th>\n",
       "      <td>579</td>\n",
       "      <td>2018-05-31 21:21:05.4020</td>\n",
       "      <td>2018-05-31 21:30:44.6750</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Howard St at 8th St</td>\n",
       "      <td>37.776513</td>\n",
       "      <td>-122.411306</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Valencia St at 16th St</td>\n",
       "      <td>37.765052</td>\n",
       "      <td>-122.421866</td>\n",
       "      <td>3743</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1971</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>48</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625339</th>\n",
       "      <td>1250</td>\n",
       "      <td>2018-09-23 18:05:18.6000</td>\n",
       "      <td>2018-09-23 18:26:09.5220</td>\n",
       "      <td>160.0</td>\n",
       "      <td>West Oakland BART Station</td>\n",
       "      <td>37.805318</td>\n",
       "      <td>-122.294837</td>\n",
       "      <td>188.0</td>\n",
       "      <td>Dover St at 57th St</td>\n",
       "      <td>37.842630</td>\n",
       "      <td>-122.267738</td>\n",
       "      <td>1165</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_sec                start_time                  end_time  \\\n",
       "485890            573  2018-01-22 13:50:14.8330  2018-01-22 13:59:48.3970   \n",
       "1223127           742  2018-07-30 13:58:41.2880  2018-07-30 14:11:03.5630   \n",
       "1248479           269  2018-07-25 19:49:58.9870  2018-07-25 19:54:28.8090   \n",
       "2252849           290  2019-01-25 16:55:16.2680  2019-01-25 17:00:06.6030   \n",
       "45712             778  2017-12-11 13:23:49.1410  2017-12-11 13:36:48.0100   \n",
       "834466            762  2018-04-09 08:25:10.5720  2018-04-09 08:37:53.5180   \n",
       "948844            515  2018-05-15 18:17:21.4160  2018-05-15 18:25:57.1070   \n",
       "517754            351  2018-01-10 21:15:44.8730  2018-01-10 21:21:36.4830   \n",
       "170920            478  2017-10-30 08:57:34.0190  2017-10-30 09:05:32.2020   \n",
       "795860            416  2018-04-18 17:05:49.4480  2018-04-18 17:12:46.1870   \n",
       "2331380           320  2019-01-11 18:11:43.5420  2019-01-11 18:17:03.5970   \n",
       "2046832           302  2018-11-06 12:29:47.9750  2018-11-06 12:34:50.0560   \n",
       "991925           1234  2018-05-08 07:34:07.8860  2018-05-08 07:54:41.9290   \n",
       "2333463           274  2019-01-11 10:23:50.0620  2019-01-11 10:28:24.8280   \n",
       "1664570           642  2018-09-17 15:24:51.5530  2018-09-17 15:35:33.8520   \n",
       "1825581           443  2018-10-20 12:25:27.3090  2018-10-20 12:32:50.4510   \n",
       "1466490           137  2018-08-20 19:13:51.4710  2018-08-20 19:16:09.2030   \n",
       "559689           2386  2018-02-22 15:31:11.7470  2018-02-22 16:10:58.2150   \n",
       "1585101          3561  2018-09-29 17:28:08.3430  2018-09-29 18:27:29.3640   \n",
       "1656148           687  2018-09-18 17:27:12.3290  2018-09-18 17:38:39.9520   \n",
       "274539            293  2017-09-26 11:45:31.7300  2017-09-26 11:50:25.1040   \n",
       "1503503           742  2018-08-14 08:56:51.4160  2018-08-14 09:09:14.0330   \n",
       "331720            727  2017-09-06 19:32:02.5880  2017-09-06 19:44:10.5560   \n",
       "1368019          2199  2018-07-06 20:26:55.2110  2018-07-06 21:03:35.1030   \n",
       "479618            826  2018-01-24 00:53:59.5360  2018-01-24 01:07:46.5300   \n",
       "1316495           301  2018-07-15 15:43:37.0470  2018-07-15 15:48:38.6170   \n",
       "1629549           412  2018-09-22 16:01:44.6920  2018-09-22 16:08:37.6900   \n",
       "670371            414  2018-03-23 09:45:01.0670  2018-03-23 09:51:56.0610   \n",
       "1531961           255  2018-08-09 08:13:19.7930  2018-08-09 08:17:34.9300   \n",
       "169306            234  2017-10-30 17:22:19.4560  2017-10-30 17:26:14.4270   \n",
       "2215663          1401  2019-01-30 17:33:34.0750  2019-01-30 17:56:55.3170   \n",
       "898956            724  2018-05-24 17:14:44.4600  2018-05-24 17:26:48.7440   \n",
       "1154212           318  2018-06-11 10:31:19.1460  2018-06-11 10:36:37.5260   \n",
       "998185           1306  2018-05-07 07:18:09.4650  2018-05-07 07:39:56.2410   \n",
       "301695             72  2017-09-17 14:34:27.0530  2017-09-17 14:35:39.6290   \n",
       "119427            471  2017-11-14 22:20:21.1840  2017-11-14 22:28:12.3840   \n",
       "1349151           642  2018-07-10 12:36:56.3040  2018-07-10 12:47:38.9960   \n",
       "2213589           217  2019-01-30 22:18:04.7650  2019-01-30 22:21:41.8380   \n",
       "148519           1210  2017-11-06 08:51:54.7410  2017-11-06 09:12:05.5080   \n",
       "2292955           351  2019-01-19 16:15:04.3310  2019-01-19 16:20:55.4490   \n",
       "516418            638  2018-01-11 09:34:58.9880  2018-01-11 09:45:37.9170   \n",
       "392501            498  2017-08-11 18:50:42.9670  2017-08-11 18:59:01.2040   \n",
       "1484516           126  2018-08-17 07:22:57.2450  2018-08-17 07:25:04.1790   \n",
       "1664078          1071  2018-09-17 16:36:35.1210  2018-09-17 16:54:26.3640   \n",
       "411722            524  2017-08-02 09:30:48.0380  2017-08-02 09:39:33.0030   \n",
       "2075935           763  2018-11-01 09:56:48.2340  2018-11-01 10:09:31.7660   \n",
       "1540824          1457  2018-08-07 18:39:14.3040  2018-08-07 19:03:31.5130   \n",
       "1026399           428  2018-05-01 13:51:59.0180  2018-05-01 13:59:07.7770   \n",
       "862347            579  2018-05-31 21:21:05.4020  2018-05-31 21:30:44.6750   \n",
       "1625339          1250  2018-09-23 18:05:18.6000  2018-09-23 18:26:09.5220   \n",
       "\n",
       "        start_station_id                                 start_station_name  \\\n",
       "485890             183.0                           Telegraph Ave at 19th St   \n",
       "1223127            175.0                           49th St at Telegraph Ave   \n",
       "1248479             66.0                              3rd St at Townsend St   \n",
       "2252849             47.0                              4th St at Harrison St   \n",
       "45712               97.0                              14th St at Mission St   \n",
       "834466              30.0     San Francisco Caltrain (Townsend St at 4th St)   \n",
       "948844              49.0                                S Park St at 3rd St   \n",
       "517754             266.0                             Parker St at Fulton St   \n",
       "170920              27.0                            Beale St at Harrison St   \n",
       "795860              27.0                            Beale St at Harrison St   \n",
       "2331380             47.0                              4th St at Harrison St   \n",
       "2046832            267.0                            Derby St at College Ave   \n",
       "991925              53.0                             Grove St at Divisadero   \n",
       "2333463            255.0                        Virginia St at Shattuck Ave   \n",
       "1664570             52.0                          McAllister St at Baker St   \n",
       "1825581             56.0                                      Koshland Park   \n",
       "1466490            336.0                        Potrero Ave and Mariposa St   \n",
       "559689             243.0                        Bancroft Way at College Ave   \n",
       "1585101              nan                                                NaN   \n",
       "1656148             21.0   Montgomery St BART Station (Market St at 2nd St)   \n",
       "274539               3.0       Powell St BART Station (Market St at 4th St)   \n",
       "1503503             14.0                              Clay St at Battery St   \n",
       "331720               3.0       Powell St BART Station (Market St at 4th St)   \n",
       "1368019            109.0                             17th St at Valencia St   \n",
       "479618              20.0    Mechanics Monument Plaza (Market St at Bush St)   \n",
       "1316495              5.0       Powell St BART Station (Market St at 5th St)   \n",
       "1629549             70.0                             Central Ave at Fell St   \n",
       "670371             321.0                                      5th at Folsom   \n",
       "1531961             67.0  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "169306              37.0                                2nd St at Folsom St   \n",
       "2215663             27.0                            Beale St at Harrison St   \n",
       "898956              93.0                       4th St at Mission Bay Blvd S   \n",
       "1154212            200.0                               2nd Ave at E 18th St   \n",
       "998185             133.0                             Valencia St at 22nd St   \n",
       "301695             243.0                        Bancroft Way at College Ave   \n",
       "119427             160.0                          West Oakland BART Station   \n",
       "1349151             16.0                            Steuart St at Market St   \n",
       "2213589            356.0                        Valencia St at Clinton Park   \n",
       "148519              41.0                         Golden Gate Ave at Polk St   \n",
       "2292955             31.0                         Raymond Kimbell Playground   \n",
       "516418              36.0                                Folsom St at 3rd St   \n",
       "392501              16.0                            Steuart St at Market St   \n",
       "1484516             61.0                                Howard St at 8th St   \n",
       "1664078            265.0                              Ninth St at Parker St   \n",
       "411722              58.0                               Market St at 10th St   \n",
       "2075935            310.0                          San Fernando St at 4th St   \n",
       "1540824             30.0     San Francisco Caltrain (Townsend St at 4th St)   \n",
       "1026399             16.0                            Steuart St at Market St   \n",
       "862347              61.0                                Howard St at 8th St   \n",
       "1625339            160.0                          West Oakland BART Station   \n",
       "\n",
       "         start_station_latitude  start_station_longitude end_station_id  \\\n",
       "485890                37.808702              -122.269927          193.0   \n",
       "1223127               37.835946              -122.262366          167.0   \n",
       "1248479               37.778742              -122.392741           47.0   \n",
       "2252849               37.780955              -122.399749           81.0   \n",
       "45712                 37.768265              -122.420110           36.0   \n",
       "834466                37.776598              -122.395282           15.0   \n",
       "948844                37.780760              -122.394989           44.0   \n",
       "517754                37.862464              -122.264791          245.0   \n",
       "170920                37.788059              -122.391865           49.0   \n",
       "795860                37.788059              -122.391865           67.0   \n",
       "2331380               37.780955              -122.399749           22.0   \n",
       "2046832               37.861804              -122.253569          243.0   \n",
       "991925                37.775946              -122.437777           36.0   \n",
       "2333463               37.876573              -122.269528          246.0   \n",
       "1664570               37.777416              -122.441838          109.0   \n",
       "1825581               37.773414              -122.427317           75.0   \n",
       "1466490               37.763281              -122.407377          124.0   \n",
       "559689                37.869360              -122.254337          162.0   \n",
       "1585101               37.400000              -121.920000            nan   \n",
       "1656148               37.789625              -122.400811           81.0   \n",
       "274539                37.786375              -122.404904           61.0   \n",
       "1503503               37.795001              -122.399970           50.0   \n",
       "331720                37.786375              -122.404904           67.0   \n",
       "1368019               37.763316              -122.421904          121.0   \n",
       "479618                37.791300              -122.399051           79.0   \n",
       "1316495               37.783899              -122.408445           47.0   \n",
       "1629549               37.773311              -122.444293           52.0   \n",
       "670371                37.780146              -122.403071           19.0   \n",
       "1531961               37.776639              -122.395526           90.0   \n",
       "169306                37.785000              -122.395936           50.0   \n",
       "2215663               37.788059              -122.391865           17.0   \n",
       "898956                37.770407              -122.391198           15.0   \n",
       "1154212               37.800214              -122.253810          163.0   \n",
       "998185                37.755213              -122.420975           66.0   \n",
       "301695                37.869360              -122.254337          239.0   \n",
       "119427                37.805318              -122.294837          230.0   \n",
       "1349151               37.794130              -122.394430          284.0   \n",
       "2213589               37.769188              -122.422285          109.0   \n",
       "148519                37.781270              -122.418740           78.0   \n",
       "2292955               37.783813              -122.434559           74.0   \n",
       "516418                37.783830              -122.398870           13.0   \n",
       "392501                37.794130              -122.394430          321.0   \n",
       "1484516               37.776513              -122.411306           60.0   \n",
       "1664078               37.858868              -122.291209          273.0   \n",
       "411722                37.776619              -122.417385           36.0   \n",
       "2075935               37.335885              -121.885660          313.0   \n",
       "1540824               37.776598              -122.395282            3.0   \n",
       "1026399               37.794130              -122.394430           21.0   \n",
       "862347                37.776513              -122.411306           98.0   \n",
       "1625339               37.805318              -122.294837          188.0   \n",
       "\n",
       "                                          end_station_name  \\\n",
       "485890                        Grand Ave at Santa Clara Ave   \n",
       "1223127                         College Ave at Harwood Ave   \n",
       "1248479                              4th St at Harrison St   \n",
       "2252849                                 Berry St at 4th St   \n",
       "45712                                  Folsom St at 3rd St   \n",
       "834466   San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "948844   Civic Center/UN Plaza BART Station (Market St ...   \n",
       "517754                              Downtown Berkeley BART   \n",
       "170920                                 S Park St at 3rd St   \n",
       "795860   San Francisco Caltrain Station 2  (Townsend St...   \n",
       "2331380                              Howard St at Beale St   \n",
       "2046832                        Bancroft Way at College Ave   \n",
       "991925                                 Folsom St at 3rd St   \n",
       "2333463                              Berkeley Civic Center   \n",
       "1664570                             17th St at Valencia St   \n",
       "1825581                           Market St at Franklin St   \n",
       "1466490                              19th St at Florida St   \n",
       "559689                               Franklin St at 9th St   \n",
       "1585101                                                NaN   \n",
       "1656148                                 Berry St at 4th St   \n",
       "274539                                 Howard St at 8th St   \n",
       "1503503                              2nd St at Townsend St   \n",
       "331720   San Francisco Caltrain Station 2  (Townsend St...   \n",
       "1368019                                 Mission Playground   \n",
       "479618                                7th St at Brannan St   \n",
       "1316495                              4th St at Harrison St   \n",
       "1629549                          McAllister St at Baker St   \n",
       "670371                                Post St at Kearny St   \n",
       "1531961                              Townsend St at 7th St   \n",
       "169306                 2nd St at Townsend St - Coming Soon   \n",
       "2215663   Embarcadero BART Station (Beale St at Market St)   \n",
       "898956   San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "1154212                          Lake Merritt BART Station   \n",
       "998185                               3rd St at Townsend St   \n",
       "301695                       Bancroft Way at Telegraph Ave   \n",
       "119427                             14th St at Mandela Pkwy   \n",
       "1349151  Yerba Buena Center for the Arts (Howard St at ...   \n",
       "2213589                             17th St at Valencia St   \n",
       "148519                                 Folsom St at 9th St   \n",
       "2292955                              Laguna St at Hayes St   \n",
       "516418                      Commercial St at Montgomery St   \n",
       "392501                                       5th at Folsom   \n",
       "1484516                               8th St at Ringold St   \n",
       "1664078                             Fulton St at Ashby Ave   \n",
       "411722                                 Folsom St at 3rd St   \n",
       "2075935                    Almaden Blvd at San Fernando St   \n",
       "1540824       Powell St BART Station (Market St at 4th St)   \n",
       "1026399   Montgomery St BART Station (Market St at 2nd St)   \n",
       "862347                              Valencia St at 16th St   \n",
       "1625339                                Dover St at 57th St   \n",
       "\n",
       "         end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "485890              37.812744            -122.247215    3440  Subscriber   \n",
       "1223127             37.848152            -122.252160     418  Subscriber   \n",
       "1248479             37.780955            -122.399749    1150  Subscriber   \n",
       "2252849             37.775880            -122.393170    4802  Subscriber   \n",
       "45712               37.783830            -122.398870     458  Subscriber   \n",
       "834466              37.795392            -122.394203    1817  Subscriber   \n",
       "948844              37.781074            -122.411738    1665  Subscriber   \n",
       "517754              37.870348            -122.267764    3410  Subscriber   \n",
       "170920              37.780760            -122.394989    3008  Subscriber   \n",
       "795860              37.776639            -122.395526     267  Subscriber   \n",
       "2331380             37.789756            -122.394643    4984  Subscriber   \n",
       "2046832             37.869360            -122.254337    2506  Subscriber   \n",
       "991925              37.783830            -122.398870    1303  Subscriber   \n",
       "2333463             37.869060            -122.270556    5514  Subscriber   \n",
       "1664570             37.763316            -122.421904    4365  Subscriber   \n",
       "1825581             37.773793            -122.421239    3116  Subscriber   \n",
       "1466490             37.760447            -122.410807    2203  Subscriber   \n",
       "559689              37.800516            -122.272080    1364  Subscriber   \n",
       "1585101             37.400000            -121.920000    4241    Customer   \n",
       "1656148             37.775880            -122.393170    2436  Subscriber   \n",
       "274539              37.776513            -122.411306    2479  Subscriber   \n",
       "1503503             37.780526            -122.390288    2397  Subscriber   \n",
       "331720              37.776639            -122.395526    2235  Subscriber   \n",
       "1368019             37.759210            -122.421339     647  Subscriber   \n",
       "479618              37.773492            -122.403673    3591  Subscriber   \n",
       "1316495             37.780955            -122.399749    3288  Subscriber   \n",
       "1629549             37.777416            -122.441838    3381  Subscriber   \n",
       "670371              37.788975            -122.403452    2817  Subscriber   \n",
       "1531961             37.771058            -122.402717    1298  Subscriber   \n",
       "169306              37.780526            -122.390288     176  Subscriber   \n",
       "2215663             37.792251            -122.397086    4737  Subscriber   \n",
       "898956              37.795392            -122.394203     467  Subscriber   \n",
       "1154212             37.797320            -122.265320    3632  Subscriber   \n",
       "998185              37.778742            -122.392741    1952  Subscriber   \n",
       "301695              37.868813            -122.258764    1229  Subscriber   \n",
       "119427              37.810743            -122.291415    1222  Subscriber   \n",
       "1349151             37.784872            -122.400876    3384  Subscriber   \n",
       "2213589             37.763316            -122.421904    5436  Subscriber   \n",
       "148519              37.773717            -122.411647    2788    Customer   \n",
       "2292955             37.776435            -122.426244    4832  Subscriber   \n",
       "516418              37.794231            -122.402923    3303  Subscriber   \n",
       "392501              37.780146            -122.403071    2291  Subscriber   \n",
       "1484516             37.774520            -122.409449    1092  Subscriber   \n",
       "1664078             37.855574            -122.263565    3417  Subscriber   \n",
       "411722              37.783830            -122.398870    2545  Subscriber   \n",
       "2075935             37.331415            -121.893200     132    Customer   \n",
       "1540824             37.786375            -122.404904    3396    Customer   \n",
       "1026399             37.789625            -122.400811     639  Subscriber   \n",
       "862347              37.765052            -122.421866    3743  Subscriber   \n",
       "1625339             37.842630            -122.267738    1165  Subscriber   \n",
       "\n",
       "         member_birth_year member_gender bike_share_for_all_trip  member_age  \\\n",
       "485890                1956          Male                      No          63   \n",
       "1223127               1953        Female                      No          66   \n",
       "1248479               1983          Male                      No          36   \n",
       "2252849               1968          Male                      No          51   \n",
       "45712                 1990          Male                     NaN          29   \n",
       "834466                1978          Male                      No          41   \n",
       "948844                1980          Male                      No          39   \n",
       "517754                1994        Female                      No          25   \n",
       "170920                1987        Female                     NaN          32   \n",
       "795860                1975          Male                      No          44   \n",
       "2331380               1980          Male                      No          39   \n",
       "2046832               1977        Female                     Yes          42   \n",
       "991925                1994        Female                      No          25   \n",
       "2333463               1968          Male                      No          51   \n",
       "1664570               1982          Male                      No          37   \n",
       "1825581               1989          Male                      No          30   \n",
       "1466490               1993          Male                      No          26   \n",
       "559689                1992          Male                      No          27   \n",
       "1585101               1989        Female                      No          30   \n",
       "1656148               1983        Female                      No          36   \n",
       "274539                1990          Male                     NaN          29   \n",
       "1503503               1979          Male                      No          40   \n",
       "331720                1986          Male                     NaN          33   \n",
       "1368019               1992          Male                      No          27   \n",
       "479618                1987          Male                      No          32   \n",
       "1316495               1991          Male                      No          28   \n",
       "1629549               1973        Female                      No          46   \n",
       "670371                1990        Female                      No          29   \n",
       "1531961               1981        Female                      No          38   \n",
       "169306                1987          Male                     NaN          32   \n",
       "2215663               1973          Male                      No          46   \n",
       "898956                1974          Male                      No          45   \n",
       "1154212               1989          Male                      No          30   \n",
       "998185                1987         Other                      No          32   \n",
       "301695                1996          Male                     NaN          23   \n",
       "119427                1997         Other                     NaN          22   \n",
       "1349151               1987          Male                      No          32   \n",
       "2213589               1993          Male                      No          26   \n",
       "148519                1993          Male                     NaN          26   \n",
       "2292955               1994        Female                      No          25   \n",
       "516418                1974        Female                      No          45   \n",
       "392501                1984          Male                     NaN          35   \n",
       "1484516               1991          Male                      No          28   \n",
       "1664078               1993        Female                      No          26   \n",
       "411722                1986          Male                     NaN          33   \n",
       "2075935               1968          Male                      No          51   \n",
       "1540824               1991          Male                      No          28   \n",
       "1026399               1969          Male                      No          50   \n",
       "862347                1971          Male                      No          48   \n",
       "1625339               1987        Female                      No          32   \n",
       "\n",
       "        age_group  \n",
       "485890      55-64  \n",
       "1223127     65-74  \n",
       "1248479     35-44  \n",
       "2252849     45-54  \n",
       "45712       25-34  \n",
       "834466      35-44  \n",
       "948844      35-44  \n",
       "517754      25-34  \n",
       "170920      25-34  \n",
       "795860      35-44  \n",
       "2331380     35-44  \n",
       "2046832     35-44  \n",
       "991925      25-34  \n",
       "2333463     45-54  \n",
       "1664570     35-44  \n",
       "1825581     25-34  \n",
       "1466490     25-34  \n",
       "559689      25-34  \n",
       "1585101     25-34  \n",
       "1656148     35-44  \n",
       "274539      25-34  \n",
       "1503503     35-44  \n",
       "331720      25-34  \n",
       "1368019     25-34  \n",
       "479618      25-34  \n",
       "1316495     25-34  \n",
       "1629549     45-54  \n",
       "670371      25-34  \n",
       "1531961     35-44  \n",
       "169306      25-34  \n",
       "2215663     45-54  \n",
       "898956      45-54  \n",
       "1154212     25-34  \n",
       "998185      25-34  \n",
       "301695      15-24  \n",
       "119427      15-24  \n",
       "1349151     25-34  \n",
       "2213589     25-34  \n",
       "148519      25-34  \n",
       "2292955     25-34  \n",
       "516418      45-54  \n",
       "392501      35-44  \n",
       "1484516     25-34  \n",
       "1664078     25-34  \n",
       "411722      25-34  \n",
       "2075935     45-54  \n",
       "1540824     25-34  \n",
       "1026399     45-54  \n",
       "862347      45-54  \n",
       "1625339     25-34  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gobike_v2.sample(50) # Check if cutting went well? Yes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the grouped ages can be plotted into a bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vXbrU/v/o6GhNnDhRM2bM0Pbt23XHHXdo48aNuvPOOx3OJygoyG1h7CxvqyspKcnranKWL/Qg0Yc38YUeJN/owxd6kNzTR1E3DtzyVbHY2FjNmTNHvXv3Vl5enjp27OiOYQEA8EnXfM/7QosXL7b/f8mSJa4cCgCAEoOLtAAAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYfxdMdP8/HyNGzdO+/btk5+fnxITE5Wenq5//vOfqlmzpiSpb9++6ty5syuGBwDAp7kkvDds2CBJWr58ubZv367ExES1bdtWgwYN0uDBg10xJAAAJYZLwrtdu3a66667JElHjhxRpUqVtGvXLu3bt0/r169XjRo1FBcXp7Jly7pieAAAfJrNsizLVTOPjY3V2rVr9fLLLys5OVl16tRRgwYN9NprryktLU2xsbGFvnbHjh0KCgpyaryIiAjVHL36asu+oj+m3qukpCSXjuGs7OxsBQcHe7qMq+ILPUj04U18oQfJN/rwhR4k9/URERHhcBqX7HmfN23aNI0aNUq9evXS8uXLVaVKFUlS+/btlZCQcMXXBgUFFakBT/C2upKSkryuJmf5Qg8SfXgTX+hB8o0+fKEHyT19FHXn0CVnm3/44Yd6/fXXJUmlS5eWzWbTsGHD9MMPP0iStm7dqvr167tiaAAAfJ5L9rw7dOigMWPGqH///jp79qzi4uJ04403KiEhQQEBAapUqZLDPW8AAHB5LgnvMmXK6KWXXvrL88uXL3fFcAAAlChcpAUAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGH8XTHT/Px8jRs3Tvv27ZOfn58SExNlWZZGjx4tm82mWrVqKT4+XqVKse0AAICzXBLeGzZskCQtX75c27dvt4d3TEyM7rjjDk2YMEHr169X+/btXTE8AAA+zSW7vu3atVNCQoIk6ciRI6pUqZJ2796tpk2bSpJatWqlLVu2uGJoAAB8nsuOW/v7+ys2NlYJCQnq2LGjLMuSzWaTJIWEhCg9Pd1VQwMA4NNcctj8vGnTpmnUqFHq1auXcnJy7M9nZmaqfPnyV3xtTk6OkpKSnBovIiKiWHU6y9m6XC07O9vranKWL/Qg0Yc38YUeJN/owxd6kLyrD5eE94cffqjk5GQ99thjKl26tGw2mxo0aKDt27frjjvu0MaNG3XnnXdecR5BQUFuC2NneVtdSUlJXleTs3yhB4k+vIkv9CD5Rh++0IPknj6KunHgkvDu0KGDxowZo/79++vs2bOKi4vTzTffrPHjx2vWrFkKDw9Xx44dXTE0AAA+zyXhXaZMGb300kt/eX7JkiWuGA4AgBKFL1oDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvL5Odl+/0ayIiIlw+BgDAe/h7ugBcLDjATzVHr3bpGH9Mvdel8wcAuBZ73gAAGIbwBgDAMA7DOysrS3/++adOnDihV199VYcPH3ZHXQAAoBAOw3vUqFHatWuXpk+froCAAE2YMMEddQEAgEI4DO+0tDTdfffdSk5O1qOPPqrc3Fx31AUAAArhMLzz8vL05ptvql69etq7d68yMzPdURcAACiEw6+KxcbGat26dRo6dKg+/vhjTZw48YrT5+XlKS4uTocPH1Zubq6GDh2qv/3tb/rnP/+pmjVrSpL69u2rzp07X4v6AQAocRyGd6NGjZSdna01a9aocePGCgsLu+L0q1atUoUKFTRjxgylpqaqW7dueuKJJzRo0CANHjz4mhUOAEBJ5TC8Z82apT///FO//fabAgICNH/+fM2aNavQ6Tt16qSOHTvaH/v5+WnXrl3at2+f1q9frxo1aiguLk5ly5a9Nh0AAFDCOPzM+7vvvtP06dNVpkwZdevWTYcOHbri9CEhISpbtqwyMjI0fPhwxcTEKDIyUs8++6yWLl2qatWq6dVXX71mDQAAUNI43PPOz89XTk6ObDab8vPzVaqU4+u6HD16VE888YT69eun+++/X2lpaSpfvrwkqX379kpISHA4j5ycHCUlJRWhhf9x9hrfxeVsXc7whR6KIzs72+tqKg768B6+0IPkG334Qg+Sd/XhMLwHDhyo7t276+TJk+rZs6cefvjhK05/4sQJDR48WBMmTFCzZs0kSY888ojGjx+vyMhIbd26VfXr13dYWFBQkNuCzFneWpczvK2HpKQkr6upOOjDe/hCD5Jv9OELPUju6aOoGwcOw/uee+5R8+bNtX//flWtWlWhoaFXnH7evHlKS0vT3LlzNXfuXEnS6NGjNWXKFAUEBKhSpUpF2vMGAACXV2h4jxkzptAXJSYmFvqzcePGady4cX95fvny5U6WBgAALqfQD7A7d+6szp076/Tp0woPD1ePHj1Up04drrAGAICHFRreUVFRioqKUnZ2toYMGaLGjRvr4Ycf1smTJ91ZHwAAuESR7iq2detWZWRkaNOmTcrLy3NHXQAAoBAOw3vy5MlatmyZevXqpRUrVmj27NnuqAsAABSi0BPWzp49K39/f1WrVk0zZ850Z00AAOAKCg3v2NhYvfDCC+rUqZNsNpskybIs2Ww2rV+/3m0FAgCAixUa3i+88IIk6amnnlKXLl3cVhAAALgyh595r1y50h11AACAInJ4hbXc3Fx17dpVYWFh9uuan98rBwAA7ucwvEeNGuWOOgAAQBE5DO+mTZu6ow4AAFBEju/vCQAAvArhDQCAYQo9bD5ixAj797svxQlrAAB4TqHh3adPH3fWAQAAiqjQ8N63b1+hL+IkNgAAPKfQ8D5+/Lg76wAAAEVUaHgPGzbM/v9jx47p7NmzsixLx44dc0thAADg8hx+zzsuLk47duzQmTNnlJ2drWrVqmnFihXuqA0AAFyGw6+K/f7771q9erVatmyp1atXKygoyB11AQCAQjgM75CQENlsNmVlZSk0NFR5eXnuqAsAABTCYXjXr19f//rXv1S5cmU9/fTTys/Pd0ddAACgEA4/8x4xYoQyMjIUHBysjRs3KjIy0h11AQCAQjgMb0kqW7asJKlt27YuLQYAADjGtc0BADBMoeE9e/ZsSdK6devcVgwAAHCs0MPm69evV+XKlbV48WKlpKRc9LPevXu7vDAAAHB5hYb3lClT9PXXXys3N5dLpQIA4EUKDe/IyEhFRkaqRYsWql69ug4cOKCqVasqNDTUnfUBAIBLODxh7dChQ+rdu7fmzZun3r1766OPPnJHXQAAoBAOvyr29ttv6/3331dISIgyMjI0cOBAdenSxR21AQCAy3C4522z2RQSEiLp3Pe9ubY5AACe5XDPu3r16po6daqaNGmib7/9VtWrV3dHXQAAoBAO97ynTJmiatWqacuWLapWrZoSEhLcURcAACiEwz1vf39/9e/f3x21AACAIuDyqAAAGIbwBgDAMA4PmycnJ2vGjBlKTU1Vx44dVadOHd12223uqA0AAFyGwz3v8ePH68EHH1Rubq6aNGmiyZMnu6MuAABQCIfhnZOTo2bNmslmsyk8PJzveQMA4GEOwzswMFCbNm1SQUGBduzYocDAQHfUBQAACuEwvBMSEvT+++8rNTVVb775piZOnOiGsgAAQGEcnrBWUFCgZ5555n8v8PdXXl6eAgICXFoYAAC4PIfh/dhjjyk5OVnh4eHat2+fSpcurbNnz+qZZ57hBiUAAHiAw8PmVatW1Zo1a7R8+XJ9/vnnuvXWW/XJJ59oyZIl7qgPAABcwmF4p6SkKDQ0VJJ03XXX6cSJE6pQoYJKleL6LgAAeILDw+b169fXiBEj1LBhQ+3YsUMRERH69NNPVbFiRXfUBwAALuEwvOPj47V+/Xr99ttveuCBB3TXXXfp999/V5s2bdxRHwAAuITDY9+nTp3SmTNnVLlyZaWmpur1119XeHi4Spcu7Y76AADAJRzueQ8fPlw1a9bUL7/8oqCgIEIbAAAPK9JZZ88//7zCwsL01ltv6fTp066uCQAAXEGRwjsnJ0dnzpyRzWZTVlaWq2sCAABX4DC8+/fvr7ffflstWrRQ69atFR4e7o66AABAIRx+5n3TTTepY8eOkqR77rlHP/30k8uLAgAAhSs0vL/99lvt3btXCxcu1KBBgySdu8750qVL9cknn7itQAAAcLFCw7t8+fI6ceKEcnNzdfz4cUmSzWa76CYll5OXl6e4uDgdPnxYubm5Gjp0qG655RaNHj1aNptNtWrVUnx8PFdoAwCgmAoN79q1a6t27drq2bOnqlSpUuQZrlq1ShUqVNCMGTOUmpqqbt26qW7duoqJidEdd9yhCRMmaP369Wrfvv01aQAAgJLG4WfeW7du1euvv67c3FxZliWbzab169cXOn2nTp3sn5FLkp+fn3bv3q2mTZtKklq1aqWvv/6a8AYAoJgchveCBQs0b9483XjjjUWaYUhIiCQpIyNDw4cPV0xMjKZNmyabzWb/eXp6usP55OTkKCkpqUhjnhcREeHU9MXlbF3O8IUeiiM7O9vraioO+vAevtCD5Bt9+EIPknf14TC8q1Wrpho1ajg106NHj+qJJ55Qv379dP/992vGjBn2n2VmZqp8+fIO5xEUFOS2IHOWt9blDG/rISkpyetqKg768B6+0IPkG334Qg+Se/oo6saBw/AODg7WP/7xD0VERNj3nkeMGFHo9CdOnNDgwYM1YcIENWvWTJJUr149bd++XXfccYc2btyoO++8s0jFAQCAv3IY3q1bt3ZqhvPmzVNaWprmzp2ruXPnSpLGjh2rSZMmadasWQoPD7/oM3EAAOAch+F9//3364MPPtDRo0d1xx13qFatWlecfty4cRo3btxfnl+yZEnxqwQAAHYOv2wdHx+vI0eO6Ouvv1ZmZqZiY2PdURcAACiEw/A+cOCAnnrqKQUGBqpt27ZFOlMcAAC4jsPwzs/P18mTJ2Wz2ZSRkcGV0QAA8DCHn3nHxMSob9++On78uHr37q24uDh31AUAAArhMLybNm2qt956S8HBwTp06JAiIyPdURcAACiEw2PgEyZM0IcffqjQ0FCtWrVKkyZNckddAACgEA7DOykpSY8//rikc18D85ZLwwEAUFI5DG/LspSamipJSktLU35+vsuLAgAAhXP4mfewYcP04IMn5xReAAAda0lEQVQPqkKFCkpLS1N8fLw76gIAAIVwGN5paWlau3atUlNTVbFiRfv1zQEAgGc4PGy+YsUK+fn5qVKlSgQ3AABewOGed25urrp27aqwsDD7BVpeeOEFlxcGAAAuz2F4jxo1yh11AACAInJ42LxevXr6+uuv9eGHH+rUqVOqUqWKO+oCAACFcBjecXFxqlatmv744w9VqlRJY8eOdUddAACgEA7D+9SpU+rRo4f8/f3VqFEjWZbljroAAEAhinSLsN9++02S9Oeff3JXMQAAPMxhEo8dO1ZxcXH66aefNHz4cI0ePdoddQEAgEJc8WzzjIwMVa9eXf/+97/dVQ8AAHCg0D3vJUuW6IEHHlCXLl20adMmd9YEAACuoNDw/uSTT7RmzRotX75cb7/9tjtrAgAAV1BoeAcGBiowMFChoaHKy8tzZ00AAOAKinTqOF8PAwDAexR6wtrevXs1cuRIWZZl//95XNscAADPKTS8X3zxRfv/+/Tp45ZiAACAY4WGd9OmTd1ZBwAAKCIulwYAgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDuCy8d+7cqejoaEnS7t27FRUVpejoaEVHR+vTTz911bAAAPg8f1fMdMGCBVq1apVKly4tSfrpp580aNAgDR482BXDAQBQorhkz7t69eqaM2eO/fGuXbv05Zdfqn///oqLi1NGRoYrhgUAoERwSXh37NhR/v7/26mPjIzUs88+q6VLl6patWp69dVXXTEsAAAlgksOm1+qffv2Kl++vP3/CQkJDl+Tk5OjpKQkp8aJiIgoVn3OcrYuZ/hCD8WRnZ3tdTUVB314D1/oQfKNPnyhB8m7+nBLeD/yyCMaP368IiMjtXXrVtWvX9/ha4KCgtwWZM7y1rqc4W09JCUleV1NxUEf3sMXepB8ow9f6EFyTx9F3ThwS3hPnDhRCQkJCggIUKVKlYq05w0AAC7PZeFdtWpVrVixQpJUv359LV++3FVDAQBQonCRFgAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDecInsvHynpo+IiHD5GADgK/w9XQB8U3CAn2qOXu3SMf6Yeq9L5w8A3oo9bwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYl4X3zp07FR0dLUnav3+/+vbtq379+ik+Pl4FBQWuGhYAAJ/nkvBesGCBxo0bp5ycHElSYmKiYmJi9M4778iyLK1fv94VwwIAUCK4JLyrV6+uOXPm2B/v3r1bTZs2lSS1atVKW7ZsccWwAACUCP6umGnHjh116NAh+2PLsmSz2SRJISEhSk9PdziPnJwcJSUlOTVuRESEc4UWk7N1OcMXepB8pw9nZWdne11NxeELffhCD5Jv9OELPUje1YdLwvtSpUr9bwc/MzNT5cuXd/iaoKAgtwWAs7y1Lmf4Qg+S9/WRlJTkdTUVhy/04Qs9SL7Rhy/0ILmnj6JuHLjlbPN69epp+/btkqSNGzeqSZMm7hgWAACf5Jbwjo2N1Zw5c9S7d2/l5eWpY8eO7hgWAACf5LLD5lWrVtWKFSskSWFhYVqyZImrhgIAoEThIi0AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeANXkJ2X79T0ERERLp0/AEiSv6cLALxZcICfao5e7bL5/zH1XpfNG4DvYs8bAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADOPvzsG6du2qcuXKSZKqVq2qxMREdw4PAIBPcFt45+TkSJIWL17sriEBAPBJbjtsvmfPHp05c0aDBw/WgAEDtGPHDncNDZRo2Xn5Tr8mIiLC5WMAKD637XkHBwfrkUceUc+ePfXHH39oyJAhWrNmjfz9L19CTk6OkpKSnBrD2T84xeVsXc7whR4k+nCGO3qoOXq1S8f4Y+q9Lu/DWdnZ2V5XU3H4Qh++0IPkXX24LbzDwsJUo0YN2Ww2hYWFqUKFCjp+/LhuvPHGy04fFBTktgBwlrfW5Qxf6EHyjT58oQfJ+/pISkryupqKwxf68IUeJPf0UdSNA7cdNn/33Xc1depUSVJycrIyMjJ0ww03uGt4AAB8htv2vHv06KExY8aob9++stlsmjJlSqGHzAEAQOHclp6BgYF64YUX3DUcAAA+i4u0AABgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBGCE7L9+p6SMiIlw+BuAp/p4uAACKIjjATzVHr3bpGH9Mvdel8weuFfa8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwbvued0FBgSZOnKiff/5ZgYGBmjRpkmrUqOGu4QEA8Blu2/Net26dcnNz9e9//1sjR47U1KlT3TU0AAA+xW3h/d133ykqKkqS1LBhQ+3atctdQwOAVyjO5Vedvcwrl3gtGdx22DwjI0Nly5a1P/bz89PZs2fl788VWgGUDL5yidfsvHwFB/gVefriXmfemTFKGptlWZY7BkpMTNRtt92mzp07S5JatWqljRs3Fjr9jh07FBQU5I7SAADwCjk5OWrYsKHD6dy229uoUSNt2LBBnTt31o4dO1S7du0rTl+U4gEAKInctud9/mzzX375RZZlacqUKbr55pvdMTQAAD7FbeENAACuDS7SAgCAYQhvAAAMQ3gDAGAYwhsAAMMQ3vC4lJQUT5eA/5+vrAv68B6+0IM3KrGXN9uzZ4/q1q3r6TKumol97Nu376LHsbGxmjZtmiQpLCzMEyWVWL6yLujDe/hCD+clJSVp69atSk9PV/ny5dW4cWNFRkZ6uixJJeirYps3b77o8YwZM/TMM89Iklq2bOmJkorFF/q46667FBwcrMqVK8uyLPsGiM1m06JFizxd3lUxbWPKV9YFfXgPX+hBkl555RX98MMPatmypUJCQpSZmanNmzerXr16iomJ8XR5JSe8u3btqlKlSqlOnTqSpE2bNtlvlJKYmOjJ0pziC32kpKQoPj5effv2VYsWLRQdHa3Fixd7uqxiMX1jylfWBX14D1/oQZL69eund95556LnLMtSr169tHLlSg9V9T8l5rD5smXL9Pzzz6tRo0bq2bOnoqOjjQm7C/lCHxUrVtSLL76oadOm6ccff/R0OVdl5syZF21MpaSkaPXqczeeMCG8fWVd0If38IUeJOns2bM6dOiQqlatan/u0KFDKlXKO04V844q3KB06dJKTExUenq64uPjlZ9v5m3zfKUPf39/jR07VlWqVPF0KVdl2bJlqlOnjho1aqTExESFhYUpMTHRqA0qX1kX5/s4f7jWVL7Qhy+8p+Li4jRs2DDde++96t27t+69914NHz5ccXFxni5NUgk6bH6hrVu36r333tPMmTM9XcpV2bp1q95991298MILni7FKd9++62aNGmigoICLVu2TD/99JMaNGigXr16yc/PzFsAvvnmm9q/f79+/fXXvxxq82a5ubk6cuSIatasqW3btmnXrl2qVauWWrdu7enSnDJr1iwNHTpUpUuX9nQpVy09PV02m01ly5bVmjVrlJ6erm7duhl1++Tvv/9e3333nc6cOaPrr79ezZs3N/ZeFhkZGcrMzFTZsmUVEhLi6XLsSlR479mzRxUqVFBoaKhef/11lSpVSoMHDzb6F37BggUaMmSIp8twyoABA7Ro0SJNmzZNWVlZatu2rbZt26bs7GzFx8d7urxiM3Fj6qmnnlJUVJROnz6tLVu2KCoqSt99951uvPFGr9nDKIqWLVvqb3/7m0aNGqU777zT0+UU27Jly/TWW29JOnfiV0pKikJDQ5WRkWHM0Zx58+bpt99+U6NGjfTVV18pPDxcBw4cULNmzdS/f39Pl1dkBw8eVGJionbv3i0/Pz8VFBSodu3aGjNmjFecNW/OptxVmjt3rrZt26aMjAzdcMMNioiIUHBwsMaNG2fUH9sRI0bIZrPZH2/btk179uyRJKP6kKQffvhBS5culSS1bt1a0dHRHq7IeTk5OVq5cqWCgoLUpUsXNWvWTJK0fPly9enTx8PVOXbixAn16NFD0dHReuutt+Tv76+HH35YPXv29HRpTgkLC9OUKVM0ZcoUzZ07V7169VJUVJSuu+46T5fmlPfff1+rV69WTk6O7r//fn3xxRey2WxGhd6mTZvsv9e9evVSTEyMXnnlFfXp08eoPsaOHauRI0fqtttusz+3Y8cOjRkzRsuXL/dgZeeUmM+8N27cqEWLFmnx4sX69ddfFRMToyFDhujYsWOeLs0ptWrV0tGjR9WjRw/16tVLN9xwg3r37q3evXt7urQiO3r0qNauXaty5crp0KFDkqTk5GRlZ2d7uDLnPfvsszp27Jj27dunfv366fTp05KkTz/91MOVFd3BgwdVu3ZtHThwwP7YNDabTdWqVdNrr72msWPHKikpSYMGDTLu8H9+fr6ys7N16tQpZWVlKSsrS3l5ecrNzfV0aUWWlZWlw4cPS5IOHDigU6dOKTs727jf79zc3IuCW5IaNmzooWr+qsTseRcUFOjIkSO66aabNHv2bElSWlqaUb8UkjR06FDVq1dPS5cu1fPPP6/y5curadOmni7LKc8++6x2796t/Px8rVu3Tg8++KD69OmjyZMne7o0p508eVIvvfSSJGnt2rUaOnSoFi5caMyJRrGxsXryySdVoUIFde/eXdWrV1dWVpZx6+LC5V2nTh371/VM8/DDD+uee+5R3bp11b17d/Xo0UOlS5c2auN85MiReuihh3TdddfpzJkzmj59uhYsWGDUXrd07n00ZswYRUVFqVy5csrMzNRXX31l/2aJp5WYz7y//fZbJSYmauXKlfZT/fv3769HHnlEbdu29XB1zjtw4ICee+45paSk6MMPP/R0OVfl2LFjqly5sqfLKJY+ffpo7ty5Cg0NlSQtXLhQ33//vY4dO+YVh9aKat++fUpNTVX58uVVo0YNBQQEeLqkq2Lye+pCe/bsUfny5XXTTTd5uhSnWJal1NRUhYaGGrsuLMvSunXr9N133ykjI0Nly5ZVo0aN1L59+4s+uvRkgSVScnKyp0u4apmZmdaqVas8XcZVi46O9nQJxbZlyxarU6dO1vHjx+3PzZ0716pfv74Hqyo+k9fFhejDe/hCD96oxHzmfalRo0Z5uoSrVqZMGa+40s/Vsgw++NOsWTN99tlnqlSpkqRze3xDhw7VV1995eHKisfkdXEh+vAepvawb9++Qv95gxLzmfelTH1DXcoX+ujUqZOnS7hmRo0apUWLFqlixYqeLqVYGjdu7OkSrglfeU/5wvowdV3ExcXp4MGDCg8Pv+jvrLdco73E7nmb+oa6lIl9fPbZZ5LOnZU6bdo0rV27VjNnzlRmZqaHK7t6pm1Mffvtt5LOndC5dOlSHT9+XMuWLTPuyn2+8p7yhfXhK+vizTffVJUqVTRjxgwtXrzY/s8bglsqQeHtK28oX+hj2bJlkqTJkyfruuuu0/jx4/W3v/1NEyZM8HBlV8+0jamXX35Z0rkbqvz888/q0KGDDhw4oEmTJnm4Muf4ynvKF9aHr6yL0qVL67nnntORI0c8XcpllZjw9pU3lK/0IUn79+/XP//5T91888166KGHdPz4cU+X5DRf2JiSzl0w5/nnn1fr1q0VGxurvXv3erqkYvGF95TkG+vDF9ZFgwYN9P/+3/+T5H1H1UpMeJ/nC28oyew+/vjjDy1cuFD+/v766aefJJ37Y2Xad+4l8zemfOWCOb7ynvKF9XF+Xfj5+Rm9Li41cOBAT5dwkRIT3pd7Q/3444/GvaF84Rdj3rx5CgkJUc2aNfXzzz8rJSVFkyZNMvq65qZuTF14wZzPP/9cycnJ6tmzp55++mlPl+aUS99TJ0+eVEJCgnHvqUsvYJSenq4+ffoYtT7Or4uwsDD777eJ6+JS3rbnXWLONp83b55++uknhYeH6+eff1a1atWUkJCgMWPGeLo0p8ybN0+7d++2/2JUqlRJkydP1sSJEz1dWpGVLl1aTZo0UZMmTWRZloYOHapp06Z5uqxiuXSPr169ekZtFG7YsEFTpkzRzp07NWrUKH322WcqU6aMypQp4+nSnJKUlKTk5GT16NFDo0aN0sKFC3XmzBmlpqZ6ujSnjBo1SuPGjVNMTIz9uQ0bNniwIueVLl1amzZtUkBAgMLCwtSvXz/l5eVp3759ioiI8HR5xeZtZ/6XmPD+888/9eqrr8rf319NmjRRuXLltGLFCvsdrkxRrlw5bdiwQZUqVVKVKlXUp08f2Ww2/fnnn8b8YgwaNEjBwcH2+xXv27fPvlVu0rqQ/rcxdX6P7/xGoSkbU+cPzc6ePVsLFixQzZo1lZycrJEjR2rJkiUerq7o3nnnHS1evFhDhw7Va6+9prCwMCUnJ+vxxx9X8+bNPV1ekdWtW1dJSUkaMGCAnnzySd1+++2eLslp48eP1+OPP6709HQ99thjWrVqlcqVK6dBgwapc+fOni6vyC69dfH5M/+95dbFJSa8582bpw8++ECWZempp55Sbm6uunXr5nWHQhyJi4vTk08+qcOHD2v48OH6z3/+o6CgIP3jH/9QmzZtPF1ekbz33nuKj49X37591aJFC0VHRxsX2ufVq1dP9erVu+guXCtWrPBgRcXj5+enmjVrSpKqVKmigoICzxbkpICAAJUpU0YhISGqVq2apHN9eMVlLJ0QFBSkCRMm6Mcff9T8+fP13HPPqVmzZqpWrZoGDBjg6fKK5OzZs2revLksy9KsWbNUpUoVSTLqfuTSuTP/Fy1apBkzZigzM1MdOnTQtm3bvOYjPrOW5lUICAhQhQoVJJ27PejAgQN14403GvfLffbsWfuNSLZv326/GIhJvxgVK1bUiy++qGnTpunHH3/0dDlXJTo6Wnl5eZf9mQnXNk9PT1f37t2VlZWllStX6oEHHtDUqVONu5Z227ZtNXToUNWuXVuPPfaYoqKitGnTJuPu7X1+Z+LWW2/VnDlzlJ6erm+++cZrrupVFH//+9/19NNPKz8/XyEhIZo9e7bKli2rG264wdOlFYu33rrYnL/4V+nvf/+7EhMT9dRTT6ls2bJ65ZVX9MgjjygtLc3TpTklLCxMY8eOVUJCgqZOnSpJmj9/vv3ynKbw9/fX2LFj9f777xt39ONC5z+jfPXVV73iUJqzPvjgA+Xm5mrPnj0KDg6WzWZT7dq11aNHD0+X5pRHH31U//d//6fNmzfrpptuUkpKiqKjo3XXXXd5ujSndO/e/aLH5cqVM+7GSdOmTdNXX32lmjVrKiQkRAsXLlRwcLCmTJni6dKccv7M/7Jly+rQoUOqWrWqV535X2LuKnb27FmtWrVK99xzj0qXLi1JOnHihF5//XWNHTvWw9UVXUFBgb744gu1a9fO/txHH32kDh062PuCe73xxhuqUaOG2rdv7+lSAFwja9eu1U8//aTdu3erefPm6tGjhx544AFNmjTJK86jKDHhDQBAUe3fv1/PP/+8fv/9dyUnJ6tBgwb6+9//rri4OK/4CIDwBgDgEo888ojGjx+vmjVraseOHfryyy/Vrl07vfzyy5o/f76nyys5F2kBAKCoMjIy7N/AaNiwob7//ns1aNDAa86TKjEnrAEAUFRVq1bVhAkT1KpVK3355ZeKiIjQ559/7jXnFnHYHACAS+Tm5mrlypXau3evIiIi9OCDD+rHH39UjRo1dP3113u6PMIbAADT8Jk3AACGIbwBADAMJ6wBBps/f74WLVqk9evXKygo6KrmtWTJEn388cf2S+02b95cTzzxxLUoE8A1xp43YLCPP/5YnTt31urVq69qPu+8847++9//atGiRVq6dKkWLlyoX375RZs3b75GlQK4ljhhDTDU9u3btWjRIo0cOVLPPPOM3nvvPf3www967rnnFBISoooVKyooKEhTp07V4sWL9cknn8hms6lz585/uUPVfffdp0WLFik0NNT+nGVZstlsOnTokIYOHaoKFSqoVatWatGihRISEuTn56egoCAlJCSooKBAI0aMsN9RrVevXpo1a5Y++OAD/f7770pJSVFaWprGjRunJk2auHU5Ab6Iw+aAoVauXKmePXsqPDxcgYGB2rlzpyZOnKjp06erVq1amj17tpKTk7V37159+umneuedd2Sz2fTwww+rZcuWCg8Pt8/r1KlT9uBeu3atFi1apOzsbDVp0kT9+/fX8ePH9d577ykwMFDdu3fX5MmTFRERoXXr1mnq1Kl69tlnC60zODhYixYt0q+//qqRI0dq1apVLl82gK/jsDlgoNOnT2vjxo1atGiRHnnkEWVkZGjJkiU6duyYatWqJUlq3LixJOmXX37RkSNH9PDDD2vgwIE6deqUDhw4cNH8QkJCdOrUKUlS+/bttXjxYj355JNKTU2VdO6CFYGBgZKkY8eOKSIiQpJ0++2369dff/1LfRce0Dt/W85atWrpxIkT13IxACUW4Q0YaNWqVXrwwQf15ptv6l//+pdWrFihr7/+WkFBQdq7d68kaefOnZKk8PBw3XLLLVq0aJEWL16s7t27q3bt2hfNr3///poyZYpyc3MlSfn5+fruu+/s97svVep/fyoqV66sPXv2SJK++eYb1axZU0FBQUpJSVF+fr7S0tJ06NAh+/S7d++WdG4jokqVKi5aIkDJwmFzwEArV67U9OnT7Y9Lly6tDh06qFKlSoqLi1OZMmUUEBCgKlWqqG7dumrWrJn69u2r3NxcRUZG/iVEBwwYoGXLlmnQoEEqVaqUMjIy1LRpUz3zzDPKysq6aNpJkyYpISFBlmXJz89PU6ZM0Q033KAWLVqoR48eql69umrUqGGfPikpSQMHDtSZM2eUkJDg2gUDlBCcsAb4kKVLl+qee+5RaGioZs+erYCAAA0bNsxj9cyZM0eVKlVS3759PVYD4IvY8wZ8SMWKFTV48GCVKVNG5cqV09SpUz1dEgAXYM8bAADDcMIaAACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADD/H8vc1j5qhDaEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8, 10]) # Highering the y-scale ensures, that smaller bars stay visible.\n",
    "(df_gobike_v2.age_group.value_counts()/len(df_gobike_v2)*100).plot.bar(color = default_color);\n",
    "plt.title(\"Percentage of Rides per Age Group\")\n",
    "plt.ylabel('Percentage of all rides')\n",
    "plt.xlabel('Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93% of all rides where provided with the users birth year.\n"
     ]
    }
   ],
   "source": [
    "print(str(round(sum(df_gobike_v2.age_group.value_counts() )/ len(df_gobike_v2) * 100)) + \n",
    "      \"% of all rides where provided with the users birth year.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows the age distribution among all rides in the period of observation. Note, this graph only accounts for 93% of all rides. 7% of the rides where done by users without their year of birth given.\n",
    "\n",
    "The graph shows, that users between 25 and 34 made up for more than 40% of all rides. Users between 35 and 44 account for the second biggest user group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?\n",
    "\n",
    "The first visualizations reveal that the majority of the rides (84%) were done by users of the subscriber type. Male users account for the most rides (74%), whereas female users account for 24%. Most users are between 25 and 34 years old. The second biggest group ages between 35 and 44 years. Unusual at this point are some users with ages far above 100 years. \n",
    "\n",
    "### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?\n",
    "\n",
    "The dataset contained outliers in the column birth year which needed to be addressed by filtering them out. In addition, this same column contained \"NaN\" values. The other two of three dimensions did not contain any unusual points. After addressing the outlier issue, these three variables may well serve as separation segments in the bivariate and multivariate exploration later on.\n",
    "\n",
    "Since the year of birth was not suitable for visualization, I decided to go for the age instead. The age appeared more intuitive to me. So adding and calculating this column has been necessary. In addition, I took a closer look at the values of the column member_age (derived from member_birth_year by sustracting \"2019\") and decided to set every value above 100 years to 0. I did the same for the NaN values. Up until around 100 years, it could theoretically be the truth, however everything above is most certainly not the truth. Note: I prefer to remove outliers in age by human logic instead of statistical logic. The statistics would have cut off rides of users older than approximately 60 years. However, this would be too drastic in human terms, where people can be perfectly capable of riding a bike even when being 95 years old. I also preferred setting outliers to 0 instead of excluding these rows, to not reduce too much on data validity for analysis which is in depended on the age.\n",
    "\n",
    "To ease the viewability of the visualizations I decided to assign the given age of each ride to its according age group. I decided for the following split, according to the prior mentioned marketing standards: 15 to 24 years, 25 to 34 years, etc. till 95 to 100 year.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next I am going to analyze which user group, segmented by age and gender uses the FordGoBike service most. This serves as a preliminary information to elaborate the amount of rides, the duration and the duration per ride and user group later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      1771680\n",
       "Female     580344\n",
       "Other       36933\n",
       "Name: member_gender, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gobike_v2.member_gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reasons of relevance, I continue the exploration with data of users which indicated their gender by female or male. The first step is to look at the distribution of gender per age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_gobike_v2_male = df_gobike_v2[(df_gobike_v2.member_gender == \"Male\")]\n",
    "df_gobike_v2_female = df_gobike_v2[(df_gobike_v2.member_gender == \"Female\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8UVWqVNF///tfhz2sWLFC999//xUnXz3xxBN67LHHNGrUKL388ssaN26cfHx8VK9ePfn5+SkoKKhA69qoUaO0fv16PfPMM0pLS1NSUpLKly+vjh07XvXogr+/vxYsWKAXXnhB8+fPV05Ojp544gndc889kqQ+ffqoe/fuuvHGG3Xvvffm+XpfxYoVNXToUCUmJqpRo0Z6/PHHr5j/E088oRdffFHz5s2Tv7+/GjRooCNHjjh8nySpVatWOnDggLp3765y5cqpTp06eY4aFLRfR+tCXFycwxoKs25da37wDJt1+fEsoJj7+eef9cMPP+jRRx+VJC1evFg//vijXnnlFQ9XZobIyEj1799f7du3d8t4ycnJWrBggZ566ikFBQVp7969euyxx7Rly5arhrSnXTwsPWXKFE+XAjjEnjeMExYWpkWLFmnVqlWy2WyqXLmywz1MeF5wcLD8/f3Vo0cP+fn5yc/PT6+88kqxDG7AFOx5AwBgGE5YAwDAMIQ3AACGIbwBADBMsT1hbc+ePXkucuAKGRkZLh/DHbyhD2/oQaKP4sQbepC8ow9v6EFyTx8ZGRn5upJdsQ3vgIAAhYeHu3SM2NhYl4/hDt7Qhzf0INFHceINPUje0Yc39CC5p4/Y2Nh8vY7D5gAAGIbwBgDAMIQ3AACGKbafeQMA3CMrK0txcXFKT0932fzz+1lucVaUfQQGBqpKlSr22xIXFOENACVcXFycypYtq+rVq7vksrVpaWn2mweZrKj6sCxLCQkJiouLU1hYWKHmwWFzACjh0tPTVaFCBa437yY2m00VKlS4riMdhDcAgOB2s+t9vwlvAEAe6Vk5RTo/m++1P9fdsWOHateurU8++STP8506ddL48eMdTrd27VrNnTs3XzVERkbqjz/+sD/OyMhQmzZt8jVtccRn3gCAPAL9fVV9/IYim9+hWQ86fU2NGjX08ccfq2PHjpKkAwcOKC0trchq8DaENwDA4+rUqaNDhw7p/PnzKleunNavX69OnTrp+PHjkqT3339fX3zxhbKzs1W2bFnNnz8/z/RLly7Vxx9/LJvNpo4dO+rRRx/N99jLli3TunXr5OPjowYNGmjcuHE6fvy4Jk+ebL8kanR0tFJSUjRq1CiFhISoZcuWGjp0aJG+BwXBYXMAQLHQtm1bbdy4UZZl6aefftJdd90lScrNzdXZs2e1ZMkSLV++XNnZ2fr555/t0/3+++/65JNPtHz5ci1fvlybNm3S//73v3yPu3btWk2cOFH//ve/VbVqVWVnZ2v27NmKjIzU0qVLNWTIEPvh+VOnTuntt9/2aHBLLtrzzsnJ0aRJk3Tw4EH5+vpq5syZsixL48ePl81m0+23366pU6fKx4dtBwDABZ06ddJzzz2nqlWrqmHDhvbnfXx85O/vr9GjR6t06dI6ceKEsrOz7T//9ddfdezYMQ0cOFCSdO7cOR05ckQ1atSwvyYgIEBZWVn2xykpKQoMDJQkzZw5U++8847mzp2r+vXry7Is/frrr3rzzTf1r3/9S5Zl2b+PXaVKFZUqVcqVb0O+uCS8v/rqK0nSypUrtWPHDnt4jxw5Uk2aNNGUKVMUExOjtm3bumJ4AICBqlatqtTUVC1dulSjR4/W0aNHJUn79+/Xpk2btHr1aqWlpenhhx+WZVn26WrUqKHbbrtN//rXv2Sz2bRkyRLVqlUrz7zvuOMOff7556pTp44kafPmzfrrX/8qSVq1apWef/55BQQEaMiQIfrhhx9Uo0YNDR48WA0aNNAff/yhnTt3SlKx2el0SXjff//9uvfeeyVJx44dU8WKFfX111+rcePGkqSWLVvqu+++I7wBAHl07NhRH374ocLCwuzhXa1aNQUFBenhhx9WqVKldOONN+rkyZP2aerUqaOmTZuqb9++yszMVEREhCpVqpRnvkOHDtWUKVPs8wgJCVF0dLQkqXbt2urRo4duuOEGVapUSXfeeafGjRun5557ThkZGUpPT9fEiRPd9ybkh+VCzz77rHXXXXdZW7ZssZo1a2Z/fuvWrdYzzzxzzWn37dvnytLcNoY7eEMf3tCDZdFHceINPViWZ/4WpmVmF+n80zKyinR+npKamlqk87vass3v8rZZ1iXHHlzg1KlT6tWrl5KTk+2HHTZt2qStW7dqypQpDqfbs2ePy296np6ebv/Mw2Te0Ic39CDRR3HiDT1I7ukjKytLt99+u8vmb1mWV1wEpqj7+O233656bfP83DPcJYfN161bp/j4eD322GMKCgqSzWZTvXr1tGPHDjVp0kSbN2/WPffcc815BAQEuOWm59wgvnjwhh4k+ihOvKEHyT19xMbGuvTa41zb/Or8/f2vWLb5vfGJS8L7gQce0IQJE9S/f39lZ2crKipKNWvW1OTJk/Xyyy+rRo0aateunSuGBgDA67kkvEuXLq158+Zd8fz777/viuEAAChRisc57wAAIN8IbwAADEN4AwDyyin8faavJvDaNxVTXFycGjRooMjISPu/1157rUhrkK68s9i16unVq1ee51asWKE33nijyGsqLG5MAgDIyzdQWl50X4my9XP+jeTbbrtNS5cuLbIxvR3hDQAotl566SXt3LlTlmVp4MCB6tChgyIjI1W7dm399ttvKl26tBo2bKhvv/1W58+f1zvvvCNfX19NnDhRSUlJSkxMVM+ePdWvXz/7PJOSkjRx4kQlJiZKkiZNmqTatWvnq56MjAw9/fTTSk5OVnp6usaOHasmTZro008/1ZIlS+Tj46O7775bY8aM0fz58/XDDz8oNTVV06dPV82aNYvsffGq8E7PylGgv2++X1+Y704WdAwAgHO///67IiMj7Y/nzp2r/fv3Ky4uTitXrlRGRoZ69eqlZs2aSZIiIiI0adIkDRkyRIGBgVq8eLHGjRunnTt3qnLlynrwwQf1wAMPKD4+XpGRkXnCe+HChbrnnnvUr18/HTp0SBMmTNCKFSuc1miz2XTkyBGdPn1aS5YsUUJCgg4dOqSzZ89q/vz5+uCDDxQUFKSxY8fqu+++k3ThuuuTJk0q4nfLy8K7qG8gfzX5uak8AKBgrnbYfP369dq7d6891LOzs3Xs2DFJF240IknlypXTbbfdZv9/RkaGKlasqHfffVdffPGFgoOD89yBTLpwF7Lt27fr008/lSSdP38+z88DAwOVmZmZ57nU1FQFBATo9ttvV//+/TV69GhlZ2crMjJSR44c0ZkzZ/SPf/xD0oU7ll28LntYWNh1vzdX41XhDQDwHjVq1FCTJk0UHR2t3NxcLViwQFWqVHE63TvvvKP69eurX79+2r59u7755psr5tu5c2d16tRJCQkJWr16dZ6fV6hQQSkpKfr999912223KScnR1u3btWAAQN04MABpaSk6K233tLJkyfVp08frVmzRpUrV9Y777wjf39/rV27VuHh4dq0aZPL7kJGeAMAiqU2bdrov//9r/r166fU1FTdf//9Cg4Odjpd69at9dxzz+mjjz5SSEiIfH198+xJP/7445o4caJWrVql5ORkPfnkk3mmt9lsmjlzpqKiouTj46OsrCzdd999atSokXx8fPT6669r3bp18vf314gRIxQaGqqBAwcqMjJSOTk5uuWWW9ShQ4cifz/y1OjqG5MUVmGv51sSD5t7wzWcvaEHiT6KE2/oQXLftc3zjJGTfuGM8yJiZafJ5se1zS93tWWb3+XN97wBAHkVYXBLUnpWkc4OIrwBADAO4Q0AgGEIbwCAiunpT17ret9vwhsASrjAwEAlJCQQ4G5iWZYSEhIUGFj4cwv4qhgAlHBVqlRRXFycTp065ZL5Z2Vlyd/fyd1JDFCUfQQGBubrO+uOEN4AUML5+/u77EpgEl/bcwUOmwMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGH8inqGWVlZioqK0p9//qnMzEwNGzZMf/nLX/T444+revXqkqS+ffuqY8eORT00AAAlQpGH9/r16xUSEqI5c+YoMTFR3bp10xNPPKFBgwZp8ODBRT0cAAAlTpGHd/v27dWuXTv7Y19fX/3yyy86ePCgYmJiVK1aNUVFRSk4OLiohwYAoEQo8s+8y5Qpo+DgYCUnJ2vEiBEaOXKkIiIi9Oyzz2rZsmWqWrWqXn/99aIeFgCAEqPI97wl6fjx43riiSfUr18/derUSefPn1e5cuUkSW3btlV0dLTTeWRkZCg2NrZA44aHhxeq3oIqaF2ulp6eXuxqKihv6EGij+LEG3qQvKMPb+hBKl59FHl4nz59WoMHD9aUKVPUtGlTSdKQIUM0efJkRUREaNu2bbrjjjuczicgIMBtYVxQxa2u2NjYYldTQXlDDxJ9FCfe0IPkHX14Qw+Se/rI78ZBkYf3woULdf78eS1YsEALFiyQJI0fP14zZsyQv7+/KlasmK89bwAAcHVFHt6TJk3SpEmTrnh+5cqVRT0UAAAlEhdpAQDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABjGr6hnmJWVpaioKP3555/KzMzUsGHDdNttt2n8+PGy2Wy6/fbbNXXqVPn4sN0AAEBhFHl4r1+/XiEhIZozZ44SExPVrVs31alTRyNHjlSTJk00ZcoUxcTEqG3btkU9NAAAJUKR7/62b99eTz/9tP2xr6+v9u7dq8aNG0uSWrZsqa1btxb1sAAAlBhFHt5lypRRcHCwkpOTNWLECI0cOVKWZclms9l/npSUVNTDAgBQYhT5YXNJOn78uJ544gn169dPnTp10pw5c+w/S0lJUbly5ZzOIyMjQ7GxsQUaNzw8vMC1FkZB63K19PT0YldTQXlDDxJ9FCfe0IPkHX14Qw9S8eqjyMP79OnTGjx4sKZMmaKmTZtKkurWrasdO3aoSZMm2rx5s+655x6n8wkICHBbGBdUcasrNja22NVUUN7Qg0QfxYk39CB5Rx/e0IPknj7yu3FQ5IfNFy5cqPPnz2vBggWKjIxUZGSkRo4cqfnz56t3797KyspSu3btinpYAABKjCLf8540aZImTZp0xfPvv/9+UQ8FAECJxJetAQAwDOENAIBhCO9iJj0rp8DTFPQEisKMAQAoPlzyVTEUXqC/r6qP3+DSMQ7NetCl8wcAuBZ73gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwCalZFBAAAgAElEQVRgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMEy+wjs3N1c5OTnatWuXMjMzXV0TAAC4Bj9nL5gzZ46qVq2qY8eOae/evapYsaJmz57tjtoAAMBVON3z/v7779WnTx/98MMPevvtt3XixAl31AUAABxwGt65ubn66aefVKVKFWVmZurMmTPuqAsAADjgNLy7dOmi6OhoDR48WHPmzNGjjz7qjroAAIADTj/z7t+/vzp37qxjx45p1KhRKl26tDvqAgAADjgN788//1xvvPGGcnJy1L59e9lsNg0fPtwdtQEAgKtweth88eLFWrVqlUJCQjR8+HBt2rTJHXUBAAAHnIa3j4+PSpUqJZvNJpvNpqCgIHfUBQAAHHAa3g0bNtTo0aMVHx+vKVOm6K9//as76gIAAA44/cx79OjR2rx5s+rWrauaNWuqdevW7qgLAAA44DC8161bl+dxxYoVde7cOa1bt05du3Z1eWEAAODqHIb3H3/8IUnas2ePgoKCdNddd+nnn39WdnY24Q0AgAc5DO9nnnlGkjRkyBC99dZb9ucHDx7s+qoAAIBDTk9YO3PmjM6fPy9JSkxM1NmzZ11eFAAAcMzpCWuPP/64unfvruDgYCUnJ2vGjBnuqAsAADjgNLzbtWundu3aKSEhQSEhIfL19XVHXQAAwAGH4T1t2jRNmTJFvXv3ls1my/OzlStXurwwAABwdQ7D++L1y19++eU8z+fm5rq2IgAAcE0OT1gLCAjQ4sWLtX37dlWuXFm33HKLkpOTNXbsWHfWBwAALuNwz/vpp59WvXr1tG/fPh0/flwVK1bUa6+9pvHjx7uzPgAAcBmH4Z2SkqLRo0fLsiy1b99et9xyiz788ENVqFDBnfUBAIDLOAzvUqVKSZJsNpsCAgL0xhtvKCAgwG2FAQCAq3P4mfelZ5iHhIQQ3AAAFBMO97z37t2rPn36yLIs/f777/b/22w2vioGAIAHOQzv9evXu7MOAACQTw7D+5ZbbnFnHQAAIJ+c3pgEAAAUL4Q3AACGcXjYfPTo0Vdc0/yil156yWUFAQCAa3MY3n369HFnHQAAIJ8chvfBgwcdTtS4cWOXFAMAAJxzGN6nTp1yZx0AACCfHIb3k08+af//yZMnlZ2dLcuydPLkSbcUBgAArs5heF8UFRWlPXv2KC0tTenp6apatapWrVrljtoAAMBVOP2q2P/+9z9t2LBBzZs314YNG7jGOQAAHuY0vMuUKSObzabU1FSFhoYqKyvLHXUBAAAHnIb3HXfcobfffls33XSTRo0apZycHHfUBQAAHHD6mffo0aOVnJyswMBAbd68WREREe6oCwAAOOA0vCUpODhYktSmTRuXFgMAAJzj2uYAABjGYXj/85//lCRt2rTJbcUAAADnHB42j4mJ0U033aSlS5cqISEhz8969+7t8sIAAMDVOQzvGTNm6LvvvlNmZiaXSgUAoBhxGN4RERGKiIhQs2bNdOutt+rIkSOqUqWKQkND3VkfAAC4jNMT1uLi4tS7d28tXLhQvXv31ocffuiOugAAgANOvyr27rvvau3atSpTpoySk5M1YMAAdenSxR21AQCAq3C6522z2VSmTBlJF77vzbXNAQDwLKd73rfeeqtmzZqlhg0bateuXbr11lvdURcAAHDA6Z73jBkzVLVqVW3dulVVq1ZVdHS0O+oCAAAOON3z9vPzU//+/d1RCwAAyAcujwoAgGEIbwAADOP0sHl8fLzmzJmjxMREtWvXTrVr19add97pjtoAAMBVON3znjx5srp3767MzEw1bNhQ06dPd0ddAADAAafhnZGRoaZNm8pms6lGjRp8zxsAAA9zGt6lSpXSli1blJubqz179qhUqVL5mvGPP/6oyMhISdLevXvVokULRUZGKjIyUp988sn1VQ0AQAnm9DPv6OhozZ49W4mJiXrnnXf03HPPOZ3pokWLtH79egUFBUmS9u3bp0GDBmnw4MHXXTAAACWd0/DOzc3V2LFj/28CPz9lZWXJ39/f4TS33nqr5s+fr2effVaS9Msvv+jgwYOKiYlRtWrVFBUVpeDg4CIoHwCAksdpeD/22GOKj49XjRo1dPDgQQUFBSk7O1tjx451eIOSdu3aKS4uzv44IiJCPXv2VL169fTGG2/o9ddf17hx4645bkZGhmJjYwvUTHh4eIFeX1gFrasgvKGHwkhPTy92NRUGfRQf3tCD5B19eEMPUvHqw2l4V6lSRe+++65CQ0N17tw5TZo0SdHR0Ro6dGi+7y7Wtm1blStXzv7//FxiNSAgwG1BVlDFta6CKG49xMbGFruaCoM+ig9v6EHyjj68oQfJPX3kd+PA6QlrCQkJCg0NlSSVL19ep0+fVkhIiHx88n99lyFDhuinn36SJG3btk133HFHvqcFAAB5Od3zvuOOOzR69GjVr19fe/bsUXh4uD755BNVqFAh34M899xzio6Olr+/vypWrMjNTQAAuA5Ow3vq1KmKiYnRH3/8oc6dO+vee+/V//73P7Vu3fqa01WpUkWrVq2SdGEDYOXKlUVTMQAAJZzTY99nz55VWlqabrrpJiUmJurNN99UjRo17F8DAwAA7uV0z3vEiBGqXr26fv31VwUEBBDaAAB4WL7OOps2bZrCwsK0ePFinTt3ztU1AQCAa8hXeGdkZCgtLU02m02pqamurgkAAFyD0/Du37+/3n33XTVr1kytWrVSjRo13FEXAABwwOln3jfffLPatWsnSerQoYP27dvn8qIAAIBjDsN7165d+v3337VkyRINGjRI0oXrnC9btkwff/yx2woEAAB5OQzvcuXK6fTp08rMzNSpU6ckSTabLc9NSgAAgPs5DO9atWqpVq1a6tmzpypVquTOmgAAwDU4/cx727ZtevPNN5WZmSnLsmSz2RQTE+OO2gAAwFU4De9FixZp4cKFqly5sjvqAQAATjgN76pVq6patWruqAUAAOSD0/AODAzU3//+d4WHh8tms0mSRo8e7fLCAADA1TkN71atWrmjDgAAkE9Or7DWqVMnZWdn6+jRo7r55psJcwAAPMxpeE+dOlXHjh3Td999p5SUFI0bN84ddQEAAAechveRI0f09NNPq1SpUmrTpo2SkpLcURcAAHDAaXjn5OTozJkzstlsSk5Olo9Pvm5EBgAAXMTpCWsjR45U3759derUKfXu3VtRUVHuqAsAADjgNLwbN26sxYsXKzAwUHFxcYqIiHBHXQAAwAGnx8CnTJmidevWKTQ0VOvXr9cLL7zgjroAAIADTsM7NjZWw4cPlyRNmjRJsbGxLi8KAAA45jS8LctSYmKiJOn8+fPKyclxeVEAAMAxp595P/nkk+revbtCQkJ0/vx5TZ061R11AQAAB5yG9/nz57Vx40YlJiaqQoUK9uubAwAAz3B62HzVqlXy9fVVxYoVCW4AAIoBp3vemZmZ6tq1q8LCwuwXaHnppZdcXhgAALg6p+E9ZswYd9QBAADyyelh87p16+q7777TunXrdPbsWVWqVMkddQEAAAechndUVJSqVq2qQ4cOqWLFipo4caI76gIAAA44De+zZ8+qR48e8vPzU4MGDWRZljvqAgAADuTrFmF//PGHJOnEiRPcVQwAAA9zmsQTJ05UVFSU9u3bpxEjRmj8+PHuqAsAADhwzbPNk5OTdeutt+rf//63u+oBAABOONzzfv/999W5c2d16dJFW7ZscWdNAADgGhyG98cff6zPPvtMK1eu1LvvvuvOmgAAwDU4DO9SpUqpVKlSCg0NVVZWljtrAgAA15CvU8f5ehgAAMWHwxPWfv/9dz3zzDOyLMv+/4u4tjkAAJ7jMLxfeeUV+//79OnjlmIAAIBzDsO7cePG7qwDAADkE5dLAwDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYfw8XUCRyknXoVkPunwM+Qa6dgwAAK7Bu8LbN1BabnPtGP0s184fAAAnOGwOAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGFcFt4//vijIiMjJUmHDx9W37591a9fP02dOlW5ubmuGhYAAK/nkvBetGiRJk2apIyMDEnSzJkzNXLkSC1fvlyWZSkmJsYVwwIAUCK4JLxvvfVWzZ8/3/547969aty4sSSpZcuW2rp1qyuGBQCgRPBzxUzbtWunuLg4+2PLsmSz2SRJZcqUUVJSktN5ZGRkKDY2tkDjhoeHF6zQQipoXQXhDT0URnp6erGrqTDoo/jwhh4k7+jDG3qQilcfLgnvy/n4/N8OfkpKisqVK+d0moCAALcFWUEV17oKorj1EBsbW+xqKgz6KD68oQfJO/rwhh4k9/SR340Dt5xtXrduXe3YsUOStHnzZjVs2NAdwwIA4JXcEt7jxo3T/Pnz1bt3b2VlZaldu3buGBYAAK/kssPmVapU0apVqyRJYWFhev/99101FAAAJQoXaQEAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeMMl0rNyCvT6wlz4oKBjAIC3cMsV1lDyBPr7qvr4DS4d49CsB106fwAortjzBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBg/TxeAy+Sk69CsB10+hnwDXTsGAMBlCO/ixjdQWm5z7Rj9LNfOHwDgUhw2BwDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDBuvcJa165dVbZsWUlSlSpVNHPmTHcODxRYelaOAv198/368PBwl84fACQ3hndGRoYkaenSpe4aErhugf6+qj5+g8vm7/Lr2APwSm47bL5//36lpaVp8ODBevTRR7Vnzx53DQ0AgFdx2553YGCghgwZop49e+rQoUMaOnSoPvvsM/n5Xb2EjIwMxcbGFmiMgh6yLKyC1lUQ3tCDRB8F4eoeCiM9Pb1Y1lUQ3tCD5B19eEMPUvHqw23hHRYWpmrVqslmsyksLEwhISE6deqUKleufNXXBwQEuC0ACqq41lUQ3tCD5B19FMceYmNji2VdBeENPUje0Yc39CC5p4/8bhy47bD5mjVrNGvWLElSfHy8kpOTdeONN7preAAAvIbb9rx79OihCRMmqG/fvrLZbJoxY4bDQ+YAAMAxt6VnqVKl9NJLL7lrOAAAvBYXaQEAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDXi49K6fA04SHh7t8DACFxw21AS8X6O+r6uM3uHSMQ7MedOn8AeTFnjcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwBgDAMIQ3AACGIbwBADAM4Q0AgGEIbwAADEN4AwBgGMIbAADDEN4AABiG8AYAwDCENwAAhiG8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYP08XAC+Vk65Dsx50+RjyDXTtGABQDBHecA3fQGm5zbVj9LNcO38AKKY4bA4AgGEIbwAADEN4AwBgGD7zBq7F1SfecdIdgEIgvIFrcfWJd5x0B6AQOGwOAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYQhvAAAMQ3gDAGAYwhsAAMNwYxLA27n6zmj/fwzujga4D+ENeDtX3xlN4u5ogJtx2BwAAMMQ3gAAGIbwBmCE9KycAr0+PDzc5WMAnsJn3gCMEOjvq+rjN7h0DJef2AcUEfa8AQAwDOENAIBhCG8AAAxDeAMAYBjCGwAAwxDeAAAYhvAGAMAwhDcAAIYhvAEAMAzhDQCAYdx2edTc3Fw999xzOnDggEqVKqUXXnhB1apVc9fwAAB4DbeF96ZNm5SZmal///vf2rNnj2bNmqU33njDXcMDMF1OuuuvPZ6TfuH+50Ax57bw/v7779WiRQtJUv369fXLL7+4a2gA3sA3UFpuc+0Y/SyXzj49K0eB/r4Fmqagd0crzBgwj9vCOzk5WcHBwfbHvr6+ys7Olp8fNzYDUDJwZzQUFZtlWa7d1Pz/Zs6cqTvvvFMdO3aUJLVs2VKbN292+Po9e/YoICDAHaUBAFAsZGRkqH79+k5f57bd3gYNGuirr75Sx44dtWfPHtWqVeuar89P8QAAlERu2/O+eLb5r7/+KsuyNGPGDNWsWdMdQwMA4FXcFt4AAKBocJEWAAAMQ3gDAGAYwhsAAMMQ3gAAGIbwhsclJCR4ugT8f96yLOij+PCGHoqjEnt5s/3796tOnTqeLuO6mdjHwYMH8zweN26cZs+eLUkKCwvzREkllrcsC/ooPryhh4tiY2O1bds2JSUlqVy5crr77rsVERHh6bIklaCvin377bd5Hs+ZM0djx46VJDVv3twTJRWKN/Rx7733KjAwUDfddJMsy7JvgNhsNr333nueLu+6mLYx5S3Lgj6KD2/oQZJee+01/fTTT2revLnKlCmjlJQUffvtt6pbt65Gjhzp6fJKTnh37dpVPj4+ql27tiRpy5Yt9hulzJw505OlFYg39JGQkKCpU6eqb9++atasmSIjI7V06VJPl1Uopm9MecuyoI/iwxt6kKR+/fpp+fLleZ6zLEu9evXS6tWrPVTV/ykxh81XrFihadOmqUGDBurZs6ciIyONCbtLeUMfFSpU0CuvvKLZs2fr559/9nQ512Xu3Ll5NqYSEhK0YcOFG0+YEN7esizoo/jwhh4kKTs7W3FxcapSpYr9ubi4OPn4FI9TxYpHFW4QFBSkmTNnKikpSVOnTlVOTo6nSyoUb+nDz89PEydOVKVKlTxdynVZsWKFateurQYNGmjmzJkKCwvTzJkzjdqg8pZlcbGPi4drTeUNfXjDOhUVFaUnn3xSDz74oHr37q0HH3xQI0aMUFRUlKdLk1SCDptfatu2bfrggw80d+5cT5dyXbZt26Y1a9bopZde8nQpBbJr1y41bNhQubm5WrFihfbt26d69eqpV69e8vU18z7E77zzjg4fPqzffvvtikNtxVlmZqaOHTum6tWra/v27frll190++23q1WrVp4urUBefvllDRs2TEFBQZ4u5bolJSXJZrMpODhYn332mZKSktStWzejbp+8e/duff/990pLS9MNN9ygv/3tb8beyyI5OVkpKSkKDg5WmTJlPF2OXYkK7/379yskJEShoaF688035ePjo8GDBxv9C79o0SINHTrU02UUyKOPPqr33ntPs2fPVmpqqtq0aaPt27crPT1dU6dO9XR5hWbixtTTTz+tFi1a6Ny5c9q6datatGih77//XpUrVy42exj50bx5c/3lL3/RmDFjdM8993i6nEJbsWKFFi9eLOnCiV8JCQkKDQ1VcnKyMUdzFi5cqD/++EMNGjTQN998oxo1aujIkSNq2rSp+vfv7+ny8u3o0aOaOXOm9u7dK19fX+Xm5qpWrVqaMGFCsThr3pxNueu0YMECbd++XcnJybrxxhsVHh6uwMBATZo0yag/tqNHj5bNZrM/3r59u/bv3y9JRvUhST/99JOWLVsmSWrVqpUiIyM9XFHBZWRkaPXq1QoICFCXLl3UtGlTSdLKlSvVp08fD1fn3OnTp9WjRw9FRkZq8eLF8vPz08CBA9WzZ09Pl1YgYWFhmjFjhmbMmKEFCxaoV69eatGihcqXL+/p0gpk7dq12rBhgzIyMtSpUyd9+eWXstlsRoXeli1b7L/XvXr10siRI/Xaa6+pT58+RvUxceJEPfPMM7rzzjvtz+3Zs0cTJkzQypUrPVjZBSXmM+/Nmzfrvffe09KlS/Xbb79p5MiRGjp0qE6ePOnp0grk9ttv1/Hjx9WjRw/16tVLN954o3r37q3evXt7urR8O378uDZu3KiyZcsqLi5OkhQfH6/09HQPV1Zwzz77rE6ePKmDBw+qX79+OnfunCTpk08+8XBl+Xf06FHVqlVLR44csT82jc1mU9WqVfXGG29o4sSJio2N1aBBg4w7/J+Tk6P09HSdPXtWqampSk1NVVZWljIzMz1dWr6lpqbqzz//lCQdOXJEZ8+eVXp6unG/35mZmXmCW5Lq16/voWquVGL2vHNzc3Xs2DHdfPPN+uc//ylJOn/+vFG/FJI0bNgw1a1bV8uWLdO0adNUrlw5NW7c2NNlFcizzz6rvXv3KicnR5s2bVL37t3Vp08fTZ8+3dOlFdiZM2c0b948SdLGjRs1bNgwLVmyxJgTjcaNG6ennnpKISEhevjhh3XrrbcqNTXVuGVx6ftdu3Zt+9f1TDNw4EB16NBBderU0cMPP6wePXooKCjIqI3zZ555Ro888ojKly+vtLQ0vfjii1q0aJFRe93ShfVowoQJatGihcqWLauUlBR988039m+WeFqJ+cx7165dmjlzplavXm0/1b9///4aMmSI2rRp4+HqCu7IkSN6/vnnlZCQoHXr1nm6nOty8uRJ3XTTTZ4uo1D69OmjBQsWKDQ0VJK0ZMkS7d69WydPniwWh9by6+DBg0pMTFS5cuVUrVo1+fv7e7qk62LyOnWp/fv3q1y5crr55ps9XUqBWJalxMREhYaGGrssLMvSpk2b9P333ys5OVnBwcFq0KCB2rZtm+ejS08WWCLFx8d7uoTrlpKSYq1fv97TZVy3yMhIT5dQaFu3brXat29vnTp1yv7cggULrDvuuMODVRWeycviUvRRfHhDD8VRifnM+3JjxozxdAnXrXTp0sXiSj/XyzL44E/Tpk316aefqmLFipIu7PENGzZM33zzjYcrKxyTl8Wl6KP4MLWHgwcPOvxXHJSYz7wvZ+oKdTlv6KN9+/aeLqHIjBkzRu+9954qVKjg6VIK5e677/Z0CUXCW9Ypb1gepi6LqKgoHT16VDVq1Mjzd7a4XKO9xO55m7pCXc7EPj799FNJF85KnT17tjZu3Ki5c+cqJSXFw5VdP9M2pnbt2iXpwgmdy5Yt06lTp7RixQrjrtznLeuUNywPb1kW77zzjipVqqQ5c+Zo6dKl9n/FIbilEhTe3rJCeUMfK1askCRNnz5d5cuX1+TJk/WXv/xFU6ZM8XBl18+0jalXX31V0oUbqhw4cEAPPPCAjhw5ohdeeMHDlRWMt6xT3rA8vGVZBAUF6fnnn9exY8c8XcpVlZjw9pYVylv6kKTDhw/r8ccfV82aNfXII4/o1KlTni6pwLxhY0q6cMGcadOmqVWrVho3bpx+//13T5dUKN6wTknesTy8YVnUq1dPd911l6Tid1StxIT3Rd6wQklm93Ho0CEtWbJEfn5+2rdvn6QLf6xM+869ZP7GlLdcMMdb1ilvWB4Xl4Wvr6/Ry+JyAwYM8HQJeZSY8L7aCvXzzz8bt0J5wy/GwoULVaZMGVWvXl0HDhxQQkKCXnjhBaOva27qxtSlF8z54osvFB8fr549e2rUqFGeLq1ALl+nzpw5o+joaOPWqcsvYJSUlKQ+ffoYtTwuLouwsDD777eJy+JyxW3Pu8Scbb5w4ULt27dPNWrU0IEDB1S1alVFR0drwoQJni6tQBYuXKi9e/fafzEqVqyo6dOn67nnnvN0afkWFBSkhg0bqmHDhrIsS8OGDdPs2bM9XVahXL7HV7duXaM2Cr/66ivNmDFDP/74o8aMGaNPP/1UpUuXVunSpT1dWoHExsYqPj5ePXr00JgxY7RkyRKlpaUpMTHR06UVyJgxYzRp0iSNHDnS/txXX33lwYoKLigoSFu2bJG/v7/CwsLUr18/ZWVl6eDBgwoPD/d0eYVW3M78LzHhfeLECb3++uvy8/NTw4YNVbZsWa1atcp+hytTlC1bVl999ZUqVqyoSpUqqU+fPrLZbDpx4oQxvxiDBg1SYGCg/X7FBw8etG+Vm7QspP/bmLq4x3dxo9CUjamLh2b/+c9/atGiRapevbri4+P1zDPP6P333/dwdfm3fPlyLV26VMOGDdMbb7yhsLAwxcfHa/jw4frb3/7m6fLyrU6dOoqNjdWjjz6qp556So0aNfJ0SQU2efJkDR8+XElJSXrssce0fv16lS1bVoMGDVLHjh09XV6+XX7r4otn/heXWxeXmPBeuHCh/vOf/8iyLD399NPKzMxUt27dit2hEGeioqL01FNP6c8//9SIESP0+eefKyAgQH//+9/VunVrT5eXLx988IGmTp2qvn37qlmzZoqMjDQutC+qW7eu6tatm+cuXKtWrfJgRYXj6+ur6tWrS5IqVar0/9q7t5Co2i6A44hsPfQAAAbLSURBVP/JxlFHoTwkpaZJSgMWQRaoJREqKOGFWiDioYRCKqLELrQkG7PRQCProqFCtoegwSStLrLCyiCTDiaWqAiFBJqSqVlMHt6LaL63t+8D/bJ33Ll+dzOzeVjzwN5r9rPXPIupqSn7BjRLWq0WFxcX9Ho9fn5+wLfvMS+2sZwFnU5Hfn4+7e3tmM1mCgoKCAsLw8/Pj7S0NHuHNyMTExOEh4czPT1NaWkp3t7eAKrqRw7fKv8VReH06dN8+vSJmJgYHj9+PG8e8alrNn+BVqtlyZIlwLf2oOnp6Sxfvlx1J/fExIStEUlLS4ttMxA1nRgeHh6cOXOG4uJi2tvb7R3OL0lNTeXr16//9TM17G0+OjpKQkIC4+PjWCwW4uPjMZlMqttLe9u2bWRlZREcHMzevXvZsmULDx8+VF1v7+83E2vXrqW8vJzR0VFaW1vnza5eM+Hj48OhQ4eYnJxEr9dTVlaGq6srXl5e9g7t/zJfWxer54r/i3x8fDh16hQHDx7E1dWVc+fOkZmZycjIiL1Dm5VVq1aRl5eH0WjEZDIBYDabbdtzqsXixYvJy8vj2rVrqlv9+LvvzyjPnz8/L5bSZquurg6r1UpnZydOTk5oNBqCg4NJSkqyd2izsmfPHp48eUJzczMrVqxgaGiI1NRUtm7dau/QZiUhIeGH125ubqprnFRcXMz9+/cJCAhAr9dTUVGBk5MTRUVF9g5tVr5X/ru6utLX14evr++8qvxfMF3FJiYmqK+vJzY2FmdnZwAGBwe5cOECeXl5do5u5qamprh37x5RUVG2965fv05MTIzte4l/18WLF/H39yc6OtreoQgh5khjYyOvXr2io6OD8PBwkpKSiI+Pp7CwcF7UUSyY5C2EEELM1Js3bzhx4gS9vb309/cTEhKCj48Pubm58+IRgCRvIYQQ4h8yMzM5duwYAQEBvHjxgqamJqKiojh79ixms9ne4S2cTVqEEEKImRobG7P9A2P9+vU8e/aMkJCQeVMntWAK1oQQQoiZ8vX1JT8/n8jISJqamjAYDNy+fXve1BbJsrkQQgjxD1arFYvFQk9PDwaDgcTERNrb2/H392fp0qX2Dk+StxBCCKE28sxbCCGEUBlJ3kIIIYTKSMGaECpmNptRFIW7d++i0+l+aayqqioaGhpsW+2Gh4ezb9++uQhTCDHH5M5bCBVraGggLi6Omzdv/tI4NTU1PH/+HEVRqK6upqKigq6uLpqbm+coUiHEXJKCNSFUqqWlBUVRyM7OJicnh9raWl6+fElBQQF6vR4PDw90Oh0mk4nKykpu3LiBRqMhLi7upw5V27dvR1EU3N3dbe9NT0+j0Wjo6+sjKyuLJUuWEBkZSUREBEajEQcHB3Q6HUajkampKQ4fPmzrqLZz505KS0upq6ujt7eXoaEhRkZGOHr0KKGhof/qPAnxJ5JlcyFUymKxsGPHDgIDA3F0dKStrY3jx49TUlJCUFAQZWVl9Pf309PTw61bt6ipqUGj0ZCRkcHmzZsJDAy0jTU8PGxL3I2NjSiKwpcvXwgNDSUlJYX3799TW1uLo6MjCQkJnDx5EoPBwJ07dzCZTBw5cuR/xunk5ISiKHR3d5OdnU19ff1vnxsh/nSybC6ECn38+JEHDx6gKAqZmZmMjY1RVVXFwMAAQUFBAGzYsAGArq4u3r17R0ZGBunp6QwPD/P27dsfxtPr9QwPDwMQHR1NZWUlBw4c4MOHD8C3DSscHR0BGBgYwGAwALBx40a6u7t/iu/vC3rf23IGBQUxODg4l9MgxIIlyVsIFaqvrycxMZHLly9z6dIlrl69yqNHj9DpdPT09ADQ1tYGQGBgIKtXr0ZRFCorK0lISCA4OPiH8VJSUigqKsJqtQIwOTnJ06dPbf3uFy36z6Vi2bJldHZ2AtDa2kpAQAA6nY6hoSEmJycZGRmhr6/PdnxHRwfw7UeEt7f3b5oRIRYWWTYXQoUsFgslJSW2187OzsTExODp6Ulubi4uLi5otVq8vb1Zs2YNYWFhJCcnY7VaWbdu3U9JNC0tjStXrrBr1y4WLVrE2NgYmzZtIicnh/Hx8R+OLSwsxGg0Mj09jYODA0VFRXh5eREREUFSUhIrV67E39/fdvzr169JT0/n8+fPGI3G3zsxQiwQUrAmxB+kurqa2NhY3N3dKSsrQ6vVsn//frvFU15ejqenJ8nJyXaLQYg/kdx5C/EH8fDwYPfu3bi4uODm5obJZLJ3SEKI30DuvIUQQgiVkYI1IYQQQmUkeQshhBAqI8lbCCGEUBlJ3kIIIYTKSPIWQgghVEaStxBCCKEyfwFMj1GEVzdw+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8, 10]) #Highering the y-scale ensures, that smaller bar stay visible.\n",
    "(df_gobike_v2_male.age_group.value_counts() / len(df_gobike_v2)*100).plot.bar(color = default_color, label='Male User');\n",
    "(df_gobike_v2_female.age_group.value_counts() / len(df_gobike_v2)*100).plot.bar(color = 'orange', label='Female User');\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Percentage of all Rides per Age Group and Gender\")\n",
    "plt.ylabel('Percentage of all Rides')\n",
    "plt.xlabel('Age Group');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph shows how the rides are proportioned across the age groups. By far, most rides were done by male users between 25 and 34 years.\n",
    "\n",
    "Next, I am going to take a different perspective on the distribution of gender and their respective amount of rides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate rides without a given year of birth / age + consideration of the gender male and female only.\n",
    "df_gobike_without_age_nan = df_gobike_v2[(df_gobike_v2.member_age.notnull()) &\n",
    "                                          ((df_gobike_v2.member_gender == \"Male\") |\n",
    "                                           (df_gobike_v2.member_gender == \"Female\")\n",
    "                                          )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1538745</th>\n",
       "      <td>200</td>\n",
       "      <td>2018-08-08 08:32:42.7010</td>\n",
       "      <td>2018-08-08 08:36:02.8050</td>\n",
       "      <td>186.0</td>\n",
       "      <td>Lakeside Dr at 14th St</td>\n",
       "      <td>37.801319</td>\n",
       "      <td>-122.262642</td>\n",
       "      <td>163.0</td>\n",
       "      <td>Lake Merritt BART Station</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.265320</td>\n",
       "      <td>2588</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>39</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316768</th>\n",
       "      <td>1110</td>\n",
       "      <td>2017-09-12 10:54:07.4980</td>\n",
       "      <td>2017-09-12 11:12:37.9100</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Irwin St at 8th St</td>\n",
       "      <td>37.766883</td>\n",
       "      <td>-122.399579</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2nd St at Folsom St</td>\n",
       "      <td>37.785000</td>\n",
       "      <td>-122.395936</td>\n",
       "      <td>2540</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>15-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188605</th>\n",
       "      <td>234</td>\n",
       "      <td>2017-10-24 16:37:26.3670</td>\n",
       "      <td>2017-10-24 16:41:21.0320</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Mechanics Monument Plaza (Market St at Bush St)</td>\n",
       "      <td>37.791300</td>\n",
       "      <td>-122.399051</td>\n",
       "      <td>15.0</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>403</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1977</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175684</th>\n",
       "      <td>344</td>\n",
       "      <td>2018-06-07 08:11:26.1740</td>\n",
       "      <td>2018-06-07 08:17:11.1670</td>\n",
       "      <td>178.0</td>\n",
       "      <td>Broadway at 30th St</td>\n",
       "      <td>37.819381</td>\n",
       "      <td>-122.261928</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19th Street BART Station</td>\n",
       "      <td>37.809013</td>\n",
       "      <td>-122.268247</td>\n",
       "      <td>2208</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1986</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255355</th>\n",
       "      <td>722</td>\n",
       "      <td>2019-01-25 10:31:54.6130</td>\n",
       "      <td>2019-01-25 10:43:57.2100</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11th St at Natoma St</td>\n",
       "      <td>37.773507</td>\n",
       "      <td>-122.416040</td>\n",
       "      <td>49.0</td>\n",
       "      <td>S Park St at 3rd St</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.394989</td>\n",
       "      <td>5359</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>15-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538789</th>\n",
       "      <td>586</td>\n",
       "      <td>2018-08-08 08:23:08.3010</td>\n",
       "      <td>2018-08-08 08:32:54.9010</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Berry St at King St</td>\n",
       "      <td>37.771762</td>\n",
       "      <td>-122.398438</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2nd St at Folsom St</td>\n",
       "      <td>37.785000</td>\n",
       "      <td>-122.395936</td>\n",
       "      <td>535</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2256300</th>\n",
       "      <td>180</td>\n",
       "      <td>2019-01-25 09:10:05.4020</td>\n",
       "      <td>2019-01-25 09:13:05.8390</td>\n",
       "      <td>6.0</td>\n",
       "      <td>The Embarcadero at Sansome St</td>\n",
       "      <td>37.804770</td>\n",
       "      <td>-122.403234</td>\n",
       "      <td>8.0</td>\n",
       "      <td>The Embarcadero at Vallejo St</td>\n",
       "      <td>37.799953</td>\n",
       "      <td>-122.398525</td>\n",
       "      <td>873</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090938</th>\n",
       "      <td>582</td>\n",
       "      <td>2018-06-21 09:23:31.1040</td>\n",
       "      <td>2018-06-21 09:33:14.0850</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Post St at Kearny St</td>\n",
       "      <td>37.788975</td>\n",
       "      <td>-122.403452</td>\n",
       "      <td>2028</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1969</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>805469</th>\n",
       "      <td>385</td>\n",
       "      <td>2018-04-16 17:03:56.0200</td>\n",
       "      <td>2018-04-16 17:10:21.7340</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19th Street BART Station</td>\n",
       "      <td>37.809013</td>\n",
       "      <td>-122.268247</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>3505</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1473568</th>\n",
       "      <td>612</td>\n",
       "      <td>2018-08-19 14:49:56.7950</td>\n",
       "      <td>2018-08-19 15:00:09.3310</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>2517</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632598</th>\n",
       "      <td>1140</td>\n",
       "      <td>2018-02-02 08:56:29.2120</td>\n",
       "      <td>2018-02-02 09:15:29.4120</td>\n",
       "      <td>147.0</td>\n",
       "      <td>29th St at Tiffany Ave</td>\n",
       "      <td>37.744067</td>\n",
       "      <td>-122.421472</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Townsend St at 7th St</td>\n",
       "      <td>37.771058</td>\n",
       "      <td>-122.402717</td>\n",
       "      <td>1908</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1985</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>800098</th>\n",
       "      <td>158</td>\n",
       "      <td>2018-04-17 18:05:12.9780</td>\n",
       "      <td>2018-04-17 18:07:51.4410</td>\n",
       "      <td>176.0</td>\n",
       "      <td>MacArthur BART Station</td>\n",
       "      <td>37.828410</td>\n",
       "      <td>-122.266315</td>\n",
       "      <td>192.0</td>\n",
       "      <td>37th St at West St</td>\n",
       "      <td>37.826696</td>\n",
       "      <td>-122.271796</td>\n",
       "      <td>1256</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1986</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512251</th>\n",
       "      <td>613</td>\n",
       "      <td>2018-01-12 12:27:24.7350</td>\n",
       "      <td>2018-01-12 12:37:38.0910</td>\n",
       "      <td>93.0</td>\n",
       "      <td>4th St at Mission Bay Blvd S</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.391198</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Howard St at 2nd St</td>\n",
       "      <td>37.787522</td>\n",
       "      <td>-122.397405</td>\n",
       "      <td>3398</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1986</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689949</th>\n",
       "      <td>881</td>\n",
       "      <td>2018-03-16 19:27:27.0800</td>\n",
       "      <td>2018-03-16 19:42:08.3800</td>\n",
       "      <td>121.0</td>\n",
       "      <td>Mission Playground</td>\n",
       "      <td>37.759210</td>\n",
       "      <td>-122.421339</td>\n",
       "      <td>284.0</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>1400</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1972</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>47</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1405450</th>\n",
       "      <td>622</td>\n",
       "      <td>2018-08-30 20:35:48.5420</td>\n",
       "      <td>2018-08-30 20:46:11.2950</td>\n",
       "      <td>66.0</td>\n",
       "      <td>3rd St at Townsend St</td>\n",
       "      <td>37.778742</td>\n",
       "      <td>-122.392741</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8th St at Ringold St</td>\n",
       "      <td>37.774520</td>\n",
       "      <td>-122.409449</td>\n",
       "      <td>3416</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007643</th>\n",
       "      <td>625</td>\n",
       "      <td>2018-05-04 14:06:37.5630</td>\n",
       "      <td>2018-05-04 14:17:03.1180</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2nd St at Folsom St</td>\n",
       "      <td>37.785000</td>\n",
       "      <td>-122.395936</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Beale St at Harrison St</td>\n",
       "      <td>37.788059</td>\n",
       "      <td>-122.391865</td>\n",
       "      <td>3779</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>40</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098203</th>\n",
       "      <td>558</td>\n",
       "      <td>2018-06-20 09:05:18.4910</td>\n",
       "      <td>2018-06-20 09:14:36.9050</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Powell St BART Station (Market St at 5th St)</td>\n",
       "      <td>37.783899</td>\n",
       "      <td>-122.408445</td>\n",
       "      <td>79.0</td>\n",
       "      <td>7th St at Brannan St</td>\n",
       "      <td>37.773492</td>\n",
       "      <td>-122.403673</td>\n",
       "      <td>2512</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>39</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1168442</th>\n",
       "      <td>602</td>\n",
       "      <td>2018-06-08 08:45:08.6540</td>\n",
       "      <td>2018-06-08 08:55:10.8470</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>49.0</td>\n",
       "      <td>S Park St at 3rd St</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.394989</td>\n",
       "      <td>2052</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622947</th>\n",
       "      <td>565</td>\n",
       "      <td>2018-02-05 13:48:13.0640</td>\n",
       "      <td>2018-02-05 13:57:38.8920</td>\n",
       "      <td>342.0</td>\n",
       "      <td>Colin P Kelly Jr St at Townsend St (Temporary ...</td>\n",
       "      <td>37.781383</td>\n",
       "      <td>-122.389841</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Clay St at Battery St</td>\n",
       "      <td>37.795001</td>\n",
       "      <td>-122.399970</td>\n",
       "      <td>2064</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1982</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>37</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624210</th>\n",
       "      <td>637</td>\n",
       "      <td>2018-09-24 07:22:26.6590</td>\n",
       "      <td>2018-09-24 07:33:03.7660</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>4419</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>15-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2253720</th>\n",
       "      <td>433</td>\n",
       "      <td>2019-01-25 15:23:30.1560</td>\n",
       "      <td>2019-01-25 15:30:43.6690</td>\n",
       "      <td>305.0</td>\n",
       "      <td>Ryland Park</td>\n",
       "      <td>37.342725</td>\n",
       "      <td>-121.895617</td>\n",
       "      <td>288.0</td>\n",
       "      <td>Mission St at 1st St</td>\n",
       "      <td>37.350964</td>\n",
       "      <td>-121.902016</td>\n",
       "      <td>1477</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1965</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>54</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2234818</th>\n",
       "      <td>358</td>\n",
       "      <td>2019-01-28 17:05:39.6480</td>\n",
       "      <td>2019-01-28 17:11:37.9540</td>\n",
       "      <td>310.0</td>\n",
       "      <td>San Fernando St at 4th St</td>\n",
       "      <td>37.335885</td>\n",
       "      <td>-121.885660</td>\n",
       "      <td>282.0</td>\n",
       "      <td>Market St at Park St</td>\n",
       "      <td>37.332426</td>\n",
       "      <td>-121.890349</td>\n",
       "      <td>131</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703712</th>\n",
       "      <td>485</td>\n",
       "      <td>2018-03-12 16:21:00.5910</td>\n",
       "      <td>2018-03-12 16:29:05.9760</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Townsend St at 5th St</td>\n",
       "      <td>37.775306</td>\n",
       "      <td>-122.397380</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Civic Center/UN Plaza BART Station (Market St ...</td>\n",
       "      <td>37.781074</td>\n",
       "      <td>-122.411738</td>\n",
       "      <td>533</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1977</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>42</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596907</th>\n",
       "      <td>683</td>\n",
       "      <td>2018-02-12 08:46:09.5220</td>\n",
       "      <td>2018-02-12 08:57:33.1580</td>\n",
       "      <td>321.0</td>\n",
       "      <td>5th at Folsom</td>\n",
       "      <td>37.780146</td>\n",
       "      <td>-122.403071</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>2540</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188731</th>\n",
       "      <td>1373</td>\n",
       "      <td>2018-06-05 09:15:17.7340</td>\n",
       "      <td>2018-06-05 09:38:11.1110</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>323.0</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>37.798014</td>\n",
       "      <td>-122.405950</td>\n",
       "      <td>2623</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>26</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356429</th>\n",
       "      <td>557</td>\n",
       "      <td>2017-08-28 09:22:05.7630</td>\n",
       "      <td>2017-08-28 09:31:22.8830</td>\n",
       "      <td>15.0</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2nd St at Townsend St - Coming Soon</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>2813</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1977</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173751</th>\n",
       "      <td>383</td>\n",
       "      <td>2017-10-28 16:00:40.2570</td>\n",
       "      <td>2017-10-28 16:07:03.9680</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Clay St at Battery St</td>\n",
       "      <td>37.795001</td>\n",
       "      <td>-122.399970</td>\n",
       "      <td>6.0</td>\n",
       "      <td>The Embarcadero at Sansome St</td>\n",
       "      <td>37.804770</td>\n",
       "      <td>-122.403234</td>\n",
       "      <td>3145</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1964</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>55-64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1893544</th>\n",
       "      <td>280</td>\n",
       "      <td>2018-10-09 17:52:39.4840</td>\n",
       "      <td>2018-10-09 17:57:19.9060</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Division St at Potrero Ave</td>\n",
       "      <td>37.769218</td>\n",
       "      <td>-122.407646</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>1106</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1985</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2371925</th>\n",
       "      <td>1616</td>\n",
       "      <td>2019-01-04 08:18:54.7010</td>\n",
       "      <td>2019-01-04 08:45:51.1740</td>\n",
       "      <td>146.0</td>\n",
       "      <td>30th St at San Jose Ave</td>\n",
       "      <td>37.742314</td>\n",
       "      <td>-122.423180</td>\n",
       "      <td>79.0</td>\n",
       "      <td>7th St at Brannan St</td>\n",
       "      <td>37.773492</td>\n",
       "      <td>-122.403673</td>\n",
       "      <td>4633</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1996</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>15-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110513</th>\n",
       "      <td>597</td>\n",
       "      <td>2018-06-18 16:02:19.0600</td>\n",
       "      <td>2018-06-18 16:12:16.8580</td>\n",
       "      <td>309.0</td>\n",
       "      <td>San Jose City Hall</td>\n",
       "      <td>37.337391</td>\n",
       "      <td>-121.886995</td>\n",
       "      <td>312.0</td>\n",
       "      <td>San Jose Diridon Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>799</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1965</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>54</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780445</th>\n",
       "      <td>502</td>\n",
       "      <td>2018-04-22 18:02:22.5860</td>\n",
       "      <td>2018-04-22 18:10:45.1570</td>\n",
       "      <td>109.0</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>84.0</td>\n",
       "      <td>Duboce Park</td>\n",
       "      <td>37.769201</td>\n",
       "      <td>-122.433812</td>\n",
       "      <td>757</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1973</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>46</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943499</th>\n",
       "      <td>547</td>\n",
       "      <td>2018-10-01 18:24:37.6450</td>\n",
       "      <td>2018-10-01 18:33:45.4640</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>3376</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354501</th>\n",
       "      <td>346</td>\n",
       "      <td>2017-08-28 20:19:10.5140</td>\n",
       "      <td>2017-08-28 20:24:57.2460</td>\n",
       "      <td>232.0</td>\n",
       "      <td>MLK Jr Way at 14th St</td>\n",
       "      <td>37.806163</td>\n",
       "      <td>-122.276040</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Grand Ave at Webster St</td>\n",
       "      <td>37.811377</td>\n",
       "      <td>-122.265192</td>\n",
       "      <td>1342</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2260854</th>\n",
       "      <td>491</td>\n",
       "      <td>2019-01-24 17:38:37.6650</td>\n",
       "      <td>2019-01-24 17:46:48.9420</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Washington St at Kearny St</td>\n",
       "      <td>37.795393</td>\n",
       "      <td>-122.404770</td>\n",
       "      <td>5410</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>26</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2374711</th>\n",
       "      <td>480</td>\n",
       "      <td>2019-01-03 17:23:44.4110</td>\n",
       "      <td>2019-01-03 17:31:44.4380</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2nd St at Folsom St</td>\n",
       "      <td>37.785000</td>\n",
       "      <td>-122.395936</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Powell St BART Station (Market St at 5th St)</td>\n",
       "      <td>37.783899</td>\n",
       "      <td>-122.408445</td>\n",
       "      <td>1759</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1963</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>56</td>\n",
       "      <td>55-64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421343</th>\n",
       "      <td>343</td>\n",
       "      <td>2017-07-27 09:21:56.7740</td>\n",
       "      <td>2017-07-27 09:27:39.9200</td>\n",
       "      <td>280.0</td>\n",
       "      <td>San Fernando at 7th St</td>\n",
       "      <td>37.337246</td>\n",
       "      <td>-121.883074</td>\n",
       "      <td>318.0</td>\n",
       "      <td>San Carlos St at Market St</td>\n",
       "      <td>37.330698</td>\n",
       "      <td>-121.888979</td>\n",
       "      <td>1860</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1364984</th>\n",
       "      <td>1600</td>\n",
       "      <td>2018-07-07 16:29:36.7890</td>\n",
       "      <td>2018-07-07 16:56:17.1510</td>\n",
       "      <td>156.0</td>\n",
       "      <td>Stanford Ave at Hollis St</td>\n",
       "      <td>37.838443</td>\n",
       "      <td>-122.288665</td>\n",
       "      <td>257.0</td>\n",
       "      <td>Fifth St at Delaware St</td>\n",
       "      <td>37.870407</td>\n",
       "      <td>-122.299676</td>\n",
       "      <td>3098</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1992</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18811</th>\n",
       "      <td>508</td>\n",
       "      <td>2017-12-19 17:03:53.3320</td>\n",
       "      <td>2017-12-19 17:12:21.7630</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>3001</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459294</th>\n",
       "      <td>898</td>\n",
       "      <td>2018-01-30 13:24:51.8520</td>\n",
       "      <td>2018-01-30 13:39:50.6530</td>\n",
       "      <td>84.0</td>\n",
       "      <td>Duboce Park</td>\n",
       "      <td>37.769201</td>\n",
       "      <td>-122.433812</td>\n",
       "      <td>284.0</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>326</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>36</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806363</th>\n",
       "      <td>959</td>\n",
       "      <td>2018-04-16 11:42:03.0270</td>\n",
       "      <td>2018-04-16 11:58:02.3970</td>\n",
       "      <td>170.0</td>\n",
       "      <td>Telegraph Ave at 58th St</td>\n",
       "      <td>37.844493</td>\n",
       "      <td>-122.261351</td>\n",
       "      <td>157.0</td>\n",
       "      <td>65th St at Hollis St</td>\n",
       "      <td>37.846784</td>\n",
       "      <td>-122.291376</td>\n",
       "      <td>778</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637870</th>\n",
       "      <td>1635</td>\n",
       "      <td>2018-02-01 07:07:34.4660</td>\n",
       "      <td>2018-02-01 07:34:49.8990</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>806</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1968</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>51</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652386</th>\n",
       "      <td>248</td>\n",
       "      <td>2018-09-19 08:45:32.7230</td>\n",
       "      <td>2018-09-19 08:49:40.7710</td>\n",
       "      <td>175.0</td>\n",
       "      <td>49th St at Telegraph Ave</td>\n",
       "      <td>37.835946</td>\n",
       "      <td>-122.262366</td>\n",
       "      <td>176.0</td>\n",
       "      <td>MacArthur BART Station</td>\n",
       "      <td>37.828410</td>\n",
       "      <td>-122.266315</td>\n",
       "      <td>1223</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1985</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>34</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1900879</th>\n",
       "      <td>380</td>\n",
       "      <td>2018-10-08 17:46:30.9800</td>\n",
       "      <td>2018-10-08 17:52:51.3450</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>140.0</td>\n",
       "      <td>Cesar Chavez St at Dolores St</td>\n",
       "      <td>37.747858</td>\n",
       "      <td>-122.424986</td>\n",
       "      <td>4376</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1959</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>60</td>\n",
       "      <td>55-64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023331</th>\n",
       "      <td>604</td>\n",
       "      <td>2018-11-09 21:17:16.5730</td>\n",
       "      <td>2018-11-09 21:27:20.8250</td>\n",
       "      <td>246.0</td>\n",
       "      <td>Berkeley Civic Center</td>\n",
       "      <td>37.869060</td>\n",
       "      <td>-122.270556</td>\n",
       "      <td>272.0</td>\n",
       "      <td>Woolsey St at Sacramento St</td>\n",
       "      <td>37.850578</td>\n",
       "      <td>-122.278175</td>\n",
       "      <td>1256</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>15-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468971</th>\n",
       "      <td>427</td>\n",
       "      <td>2018-01-27 10:20:30.6690</td>\n",
       "      <td>2018-01-27 10:27:38.4100</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Page St at Scott St</td>\n",
       "      <td>37.772406</td>\n",
       "      <td>-122.435650</td>\n",
       "      <td>107.0</td>\n",
       "      <td>17th St at Dolores St</td>\n",
       "      <td>37.763015</td>\n",
       "      <td>-122.426497</td>\n",
       "      <td>3741</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1981</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>38</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1593278</th>\n",
       "      <td>668</td>\n",
       "      <td>2018-09-28 09:35:34.7000</td>\n",
       "      <td>2018-09-28 09:46:43.4130</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>2445</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>31</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855507</th>\n",
       "      <td>1048</td>\n",
       "      <td>2018-04-02 20:36:26.2010</td>\n",
       "      <td>2018-04-02 20:53:54.7270</td>\n",
       "      <td>342.0</td>\n",
       "      <td>Colin P Kelly Jr St at Townsend St (Temporary ...</td>\n",
       "      <td>37.781383</td>\n",
       "      <td>-122.389841</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Bryant St at 15th St</td>\n",
       "      <td>37.767100</td>\n",
       "      <td>-122.410662</td>\n",
       "      <td>2818</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1983</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>36</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189577</th>\n",
       "      <td>464</td>\n",
       "      <td>2017-10-24 09:54:19.7410</td>\n",
       "      <td>2017-10-24 10:02:03.9030</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>28.0</td>\n",
       "      <td>The Embarcadero at Bryant St</td>\n",
       "      <td>37.787168</td>\n",
       "      <td>-122.388098</td>\n",
       "      <td>2149</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1969</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816422</th>\n",
       "      <td>1960</td>\n",
       "      <td>2018-04-12 20:07:53.2120</td>\n",
       "      <td>2018-04-12 20:40:33.4650</td>\n",
       "      <td>323.0</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>37.798014</td>\n",
       "      <td>-122.405950</td>\n",
       "      <td>121.0</td>\n",
       "      <td>Mission Playground</td>\n",
       "      <td>37.759210</td>\n",
       "      <td>-122.421339</td>\n",
       "      <td>2677</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1975</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>44</td>\n",
       "      <td>35-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754709</th>\n",
       "      <td>1950</td>\n",
       "      <td>2018-09-01 16:35:43.4340</td>\n",
       "      <td>2018-09-01 17:08:14.4050</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2nd St at Townsend St</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>323.0</td>\n",
       "      <td>Broadway at Kearny</td>\n",
       "      <td>37.798014</td>\n",
       "      <td>-122.405950</td>\n",
       "      <td>776</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>25-34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_sec                start_time                  end_time  \\\n",
       "1538745           200  2018-08-08 08:32:42.7010  2018-08-08 08:36:02.8050   \n",
       "316768           1110  2017-09-12 10:54:07.4980  2017-09-12 11:12:37.9100   \n",
       "188605            234  2017-10-24 16:37:26.3670  2017-10-24 16:41:21.0320   \n",
       "1175684           344  2018-06-07 08:11:26.1740  2018-06-07 08:17:11.1670   \n",
       "2255355           722  2019-01-25 10:31:54.6130  2019-01-25 10:43:57.2100   \n",
       "1538789           586  2018-08-08 08:23:08.3010  2018-08-08 08:32:54.9010   \n",
       "2256300           180  2019-01-25 09:10:05.4020  2019-01-25 09:13:05.8390   \n",
       "1090938           582  2018-06-21 09:23:31.1040  2018-06-21 09:33:14.0850   \n",
       "805469            385  2018-04-16 17:03:56.0200  2018-04-16 17:10:21.7340   \n",
       "1473568           612  2018-08-19 14:49:56.7950  2018-08-19 15:00:09.3310   \n",
       "632598           1140  2018-02-02 08:56:29.2120  2018-02-02 09:15:29.4120   \n",
       "800098            158  2018-04-17 18:05:12.9780  2018-04-17 18:07:51.4410   \n",
       "512251            613  2018-01-12 12:27:24.7350  2018-01-12 12:37:38.0910   \n",
       "689949            881  2018-03-16 19:27:27.0800  2018-03-16 19:42:08.3800   \n",
       "1405450           622  2018-08-30 20:35:48.5420  2018-08-30 20:46:11.2950   \n",
       "1007643           625  2018-05-04 14:06:37.5630  2018-05-04 14:17:03.1180   \n",
       "1098203           558  2018-06-20 09:05:18.4910  2018-06-20 09:14:36.9050   \n",
       "1168442           602  2018-06-08 08:45:08.6540  2018-06-08 08:55:10.8470   \n",
       "622947            565  2018-02-05 13:48:13.0640  2018-02-05 13:57:38.8920   \n",
       "1624210           637  2018-09-24 07:22:26.6590  2018-09-24 07:33:03.7660   \n",
       "2253720           433  2019-01-25 15:23:30.1560  2019-01-25 15:30:43.6690   \n",
       "2234818           358  2019-01-28 17:05:39.6480  2019-01-28 17:11:37.9540   \n",
       "703712            485  2018-03-12 16:21:00.5910  2018-03-12 16:29:05.9760   \n",
       "596907            683  2018-02-12 08:46:09.5220  2018-02-12 08:57:33.1580   \n",
       "1188731          1373  2018-06-05 09:15:17.7340  2018-06-05 09:38:11.1110   \n",
       "356429            557  2017-08-28 09:22:05.7630  2017-08-28 09:31:22.8830   \n",
       "173751            383  2017-10-28 16:00:40.2570  2017-10-28 16:07:03.9680   \n",
       "1893544           280  2018-10-09 17:52:39.4840  2018-10-09 17:57:19.9060   \n",
       "2371925          1616  2019-01-04 08:18:54.7010  2019-01-04 08:45:51.1740   \n",
       "1110513           597  2018-06-18 16:02:19.0600  2018-06-18 16:12:16.8580   \n",
       "780445            502  2018-04-22 18:02:22.5860  2018-04-22 18:10:45.1570   \n",
       "1943499           547  2018-10-01 18:24:37.6450  2018-10-01 18:33:45.4640   \n",
       "354501            346  2017-08-28 20:19:10.5140  2017-08-28 20:24:57.2460   \n",
       "2260854           491  2019-01-24 17:38:37.6650  2019-01-24 17:46:48.9420   \n",
       "2374711           480  2019-01-03 17:23:44.4110  2019-01-03 17:31:44.4380   \n",
       "421343            343  2017-07-27 09:21:56.7740  2017-07-27 09:27:39.9200   \n",
       "1364984          1600  2018-07-07 16:29:36.7890  2018-07-07 16:56:17.1510   \n",
       "18811             508  2017-12-19 17:03:53.3320  2017-12-19 17:12:21.7630   \n",
       "459294            898  2018-01-30 13:24:51.8520  2018-01-30 13:39:50.6530   \n",
       "806363            959  2018-04-16 11:42:03.0270  2018-04-16 11:58:02.3970   \n",
       "637870           1635  2018-02-01 07:07:34.4660  2018-02-01 07:34:49.8990   \n",
       "1652386           248  2018-09-19 08:45:32.7230  2018-09-19 08:49:40.7710   \n",
       "1900879           380  2018-10-08 17:46:30.9800  2018-10-08 17:52:51.3450   \n",
       "2023331           604  2018-11-09 21:17:16.5730  2018-11-09 21:27:20.8250   \n",
       "468971            427  2018-01-27 10:20:30.6690  2018-01-27 10:27:38.4100   \n",
       "1593278           668  2018-09-28 09:35:34.7000  2018-09-28 09:46:43.4130   \n",
       "855507           1048  2018-04-02 20:36:26.2010  2018-04-02 20:53:54.7270   \n",
       "189577            464  2017-10-24 09:54:19.7410  2017-10-24 10:02:03.9030   \n",
       "816422           1960  2018-04-12 20:07:53.2120  2018-04-12 20:40:33.4650   \n",
       "1754709          1950  2018-09-01 16:35:43.4340  2018-09-01 17:08:14.4050   \n",
       "\n",
       "        start_station_id                                 start_station_name  \\\n",
       "1538745            186.0                             Lakeside Dr at 14th St   \n",
       "316768             102.0                                 Irwin St at 8th St   \n",
       "188605              20.0    Mechanics Monument Plaza (Market St at Bush St)   \n",
       "1175684            178.0                                Broadway at 30th St   \n",
       "2255355             77.0                               11th St at Natoma St   \n",
       "1538789             91.0                                Berry St at King St   \n",
       "2256300              6.0                      The Embarcadero at Sansome St   \n",
       "1090938             81.0                                 Berry St at 4th St   \n",
       "805469             182.0                           19th Street BART Station   \n",
       "1473568             70.0                             Central Ave at Fell St   \n",
       "632598             147.0                             29th St at Tiffany Ave   \n",
       "800098             176.0                             MacArthur BART Station   \n",
       "512251              93.0                       4th St at Mission Bay Blvd S   \n",
       "689949             121.0                                 Mission Playground   \n",
       "1405450             66.0                              3rd St at Townsend St   \n",
       "1007643             37.0                                2nd St at Folsom St   \n",
       "1098203              5.0       Powell St BART Station (Market St at 5th St)   \n",
       "1168442            324.0                Union Square (Powell St at Post St)   \n",
       "622947             342.0  Colin P Kelly Jr St at Townsend St (Temporary ...   \n",
       "1624210             67.0  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "2253720            305.0                                        Ryland Park   \n",
       "2234818            310.0                          San Fernando St at 4th St   \n",
       "703712              80.0                              Townsend St at 5th St   \n",
       "596907             321.0                                      5th at Folsom   \n",
       "1188731             67.0  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "356429              15.0  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "173751              14.0                              Clay St at Battery St   \n",
       "1893544             89.0                         Division St at Potrero Ave   \n",
       "2371925            146.0                            30th St at San Jose Ave   \n",
       "1110513            309.0                                 San Jose City Hall   \n",
       "780445             109.0                             17th St at Valencia St   \n",
       "1943499             81.0                                 Berry St at 4th St   \n",
       "354501             232.0                              MLK Jr Way at 14th St   \n",
       "2260854             23.0                      The Embarcadero at Steuart St   \n",
       "2374711             37.0                                2nd St at Folsom St   \n",
       "421343             280.0                             San Fernando at 7th St   \n",
       "1364984            156.0                          Stanford Ave at Hollis St   \n",
       "18811              324.0                Union Square (Powell St at Post St)   \n",
       "459294              84.0                                        Duboce Park   \n",
       "806363             170.0                           Telegraph Ave at 58th St   \n",
       "637870             324.0                Union Square (Powell St at Post St)   \n",
       "1652386            175.0                           49th St at Telegraph Ave   \n",
       "1900879            134.0                             Valencia St at 24th St   \n",
       "2023331            246.0                              Berkeley Civic Center   \n",
       "468971              72.0                                Page St at Scott St   \n",
       "1593278             81.0                                 Berry St at 4th St   \n",
       "855507             342.0  Colin P Kelly Jr St at Townsend St (Temporary ...   \n",
       "189577              67.0  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "816422             323.0                                 Broadway at Kearny   \n",
       "1754709             50.0                              2nd St at Townsend St   \n",
       "\n",
       "         start_station_latitude  start_station_longitude end_station_id  \\\n",
       "1538745               37.801319              -122.262642          163.0   \n",
       "316768                37.766883              -122.399579           37.0   \n",
       "188605                37.791300              -122.399051           15.0   \n",
       "1175684               37.819381              -122.261928          182.0   \n",
       "2255355               37.773507              -122.416040           49.0   \n",
       "1538789               37.771762              -122.398438           37.0   \n",
       "2256300               37.804770              -122.403234            8.0   \n",
       "1090938               37.775880              -122.393170           19.0   \n",
       "805469                37.809013              -122.268247          197.0   \n",
       "1473568               37.773311              -122.444293           70.0   \n",
       "632598                37.744067              -122.421472           90.0   \n",
       "800098                37.828410              -122.266315          192.0   \n",
       "512251                37.770407              -122.391198           25.0   \n",
       "689949                37.759210              -122.421339          284.0   \n",
       "1405450               37.778742              -122.392741           60.0   \n",
       "1007643               37.785000              -122.395936           27.0   \n",
       "1098203               37.783899              -122.408445           79.0   \n",
       "1168442               37.788300              -122.408531           49.0   \n",
       "622947                37.781383              -122.389841           14.0   \n",
       "1624210               37.776639              -122.395526          324.0   \n",
       "2253720               37.342725              -121.895617          288.0   \n",
       "2234818               37.335885              -121.885660          282.0   \n",
       "703712                37.775306              -122.397380           44.0   \n",
       "596907                37.780146              -122.403071           24.0   \n",
       "1188731               37.776639              -122.395526          323.0   \n",
       "356429                37.795392              -122.394203           50.0   \n",
       "173751                37.795001              -122.399970            6.0   \n",
       "1893544               37.769218              -122.407646           67.0   \n",
       "2371925               37.742314              -122.423180           79.0   \n",
       "1110513               37.337391              -121.886995          312.0   \n",
       "780445                37.763316              -122.421904           84.0   \n",
       "1943499               37.775880              -122.393170           23.0   \n",
       "354501                37.806163              -122.276040          181.0   \n",
       "2260854               37.791464              -122.391034           10.0   \n",
       "2374711               37.785000              -122.395936            5.0   \n",
       "421343                37.337246              -121.883074          318.0   \n",
       "1364984               37.838443              -122.288665          257.0   \n",
       "18811                 37.788300              -122.408531           58.0   \n",
       "459294                37.769201              -122.433812          284.0   \n",
       "806363                37.844493              -122.261351          157.0   \n",
       "637870                37.788300              -122.408531           70.0   \n",
       "1652386               37.835946              -122.262366          176.0   \n",
       "1900879               37.752428              -122.420628          140.0   \n",
       "2023331               37.869060              -122.270556          272.0   \n",
       "468971                37.772406              -122.435650          107.0   \n",
       "1593278               37.775880              -122.393170           16.0   \n",
       "855507                37.781383              -122.389841          100.0   \n",
       "189577                37.776639              -122.395526           28.0   \n",
       "816422                37.798014              -122.405950          121.0   \n",
       "1754709               37.780526              -122.390288          323.0   \n",
       "\n",
       "                                          end_station_name  \\\n",
       "1538745                          Lake Merritt BART Station   \n",
       "316768                                 2nd St at Folsom St   \n",
       "188605   San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "1175684                           19th Street BART Station   \n",
       "2255355                                S Park St at 3rd St   \n",
       "1538789                                2nd St at Folsom St   \n",
       "2256300                      The Embarcadero at Vallejo St   \n",
       "1090938                               Post St at Kearny St   \n",
       "805469                         El Embarcadero at Grand Ave   \n",
       "1473568                             Central Ave at Fell St   \n",
       "632598                               Townsend St at 7th St   \n",
       "800098                                  37th St at West St   \n",
       "512251                                 Howard St at 2nd St   \n",
       "689949   Yerba Buena Center for the Arts (Howard St at ...   \n",
       "1405450                               8th St at Ringold St   \n",
       "1007643                            Beale St at Harrison St   \n",
       "1098203                               7th St at Brannan St   \n",
       "1168442                                S Park St at 3rd St   \n",
       "622947                               Clay St at Battery St   \n",
       "1624210                Union Square (Powell St at Post St)   \n",
       "2253720                               Mission St at 1st St   \n",
       "2234818                               Market St at Park St   \n",
       "703712   Civic Center/UN Plaza BART Station (Market St ...   \n",
       "596907                               Spear St at Folsom St   \n",
       "1188731                                 Broadway at Kearny   \n",
       "356429                 2nd St at Townsend St - Coming Soon   \n",
       "173751                       The Embarcadero at Sansome St   \n",
       "1893544  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "2371925                               7th St at Brannan St   \n",
       "1110513                           San Jose Diridon Station   \n",
       "780445                                         Duboce Park   \n",
       "1943499                      The Embarcadero at Steuart St   \n",
       "354501                             Grand Ave at Webster St   \n",
       "2260854                         Washington St at Kearny St   \n",
       "2374711       Powell St BART Station (Market St at 5th St)   \n",
       "421343                          San Carlos St at Market St   \n",
       "1364984                            Fifth St at Delaware St   \n",
       "18811                                 Market St at 10th St   \n",
       "459294   Yerba Buena Center for the Arts (Howard St at ...   \n",
       "806363                                65th St at Hollis St   \n",
       "637870                              Central Ave at Fell St   \n",
       "1652386                             MacArthur BART Station   \n",
       "1900879                      Cesar Chavez St at Dolores St   \n",
       "2023331                        Woolsey St at Sacramento St   \n",
       "468971                               17th St at Dolores St   \n",
       "1593278                            Steuart St at Market St   \n",
       "855507                                Bryant St at 15th St   \n",
       "189577                        The Embarcadero at Bryant St   \n",
       "816422                                  Mission Playground   \n",
       "1754709                                 Broadway at Kearny   \n",
       "\n",
       "         end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "1538745             37.797320            -122.265320    2588  Subscriber   \n",
       "316768              37.785000            -122.395936    2540  Subscriber   \n",
       "188605              37.795392            -122.394203     403  Subscriber   \n",
       "1175684             37.809013            -122.268247    2208  Subscriber   \n",
       "2255355             37.780760            -122.394989    5359  Subscriber   \n",
       "1538789             37.785000            -122.395936     535  Subscriber   \n",
       "2256300             37.799953            -122.398525     873  Subscriber   \n",
       "1090938             37.788975            -122.403452    2028  Subscriber   \n",
       "805469              37.808848            -122.249680    3505  Subscriber   \n",
       "1473568             37.773311            -122.444293    2517    Customer   \n",
       "632598              37.771058            -122.402717    1908  Subscriber   \n",
       "800098              37.826696            -122.271796    1256  Subscriber   \n",
       "512251              37.787522            -122.397405    3398    Customer   \n",
       "689949              37.784872            -122.400876    1400  Subscriber   \n",
       "1405450             37.774520            -122.409449    3416  Subscriber   \n",
       "1007643             37.788059            -122.391865    3779  Subscriber   \n",
       "1098203             37.773492            -122.403673    2512  Subscriber   \n",
       "1168442             37.780760            -122.394989    2052  Subscriber   \n",
       "622947              37.795001            -122.399970    2064  Subscriber   \n",
       "1624210             37.788300            -122.408531    4419  Subscriber   \n",
       "2253720             37.350964            -121.902016    1477  Subscriber   \n",
       "2234818             37.332426            -121.890349     131  Subscriber   \n",
       "703712              37.781074            -122.411738     533  Subscriber   \n",
       "596907              37.789677            -122.390428    2540  Subscriber   \n",
       "1188731             37.798014            -122.405950    2623  Subscriber   \n",
       "356429              37.780526            -122.390288    2813  Subscriber   \n",
       "173751              37.804770            -122.403234    3145  Subscriber   \n",
       "1893544             37.776639            -122.395526    1106  Subscriber   \n",
       "2371925             37.773492            -122.403673    4633  Subscriber   \n",
       "1110513             37.329732            -121.901782     799  Subscriber   \n",
       "780445              37.769201            -122.433812     757  Subscriber   \n",
       "1943499             37.791464            -122.391034    3376  Subscriber   \n",
       "354501              37.811377            -122.265192    1342  Subscriber   \n",
       "2260854             37.795393            -122.404770    5410  Subscriber   \n",
       "2374711             37.783899            -122.408445    1759  Subscriber   \n",
       "421343              37.330698            -121.888979    1860  Subscriber   \n",
       "1364984             37.870407            -122.299676    3098  Subscriber   \n",
       "18811               37.776619            -122.417385    3001  Subscriber   \n",
       "459294              37.784872            -122.400876     326  Subscriber   \n",
       "806363              37.846784            -122.291376     778  Subscriber   \n",
       "637870              37.773311            -122.444293     806  Subscriber   \n",
       "1652386             37.828410            -122.266315    1223  Subscriber   \n",
       "1900879             37.747858            -122.424986    4376  Subscriber   \n",
       "2023331             37.850578            -122.278175    1256  Subscriber   \n",
       "468971              37.763015            -122.426497    3741  Subscriber   \n",
       "1593278             37.794130            -122.394430    2445  Subscriber   \n",
       "855507              37.767100            -122.410662    2818  Subscriber   \n",
       "189577              37.787168            -122.388098    2149  Subscriber   \n",
       "816422              37.759210            -122.421339    2677  Subscriber   \n",
       "1754709             37.798014            -122.405950     776    Customer   \n",
       "\n",
       "         member_birth_year member_gender bike_share_for_all_trip  member_age  \\\n",
       "1538745               1980        Female                      No          39   \n",
       "316768                1995          Male                     NaN          24   \n",
       "188605                1977          Male                     NaN          42   \n",
       "1175684               1986        Female                      No          33   \n",
       "2255355               1995          Male                      No          24   \n",
       "1538789               1989          Male                      No          30   \n",
       "2256300               1990          Male                      No          29   \n",
       "1090938               1969        Female                      No          50   \n",
       "805469                1987          Male                      No          32   \n",
       "1473568               1969        Female                      No          50   \n",
       "632598                1985        Female                      No          34   \n",
       "800098                1986          Male                      No          33   \n",
       "512251                1986          Male                      No          33   \n",
       "689949                1972          Male                      No          47   \n",
       "1405450               1987          Male                      No          32   \n",
       "1007643               1979          Male                     Yes          40   \n",
       "1098203               1980        Female                      No          39   \n",
       "1168442               1989          Male                      No          30   \n",
       "622947                1982          Male                      No          37   \n",
       "1624210               1995        Female                      No          24   \n",
       "2253720               1965          Male                      No          54   \n",
       "2234818               1991          Male                     Yes          28   \n",
       "703712                1977          Male                      No          42   \n",
       "596907                1987          Male                      No          32   \n",
       "1188731               1993          Male                      No          26   \n",
       "356429                1977          Male                     NaN          42   \n",
       "173751                1964          Male                     NaN          55   \n",
       "1893544               1985          Male                      No          34   \n",
       "2371925               1996          Male                      No          23   \n",
       "1110513               1965          Male                      No          54   \n",
       "780445                1973          Male                      No          46   \n",
       "1943499               1991          Male                      No          28   \n",
       "354501                1987          Male                     NaN          32   \n",
       "2260854               1993          Male                      No          26   \n",
       "2374711               1963          Male                      No          56   \n",
       "421343                1993          Male                     NaN          26   \n",
       "1364984               1992          Male                      No          27   \n",
       "18811                 1987        Female                     NaN          32   \n",
       "459294                1983          Male                      No          36   \n",
       "806363                1984          Male                      No          35   \n",
       "637870                1968        Female                      No          51   \n",
       "1652386               1985          Male                     Yes          34   \n",
       "1900879               1959        Female                      No          60   \n",
       "2023331               1995          Male                      No          24   \n",
       "468971                1981          Male                      No          38   \n",
       "1593278               1988          Male                      No          31   \n",
       "855507                1983          Male                      No          36   \n",
       "189577                1969          Male                     NaN          50   \n",
       "816422                1975          Male                      No          44   \n",
       "1754709               1991          Male                      No          28   \n",
       "\n",
       "        age_group  \n",
       "1538745     35-44  \n",
       "316768      15-24  \n",
       "188605      35-44  \n",
       "1175684     25-34  \n",
       "2255355     15-24  \n",
       "1538789     25-34  \n",
       "2256300     25-34  \n",
       "1090938     45-54  \n",
       "805469      25-34  \n",
       "1473568     45-54  \n",
       "632598      25-34  \n",
       "800098      25-34  \n",
       "512251      25-34  \n",
       "689949      45-54  \n",
       "1405450     25-34  \n",
       "1007643     35-44  \n",
       "1098203     35-44  \n",
       "1168442     25-34  \n",
       "622947      35-44  \n",
       "1624210     15-24  \n",
       "2253720     45-54  \n",
       "2234818     25-34  \n",
       "703712      35-44  \n",
       "596907      25-34  \n",
       "1188731     25-34  \n",
       "356429      35-44  \n",
       "173751      55-64  \n",
       "1893544     25-34  \n",
       "2371925     15-24  \n",
       "1110513     45-54  \n",
       "780445      45-54  \n",
       "1943499     25-34  \n",
       "354501      25-34  \n",
       "2260854     25-34  \n",
       "2374711     55-64  \n",
       "421343      25-34  \n",
       "1364984     25-34  \n",
       "18811       25-34  \n",
       "459294      35-44  \n",
       "806363      35-44  \n",
       "637870      45-54  \n",
       "1652386     25-34  \n",
       "1900879     55-64  \n",
       "2023331     15-24  \n",
       "468971      35-44  \n",
       "1593278     25-34  \n",
       "855507      35-44  \n",
       "189577      45-54  \n",
       "816422      35-44  \n",
       "1754709     25-34  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gobike_without_age_nan.sample(50) #Check if filtering went well. Yes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2352024, 18)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gobike_without_age_nan.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last bar plot did its job well, however, I would like to check if a violin plot can reveal more detailed information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\scorneli\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 8])\n",
    "sb.violinplot(data = df_gobike_without_age_nan, x = 'member_gender', y = 'member_age', color = default_color)\n",
    "plt.title(\"Distribution of Rides and Age per Gender\")\n",
    "plt.ylabel(\"Age\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.ylim([0,100]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These violin plots make three things more visible compared to the previous bar plot. First, it shows more clearly, that within the group of rides done by male users, the age distribution a bit more stretched towards higher ages. Second, it shows the relative frequency of rides across both groups (male / female). Thereby it reveals on first sight, that the group of female users is denser around users aging around 30 years (indicated by the width of the \"violin\"). Third, the interquartile distance (indicated by the thicker line in the middle of each violin) is shorter for the group of female users, which also tells us, that the age distribution is denser."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To visualize the usage, i.e. the amount and duration the dataset needs to be segmented by the membership models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_gobike_customer = df_gobike_v2[(df_gobike_v2.user_type == \"Customer\")]\n",
    "df_gobike_subscriber = df_gobike_v2[(df_gobike_v2.user_type == \"Subscriber\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\scorneli\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\scorneli\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Converting seconds to minutes in an additional column, to make plots more readable.\n",
    "df_gobike_customer['duration_min'] = df_gobike_customer.duration_sec/60 \n",
    "df_gobike_subscriber['duration_min'] = df_gobike_subscriber.duration_sec/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if the separation went well. Yes.\n",
    "len(df_gobike_subscriber) + len(df_gobike_customer) == len(df_gobike_v2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I am going to explore how the length of the rides is distributed across the membership models as a preparation to segment those by age group and gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(2, 1, 1)\n",
    "sb.violinplot(x='duration_min', data = df_gobike_customer, color = default_color, label =\"Customer\")\n",
    "plt.title(\"Distribution of Ride Duration per User Type\")\n",
    "plt.xlabel(\"Duration (in min)\")\n",
    "plt.ylabel(\"Customer\")\n",
    "plt.xlim(0,60);\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "sb.violinplot(x='duration_min', data = df_gobike_subscriber, color = 'orange', label =\"Subscriber\")   \n",
    "plt.xlabel(\"Duration (in min)\")\n",
    "plt.ylabel(\"Subscriber\")\n",
    "plt.xlim(0,60);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These plots show the relative distribution of the duration of trips for the group of subscribers and customers. Noteworthy is, that customers have longer enduring trips/rides than the group of subscribers. The plot also gives a hint, that we have to deal with outliers in this variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, I shall exclude the outliers in duration from further analysis to generate meaningful insights, valid for the majority of rides. But first, I am going to check at which duration it would make sense to exclude data.\n",
    "\n",
    "From now on, I want to focus the exploration on the group of subscribers only.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36025.266667\n",
       "mean        11.236679\n",
       "std         24.298027\n",
       "min          1.016667\n",
       "5%           2.883333\n",
       "10%          3.683333\n",
       "25%          5.550000\n",
       "50%          8.616667\n",
       "75%         13.033333\n",
       "90%         18.766667\n",
       "95%         23.666667\n",
       "99.5%       60.316667\n",
       "99.8%      125.566667\n",
       "99.9%      248.366167\n",
       "max       1438.016667\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From the prior violin plot it became clear, that I have to look especially at last quartile of rides.\n",
    "df_gobike_subscriber.duration_sec.describe(percentiles = [0.05, 0.10, 0.25, 0.5, 0.75, 0.9, 0.95, 0.995, 0.998, 0.999])/60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This description of the percentiles shows, that 99.9% of the rides do not take more than 248.37 minutes, which equals approximately 4 hours and 8 minutes. For further exploration it makes sense to include as much values as possible to make the analysis on aspects of duration and usage as complete as possible. This handling ensures, that only an absolute minimum of data points is excluded, whereas this plot on its own, would have suggested to remove more outlying values. \n",
    "\n",
    "Note: To me, 4 hours still seem to be a reasonable trip duration, which might occur from time to time. From a business logic, it makes sense to include those in the analysis, because the happen to be the truth. In addition, the plot shows that only very few trips endure more than 40 minutes. Trips longer than 4 hours may be reasoned by technical issues. The bike might not have been locked properly. \n",
    "\n",
    "Next, I will exclude rides which took more than 4 hours (14.400 seconds) from the segment of subscribers before continuing with the multivariate exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gobike_subscriber_without_outliers = df_gobike_subscriber[(df_gobike_subscriber.duration_sec <= 14400)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This action removed 2248 outlying rides from the segment of subscribers.\n"
     ]
    }
   ],
   "source": [
    "print(\"This action removed \" + str(len(df_gobike_subscriber) - len(df_gobike_subscriber_without_outliers)) +\n",
    "      \" outlying rides from the segment of subscribers.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "In addition to the fact that female users used the service far less than male users, this part also revealed that female users are generally younger than male users. The relative distribution of age, segmented by gender shows, that the values for age are denser distributed than for males.  \n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "Regarding the duration of rides, it became visible that subscribers have shorter trips than regular customers. Important at this point, is to handle the outlying values. Although there were very few outlying values, they might cause inaccurate evaluations, as their values (duration) were multiples from the average.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "Next I want to explore, how the amount of rides and the duration differs among subscribers for each combination/segment of gender and age group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing \"Other\" values from the column member_gender. This group of users is too insignificant compared to the \n",
    "# groups of male or female users. So excluding the group of \"Other\" will enhance readibility in the visualization.\n",
    "df_gobike_subscriber_female_male_without_outliers = df_gobike_subscriber_without_outliers[\n",
    "    (df_gobike_subscriber_without_outliers.member_gender != \"Other\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      1590680\n",
       "Female     495870\n",
       "Name: member_gender, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gobike_subscriber_female_male_without_outliers.member_gender.value_counts() # Check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to visualize amount of rides and duration segmented by age group and gender, I need to pivot these settings first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_rides_subsriber = df_gobike_subscriber_female_male_without_outliers.groupby(['member_gender', 'age_group']).size()\n",
    "ct_rides_subsriber = ct_rides_subsriber.reset_index(name = 'count')\n",
    "ct_rides_subsriber = ct_rides_subsriber.pivot(index = 'age_group', columns = 'member_gender', values = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ct_duration_subsriber = df_gobike_subscriber_female_male_without_outliers.groupby(['member_gender', 'age_group'])['duration_sec'].agg('sum')\n",
    "ct_duration_subsriber = ct_duration_subsriber\n",
    "ct_duration_subsriber = ct_duration_subsriber.reset_index(name = 'duration_min')\n",
    "ct_duration_subsriber = ct_duration_subsriber.pivot(index = 'age_group', columns = 'member_gender', values = 'duration_min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "member_gender    Female      Male\n",
       "age_group                        \n",
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     "metadata": {},
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   "source": [
    "# Check, if the pivoting actions went well?\n",
    "ct_rides_subsriber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
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       "member_gender       Female         Male\n",
       "age_group                              \n",
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       "95-100                 NaN     186303.0"
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     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct_duration_subsriber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "member_gender      Female        Male\n",
       "age_group                            \n",
       "15-24          696.016642  597.786632\n",
       "25-34          701.952873  604.053364\n",
       "35-44          680.881954  596.168450\n",
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       "85-94          609.912892  492.447368\n",
       "95-100                NaN  663.000000"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using those two pivot tables to calculate the average per ride on each segment.\n",
    "ct_duration_per_ride_subscriber = ct_duration_subsriber / ct_rides_subsriber\n",
    "ct_duration_per_ride_subscriber"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, that the data is pivoted per age group and gender, I want to use heatmaps, where the colorization indicates differences in the amount of rides, the duration and the duration per ride, segmented by age group and gender, across the group of subscribers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [16, 14])\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "sb.heatmap(ct_rides_subsriber/1000, annot = True, fmt =  '.0f',  cmap = 'Blues')\n",
    "plt.title(\"Amount of Rides (in k) per Age Group and Gender\")\n",
    "plt.ylabel(\"Age Group (years)\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.yticks(rotation=360)\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "sb.heatmap(ct_duration_subsriber/60/60/1000, annot = True, fmt =  '.2f',  cmap = 'Blues', yticklabels=False)\n",
    "plt.title(\"Sum of all Rides Duration (in k-Hours) per Age Group and Gender\")\n",
    "plt.ylabel(\"Age Group\")\n",
    "plt.xlabel(\"Gender\")\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "sb.heatmap(ct_duration_per_ride_subscriber/60, annot = True, fmt =  '.2f',  cmap = 'Blues');\n",
    "plt.title(\"Average Ride Duration (min) per Age Group and Gender\")\n",
    "plt.ylabel(\"Age Group (years)\")\n",
    "plt.xlabel(\"Gender\")\n",
    "plt.yticks(rotation=360);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "\n",
    "In this part, I observed that the usage in terms of amount of rides and the duration do not diverge much among the segments of age group and gender. That means, the group with most rides also has the highest total ride duration. The same goes for the other segments. This can easily be identified by the colorization of the first two heatmaps. The highest usage is achieved by male users between 25 and 34 years. The second highest by males between 35 and 44 years. \n",
    "The colorization hue is identical per sub-segment in both heatmaps. Thinking the other way around it means, there is no segment which uses the service in such a degree that it would drive over proportional usage, which could mean that it blocks important bike-capacities for other groups.\n",
    "\n",
    "The first two heatmaps also show, which sub segments are using FordGoBike extensively and which are not. The darker the colorization, the more important the segment is, in terms of usage. Using these heatmaps, one can easily identify which segment can be marketed on. The lighter the colorization, the higher the unused potential for FordGoBike can be. Given that the segments population in San Francisco high big enough. Hence, it could make sense for FordGoBike to get more woman using the service by either putting marketing efforts into it, or by making the corporate identity more appealing to woman. Next, the group of 45 to 54-year-old people could become an asset when targeting those dedicatedly with marketing activities. This group does not use the bikes as much as the younger groups, but could still be very capable of riding bicycles. \n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "The third heatmap shows interesting differentiations among the user segments. The duration per trip (in the group of subscribers, outliers excluded) shows that woman tend to have little longer enduring rides than man. Furthermore, rides of older users tend to endure longer than rides of younger users. However, since both female and older users represent rather smaller user groups, they do not increase the total trip duration to such a degree, that it would affect the colorization of the second heatmap.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary und Conclusion\n",
    "\n",
    "The here analyzed dataset from FordGoBike (San Francisco) is based on the ride data from 28th of June 2017 till 31st of January 2019. This period is also the period of observation. The dataset has been explored by segmenting the data using the variables of user type (subscriber or regular customer), gender, age and by quantitate variables such as amount of rides and their duration.\n",
    "\n",
    "The exploration revealed that most rides have been done by subscribers (84%). Male users account by far for the highest proportion of rides (74%). Female and male users between 25 and 34 years are the group which used the service most often. Interestingly, rides of female users were done at a younger age compared to male users. The relative distribution of rides shows a higher density around the age of thirty for woman, compared to man.\n",
    "Most rides took less than 40 minutes, whereas rides done by customers have a higher duration than those of subscribers. Visualizations show, that especially younger and male users show very frequent usage behavior, where older uses aging between 45 and 54 years show very low usage.  Furthermore, the group of female users shows overall lower usage compared to their counterparts. That means, those segments can be of high interest for future marketing activities in order to drive utilization and hence revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlook\n",
    "\n",
    "Now, that is theoretically explored in which segments FordGoBike could experience further growth, to put the opportunities into the real worlds setting.\n",
    "\n",
    "First, FordGoBike could check with community data, how the population in the area of San Francisco is distributed. Are the enormous potentials in the segment of female users really existent? How is the population divided between males and females? Matches the age group distribution the theoretical potential? Since bike sharing is an option for daily commuters to get to work, are there as much employees and commuters among female citizens as male citizens? Do as much females work within the city center or tend they to work rather outside, compared to male users? Are the reasons and motivations to use a bike identic for man and woman? Do these motives differentiate across the age groups? Could it be, that people between 45 and 54 years own more cars per capita than users between 25 and 34 years? And how differs the bike possession rate among the segments of gender and age group? \n",
    "\n",
    "These and other questions shall be asked when reflecting the theoretical value of the exploration against real business environment. The next step could be to derive hypothesis’s and testing those using additional data from San Francisco’s population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ResourcesUsed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "    <li>https://stackoverflow.com/questions/3451111/unzipping-files-in-python</li>\n",
    "    <li>https://stackoverflow.com/questions/3207219/how-do-i-list-all-files-of-a-directory</li>\n",
    "        <li>https://stackoverflow.com/questions/39173992/drop-all-data-in-a-pandas-dataframe</li>\n",
    "        <li>https://stackoverflow.com/questions/39922986/pandas-group-by-and-sum</li>\n",
    "    <li>https://seaborn.pydata.org/generated/seaborn.heatmap.html</li>\n",
    "    </ul>"
   ]
  }
 ],
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