Divvy'ing_Up_Data_Final_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DIVVY'IN UP DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project analyzes anonymized data from Divvy, a rideshare biking service in Chicago, IL. We chose to examine data in the third quarter of 2019 which includes the summer months and high volume of riders compared to other quarters. \n",
    "\n",
    "We wanted to uncover interesting patterns in the data. **We asked ourselves:**\n",
    "* Are there routes and stations that are more popular than others?\n",
    "* Are there any specific bikes that were rented a surprising amount?\n",
    "* Are there any relationships between the age and/or gender when comparing one-time customers or yearly subscribers?\n",
    "* How long, on average, are trips made by Divvy bike?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Popular Stations**\n",
    "* The most popular station by far is Streeter Dr. and Grand Ave. which is located on Navy Pier. It is a popular destination for both tourists and locals. Indeed, this station appeared as either the starting or ending station on many of the most popular routes. \n",
    "\n",
    "**Popular Routes**\n",
    "* All of the most frequented routes were in the loop and along the lakefront. Without knowing, we suspect that this is again a combination of tourists and commuters making use of the bikes in downtown Chicago. \n",
    "\n",
    "**Customers vs. Subscribers:** \n",
    "* There are far more subscribers than customers who ride in the Chicago Divvy network, about 84% to 16%\n",
    "* This data may be skewed. We made an early call to drop any data with null values. We suspect that the dropped cells are more likely to be customers, one-time or infrequent users.\n",
    "\n",
    "**Gender:**\n",
    "* There are far more male Divvy riders than female - about 70% to 30%.\n",
    "* It is fairly the same gender breakdown when it comes to customer (about 70/30) and subscriber (about 60/40).\n",
    "* Divvy did not collect gender data beyond male and female, which means portions of Chicago's non-binary population are not accounted for in this dataset. \n",
    "    \n",
    "**Age:**\n",
    "* Riders aged 20-29 made up 47% of overall ridership with people aged 30-39 coming in second with 30% of overall ridership.\n",
    "* The breakdown per usertype, customer versus subscriber, was similar to the overall categories. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing & Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dependencies\n",
    "from matplotlib import pyplot as plt\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import linregress\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import requests\n",
    "import json\n",
    "import os\n",
    "import gmaps\n",
    "\n",
    "# Import API key\n",
    "from config import g_key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data file\n",
    "divvy_df = pd.read_csv('resources/Divvy_Trips_2019_Q3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1640718, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove null rows (if needed)\n",
    "divvy_df.dropna()\n",
    "divvy_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23479388</td>\n",
       "      <td>2019-07-01 00:00:27</td>\n",
       "      <td>2019-07-01 00:20:41</td>\n",
       "      <td>3591</td>\n",
       "      <td>1,214.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>497</td>\n",
       "      <td>Kimball Ave &amp; Belmont Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23479389</td>\n",
       "      <td>2019-07-01 00:01:16</td>\n",
       "      <td>2019-07-01 00:18:44</td>\n",
       "      <td>5353</td>\n",
       "      <td>1,048.0</td>\n",
       "      <td>381</td>\n",
       "      <td>Western Ave &amp; Monroe St</td>\n",
       "      <td>203</td>\n",
       "      <td>Western Ave &amp; 21st St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23479390</td>\n",
       "      <td>2019-07-01 00:01:48</td>\n",
       "      <td>2019-07-01 00:27:42</td>\n",
       "      <td>6180</td>\n",
       "      <td>1,554.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23479391</td>\n",
       "      <td>2019-07-01 00:02:07</td>\n",
       "      <td>2019-07-01 00:27:10</td>\n",
       "      <td>5540</td>\n",
       "      <td>1,503.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23479392</td>\n",
       "      <td>2019-07-01 00:02:13</td>\n",
       "      <td>2019-07-01 00:22:26</td>\n",
       "      <td>6014</td>\n",
       "      <td>1,213.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trip_id           start_time             end_time  bikeid tripduration  \\\n",
       "0  23479388  2019-07-01 00:00:27  2019-07-01 00:20:41    3591      1,214.0   \n",
       "1  23479389  2019-07-01 00:01:16  2019-07-01 00:18:44    5353      1,048.0   \n",
       "2  23479390  2019-07-01 00:01:48  2019-07-01 00:27:42    6180      1,554.0   \n",
       "3  23479391  2019-07-01 00:02:07  2019-07-01 00:27:10    5540      1,503.0   \n",
       "4  23479392  2019-07-01 00:02:13  2019-07-01 00:22:26    6014      1,213.0   \n",
       "\n",
       "   from_station_id              from_station_name  to_station_id  \\\n",
       "0              117       Wilton Ave & Belmont Ave            497   \n",
       "1              381        Western Ave & Monroe St            203   \n",
       "2              313  Lakeview Ave & Fullerton Pkwy            144   \n",
       "3              313  Lakeview Ave & Fullerton Pkwy            144   \n",
       "4              168         Michigan Ave & 14th St             62   \n",
       "\n",
       "             to_station_name    usertype gender  birthyear  \n",
       "0  Kimball Ave & Belmont Ave  Subscriber   Male     1992.0  \n",
       "1      Western Ave & 21st St    Customer    NaN        NaN  \n",
       "2  Larrabee St & Webster Ave    Customer    NaN        NaN  \n",
       "3  Larrabee St & Webster Ave    Customer    NaN        NaN  \n",
       "4            McCormick Place    Customer    NaN        NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display sample of dataframe\n",
    "divvy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['trip_id', 'start_time', 'end_time', 'bikeid', 'tripduration',\n",
       "       'from_station_id', 'from_station_name', 'to_station_id',\n",
       "       'to_station_name', 'usertype', 'gender', 'birthyear'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find column names\n",
    "divvy_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23479388</td>\n",
       "      <td>2019-07-01 00:00:27</td>\n",
       "      <td>2019-07-01 00:20:41</td>\n",
       "      <td>3591</td>\n",
       "      <td>1,214.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>497</td>\n",
       "      <td>Kimball Ave &amp; Belmont Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23479389</td>\n",
       "      <td>2019-07-01 00:01:16</td>\n",
       "      <td>2019-07-01 00:18:44</td>\n",
       "      <td>5353</td>\n",
       "      <td>1,048.0</td>\n",
       "      <td>381</td>\n",
       "      <td>Western Ave &amp; Monroe St</td>\n",
       "      <td>203</td>\n",
       "      <td>Western Ave &amp; 21st St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23479390</td>\n",
       "      <td>2019-07-01 00:01:48</td>\n",
       "      <td>2019-07-01 00:27:42</td>\n",
       "      <td>6180</td>\n",
       "      <td>1,554.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23479391</td>\n",
       "      <td>2019-07-01 00:02:07</td>\n",
       "      <td>2019-07-01 00:27:10</td>\n",
       "      <td>5540</td>\n",
       "      <td>1,503.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23479392</td>\n",
       "      <td>2019-07-01 00:02:13</td>\n",
       "      <td>2019-07-01 00:22:26</td>\n",
       "      <td>6014</td>\n",
       "      <td>1,213.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trip_id           start_time             end_time  bikeid tripduration  \\\n",
       "0  23479388  2019-07-01 00:00:27  2019-07-01 00:20:41    3591      1,214.0   \n",
       "1  23479389  2019-07-01 00:01:16  2019-07-01 00:18:44    5353      1,048.0   \n",
       "2  23479390  2019-07-01 00:01:48  2019-07-01 00:27:42    6180      1,554.0   \n",
       "3  23479391  2019-07-01 00:02:07  2019-07-01 00:27:10    5540      1,503.0   \n",
       "4  23479392  2019-07-01 00:02:13  2019-07-01 00:22:26    6014      1,213.0   \n",
       "\n",
       "   from_station_id              from_station_name  to_station_id  \\\n",
       "0              117       Wilton Ave & Belmont Ave            497   \n",
       "1              381        Western Ave & Monroe St            203   \n",
       "2              313  Lakeview Ave & Fullerton Pkwy            144   \n",
       "3              313  Lakeview Ave & Fullerton Pkwy            144   \n",
       "4              168         Michigan Ave & 14th St             62   \n",
       "\n",
       "             to_station_name    usertype gender  birthyear   age  \n",
       "0  Kimball Ave & Belmont Ave  Subscriber   Male     1992.0  27.0  \n",
       "1      Western Ave & 21st St    Customer    NaN        NaN   NaN  \n",
       "2  Larrabee St & Webster Ave    Customer    NaN        NaN   NaN  \n",
       "3  Larrabee St & Webster Ave    Customer    NaN        NaN   NaN  \n",
       "4            McCormick Place    Customer    NaN        NaN   NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert birthyear to age\n",
    "divvy_df['age'] = (2019 - divvy_df['birthyear'])\n",
    "divvy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
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       "      <th>Age Group</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>23479388</td>\n",
       "      <td>2019-07-01 00:00:27</td>\n",
       "      <td>2019-07-01 00:20:41</td>\n",
       "      <td>3591</td>\n",
       "      <td>1,214.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>497</td>\n",
       "      <td>Kimball Ave &amp; Belmont Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23479389</td>\n",
       "      <td>2019-07-01 00:01:16</td>\n",
       "      <td>2019-07-01 00:18:44</td>\n",
       "      <td>5353</td>\n",
       "      <td>1,048.0</td>\n",
       "      <td>381</td>\n",
       "      <td>Western Ave &amp; Monroe St</td>\n",
       "      <td>203</td>\n",
       "      <td>Western Ave &amp; 21st St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23479390</td>\n",
       "      <td>2019-07-01 00:01:48</td>\n",
       "      <td>2019-07-01 00:27:42</td>\n",
       "      <td>6180</td>\n",
       "      <td>1,554.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23479391</td>\n",
       "      <td>2019-07-01 00:02:07</td>\n",
       "      <td>2019-07-01 00:27:10</td>\n",
       "      <td>5540</td>\n",
       "      <td>1,503.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23479392</td>\n",
       "      <td>2019-07-01 00:02:13</td>\n",
       "      <td>2019-07-01 00:22:26</td>\n",
       "      <td>6014</td>\n",
       "      <td>1,213.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trip_id           start_time             end_time  bikeid tripduration  \\\n",
       "0  23479388  2019-07-01 00:00:27  2019-07-01 00:20:41    3591      1,214.0   \n",
       "1  23479389  2019-07-01 00:01:16  2019-07-01 00:18:44    5353      1,048.0   \n",
       "2  23479390  2019-07-01 00:01:48  2019-07-01 00:27:42    6180      1,554.0   \n",
       "3  23479391  2019-07-01 00:02:07  2019-07-01 00:27:10    5540      1,503.0   \n",
       "4  23479392  2019-07-01 00:02:13  2019-07-01 00:22:26    6014      1,213.0   \n",
       "\n",
       "   from_station_id              from_station_name  to_station_id  \\\n",
       "0              117       Wilton Ave & Belmont Ave            497   \n",
       "1              381        Western Ave & Monroe St            203   \n",
       "2              313  Lakeview Ave & Fullerton Pkwy            144   \n",
       "3              313  Lakeview Ave & Fullerton Pkwy            144   \n",
       "4              168         Michigan Ave & 14th St             62   \n",
       "\n",
       "             to_station_name    usertype gender  birthyear   age Age Group  \n",
       "0  Kimball Ave & Belmont Ave  Subscriber   Male     1992.0  27.0     20-29  \n",
       "1      Western Ave & 21st St    Customer    NaN        NaN   NaN       NaN  \n",
       "2  Larrabee St & Webster Ave    Customer    NaN        NaN   NaN       NaN  \n",
       "3  Larrabee St & Webster Ave    Customer    NaN        NaN   NaN       NaN  \n",
       "4            McCormick Place    Customer    NaN        NaN   NaN       NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create bins for age\n",
    "bins = [10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "\n",
    "# Create the names for the five bins\n",
    "group_names = [\"11-19\", \"20-29\", \"30-39\", \"40-49\", \"50-59\", \"60-69\", \"70-79\", \"80-89\"]\n",
    "divvy_df[\"Age Group\"] = pd.cut(divvy_df[\"age\"], bins, labels=group_names, include_lowest=True)\n",
    "divvy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>time difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23479388</td>\n",
       "      <td>2019-07-01 00:00:27</td>\n",
       "      <td>2019-07-01 00:20:41</td>\n",
       "      <td>3591</td>\n",
       "      <td>1,214.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>497</td>\n",
       "      <td>Kimball Ave &amp; Belmont Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-29</td>\n",
       "      <td>20.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23479389</td>\n",
       "      <td>2019-07-01 00:01:16</td>\n",
       "      <td>2019-07-01 00:18:44</td>\n",
       "      <td>5353</td>\n",
       "      <td>1,048.0</td>\n",
       "      <td>381</td>\n",
       "      <td>Western Ave &amp; Monroe St</td>\n",
       "      <td>203</td>\n",
       "      <td>Western Ave &amp; 21st St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23479390</td>\n",
       "      <td>2019-07-01 00:01:48</td>\n",
       "      <td>2019-07-01 00:27:42</td>\n",
       "      <td>6180</td>\n",
       "      <td>1,554.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23479391</td>\n",
       "      <td>2019-07-01 00:02:07</td>\n",
       "      <td>2019-07-01 00:27:10</td>\n",
       "      <td>5540</td>\n",
       "      <td>1,503.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23479392</td>\n",
       "      <td>2019-07-01 00:02:13</td>\n",
       "      <td>2019-07-01 00:22:26</td>\n",
       "      <td>6014</td>\n",
       "      <td>1,213.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640713</th>\n",
       "      <td>25223635</td>\n",
       "      <td>2019-09-30 23:56:27</td>\n",
       "      <td>2019-10-01 00:06:20</td>\n",
       "      <td>5690</td>\n",
       "      <td>593.0</td>\n",
       "      <td>84</td>\n",
       "      <td>Milwaukee Ave &amp; Grand Ave</td>\n",
       "      <td>134</td>\n",
       "      <td>Peoria St &amp; Jackson Blvd</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>20-29</td>\n",
       "      <td>9.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640714</th>\n",
       "      <td>25223636</td>\n",
       "      <td>2019-09-30 23:57:02</td>\n",
       "      <td>2019-10-01 00:02:13</td>\n",
       "      <td>5079</td>\n",
       "      <td>310.0</td>\n",
       "      <td>268</td>\n",
       "      <td>Lake Shore Dr &amp; North Blvd</td>\n",
       "      <td>85</td>\n",
       "      <td>Michigan Ave &amp; Oak St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-29</td>\n",
       "      <td>5.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640715</th>\n",
       "      <td>25223637</td>\n",
       "      <td>2019-09-30 23:57:45</td>\n",
       "      <td>2019-10-01 00:01:41</td>\n",
       "      <td>5535</td>\n",
       "      <td>235.0</td>\n",
       "      <td>423</td>\n",
       "      <td>University Ave &amp; 57th St</td>\n",
       "      <td>426</td>\n",
       "      <td>Ellis Ave &amp; 60th St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Male</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11-19</td>\n",
       "      <td>3.933333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640716</th>\n",
       "      <td>25223638</td>\n",
       "      <td>2019-09-30 23:57:47</td>\n",
       "      <td>2019-10-01 00:04:41</td>\n",
       "      <td>6120</td>\n",
       "      <td>414.0</td>\n",
       "      <td>110</td>\n",
       "      <td>Dearborn St &amp; Erie St</td>\n",
       "      <td>31</td>\n",
       "      <td>Franklin St &amp; Chicago Ave (Temp)</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30-39</td>\n",
       "      <td>6.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640717</th>\n",
       "      <td>25223639</td>\n",
       "      <td>2019-09-30 23:59:37</td>\n",
       "      <td>2019-10-01 00:05:17</td>\n",
       "      <td>60</td>\n",
       "      <td>339.0</td>\n",
       "      <td>265</td>\n",
       "      <td>Cottage Grove Ave &amp; Oakwood Blvd</td>\n",
       "      <td>179</td>\n",
       "      <td>MLK Jr Dr &amp; Pershing Rd</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Female</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20-29</td>\n",
       "      <td>5.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1640718 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          trip_id           start_time             end_time  bikeid  \\\n",
       "0        23479388  2019-07-01 00:00:27  2019-07-01 00:20:41    3591   \n",
       "1        23479389  2019-07-01 00:01:16  2019-07-01 00:18:44    5353   \n",
       "2        23479390  2019-07-01 00:01:48  2019-07-01 00:27:42    6180   \n",
       "3        23479391  2019-07-01 00:02:07  2019-07-01 00:27:10    5540   \n",
       "4        23479392  2019-07-01 00:02:13  2019-07-01 00:22:26    6014   \n",
       "...           ...                  ...                  ...     ...   \n",
       "1640713  25223635  2019-09-30 23:56:27  2019-10-01 00:06:20    5690   \n",
       "1640714  25223636  2019-09-30 23:57:02  2019-10-01 00:02:13    5079   \n",
       "1640715  25223637  2019-09-30 23:57:45  2019-10-01 00:01:41    5535   \n",
       "1640716  25223638  2019-09-30 23:57:47  2019-10-01 00:04:41    6120   \n",
       "1640717  25223639  2019-09-30 23:59:37  2019-10-01 00:05:17      60   \n",
       "\n",
       "        tripduration  from_station_id                 from_station_name  \\\n",
       "0            1,214.0              117          Wilton Ave & Belmont Ave   \n",
       "1            1,048.0              381           Western Ave & Monroe St   \n",
       "2            1,554.0              313     Lakeview Ave & Fullerton Pkwy   \n",
       "3            1,503.0              313     Lakeview Ave & Fullerton Pkwy   \n",
       "4            1,213.0              168            Michigan Ave & 14th St   \n",
       "...              ...              ...                               ...   \n",
       "1640713        593.0               84         Milwaukee Ave & Grand Ave   \n",
       "1640714        310.0              268        Lake Shore Dr & North Blvd   \n",
       "1640715        235.0              423          University Ave & 57th St   \n",
       "1640716        414.0              110             Dearborn St & Erie St   \n",
       "1640717        339.0              265  Cottage Grove Ave & Oakwood Blvd   \n",
       "\n",
       "         to_station_id                   to_station_name    usertype  gender  \\\n",
       "0                  497         Kimball Ave & Belmont Ave  Subscriber    Male   \n",
       "1                  203             Western Ave & 21st St    Customer     NaN   \n",
       "2                  144         Larrabee St & Webster Ave    Customer     NaN   \n",
       "3                  144         Larrabee St & Webster Ave    Customer     NaN   \n",
       "4                   62                   McCormick Place    Customer     NaN   \n",
       "...                ...                               ...         ...     ...   \n",
       "1640713            134          Peoria St & Jackson Blvd  Subscriber    Male   \n",
       "1640714             85             Michigan Ave & Oak St  Subscriber    Male   \n",
       "1640715            426               Ellis Ave & 60th St    Customer    Male   \n",
       "1640716             31  Franklin St & Chicago Ave (Temp)  Subscriber    Male   \n",
       "1640717            179           MLK Jr Dr & Pershing Rd  Subscriber  Female   \n",
       "\n",
       "         birthyear   age Age Group  time difference  \n",
       "0           1992.0  27.0     20-29        20.233333  \n",
       "1              NaN   NaN       NaN        17.466667  \n",
       "2              NaN   NaN       NaN        25.900000  \n",
       "3              NaN   NaN       NaN        25.050000  \n",
       "4              NaN   NaN       NaN        20.216667  \n",
       "...            ...   ...       ...              ...  \n",
       "1640713     1993.0  26.0     20-29         9.883333  \n",
       "1640714     1992.0  27.0     20-29         5.183333  \n",
       "1640715     2000.0  19.0     11-19         3.933333  \n",
       "1640716     1986.0  33.0     30-39         6.900000  \n",
       "1640717     1996.0  23.0     20-29         5.666667  \n",
       "\n",
       "[1640718 rows x 15 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divvy_df['time difference'] = ((pd.to_datetime(divvy_df['end_time']) - \n",
    "                            pd.to_datetime(divvy_df['start_time']))\n",
    "                                .dt.total_seconds() / 60)\n",
    "divvy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert date columns to datetime format\n",
    "divvy_df['start_time'] = pd.to_datetime(divvy_df['start_time'])\n",
    "divvy_df['end_time'] = pd.to_datetime(divvy_df['end_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>time difference</th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23479388</td>\n",
       "      <td>00:00:27</td>\n",
       "      <td>00:20:41</td>\n",
       "      <td>3591</td>\n",
       "      <td>1,214.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>497</td>\n",
       "      <td>Kimball Ave &amp; Belmont Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-29</td>\n",
       "      <td>20.233333</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23479389</td>\n",
       "      <td>00:01:16</td>\n",
       "      <td>00:18:44</td>\n",
       "      <td>5353</td>\n",
       "      <td>1,048.0</td>\n",
       "      <td>381</td>\n",
       "      <td>Western Ave &amp; Monroe St</td>\n",
       "      <td>203</td>\n",
       "      <td>Western Ave &amp; 21st St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.466667</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23479390</td>\n",
       "      <td>00:01:48</td>\n",
       "      <td>00:27:42</td>\n",
       "      <td>6180</td>\n",
       "      <td>1,554.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23479391</td>\n",
       "      <td>00:02:07</td>\n",
       "      <td>00:27:10</td>\n",
       "      <td>5540</td>\n",
       "      <td>1,503.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.050000</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23479392</td>\n",
       "      <td>00:02:13</td>\n",
       "      <td>00:22:26</td>\n",
       "      <td>6014</td>\n",
       "      <td>1,213.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.216667</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2019-07-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trip_id start_time  end_time  bikeid tripduration  from_station_id  \\\n",
       "0  23479388   00:00:27  00:20:41    3591      1,214.0              117   \n",
       "1  23479389   00:01:16  00:18:44    5353      1,048.0              381   \n",
       "2  23479390   00:01:48  00:27:42    6180      1,554.0              313   \n",
       "3  23479391   00:02:07  00:27:10    5540      1,503.0              313   \n",
       "4  23479392   00:02:13  00:22:26    6014      1,213.0              168   \n",
       "\n",
       "               from_station_name  to_station_id            to_station_name  \\\n",
       "0       Wilton Ave & Belmont Ave            497  Kimball Ave & Belmont Ave   \n",
       "1        Western Ave & Monroe St            203      Western Ave & 21st St   \n",
       "2  Lakeview Ave & Fullerton Pkwy            144  Larrabee St & Webster Ave   \n",
       "3  Lakeview Ave & Fullerton Pkwy            144  Larrabee St & Webster Ave   \n",
       "4         Michigan Ave & 14th St             62            McCormick Place   \n",
       "\n",
       "     usertype gender  birthyear   age Age Group  time difference  start_date  \\\n",
       "0  Subscriber   Male     1992.0  27.0     20-29        20.233333  2019-07-01   \n",
       "1    Customer    NaN        NaN   NaN       NaN        17.466667  2019-07-01   \n",
       "2    Customer    NaN        NaN   NaN       NaN        25.900000  2019-07-01   \n",
       "3    Customer    NaN        NaN   NaN       NaN        25.050000  2019-07-01   \n",
       "4    Customer    NaN        NaN   NaN       NaN        20.216667  2019-07-01   \n",
       "\n",
       "     end_date  \n",
       "0  2019-07-01  \n",
       "1  2019-07-01  \n",
       "2  2019-07-01  \n",
       "3  2019-07-01  \n",
       "4  2019-07-01  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split date from time and create columns\n",
    "divvy_df['start_date'] = divvy_df['start_time'].dt.date\n",
    "divvy_df['start_time'] = divvy_df['start_time'].dt.time\n",
    "divvy_df['end_date'] = divvy_df['end_time'].dt.date\n",
    "divvy_df['end_time'] = divvy_df['end_time'].dt.time\n",
    "divvy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>trip_id</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_time</th>\n",
       "      <th>time difference</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_time</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23479388</td>\n",
       "      <td>3591</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:00:27</td>\n",
       "      <td>20.233333</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:20:41</td>\n",
       "      <td>1,214.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>497</td>\n",
       "      <td>Kimball Ave &amp; Belmont Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23479389</td>\n",
       "      <td>5353</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:01:16</td>\n",
       "      <td>17.466667</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:18:44</td>\n",
       "      <td>1,048.0</td>\n",
       "      <td>381</td>\n",
       "      <td>Western Ave &amp; Monroe St</td>\n",
       "      <td>203</td>\n",
       "      <td>Western Ave &amp; 21st St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23479390</td>\n",
       "      <td>6180</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:01:48</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:27:42</td>\n",
       "      <td>1,554.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23479391</td>\n",
       "      <td>5540</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:07</td>\n",
       "      <td>25.050000</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:27:10</td>\n",
       "      <td>1,503.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23479392</td>\n",
       "      <td>6014</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:13</td>\n",
       "      <td>20.216667</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:22:26</td>\n",
       "      <td>1,213.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23479393</td>\n",
       "      <td>4941</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:21</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:07:31</td>\n",
       "      <td>310.0</td>\n",
       "      <td>300</td>\n",
       "      <td>Broadway &amp; Barry Ave</td>\n",
       "      <td>232</td>\n",
       "      <td>Pine Grove Ave &amp; Waveland Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1990.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23479394</td>\n",
       "      <td>3770</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:24</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:23:12</td>\n",
       "      <td>1,248.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23479395</td>\n",
       "      <td>5442</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:26</td>\n",
       "      <td>25.833333</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:28:16</td>\n",
       "      <td>1,550.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23479396</td>\n",
       "      <td>2957</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:34</td>\n",
       "      <td>26.383333</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:28:57</td>\n",
       "      <td>1,583.0</td>\n",
       "      <td>43</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>195</td>\n",
       "      <td>Columbus Dr &amp; Randolph St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23479397</td>\n",
       "      <td>6091</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:45</td>\n",
       "      <td>26.483333</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:29:14</td>\n",
       "      <td>1,589.0</td>\n",
       "      <td>43</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>195</td>\n",
       "      <td>Columbus Dr &amp; Randolph St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    trip_id  bikeid  start_date start_time  time difference    end_date  \\\n",
       "0  23479388    3591  2019-07-01   00:00:27        20.233333  2019-07-01   \n",
       "1  23479389    5353  2019-07-01   00:01:16        17.466667  2019-07-01   \n",
       "2  23479390    6180  2019-07-01   00:01:48        25.900000  2019-07-01   \n",
       "3  23479391    5540  2019-07-01   00:02:07        25.050000  2019-07-01   \n",
       "4  23479392    6014  2019-07-01   00:02:13        20.216667  2019-07-01   \n",
       "5  23479393    4941  2019-07-01   00:02:21         5.166667  2019-07-01   \n",
       "6  23479394    3770  2019-07-01   00:02:24        20.800000  2019-07-01   \n",
       "7  23479395    5442  2019-07-01   00:02:26        25.833333  2019-07-01   \n",
       "8  23479396    2957  2019-07-01   00:02:34        26.383333  2019-07-01   \n",
       "9  23479397    6091  2019-07-01   00:02:45        26.483333  2019-07-01   \n",
       "\n",
       "   end_time tripduration  from_station_id              from_station_name  \\\n",
       "0  00:20:41      1,214.0              117       Wilton Ave & Belmont Ave   \n",
       "1  00:18:44      1,048.0              381        Western Ave & Monroe St   \n",
       "2  00:27:42      1,554.0              313  Lakeview Ave & Fullerton Pkwy   \n",
       "3  00:27:10      1,503.0              313  Lakeview Ave & Fullerton Pkwy   \n",
       "4  00:22:26      1,213.0              168         Michigan Ave & 14th St   \n",
       "5  00:07:31        310.0              300           Broadway & Barry Ave   \n",
       "6  00:23:12      1,248.0              168         Michigan Ave & 14th St   \n",
       "7  00:28:16      1,550.0              313  Lakeview Ave & Fullerton Pkwy   \n",
       "8  00:28:57      1,583.0               43   Michigan Ave & Washington St   \n",
       "9  00:29:14      1,589.0               43   Michigan Ave & Washington St   \n",
       "\n",
       "   to_station_id                to_station_name    usertype gender  birthyear  \\\n",
       "0            497      Kimball Ave & Belmont Ave  Subscriber   Male     1992.0   \n",
       "1            203          Western Ave & 21st St    Customer    NaN        NaN   \n",
       "2            144      Larrabee St & Webster Ave    Customer    NaN        NaN   \n",
       "3            144      Larrabee St & Webster Ave    Customer    NaN        NaN   \n",
       "4             62                McCormick Place    Customer    NaN        NaN   \n",
       "5            232  Pine Grove Ave & Waveland Ave  Subscriber   Male     1990.0   \n",
       "6             62                McCormick Place    Customer    NaN        NaN   \n",
       "7            144      Larrabee St & Webster Ave    Customer    NaN        NaN   \n",
       "8            195      Columbus Dr & Randolph St    Customer    NaN        NaN   \n",
       "9            195      Columbus Dr & Randolph St    Customer    NaN        NaN   \n",
       "\n",
       "    age Age Group  \n",
       "0  27.0     20-29  \n",
       "1   NaN       NaN  \n",
       "2   NaN       NaN  \n",
       "3   NaN       NaN  \n",
       "4   NaN       NaN  \n",
       "5  29.0     20-29  \n",
       "6   NaN       NaN  \n",
       "7   NaN       NaN  \n",
       "8   NaN       NaN  \n",
       "9   NaN       NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Organize columns in dataframe\n",
    "divvy_df = divvy_df[[\"trip_id\", \"bikeid\", \"start_date\", \"start_time\", \"time difference\", \"end_date\", \"end_time\", \"tripduration\", \"from_station_id\", \"from_station_name\", \"to_station_id\", \"to_station_name\", \"usertype\", \"gender\", \"birthyear\", \"age\", \"Age Group\"]]\n",
    "divvy_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bike Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exploring the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5787"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of unique bikes in circulation\n",
    "divvy_df['bikeid'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5724    535\n",
       "3115    532\n",
       "5344    531\n",
       "3088    528\n",
       "717     521\n",
       "       ... \n",
       "1827      1\n",
       "1806      1\n",
       "3089      1\n",
       "2705      1\n",
       "3299      1\n",
       "Name: bikeid, Length: 5787, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bike IDs with most activity\n",
    "divvy_df['bikeid'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Trip Duration Statistics\n",
      "Max: 150943.9\n",
      "Min: 1.0166666666666668\n",
      "Mean: 29.036642575588115\n",
      "Median: 13.55\n",
      "Mode: 0    5.716667\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Identify min / max duration of rides\n",
    "max_duration = divvy_df['time difference'].max()\n",
    "min_duration = divvy_df['time difference'].min()\n",
    "mean = divvy_df['time difference'].mean()\n",
    "median = divvy_df['time difference'].median()\n",
    "mode = divvy_df['time difference'].mode()\n",
    "\n",
    "# Print Summary Trip Duration Stats\n",
    "print(f'Summary Trip Duration Statistics')\n",
    "print(f'Max: {max_duration}')\n",
    "print(f'Min: {min_duration}')\n",
    "print(f'Mean: {mean}')\n",
    "print(f'Median: {median}')\n",
    "print(f'Mode: {mode}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Trip Duration**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few charts explore the trip duration of bikes. In the first scatterplot and boxplot, it's clear that most of the data is clustered to the left with some significant outliers. The next two charts reduce the data to rides under or equal to 100 minutes, showing how dense the datapoints are in the left section of the first two charts. These visualization help us determine that the median is the most useful number for getting a summary of most of the data. \n",
    "\n",
    "**Summary Statistics**\n",
    "* Mean - 29.03 minutes\n",
    "* Median - 13.55 minutes\n",
    "* Mode - 5.72 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Trip Duration for Whole Dataset\n",
    "plt.figure(figsize=(13,10))\n",
    "Trip_Duration_All_data = plt.scatter(divvy_df['time difference'], divvy_df['bikeid'], color=\"deepskyblue\", alpha = .3, edgecolors='deepskyblue')\n",
    "plt.title(\"Trip Duration for Whole Dataset\")\n",
    "plt.xlabel(\"Trip Duration (Minutes)\")\n",
    "plt.ylabel(\"Bike ID\")\n",
    "plt.savefig('Images/Trip_Duration_Scatter_All.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Trip Duration Data Points\n",
    "plt.figure(figsize=(13,10))\n",
    "plt.boxplot(divvy_df['time difference'], vert=False)\n",
    "plt.title(\"Trip Duration for Whole Dataset\")\n",
    "plt.xlabel(\"Trip Duration (Minutes)\")\n",
    "plt.grid(True, alpha=.5)\n",
    "plt.savefig('Images/Trip_Duration_Box_All.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>trip_id</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_time</th>\n",
       "      <th>time difference</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_time</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23479388</td>\n",
       "      <td>3591</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:00:27</td>\n",
       "      <td>20.233333</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:20:41</td>\n",
       "      <td>1,214.0</td>\n",
       "      <td>117</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>497</td>\n",
       "      <td>Kimball Ave &amp; Belmont Ave</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23479389</td>\n",
       "      <td>5353</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:01:16</td>\n",
       "      <td>17.466667</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:18:44</td>\n",
       "      <td>1,048.0</td>\n",
       "      <td>381</td>\n",
       "      <td>Western Ave &amp; Monroe St</td>\n",
       "      <td>203</td>\n",
       "      <td>Western Ave &amp; 21st St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23479390</td>\n",
       "      <td>6180</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:01:48</td>\n",
       "      <td>25.900000</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:27:42</td>\n",
       "      <td>1,554.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23479391</td>\n",
       "      <td>5540</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:07</td>\n",
       "      <td>25.050000</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:27:10</td>\n",
       "      <td>1,503.0</td>\n",
       "      <td>313</td>\n",
       "      <td>Lakeview Ave &amp; Fullerton Pkwy</td>\n",
       "      <td>144</td>\n",
       "      <td>Larrabee St &amp; Webster Ave</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23479392</td>\n",
       "      <td>6014</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:02:13</td>\n",
       "      <td>20.216667</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>00:22:26</td>\n",
       "      <td>1,213.0</td>\n",
       "      <td>168</td>\n",
       "      <td>Michigan Ave &amp; 14th St</td>\n",
       "      <td>62</td>\n",
       "      <td>McCormick Place</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640713</th>\n",
       "      <td>25223635</td>\n",
       "      <td>5690</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>23:56:27</td>\n",
       "      <td>9.883333</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>00:06:20</td>\n",
       "      <td>593.0</td>\n",
       "      <td>84</td>\n",
       "      <td>Milwaukee Ave &amp; Grand Ave</td>\n",
       "      <td>134</td>\n",
       "      <td>Peoria St &amp; Jackson Blvd</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640714</th>\n",
       "      <td>25223636</td>\n",
       "      <td>5079</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>23:57:02</td>\n",
       "      <td>5.183333</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>00:02:13</td>\n",
       "      <td>310.0</td>\n",
       "      <td>268</td>\n",
       "      <td>Lake Shore Dr &amp; North Blvd</td>\n",
       "      <td>85</td>\n",
       "      <td>Michigan Ave &amp; Oak St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640715</th>\n",
       "      <td>25223637</td>\n",
       "      <td>5535</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>23:57:45</td>\n",
       "      <td>3.933333</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>00:01:41</td>\n",
       "      <td>235.0</td>\n",
       "      <td>423</td>\n",
       "      <td>University Ave &amp; 57th St</td>\n",
       "      <td>426</td>\n",
       "      <td>Ellis Ave &amp; 60th St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Male</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>11-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640716</th>\n",
       "      <td>25223638</td>\n",
       "      <td>6120</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>23:57:47</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>00:04:41</td>\n",
       "      <td>414.0</td>\n",
       "      <td>110</td>\n",
       "      <td>Dearborn St &amp; Erie St</td>\n",
       "      <td>31</td>\n",
       "      <td>Franklin St &amp; Chicago Ave (Temp)</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1640717</th>\n",
       "      <td>25223639</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>23:59:37</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>00:05:17</td>\n",
       "      <td>339.0</td>\n",
       "      <td>265</td>\n",
       "      <td>Cottage Grove Ave &amp; Oakwood Blvd</td>\n",
       "      <td>179</td>\n",
       "      <td>MLK Jr Dr &amp; Pershing Rd</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Female</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1604278 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          trip_id  bikeid  start_date start_time  time difference    end_date  \\\n",
       "0        23479388    3591  2019-07-01   00:00:27        20.233333  2019-07-01   \n",
       "1        23479389    5353  2019-07-01   00:01:16        17.466667  2019-07-01   \n",
       "2        23479390    6180  2019-07-01   00:01:48        25.900000  2019-07-01   \n",
       "3        23479391    5540  2019-07-01   00:02:07        25.050000  2019-07-01   \n",
       "4        23479392    6014  2019-07-01   00:02:13        20.216667  2019-07-01   \n",
       "...           ...     ...         ...        ...              ...         ...   \n",
       "1640713  25223635    5690  2019-09-30   23:56:27         9.883333  2019-10-01   \n",
       "1640714  25223636    5079  2019-09-30   23:57:02         5.183333  2019-10-01   \n",
       "1640715  25223637    5535  2019-09-30   23:57:45         3.933333  2019-10-01   \n",
       "1640716  25223638    6120  2019-09-30   23:57:47         6.900000  2019-10-01   \n",
       "1640717  25223639      60  2019-09-30   23:59:37         5.666667  2019-10-01   \n",
       "\n",
       "         end_time tripduration  from_station_id  \\\n",
       "0        00:20:41      1,214.0              117   \n",
       "1        00:18:44      1,048.0              381   \n",
       "2        00:27:42      1,554.0              313   \n",
       "3        00:27:10      1,503.0              313   \n",
       "4        00:22:26      1,213.0              168   \n",
       "...           ...          ...              ...   \n",
       "1640713  00:06:20        593.0               84   \n",
       "1640714  00:02:13        310.0              268   \n",
       "1640715  00:01:41        235.0              423   \n",
       "1640716  00:04:41        414.0              110   \n",
       "1640717  00:05:17        339.0              265   \n",
       "\n",
       "                        from_station_name  to_station_id  \\\n",
       "0                Wilton Ave & Belmont Ave            497   \n",
       "1                 Western Ave & Monroe St            203   \n",
       "2           Lakeview Ave & Fullerton Pkwy            144   \n",
       "3           Lakeview Ave & Fullerton Pkwy            144   \n",
       "4                  Michigan Ave & 14th St             62   \n",
       "...                                   ...            ...   \n",
       "1640713         Milwaukee Ave & Grand Ave            134   \n",
       "1640714        Lake Shore Dr & North Blvd             85   \n",
       "1640715          University Ave & 57th St            426   \n",
       "1640716             Dearborn St & Erie St             31   \n",
       "1640717  Cottage Grove Ave & Oakwood Blvd            179   \n",
       "\n",
       "                          to_station_name    usertype  gender  birthyear  \\\n",
       "0               Kimball Ave & Belmont Ave  Subscriber    Male     1992.0   \n",
       "1                   Western Ave & 21st St    Customer     NaN        NaN   \n",
       "2               Larrabee St & Webster Ave    Customer     NaN        NaN   \n",
       "3               Larrabee St & Webster Ave    Customer     NaN        NaN   \n",
       "4                         McCormick Place    Customer     NaN        NaN   \n",
       "...                                   ...         ...     ...        ...   \n",
       "1640713          Peoria St & Jackson Blvd  Subscriber    Male     1993.0   \n",
       "1640714             Michigan Ave & Oak St  Subscriber    Male     1992.0   \n",
       "1640715               Ellis Ave & 60th St    Customer    Male     2000.0   \n",
       "1640716  Franklin St & Chicago Ave (Temp)  Subscriber    Male     1986.0   \n",
       "1640717           MLK Jr Dr & Pershing Rd  Subscriber  Female     1996.0   \n",
       "\n",
       "          age Age Group  \n",
       "0        27.0     20-29  \n",
       "1         NaN       NaN  \n",
       "2         NaN       NaN  \n",
       "3         NaN       NaN  \n",
       "4         NaN       NaN  \n",
       "...       ...       ...  \n",
       "1640713  26.0     20-29  \n",
       "1640714  27.0     20-29  \n",
       "1640715  19.0     11-19  \n",
       "1640716  33.0     30-39  \n",
       "1640717  23.0     20-29  \n",
       "\n",
       "[1604278 rows x 17 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reduce dataset to only rides less than or equal to 100 minutes\n",
    "abridged_duration = divvy_df.loc[divvy_df['time difference']<=100]\n",
    "abridged_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Trip Duration by Bike ID\n",
    "plt.figure(figsize=(13,10))\n",
    "plt.scatter(abridged_duration['time difference'], abridged_duration['bikeid'], color=\"deepskyblue\", alpha = .3, edgecolors='deepskyblue')\n",
    "plt.title(\"Trip Duration for Trips Less Than or Equal to 100 Minutes\")\n",
    "plt.xlabel(\"Trip Duration (Minutes)\")\n",
    "plt.ylabel(\"Bike ID\")\n",
    "plt.savefig('Images/Trip_Duration_Scatter_Under_100.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgmNP2ewAsrn3ta+e8887b72FcTD/6mqnHfHq/h3EgnH766Tn33HP3exgAAElE/IFx3nnnpbv3exgXd9ZXHbwxHcXhw4dz6NChE77eqjrh6wQAOF4OpwEAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGCYy2zEV9V+DwHguPk/DIAjucxGPAAAXFaJeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYJjTjnaBqnpyknsm+Wh3f+OlPySAU1dV7fg9APunu/d7CF9hL3vin5Lkuy/lcQCc8kQ7wMF0EP9/PmrEd/fLkpx7EsYCAADswVEPp9mrqvrxJD+eJGeeeWYOHz58ola9q0984hNHG9OlPoYT6WTcZ8fiUA7emI7maHPikpg2nwCAE+egNdEJi/ju/t0kv5skt7vd7frQoUMnatVHdKTtHMTjl3ZTVUe8LfvlII7paC6tMU+aT1zk8OHDY+axJ4oAB9dB+13i02kAAGAYEQ9wQHi1B+BgOoj/P+/lIyafmeTOSc6oqnOSPLq7/8ulPTCAU9HWL4qqOpC/NC4rJh1mxclhTrDdQZ8TR4347r7vyRgIAACwNw6nAQCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhrnMRnx37/cQAI6b/8MAOJLLbMQDAMBllYgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADHPafg+Ai1TVfg/hYvrR1zxwY9ovp59++n4PAQDgH4j4A6K793sIO+qz9nsEx+bw4cM5dOjQfg8DAOBS5XAaAAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGFEPAAADCPiAQBgGBEPAADDiHgAABhGxAMAwDAiHgAAhhHxAAAwjIgHAIBhRDwAAAwj4gEAYBgRDwAAw4h4AAAYRsQDAMAwIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBAGAYEQ8AAMOIeAAAGEbEAwDAMCIeAACGEfEAADCMiAcAgGGqu0/8Sqs+luR9J3zFX+mMJB8/CdthDnOC7cwJdmJesJ05wXYHYU7cqLuvs9MZl0rEnyxV9Zruvt1+j4ODw5xgO3OCnZgXbGdOsN1BnxMOpwEAgGFEPAAADDM94n93vwfAgWNOsJ05wU7MC7YzJ9juQM+J0cfEAwDAqWj6nngAADjliHgAABhmZMRX1XdX1dur6l1V9cj9Hg8nX1XdoKpeUlVvraq3VNW/Xpdfu6r+Z1W9c/339P0eKydXVV2+ql5fVX+6njYnTnFVda2qek5VvW39P+OO5sWprap+Zv3d8eaqemZVXdmcOPVU1ZOr6qNV9eaNZbvOg6r6ubU9315V37U/o77IuIivqssneXySuye5ZZL7VtUt93dU7IMvJnlYd39Dkjsk+al1Hjwyyf/q7psn+V/raU4t/zrJWzdOmxM8LsmfdffXJ7l1lvlhXpyiqurMJA9Jcrvu/sYkl09yn5gTp6KnJPnubct2nAdrY9wnya3W6zxhbdJ9My7ik3xzknd193u6+8Ikz0py730eEydZd3+ou1+3fv+ZLL+Uz8wyF566XuypSb5vXwbIvqiq6ye5R5InbSw2J05hVXXNJN+Z5L8kSXdf2N2fjHlxqjstyVWq6rQkV03ywZgTp5zuflmSc7ct3m0e3DvJs7r789393iTvytKk+2ZixJ+Z5OyN0+esyzhFVdWhJLdN8qok1+3uDyVL6Cf5mn0cGiffbyZ5RJIvbywzJ05tN0nysSS/vx5m9aSqulrMi1NWd38gyWOTvD/Jh5J8qrtfHHOCxW7z4MD158SIrx2W+ZzMU1RVXT3JHyZ5aHd/er/Hw/6pqnsm+Wh3v3a/x8KBclqSb0ryO9192yQXxGESp7T1GOd7J7lxkusluVpV3X9/R8UAB64/J0b8OUlusHH6+lleBuMUU1VXyBLwz+ju566LP1JVX7ue/7VJPrpf4+Ok+7Yk96qqw1kOs/tnVfX0mBOnunOSnNPdr1pPPydL1JsXp667JXlvd3+su7+Q5LlJvjXmBIvd5sGB68+JEf83SW5eVTeuqitmeZPB8/d5TJxkVVVZjnF9a3f/xsZZz0/ywPX7Byb545M9NvZHd/9cd1+/uw9l+X/hL7r7/jEnTmnd/eEkZ1fV162L7prk72JenMren+QOVXXV9XfJXbO8r8qcINl9Hjw/yX2q6kpVdeMkN0/y6n0Y3z8Y+Rdbq+p7shz7evkkT+7uX9nfEXGyVdW3J3l5kjflouOffz7LcfHPTnLDLP9R/4vu3v6mFS7jqurOSR7e3fesqq+OOXFKq6rbZHmz8xWTvCfJv8qyE8u8OEVV1WOS/FCWTzp7fZIfTXL1mBOnlKp6ZpI7JzkjyUeSPDrJ87LLPKiqRyX5kSzz5qHd/aKTP+qLjIx4AAA4lU08nAYAAE5pIh4AAIYR8QAAMIyIBwCAYUQ8AAAMI+IBtqmqr66qN6xfH66qD2ycvuJ6mXtV1Z7/8mdVHaqqz1XV66vqrVX16qp64NGveUzjfmhVXXXj9Aur6loncN0PWL9/SlV9tqqusXH+46qqq+qM9fRfX4Jt3bmqvvU4r3udqvqz4902wBSn7fcAAA6a7v5EktskSVWdleT87n7s1vlVdVp3Pz/H/ofm3t3dt13XcZMkz62qy3X37+/lyusfpqnu/vIuF3lokqcn+ex6O77nGMe323ZPy/LZyN+0sfhdWf50/dOr6nJJ7pLkA1tndvdxRfjqzknOT3LMTwS6+2NV9aGq+rbu/qtLMAaAA82eeIA9WPc+/0ZVvSTJr1fVg6rqtzfOe2JVvbyq3lFV9zza+rr7PUl+NslD1nWcVVUP39jem9e994fWPfdPSPK6JDeoqt+pqtdU1VvWP1qTqnpIkusleck6xlTV4Y094z+7rvPNVfXQddnWun9vXdeLq+oqOwz3nyV5XXd/cWPZM7P8sZxkie6/yvIHULbGf/76752r6qVV9ZyqeltVPWN9MrJ9fLdbL3coyYOT/Mz6ysd3rHvX/7Cq/mb9+rb1OnfaeIXk9RuvDDwvyf2O9hgATCbiAfbuFknu1t0P2+G8Q0nulOQeSZ5YVVfew/pel+Tr93C5r0vyX7v7tt39viSP6u7bJfknSe5UVf+ku/9Tkg8muUt332XzylX1T7P8ldJvSXKHJD9WVbddz755ksd3962SfDLJP99h+9+W5LXblr0zyXWq6vQk903yrCOM/7ZZXiW4ZZKbrOvbUXcfTvLEJP+xu2/T3S9P8rj19O3X8T1pvfjDk/xUd98myXck+dy6/DXraYDLLBEPsHf/vbu/tMt5z+7uL3f3O5O8J3uL89rjdt/X3a/cOP2DVfW6LH8u/lZZ4vhIvj3JH3X3Bd19fpLn5qLIfW93v2H9/rVZnoxs97VJPrbD8ucmuU+WJwcvP8L2X93d56yHAb1hl20cyd2S/HZVvSHLIUzXXPe6/1WS31hfhbjWxisFH83yqgTAZZZj4gH27oIjnNdHOb2T2yZ56/r9F3PxHSube/L/YbtVdeMse6Bv393nVdVTtl12J0d6svD5je+/lGSnw2k+t8s2npXl1YSndveX16Nk9rKNrd89m7f5SLfhcknu2N2f27b816rqBUm+J8krq+pu3f22dV3bLwtwmWJPPMCJ8S+q6nJVddMsh4y8/UgXXo/9fmyS31oXHc76xtGq+qYkN97lqtfMEvWfqqrrJrn7xnmfSXKNHa7zsiTfV1VXraqrJfn+HHnP+XZvTXKz7Qu7+/1JHpXkCcewrk2Hk/zT9fvNw3i2344XJ/nprRNVdZv135t295u6+9ezHEKz9erHLZK8+TjHBDCCiAc4Md6e5C+TvCjJg7v773e4zE23PmIyybOT/NbGJ9P8YZJrr4eM/ESSd+y0ke5+Y5bDaN6S5MlZDinZ8rtJXrT1xtaN67wuyVOSvDrJq5I8qbtffwy37UVJvnOX8fzn7n73Maxr02OSPK6qXp5lD/2WP0ny/VtvbM3y5t/bVdXfVtXfZXnja5I8dH2j7huz7Hl/0br8LklecJxjAhihuvfyii8Au1kPafnT7n7Ofo/l0lJVf5TkEesx/wdaVb0syb27+7z9HgvApcWeeAD24pFZ3uB6oFXVdZL8hoAHLuvsiQcAgGHsiQcAgGFEPAAADCPiAQBgGBEPAADDiHgAABjm/wcVfH8bAEwCzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,10))\n",
    "plt.boxplot(abridged_duration['time difference'], vert=False)\n",
    "plt.title(\"Trip Duration for Trips Less Than or Equal to 100 Minutes\")\n",
    "plt.xlabel(\"Trip Duration (Minutes)\")\n",
    "plt.grid(True, alpha=.5)\n",
    "plt.savefig('Images/Trip_Duration_Box_Under_100.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TTEST\n",
    "def gendata(loc=0):\n",
    "    duration_sample = abridged_duration['time difference']\n",
    "    duration_all = divvy_df['time difference']\n",
    "    \n",
    "    # Scatter Plot of Data\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.scatter(range(len(duration_all)), duration_all, label=\"trip duration\")\n",
    "    plt.scatter(range(len(duration_sample)), duration_sample, label=\"sample duration\")\n",
    "    plt.legend()\n",
    "\n",
    "    # Histogram Plot of Data\n",
    "    plt.subplot(2, 1, 2)\n",
    "    plt.hist(duration_all, 20, density=True, alpha=0.7, label=\"trip duration\")\n",
    "    plt.hist(duration_sample, 20, density=True, alpha=0.7, label=\"sample duration\")\n",
    "    plt.axvline(duration_all.mean(), color='k', linestyle='dashed', linewidth=1)\n",
    "    plt.axvline(duration_sample.mean(), color='k', linestyle='dashed', linewidth=1)\n",
    "    plt.legend()  \n",
    "    \n",
    "    return duration_all, duration_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "duration_all, duration_sample = gendata()\n",
    "plt.savefig('Images/TTest.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=-874.803596673298, pvalue=0.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_1samp(duration_sample, duration_all.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5724    535\n",
       "3115    532\n",
       "5344    531\n",
       "3088    528\n",
       "717     521\n",
       "5634    520\n",
       "6079    519\n",
       "4638    518\n",
       "320     517\n",
       "3770    516\n",
       "6425    515\n",
       "6198    514\n",
       "4877    513\n",
       "522     509\n",
       "749     507\n",
       "5501    506\n",
       "3333    506\n",
       "682     505\n",
       "4781    505\n",
       "4020    505\n",
       "Name: bikeid, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_bikes = divvy_df['bikeid'].value_counts().head(20)\n",
    "popular_bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rides for top 20 bikes\n",
    "divvy_df['bikeid'] = divvy_df['bikeid'].astype(str)\n",
    "divvy_df['bikeid'].dtypes\n",
    "plt.figure(figsize=(13,7))\n",
    "plt.bar(divvy_df['bikeid'].head(20), popular_bikes, color='deepskyblue')\n",
    "plt.title(\"Number of Rides for The Top 20 Bikes\")\n",
    "plt.xlabel(\"Bike ID\")\n",
    "plt.ylabel(\"Number of Rides\")\n",
    "plt.grid(True, alpha=.5)\n",
    "plt.savefig('Images/Rides_per_Top_20_Bikes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5724    535\n",
       "3115    532\n",
       "5344    531\n",
       "3088    528\n",
       "717     521\n",
       "5634    520\n",
       "6079    519\n",
       "4638    518\n",
       "320     517\n",
       "3770    516\n",
       "6425    515\n",
       "6198    514\n",
       "4877    513\n",
       "522     509\n",
       "749     507\n",
       "3333    506\n",
       "5501    506\n",
       "4781    505\n",
       "682     505\n",
       "4020    505\n",
       "Name: bikeid, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find 20 most used bike IDs\n",
    "popular_bikes = divvy_df['bikeid'].value_counts().head(20)\n",
    "popular_bikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert bike ID to string\n",
    "divvy_df['bikeid'] = divvy_df['bikeid'].astype(str)\n",
    "divvy_df['bikeid'].dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Popular Stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>total trips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Streeter Dr &amp; Grand Ave</th>\n",
       "      <td>39726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Shore Dr &amp; Monroe St</th>\n",
       "      <td>26309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lake Shore Dr &amp; North Blvd</th>\n",
       "      <td>21845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan Ave &amp; Oak St</th>\n",
       "      <td>20300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canal St &amp; Adams St</th>\n",
       "      <td>20176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton St &amp; Madison St</th>\n",
       "      <td>19019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Theater on the Lake</th>\n",
       "      <td>18491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Millennium Park</th>\n",
       "      <td>18337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clinton St &amp; Washington Blvd</th>\n",
       "      <td>16854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan Ave &amp; Washington St</th>\n",
       "      <td>16073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Columbus Dr &amp; Randolph St</th>\n",
       "      <td>14766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daley Center Plaza</th>\n",
       "      <td>14653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Franklin St &amp; Monroe St</th>\n",
       "      <td>13524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shedd Aquarium</th>\n",
       "      <td>13045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kingsbury St &amp; Kinzie St</th>\n",
       "      <td>12439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clark St &amp; Elm St</th>\n",
       "      <td>11310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wabash Ave &amp; Grand Ave</th>\n",
       "      <td>10701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canal St &amp; Madison St</th>\n",
       "      <td>10597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clark St &amp; Armitage Ave</th>\n",
       "      <td>10301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wells St &amp; Concord Ln</th>\n",
       "      <td>10185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wells St &amp; Huron St</th>\n",
       "      <td>10048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              total trips\n",
       "Streeter Dr & Grand Ave             39726\n",
       "Lake Shore Dr & Monroe St           26309\n",
       "Lake Shore Dr & North Blvd          21845\n",
       "Michigan Ave & Oak St               20300\n",
       "Canal St & Adams St                 20176\n",
       "Clinton St & Madison St             19019\n",
       "Theater on the Lake                 18491\n",
       "Millennium Park                     18337\n",
       "Clinton St & Washington Blvd        16854\n",
       "Michigan Ave & Washington St        16073\n",
       "Columbus Dr & Randolph St           14766\n",
       "Daley Center Plaza                  14653\n",
       "Franklin St & Monroe St             13524\n",
       "Shedd Aquarium                      13045\n",
       "Kingsbury St & Kinzie St            12439\n",
       "Clark St & Elm St                   11310\n",
       "Wabash Ave & Grand Ave              10701\n",
       "Canal St & Madison St               10597\n",
       "Clark St & Armitage Ave             10301\n",
       "Wells St & Concord Ln               10185\n",
       "Wells St & Huron St                 10048"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the most popular starting stations\n",
    "top_routes = divvy_df['from_station_name'].value_counts()\n",
    "total_trips_df = pd.DataFrame({\n",
    "    'total trips': top_routes\n",
    "})\n",
    "\n",
    "# Reduce the results to any stations with trip greater than or equal to 10,000\n",
    "total_trips_reduced = total_trips_df.loc[total_trips_df['total trips'] >= 10000]\n",
    "total_trips_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a bar chart of the 25 most popular stations\n",
    "total_trips_reduced.plot(kind='bar', color='deepskyblue', alpha=.5, align='center', figsize=(20,3))\n",
    "plt.title('Most Popular Stations')\n",
    "plt.xlabel('Stations')\n",
    "plt.ylabel('Trips')\n",
    "plt.savefig('Images/Most_Popular_Stations.png')\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Popular Routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>from_station</th>\n",
       "      <th>to_station</th>\n",
       "      <th>rides_taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2112 W Peterson Ave</td>\n",
       "      <td>2112 W Peterson Ave</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2112 W Peterson Ave</td>\n",
       "      <td>Broadway &amp; Argyle St</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2112 W Peterson Ave</td>\n",
       "      <td>Broadway &amp; Barry Ave</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2112 W Peterson Ave</td>\n",
       "      <td>Broadway &amp; Berwyn Ave</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2112 W Peterson Ave</td>\n",
       "      <td>Broadway &amp; Granville Ave</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101526</th>\n",
       "      <td>Yates Blvd &amp; 75th St</td>\n",
       "      <td>South Shore Dr &amp; 71st St</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101527</th>\n",
       "      <td>Yates Blvd &amp; 75th St</td>\n",
       "      <td>South Shore Dr &amp; 74th St</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101528</th>\n",
       "      <td>Yates Blvd &amp; 75th St</td>\n",
       "      <td>Stony Island Ave &amp; 82nd St</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101529</th>\n",
       "      <td>Yates Blvd &amp; 75th St</td>\n",
       "      <td>Woodlawn Ave &amp; Lake Park Ave</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101530</th>\n",
       "      <td>Yates Blvd &amp; 75th St</td>\n",
       "      <td>Yates Blvd &amp; 75th St</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101531 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                from_station                    to_station  rides_taken\n",
       "0        2112 W Peterson Ave           2112 W Peterson Ave            6\n",
       "1        2112 W Peterson Ave          Broadway & Argyle St            2\n",
       "2        2112 W Peterson Ave          Broadway & Barry Ave            2\n",
       "3        2112 W Peterson Ave         Broadway & Berwyn Ave            3\n",
       "4        2112 W Peterson Ave      Broadway & Granville Ave            6\n",
       "...                      ...                           ...          ...\n",
       "101526  Yates Blvd & 75th St      South Shore Dr & 71st St            4\n",
       "101527  Yates Blvd & 75th St      South Shore Dr & 74th St            3\n",
       "101528  Yates Blvd & 75th St    Stony Island Ave & 82nd St            1\n",
       "101529  Yates Blvd & 75th St  Woodlawn Ave & Lake Park Ave            1\n",
       "101530  Yates Blvd & 75th St          Yates Blvd & 75th St           21\n",
       "\n",
       "[101531 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe shows the most popular ending station for popular starting stations\n",
    "divvy_rides = {'from_station': divvy_df['from_station_name'],\n",
    "               'to_station': divvy_df['to_station_name']\n",
    "              }\n",
    "\n",
    "divvy_rides_df = pd.DataFrame(divvy_rides, columns=['from_station', 'to_station'])\n",
    "dup_to_from = divvy_rides_df.pivot_table(index=['from_station', 'to_station'], aggfunc='size')\n",
    "\n",
    "# Reconfigure dataframe to display data side by side\n",
    "dup_to_from.columns = dup_to_from.droplevel(0)\n",
    "dup_to_from.columns.name = None\n",
    "dup_to_from_II = dup_to_from.reset_index()\n",
    "\n",
    "# Rename dataframe columns\n",
    "to_from_III = dup_to_from_II.rename(columns={'from_station': 'from_station', 'to_station':'to_station', 0:'rides_taken'})\n",
    "to_from_III"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>from_station</th>\n",
       "      <th>to_station</th>\n",
       "      <th>rides_taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Shore Dr &amp; Monroe St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>5055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>Lake Shore Dr &amp; North Blvd</td>\n",
       "      <td>2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shedd Aquarium</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Millennium Park</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lake Shore Dr &amp; North Blvd</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Theater on the Lake</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dusable Harbor</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Canal St &amp; Adams St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Michigan Ave &amp; Oak St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adler Planetarium</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Columbus Dr &amp; Randolph St</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MLK Jr Dr &amp; 29th St</td>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Canal St &amp; Madison St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clinton St &amp; Madison St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Loomis St &amp; Lexington St</td>\n",
       "      <td>Morgan St &amp; Polk St</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Morgan St &amp; Polk St</td>\n",
       "      <td>Loomis St &amp; Lexington St</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>MLK Jr Dr &amp; 29th St</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wacker Dr &amp; Washington St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Calumet Ave &amp; 33rd St</td>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Broadway &amp; Belmont Ave</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Wabash Ave &amp; Roosevelt Rd</td>\n",
       "      <td>Wabash Ave &amp; 16th St</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Michigan Ave &amp; 8th St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Michigan Ave &amp; Lake St</td>\n",
       "      <td>Canal St &amp; Adams St</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    from_station                    to_station  rides_taken\n",
       "0      Lake Shore Dr & Monroe St       Streeter Dr & Grand Ave         5055\n",
       "1        Streeter Dr & Grand Ave    Lake Shore Dr & North Blvd         2132\n",
       "2                 Shedd Aquarium       Streeter Dr & Grand Ave         1813\n",
       "3                Millennium Park       Streeter Dr & Grand Ave         1775\n",
       "4     Lake Shore Dr & North Blvd       Streeter Dr & Grand Ave         1703\n",
       "5            Theater on the Lake       Streeter Dr & Grand Ave         1559\n",
       "6                 Dusable Harbor       Streeter Dr & Grand Ave         1414\n",
       "7            Canal St & Adams St  Michigan Ave & Washington St         1355\n",
       "8          Michigan Ave & Oak St       Streeter Dr & Grand Ave         1334\n",
       "9   Michigan Ave & Washington St  Clinton St & Washington Blvd         1056\n",
       "10             Adler Planetarium       Streeter Dr & Grand Ave          907\n",
       "11     Columbus Dr & Randolph St  Clinton St & Washington Blvd          850\n",
       "12           MLK Jr Dr & 29th St            State St & 33rd St          801\n",
       "13         Canal St & Madison St  Michigan Ave & Washington St          775\n",
       "14       Clinton St & Madison St  Michigan Ave & Washington St          769\n",
       "15      Loomis St & Lexington St           Morgan St & Polk St          758\n",
       "16  Clinton St & Washington Blvd  Michigan Ave & Washington St          750\n",
       "17           Morgan St & Polk St      Loomis St & Lexington St          746\n",
       "18            State St & 33rd St           MLK Jr Dr & 29th St          712\n",
       "19     Wacker Dr & Washington St  Michigan Ave & Washington St          693\n",
       "20         Calumet Ave & 33rd St            State St & 33rd St          645\n",
       "21        Broadway & Belmont Ave      Wilton Ave & Belmont Ave          631\n",
       "22     Wabash Ave & Roosevelt Rd          Wabash Ave & 16th St          605\n",
       "23         Michigan Ave & 8th St       Streeter Dr & Grand Ave          592\n",
       "24        Michigan Ave & Lake St           Canal St & Adams St          589"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows where start and end station were identical\n",
    "to_from_removed_dups = to_from_III[to_from_III['from_station'] != to_from_III['to_station']]\n",
    "\n",
    "# Sort dataframe of start/end stations, sort, and identify 25 most popular routes\n",
    "most_pop_rides = to_from_removed_dups.loc[to_from_removed_dups.groupby('from_station')['rides_taken'].idxmax()]\n",
    "most_popular_routes = most_pop_rides.sort_values(ascending=False, by='rides_taken').reset_index(drop=True)\n",
    "most_popular_reduced = most_popular_routes[0:25]\n",
    "most_popular_reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart of the 25 most popular routes\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.bar(most_popular_reduced['from_station'], most_popular_reduced['rides_taken'], color='deepskyblue', alpha=.5, align='center')\n",
    "plt.xticks(most_popular_reduced['from_station'], rotation='vertical')\n",
    "plt.title('25 Most Popular Divvy Routes (origin)')\n",
    "plt.xlabel('Divvy Locations')\n",
    "plt.ylabel('Number of Rides')\n",
    "plt.savefig('Images/Most Popular Routes.png')\n",
    "plt.show()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpr = most_popular_reduced.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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>from_station</th>\n",
       "      <th>to_station</th>\n",
       "      <th>rides_taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake Shore Dr &amp; Monroe St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>5055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>Lake Shore Dr &amp; North Blvd</td>\n",
       "      <td>2132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shedd Aquarium</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Millennium Park</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lake Shore Dr &amp; North Blvd</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Theater on the Lake</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dusable Harbor</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Canal St &amp; Adams St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>1355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Michigan Ave &amp; Oak St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adler Planetarium</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Columbus Dr &amp; Randolph St</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MLK Jr Dr &amp; 29th St</td>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Canal St &amp; Madison St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clinton St &amp; Madison St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Loomis St &amp; Lexington St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Morgan St &amp; Polk St</td>\n",
       "      <td>Loomis St &amp; Lexington St</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>MLK Jr Dr &amp; 29th St</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wacker Dr &amp; Washington St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Calumet Ave &amp; 33rd St</td>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Broadway &amp; Belmont Ave</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Wabash Ave &amp; Roosevelt Rd</td>\n",
       "      <td>Wabash Ave &amp; 16th St</td>\n",
       "      <td>605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Michigan Ave &amp; 8th St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Michigan Ave &amp; Lake St</td>\n",
       "      <td>Canal St &amp; Adams St</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    from_station                    to_station  rides_taken\n",
       "0      Lake Shore Dr & Monroe St       Streeter Dr & Grand Ave         5055\n",
       "1        Streeter Dr & Grand Ave    Lake Shore Dr & North Blvd         2132\n",
       "2                 Shedd Aquarium       Streeter Dr & Grand Ave         1813\n",
       "3                Millennium Park       Streeter Dr & Grand Ave         1775\n",
       "4     Lake Shore Dr & North Blvd       Streeter Dr & Grand Ave         1703\n",
       "5            Theater on the Lake       Streeter Dr & Grand Ave         1559\n",
       "6                 Dusable Harbor       Streeter Dr & Grand Ave         1414\n",
       "7            Canal St & Adams St  Michigan Ave & Washington St         1355\n",
       "8          Michigan Ave & Oak St       Streeter Dr & Grand Ave         1334\n",
       "9   Michigan Ave & Washington St  Clinton St & Washington Blvd         1056\n",
       "10             Adler Planetarium       Streeter Dr & Grand Ave          907\n",
       "11     Columbus Dr & Randolph St  Clinton St & Washington Blvd          850\n",
       "12           MLK Jr Dr & 29th St            State St & 33rd St          801\n",
       "13         Canal St & Madison St  Michigan Ave & Washington St          775\n",
       "14       Clinton St & Madison St  Michigan Ave & Washington St          769\n",
       "15      Loomis St & Lexington St       Streeter Dr & Grand Ave          758\n",
       "16  Clinton St & Washington Blvd  Michigan Ave & Washington St          750\n",
       "17           Morgan St & Polk St      Loomis St & Lexington St          746\n",
       "18            State St & 33rd St           MLK Jr Dr & 29th St          712\n",
       "19     Wacker Dr & Washington St  Michigan Ave & Washington St          693\n",
       "20         Calumet Ave & 33rd St            State St & 33rd St          645\n",
       "21        Broadway & Belmont Ave      Wilton Ave & Belmont Ave          631\n",
       "22     Wabash Ave & Roosevelt Rd          Wabash Ave & 16th St          605\n",
       "23         Michigan Ave & 8th St       Streeter Dr & Grand Ave          592\n",
       "24        Michigan Ave & Lake St           Canal St & Adams St          589"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
    "mask = mpr['from_station'].str.startswith('L')\n",
    "mpr.loc[mask, 'to_station'] = 'Streeter Dr & Grand Ave'\n",
    "mpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Lake+Shore+Dr+and+Monroe+St\n",
       "1          Streeter+Dr+and+Grand+Ave\n",
       "2                     Shedd+Aquarium\n",
       "3                    Millennium+Park\n",
       "4       Lake+Shore+Dr+and+North+Blvd\n",
       "5                Theater+on+the+Lake\n",
       "6                     Dusable+Harbor\n",
       "7              Canal+St+and+Adams+St\n",
       "8            Michigan+Ave+and+Oak+St\n",
       "9     Michigan+Ave+and+Washington+St\n",
       "10                 Adler+Planetarium\n",
       "11       Columbus+Dr+and+Randolph+St\n",
       "12             MLK+Jr+Dr+and+29th+St\n",
       "13           Canal+St+and+Madison+St\n",
       "14         Clinton+St+and+Madison+St\n",
       "15        Loomis+St+and+Lexington+St\n",
       "16    Clinton+St+and+Washington+Blvd\n",
       "17             Morgan+St+and+Polk+St\n",
       "18              State+St+and+33rd+St\n",
       "19       Wacker+Dr+and+Washington+St\n",
       "20           Calumet+Ave+and+33rd+St\n",
       "21          Broadway+and+Belmont+Ave\n",
       "22       Wabash+Ave+and+Roosevelt+Rd\n",
       "23           Michigan+Ave+and+8th+St\n",
       "24          Michigan+Ave+and+Lake+St\n",
       "Name: from_station, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpr_I = mpr[\"from_station\"] = mpr[\"from_station\"].str.replace(' ', '+')\n",
    "mpr_I\n",
    "mpr_I = mpr[\"from_station\"] = mpr[\"from_station\"].str.replace('&', 'and')\n",
    "mpr_I = mpr[\"from_station\"] = mpr[\"from_station\"].str.lstrip()\n",
    "\n",
    "mpr_I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"South Lake Shore Drive & East Monroe Street\",\n",
      "                    \"short_name\": \"S Lake Shore Dr & E Monroe St\",\n",
      "                    \"types\": [\n",
      "                        \"intersection\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago Loop\",\n",
      "                    \"short_name\": \"Chicago Loop\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60601\",\n",
      "                    \"short_name\": \"60601\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"S Lake Shore Dr & E Monroe St, Chicago, IL 60601, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.88093629999999,\n",
      "                    \"lng\": -87.617496\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.88228528029149,\n",
      "                        \"lng\": -87.61614701970849\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8795873197085,\n",
      "                        \"lng\": -87.61884498029151\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"EjVTIExha2UgU2hvcmUgRHIgJiBFIE1vbnJvZSBTdCwgQ2hpY2FnbywgSUwgNjA2MDEsIFVTQSJmImQKFAoSCYWqZsdYKw6IEcZYHrRu5zxqEhQKEgmFqmbHWCsOiBHGWB60buc8ahoUChIJU1CtTtsrDogRylfSHF_zSiEaFAoSCSt7nremLA6IEf1ajy5dO4G2IgoNE4b2GBWQocbL\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"East Grand Avenue & North Streeter Drive\",\n",
      "                    \"short_name\": \"E Grand Ave & N Streeter Dr\",\n",
      "                    \"types\": [\n",
      "                        \"intersection\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Streeterville\",\n",
      "                    \"short_name\": \"Streeterville\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60611\",\n",
      "                    \"short_name\": \"60611\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"E Grand Ave & N Streeter Dr, Chicago, IL 60611, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.8921227,\n",
      "                    \"lng\": -87.61004299999999\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.8934716802915,\n",
      "                        \"lng\": -87.60869401970848\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8907737197085,\n",
      "                        \"lng\": -87.61139198029149\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"EjNFIEdyYW5kIEF2ZSAmIE4gU3RyZWV0ZXIgRHIsIENoaWNhZ28sIElMIDYwNjExLCBVU0EiZiJkChQKEglt7hBeUisOiBGndgO8wkvEARIUChIJbe4QXlIrDogRp3YDvMJLxAEaFAoSCfPXGWVUKw6IEdNQLy7Cd5qyGhQKEgnx0lpIUisOiBFXTK8L_-zUpCIKDQs7-BgVssTHyw\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"1200\",\n",
      "                    \"short_name\": \"1200\",\n",
      "                    \"types\": [\n",
      "                        \"street_number\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"South Lake Shore Drive\",\n",
      "                    \"short_name\": \"S Lake Shore Dr\",\n",
      "                    \"types\": [\n",
      "                        \"route\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Near South Side\",\n",
      "                    \"short_name\": \"Near South Side\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60605\",\n",
      "                    \"short_name\": \"60605\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"1200 S Lake Shore Dr, Chicago, IL 60605, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.8675726,\n",
      "                    \"lng\": -87.614038\n",
      "                },\n",
      "                \"location_type\": \"ROOFTOP\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.86892158029149,\n",
      "                        \"lng\": -87.61268901970848\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8662236197085,\n",
      "                        \"lng\": -87.6153869802915\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"ChIJ-XW3X2MrDogR3_tQ3-OdBTI\",\n",
      "            \"plus_code\": {\n",
      "                \"compound_code\": \"V99P+29 Chicago, IL, USA\",\n",
      "                \"global_code\": \"86HJV99P+29\"\n",
      "            },\n",
      "            \"types\": [\n",
      "                \"aquarium\",\n",
      "                \"establishment\",\n",
      "                \"point_of_interest\",\n",
      "                \"tourist_attraction\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"Millennium Park\",\n",
      "                    \"short_name\": \"Millennium Park\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"Millennium Park, Chicago, IL, USA\",\n",
      "            \"geometry\": {\n",
      "                \"bounds\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.8844714,\n",
      "                        \"lng\": -87.6143578\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8808272,\n",
      "                        \"lng\": -87.6244517\n",
      "                    }\n",
      "                },\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.8827024,\n",
      "                    \"lng\": -87.6193938\n",
      "                },\n",
      "                \"location_type\": \"APPROXIMATE\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.8844714,\n",
      "                        \"lng\": -87.6143578\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8808272,\n",
      "                        \"lng\": -87.6244517\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"ChIJgfAAC6csDogRK6Ew7nIj9ss\",\n",
      "            \"types\": [\n",
      "                \"neighborhood\",\n",
      "                \"political\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"North Lake Shore Drive\",\n",
      "                    \"short_name\": \"N Lake Shore Dr\",\n",
      "                    \"types\": [\n",
      "                        \"route\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"N Lake Shore Dr, Illinois, USA\",\n",
      "            \"geometry\": {\n",
      "                \"bounds\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.9874997,\n",
      "                        \"lng\": -87.60876379999999\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8800529,\n",
      "                        \"lng\": -87.658113\n",
      "                    }\n",
      "                },\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.9345118,\n",
      "                    \"lng\": -87.6324027\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.9874997,\n",
      "                        \"lng\": -87.60876379999999\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8800529,\n",
      "                        \"lng\": -87.658113\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"ChIJ5eex5XXTD4gRZYOqabXce9Q\",\n",
      "            \"types\": [\n",
      "                \"route\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"2401\",\n",
      "                    \"short_name\": \"2401\",\n",
      "                    \"types\": [\n",
      "                        \"street_number\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"North Lake Shore Drive\",\n",
      "                    \"short_name\": \"N Lake Shore Dr\",\n",
      "                    \"types\": [\n",
      "                        \"route\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Lincoln Park\",\n",
      "                    \"short_name\": \"Lincoln Park\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60614\",\n",
      "                    \"short_name\": \"60614\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"2401 N Lake Shore Dr, Chicago, IL 60614, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.9270753,\n",
      "                    \"lng\": -87.6306663\n",
      "                },\n",
      "                \"location_type\": \"ROOFTOP\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.92842428029149,\n",
      "                        \"lng\": -87.62931731970849\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.9257263197085,\n",
      "                        \"lng\": -87.63201528029151\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"ChIJ9wm2tXHTD4gRaSxHG55KHFQ\",\n",
      "            \"plus_code\": {\n",
      "                \"compound_code\": \"W9G9+RP Chicago, IL, USA\",\n",
      "                \"global_code\": \"86HJW9G9+RP\"\n",
      "            },\n",
      "            \"types\": [\n",
      "                \"establishment\",\n",
      "                \"food\",\n",
      "                \"point_of_interest\",\n",
      "                \"restaurant\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
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      "                \"point_of_interest\"\n",
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      "    \"status\": \"OK\"\n",
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      "    \"status\": \"OK\"\n",
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      "                },\n",
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      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n"
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      "                \"point_of_interest\",\n",
      "                \"tourist_attraction\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
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      "                },\n",
      "                {\n",
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      "            \"address_components\": [\n",
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      "    \"status\": \"OK\"\n",
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      "                },\n",
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      "            \"formatted_address\": \"W Lexington St & S Loomis St, Chicago, IL 60607, USA\",\n",
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      "    ],\n",
      "    \"status\": \"OK\"\n",
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      "                {\n",
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      "                },\n",
      "                {\n",
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      "    \"status\": \"OK\"\n",
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      "            \"address_components\": [\n",
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      "                {\n",
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      "                {\n",
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      "                    \"types\": [\n",
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      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
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      "                },\n",
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      "            \"plus_code\": {\n",
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      "            \"types\": [\n",
      "                \"establishment\",\n",
      "                \"point_of_interest\"\n",
      "            ]\n",
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      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
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      "            \"address_components\": [\n",
      "                {\n",
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      "                },\n",
      "                {\n",
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      "            \"types\": [\n",
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      "    ],\n",
      "    \"status\": \"OK\"\n",
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      "                },\n",
      "                {\n",
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      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60606\",\n",
      "                    \"short_name\": \"60606\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"W Washington St & N Upper Wacker Dr, Chicago, IL 60606, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.8832047,\n",
      "                    \"lng\": -87.6367896\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.8845536802915,\n",
      "                        \"lng\": -87.6354406197085\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8818557197085,\n",
      "                        \"lng\": -87.63813858029151\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"EjtXIFdhc2hpbmd0b24gU3QgJiBOIFVwcGVyIFdhY2tlciBEciwgQ2hpY2FnbywgSUwgNjA2MDYsIFVTQSJmImQKFAoSCTs46V64LA6IEY9L1xPIYiOTEhQKEgk7OOleuCwOiBGPS9cTyGIjkxoUChIJj_zlg7ksDogRy8A7eGJzm08aFAoSCaWYeUS4LA6IERcIrg2h8cUhIgoNr972GBXor8PL\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"South Calumet Avenue & East 33rd Boulevard\",\n",
      "                    \"short_name\": \"S Calumet Ave & E 33rd Blvd\",\n",
      "                    \"types\": [\n",
      "                        \"intersection\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Douglas\",\n",
      "                    \"short_name\": \"Douglas\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60616\",\n",
      "                    \"short_name\": \"60616\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"S Calumet Ave & E 33rd Blvd, Chicago, IL 60616, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.83475079999999,\n",
      "                    \"lng\": -87.6181951\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.83609978029149,\n",
      "                        \"lng\": -87.61684611970848\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.83340181970849,\n",
      "                        \"lng\": -87.6195440802915\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"EjNTIENhbHVtZXQgQXZlICYgRSAzM3JkIEJsdmQsIENoaWNhZ28sIElMIDYwNjE2LCBVU0EiZiJkChQKEgmBVl5a9SsOiBGAzsqbxCmzQRIUChIJgVZeWvUrDogRgM7Km8Qps0EaFAoSCUm_q73SKA6IEdUY1dAeG51QGhQKEgm3H1O7DCwOiBHzau0hrWkKlyIKDfR57xgVQYbGyw\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"West Belmont Avenue & North Broadway\",\n",
      "                    \"short_name\": \"W Belmont Ave & N Broadway\",\n",
      "                    \"types\": [\n",
      "                        \"intersection\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Lake View East\",\n",
      "                    \"short_name\": \"Lake View East\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60657\",\n",
      "                    \"short_name\": \"60657\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"W Belmont Ave & N Broadway, Chicago, IL 60657, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.9400607,\n",
      "                    \"lng\": -87.6444035\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.9414096802915,\n",
      "                        \"lng\": -87.6430545197085\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.9387117197085,\n",
      "                        \"lng\": -87.6457524802915\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"EjJXIEJlbG1vbnQgQXZlICYgTiBCcm9hZHdheSwgQ2hpY2FnbywgSUwgNjA2NTcsIFVTQSJmImQKFAoSCb8t_qCm0w-IETCNUfgD6vdxEhQKEgm_Lf6gptMPiBEwjVH4A-r3cRoUChIJv4LgTFbMD4gRdkaK7Vps9sAaFAoSCZ9ltXW60w-IEWL0St5kn-yoIgoNn4v_GBV9hsLL\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"Roosevelt Road & South Wabash Avenue\",\n",
      "                    \"short_name\": \"Roosevelt Rd & S Wabash Ave\",\n",
      "                    \"types\": [\n",
      "                        \"intersection\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Near South Side\",\n",
      "                    \"short_name\": \"Near South Side\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60605\",\n",
      "                    \"short_name\": \"60605\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"Roosevelt Rd & S Wabash Ave, Chicago, IL 60605, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.867404,\n",
      "                    \"lng\": -87.62581449999999\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.8687529802915,\n",
      "                        \"lng\": -87.6244655197085\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8660550197085,\n",
      "                        \"lng\": -87.6271634802915\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"EjNSb29zZXZlbHQgUmQgJiBTIFdhYmFzaCBBdmUsIENoaWNhZ28sIElMIDYwNjA1LCBVU0EiZiJkChQKEgkrsXUDmywOiBEB-47o8ZbaORIUChIJK7F1A5ssDogRAfuO6PGW2jkaFAoSCa0aMBNZUg6IETWMfEml2eyvGhQKEgk3ZzaIKS8OiBEzftSDjZz2ciIKDXh19BgVn1zFyw\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"Michigan Avenue & East 8th Street\",\n",
      "                    \"short_name\": \"Michigan Ave & E 8th St\",\n",
      "                    \"types\": [\n",
      "                        \"intersection\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago Loop\",\n",
      "                    \"short_name\": \"Chicago Loop\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60605\",\n",
      "                    \"short_name\": \"60605\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"Michigan Ave & E 8th St, Chicago, IL 60605, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.8718705,\n",
      "                    \"lng\": -87.62424999999999\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.8732194802915,\n",
      "                        \"lng\": -87.6229010197085\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8705215197085,\n",
      "                        \"lng\": -87.6255989802915\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"Ei9NaWNoaWdhbiBBdmUgJiBFIDh0aCBTdCwgQ2hpY2FnbywgSUwgNjA2MDUsIFVTQSJmImQKFAoSCXdkVjeZLA6IEWc3cS8neBOKEhQKEgl3ZFY3mSwOiBFnN3EvJ3gTihoUChIJ4V4QFqQsDogR70KPar1JHQwaFAoSCVObtwuZLA6IEY2e5mSbBwTRIgoN8SP1GBW8mcXL\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n",
      "{\n",
      "    \"results\": [\n",
      "        {\n",
      "            \"address_components\": [\n",
      "                {\n",
      "                    \"long_name\": \"Michigan Avenue & East Lake Street\",\n",
      "                    \"short_name\": \"Michigan Ave & E Lake St\",\n",
      "                    \"types\": [\n",
      "                        \"intersection\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago Loop\",\n",
      "                    \"short_name\": \"Chicago Loop\",\n",
      "                    \"types\": [\n",
      "                        \"neighborhood\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Chicago\",\n",
      "                    \"short_name\": \"Chicago\",\n",
      "                    \"types\": [\n",
      "                        \"locality\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Cook County\",\n",
      "                    \"short_name\": \"Cook County\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_2\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"Illinois\",\n",
      "                    \"short_name\": \"IL\",\n",
      "                    \"types\": [\n",
      "                        \"administrative_area_level_1\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"United States\",\n",
      "                    \"short_name\": \"US\",\n",
      "                    \"types\": [\n",
      "                        \"country\",\n",
      "                        \"political\"\n",
      "                    ]\n",
      "                },\n",
      "                {\n",
      "                    \"long_name\": \"60601\",\n",
      "                    \"short_name\": \"60601\",\n",
      "                    \"types\": [\n",
      "                        \"postal_code\"\n",
      "                    ]\n",
      "                }\n",
      "            ],\n",
      "            \"formatted_address\": \"Michigan Ave & E Lake St, Chicago, IL 60601, USA\",\n",
      "            \"geometry\": {\n",
      "                \"location\": {\n",
      "                    \"lat\": 41.8857715,\n",
      "                    \"lng\": -87.6244155\n",
      "                },\n",
      "                \"location_type\": \"GEOMETRIC_CENTER\",\n",
      "                \"viewport\": {\n",
      "                    \"northeast\": {\n",
      "                        \"lat\": 41.8871204802915,\n",
      "                        \"lng\": -87.62306651970849\n",
      "                    },\n",
      "                    \"southwest\": {\n",
      "                        \"lat\": 41.8844225197085,\n",
      "                        \"lng\": -87.62576448029151\n",
      "                    }\n",
      "                }\n",
      "            },\n",
      "            \"place_id\": \"EjBNaWNoaWdhbiBBdmUgJiBFIExha2UgU3QsIENoaWNhZ28sIElMIDYwNjAxLCBVU0EiZiJkChQKEgn3jp1crywOiBG_1mkTyMKXGxIUChIJ946dXK8sDogRv9ZpE8jClxsaFAoSCeFeEBakLA6IEe9Cj2q9SR0MGhQKEgmLE-ZlrywOiBHOmYBDmZh88SIKDfNC9xgVRZPFyw\",\n",
      "            \"types\": [\n",
      "                \"intersection\"\n",
      "            ]\n",
      "        }\n",
      "    ],\n",
      "    \"status\": \"OK\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# stations = mpr_I[\"from_station\"]   \n",
    "place_id = []\n",
    "latitude = []\n",
    "longitude = []\n",
    "\n",
    "for station in mpr_I:\n",
    "    url = \"https://maps.googleapis.com/maps/api/geocode/json?\"\n",
    "    address = 'address=' + station\n",
    "    api_key = '&key=' + g_key\n",
    "    city_state = ',+Chicago,+IL\"'\n",
    "    \n",
    "    \n",
    "    response = requests.get(url +  address + city_state + api_key).json()\n",
    "    print(json.dumps(response, indent=4, sort_keys=True))\n",
    "    \n",
    "    place_id.append(response['results'][0][\"place_id\"])\n",
    "    latitude.append(response['results'][0]['geometry']['location']['lat'])\n",
    "    longitude.append(response['results'][0]['geometry']['location']['lng'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "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>from_station</th>\n",
       "      <th>to_station</th>\n",
       "      <th>rides_taken</th>\n",
       "      <th>Place ID</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lake+Shore+Dr+and+Monroe+St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>5055</td>\n",
       "      <td>EjVTIExha2UgU2hvcmUgRHIgJiBFIE1vbnJvZSBTdCwgQ2...</td>\n",
       "      <td>41.880936</td>\n",
       "      <td>-87.617496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Streeter+Dr+and+Grand+Ave</td>\n",
       "      <td>Lake Shore Dr &amp; North Blvd</td>\n",
       "      <td>2132</td>\n",
       "      <td>EjNFIEdyYW5kIEF2ZSAmIE4gU3RyZWV0ZXIgRHIsIENoaW...</td>\n",
       "      <td>41.892123</td>\n",
       "      <td>-87.610043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shedd+Aquarium</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1813</td>\n",
       "      <td>ChIJ-XW3X2MrDogR3_tQ3-OdBTI</td>\n",
       "      <td>41.867573</td>\n",
       "      <td>-87.614038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Millennium+Park</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1775</td>\n",
       "      <td>ChIJgfAAC6csDogRK6Ew7nIj9ss</td>\n",
       "      <td>41.882702</td>\n",
       "      <td>-87.619394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lake+Shore+Dr+and+North+Blvd</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1703</td>\n",
       "      <td>ChIJ5eex5XXTD4gRZYOqabXce9Q</td>\n",
       "      <td>41.934512</td>\n",
       "      <td>-87.632403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Theater+on+the+Lake</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1559</td>\n",
       "      <td>ChIJ9wm2tXHTD4gRaSxHG55KHFQ</td>\n",
       "      <td>41.927075</td>\n",
       "      <td>-87.630666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dusable+Harbor</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1414</td>\n",
       "      <td>ChIJUe7Hi1ArDogRkwk1xuXospA</td>\n",
       "      <td>41.885597</td>\n",
       "      <td>-87.611850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Canal+St+and+Adams+St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>1355</td>\n",
       "      <td>Ei9TIENhbmFsIFN0ICYgVyBBZGFtcyBTdCwgQ2hpY2Fnby...</td>\n",
       "      <td>41.879304</td>\n",
       "      <td>-87.639607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Michigan+Ave+and+Oak+St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>1334</td>\n",
       "      <td>Ei9NaWNoaWdhbiBBdmUgJiBFIE9hayBTdCwgQ2hpY2Fnby...</td>\n",
       "      <td>41.900798</td>\n",
       "      <td>-87.624239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Michigan+Ave+and+Washington+St</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>1056</td>\n",
       "      <td>EjZNaWNoaWdhbiBBdmUgJiBFIFdhc2hpbmd0b24gU3QsIE...</td>\n",
       "      <td>41.883235</td>\n",
       "      <td>-87.624244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adler+Planetarium</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>907</td>\n",
       "      <td>ChIJtRSxt28rDogRpo4hEqqjIGk</td>\n",
       "      <td>41.866333</td>\n",
       "      <td>-87.606783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Columbus+Dr+and+Randolph+St</td>\n",
       "      <td>Clinton St &amp; Washington Blvd</td>\n",
       "      <td>850</td>\n",
       "      <td>EjVOIENvbHVtYnVzIERyICYgRSBSYW5kb2xwaCBTdCwgQ2...</td>\n",
       "      <td>41.884464</td>\n",
       "      <td>-87.620717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MLK+Jr+Dr+and+29th+St</td>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>801</td>\n",
       "      <td>EjtFIDI5dGggU3QgJiBTIE1hcnRpbiBMdXRoZXIgS2luZy...</td>\n",
       "      <td>41.842067</td>\n",
       "      <td>-87.617221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Canal+St+and+Madison+St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>775</td>\n",
       "      <td>EjFTIENhbmFsIFN0ICYgVyBNYWRpc29uIFN0LCBDaGljYW...</td>\n",
       "      <td>41.881867</td>\n",
       "      <td>-87.639677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Clinton+St+and+Madison+St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>769</td>\n",
       "      <td>EjNXIE1hZGlzb24gU3QgJiBOIENsaW50b24gU3QsIENoaW...</td>\n",
       "      <td>41.881853</td>\n",
       "      <td>-87.641183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Loomis+St+and+Lexington+St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>758</td>\n",
       "      <td>EjRXIExleGluZ3RvbiBTdCAmIFMgTG9vbWlzIFN0LCBDaG...</td>\n",
       "      <td>41.872521</td>\n",
       "      <td>-87.661735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Clinton+St+and+Washington+Blvd</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>750</td>\n",
       "      <td>EjhXIFdhc2hpbmd0b24gQmx2ZCAmIE4gQ2xpbnRvbiBTdC...</td>\n",
       "      <td>41.883189</td>\n",
       "      <td>-87.641216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Morgan+St+and+Polk+St</td>\n",
       "      <td>Loomis St &amp; Lexington St</td>\n",
       "      <td>746</td>\n",
       "      <td>ChIJcT7Wyd0sDogR8UoGWQBImBk</td>\n",
       "      <td>41.878372</td>\n",
       "      <td>-87.652257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>State+St+and+33rd+St</td>\n",
       "      <td>MLK Jr Dr &amp; 29th St</td>\n",
       "      <td>712</td>\n",
       "      <td>Ei5XIDMzcmQgU3QgJiBTIFN0YXRlIFN0LCBDaGljYWdvLC...</td>\n",
       "      <td>41.834688</td>\n",
       "      <td>-87.626680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Wacker+Dr+and+Washington+St</td>\n",
       "      <td>Michigan Ave &amp; Washington St</td>\n",
       "      <td>693</td>\n",
       "      <td>EjtXIFdhc2hpbmd0b24gU3QgJiBOIFVwcGVyIFdhY2tlci...</td>\n",
       "      <td>41.883205</td>\n",
       "      <td>-87.636790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Calumet+Ave+and+33rd+St</td>\n",
       "      <td>State St &amp; 33rd St</td>\n",
       "      <td>645</td>\n",
       "      <td>EjNTIENhbHVtZXQgQXZlICYgRSAzM3JkIEJsdmQsIENoaW...</td>\n",
       "      <td>41.834751</td>\n",
       "      <td>-87.618195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Broadway+and+Belmont+Ave</td>\n",
       "      <td>Wilton Ave &amp; Belmont Ave</td>\n",
       "      <td>631</td>\n",
       "      <td>EjJXIEJlbG1vbnQgQXZlICYgTiBCcm9hZHdheSwgQ2hpY2...</td>\n",
       "      <td>41.940061</td>\n",
       "      <td>-87.644403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Wabash+Ave+and+Roosevelt+Rd</td>\n",
       "      <td>Wabash Ave &amp; 16th St</td>\n",
       "      <td>605</td>\n",
       "      <td>EjNSb29zZXZlbHQgUmQgJiBTIFdhYmFzaCBBdmUsIENoaW...</td>\n",
       "      <td>41.867404</td>\n",
       "      <td>-87.625814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Michigan+Ave+and+8th+St</td>\n",
       "      <td>Streeter Dr &amp; Grand Ave</td>\n",
       "      <td>592</td>\n",
       "      <td>Ei9NaWNoaWdhbiBBdmUgJiBFIDh0aCBTdCwgQ2hpY2Fnby...</td>\n",
       "      <td>41.871870</td>\n",
       "      <td>-87.624250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Michigan+Ave+and+Lake+St</td>\n",
       "      <td>Canal St &amp; Adams St</td>\n",
       "      <td>589</td>\n",
       "      <td>EjBNaWNoaWdhbiBBdmUgJiBFIExha2UgU3QsIENoaWNhZ2...</td>\n",
       "      <td>41.885771</td>\n",
       "      <td>-87.624415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      from_station                    to_station  rides_taken  \\\n",
       "0      Lake+Shore+Dr+and+Monroe+St       Streeter Dr & Grand Ave         5055   \n",
       "1        Streeter+Dr+and+Grand+Ave    Lake Shore Dr & North Blvd         2132   \n",
       "2                   Shedd+Aquarium       Streeter Dr & Grand Ave         1813   \n",
       "3                  Millennium+Park       Streeter Dr & Grand Ave         1775   \n",
       "4     Lake+Shore+Dr+and+North+Blvd       Streeter Dr & Grand Ave         1703   \n",
       "5              Theater+on+the+Lake       Streeter Dr & Grand Ave         1559   \n",
       "6                   Dusable+Harbor       Streeter Dr & Grand Ave         1414   \n",
       "7            Canal+St+and+Adams+St  Michigan Ave & Washington St         1355   \n",
       "8          Michigan+Ave+and+Oak+St       Streeter Dr & Grand Ave         1334   \n",
       "9   Michigan+Ave+and+Washington+St  Clinton St & Washington Blvd         1056   \n",
       "10               Adler+Planetarium       Streeter Dr & Grand Ave          907   \n",
       "11     Columbus+Dr+and+Randolph+St  Clinton St & Washington Blvd          850   \n",
       "12           MLK+Jr+Dr+and+29th+St            State St & 33rd St          801   \n",
       "13         Canal+St+and+Madison+St  Michigan Ave & Washington St          775   \n",
       "14       Clinton+St+and+Madison+St  Michigan Ave & Washington St          769   \n",
       "15      Loomis+St+and+Lexington+St       Streeter Dr & Grand Ave          758   \n",
       "16  Clinton+St+and+Washington+Blvd  Michigan Ave & Washington St          750   \n",
       "17           Morgan+St+and+Polk+St      Loomis St & Lexington St          746   \n",
       "18            State+St+and+33rd+St           MLK Jr Dr & 29th St          712   \n",
       "19     Wacker+Dr+and+Washington+St  Michigan Ave & Washington St          693   \n",
       "20         Calumet+Ave+and+33rd+St            State St & 33rd St          645   \n",
       "21        Broadway+and+Belmont+Ave      Wilton Ave & Belmont Ave          631   \n",
       "22     Wabash+Ave+and+Roosevelt+Rd          Wabash Ave & 16th St          605   \n",
       "23         Michigan+Ave+and+8th+St       Streeter Dr & Grand Ave          592   \n",
       "24        Michigan+Ave+and+Lake+St           Canal St & Adams St          589   \n",
       "\n",
       "                                             Place ID   latitude  longitude  \n",
       "0   EjVTIExha2UgU2hvcmUgRHIgJiBFIE1vbnJvZSBTdCwgQ2...  41.880936 -87.617496  \n",
       "1   EjNFIEdyYW5kIEF2ZSAmIE4gU3RyZWV0ZXIgRHIsIENoaW...  41.892123 -87.610043  \n",
       "2                         ChIJ-XW3X2MrDogR3_tQ3-OdBTI  41.867573 -87.614038  \n",
       "3                         ChIJgfAAC6csDogRK6Ew7nIj9ss  41.882702 -87.619394  \n",
       "4                         ChIJ5eex5XXTD4gRZYOqabXce9Q  41.934512 -87.632403  \n",
       "5                         ChIJ9wm2tXHTD4gRaSxHG55KHFQ  41.927075 -87.630666  \n",
       "6                         ChIJUe7Hi1ArDogRkwk1xuXospA  41.885597 -87.611850  \n",
       "7   Ei9TIENhbmFsIFN0ICYgVyBBZGFtcyBTdCwgQ2hpY2Fnby...  41.879304 -87.639607  \n",
       "8   Ei9NaWNoaWdhbiBBdmUgJiBFIE9hayBTdCwgQ2hpY2Fnby...  41.900798 -87.624239  \n",
       "9   EjZNaWNoaWdhbiBBdmUgJiBFIFdhc2hpbmd0b24gU3QsIE...  41.883235 -87.624244  \n",
       "10                        ChIJtRSxt28rDogRpo4hEqqjIGk  41.866333 -87.606783  \n",
       "11  EjVOIENvbHVtYnVzIERyICYgRSBSYW5kb2xwaCBTdCwgQ2...  41.884464 -87.620717  \n",
       "12  EjtFIDI5dGggU3QgJiBTIE1hcnRpbiBMdXRoZXIgS2luZy...  41.842067 -87.617221  \n",
       "13  EjFTIENhbmFsIFN0ICYgVyBNYWRpc29uIFN0LCBDaGljYW...  41.881867 -87.639677  \n",
       "14  EjNXIE1hZGlzb24gU3QgJiBOIENsaW50b24gU3QsIENoaW...  41.881853 -87.641183  \n",
       "15  EjRXIExleGluZ3RvbiBTdCAmIFMgTG9vbWlzIFN0LCBDaG...  41.872521 -87.661735  \n",
       "16  EjhXIFdhc2hpbmd0b24gQmx2ZCAmIE4gQ2xpbnRvbiBTdC...  41.883189 -87.641216  \n",
       "17                        ChIJcT7Wyd0sDogR8UoGWQBImBk  41.878372 -87.652257  \n",
       "18  Ei5XIDMzcmQgU3QgJiBTIFN0YXRlIFN0LCBDaGljYWdvLC...  41.834688 -87.626680  \n",
       "19  EjtXIFdhc2hpbmd0b24gU3QgJiBOIFVwcGVyIFdhY2tlci...  41.883205 -87.636790  \n",
       "20  EjNTIENhbHVtZXQgQXZlICYgRSAzM3JkIEJsdmQsIENoaW...  41.834751 -87.618195  \n",
       "21  EjJXIEJlbG1vbnQgQXZlICYgTiBCcm9hZHdheSwgQ2hpY2...  41.940061 -87.644403  \n",
       "22  EjNSb29zZXZlbHQgUmQgJiBTIFdhYmFzaCBBdmUsIENoaW...  41.867404 -87.625814  \n",
       "23  Ei9NaWNoaWdhbiBBdmUgJiBFIDh0aCBTdCwgQ2hpY2Fnby...  41.871870 -87.624250  \n",
       "24  EjBNaWNoaWdhbiBBdmUgJiBFIExha2UgU3QsIENoaWNhZ2...  41.885771 -87.624415  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpr[\"Place ID\"] = place_id\n",
    "mpr[\"latitude\"] = latitude\n",
    "mpr[\"longitude\"] = longitude\n",
    "mpr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "92b15207e75d4d6892fab5db90d5c944",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(layout=FigureLayout(height='420px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gmaps.configure(api_key=g_key)\n",
    "\n",
    "locations = mpr[[\"latitude\", \"longitude\"]].astype(float)\n",
    "rides = mpr['rides_taken'].astype(int)\n",
    "\n",
    "fig = gmaps.figure()\n",
    "\n",
    "heat_layer = gmaps.heatmap_layer(locations, weights=rides, max_intensity=100,\n",
    "                                 point_radius = 10)\n",
    "\n",
    "fig.add_layer(heat_layer)\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Subscribers & Customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are far more subscribers than customers who ride in the Chicago Divvy network, about 84% to 16%. It's important to note that this data may be skewed. We made an early call to drop any data with null values. We suspect that the dropped cells are more likely to be customers, one-time or infrequent users who are less likely to fill out their full profile on the app. This caveat applies to subsequent analysis on age and gender. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>trip_id</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_time</th>\n",
       "      <th>time difference</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_time</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "      <th>Age Group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Customer</th>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>491694</td>\n",
       "      <td>214116</td>\n",
       "      <td>216619</td>\n",
       "      <td>216619</td>\n",
       "      <td>216591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subscriber</th>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1149024</td>\n",
       "      <td>1139252</td>\n",
       "      <td>1146005</td>\n",
       "      <td>1146005</td>\n",
       "      <td>1145657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            trip_id   bikeid  start_date  start_time  time difference  \\\n",
       "usertype                                                                \n",
       "Customer     491694   491694      491694      491694           491694   \n",
       "Subscriber  1149024  1149024     1149024     1149024          1149024   \n",
       "\n",
       "            end_date  end_time  tripduration  from_station_id  \\\n",
       "usertype                                                        \n",
       "Customer      491694    491694        491694           491694   \n",
       "Subscriber   1149024   1149024       1149024          1149024   \n",
       "\n",
       "            from_station_name  to_station_id  to_station_name   gender  \\\n",
       "usertype                                                                 \n",
       "Customer               491694         491694           491694   214116   \n",
       "Subscriber            1149024        1149024          1149024  1139252   \n",
       "\n",
       "            birthyear      age  Age Group  \n",
       "usertype                                   \n",
       "Customer       216619   216619     216591  \n",
       "Subscriber    1146005  1146005    1145657  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Groupby usertype for ridership breakdown\n",
    "usertype_df = divvy_df.groupby('usertype')\n",
    "usertype_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    1149024\n",
       "Customer       491694\n",
       "Name: usertype, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find amount ot Subscribers and Customers\n",
    "divvy_df['usertype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Total Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usertype</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Subscriber</th>\n",
       "      <td>1149024</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer</th>\n",
       "      <td>491694</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total Count  Percentage\n",
       "usertype                           \n",
       "Subscriber      1149024        70.0\n",
       "Customer         491694        30.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find Usertype Percentage\n",
    "\n",
    "# Count number of trips for each usertype\n",
    "count_number_usertype = divvy_df['trip_id'].nunique()\n",
    "\n",
    "# Count number of each usertype\n",
    "usertype_number = usertype_df['trip_id'].nunique()\n",
    "\n",
    "# Percentage Usertype Calculation\n",
    "percent_usertype =  round((usertype_number / count_number_usertype * 100),)\n",
    "\n",
    "# Display usertype demographics\n",
    "usertype_demographics_df = pd.DataFrame({'Total Count':usertype_number,\n",
    "                                       'Percentage':percent_usertype})\n",
    "usertype_demographics_df.sort_values('Total Count', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use by Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all customers the data shows that there are far more male Divvy riders than female - about 70% to 30%. If you just look at customers, that breakdown remains consistent. Subscribers are slightly more balanced between genders with 60% male rides and 40% female riders.\n",
    "\n",
    "Here again, we need to examine the data available. There are two factors to consider. The first, we've already discussed. Our choice to remove any cells with null values. We wanted to check where those null values were in our data and discovered that 287,350 cells with null-gender data were dropped (see below). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1640718 entries, 0 to 1640717\n",
      "Data columns (total 17 columns):\n",
      " #   Column             Non-Null Count    Dtype   \n",
      "---  ------             --------------    -----   \n",
      " 0   trip_id            1640718 non-null  int64   \n",
      " 1   bikeid             1640718 non-null  object  \n",
      " 2   start_date         1640718 non-null  object  \n",
      " 3   start_time         1640718 non-null  object  \n",
      " 4   time difference    1640718 non-null  float64 \n",
      " 5   end_date           1640718 non-null  object  \n",
      " 6   end_time           1640718 non-null  object  \n",
      " 7   tripduration       1640718 non-null  object  \n",
      " 8   from_station_id    1640718 non-null  int64   \n",
      " 9   from_station_name  1640718 non-null  object  \n",
      " 10  to_station_id      1640718 non-null  int64   \n",
      " 11  to_station_name    1640718 non-null  object  \n",
      " 12  usertype           1640718 non-null  object  \n",
      " 13  gender             1353368 non-null  object  \n",
      " 14  birthyear          1362624 non-null  float64 \n",
      " 15  age                1362624 non-null  float64 \n",
      " 16  Age Group          1362248 non-null  category\n",
      "dtypes: category(1), float64(3), int64(3), object(10)\n",
      "memory usage: 201.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Examine count of non-null values\n",
    "divvy_df.dtypes\n",
    "divvy_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also noticed that Divvy only collected data for two genders, male and female, not allowing for any other gender identities across the spectrum. In a city like Chicago, with a significant LGBTQ population, this makes for inaccurate data on gender. Our speculation is that some of the dropped rides included riders who didn't select male or female because those labels did not describe them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      957353\n",
       "Female    396015\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find amount of Male and Female riders\n",
    "divvy_df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Total Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>957353</td>\n",
       "      <td>58.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>396015</td>\n",
       "      <td>24.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total Count  Percentage\n",
       "gender                         \n",
       "Male         957353       58.35\n",
       "Female       396015       24.14"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by gender\n",
    "usertype_gender_df = divvy_df.groupby('gender')\n",
    "\n",
    "# Count number of trips for each gender\n",
    "count_number_gender = divvy_df['trip_id'].nunique()\n",
    "\n",
    "# Count number of each gender\n",
    "gender_type_number = usertype_gender_df['trip_id'].nunique()\n",
    "\n",
    "# Calculate percentage for gender\n",
    "percent_gender =  round((gender_type_number / count_number_gender * 100),2)\n",
    "\n",
    "# Display gender demographics\n",
    "gender_demographics_df = pd.DataFrame({'Total Count':gender_type_number,\n",
    "                                       'Percentage':percent_gender})\n",
    "gender_demographics_df.sort_values('Total Count', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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G7TASPipBya9UuhY4HTgL7xh7IvnQgbfe8Pc0N2UsZ5EQUQlKfqTSDcCPgdOI1iGJpLhc4F7g/2huest2GCl9KkHpGa/8fgV8H+hlOY1ERwdwB/Abmpvetx1GSpdKUDZNKt0Xb33fT4Aay2kkutqAm4BzaW762HIWKUEqQdk4qXQlcCre6E87sZagaAGuA86nuWmu7TBSOlSC0n2p9JHABcAY21FE1mEF3u/ohTQ3rbIdRoJPJSgblkrvClwM7GU7ikg3zQR+THPTA7aDSLCpBGXdUmkH+BPwLcBYTiOyKR4FzqS56T3bQSSYVILStVT6ROAioM52FJEeWg1cApxHc9Myy1kkYFSCsqZUegu83VVNsB1FJM8+BX5Bc9NttoNIcKgExZNKV+Ft8Xk2UGk5jUghTQJOornpA9tBxD6VoEAqvS9wNdBoO4pIkSzH+4fvKpqb9CEYYSrBKPP283kp8G3bUUQseQr4Ls1Ns2wHETtitgOIJal0E/AaKkCJtn2B10mlf2A7iNihkWDUpNIVwO/wFgXpnyCRLzyOt65Qu1+LEJVglKTS44HbgJ1sRxEJqCV4k+wn2g4ixaESjIpU+hS8vb70th1FpATcAXxf8wrDTyUYdql0PXADcIjtKCIl5l3gKJqb3rAdRApH64TCzNv4ZRoqQJFNMR54gVRaG4+FmEaCYZVKn4m3+LPcdhSRELgROJ3mppW2g0h+qQTDJpXuBVyLt9NrEcmfacD/aGfc4aISDJNUejhwH7Cj7SgiIbUUOJnmprttB5H80DrBsEil/wt4BRWgSCH1Be4ilf6d7SCSHxoJhkEq/RPgQqDMdhSRCLkdb5drLbaDyKZTCZayVLoSb/rDN21HEYmoZ4HDaG6aZzuIbBqVYKlKpfvhrf/b13YUkYj7APgazU3v2A4iG0/rBEtRKj0ESKMCFAmC0cBz/iHJpMSoBEuNd+T354DtbUcRkf/oDzxCKn2S7SCycVSCpSSV3h34NzDCdhQR+ZIK4HpS6XNtB5HuUwmWilT6EOBJoM52FBFZr1+TSv/ZdgjpHm0YUwpS6e8BV6MpECKl5FrgVJqb9CEbYBoJBl0q/VPgr6gARUrND4CbSaX1txtgGgkGmTcJ/mLbMUSkR+4BjqO5qdV2EPkylWBQpdI/BrReQSQc/oV3bMJVtoPImlSCQZRK/wj4i+0YIpJXTwGH0ty03HYQ+YJKMGhUgCJhlgYO0nEJg0MbxgRJKn0WKkCRMGsC/k4qrYNdB4RKMCi8I8FfYjuGiBTc1/C2GtXnbwDohxAE3q6WLrUdQ0SK5jjgctshROsE7Uulvwo8AGjxiEj0nE9z0zm2Q0SZStCmVHoX4Gmgj+0oImLNT2lu0nQoS1SCtqTSY/COBlFvO4qIWOUCJ9HcdKPtIFGkErQhla7HOyL1WNtRRCQQ2oEjaW56wHaQqFEJFlsq3RtvEeiutqOISKCsAPamuelV20GiRFuHFpO3I927UQGKyJf1Bv5JKr2Z7SBRohIsrivx5giJiHRlKPAgqbQ2lisSlWCxpNKn4h1aRURknVzYZlJNwx+SmayxnSUKNDetGFLp3dHeYERkA1zI3tN/89nv9+p7JjAfONd2prDThjGFlkoPAl7FW8whIfDxvM/51sXnM3fhAmImxvcPOoSzDjuK1z6YwSlXXMyylSsZOWgwt/3i1/TrveZSrVWrW2j6xZm0tLbS1t7OUXtN4Hff/C4AZ0+8htTLL7DD6LHc8rNfAXDrk4+yYOlSzjrsqKK/TimuVsz0G5wx1YvKK4f5F7nA4Ym4oy1GC0iLQwvJ20nu31EBhkp5WRkXn3w6b197K8//+WqufOg+3vroQ06+9EKSJ/6A16++icP32JuL7rnzS/etqqjkqT/+hdeunMjUK27gkZdf5Pl33mTx8mU8+/YbTLvqRto7Onh95vusbGnhpice4bSDDyv+i5SiWhyrePGyhsYhOQUIYIBbk5nsVrZyRYFKsLAuBva2HULya8jAOnYc2whA39692XL4CGZn5/HuJx/TtM32ABwQ34V/TJn8pfsaY6ip7g1Aa1sbre1tGAwxE2N1axuu67JydQsV5eVc9I87OPPQI6ko11qLMJtRVTP56vqxO7fGymq6uLov8EAykx1Q7FxRoRIslFT6eOBM2zGksD78bA6Z96ez2xZbsc3IUTz4/BQA/v7M03yc/bzL+7S3t7PDD0+i4bjDOCC+M7ttsRV9e/fmyD2biJ9xMqMGDaG2Tw0vvfcOX999r2K+HCkiF1om1TRMuWfA8AkYs77P4rHATUWKFTlaJ1gIqfT2eLtEq7YdRQpn2coVTDj7LH519AkcsWcT73w8izOvuYz5S5dw6G57ctmD/2D+Xf9c5/0XLVvK4b8/h8tPOYttRo5e47qTL7mQ0w85jFemv8djr77EdqPGcM6x3yr0S5Ii6YDP7xow/PNZVTXbbMTdTk3EnWsKFiqiNBLMt1S6FrgXFWCotba1ceT5/8fx++zPEXs2AbDF5iN47PyLeeWy6zh2wn6MGbL+VcH9a/qyz7ZxHnnlxTUuz7z/HgCNm23OLU8+yt2//B1vzJrJ9NmfFObFSFGtNuada52xbRtZgAB/TmayWxYkVISpBPPvamD0Bm8lJct1XU665AK23HwEPzni6P9c/vmihQB0dHTw+ztv4ZSvHvql+85bvIhFy5YCsLKlhSemvswWw4avcZtf3zKRc0/4Lq1tbbR3dAAQM4YVLasK9ZKkSBaWVTx3WcP44YvLKzdlY7lq4I5kJluV71xRpjXu+ZRKnwAcazuGFNaUt17n1qceY9uRo9nhhycB8Idvf4/psz/hyofuA+CIPZs48YCvAvDp/CwnX3ohD597IXMWzOfbF/+B9o4OOlyXb+y9Dwfvtsd/Hvv+Z59hl8YtGFrnALD7lluz7anfYbtRY9h+tPa3XqpccN+t6pu+v/+wJozpyST47YELgB/lJ5lonWC+pNKjgal4W3OJiADgwson+w6a+nKfut3z95B8NRF3HsnT40WaFofmQTKTLVsWK78AFaCI5OiAObcNHPlhHgsQvPmDNyUz2YY8PmZkqQTz4+yr6sceOqOqZrILHbbDiIh9LSb25lX142KfVPYuxMYsg/CKUPsX7SGVYA8lM9ntgd90mFjlPQOGT7in/+avt4M24xOJsGxZ5ZTLGhrHLCurGFTAp2kGzijg40eC1gn2QDKTrQReArbLvbyyo33JNxd8OK2hrUUznUUixAX3zV616Yf6bzahSE/ZAuyaiDvTivR8oaORYM/8lrUKEGB1rKzfRGfMXo/3HfycCwuLH0tEis2F5Y/0G/JiEQsQoAqYmMxk9Vm+ifTGbaJkJrsb8Iv13eaVPgN3v6p+XMuyWNkrRYolIhZ0wOxbB4785LXeA3az8PQ7AadbeN5Q0OLQTZDMZMvwFoPGu3UH13X3X/pZeqcVC3Yz0Kug4USkqFaZ2LTrnLFDlpeV11uMsQTYIhF35ljMUJI0Etw0p9LdAgQwxjzRb/CEG+tGzV5tzLuFiyUixfRZedW/L2to3MJyAQL0A/5iOUNJ0khwI/lzc94F+m/K/WNux+ojF37y7OjVy5qM/gkRKUkudEyt7v/Mo7VDi7n+rzsOTMSdx2yHKCX6EN54F7KJBQjQYWKVfx84fB9NpRApTS4seah26CsBLECAK5OZrFa5bASV4EZIZrJ7AHk5ns37vfpuf2nD+H7zyqum5OPxRKTw2uGjG+tGzXuzuv8utrOsw1jgl7ZDlBItDu0mf2OYl4Ed8v3YOy1f8Nz+S+duYUBHjxYJqBWmbOp19WOGr4yVD7SdZQNWA9sl4o62P+gGjQS77zQKUICgqRQiQfdpRa9nrmho3LoEChCgErjKdohSoZFgN/gbw7wH1Bb0iVzXPWDp3PSOKxZqKoVIALjQ/nLvgVOe7De4yXaWTXBCIu78zXaIoFMJdkMyk72ZPK0L7I5BrStnHL/gw/ZK1x1frOcUkTW5sPiB2s1mvFNdu5PtLJtoLjAmEXdW2A4SZFocugHJTHZP4IRiPudnFdVjL2kYP+r9yppJOiqFSPG1YWbeUDd6QQkXIMBg4CzbIYJOI8ENSGayzwM2doUEwJhVS187YtHHdWUwzFYGkShZFit75Tpn7NiWWFlhV38UxyJgVCLuLLKcI7A0ElyPZCb7NSwWIGgqhUgxfVTRO31lfeMOISlA8OY0r3cfx1GnkeB6JDPZV4AdbefotPPy+c/tt/SzLU0PJuuLyJe50Pp8n7rnJ/cdtLftLAWwHG/d4Ge2gwSRRoLrkMxkDyNABQjwcp+63a+uH7dyeazsVdtZRMLChQX39t/8zZAWIEAf4BzbIYJKI8EuJDNZA0yli2MFBoKmUojkRRvm/YnO6IoF5VXDbWcpsNVAYyLuzLIdJGg0EuzaUQS1AAGMMY/3GzLhprpRn7Sio1KIbIolsfKXLmtorI9AAYI3gf63tkMEkUaCa/GP0Pw6sJXtLN2ho1KIbLwPKvtMvnvA8L0xJkp/M+3Atom487btIEESpV+A7jqGEilA+OKoFP/ov/m0DphtO49IkLmwOl1T/++7B46YELECBCgDzrMdImg0Eszh7yT7LaDRdpZNUdXRvvibCz58o76tZU/bWUSCpgPm/X3A8Lkzq2q2tZ3Fsl0Scedl2yGCImr/CW3ICZRoAQK0xMpqb3DG7PlE30HPud4kWREBWjHv/dUZ26ICBOBs2wGCRCNBXzKTLcc7Yvxo21nyoV9765xvz/9gTp+O9kBN8xAptkVlFS/cUDdmm9ZYrI/tLAHRjjdvUFuKopFgrsMISQECLCmrGHJ5fWP8ld4DJruwynYeERumV9VMvsYZu6sKcA1lwBm2QwSFRoK+ZCY7GSjFw6Vs0KDWlTO+Of/D9gp0VAqJBhdWPd130Ksv9qnbw3aWgFoMDEvEnWW2g9imkSCQzGS3I6QFCN5RKf4yaPyoDyr7TNZRKSTsOuCzOwaMeF8FuF61wIm2QwSBStAT+kUDHSZWeffAERM0lULCbLWJvX1N/biOj6r6bG07Swk4y58XHWmRfwOSmexA4HjbOYplRq++O1zaML5GR6WQsFlQVvncZQ2NI5eUVQyxnaVEjAEOsR3CtsiXIHASUG07RDHlTKV4VlMppNS54L7Vq9/kv9aP3b3NxCL1t5wHP7IdwLZIbxjjLwqYAYyyncWWfu2r53x7/kxNpZCS5MKKx/oNnpbpPfArtrOUsHgi7ky1HcKWqI8EDybCBQiwpKxSUymkJHXAnL8NHPmRCrDHfmw7gE1RL8HQbxDTLf5RKW6uG/VxK+Y923FENmSVib1xVf24stmVvbewnSUEjklmsoNth7AlsiWYzGS3BPa3nSNI5lZUj/vLoPEjP6jsM0lTKSSo5pVXTbm8oXHcsrKKBttZQqISb5eRkRTZEgROtx0giPypFPvcq6kUEjAudEyrrp18gzNmz3YTq7KdJ2S+aTuALZHcMCaZyVYCc4EBtrMEWVVH++IT5s98w2lfraNSiFUuLEv1G/LWtN4DdrWdJcS2S8Sd122HKLaojgQPRAW4QS2xstrr68dqKoVY1Q6f3Fw3ao4KsOAiORqMagkeYztAKXm5T90eV9ePXbk8Vvaq7SwSLStN7LUr6xt7za2oHmc7SwQcl8xkje0QxRa5Ekxmsr2Br9vOUWo6p1K8Wj1gsgsttvNI+M0t7/XM5Q2NW64oK3dsZ4mIYcA+tkMUW+RKEG9uoA6rsimMMY/VDplwc92ojzSVQgrFhfZXqwdMvskZvXeHiVXazhMxkVskGsUSPNZ2gFI3t6J63CWDxo/QUSkk31xY/M/azTKP1Q6ZYDtLRB2ZzGR72Q5RTJEqwWQmWws0284RBu0mVnX3wBETNJVC8qUdZk2sGz3/reranW1nibBaIrZT7UiVIHA4oPlFeTTdPypFtqzyWdtZpHQtj5Vlrmho7Devotdo21kkWotEo1aC2iq0APypFHs86U2lWGw7j5SW2RXV6SvqG7ddGSvXtKVgaE5msnW2QxRLZEowmcnWA/vZzhFmL3lTKVYsj5VlbGeR4HOh7YXedelb60Y1ucaU284j/1EBHGY7RLFEpgSBowD9oRWYP5Vih4ymUsh6uLDo/v7Dpj3db1CT7SzSpchsOxGlEjzCdoDIMMY8qqkUsg5tmA+ud8YsfrdXPx3DMrj2T2aykRg0RKIEk5lsNbC37RxR0zmVYqY3lSJ6O6mVL1kWK3/58obGuvnlVSNsZ5H1qgUicZzGSJQgXgFqq1AL2k2s6q6BIybc23/Y1A741HYesWdWZe/0lfXj4i2xslrbWaRbIrFINCol+N+2A0Td9F794pc2jO+TLaucYjuLFJcLrVP6OM/cMXBkk2tMme080m0H2Q5QDFEpwQNsB5AvjkqhqRTR4cL8e/pv/tYzfRu0OqL0xJOZ7CDbIQot9CXo/xC3tZ1DvvBSn7o9rnHGLtdUinBrxUy/1hm78v1efbe3nUU2icE77Fyohb4Egf3xfpgSIIvLK4dqKkV4LYmVv3h5Q+PgReWVw2xnkR4J/XrBKJSgFoUG1ZpTKabbjiP58X5lzeSr6sftvDpW1td2FumxA5KZbKh7ItQvzqcSDDh/KsVwTaUobS60TKppmPL3gcMnYEwUPluioA7Y1XaIQgr1L2oyk90aGGo7h2yYplKUtg6Yd+eA4dOfr3H2tJ1F8i7UW4mGugTRKLDk5Eyl0FEpSsRqY9691hnbOquqZhvbWaQg9rIdoJBUghI4nUeleEpTKQJvUVnF85fXjx+2uLxSS1zCa+dkJhvajQvDXoKR2O1PWL3oT6VYYTSVIojereo7+Rpn7G6tsVgf21mkoGqBRtshCiW0JZjMZEcCA23nkJ5ZXF459LIGTaUIEhdWPtF30LP3Ddh8AsaEdoQgawjtxjGhLUFgJ9sBJE80lSIwOmDu7QNHzHy5T90etrNIUe1iO0ChhLkEdZiWkNFUCrtaTOytq+vH8XFln61sZ5Gi00iwBGkkGEKdUynu86ZSzLGdJyrml1U+e3lD46ilZRWDbWcRK3ZIZrIVtkMUQphLMG47gBTOe95Uit6aSlFYLrhv9uo3+br6sXu0mVi17TxiTRWwne0QhRDKEkxmssOABts5pLA0laKwXFj+aL/BL/yz/7AJtrNIIIRykWgoSxAtCo0UTaXIvw6YfevAkZ9M7T1Q04ykUyg3jglrCWqjmIj5YipFf02l6KFVJvb6lfWNlZ9W9h5vO4sEikaCJUQjwSgyxjxaO3TCLQNHzdJUik3zeXnVvy9vaGxcXlZebzuLBM6WyUw2dDtGCGsJaiQYYXMqqxsvGTR++IeaStFtLnRMre4/eaIzZq92E6uynUcCKQaEbulA6ErQP5L8ENs5xK52E6u6U1MpusWFpf+qHfrKI7VDtQGMbIhKsASMsx1AgqNzKsV8TaXoUjt8dFPdqM/eqO4fyo0eJO9Ctw/RMJbgSNsBJFhaYmW119WP3eOpvg1TNJXiCytM2dQrGhprPquoHms7i5QMlWAJGGk7gATTi32cPTWVwjOnvNczVzQ0br0yVq6dzMvGUAmWgFG2A0hwdU6lmOpNpVhtO0+xudD+cu+Bk292Ru/dYUwod4MlBaUSLAEjbQeQgDPGPOJNpfgwSlMpXFj8YO1mU5/oN1gbwMim6pfMZOtsh8gnlaBEVpSmUrRjPpxYN3rB29W1mkMrPTXSdoB8ClUJJjPZGLC57RxSOjqnUtzff1gmrFMplsfKXr28obH/vIpeWlUg+TDSdoB8ClUJApsBWs8hG+3dXv12vKxhfHXYplJ8XFGdvqK+cbtVsbL+trNIaITqn6mwlWCofjhSXKtiZf3DMpXChbbn+9Slb6sb1eQaU247j4TKSNsB8ilsJTjSdgApff5UimUrTNlU21k2hQsL7+0/7PVJfQc12c4ioTTCdoB8UgmKdGFxeeVmlzU0bldqUynaMO9f54xZMr1XPx1UWgolVDtXD1sJhuo/FLHMmNgjtUMn3Dpw5MxSmEqxNFb+0mUNjfULyqv0dyCFNMB2gHwKWwmG6j8UCYZPK3uP96ZS9A7sVIqZlX0mX1k/bqfVsbJ+trNI6KkEAyxUPxwJDm8qxcjATaVwYfUzNfX/vmvgiAkYE7a/ZwmmUH3Ohu2PJlQ/HAmeIE2lcCF794Dh70ypqd/LdhaJlPJkJltjO0S+hK0EtTNgKbjOqRRP19ibStGKee9aZ+yqmVU129l4fom80Aw4wlaCofnBSPC9UGNnKsXiWMULlzU0Dl1UXjmsmM8rkiM0A47QlGAyk60CetnOIdFS7KkU06tqJl1dP3bX1lhZaBZHSUkKzYAjNCUIaKs4saMIUylcWPV0TcOUfwwYvg/GmEI8h8hGUAkGUB/bASTaCjWVogM+v3PAiPdfqHH2zNdjivSQSjCAVIJiXb6nUqw25u1r6se2z6rqs3U+8onkiUowgFSCEhj5mEqxoKzyucsaxo9YUlY5JJ/ZRPJAJRhAKkEJlE2dSuGC+3avfpP+6oz5SpuJ9S5kRpFNFJpD1qkERQpsY6ZSuLDiib6Dn3+g/zBtACNBFsjdB26KMJVgmF6LhEznVIrXqvtPWtdUig6Y87eBI2e90mfg7sXOJ7KRVIIB1GY7gMh6GRNL1Q7dp6upFC0m9uZV9ePKZlf23tJWPJGNoBIMIJWglAR/KsXms/ypFPPKq6Zc1tA4ZllZRYPtbCJRU247QB612w4g0l3tJtbrjoEjJzS0rnz/84pqzf+TUqORYABpJCgl5/OK6jG2M4hsApVgAKkERUSKQyUYQFocKiIiGyVMJaiRoIhIcWgkGEAqQRGR4lAJBpBKUESkOFSCAaR1giIixbHMdoB8CVMJttoOICISEQtsB8iXMJXgUtsBREQiQiUYQFlCtJxaRCTA5tsOkC+hKcFE3GknRP+diIgEWGg+a0NTgr7PbQcQEYkAjQQDap7tACIiEaCRYEBpJCgiUljLE3GnywNDl6KwlaBGgiIihRWaRaEQvhLUSFBEpLBCsygUwleCGgmKiBSWRoIBppGgiEhhaSQYYCpBEZHC+sh2gHxSCYqIyMaYYTtAPoWtBD9Cu04TESmk6bYD5FOoSjARd5YDs23nEBEJMY0EA+4d2wFEREKqBfjYdoh8CmMJvms7gIhISH2QiDsdtkPkUxhLUCNBEZHCCNWiUAhnCWokKCJSGKHaKAbCWYIaCYqIFIZGgiXgE2C57RAiIiGkEgy6RNxxgfds5xARCSEtDi0RWi8oIpJfqwnZLtMgvCWo9YIiIvn1WtimR4BKUEREuucF2wEKIawl+IrtACIiIfOi7QCFEMoSTMSdGeiIEiIi+aSRYIl5znYAEZGQWEgItwyFcJfgs7YDiIiExEv+9LPQUQmKiMiGhHJRKIS7BF/Gm9ciIiI9oxIsNYm4swrI2M4hIhICKsESpY1jRER6ZmYi7mRthyiUsJeg1guKiPRMaEeBoBIUEZH1e952gEIKdQkm4s5sQrjDVxGRInrUdoBCCnUJ+p6xHUBEpETNTMSdUO+LOQolmLIdQESkRIX+8zMqJdhuO4SISAlSCZa6RNxZgDaQERHZWC3AU7ZDFFroS9D3T9sBRERKzORE3FlhO0ShqQRFRKQrD9sOUAyRKEF/66YZtnOIiJSQ0K8PhIiUoE+jQRGR7pmRiDvv2Q5RDCpBERFZWyRGgRCtEnwGWGQ7hIhICVAJhk0i7rQBj9jOISIScIuBp22HKJbIlKDvIdsBREQC7h7/eKyREMUSjMwPV0RkE9xqO0AxRaoEE3FnMXCf7RwiIgE1C0jbDlFMkSpB3022A4iIBNTfEnHHtR2imKJYgk8AH9sOISISQJFaFAoRLMFE3OkAbrGdQ0QkYF5KxJ13bYcotsiVoO8m2wFERAImcqNAiGgJJuLODODftnOIiAREK3CH7RA2RLIEfTfZDiAiEhCPJOJO1nYIG6JcgncDoT9WlohIN0RyUShEuAQTcWcpcI/tHCIili0kwgcYiGwJ+m6yHUBExLLro7SbtLVFvQQnoYPtikh0tQNX2g5hU6RL0N8zwp9t5xARseTBRNyZZTuETZEuQd9NwDzbIURELLjUdgDbIl+CibizErjCdg4RkSJ7LRF3JtsOYVvkS9B3JZouISLRcontAEGgEgQScWc+MNF2DhGRIpkN3GY7RBCoBL/wZ7wtpUREwu6SRNxptR0iCFSCvkTcmYkmz4tI+C0G/mo7RFCoBNd0oe0AIiIFdm0i7iyxHSIoVII5EnHnVeBJ2zlERAqkBU2LWINK8Msush1ARKRArkrEnU9thwgSleBaEnHnUeBV2zlERPJsCfAH2yGCRiXYtV/ZDiAikmcXR/WYgeujEuxCIu48grdzbRGRMPgc7Se5SyrBdftf2wFERPLk94m4s8x2iCBSCa5DIu48D9xvO4eISA/NBK61HSKoVILr9yu0FxkRKW2/ScSd1bZDBJVKcD0ScecttE9RESldr6N9hK6XSnDDfg0stR1CRGQT/CoRdzpshwgyleAGJOLOZ0DSdg4RkY00JRF3/mk7RNCpBLvnz8BHtkOIiHSTC/zMdohSoBLshkTcWYWmTIhI6firv4W7bIBKsPvuACbbDiEisgGfAQnbIUqFSrCbEnHHBb4PrLKdRURkPX6ciDuLbIcoFSrBjZCIO+8B59nOISKyDo8m4s4dtkOUEpXgxrsQmGY7hIjIWlYCp9kOUWpUghspEXfagJPRnmREJFjOS8SdD2yHKDUqwU2QiDsvAZfZziEi4nsT+JPtEKVIJbjpzsHbMa2IiE0u8INE3Gm1HaQUqQQ3USLurABOsZ1DRCLv+kTcmWI7RKlSCfZAIu48BtxqO4eIRNYc4GzbIUqZSrDnfgzMsx1CRCLHBb6ViDsLbQcpZSrBHkrEnfnAqbZziEjkXJSIO0/YDlHqjOu6tjOEQjKTvQqVofjmfTiDOxIn/+f7BbNnsf8pZ7PjwUdzR+J7LPz0IwYMHc5xF1xPdb/+3brvXsefQurSc3lvypMMGb8N3zjvSgBefehuVi5ZyJ7H/aAor00C4UVgL20M03MqwTxJZrJVwPPADpajSMB0tLfzx4O25bSbH+X5uydSXduffU48i0k3XsrKJYtpPuv/unXf6r613HzWcfxg4kPc+atT2Oc7Z1K3+ShuPut4TrziLsoqKor4qsSiJUBccwLzQ4tD8yQRd1qAbwDLbGeRYJnxYpq6YSMZMHRz3pqcYseDjwZgx4OP5q1JD3f7viYWo62tFdd1aVu1ilh5BelbrmSPY7+nAoyW01SA+aMSzKNE3JkOaJmUrGHao/ex3YFHALBs/jz61Q8GoF/9YJYtyHb7vlV9athm34O5/Nj/YsBmw+lV05dP3syw1T7NhX0BEiS3JuLObbZDhIlKMM8Sced24AbbOSQY2lpX83b6UbY94NC83HfCd87gzDsn8bWfnMvjVyc54NSzeem+W7n97JN46vqL8xldgmc62jdo3qkEC+MMvN0YScS9N+VJhm6xHX3rGgCoqatnyby5ACyZN5eagU6375vr03e8fbg7I8bw6kN3c9wFN/DZjHfIfvR+AV6FBEArcGwi7mh1S56pBAsgEXdW4q0fXGE7i9j12iP3sv2Bh//n+y2bDuLVh+4C4NWH7mKrCetelLn2fXM9dlWSA05N0N7Whtvh7cvdxGK0rlqZx/QSIL9MxJ1XbIcII5VggSTizlvAD23nEHtWr1zB9Bcms82+B//nsgknnsmM5yfzp6/vyoznJzPhxDMBb1R44xnHrPe+nd58+mGGbR2nX/1gqvvWMny7XbjkG01gDEMatyn8C5NiewjQsu4C0RSJAktmsrcAJ9jOISIl6XVgz0TcWWo7SFhpJFh4PwBesh1CRErO58AhKsDCUgkWmL9+8FDgI9tZRKRktACHJ+LOLNtBwk4lWASJuDMX+Brenh5ERDbke4m486ztEFGgEiySRNx5A/gfoM12FhEJtGQi7ugQbUWiEiwi//iDp9vOISKBdR/wS9shokRbh1qQzGQvAn5mO4eIBMpUvCNDLLcdJEo0ErTjF8C9tkOISGDMBQ5VARafRoKWJDPZamAysIvtLCJi1XJgv0TcecF2kCjSSNASf+rEIYA2gRaJrlXA11WA9qgELUrEnc+Ag4DPbGcRkaJbDRyZiDtP2g4SZSpByxJx5x1gX2Ce7SwiUjRteEeFWP9RlaXgVIIB4O9se19g/UdYFZEw6AC+nYg72jguAFSCAeFPpt8PmG87i4gUjIu3N5jbbQcRj0owQBJxZxpwALDQdhYRKYgzEnFnou0Q8gWVYMAk4k4GrwgXWY4iIvn180TcudJ2CFmTSjCA/CNI/zew2HYWEcmL3ybizp9sh5AvUwkGVCLuvIQ3fULHEhMpbb9JxJ3f2Q4hXdMeYwIumcnuAaSAfraziMhGaQdOT8Sda20HkXVTCZaAZCa7HfAwsJntLCLSLauA4xJx5z7bQWT9VIIlIpnJbo43ItzadhYRWa/FeDvDTtsOIhumdYIlIhF3Pgb2wtvptogE06fA3irA0qESLCGJuLMIOBC403IUEfmyd4E9EnHnddtBpPtUgiUmEXdagOMAbW4tEhwv4h0QV0eFKTFaJ1jCkpnsGcAl6J8ZEZseAY7SAXFLkz48S1gi7lwOHIW3JZqIFN9fgUNUgKVLI8EQ8OcS3g/UW44iEhWr8OYAaj+gJU4lGBLJTHYz4G5gD9tZREJuFt7BcF+xHUR6TotDQyIRd2YDE4C/2M4iEmJPADupAMNDI8EQSmayRwA3ol2tieRTEjgnEXfabQeR/FEJhlQykx0H3ANsZzuLSIlbinckeO0CLYS0ODSkEnFnOvAV4CbLUURK2dvALirA8NJIMAKSmexJwBVAL9tZRErI34HvJuLOMttBpHBUghGRzGR3wFs8OsZyFJGgWwr8SNMfokGLQyMiEXemAnHgWkD/+Yh0bQqwvQowOjQSjKBkJrsfcD0w0nIUkaBoBX4DXJCIOx22w0jxqAQjKpnJ1gAXAKcCxnIcEZteA05MxJ2M7SBSfCrBiEtmsvsANwCjLUcRKbZW4HzgD4m402o7jNihEhSSmWwf4A/AGWhUKNHwKt7ob5rtIGKXSlD+I5nJ7g1MBMbaziJSICvxRn8XJOJOm+0wYp9KUNaQzGSrgd8DZwLlluOI5NPfgZ/rwLeSSyUoXUpmslsDlwL72c4i0kOvAWcl4s5k20EkeFSCsl7JTPYw4M/AKMtRRDbWfOAc4Drt9FrWRSUoG5TMZKuAnwL/C9RYjiOyIW3A1cBvEnFnoe0wEmwqQem2ZCY7CPgdcDJQZjmOSFeexFv0+abtIFIaVIKy0ZKZ7BZ4E+0PtZ1FxDcDb6OX+20HkdKiEpRNlsxkJwB/BHa3nUUi6128KQ+3a72fbAqVoPRYMpPdF/gl2pJUiuctvKk8d2lfn9ITKkHJm2QmuyteGR6K9jwjhTENOA/4RyLu6MNLekwlKHmXzGS3wduS9Gi0AY3kx6t45feAyk/ySSUoBZPMZEcDZwPfBqosx5HS9CJwXiLuPGQ7iISTSlAKLpnJDsWbZ/h9NM9QNmw18A/gmkTcSdsOI+GmEpSiSWayfYFjge8BO1uOI8EzE7gWmJiIO/Nsh5FoUAmKFclMdge8MjweqLWbRixqB/6Ft4eXR7W+T4pNJShWJTPZ3sD/4BXinpbjSPHMAa7H26/nx7bDSHSpBCUwkpnsVni7ZPsWUGc5juRfK/A43jErH9Dx/CQIVIISOP4Ouw8FjgS+CvS1m0h6oAWv+P4OPJiIO4vsxhFZk0pQAs0vxP2Aw/GKscFuIumGVcCjwD14xbfEch6RdVIJSslIZrIxvPWGh/unkVYDSa6VwCN4I76HEnFnqeU8It2iEpSS5W9h2lmI29pNE0kzgKfxFnemEnFnmeU8IhtNJSihkMxkBwNNOadt0P5L820W8BRe8T2diDufWM4j0mMqQQmlZCY7ANjbPzUBOwLlVkOVntn4hYdXejMt5xHJO5WgREIyk+0D7IFXiHsB2wMDrIYKloV4R2h4zT89k4g70+1GEik8laBElr9P063xFp12ft2KcE/JaAems2bhTdOEdYkqlaBIjmQma4DheKXYWYxbAsPwpmeUwqGh2oBPgU+Aj/2vb+MV3puJuLPSYjaRQFEJinRTMpMtwyvCITmnoV2cHwRUFCBCC7AEWArM5YuC6/zaeX6ujrYu0j0qQZECSGay5UAvvOMorv117csq8UZvq/CKriXn/DL84kvEndbivgqR8FMJiohIZMVsBxAREbFFJSgiIpGlEhQRkchSCYqISGSpBEVEJLJUgiIiElkqQRERiSyVoIiIRJZKUEREIkslKCIikaUSFBGRyFIJigScMcY1xtya8325MWaeMeahDdxvnw3dRiTqVIIiwbcc2MYYU+1/fwAw22IekdBQCYqUhhTwNf/8scAdnVcYY3Y1xjxrjMn4X8evfWdjTB9jzERjzEv+7b5epNwigaYSFCkNdwLHGGN6AdsBL+Rc9w7Q5LpuHPg/4A9d3P9XwFOu6+4C/BdwkTGmT4EziwReue0AIrJhrutOM8aMxBsFPrzW1bXAzcaYcYBL10e1/2/gUGPMz/zvewHDgbcLk1ikNKgERUrHg8CfgH2AupzLzwOedl33cL8oJ3VxXwMc6bruuwXOKFJStDhUpHRMBM51Xff1tS6v5YsNZb6zjvs+CpxhjDEAxph4QRKKlBiVoEiJcF33E9d1L+3iqguBPxpjpgBl67j7eXiLSacZY97wvxeJPOO6ru0MIiIiVmgkKCIikaUSFBGRyFIJiohIZKkERUQkslSCIiISWSpBERGJLJWgiIhElkpQREQiSyUoIiKRpRIUEZHIUgmKiEhkqQRFRCSyVIIiIhJZKkEREYkslaCIiESWSlBERCJLJSgiIpGlEhQRkchSCYqISGSpBEVEJLJUgiIiElkqQRERiSyVoIiIRJZKUEREIkslKCIikaUSFBGRyFIJiohIZKkERUQkslSCIiISWf8PRopC5afv+fIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Divvy Riders based on Gender- PIE CHART\n",
    "gender_data = divvy_df['gender'].value_counts()\n",
    "gender_labels = ['Male','Female']\n",
    "colours = ['skyblue', 'lightpink']\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(gender_data, labels= gender_labels, colors=colours, autopct=\"%1.1f%%\", startangle=145)\n",
    "plt.title(\"Total Divvy Riders based on Gender\")\n",
    "plt.savefig('Images/Gender_Overall.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  usertype  \n",
       "Female  Subscriber    313303\n",
       "        Customer       82712\n",
       "Male    Subscriber    825949\n",
       "        Customer      131404\n",
       "Name: usertype, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find breakdown of each gender for each usertype\n",
    "usertype_gender_df['usertype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V1X2dq4Gt+ybbAleMZHFlT9u2vx6ffqdtqmopcCOw5SR53UmSg5MsTLJw8eLFq76zkiRJs8R0NstuTqtZuz9wX2CjJC+bbJMJ0mqS9FXdZllC1VFVtXtV7T5//vxJiiZJkjQ3TGez7DOAy6pqcVXdBnwWeAJwTW9qpT9f29e/Eth+ZPvtaM24V/bX49PvtE1v+t0UWDJJXpIkSYM2ncHd5cCeSTbs/eD2Bi4ETgHGRq8eCJzcX58CLOgjYO9PGzhxTm+6vTnJnj2fV4zbZiyvFwBn9H55pwL7JNm81yDu09MkSZIGbd50ZVxVZyc5CfgesBT4PnAUcC/gxCQH0QLAF/b1L0hyIvDjvv7rq+r2nt1rgWOADYAv9wfAx4Djkyyi1dgt6HktSXI4cG5f751VtWS69lWSJGm2SKvo0u67714LFy6c6WJI0rTZ9/AvznQRZqVT//YPZ7oI0kpL8t2q2n2iZd6hQpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQKYtuEvykCTnjTxuSvLmJFskOS3JJf1585FtDk2yKMnFSfYdSd8tyfl92QeSpKevl+QzPf3sJDuObHNg/4xLkhw4XfspSZI0m0xbcFdVF1fVrlW1K7Ab8Bvgc8AhwOlVtRNwen9Pkp2BBcAuwH7AkUnW7tl9GDgY2Kk/9uvpBwE3VNWDgPcC7+l5bQEcBjwO2AM4bDSIlCRJGqo11Sy7N/CTqvoZsD9wbE8/Fjigv94fOKGqbq2qy4BFwB5J7gNsUlVnVVUBx43bZiyvk4C9e63evsBpVbWkqm4ATmNZQChJkjRYayq4WwB8ur/epqquBujPW/f0bYErRra5sqdt21+PT7/TNlW1FLgR2HKSvO4kycFJFiZZuHjx4lXeOUmSpNli2oO7JOsCzwX+c0WrTpBWk6Sv6jbLEqqOqqrdq2r3+fPnr6B4kiRJs9+aqLl7JvC9qrqmv7+mN7XSn6/t6VcC249stx1wVU/fboL0O22TZB6wKbBkkrwkSZIGbU0Edy9hWZMswCnA2OjVA4GTR9IX9BGw96cNnDinN93enGTP3p/uFeO2GcvrBcAZvV/eqcA+STbvAyn26WmSJEmDNm86M0+yIfAHwJ+NJL8bODHJQcDlwAsBquqCJCcCPwaWAq+vqtv7Nq8FjgE2AL7cHwAfA45PsohWY7eg57UkyeHAuX29d1bVkmnZSUmSpFlkWoO7qvoNbYDDaNr1tNGzE61/BHDEBOkLgYdPkH4LPTicYNnRwNErX2pJkqS5yztUSJIkDYjBnSRJ0oAY3EmSJA2IwZ0kSdKAGNxJkiQNiMGdJEnSgBjcSZIkDYjBnSRJ0oAY3EmSJA2IwZ0kSdKAGNxJkiQNiMGdJEnSgBjcSZIkDYjBnSRJ0oAY3EmSJA2IwZ0kSdKAGNxJkiQNiMGdJEnSgBjcSZIkDYjBnSRJ0oAY3EmSJA2IwZ0kSdKAGNxJkiQNiMGdJEnSgBjcSZIkDYjBnSRJ0oAY3EmSJA2IwZ0kSdKAGNxJkiQNiMGdJEnSgBjcSZIkDYjBnSRJ0oAY3EmSJA2IwZ0kSdKAGNxJkiQNiMGdJEnSgBjcSZIkDYjBnSRJ0oAY3EmSJA2IwZ0kSdKAGNxJkiQNyLQGd0k2S3JSkouSXJjk8Um2SHJakkv68+Yj6x+aZFGSi5PsO5K+W5Lz+7IPJElPXy/JZ3r62Ul2HNnmwP4ZlyQ5cDr3U5IkabaY7pq79wNfqaqHAo8CLgQOAU6vqp2A0/t7kuwMLAB2AfYDjkyyds/nw8DBwE79sV9PPwi4oaoeBLwXeE/PawvgMOBxwB7AYaNBpCRJ0lBNW3CXZBPgKcDHAKrqd1X1S2B/4Ni+2rHAAf31/sAJVXVrVV0GLAL2SHIfYJOqOquqCjhu3DZjeZ0E7N1r9fYFTquqJVV1A3AaywJCSZKkwZrOmrsHAIuBjyf5fpL/SLIRsE1VXQ3Qn7fu628LXDGy/ZU9bdv+enz6nbapqqXAjcCWk+R1J0kOTrIwycLFixevzr5KkiTNCtMZ3M0DHgN8uKoeDfya3gS7HJkgrSZJX9VtliVUHVVVu1fV7vPnz5+kaJIkSXPDdAZ3VwJXVtXZ/f1JtGDvmt7USn++dmT97Ue23w64qqdvN0H6nbZJMg/YFFgySV6SJEmDNm3BXVX9ArgiyUN60t7Aj4FTgLHRqwcCJ/fXpwAL+gjY+9MGTpzTm25vTrJn70/3inHbjOX1AuCM3i/vVGCfJJv3gRT79DRJkqRBmzfN+b8R+GSSdYFLgVfRAsoTkxwEXA68EKCqLkhyIi0AXAq8vqpu7/m8FjgG2AD4cn9AG6xxfJJFtBq7BT2vJUkOB87t672zqpZM545KkiTNBtMa3FXVecDuEyzaeznrHwEcMUH6QuDhE6TfQg8OJ1h2NHD0ShRXkiRpzvMOFZIkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCArFdwlWSvJJtNVGEmSJK2eFQZ3ST6VZJMkGwE/Bi5O8tbpL5okSZJW1lRq7nauqpuAA4AvATsAL5/OQkmSJGnVTCW4WyfJOrTg7uSqug2oaS2VJEmSVslUgruPAD8FNgK+keR+wE3TWShJkiStmkmDuyRrAddU1bZV9ayqKuBy4GlTyTzJT5Ocn+S8JAt72hZJTktySX/efGT9Q5MsSnJxkn1H0nfr+SxK8oEk6enrJflMTz87yY4j2xzYP+OSJAeuzB9FkiRprpo0uKuqO4A3jEurqlq6Ep/xtKratap27+8PAU6vqp2A0/t7kuwMLAB2AfYDjkyydt/mw8DBwE79sV9PPwi4oaoeBLwXeE/PawvgMOBxwB7AYaNBpCRJ0lBNpVn2tCR/lWT7Xuu2RQ+eVtX+wLH99bG0vnxj6SdU1a1VdRmwCNgjyX2ATarqrF5zeNy4bcbyOgnYu9fq7QucVlVLquoG4DSWBYSSJEmDNW8K67y6P79+JK2AB0xh2wK+mqSAj1bVUcA2VXU1QFVdnWTrvu62wHdGtr2yp93WX49PH9vmip7X0iQ3AluOpk+wze8lOZhWI8gOO+wwhd2RJEma3VYY3FXV/Vcj/ydW1VU9gDstyUWTrJuJPn6S9FXdZllCCzaPAth9990dASxJkua8qUxivGGSv0lyVH+/U5JnTyXzqrqqP18LfI7W/+2a3tRKf762r34lsP3I5tsBV/X07SZIv9M2SeYBmwJLJslLkiRp0KbS5+7jwO+AJ/T3VwLvWtFGSTZKsvHYa2Af4EfAKcDY6NUDgZP761OABX0E7P1pAyfO6U24NyfZs/ene8W4bcbyegFwRu+XdyqwT5LN+0CKfXqaJEnSoE2lz90Dq+rFSV4CUFW/HZuKZAW2AT7XV50HfKqqvpLkXODEJAfRplV5Yc/3giQn0m5xthR4fVXd3vN6LXAMsAHw5f4A+BhwfJJFtBq7BT2vJUkOB87t672zqpZMocySJElz2lSCu98l2YDeZy3JA4FbV7RRVV0KPGqC9OuBvZezzRHAEROkLwQePkH6LfTgcIJlRwNHr6ickiRJQzKV4O4w4CvA9kk+CTwReOV0FkqSJEmrZiqjZU9L8j1gT9oo1DdV1XXTXjJJkiSttOUGd0keMy7p6v68Q5Idqup701csSZIkrYrJau7+X39eH9gd+AGt5u6RwNnAk6a3aJIkSVpZy50KpaqeVlVPA34GPKaqdq+q3YBH024NJkmSpFlmKvPcPbSqzh97U1U/AnadthJJkiRplU1ltOyFSf4D+ARtOpSXARdOa6kkSZK0SqYS3L2KNonwm/r7bwAfnrYSSZIkaZVNZSqUW4D39ockSZJmscmmQjmxql6U5Hz63SlGVdUjp7VkkiRJWmmT1dyNNcM+e00URJIkSatvucFdVV3dn382mp5kbWABbYoUSZIkzSLLnQolySZJDk3yoST7pHkjcCnwojVXREmSJE3VZM2yxwM3AGcBfwK8FVgX2L+qzpv+okmSJGllTRbcPaCqHgHQ57m7Dtihqm5eIyWTJEnSSpvsDhW3jb2oqtuBywzsJEmSZrfJau4eleSm/jrABv19gKqqTaa9dJIkSVopk42WXXtNFkSSJEmrb7JmWUmSJM0xBneSJEkDMtk8d+utyYJIkiRp9U1Wc3cWQJLj11BZJEmStJomGy27bpIDgScked74hVX12ekrliRJklbFZMHda4CXApsBzxm3rACDO0mSpFlmsqlQvgl8M8nCqvrYGiyTJEmSVtFkNXdjjk/y58BT+vuvAx+pqtsm2UaSJEkzYCrB3ZHAOv0Z4OXAh4E/ma5CSZIkadVMJbh7bFU9auT9GUl+MF0FkiRJ0qqbyiTGtyd54NibJA8Abp++IkmSJGlVTaXm7q3A15JcCgS4H/CqaS2VJEmSVskKg7uqOj3JTsBDaMHdRVV167SXTJIkSSttKjV39GDuh9NcFkmSJK2mqfS5kyRJ0hxhcCdJkjQgKwzukpw+lTRJkiTNvOX2uUuyPrAhsFWSzWmDKQA2Ae67BsomSZKklTTZgIo/A95MC+S+y7Lg7ibg36a3WJIkSVoVyw3uqur9wPuTvLGqPrgGyyRJkqRVNJV57j6Y5AnAjqPrV9Vx01guSZIkrYIVBndJjgceCJzHstuOFWBwJ0mSNMtMZRLj3YGdq6qmuzCSJElaPVOZ5+5HwL2nuyCSJElafVMJ7rYCfpzk1CSnjD2m+gFJ1k7y/SRf6O+3SHJakkv68+Yj6x6aZFGSi5PsO5K+W5Lz+7IPJElPXy/JZ3r62Ul2HNnmwP4ZlyQ5cKrllSRJmsum0iz7jtX8jDcBF9LmxwM4BDi9qt6d5JD+/m1JdgYWALvQpl/5nyQPrqrbgQ8DBwPfAb4E7Ad8GTgIuKGqHpRkAfAe4MVJtgAOozUpF/DdJKdU1Q2ruS+SJEmz2gpr7qrq6xM9ppJ5ku2APwT+YyR5f+DY/vpY4ICR9BOq6taqugxYBOyR5D7AJlV1Vu/3d9y4bcbyOgnYu9fq7QucVlVLekB3Gi0glCRJGrSp3H7s5iQ39cctSW5PctMU838f8NfAHSNp21TV1QD9eeuevi1wxch6V/a0bfvr8el32qaqlgI3AltOktf4fTs4ycIkCxcvXjzFXZIkSZq9plJzt3FVbdIf6wPPBz60ou2SPBu4tqq+O8WyZIK0miR9VbdZllB1VFXtXlW7z58/f4rFlCRJmr2mMqDiTqrq88DTp7DqE4HnJvkpcALw9CSfAK7pTa3052v7+lcC249svx1wVU/fboL0O22TZB6wKbBkkrwkSZIGbSrNss8bebwgybuZoBZsvKo6tKq2q6odaQMlzqiqlwGnAGOjVw8ETu6vTwEW9BGw9wd2As7pTbc3J9mz96d7xbhtxvJ6Qf+MAk4F9kmyeR+Nu09PkyRJGrSpjJZ9zsjrpcBPaQMZVtW7gROTHARcDrwQoKouSHIi8OP+Oa/vI2UBXgscA2xAGyX75Z7+MeD4JItoNXYLel5LkhwOnNvXe2dVLVmNMkuSJM0JU7m37KtW90Oq6kzgzP76emDv5ax3BHDEBOkLgYdPkH4LPTicYNnRwNGrWmZJkqS5aCrNstsl+VySa5Nck+S/+hQnkiRJmmWmMqDi47S+bfelTSfy3z1NkiRJs8xUgrv5VfXxqlraH8cAzhsiSZI0C00luLsuycv6PWLXTvIy4PrpLpgkSZJW3lSCu1cDLwJ+AVxNm3Lk1dNZKEmSJK2aqYyWvRx47hooiyRJklbTCoO7PqHwG4EdR9evKgM+SZKkWWYqkxh/njZZ8H8Dd0xraSRJkrRaphLc3VJVH5j2kkiSJGm1TSW4e3+Sw4CvAreOJVbV96atVJIkSVolUwnuHgG8HHg6y5plq7+XJEnSLDKV4O6PgAdU1e+muzCSJElaPVOZ5+4HwGbTXA5JkiTdDaZSc7cNcFGSc7lznzunQpEkSZplphLcHTbtpZAkSdLdYip3qPj66PskTwT+GPj6xFtIkiRppkyl5o4ku9ICuhcBlwH/NY1lkiRJ0ipabnCX5MHAAuAlwPXAZ4BU1dPWUNkkSZK0kiarubsI+F/gOVW1CCDJW9ZIqSRJkrRKJpsK5fnAL4CvJfn3JHsDWTPFkiRJ0qpYbnBXVZ+rqhcDDwXOBN4CbJPkw0n2WUPlkyRJ0kpY4STGVfXrqvpkVT0b2A44DzhkugsmSZKklTeVO1T8XlUtqaqPVpX3lZUkSZqFViq4kyRJ0uxmcCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQg82a6ALqrnDnTJZidaq+ZLoEkSbOfNXeSJEkDYnAnSZI0IAZ3kiRJAzJtwV2S9ZOck+QHSS5I8vc9fYskpyW5pD9vPrLNoUkWJbk4yb4j6bslOb8v+0CS9PT1knymp5+dZMeRbQ7sn3FJkgOnaz8lSZJmk+msubsVeHpVPQrYFdgvyZ7AIcDpVbUTcHp/T5KdgQXALsB+wJFJ1u55fRg4GNipP/br6QcBN1TVg4D3Au/peW0BHAY8DtgDOGw0iJQkSRqqaQvuqvlVf7tOfxSwP3BsTz8WOKC/3h84oapurarLgEXAHknuA2xSVWdVVQHHjdtmLK+TgL17rd6+wGlVtaSqbgBOY1lAKEmSNFjT2ucuydpJzgOupQVbZwPbVNXVAP156776tsAVI5tf2dO27a/Hp99pm6paCtwIbDlJXuPLd3CShUkWLl68eDX2VJIkaXaY1uCuqm6vql2B7Wi1cA+fZPVMlMUk6au6zWj5jqqq3atq9/nz509SNEmSpLlhjYyWrapfAmfSmkav6U2t9Odr+2pXAtuPbLYdcFVP326C9Dttk2QesCmwZJK8JEmSBm06R8vOT7JZf70B8AzgIuAUYGz06oHAyf31KcCCPgL2/rSBE+f0ptubk+zZ+9O9Ytw2Y3m9ADij98s7FdgnyeZ9IMU+PU2SJGnQpvP2Y/cBju0jXtcCTqyqLyQ5CzgxyUHA5cALAarqgiQnAj8GlgKvr6rbe16vBY4BNgC+3B8AHwOOT7KIVmO3oOe1JMnhwLl9vXdW1ZJp3FdJkqRZYdqCu6r6IfDoCdKvB/ZezjZHAEdMkL4QuEt/vaq6hR4cTrDsaODolSu1JEnS3OYdKiRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBmTagrsk2yf5WpILk1yQ5E09fYskpyW5pD9vPrLNoUkWJbk4yb4j6bslOb8v+0CS9PT1knymp5+dZMeRbQ7sn3FJkgOnaz8lSZJmk+msuVsK/GVVPQzYE3h9kp2BQ4DTq2on4PT+nr5sAbALsB9wZJK1e14fBg4GduqP/Xr6QcANVfUg4L3Ae3peWwCHAY8D9gAOGw0iJUmShmragruqurqqvtdf3wxcCGwL7A8c21c7Fjigv94fOKGqbq2qy4BFwB5J7gNsUlVnVVUBx43bZiyvk4C9e63evsBpVbWkqm4ATmNZQChJkjRYa6TPXW8ufTRwNrBNVV0NLQAEtu6rbQtcMbLZlT1t2/56fPqdtqmqpcCNwJaT5DW+XAcnWZhk4eLFi1djDyVJkmaHaQ/uktwL+C/gzVV102SrTpBWk6Sv6jbLEqqOqqrdq2r3+fPnT1I0SZKkuWFag7sk69ACu09W1Wd78jW9qZX+fG1PvxLYfmTz7YCrevp2E6TfaZsk84BNgSWT5CVJkjRo0zlaNsDHgAur6l9HFp0CjI1ePRA4eSR9QR8Be3/awIlzetPtzUn27Hm+Ytw2Y3m9ADij98s7FdgnyeZ9IMU+PU2SJGnQ5k1j3k8EXg6cn+S8nvZ24N3AiUkOAi4HXghQVRckORH4MW2k7eur6va+3WuBY4ANgC/3B7Tg8fgki2g1dgt6XkuSHA6c29d7Z1Utmab9lCRJmjWmLbirqm8ycd83gL2Xs80RwBETpC8EHj5B+i304HCCZUcDR0+1vJIkSUPgHSokSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUDmzXQBJEnS7HPyty6b6SLMSvs/8f4zXYQVsuZOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGxOBOkiRpQAzuJEmSBsTgTpIkaUAM7iRJkgbE4E6SJGlADO4kSZIGZNqCuyRHJ7k2yY9G0rZIclqSS/rz5iPLDk2yKMnFSfYdSd8tyfl92QeSpKevl+QzPf3sJDuObHNg/4xLkhw4XfsoSZI020xnzd0xwH7j0g4BTq+qnYDT+3uS7AwsAHbp2xyZZO2+zYeBg4Gd+mMsz4OAG6rqQcB7gff0vLYADgMeB+wBHDYaREqSJA3ZtAV3VfUNYMm45P2BY/vrY4EDRtJPqKpbq+oyYBGwR5L7AJtU1VlVVcBx47YZy+skYO9eq7cvcFpVLamqG4DTuGuQKUmSNEhrus/dNlV1NUB/3rqnbwtcMbLelT1t2/56fPqdtqmqpcCNwJaT5HUXSQ5OsjDJwsWLF6/GbkmSJM0Os2VARSZIq0nSV3WbOydWHVVVu1fV7vPnz59SQSVJkmazNR3cXdObWunP1/b0K4HtR9bbDriqp283QfqdtkkyD9iU1gy8vLwkSZIGb00Hd6cAY6NXDwROHklf0EfA3p82cOKc3nR7c5I9e3+6V4zbZiyvFwBn9H55pwL7JNm8D6TYp6dJkiQN3rzpyjjJp4G9gK2SXEkbwfpu4MQkBwGXAy8EqKoLkpwI/BhYCry+qm7vWb2WNvJ2A+DL/QHwMeD4JItoNXYLel5LkhwOnNvXe2dVjR/YIUmSNEjTFtxV1UuWs2jv5ax/BHDEBOkLgYdPkH4LPTicYNnRwNFTLqwkSdJAzJYBFZIkSbobGNxJkiQNiMGdJEnSgExbnztJa8a7v3/bTBdhVjrk0evMdBEkaUZYcydJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQgBneSJEkDYnAnSZI0IAZ3kiRJA2JwJ0mSNCAGd5IkSQNicCdJkjQggw7ukuyX5OIki5IcMtPlkSRJmm6DDe6SrA38G/BMYGfgJUl2ntlSSZIkTa/BBnfAHsCiqrq0qn4HnADsP8NlkiRJmlbzZroA02hb4IqR91cCjxtdIcnBwMH97a+SXLyGyjaXbAVcN9OFAMhMF0BTMWuOl0NnugBakVlzrOTvZroEmoJZc7zMIvdb3oIhB3cTxQJ1pzdVRwFHrZnizE1JFlbV7jNdDs0NHi+aKo8VrQyPl5Uz5GbZK4HtR95vB1w1Q2WRJElaI4Yc3J0L7JTk/knWBRYAp8xwmSRJkqbVYJtlq2ppkjcApwJrA0dX1QUzXKy5yGZrrQyPF02Vx4pWhsfLSkhVrXgtSZIkzQlDbpaVJEm6xzG4kyRJGhCDO0mSpJWQZIP+PCunYDW402Ak2SXJYUn26u9n5T+dZlaSTZJsM9Pl0OyU5OAk+8x0OTR7JXk8cGySHWqWDlwwuNOclmTtJGsB9NHQ/w38Q5L7zNZ/Os2MNGsDfwz8Y5KtZrpMmh2SrJVkbPaIxcAzkzy/L/NHosbuVw9AVZ0FnAgckeT+M1eq5TO405wzerKtqtur6o4kWyZ5ZlV9D/gm8FprZwTtwg1Qze1V9RHgGuAtSTad2dJpNqiqO/r0WfOr6nPAp4E3JtnWH4n3XOOvNT1tryT7VtVJwPXAW2aqfJMxuNOcMXqRHknbLck/AO8Cdu3JHwO2AJ62psuomTdSQ0eSVNUd/fXDkrw3yZOBDwIbAy+ewaJqDRs9Nsal75nkcODbSR5ZVecAPwPePLbdmi2pZtLY9z3uWrNPklOAVwBjPwqPBPaYjbV3BneatcafUEcu0k9O8qyevBbtl9O5VfWPfb2LgcuBBybZbM2VWLPBWA3d2Oteq3sK8EbgPNqxchWthvdRSR40c6XVmjR6bPS7F81LsjnwV8ATqmqnqvphX/3fgOeObTczJdaaMMG1ppJsk+SgJHv35KXAs4HDq+rEvt7/0W51+sQ1WuApMLjTrJJkiyS7wF1PqEkenuRc2q/ppyc5pKrOpd1q7ua+zjp99Z8A9qkauN5XKuPS5id5c5JPJnlMVV1Paz7ZFvhUVd3SV/0R8FtglzVbak23JBuOnAtG09fvx8aPgX8C3k47d5xKq8n9fd+qXnu3VpI91lzJNRMmuNYcCJwMPAB4VZIFwDnA94CH9HXW76v/L3BAT5s1MdWsKYjumXozybwkz+hJBwF/25dtk+QJI6s/E3hzVT2fVi3+kt6v7lTaLyqAO/rzd4EnV9Uvp3sftGaN6wdzx7imkw2A/wdsBHwS+IskBwCfAm4BNhzJ6gogwK1roNiaZknWSbJBkmcD/whs0tM3HWmKvS+wDfBo4M+Bt9IuzOcAP0/yqKq6fSQwPBF4Ss/HptkB6Nec8T8In5bk8Wn3oQd4EPBnwPtp3X2eDawP/Bfwkr7O0v78eWDL0S4gs4HBnWZMkrX6hXkzYKzq++vAvCQfAr4A/FOSv+nNq9sDJyX5Fq3G5SVVdQ3wCWDPJJv0E3Oq6qe0k7VNbgOxnD6XT0rywZEfB3sBvwZOAp4HPIZWI3MOsDWw09i2VXUzcB/gXj0vL95zVJJ705rd7w/8FNgc2DfJCcC3gX9JsiGwB/AI2gX588BHga/1bX4M/GHPcizAW9LTbZodiN40X0l2SrJ5kncA/wIcThuINw94FK3m7mjgvcCrquo62jHz6CQbVtVYcLcj8D/c+YfjjJu34lWk6TH2K6eqrkvyoSS7V9U5SX4NPKyqHpvkIcAbaCfu7wHb9po7AHrn5x8m+S2tZu8z/R/3wbTg8Ko1vmOaFiN9Lh9O+2V9M/BS4JfAm5LcCvwceBVwb+AU4PVVdWvf7kLgWUnOGzkxn0M7OXvxnmN6sD92rf4F8K9Jturnk+uABcDnqmpBklNpNTHX0mp1P9BHxY7ltQHwA+D1wD/0Y+2OJA+l/eDUHNOPj4z0sVyL9t0/hfYj8IW0rhkXVtVuSZ4KfIA2IO964J+q6si+7bwkOwMX00baPwX4Sv+odYAbq+rXa2rfpsKaO60RGZmPbiRtuyT/mORgWs3dCX3RQmDL/vpS4MvAfNo/1n2S/EmS/Xsn+df0GpdnVdVnRmpfrgS+UlW/meZd092st5qsPf59kqck+UfaCfilwL8DH6yqvwDOpp2w76CddN9bVR+vqluTPDvJtsDnaL+u5/V81wIuodXyaQ7ox8HYSMY7+g+5ByR5TZIXAu9Lsivth93WtIFV0GpldqN931eyrN/Uc/t5ZFvgdFpT7eicZh8CLlsjO6fVNnqN6cfH7Wn9crfuAfszgP8EflhVO9IGWO3QW5G+TgvUHkK7Fj03yVuSHEKrWHhODxT3r6qvjHzW14Dj19Q+TpXBndaIkfnoNk6ybpI/Ar5Ia/b4Cm1eqc2TbAF8CbgyyW5VdRut2exe1SaOfCuwHfA64LPAW/ov96v651R//k1VXbGm91OrJss6J/9+RGO/kD+NFowVrd/LfsDzquqFtPPXWLP7D2jT38yjBXHvSvK+JN+hDcDZrKpOq6pDxgZU9JP913oTvuaAfh6pHvA/NMlhtP51twH/R2te3ZM2yOpS+jWuqr4JPAG4gRbobdu7d7yRdh65rKqur6ofjH1Of/5BH5CjWSzJ/ZI8abTPW9rAquNotfMfTPLEXlv7E9pE1QBn0mrpHtvffwE4uKpOpfX9nk+73ryiqt4D7doy2r+uH5M3Tv9erhybZXW36r+q1xqpCk8/GT8H+BPaCNaX0JpLt6fVrl3e1z0X+NOqek+S64F3JHkJrVbveoCq+laSb4/rdxWb1Oam3r9lN9pJ9Asj6W8EXk0L/q+nXcD/i3bM7EzrR/VvtGa0/6T9st6LNojmg0m+Tmt2+bOxC/ZI3r8/XqzZnZ3Gn0dG0l4KPId2rHwdeDpwQVV9rK/zSOBJtFrd/wNeneQyWlB3AbB2VV2U5K+B9avqhjW4W7ob9drV6kFWgDf3fpWPpY2E/kNaM/yrgdfQum5cCXyVNgfqV4BFtPPLE2i1/8cBn02ybi2bieEu5sL1xpo7rbKRJtDfG6t16cvv1583Bw4EPgM8tQdzl9Au1k8e2fyDwMv7668CD6dNEvlbWlPc7z+j/3K/Swd7zX658y3jltJGPv9Jkg8neVuS7WjTk7ylqvYGLgQOof3i/gXw4J7VR4DHJNm4qn5Oq7VZkmT9qvppVf3zWGA3eqx6vMx+o+eREW+i9aM7CfhiP498D7g0ySZ9nf8Dbgd2p/W5fBitBuYM4Dt9OVX126q6YfQ8orllpDVoQ1os81jaD76te4vPHwHX9nPM8bRAbQHwH8C+PZsraeeUXdIGSfwIeERV/W7snNGPkTk32MqDWqskyWY9yFp7XPqWSQ5PcgFtcuH1aL+2f1dVn6plHdlvpP3yftLYtlX1RWDntEEUX6T1o3tFVb15fBNrP/nPmmHnmty44GrspLxNki1pfVyeRRsE8T5a7e79gbHJZN8NPA74Ha3/045J7l1ttOvFLBvh+KGq+mQtm8duwpnmNXtk2Z1Exp9HHpzWH/es3vy6Lu088taq+k/aIBpoUx7tQBv1DK0pdgltepMf0kY7vrOqHlVVR4w/Z3gembuSPD/J/9BGum5KGwhxalW9qa9yCr1vZW82vY3WvedCYJu0W4gtpY2K/ave3LpWtX66YzM5/H7EzhrevdVmcKeVluRhtClJtup9ozYf+eW8L60f1G49KLuF9ut6177t2ASht9M6s26ZO08S+mTgJ1X1y/5POGd/OWmZcc3ou/eT8pnAC2i/qt8CfL+qbq2q82gX6x36tr8FrqbV5J5Pa5rdsWf35Ko6YaT5/y4zzU/nfmnVJNkkyd/S55Dr55G1+rINaRfs3wAHVtVFtMER57AsiBvrUnQGbSqlsYv4Ytq0FF+oqqVV9YGq+knP1+vdHDH+uxo5NsZag54KvBb4O+Cvq+r7tKb4XZLsmjaNzdeBxyfZvWezJ+38Qd92bIqbK6vPh1rL+tHN+YDfPndaFb8ELqJNELsl8HjgW0neQ+vb8ijgZWlTmlxP+yf7TZIF/UJ8f1rNzHnAabQRSkDrUzf+w7xAzx1JtgZu6M0iY3MZ3pFke+CxVfVZ4I9pU1GcMrLdZbT5o3arqu/SmuzfkuSTtEDuMlqz2kbARVV1NUBV3TL2Gf29x8osNhaEV9VN/TxwfZJ70Zpcv5fkeFr/qZ/TvvO1+7EDrf/U3sAZvdls56r6cZLFtFH061fVLVV15nI+c85fsIcsyX1p/eP2AI5Ocko/d2xcVTf3SoB3J3kDLXbZCNgA2DXJDVV1QZKf0s4z5wGLknyAdh55Aq2P3bcAqurza3r/1rR4LtREkqw99mu6/4ONPT+iqs5P8lLgb4C/p/WBORK4CTiCNnDid7RbtzyB1tR2LfAyWlC3Ja255H0T9KvRHJNkPu2X8LNo/eKOqarTkjyoqhb1dV5Iu+H2H9GaS66mXbyvojWlXQj8NfCDqjquN80/EPgL2rxSR1WbqkBzTJJ5I90xRtOfSwv0f0e7g8hDaE2vB9Du6fpM2o/IF9HOId+k1eidT+tLtw7wp8DPa9zAmLGAbnr2SHe3fiy8i1YR8CVaBcLPaAMf9qyqN/b1/pM2TcnnadPU/IY2YfUj+vtLaTMp3Au4uqoOTrIV8Jvxx8jQGdzp99LmAnslfSBDVf1vT9+gqn7bm1Rvo92+Zz1af5c/rqrT00apvZI2tcR/j+T5duCaqvpY/4X+yKr69rjP9UQ8RyVZj9ZB+Ve0gP2HwEOr6gdJltCmEPhC2sTDb6CNbP0xbVqT22gd3/ejNefvAryzZ/3uXss7YWCg2avXwBwInF9VXxi3bGNaQLY57WJ+Eq3T+0F9+eeBj1fVySPbvIR2u7BDac2yTwVuGj3PaO7q14UTgHdV1XfGLXsurU/tB6vqR0neSruF3GFVddPIeq8Cdqyqw5LsTbszzYlV9bORdX4/6fX079XMs1lWACTZl3ZPzjNptXA/6+lvo92K5+iqWpLkS8BBVfXuJJ+i1cydTpssdEvarO470G7IvRutc/OrAKrqV7QpLH7fXNfT7xH/bAP1VGCDqnr5SNrY1CMfon33Y3cK+RHw7Ko6ndY/hiQn0ka/3lFVp6TdaeSi6gNoqmrpPe2kPFf1Gtx30/7vfwh8O23+wo2BE6qNfP4V7dyya9/sXOC2tAEyv6BNV7IfcHKSfYB9aFPcfK7X8l9Ju2fw2Gf6w3DuK9oclddCu0cwsLR/rz+mDbp7Iu388WXgWOB+SX4BvJh2jOxIn2mhn19Ov8uH3MOa5e1gqjF/Bryhqt7Qa+zGRqdeQ/vHGevIfCSt2RXgG7S+dZvTmkgeQGteWwz8L22y2X2rTzA86p72jzY0IwMXnkQfudhr8UY7Q3+Efs/gqlpCq6m7X5L7JPmDJF+jXdxPp01HQLWJhq8YHRhR/U4E079XWhUj39UDaZNF71ptlPvXex+4xcBuSfbq3+MlwC20vrqnAY9k2ZRIVwK/7XnuQuvD+7yqOmKiz/S4GIRH0H4M3Bugqm4b+V4vpU1f8/j+/kLgfrTm2nVod5w5qqoeWSPzWY4fWHVPZM3dPdxY3zra3FDPS/Ir4KHAZWnTmXwe+APaRfmiqvpSkhPT7rn4P7QfCJ+h/aOdRuvn8Fv6r2tPwnPXaO3qJM6izSNG9Xu4jtTIXpVkUZI/q6qP0kY83pt2oj6b1gxzl1/YfVuPlzli5LvaCfhl7+O0NS2Au643n/0b7dZeZ9L6W471p/p72gCKg5M8j9ZM/6Ke53vHPmP8ecTjY3br31emcP6AdiysRTt+vj3y43At2rRI3wT2Tbs/8Fj/zG/1SoN/GvlMW4NG2OfuHmAqF+m06U0+QKul+xZtxOsdtCrvVwOb0AZLhNZp/otV9adJnk0L6L47fXugNWUlT8r0EY/H0KYjODvJOlV1W9qUBUUbQPMmWh+Yr9Dmojqv+mjansdUgkjNUmNNo73f7Wtod6D5Hq2z+3a0PnaXA/9XVZv1bf6dNmXS82jNab+hHRcLJ8p7Te2LVl2/hvy2Rm7n188njwO+O/o/P8G2r6edI46qqrN72tOAnarqqCQPoE2bc0b1OxqNfobHyF0Z3A3Uyl6k+zYb0Grw7kObiuDTtNFqR9L60O1Kaz77PnBJtclER7f3Ij0H9UDsFVV1+Mgo6dDmoJsHnLSCE/M/0W4J9rqqurzn9yfA16vqf5LcG5hXVVeugd3R3WgVgv0taB3e16ONWHwCsEtVvazXvPyY9qNxS1qT27Gjnd57Hp5H5pi06Wr+Griiqv6p95t7B61y4CLad/6XVXXd6CCpkR8GG9HuAf1s2gT2T6UdIx+qqqPHfZatQVNgcDcgq3uRniC/vwV+XVX/mmTTns9Xa9zdIjQ3jZxYN6fNR7hhtXnjHkcbyfhT4Ne0QTEfraprlpPPJrTbg+1MGwW5KfA1WrPr9SPreVKeA1anBmaCvPahTYH0alrQ90e0+wh/qA+g+H3+HhdzR+58X9ex9y+n1b4dQjsHvJg2kn5d2rRH/1BV7xmXz52+997d5yXAwnI09GoxuBuAu/EivRGtg/z9aPNMbQksGP/Luq/rr+s5KMvu6fr7k3L/EfA12ojEDyR5Je2m7UcnOZbW4f11VXVW32bdqvrdBHk/Cti8xk0iq7ljVWtgRrZfjzbyEeDpwEG0prR3LefzDOrmgLTbvz2w+l2DRtI3pY10vznJE2lT4BxJ6z/3dtrguo1oo1yPrDbjwo60Wrq/Gj1+JjoWvM6sOkfLzlFJ1hq5UFe/SN9AmwTy4L7aw4BPV9XYvFJ/RBvROpbHuqN5VtWvadXhT6f9Iz5+osCur+s/3BxUbeTpHWn3AH4a7Rc2wMfpU9bQTsyHJfkWrX/ls0YCu3+hncDvcougqvrBWGA3fplmpyRrj/uurqLNX7ld2m3AtqJdoJ9G6zt5AC1gY/yFub+8nTZg5r9pTWz/Oj6wG1nXWty543LgP5LcByDJ3kn+mzZjwl/2ioSFtOlMnkq7LdxDgT+vqif2Y2Bp7/qzlHZcPWL0A8bV4I3V8nudWUWegOeoabxI/01VLaiqL/Vl9/gh5XPVRN9dkm2TfJY20vkFtMEQVNVxwAPSbh92HW1y2X2q6p1VdXWSx/QsxiYghjZgYkKelGenJOv2Zleg3dO1n0c2TbvN0+20qUrWp80/+CTaeeKztIDtn1k2R+GOSd7Xa/Cq17wspR1bT6qql9S4SYz7ZxrQzWJJdk7ykt5/krRbTJ5Fa/nZs6/2QNqchk+kXXveXG20/A9pE9VfTwv8Xphkl7Q7Gp3Qt1+X9iPiJ8srg8fI6jO4mwPW5EW6n+jXsn/U3DTRd5fkcUke3Fe5ETiKNtHsR4Bn9hMvtF/bfwocTzthfzDJgUlOB/6/XpNzDrCwN5d4bMw901YDM3Y8VNVNvVvIWhOduzS7pNkkyc496W9pU1mNtQBtRpuI+qu0YA7a9WZH2hyVu9Bqep9Am+LotiRPpt2x6Ne0ZtoDgH+rqq/RjsGP1cgdJnT3M7ibpWbyIl1OGjtnjX13SbZPsn6SY4CP0i7oT6l2l5BLafdv/HvaHIUH9s3fC/xpVV1Hu2/wD2hN9P9aVc+vdm/GS6rqcGvmZr+ZroHxPDI39O9oP+AtPen9tO90z37O+AltfrkC1knyCNrkwU8H3lhV+9AGTOzTu/EsAZ5bVVdX1b/29BdW1Rd7/rdX1cVrbAfvoRxQMcv1Ds6LaQHcrsBNtKbTb/RA7wO0OaJ+A2xdVfskeQpwXFXtmDYNxYtZdq+9L/Z81+sncc1BvSk9vRltLG1j2j04X047WZ8JnFNVH0xyKPCEqnpOkhcD+1bVq/sF/2rgUVV1YZIbaU1q50/wmXZ+n8X6j8GNge2q6sdJPk373397tdsFPpAW6J8M3K+q/qr3u30hbYLhG4ENgLfRpkI6lFaDs4g2gnF/Wo3eMVX1xSTzaJ3svVDPcUleR5uv9HNpA+tOpN0N4nraXUJuod06bjdaq8+5tAFYD+vH1XuBHWgjqrcDbuiDJ9Yaaw3CWwiuUdbczbBeQ7f2uLSNkzwlbaLPb9P6uHy3qnaljTp6a1/10cBVVfU8WmfnvZI8rKq+AWye5BFV9Yuqen9VHTgS2MXAbu7pTWgbwO9rRW7v6WP9Ld9Gq639HO3YWJvWbwrgaGDntLsHbEm7X/CjaRftG4DH9vXuV1Xnj/XD7E02NtHPAdbAaDXsTJslYWxg3bdpo1w/Rxv1+lLaPaIvBXarqouAr6b15/4c7bzzwqq6tap+Uu12g6N3q7EWdw0zuJsBXqQ1VUkektZp/Ru0W+3s2NPvl+S4JGcDh/b+MkfSpqq4uDetngysleQ+1aa/uQR4Dq1Z7br+vAmtpu44gKr6ZcbdxsfjZU7ZitbkDnA+7b6ci4AnJnkNrUbmF7Sa/l1pt4N7QlWd02tgNgMOSJvS5GPAP8Lvp6S41f64g3Um8KKR96fQJqD+P9qdia6nzZV6Ma1/3R5V9SbgtdXu6/qfVXXJGi6zJmFwt4Z4kdZUTNC5+VDanIPPqqrdetPp2j3tZFqn9zuA/1ftXotX0C7a0C7qS2knaWg/Fl7Zf1W/o6oeUlX/MP6kbH+6Oc0aGK2K04FNkuzQ319IG/jw/Gr3hf6Lav11L6QF/N8HqKofgrMqzEYGd9PEi7RWxUjT2pt70jHApVX1qyTbJPlj2q2dDgCeTAvsn0Y7JujPf9xfX0brR/WEnvdHgWf217eC89EN0JlYA6OVVG2O1O/RR8hWm9Lma8Dr+vur+vPiqrrLXUoM+GcfB1RMoyQvAp5RVQcn2Ys2EOIZSbYB9gY+T5s76nxaTd42wMer3Sj5LbSauOf30W5vBjaqqr/seW9YbfTi2Gc5k/dAjOvcvD7tgr0uLcA/A/gw8He0GprXj/V7ShsFvS5wJe2G21f3QTfX1Z07NzswYqDS7lLzv7QfkZf3QQ+fow2w+s8k962qq5LMp3WA/+Hohdpj454ryWNpA/ReVFVX9GNp26r60QwXTatg3kwXYOC2og2AAPgOcK8k32PZRfpbtFuDPZy7XqQ/Dhzem2KvTvIJWhPsWCD3m9ETsYHdoOxMC9KoNl/YF4D9qupJYyskOQN4eVVd3PtHvbqv/+Ekf027UwBV9X9j24w20a+xPdEaVVU39HPMwbQfk0vTbi33OuA/R2tgaKPwx2/vsXEPVVXnJvk48NdJPlZV59H6cWsOsklmeo32f7mF1tflN1W1R1UdUm1E2hnA0rGLdJLXAgdW1S9p93j8/UV6gv4vnoiH6Uzu3LT2eWDDXgszFtx/ErgiyYm05pR9+jNVdWRVXbtGS6zZ5IPA3mnTKEH7ofjGGSyP5oiqOgo4lVarqznMZtlplOQFtI6oT+jvHw4cB+zRf1GPNZP9O20C0V1ofWPeXVVnz1jBNaOW07T2X8BnqupTSdatqt/1dR8ALK6qm8flYfPaPViSg2l3jhirgZGmzPPH3GdwN428SGtVJTkOuLyq/qa//wvgOVX1tOWs7yShupMkzwbWqqpTZrosktYsg7tp5kVaq8LOzbo7+ONQumcyuJtmXqS1qmxakyStCoO7NcCLtFaVTWuSpJVlcLeGeJHWqrJpTZK0Mgzu1iAv0pIkaboZ3EmSJA2IkxhLkiQNiMGdJEnSgBjcSZIkDYjBnaQ5J8k2ST6V5NIk301yVpI/upvy3ivJF+6OvHp+vxr3/pVJPnQ35LtZktetbj6ShsfgTtKckiTA54FvVNUDqmo3YAGw3QyVZ94MfObawGaAwZ2kuzC4kzTXPB34XVV9ZCyhqn5WVR+EFvgk+eck5yb5YZI/6+l7JTkzyUlJLkryyR4okmS/nvZN4Hlj+SbZKMnRPa/vJ9m/p78yyX8m+W/gq6u6I0lemORHSX6Q5BtTKP/XknwKOB94N/DAJOf19Y8fK19f/5NJntvLenKSryS5OMlhI+u8LMk5PY+P9qBR0hy3xn9xStJq2gX43iTLDwJurKrHJlkP+FaSsQDs0X37q4BvAU9MshD4d1rQuAj4zEhe/x9wRlW9OslmwDlJ/qcvezzwyKpashr78nfAvlX1857/isq/B/DwqrosyY799a4ASZ4KvAU4OcmmwBOAA4GXjW0H/AY4N8kXgV8DLwaeWFW3JTkSeClw3Grsj6RZwOBO0pyW5N+AJ9Fq8x4L7AM8MskL+iqbAjsBvwPOqaor+3bnATsCvwIuq6pLevongIP7tvsAz03yV/39+sAO/fVpqxHYjU0w+i3gmCQnAp8d+czJyn/ZhBlWfT3JvyXZmlb7+F9VtbRXTp5WVdf3/fss7e+1FNiNFuwBbABcu4r7I2kWMbiTNNdcADx/7E1VvT7JVsDCnhTgjVV16uhGSfYCbh1Jup1l58DlzeYe4PlVdfG4vB5Hq/mait8mWbeqftffbwFc18v+mp7XHwLnJdl1BeVf0WceT6t9WwC8eiR9/P5V/5xjq+rQKe6HpDnCPneS5pozgPWTvHYkbcOR16cCr02yDkCSByfZaJL8LgLun+SB/f1LxuX1xpG+eY9eXiZJLlrOoq/TmkZJsgHwIuBr/f0Dq+rsqvo7WsC3/UqU/2Zg43FpxwBvBqiqC0bS/yDJFv3zD6DVGJ4OvKDX9NGX3295+ydp7rDmTtKcUlWV5ADgvUn+GlhMq9F6W1/lP2jNrd/rQdliWkCzvPxuSXIw8MUk1wHfpPVPAzgceB/ww57XT4Fnj8+j1xxmOR/xJuCjSf68r3NcVX2jL/vnJDv19NOBHwA/nEr5q+r6JN9K8iPgy1X11qq6JsmFtNHEo75Jq9V7EPCpqlrYy/03wFeTrAXcBrwe+Nly9kPSHOG9ZSVpNSV5NvCAqvrADJdjQ9pI2sdU1Y097ZXA7lX1hpksm6Q1x5o7SVpNVXW3TXq8qpI8Azga+NexwE7SPZM1d5IkSQPigApJkqQBMbiTJEkaEIM7SZKkATG4kyRJGhCDO0mSpAH5/wGD3fgdHWHZYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Divvy Riders based on Gender and Usertype- Bar Graph\n",
    "split_gender_data = usertype_gender_df['usertype'].value_counts()\n",
    "blue_colors = [\"deepskyblue\", \"lightskyblue\", \"steelblue\", \"lightsteelblue\"]\n",
    "plt.figure(figsize=(10,8))\n",
    "gender_chart = split_gender_data.plot(kind = 'bar', color = blue_colors, rot = 20)\n",
    "gender_chart.set_ylabel(\"Amount of Riders\")\n",
    "gender_chart.set_xlabel(\"Gender, Usertype\")\n",
    "gender_chart.set_title(\"Total Divvy Riders based on Gender and Usertype\")\n",
    "plt.savefig('Images/Gender_Customers_Only.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QRERiSyUoIiKxpYPqipRAY1MuQXBswY2LTpsUfT8E6AfUhacUwVHsizmCA/c2h6eW1b6u/P4DYEFDfV17mZ6OSGzoUEoi69DYlBsAbLeW0xbAUIIjyFfSMmABML/o68rv3wHmNtTXLapwJpGaohIUCTU25bYC9i467QXsRFBytWop8Aowe7WvrzXU1+V8BhOpBipBiZ3Gplx/4ABgX4Ky24eg8Db3mavCuoDXgOeB6eFpRkN9XavPUCKVphKUyGtsym0JHAkcEX49gGD5nKwqD8zio1KcDvynob4u7zWVSBmpBCVyGptyewFHERTekQRTmrJ+WoGngMeBx4Cmhvq6gt9IIqWjEpSa19iUGwZ8CjgeOA4Y6TdRpC0BniQoxMca6uvmec4jskFUglKTGpty+xGU3vEE05za3MeP1wlGiQ8A4xvq6zo85xHpE5Wg1ITGplwKOAb4KkHxbe03kXQjCzwI/A14WCvZSC1QCUrVamzKGXA0cBJB+Q33m0j6oBV4GPg78EBDfV3Wcx6RbqkEpeo0NuUOAr4OnABs4zmObLgOYDxwJ3CvpkylmqgEpSo0NuW2B75LUH67eI4j5bOEoAxvbKivm+k7jIhKULwJ97f5WeAsgrU6tUP3eHkW+BNwV0N93QrfYSSeVIJSceHuyU4HziDY96bEWyvByjQ3NtTXTfQdRuJFJSgVEa7kcgzBqO+LaJMG6d5M4Argz1p2KJWgEpSyamzK9QNOAc4D9vQcR2rH+8C1wFUN9XXv+w4j0aUSlLII9+JyFvBjYITnOFK72oFbgEsa6ute85xFIkglKCXV2JTbHDgXOJvgGHsipVAA/gFc0FBfN8N3GIkOlaCURGNTbiTwM+BMYJDnOBJt9wK/aqiv+4/vIFL7VIKyQRqbcpsCvyQY+fX3HEfiowD8Bfh1Q33dHN9hpHapBGW9NDblBgI/Ac4HhvlNIzHWBdwG/K6hvu5N32Gk9qgEpU8am3JJ4DTgN+iQRVI9OoEbgDEN9XULfIeR2qESlF5rbMr9F3ABsIfnKCI9aQUagYsb6uvafYeR6qcSlHVqbModTLAB8xGeo4j01jzg3Ib6unt9B5HqphKUHjU25TYmGPmdifbrKbVpPPDjhvq62b6DSHVSCUq3Gpty3wIuBrbwnUVkA+WAccBvtaNuWZ1KUFbR2JT7GHANMMp3FpESew9oaKivu9V3EKkeKkEBoLEpNwj4NXAOUOc5jkg5PQacoU0qBFSCAjQ25Y4FrkeHNZL4yBJs43ptQ32d3gRjTCUYY41NuY2ASwh2dC0SR08A322or3vDdxDxQyUYU41NucMJ9rSxi+8sIp41Aw3A1RoVxo9KMGbC4/v9lmBn10nPcUSqSYZgVPi67yBSOSrBGGlsyu0D3A7s5zuLSJVqBs5qqK+703cQqQyVYAw0NuUM+CnwB6Cf5zgiteAm4IcN9XWtvoNIeakEIy481NHtwGd8ZxGpMbOAExvq62b5DiLlo11hRVhjU+4gYAYqQJH1sRcwvbEpd5rvIFI+GglGVGNT7iyCnV7rQLciG+4O4PsN9XXNvoNIaakEIybc88u1wCm+s4hEzKvAVxrq6/7jO4iUjkowQhqbcrsBfwf29p1FJKKywDca6uvu9x1ESkPLBCOisSn3BeAZVIAi5TQEuLexKfdz30GkNDQSjIDGptw5BLs/04cakcq5DTizob6uw3cQWX8qwRrW2JRLAn8EzvadRSSmngK+1FBft9B3EFk/KsEa1diUGwzcDXzWdxaRmHsL+EJDfd0LvoNI32n6rAY1NuW2BiahAhSpBtsBU8Ll8lJjVII1prEptz/wNLC/3yQiUmQj4B+NTbnv+A4ifaMSrCHhwW8nAVv7ziIia0gCNzU25c71HUR6TyVYIxqbcl8G7gMG+84iIj0y4NLGptwY30Gkd7RiTA1obMqdDNwKpHxnEZFeuxY4u6G+ruA7iPRMJVjlGpty3wWuR6N2kVr0F+CUhvq6nO8g0j29sVaxxqbcD4Eb0O9JpFadCNwf7tNXqpDeXKtUY1PufOBKgmUMIlK7jgUeaGzKDfQdRNakEqxCjU253wKNvnOISMl8HPhnY1NOhzarMirBKtPYlPsN8L++c4hIyR0L/LWxKVfnO4h8RCvGVJHGptxPgMt95xCRsvo7cGJDfV3edxBRCVaNxqbcqcCNaBmgSBz8mWCtUW0+4ZlKsAo0NuW+QrAqddJ3FhGpmJuB7zbU1+lN2CMtE/SssSn3aYJPhSpAkXg5Fbjad4i4Uwl61NiUOwL4J9DPdxYR8eKsxqbcr32HiDNNh3rS2JTbD8gAG/tNIiJV4PSG+robfYeII5WgB41NuW0JDoc0wncWEakKXQQH5n3Yd5C40XRohTU25YYAD6ACFJGPpN7qsnMtQ73vIHGjEqygxqZcErgb2Nd3FhGpHk2dlrmmJfVJ4H7LMNJ3njhRCVbQ3C77JfAZ3zlEpDo4R+fDbYkp97Sl0uFZWxMUoXa4XSFaJlghluEM4PqvDezK1Ne50WbaKF4kzpzjg9tak2/M7krs383F/wS+4tLoDbrMVIIVYBmOBv4N1AEc1i8/7QsDCvua6dOeSBzlHW//X3Mq917BdlrL1S5yac6vWKiYUgmWmWXYDngWGF58/g7JwstnbJTfJGFs5SeZlMoH773NX//3NLKL38MSCQ758ukcefIP+fP5J7P4zVcBaMsuZ+CQYfzo7mfXuP2Fn92V/hsNJpFIkkim+MGd0wB4+I//j1enPMqI3ffjhN/fDMCMB+6gbcUyjjz5h5V7glJS7Y6XLs2mhjc7G77ua3OiS3NP2UPFWMp3gCgL5/XvY7UCBHgjn9jzoqy9d86Qrpf7G3tWPp2USiKZ4jPnXMTWe9bT0ZJl3DcOZZfDjuHkC//84XUevOznDBg8tMf7OOO6x9hok80//Lk9u5y3XpjGj++Zwd2//BbvzXmRzbbdhRn3386p//dAWZ+PlM/SAk9fkU3tk8N6Owt0g2WY4dLMLWuwGNOKMeV1I7BfTxcud7bVmBWp7ZfkmVbBTFJiQ4ePYOs9gzXb+280hC123IMV78//8HLnHC8+9jf2O+7EXt+nJRJ0dXXinKOrvY1Eqo6Jt13KEV8/m2SdjsRTi17rsgmXZFMH96EAAYYC91gGHYewTFSCZWIZTgNOWtf1ctigS5pTh76Us0z5U0m5LZv/BvNfeYFt9z7kw/PemDGZwZtuwebb7drtbcyMm87+DONOPpTpf/8TEJTp3p/4EuO+fjCbbL0jAwYP451Zz/Kx9Bcq8jykdJyjMLUjMeFPLanRDluf99x64IoSx5KQlgmWgWXYHXgO2KgvtxvVLz/luAGFg8z0qa8WdbQ2c/3px/Dx7zaw9zFf+vD8e8f+gM223ZmjTzmn29utWDSfocNH0rz0fW78/vF84edXsOOBR69ynb//7nscfsJZvPtyE3OmPcZWu+7DJ07/RVmfj2w452j7Z1ty5jO5xKEluLuTXJq/lOB+pIhGgiVmGfoBd9HHAgSY2Jk88qbW5KsFx6LSJ5Nyyudy3PnTE9n/M19fpQDzXV3MeuJe9v3013q87dDhwbbRgzfdgr0+/kXenvXMKpfPn90EwObb78aMB+7g5AvvYuHcWSx+a04ZnomUSsGx6E8tyddLVIAQLB/cpUT3JSGVYOk1wvrv+mhuV2KfS5tTHZ0OvcPVCOccf//dmQzfcQ+O/uZPVrls7tP/ZvgOuzNsy226vW1nWwsdLdkPv58z7XG23HmvVa4z/urf8qnv/5p8Vw5XCA5GbokEufbW0j8ZKYkux+uXNafaX88n9lr3tXttCPBXLR8sLZVgCVmG44CfbOj9LC3YNmNWpEZ8UGD6hqeScnvz+ak0PXgnrz/zJFeedBBXnnQQsycH+0GeOf6eNVaIWbFoPjf/MFi217xkIdeeluaPJx7IVaccwR5HHc/uRx774XVnPfkvttnrQIYOH8nAIRuz3b6HccUJ9WDGiN16XOdKPGot8MLYbGrTJQXbtgx3vz9aPlhSWiZYIpZhS2AmsEXJ7hNXOHVQftKudW50qe5TRMrn/TxTrmxOHZzHyn2M0K+7NHeX+TFiQSVYApbBgEeAT5fj/j/ZPz/pE/0Lh5mhdeNFqtRLOZtwe2tyFFgldomYBQ50aS022VCaDi2NcylTAQI83pE8+vbW5CznWFauxxCR9eMcXf9uT0y6vTU1ukIFCMHywVsto/fwDaWR4AayDAcCU4FyT38wPOHe/NHgrkLK2LHcjyUi6+Yc2bvbkq/OzCUO9BThRy7NOE+PHQkqwQ1gGQYALwC7VeoxB+CWnzuka+6QBL7+6UQEKDgWXNOSzL6TT1Ts/78bzcBeLs1bHjPUNA2lN8wvqWABArRjwxqzqf3e7LKJlXxcEflIp+OVi7IpPBcgwGDgOs8ZappGguvJMuxBMAos+zRoTz47ID/hyH6Fo8xI+sogEjcrCjx7WTa1ewc2xHeWIqe4NHf4DlGLVILryTI8CaR959i3rvDsSQPzu5oxzHcWkah7p8smXdOSPLyAVdsReJYAe7q09jbVV5oOXQ+W4dtUQQECzMwlDhrXnFqcd1omIFIuzuGe67QJV7Wkjq7CAgTYDLjSd4hapJFgH1mGzYDZwObrum4lDTK39LzBXW8PSvR86CYR6Tvn6HioPfHc5M7kEb6z9MLnXRodcLIPNBLsu4uosgIEaHW26dhsas/5eSb7ziISFc6x7NbW5OwaKUCAayxDz0dvljWoBPvAMhwNnOo7R0/yWL9xzXVHPdNpE5yj4DuPSC3LO976Y3Pqg1e6ErU0u7INwU78pZc0HdpLlqEOeB74mOcovXJQXWH6lwfmP2bGYN9ZRGpNu2PWpdnUls3Oqm7WpxcccLhL87TvILVAI8He+xk1UoAAz+YSh1zTknw373jXdxaRWrK0wLQxK1I71WgBAhhwse8QtUIjwV6wDDsCs4CBvrP01RBzi84d0vX+AKOUxzUTiaTXumzCjS3Jox0WhQHCf7k0//IdotpF4RddCY3UYAECZJ0NH7MitfP7eab6ziJSrZyjMKUjMeFPLanRESlAgAstQzVuzlFVovLLLhvLUA98zXeODdGFDbi8ue6IFzot4xwa+osUcY7Wf7Qln32gPRm143buDpzuO0S103ToOliGh4HjfOcolSP65Z/63IDC/ma1ObIVKaWCY9GfWpKL5uUTNbO8v48WAru4NM2+g1QrjQTXwjKMIkIFCDC1M3n4DS3JeQXHQt9ZRHzKOV67rDnVEeECBNiSYKU+6YFGgmthGSYDR/rOUQ4bm1twzpCu5f2MPXxnEam0lgLPX5JN7diOxWGfuy3Ari7NAt9BqpFGgj2wDJ8logUI8IGzEWNWpLZdWmCa7ywilbQwz5QLsqmPxaQAATYCfus7RLXSSLAblsGAJojDfjid+9ag/IQ961zadxKRcpuVswl3tKaitgJMb+SBfV2al3wHqTYaCXbvRGJRgABmt7Wm0o+2JyY7R6fvNCLl4Bxdj7cnJsW0AAGSwIW+Q1QjjQRXE25X8zKwi+8slbZrqjDzO4PyIxNWfTsIF1lfzrHirrbknBdziQN9Z6kCh2l3aqvSSHBNpxHDAgSY05XY97LmVFvOMdd3FpFSKDjmX9WSXKgC/NDPfQeoNhoJFrEMA4A5BHtij63+uBXnDOl6dViCg3xnEVlfnY7Zl2VTGy93tpXvLFWkAOzh0szxHaRaaCS4qlOIeQECdGBDL8ym6l/rsgm+s4isj+UFnh2zIrWNCnANCeA83yGqiUaCRSzDf0A7mi726f75Sen+hcPNtA9CqQ1vddnEa1uSRzos6TtLlWoHtndp3vcdpBpoJBiyDJ9CBbiG8R3Jo+9sTb7oHB/4ziKyNs7hnu20zDUtqVEqwLUaAPzId4hqoZFgyDI8BBzvO0e12jLh5v1gcJeljB18ZxFZnXN0PNCemDG1M3m47yw1YimwnUvT4juIbxoJApZhdyK2j9BSW1iwHcdmUxs3F2jynUWkmHMsvbk1+YoKsE82RUeYAFSCK/2Y4GjMshZtzja+IJva5+0um+Q7iwhA3vHmH5tTy+d0Jfb1naUGnaPjDaoEsQybAN/ynaNWFLDU1S2po6d2JCY4R953HomvNseLF2RTgxcWbEffWWrU9sAJvkP4FvsSBM4k2MGs9MH97cnR97Qlm5xjhe8sEj9L8kwbuyK1a4uzzXxnqXGxP8xSrFeMCacCXge29Z2lVo1MuLn/Pbirf9L0GkplzMnZhJtak6PAtAijND7h0jzpO4QvcR8JfgUV4AaZX7BdGrOpga0FZvrOItHmHPlJHYmJN7WmRqsAS+o03wF8ivtIcBpwqO8cUZDEdZ49uGv6iCRH+c4i0eMcLX9rS86akUsc4jtLBLUCI1w6nos2YjsStAz7owIsmTzW78rmuqOe67SMc8T3k5WUXMHx/vUtybdUgGUziBivIBPbEgS+6TtAFP2tLZW+tz3xtHPaCFc2XM7x2qXNqc438ok9fWeJuO/4DuBLLKdDLUMCeBsY6TtLVG2XLMz+3kb5YQljhO8sUptaCjRdkk3t1I4N850lJnaL49El4joS/DgqwLJ6K5/Y48JsKtHueMl3Fqk9C/JMHptN7a0CrKjv+A7gQ1xL8Bu+A8TBCmdbjlmR2nFxnqd8Z5Ha8WLOJlzZXHdUAavznSVmTglnyWIldtOh4YFzFwJDfWeJD+dOHpSfuE+dG+07iVQv58g93pF4+omOpNYw9ufTLs1jvkNUUuxaH/gcKsAKM/tza2r0g22Jqc7R7juNVB/nWH5na/JFFaB3p/oOUGlxHAneC3zRd4642jlZmHXaRvnhCWML31mkOuQd717Tkmx9N5/Y1XcWoY1gm8HlvoNUSqxGguHOsnXMQI9eyyf2uiSb6up0vOI7i/jX4Xj5omwqpQKsGgOBE32HqKRYlSDwNaCf7xBxt8zZyDErUlsvKzDddxbxZ3mBZ8auSG23wtmWvrPIKr7mO0Alxa0EtYF8lejEBl+cTR30Ss4m+M4ilfdml028MJs6oBPTEVyqzyjLMMR3iEqJTQlahu1A+7WsJg5L3NKaGv14e2Kyc3T6ziPl5xzumU6bcG1LapTDkr7zSLf6AZ/2HaJSYlOCBGuFas/zVejfHcmjbmtNvuwcS31nkfJxjvb72xPT/tGW0qYy1e9zvgNUSpxK8FjfAaRns7sS+13WnGrOOV7znUVKzzmW3NyanPNUZ/Jw31mkVz5jmXgMGmKxiYRlqAOWAoN9Z5G1G4Bbfs6QrjlDExzkO4uURpfjzSubUywq2Pa+s0ifHObSPO07RLnFZSR4JCrAmtCODWvMpurndWmFmShoc7zYmE0NVgHWpFhMicalBDUVWkMclry+JTV6UkdionN0+c4j62dxnqfGrkjt2uJsM99ZZL3EogTjMh06A6j3nUP6bu9UYcbJg/I7m6GjCdSQV3I24ZbW5CiwWCxXirBtXJp3fYcop8iPBC3DFsD+vnPI+vlPV+KAPzanlnY53vSdRdbNOfITOxITb2lNjVYBRkLkR4ORL0GCqVD9M9awhQXb8YJsamhLged9Z5GeOUfL39qSMx5uT47ynUVKRiUYAVoeGAGtzjYZm03t9U6XTfKdRdZUcCy8riX51oxc4mDfWaSkjgkPPxdZkS7BcDuX2Oz5IOoKWN1VLamjn+5ITHCOgu88Esg55lySTXW9mU/s6TuLlNxAINIfbCJdggQrwwz3HUJK69725Oi/tSWfdY6s7yxx11xgxtgVqS2WOdvadxYpm8N8ByinqJegRoERNSOXOOSqluR7ecc7vrPE1fw8ky/IpvZpx7TmbrRFei8/US/BI3wHkPJ5N5/YtTGb6t/meNF3lrh5odMy45rrjipgdb6zSNlpJFjDtOutiGt2NnzMitRuC/NM8Z0lDpwj92h7YvLdbam07yxSMSMsQ2T3+BPZErQMI4ERvnNI+eWx/lc01x3Z1GkZ54j+3h88cY7ld7Qm/5PpSOqQZPET2SnRyJYgGgXGzj1tqfR97YmnnaPVd5aoyTveGdecWvxSV0J7XoqnyE6JqgQlUqZ1Jg+7viX5ZsHxnu8sUdHhePnCbKrfgoLt7DuLeKORYA1SCcbUG/nEnhdlU3Q4XvadpdZ9UGD62BWp7bLOtvCdRbyqj+pG81EuwQN9BxB/ljvbasyK1PZL8kzznaVWvdFlEy/Kpg7sxDbynUW8qwMO8B2iHCJZgpZhO0CfXGMuhw26pDl16Es5y/jOUkucwz3dkZhwXUtqlMOSvvNI1YjklGgkSxBNhcqHzG5vTaUfbktMcY4O32mqnXO039eeePre9uRo31mk6qgEa4hKUFYxsTN55E2tyVcLjkW+s1SrgmPxTa3JOdM6k5FdE1A2yL6+A5SDSlBiY25XYp9Lm1MdnY45vrNUmy7HvCuaUy1zuxL7+M4iVWsny9Dfd4hSi2oJaqUY6dbSgm0zdkVqqw8KTPedpVq0Fph5QTY1bFHBIrtXECmJJLCr7xClFrkStAxbApv6ziHVqwMbclE2ddDcLpvgO4tvi/JMHZtN7d7qTP8z0huRO1xW5EqQCH5SkdJzWOLGltToJ9oTk5wj5zuPD7NzlrmsOXV4HovcFJeUjUqwBuziO4DUjsc6kkff3pqc5RzLfGepFOfIZ9oTE29tTaXBzHceqSl7+A5QaipBib2XuxL7X96cWtHlmOc7S7k5R/M9bcmmRzuSo3xnkZq0m+8ApaYSFAEWFWz7MStSm2YLPOc7S7kUHO9d05J89/lcQmtPy/qK3P5jVYIioXZsWGM2td+bXTbRd5ZS63TMuSSbKrydT+zuO4vUtI0twya+Q5RSFEtwR98BpHYVsNS1LalRkzsSE5wj7ztPKWQLPDd2RWrLZc5G+s4ikRCp0WCkStAyDEabR0gJPNieHH13W7LJOZb7zrIh5ueZ3JhN7deBDfWdRSJjJ98BSilSJQhs5zuARMfMXOKgcc2pxXnHW76zrI/nOy0zrrnuqAKW8p1FIkUjwSqmEpSSWlCwnS/IpjZqLfCC7yy95RydD7clpvylLZX2nUUiKVLvsypBkXVocbbZ2Gxqz/l5JvvOsi7Osfz21uRLEzuTR/rOIpG1me8ApaQSFOmFPNZvXHPdUc90WsY5Cr7zdCfveGdcc2rxy12J/X1nkUhTCVaxEb4DSLT9oy2V/mdb8hnnaPGdpVi746ULs6n+CwoWqeU1UpVUglVsmO8AEn3P5BKHXtOSfKfgmO87C8CyAtPHrkjtkHU23HcWiQWVYBVTCUpFvJ1P7N6YTdW1O2b5zDGvyyZcnE0dlMMG+cwhsaISrGLaFkoqJuts+JgVqZ3fzzO10o/tHIWnOhITrm9JjXZY1P6PpboNtAyR+dAVtX8ejQSlorqwAZc31x3xQrDCjKvEYzpH273tien3tSdHV+LxRLoRmdGgSlCkBO5uS6UfaE9Mc462cj5OwbH4xpbk69M7k4eV83FE1kElWKVUguLN1M7k4Te0JOcVHAvLcf9djnmXN6daX8sn9irH/Yv0gUqw2liGFDDQdw6Jt3n5xMcuzqYKnY7Zpbzf1gIvXJBNbby4YNoWVqqBSrAKaRQoVeEDZyPGrEhtu7TAtFLc3/t5po7NpvZodRapQ9hITVMJViGVoFSNTmyji7OpQ2fnLLMh9/NyziZc3pw6PI/1L1E0kVKIzNF6olSC2jxCqozZra2p9Pj2xGTn6OzLLZ2j68n2xKTbWlOjwaxcCUXWU2SOTBKlEtRIUKrSkx3Jo25pTc52jiW9ub5zZO9uSz4/viN5dLmziaynyHwwi1IJDvEdQKQnr3Yl9r20OdWac8xd2/UKjgXXtCTnz8wlDqpUNpH1oBIUkb5ZUrBtx6xIbbG8wLPdXd7peOWibIq384ndK51NpI9UglUo7zuAyLp0YEMvzKbqX+uyCcXnZws8N3ZFauRyZzoSikgFRakEu3wHEOkNhyX/1JIa/WR7YpJzdL3TZZMas6n9OjBN6UutiMxIMDJr+KASlBozviN59Au5xLyFBdMKMFJrIlOCURoJajpUas7Cgu3oO4PIelAJViGNBEVEKkMlWIU0EhQRqQyVYBXSSFBEpDJUglVII0ERkcpQCVYhjQRFRCqjrAePrqQolaBGgiIilbHUd4BSiVIJ5nwHEBGJiWW+A5RKlEqw2XcAEZGY0EiwCi0CnO8QIiIxoJFgtXFp8kTo04mISBVTCVap930HEBGJgcgMOFSCIiLSVxoJVqlFvgOIiERcq0vT6TtEqUStBDUSFBEpr8hMhYJKUERE+iYyU6GgEhQRkb7RSLCKqQRFRMorUu+zKkEREemL13wHKCWVoIiI9IVKsIrN9x1ARCTi5voOUEqRKkGXJouKUESknFSCVW627wAiIhHVDrzrO0QpqQRFRKS3XnfpaB2tJ4ol+LLvACIiERWpqVCIZglqJCgiUh6RWjMUVIIiItJ7GglWO5fmHSDrO4eISASpBGvEK74DiIhEkKZDa4SmREVESqsDeNN3iFJTCYqISG8879J0+Q5RalEtQW0mISJSWtN9ByiHqJbgLN8BREQi5mnfAcohqiX4KhE78KOIiGcaCdaKcLc+U33nEBGJiKUuzRzfIcohkiUYmuI7gIhIRDzjO0C5qARFRGRdIrk8EKJdgs8Anb5DiIhEQCSXB0KES9ClaQdm+M4hIhIBGgnWKE2JiohsmHkuzWLfIcol6iU42XcAEZEaF9lRIES/BLWZhIjIhpnmO0A5RboEXZr3IZrbtoiIVMh43wHKKdIlGNJyQRGR9TPPpaO9L+Y4lOBE3wFERGrUw74DlFscSvAhwPkOISJSgx7yHaDcIl+CLs1CIr52k4hIGbQDT/gOUW6RL8HQv3wHEBGpMRNcmjbfIcotLiV4n+8AIiI1JvJToRCTEnRpXgLm+s4hIlJDHvQdoBJiUYIhTYmKiPTOHJfmNd8hKiFOJagpURGR3onFVCjEqwSnAEt8hxARqQGR3z5wpdiUoEuTBx7wnUNEpMplgYzvEJUSmxIMaUpURGTt/ubSdPgOUSlxK8FHCTYAFRGR7t3mO0AlxaoEXZoW4BHfOUREqtSbwATfISopViUYutl3ABGRKnWHS8drX8txLMGHgPd8hxARqUKxmgqFGJagS9MF3Oo7h4hIlZnu0rzqO0Slxa4EQzf5DiAiUmViNwqEmJZg+Glnku8cIiJVohO423cIH2JZgqEbfQcQEakSD7l0PPeoFecS/CuwwncIEZEqEMupUIhxCbo0rcBdvnOIiHi2hJgcNqk7sS3BkKZERSTubnNpOn2H8CXWJejSPAO86DuHiIgnBWCc7xA+xboEQxoNikhc3efSzPMdwieVINxCcOgQEZG4ucJ3AN9iX4IuzXLgBt85REQq7HmXjtfOsrsT+xIMXQHkfIcQEamgP/oOUA1UgoBL8zYx3VuCiMTSu8CffYeoBirBj1ziO4CISIVcEefNIoqpBEMuzUx0wF0Rib4PgOt8h6gWKsFVjfEdQESkzK52aa0Rv5JKsIhLMxm0tpSIRFY7WiFmFSrBNf3edwARkTK50aV533eIamLOOd8Zqo5lmAoc7juHiEgJtQC7uDTv+Q5STTQS7N4ffAcQESmxy1SAa9JIsAcaDYpIhCwCdtYKMWvSSLBn5/oOICJSIn9QAXZPJdgDl2Ya8BffOURENtDrwLW+Q1QrleDanU+wSrGISK36lfYO0zOV4Fq4NG+ibWpEpHY1AXf5DlHNVILrNha0XY2I1KQGl0ZrP66FSnAdXJoVwK995xAR6aPHXZrxvkNUO5Vg79wAzPIdQkSklxzQ4DtELVAJ9oJLkwd+6juHiEgv3enSPOc7RC1QCfaSS/MI8KjvHCIi67AEbefcayrBvjkPyPsOISKyFue5NIt8h6gVKsE+cGlmAZf7ziEi0oN/uzS3+g5RS1SCffcr4FXfIUREVtMGfM93iFqjEuwjl6YdOA0o+M4iIlLkty7Na75D1BqV4HpwaaYA43znEBEJvQBc6jtELVIJrr9fgD51iYh3BeAMl6bLd5BapBJcTy5NK/Bd0C6JRMSrcS7NM75D1CqV4AZwaSYA1/jOISKx9RbwP75D1DKV4IY7H3jDdwgRiaXvuzTNvkPUMpXgBgr/AM/wnUNEYudql+Yh3yFqnTmnRVqlYBmuR2UYX2+9Ar878aOfF7wOp/4OFr8LU++Hun4wcmc4/2YYvPGatz9pBxg0BBJJSKbgumeD8687H6Y/DDvvD7+4LThv/O2wYil89cdlflJSxV4ADnVpOnwHqXUqwRKxDEOBGcDOvrOIZ/k8fG1ruPppePsVOOATYbGdH1z+vQvXvM1JOwTFN2zzj85rXg6/+BxcOQn+8A04uQG23gX+3+fgokcgVVeRpyNVpxk40KW1045S0HRoiYTHHfwq0O47i3g249/BqG+r7eHgTwcFCPCxw2DRO72/n0QCcp3gHHS2BaV398Xw5R+pAOPt+yrA0lEJlpBL8zzwA985xLMn7oZjvr7m+Q/fBIce3/1tzOBnn4YzD4T7rw/OGzQERn0FzqiHrXaEjYbBK8/AUV8sX3apdre4NHf4DhElmg4tA8twE3Cq7xziQa4TvjoSbp4Fm2750fl3jIFXnoXf/SMovNUtng+bj4Rl78NPPwU/Ggf7jVr1OhefDv91Nrz6HDwzHnbeF07R2vEx8jJwsEvT4jtIlGgkWB5nAzN9hxAPnn4Ydjtg1QJ85FZ46gH45Z3dFyAEBQiwyRZw9Jdg9vRVL5/TFHzdZjcYfxv85h6Y9x94Z07pn4NUo3bgRBVg6akEy8ClaSNYPrjCdxapsCfugk8UTYVOfwTuvhDG3AcDBnV/m7YWaM1+9P2z42HHvVe9zk2/CtY2zeeCFW8ALAHtraV/DlKNfuLSvOg7RBSpBMvEpZmDpkTjpb0VnnsMjv7yR+f98QdBwf30U3D6/nDZWcH5i+dDw2eC75cthB8eBd/dD75/CBz2WTjkuI/uY/K9sMfBwWhx8Maw1+Fw2j7BqHKX/Sr05MSje1ya63yHiCotEywzy3ApcK7vHCJSk14DDgjXPpcy0Eiw/M4HpvgOISI15wPgcyrA8lIJlll4eJMTgPd9ZxGRmpEDvuLSzPYdJOpUghXg0swHvow2pBeR3jnLpXnCd4g4UAlWSHg0+m8SHABTRKQnjS7NTb5DxIVKsIJcmr8D5/jOISJV6x7gF75DxInWDvXAMlwM/NR3DhGpKtOAj7u0FptUkkaCfvwcuMt3CBGpGvOAL6oAK08jQU8sQz/gEeDjvrOIiFfLgcNdmpd9B4kjjQQ9cWk6gS+BdoUkEmNdwFdVgP6oBD1yaZYDnwH6cJA5EYmIPPBtl+Zx30HiTCXomUvzDnA8wZSIiMRDATjVpfmz7yBxpxKsAi7Nf4DPA82+s4hI2RWA77o0t/sOIirBquHSTAKOA7K+s4hI2TjgDJfmFt9BJKASrCLhXmU+haZGRaLIAd/T3mCqi0qwyrg0TwPHAMt8ZxGRkjrbpbnBdwhZlUqwCrk0zwGfAJb4ziIiJfEDl+Ya3yFkTSrBKuXSPE+wIb0OwSRS237s0lzlO4R0TyVYxVyaF4E08J7nKCKyfs5zaa70HUJ6pt2m1QDLsBvwBLC17ywi0itdaCWYmqASrBGWYWeCItzOdxYRWatm4GsuzSO+g8i6aTq0Rrg0rwFHAE2+s4hIjxYAo1SAtUMlWENcmneBo4H7fGcRkTW8BBzm0vqgWktUgjXGpWkhOPrEpb6ziMiHMsCRLs1bvoNI32iZYA2zDGcAVwMp31lEYuwu4Dvh4dGkxmgkWMPCvU8cD3zgOYpIXF0IfEMFWLs0EowAy7AH8CCwk+8sIjGRB37k0lztO4hsGJVgRFiGzYF/Akf5ziIScYsIRn+P+Q4iG07ToRHh0iwm2PG2jlEmUj6Tgf1VgNGhkWAEWYb/Jlh7dIDvLCIR4YBLgF+4NF2+w0jpqAQjyjLsC9wN7Ok7i0iNWwZ826W533cQKT1Nh0aUSzMTOAi40XcWkRr2DHCACjC6NBKMActwEnAdMNR3FpEa8n8ER4HQ5g8RphKMCcuwE8FGvYf4ziJS5bLA6S7NPb6DSPlpOjQmXJrXCTafuJhgIb+IrGkGcJAKMD40Eowhy3AscBuwhe8sIlUiB/weuEBrf8aLSjCmLMNWwDXAf3mOIuLb8wT7/nzBdxCpPJVgzFmGrxKsALCl7ywiFZYDxgJjXJqc7zDih0pQsAybEGxcf6rvLCIVMh04I9yUSGJMJSgfsgyfJNiUQjvilqhqBv4HGOfSFHyHEf9UgrIKyzAA+AXwc6C/5zgipfQg8N868K0UUwlKtyzDbsBVwCd9ZxHZQG8AP3dp/uo7iFQflaCsVbi3mcuAEb6ziPTRBwQrvlzp0nR4ziJVSiUo62QZNgLOAX6Gdr0m1S9HsPnP71yaJb7DSHVTCUqvWYbNgP8HnI0O0yTV6R/A+S7NXN9BpDaoBKXPLMO2wG+AbwNJv2lEgGCTh/Ncmsm+g0htUQnKerMMewJjgC/5ziKx9QbB7MRfXFr7xJW+UwnKBrMMhwIXAB/3nUVi4x2CHTxco5VeZEOoBKVkwh1z/4HgYL4i5fAycBFwp3Z1JqWgEpSSswyjgHOBz6PDdUlpTCEov/s17SmlpBKUsrEMuwA/Jtgn6Uae40jtccADwIUuzRTfYSSaVIJSduEOus8EfgBs4zmOVL8ccBdwkUszy3cYiTaVoFSMZUgBJxBMlR7oOY5UnyUEB3u+3KV523cYiQeVoHhhGY4m2AvN54GU5zjiTwEYD9wE/Mul6fScR2JGJSheWYbhwInAKcAhnuNI5cwFbgZuc2ne8R1G4kslKFUjPHLFN8PTjp7jSOm1AH8FbnZpJvoOIwIqQalSluFIgtHhCcAmnuPIhplCMOr7i0vT7DuMSDGVoFQ1y9AP+CzB6PBzQD+/iaQXOoEJwH0E2/W96TmPSI9UglIzLMNg4BjgOOB4YHu/iaTIEuAhguJ71KXJes4j0isqQalZ4Q68VxbiKKC/30SxMxu4PzxNdWnynvOI9JlKUCLBMgwi2IH3ylLc2W+iSFoGTAMeJ5jmnOM5j8gGUwlKJIW7bDsGODg87YWOfdgXDngFmFp0mq39dkrUqAQlFsKRYj3Btogri3EXr6GqSwvBgWmfIii8p1yapX4jiZSfSlBiK9yn6UEEhXgIsD+wLdE+8kUX8BrBKG/lqQl4Qcv0JI5UgiJFwk0ydiBYprhTN18HeQvXN4sIVlx5ZbXT6y5Nl89gItVEJSjSB5ZhKz4qxB2BzYCNezgNAaxED+2ApcBigoJb+XVRN+e97tIsK9HjikSaSlCkTCxDAhhGUIibhF9XbsZhq50KBIcQyhFsbL7y+3aCcluq6UqR0lMJiohIbEV5BQAREZG1UgmKiEhsqQRFRCS2VIIiIhJbKkEREYktlaCIiMSWSlBERGJLJSgiIrGlEhQRkdhSCYqISGypBEVEJLZUgiJVwsycmd1e9HPKzBaZ2QPruF16XdcRke6pBEWqRwuwt5kNDH/+FPCuxzwikacSFKkuDwOfDb//OnDXygvM7BAzm2pmTeHX3Ve/sZltZGY3mdkz4fW+WKHcIjVJJShSXe4GTjKzAcC+wNNFl80GRjnn6oH/BcZ2c/tfAk845w4GPg5cbGYblTmzSM1K+Q4gIh9xzs00sx0IRoEPrXbxMOBWM9uV4Ejzdd3cxaeBL5jZT8OfBwDbAS+XJ7FIbVMJilSf+4BLgDSwWdH5vweedM59KSzKTDe3NeArzrlXypxRJBI0HSpSfW4Cfuece3G184fx0Yoy3+nhto8CPzQzAzCz+rIkFIkIlaBIlXHOveOc+2M3F10EXGBmU4BkDzf/PcE06Uwz+0/4s4j0wJxzvjOIiIh4oZGgiIjElkpQRERiSyUoIiKxpRIUEZHYUgmKiEhsqQRFRCS2VIIiIhJbKkEREYktlaCIiMSWSlBERGJLJSgiIrGlEhQRkdhSCYqISGypBEVEJLZUgiIiElsqQRERiS2VoIiIxJZKUEREYkslKCIisaUSFBGR2FIJiohIbKkERUQktlSCIiISWypBERGJLZWgiIjElkpQRERiSyUoIiKxpRIUEZHYUgmKiEhs/X9v7yO1Cl4XdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Divvy Riders based on Gender- Customers Only\n",
    "sizes = [825949,313303]\n",
    "skyblue_colors = [\"deepskyblue\", \"lightskyblue\"]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(sizes, labels= gender_labels, colors=skyblue_colors, autopct=\"%1.1f%%\", startangle=140)\n",
    "plt.title(\"Divvy Riders based on Gender- Customers Only\")\n",
    "plt.savefig('Images/Gender_Customers_and_Subscribers.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Divvy Riders based on Gender- Subscribers Only\n",
    "sizes = [131404,82712]\n",
    "steel_colors = [\"steelblue\", \"lightsteelblue\"]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(sizes, labels= gender_labels, colors=steel_colors, autopct=\"%1.1f%%\", startangle=160)\n",
    "plt.title(\"Divvy Riders based on Gender- Subscribers Only\")\n",
    "plt.savefig('Images/Genders_Subscribers_Only.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use by Age Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trip_id</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_time</th>\n",
       "      <th>time difference</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_time</th>\n",
       "      <th>tripduration</th>\n",
       "      <th>from_station_id</th>\n",
       "      <th>from_station_name</th>\n",
       "      <th>to_station_id</th>\n",
       "      <th>to_station_name</th>\n",
       "      <th>usertype</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11-19</th>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "      <td>24148</td>\n",
       "      <td>24439</td>\n",
       "      <td>24439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "      <td>633811</td>\n",
       "      <td>637735</td>\n",
       "      <td>637735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "      <td>402889</td>\n",
       "      <td>405750</td>\n",
       "      <td>405750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "      <td>151754</td>\n",
       "      <td>152741</td>\n",
       "      <td>152741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "      <td>106465</td>\n",
       "      <td>107283</td>\n",
       "      <td>107283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "      <td>31272</td>\n",
       "      <td>31557</td>\n",
       "      <td>31557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "      <td>2656</td>\n",
       "      <td>2710</td>\n",
       "      <td>2710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-89</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           trip_id  bikeid  start_date  start_time  time difference  end_date  \\\n",
       "Age Group                                                                       \n",
       "11-19        24439   24439       24439       24439            24439     24439   \n",
       "20-29       637735  637735      637735      637735           637735    637735   \n",
       "30-39       405750  405750      405750      405750           405750    405750   \n",
       "40-49       152741  152741      152741      152741           152741    152741   \n",
       "50-59       107283  107283      107283      107283           107283    107283   \n",
       "60-69        31557   31557       31557       31557            31557     31557   \n",
       "70-79         2710    2710        2710        2710             2710      2710   \n",
       "80-89           33      33          33          33               33        33   \n",
       "\n",
       "           end_time  tripduration  from_station_id  from_station_name  \\\n",
       "Age Group                                                               \n",
       "11-19         24439         24439            24439              24439   \n",
       "20-29        637735        637735           637735             637735   \n",
       "30-39        405750        405750           405750             405750   \n",
       "40-49        152741        152741           152741             152741   \n",
       "50-59        107283        107283           107283             107283   \n",
       "60-69         31557         31557            31557              31557   \n",
       "70-79          2710          2710             2710               2710   \n",
       "80-89            33            33               33                 33   \n",
       "\n",
       "           to_station_id  to_station_name  usertype  gender  birthyear     age  \n",
       "Age Group                                                                       \n",
       "11-19              24439            24439     24439   24148      24439   24439  \n",
       "20-29             637735           637735    637735  633811     637735  637735  \n",
       "30-39             405750           405750    405750  402889     405750  405750  \n",
       "40-49             152741           152741    152741  151754     152741  152741  \n",
       "50-59             107283           107283    107283  106465     107283  107283  \n",
       "60-69              31557            31557     31557   31272      31557   31557  \n",
       "70-79               2710             2710      2710    2656       2710    2710  \n",
       "80-89                 33               33        33      33         33      33  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count number of trips for each age group\n",
    "count_number_age = divvy_df['trip_id'].nunique()\n",
    "\n",
    "# Count number in each age group and display\n",
    "usertype_age_df = divvy_df.groupby('Age Group')\n",
    "age_type_number = usertype_age_df['trip_id'].nunique()\n",
    "usertype_age_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "170281.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average Trip Duration\n",
    "average_duration = usertype_age_df['tripduration'].count().mean()\n",
    "average_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Total Count</th>\n",
       "      <th>Percentage of Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11-19</th>\n",
       "      <td>24439</td>\n",
       "      <td>1.489531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>637735</td>\n",
       "      <td>38.869263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>405750</td>\n",
       "      <td>24.730027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>152741</td>\n",
       "      <td>9.309400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>107283</td>\n",
       "      <td>6.538784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>31557</td>\n",
       "      <td>1.923365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>2710</td>\n",
       "      <td>0.165172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-89</th>\n",
       "      <td>33</td>\n",
       "      <td>0.002011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total Count  Percentage of Age\n",
       "Age Group                                \n",
       "11-19            24439           1.489531\n",
       "20-29           637735          38.869263\n",
       "30-39           405750          24.730027\n",
       "40-49           152741           9.309400\n",
       "50-59           107283           6.538784\n",
       "60-69            31557           1.923365\n",
       "70-79             2710           0.165172\n",
       "80-89               33           0.002011"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate percentage for age\n",
    "percent_age =  age_type_number / count_number_age * 100\n",
    "\n",
    "# Make df of age demographics\n",
    "age_demographics_df = pd.DataFrame({'Total Count':age_type_number,\n",
    "                                       'Percentage of Age':percent_age})\n",
    "age_demographics_df.sort_values('Age Group', ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Percentage Divvy Riders Based on Age')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentage Divvy Riders based on Age- Bar Chart\n",
    "rainbow_colors = [\"dodgerblue\", \"deepskyblue\", \"cornflowerblue\", \"royalblue\", \"steelblue\", \"mediumblue\", \"skyblue\", \"navy\"]\n",
    "plt.figure(figsize=(10,8))\n",
    "percent_chart = percent_age.plot(kind = 'bar', color = rainbow_colors, rot = 20)\n",
    "plt.savefig('Images/Age_Overall.png')\n",
    "plt.figure(figsize=(10,8))\n",
    "percent_chart.set_ylabel(\"Percent\")\n",
    "percent_chart.set_xlabel(\"Age Group\")\n",
    "percent_chart.set_title(\"Percentage Divvy Riders Based on Age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age Group  usertype  \n",
       "11-19      Customer       12232\n",
       "           Subscriber     12207\n",
       "20-29      Subscriber    515465\n",
       "           Customer      122270\n",
       "30-39      Subscriber    357309\n",
       "           Customer       48441\n",
       "40-49      Subscriber    133147\n",
       "           Customer       19594\n",
       "50-59      Subscriber     95445\n",
       "           Customer       11838\n",
       "60-69      Subscriber     29468\n",
       "           Customer        2089\n",
       "70-79      Subscriber      2586\n",
       "           Customer         124\n",
       "80-89      Subscriber        30\n",
       "           Customer           3\n",
       "Name: usertype, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age seperated by usertype\n",
    "usertype_age_df['usertype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Divvy Riders based on Age, Separated by Usertype- Stacked Histogram\n",
    "subscriber_age_totals = [12093,512720,355281,132359,94713,29201,2535,30]\n",
    "customer_age_totals = [12055,121091,47608,19395,11752,2071,121,3]\n",
    "plotdata = pd.DataFrame({\n",
    "    \"Subscriber\":subscriber_age_totals,\n",
    "    \"Customer\":customer_age_totals\n",
    "    }, \n",
    "    index= group_names\n",
    "    )\n",
    "plotdata[[\"Subscriber\", \"Customer\"]].plot(kind=\"bar\", color=['dodgerblue','powderblue'], stacked=True)\n",
    "plt.title(\"Total Divvy Riders based on Age, Separated by Usertype\")\n",
    "plt.xlabel(\"Age Groups\")\n",
    "plt.ylabel(\"Amount of Riders\")\n",
    "plt.savefig('Images/Age_Customers_and_Subscribers.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Divvy Riders based on Age, Customers Only- PIE CHART\n",
    "customer_age_totals = [12055,121091,47608,19395,11752,2071,121,3]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(customer_age_totals, labels= group_names, colors=rainbow_colors)\n",
    "#autopct=\"%1.1f%%\"\n",
    "#startangle=90\n",
    "plt.title(\"Divvy Riders based on Age, Customers Only\")\n",
    "plt.savefig('Images/Age_Customers_Only.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EERERQIUoIiICqBBFREQAFaKIiAigQhQREQFUiCIiIoAKUUREBFAhioiIACpEERERQIUoIiICaPonkbyxNAEwB5hPOCv98OfpwOhZese6qv5Y92WBDsIZKA5Enw8CB10Tg/lJLiKg2S5EJmVpTiKcOml00Y3+PBeIFTFaF7CfcEqm8T52uCayRcwkUrZUiCIRS1MLnAWcC5wz4vM8n7lOUD/wEPBA9HE/cL9renzOQxGJqBCl6lgaA07licV3OtWzX30PRwpyuCwfcU0MeU0l4pEKUSqepTkNeCZwEWHxnUW4X0+ONgg8zJGCvAu4yzWR8ZpKpEhUiFJxLM1s4BnA1dHHKX4TlbUu4P+AXwG/ck1s8JxHpGBUiFL2LE0SuJyw/J4FXEj1DH0W2xbgFsKCvNU10ek3jkj+qBClLFmasziyBXgVMM1voqqUBe4h2noE7tYRrVLOVIhSFqKtwOcBLyIswUVeA8lYDgHrgXXAD10T+zznEZkSFaKUNEtzKfC3wCsIT3qX8pAhLMYbgR+7JgY85xGZlApRSo6laQReE32s8BxHTlwH8F3gRtfEXb7DiIxHhSglwdLMAF5KuDV4FU+81JlUhkeBrwPfcE3s8B1GZCQVonhjaWKE5wf+LeG+wTqvgaSYcoSnc9wIfN810es5j4gKUYrP0qwCXg/8NTo4RsJzHb9HWI63u6YxL3IuUnAqRCkaS/Nk4J+B1WhIVMb2KPBxwiFVXUZOikqFKAVnaZ4HvA+4wncWKRs7gE8CX9ZwqhSLClEKIto/+HLgvcCTPMeR8rUf+E/gv3VVHCk0FaLklaVJAa8D3k04o4RIPhwGPg98Wif8S6GoECUvLM1M4M3AO4AFftNIBesHbgA+4ZrY6jmLVBgVopwQSzOfsATfDNT7TSNVJAN8G7jONfGQ7zBSGVSIclwszSzgX4E3ATV+00gVc8CPgH9xTTzoO4yUNxWiTImlCYA3Ah8B5nqOIzIsC3wBWOua6PAdRsqTClGOmaV5CvBZ4HzfWUTGcRD4ANDqmsj5DiPlRYUok7I0i4FPAK/ynUXkGP0JeKtr4g7fQaR8qBBlXNEpFO8ivLqMJuCVcvRN4D2uid2+g0jpUyHKmCzNi4Dr0bmEUv66gQ8TnsM46DuMlC4VohzF0pwJfIZwVnqRSrIBeIdr4ue+g0hpUiEKAJamHvgQ8A9A3HMckUL6GWExbvQdREqLClGwNC8EvgTM951FpEgGgY8CH3ZNZH2HkdKgQqxi0eXW/hN4recoIr78Dni1a2Kz7yDiX+A7gPhhaZqAB1AZSnW7HLjP0vyt7yDin7YQq4ylqSEcKnoHmqRXZKTvAG/WNFPVS4VYRSzNeYTnZZ3pOYpIqdpOOIT6G99BpPhUiFXC0rwN+DiQ8p1FpMTlgI8BH3RNZHyHkeJRIVY4SzOHcP64F/rOIlJm7gH+2jWxyXcQKQ4dVFPBLM2VwH2oDEWOxyWEB9y81ncQKQ5tIVYgSxMD1gL/D4h5jiNSCW4G/l4H3FQ2FWKFia44czPwLN9ZRCrMDuCFron7fAeRwtCQaQWxNMuAO1AZihTCUuA3lub5voNIYagQK4SluQy4GzjLdxaRCjYd+GF01LZUGA2ZVgBL83LgRqDGdxaRKvJZ4J26Fmrl0BZimbM07ye8wobKUKS43gr8yNJM9x1E8kNbiGXK0iQIZ6h4recoItXuPuD5roldvoPIiVEhliFLMxv4X6DJcxQRCe0mLMU/+Q4ix09DpmXG0pwO3IXKUKSULCI8AvUFvoPI8VMhlhFL81TCMlzuO4uIPME0wiNQ3+47iBwfDZmWCUvzauArQNJ3FhGZ1H8Db9cRqOVFhVgGLM1bgf/ynUNEpuQnwMtcEwO+g8ixUSGWOEvTTPhuU0TKz8+Bl6gUy4MKsYRZmjXAF9HM9iLl7BfAi1WKpU+FWKIszesI9xmqDEXK3zrgRa6Jft9BZHwqxBJkaV4DfA0dBSxSSX4FXKNSLF0qxBJjaV4FfAPNYyhSiW4hnEJKpViCtAVSQizNy1AZilSyq4HvR5delBKjQiwRlubFwLdQGYpUuucB37K0/q+XGhViCYgu9/RdIO47i4gUxUuBL1taB82VEhWiZ5bmecD3QEMoIlXmteiCGyVFheiRpXkW4awVuhybSHV6i6X5qO8QEtJRpp5YmguA3wB1vrOIiHfvck1c7ztEtVMhemBp5gO/B5b6ziIiJSEHvMA18XPfQaqZCrHILE0S+D/gyb6ziEhJOQRc5pp4xHeQaqV9iMX3BVSGIvJE9cCPLM0s30GqlQqxiCzNO4HX+c4hIiVrOfBtS+tvsw/6pheJpXk28AnfOUSk5D0H+A/fIaqR9iEWgaVZDtwNGgoRkWP2t66Jb/gOUU1UiAUW7Q+4C1jhOYqIlJd+4ErXxO99B6kWGjItoOhahd9GZSgiU1cD/NDSLPQdpFqoEAvr44T7A0REjsci4AeWJuU7SDVQIRaIpbkW+EffOUSk7F0KfMl3iGqgfYgFYGkuA9Kgd3Uikjf/5Jr4lO8QlUyFmGeWZibwINDoO4uIVJQs8AzXxG2+g1QqDZnm32dQGYpI/sWAr1ma6b6DVCoVYh5FE/3qSjQiUijLQMOmhaIh0zyxNHOBPwPzfWcRkYr3XNfEL32HqDTaQsyfz6MyFJHi+LIuAp5/KsQ8sDR/DbzUdw4RqRqLgc/6DlFpNGR6gizNIsKh0tm+s4hI1XmJa+IHvkNUCm0hnrivoDIUET++EB2/IHmgQjwBlubv0aXZRMSfkwgnHZc80JDpcbI0pwL3g84JEhHv/sY18S3fIcqdCvE4RLNZ3wY81XcWERGgAzjLNbHHd5BypiHT4/OPqAxFpHTMBr7sO0S50xbiFFmaFYRDpbpwt4iUmje6Jr7iO0S50hbi1H0KlaGIlKbrLc0c3yHKlQpxCizN1cDzfOcQERlHPfCvvkOUKw2ZHqPoQJr7gHM8RxERmcgQcLZr4jHfQcqNthCP3etRGYpI6UsA/+E7RDnSFuIxiOYf2wAs8J1FROQYXeWauN13iHKiLcRj8z5UhiJSXq63NOY7RDlRIU7C0iwlPO9QRKScXAT8te8Q5USFOLmPAbW+Q4iIHIePWpoa3yHKhQpxApbmYvQOS0TKVyPwDt8hyoUKcWKfAo3Bi0hZ+2dLM893iHKgQhyHpXkpul6piJS/mcAHfYcoBzrtYgyWJgk8DJzqO4uISB5kgHNcE4/4DlLKtIU4tregMhSRyhEHPu47RKnTFuIo0RFZ2whnohYRqSRXuCZ+6ztEqdIW4hO9BpWhiFSm9/gOUMq0hThCdFWHh4CVvrOIiBSAA1a4Jjb4DlKKtIV4tNWoDEWkchnwTt8hSpUK8Wjv8h1ARKTArtUkwmNTIUYszYXAVb5ziIgUWB3wJt8hSpEK8QhtHYpItXhLdL61jKBCBCxNI/BS3zlERIpkAbpO8xOoEEPvIDxxVUSkWmhau1Gq/rQLS1MP7ABm+M4iIlJkz3JN3OI7RKnQFiL8HSpDEalO2kocoaq3EC1NAtgMLPGdRUTEk7NdE3/xHaIUVPsW4itQGYpIddOJ+pFq30L8E3Ce7xwiIh4NAI2uiTbfQXyr2i3E6ET883znEBHxLAX8ve8QpaBqCxF4le8AIiIlQuckUqVDptGsFtvR/kMRkWHnuybu8x3Cp2rdQrwClaGIyEiv9B3At2otRA0PiIgc7RW+A/hWdUOm0bmHe0DTn4iIjPJk18TvfIfwpRq3EK9GZSgiMpaqHjatxkLU0aUiImN7maWrsheAKitES1MLvMh3DhGRErUQaPIdwpeqKkTgBcB03yFEREpY1Q6bVlsharhURGRifxUdfFh1qqYQo3kPn+s7h4hIiWsAnuU7hA9VU4jAXxFes09ERCZWlcOm1VSIGi4VETk210QHIVaVqihESzMXeJrvHCIiZWIGsNp3iGKrikIEngHEfIcQESkjVXfMRbUU4tW+A4iIlJkm3wGKrVoK8Zm+A4iIlJlTLc1S3yGKqeIL0dKcAZzsO4eISBlq8h2gmCq+ENHWoYjI8WryHaCYqqEQtf9QROT4XOU7QDFV9HyI0VXbDwCzfWcRESlTS10TO32HKIa47wCF9MaH2s93kDmcCO7aNiM5uGlWcvHBmvhpvnOJiJSRJuAm3yGKoaILEbjCYF79UG7eue39nNvej4P9h5PBxm0zkpmN9cnF7TXxU32HFBEpYU2oECvClaPvMJhXP5ibd+7Bfs492E8O2g4ng03bZiSHNtYnl3bUxE/xEVREpEQ1+Q5QLBW9D3FNS3sbMG8qr8nBvsPJYNPWGcnsxvrk0s6a+LLCpBMRKRtVsR+xYgtxTUv7KuChE11ODvYcSgZbts5MZjfWp04+lIo15iGeiEg5ebVr4pu+QxRaJQ+ZXpqPhQSwcPZgbuHsA/2cf6CfHOzpTMU2b52RcJvqU40qSBGpAk2gQixn5xdioQEsbBjILmwYyHJBWJC7OlOxrVtmJt2mmcllh1OxJYVYr4iIR02+AxRDJQ+Z3sYYB9UUWg52doQFaZvqkyd3JVWQIlIRFrsmdvsOUUiVvIX4JB8rDWDJnIHskjn7+7hof9/oglzWlYwt9pFLROQEnQeVXYgVuYW4pqX9FGCz7xxjycGO9prY1i0zk8GmmclTu5Oxhb4ziYgcg39yTXzKd4hCqtQtxPN8BxhPAEvn9meXzu3v4+K2PrKwrb0mtmPLzGSwqT55Sk9CBSkiJWml7wCFpkL0LAYnz+vPnjyvv49L2vrIGtvaU7Htm2cmY5vqU6f1JoL5vjOKiACrfAcotEotRC/7D/Mh5o4U5KVhQW45mIrt3FyfjG2emTq9NxGc5DujiFSlit9CrNR9iFup0EmBs8aWAzWxHZtnphKbZyZP70sEU7oSj4jICZjrmjjoO0ShVFwhrmlpnwV0+M5RLBlj08Ga+K7NM5OJzfXJ0/viKkgRKZinuibu8B2iUCpxyPRc3wGKKe44bX5f5rT5fRku39dLxth0oCa+a3N9MrF5ZnJ5fzyY4zujiFSMVaBCLCen+w7gU9xx2oK+zGkL+jJcvrfXZY2NB2rjuzbNTKa2hAXZ4DujiJStit6PWImFuMx3gFJhYHHH6Qt6M6cv6M3w5LAgN+yvje/ZNDOZ3DIzuWIgHsz2nVNEyoYKscws8x2gVEUFecbC3swZC3szPCUsyMfaauN7NtUna7fOTC4fiAWzfOcUkZJV0YVYiQfVeLmGaSVwkMuEW5B7N9anarfOTKwYjAX1vnOJSMnIAtNcEwO+gxSCthDlcQZBwrFiUW9mxaLeDFfsIZcxHmmri+/bVJ+q2zojsVwFKVLVYsBy4EHfQQqhorYQ17S0x4F+wh+a5JmDbCbgsX218baoIFcMxYKZvnOJSFG93DXxP75DFEKlbSEuRWVYMAaxRI5VS3oyq5b0ZLgSskMBD+2rTbRtqk9O3zYjuWIoZjN85xSRgqrY/YiVVojLfAeoJgaxZI4zl/YMnbm0ZwhHT3Yo4C/76hIHNtYnp22bnlyZidl03zlFJK8q8ipgUHmFWLE/qHIQFeRZS7uHWNo9hKMnMxTYX/bWxfdvrE/O2D4juTIT2DTfOUXkhFTs1bAqrRCX+Q4gRxjEkzl3VmP3EI1hQQ4NBvbg3rp4+8b65PQdM5KrMoHV+c4pIlOiQiwTi3wHkPEZJFI5d87J3UOcPKIg90yLH9xYn6rfMT2xMhtYre+cIjKhub4DFEqlFaKuulJGhgtyWdcQy7qGcDA4GNgDe6bFOzbUp2bunJ5YlQ2sxndOETmKthDLxCzfAeT4GSRTOXfuiIIcGIjZA3vq4u0b61OzdoZbkCpIEb9mWZqEa2LId5B8UyFKyTJI1WTduad0DXHKkYK8b/e0xKGN9clZO6clVuYCS/nOKVKF5gJ7fIfINxWilI2oIM879fAgpx4exEH/cEFuqE/O2jUtsSoXWNJ3TpGK43DAYaDLcnQn+2hAhVjyZvkOIMVjUDOqIPv6Y/an3dMShzfWJ2fvCrcgVZAiwxyHgcPm6LEcfUGW/iDLUJAhG8tAkCGIDRGPZUjFhqiNZZgeZJgRZJlpUE/4AXk8sMbMbgCeD7Q5586O7nsZ8EHC+Rcvcc7dO85rx3yemSWBLwIXATng7c659GRZKq0QdZ3NKmZQW5t15592eJDTwoLs7Y/ZX3ZNSxzeOCs5e9e0xCpnlvCdU+SEHF1qvZZlIMgyFMuSC4ZwsQyx2BCxEaU2LSq1eoOZhB8nalYeljHsa8DngK+PuO/PwEsIS20i4z1vDYBz7hwzOwn4hZld7JzLTbSwiinENS3t0wD9sZPHGdTVZt35px8e5PSoIPti9sCu6YnujfWpObunxVeoIMULRxdhqXVbjr6o1AZHlNrwllqygKV2IvK28eGcu93Mlo2672EAM5vsteM970zg1ug5bWbWSbi1eM9Ey6uYQkTDpTIJg7q6rLvwjEODnHFoEAc9fXF7YOe0sCD3TIuvdGaV9H9CCsnRTVhqXVGp9T++pZbBxYYIYhnisSFSsSFqRpXaDMKPclXql2S8H7jGzL5DeI3rC6PPKkSRsRhMq8u4C5cfGmR5WJDdfXF7dOe0RNeGWal5e+viK52ZLhZfyY6UWrfl6LVcNPyYITuq1JKxDLVBhrpYhhlBhnoLS6HUi6FQSv0KUzcQ7le8F9gG3AlkJntRJRWi9h/KCTGYPqogu3rj9ujO6YmeDfWpefvq4itUkCXI0cPRpTbyQJHh4cdYbIhULENNkGGaSu2EebmilJl9FTgf2O2ce954z3POZYB3jnjdncCGyZZfSYWo89EkrwxmTMu4i1Z0DrKicxAHh3vj9uiO6cnejfXJk/bVxZerIPPk+EptepBhlsE0wg8pHi8XyHDOve5YnmdmdYTz/faY2dVAxjn30GSvq6RCFCkog5nTMu7ilZ0DrOwcwMGhnnjw2I7pid6Ns5In7auNr8As8J3TG0cvcGjU8OPgiFKz4QNFgmifWmx4n5pTqZWZvJ3OZGbfBlYDM8xsCPgjcD3waWAhcFd0UMwZzrmOUa99MfAFwsvJ/c7MeoGrgTZgnZnNJ+y57WbWNNmpF+acy9fX5dWalvanAet955DqFRXko9tnJPo21icXtNXGzyi7gnT0EZZa13ilFgzvU3tiqemcz+rx2W1v5235WJCZXQH8GngEcMBZwP8AB4Dnjnjqz5xz7xjj9XuBHqCLsECnOeemm9nbgLcC/UBAWIyrJjr1opK2ECc+PlekwAzqp2dyl5zZMcCZHQM46OxOBI/tmJ7o31Cfmr+/NrZ80uPI8+FIqY0cfhwMsuRiQ+TGKLW6IMOMWFhqtYT7hxYUPKeUs3y+0dtMuEX3NAgPcgJ+DvwX8Hnn3AfM7CPAm4B3jPH6+4EbnHPfNbOfAo3R/X9FuK/xquhcxI3AxcDd4wWppEIUKSkGs2YMHVWQHd2J4LHtMxL9G+tTi/bXxE4ftyAd/cAhHN1Bjp4RpZaNZXDBEBYd/ZiIZaiNDY1ZaiKFkrdCdM7tMrNPAtsJjwTtcs7dZGY3Ep6wT/T5feMs4h2Ew6OfJNxCfEZ0fw6os/BUqmmE/yeaUCGK+Gcwe8ZQ7tKz2gc4q30AB+07B4d+t23njkzXjkS2d0cykTlkNbGYBfFEQJCAeNxckLBcLG7EkrhY+G8XTxhB3Fw8AUHcXCyOxRLmYglzsZhZkMBZDGJxiMWNIG7E4xDEjSCGBXEjFsNicbMgRnhfDIvFwsctwIKYWWz4dkAQBGYWYGbOsDyPyBRiz43DFWSXUHhdz7xv6Tsg73ld/hcJ0J8N2mBOXpZlZrOBa4BTgI8DF5jZq4HAObcHwDm3x8YfXXkz4RGl+4GvAmuB24DvAv9IeOrF/ui5E04RWEmFqCFTKSsGDXOHglMHB2fWLD1ro9nZbllmwLoP7Upsa9+cbO/clsx17E/UD/VaI1iD77yjuCBONohZJoiRCWLkYnHLBDGyQcyyQZxsLGa5IGHZWIxsELdcLE4uiFs2FscFMcvGEuZicXKx8DEXxC0X3edi4W2O3A+xmLkgui8WNxeEbwLC++NYLG4uCEueIAZBWPYWhCUfHPmMWWAWxAiCALOYmQXEAsMssMACwg8jZkZ4T3QbiJkRwwgM4tjj+6bi4WMnemGHcvkz5v6cx4U9E9gCHAJeQHht0icDOTNbGJXhQqK3TWOcenEt8HagJfr412i5XwZWEA7F9hIOx+6cKEglFaJI2UnVBEvI1MzI7FvZGZ/32J/iqez5c04dPGvOqYNHPa//ULC3Y1tyZ/vmVM+hnYl4b0dsXm7IloG3i5dbLkM8l3Ej/oZUxgF6J8oCckFAJohbNnyzYLkgTiaIWTYWe/xNQSaIWy56I5GLxYffNDz+xmH4TUL26DcJ4ZuBcKQAF4uZG/HGwUUjAi6Im8USEMSi0YO4EcQhiJkFMVwsbkE4MhC9aYibhW8OsCAwsxhBEL0VCIbfIIQjBDEzrL87dyiP37LtwGWEW4l/JNzPdy9hQb4F+ED0ebx17gaeTnhN07dx5HzDJPD/Rpx60cQkB15WUiGWy1srkccFMZsB7MfF52XaVp0Tm7Pp9iDZd+Xo59XU5xYsPLd/wcJz+x+/L5cl090W39S+JdXWsSU50LU3UTfQFSxyOVtSzK9BjuZyBNkcyWxm+A1CRb5R+MXHXH6Ou3LO3W1m3wO+QnhAzT7gS8By4BVm9nLCv+83Rc8ffS7iGsKh0mmEQ6R/F93fCPzYzLJAH/DIZOciVlIhipSlIGBvLsc8sHj24OlXupm7bwvqDj7VjAlP+g9ixGcuzJw2c2HmtGVP7nn8/qF+O3xoZ2Jb++ZUR+f2JN374/WZPlsGpqs5Sb4M5Hl5/0G4FXiWc+4QgJl9kPAi3Y2El1/74FgvdM791szuAO5yzn1hxEP9HHk30g68YbIQlVSIWd8BRI5HPBkcGuw/cmpU7vCiq9xg7b2xWTuXm019VoNEjZs59/TBc+aefvSwa19HbE/HtuTO9i3JnkM7E8m+jvi8XIZloBk/ZMryWojOuV5GHaXjnDvIkSNGJ3v9a8e4byvhPsRjVkmF2OU7gMjxSKaCzMhCBHD9sy/KHKjZFJ+78ZAZS/OxntrZ2YW1s/sWLjqv7/H7clmGuvYmNnRsSba1b00Ode1NTBvsChY7Z4vysU6pWPneQiwJlVSI+dzJK1I0NXVBqnus395M7WmZtlUH4/Mee8CC7LmFWHcQI1G/eOiM+sVDZyx76ohh1z471Lkjua19c7Kzc3uSngPx2Zl+OxnM9zx8UhpUiCWu03cAkeORqo2Nf0pFLj4ns2/ljPjcTb+1RP9Ti5UpUevq5y0fOHfe8qP/7vW2x3Z2bE3uad+S7Dm0K5ns74ydFA27VtLfEpmcCrHEaQtRylIyFSye+BlBMnPgjKfG6nemrbbjyuh8OC/qGrJL6hr6liy+YMSwa4bBw3sSj7ZvSR7o2Joc6t6XmD7QHSzBmS7/VrkqshAr5uLeAGta2nvRJaukDG36c3cbcNJkz7Pa9rtj9bvONiv9mSEGe62jc3tyW/vm1OHO7QnrPRifnRmwZWCaf7D8vdS5C77vO0S+VdIWIoRbiSpEKTtBjD257OSF6PoaLs1mah6Nzdk0w4ySPvAlWedmn7RyYPZJK49sTDiH6z0Y2x4Ou6b6D+9KJPsPxebnspwMmluyjOyf/CnlpxILUcM0UnbiieDwYHbcWWmO4obqVmTaVrbF5z32FwtyZxU4Wl6ZYdPmZhunze1rXHLRkWHX7BD9h3cnNnRsSR1o35bMdu+LzxjsCZbgbNI3CeKFCrEMdPoOIHI8UjVBdvSpFxPKJU7K7Fs1Mz5vw50WH3xy4ZIVRyxBzeyTh1bOPnmIU0fcP9gTHOzYltjRviV1qHN7Mug9GJuTHbRlYHW+sgqgQiwLOrBGylKqNlbT1ZmZ4quCmsz+FU+Ozdp+m9UcutLyPQNFCUhOy82Zf+bAnPlnjhh2zZHrORjf1rElubd9S7Lv8O5ETf/h2AKXpRHKbELm8pQjvPJLXpjZLMILcZ9NeGWZ1wOPEs5WsQzYCrzcOdcxzuvfSniVmwzhJMLvMbMk8EXgoijv251z6cmyqBBFSkCqNjju2SyynY1XBXUHfhfM3HOeWeXvQ7eAYPq8zMnT52VOXnpJ7+P3Zwet99DuxNb2zcn2jm3JbE9bfMZgT9AINtdj3Ep00LkLpjCcMan/BH7pnHtpVGR1wPuBW51z15nZ+wjnQnzv6Bea2dMILwp+rnNuIJoIGMLrm+KcOye67xdmdrFzbsLclVaI+3wHEDkeyZrJTr2YWK537uUuU/NQrGHLHDPm5ytXOYklXV3DssEzG5Ydfcm6ga5gf8e25I72LcmuQzuSsd72x4ddK/7NQ4HkbbjUzGYCVwKvBXDODQKDZnYN4ewUADcCacYoRMK5EK9zzg1Er2+L7j8TuHX4PjPrJNxavGeiPJVWiNt8BxA5HkFg04C9nMBBYW5w+pmZ/Sv2xOc+9ogFbmX+0pW31IzcvAVn989bcPaRmUJcjmz3/viWjq3Jve2bk4OH9yRqBg7HFrgcjTDuRLQS2pPHZZ1KNLGvmT0J+APh3IbzR00OPN7BVcuBK8zsI4QX836Xc+73wP3ANWb2HWApcGH0WYUoUg6CGHtz2RM8SjqbXJhpWzUzPnfDXRYfuixP0SqOBcRmzM+cMmN+5pTGS48Muw5P0NyxJdnesTXpuvcnZpboBM0+TTjJ7hTFgQuAt0bTQP0n4fDoVF4/m3A+xYuBm83sVOAGYBXhvIrbgDsJ9zFOurBKokKUspVIBF0Dx3jqxYRcbFpm/4pLY7O3pYOarqYTX2D1iKfc9HEmaN4XDrumeg7tSMR7O2JzowmaU36SepXPQtwJ7HTO3R39+3uEhbjPzBZGW4cLgTYAM/sqcD6w2zn3vOj1/+vCK8zcY2Y5YK5zbj/wzuGVmNmdHJk4eFwqRJESkawJsgNTOfViQmbZjmVNblrbb4MZ+y4xI5mnBVelmvrc/IXn9s8fa4Lmjq3JtvYtqYGuPcMTNLO4woddd+VrQc65vWa2w8xWOOceJZzu6aHo41rguujzj6Lnj54c+IfA04G0mS0HksABM6sjvBJbj5ldDWQmmxwYKq8Q2wjHkWt8BxGZqlRtrHbqp15MLNdz0lNdpvaB2Oyti8zQ0ZZ5NHKC5pMvP2rYtevQjsTWg1tSnZ3bkq5nf7x+qM9OBpvlL21e5XMLEeCtwDejI0w3A68DAsLhzzcA24GXjfPaG4AbzOzPwCBwrXPORfsc10VbjLuA1xxLkIq6linAmpb2R5jipJAipaC/N7th1+a+Mwqy8NjAzvi8DX1mrjDLl0n1dcb2dGxL7GrfnOo5tDMR7+uMz8sNsQys3LbeL3Dugj/5DlEIlbaFCOGwqQpRyk6yJlhCeGJy/ofbsqklmX2ruuLzHvu9xTIX5335MqnaWdmFtbOyCxc96ahh13CC5q3J/e1bkkNdexN1g93BIpezEzoNp8B2+A5QKJVaiCJlJwislvCQ9oUFWYGLzci0rbww1rDltiDVc1VB1iFTctQEzU954gTNHVuSnR3bktZzIF6f6bdlJTBBc4dzFxzwnKFgVIgiJSQ69aIwhQiABdn2U69y0/f+Jpi+/zIzEoVblxyvCSZo3tWxLbmrfXOy19MEzQ8XaT1eVGIhbvUdQOR4JZJB90BfPq+KNbZc94Ir3FDtn2Kzty8zY3bBVyh5UdeQXVzX0Ld48flPmKD5sY6tyf0dW5NDXXsT0we6g8U4K8QbKxVimfmz7wAixytZE2SLUYgAbqD+/MyBM7bG527sNHOnFGWlkndBnOSspUPLZy0dWn7KFSOGXXuts2N7clv7lmRn5/Zk0HsgLxM0T3rqQjmrxEJ8GBgCDQVJ+ampjdV2deT31IsJZWqWZfatOhSf99gfLZa5oHgrlkJL1LlZJ60cmPWECZrbYzuiCZr7Du9KhMOuWZYd4wTNFV2IFXfaBcCalvb7gCf5ziEyVQN92Y07N/WdXvw1u0xszuY7g2TvlcVft/iWHaK/a09ia/uW5IGOrals17749MGeYOkYEzQvc+6Cij1OoxK3ECG8sKsKUcpOIlXAUy8mZPHswdOudDN33x7UHXyKGceytSAVIpagZlbj0MpZjUNw1ZFh18Ge4GDH9sT2js2pwx3bk5muPYntHmMWXKVOpnm/7wAixyMIrAbY7Wv9ucOLrsx2Lv2Tc5pbVKIJmlcNnL9y9eGrLn/zgTrnLqi8IcURVIgiJSaIWdvkzyoc1z/rosyB0w84R0VvDciU3ec7QKGpEEVKTCJpXb4zkKk9LdO2arrLxfR/SYbd5ztAoVVkIbY2NxzA47CTyIlI1QSlMSyVizdk9q1c5YZqfus7ipSEirx+6UgVWYgRvbOVspSqjdX6znBEkMwcOOOpud7ZtzlHcU6QlFKUBR70HaLQKrkQ7/MdQOR4pGqDOb4zjJY9tOSq7KHF9zpHt+8s4sVf1q1d3T/508pbJRfivb4DiByPZHjqRcltjbm+hkuyB0/b7Vz+JoiVsnGb7wDFUMmFqP0eUpYssBThrBclxw3VLc+0rUy6XKBLJFYXFWI5a21uaAMe9Z1D5HjEYrbPd4Zx5RLzMvtWne6GUnf4jiJFc7vvAMVQsYUY+Y3vACLHI560Et9XF9RkDix/Sq6v/jbnKI2jYqVQHl63dvV+3yGKQYUoUoJStSVy6sUksp2NV+UOL7zLOfomf7aUqaoYLoXKL8Sq2MyXylNap15MLNc79/Js+6lbnGOv7yxSECrEStDa3LAV2OI7h8hUpWqDeb4zTIUbnHZmpm2Fc7mgoieQrVIqxAqy3ncAkalKJoPFhCdDl49ccmGmbdXJLpO8y3cUyZuN69auLskjngtBhShSgiywJOV4+UEX1GX2L7801z+jarYqKlxV/RxViCIlKhb3O+vF8TPLdiy7Knt4/h3OMTD586WEqRArSWtzw15AJxFL2UkkrWfyZ5WuXM9JT8m2L3vMOarikP0K5KiyDYqKL8TIj3wHEJmqZE2sLE69mIgbnHFOZv/yAedsg+8sMmX3rlu7uqou01cthfhD3wFEpqqmNqjznSEvsqklmX2rFrhs4h7fUWRKfuA7QLFVRSG2NjfcC+zwnUNkKlK1wVzfGfLGxWZk2lZclBuYXlX7pMqcCrGCadhUykoinPWivE69mJAF2fZTrsp2nfQb5xjynUYm9Mi6tasf8R2i2KqpEKvu3Y6UNzNLADt958i3XPf8K7IdJ//FOdp9Z5Fx/dB3AB+qqRBvB/0HlPISi1tFHqHpBmael9l/Rpdzttl3FhlTVW5AVE0htjY3ZICf+s4hMhWJpPX6zlAw2ZqTM/tWzXHZ+B98R5Gj7AJ+7zuED1VTiJGqfNcj5StVAadeTMjF6jNtK5+UG6zThfhLxw/XrV1d2b9346i2QvwVaJoaKR+pumCa7wyFZ/HswdOuzHbPvd05Mr7TSPVuOFRVIbY2N/QCv/SdQ+RYpWpiZTXrxYnIdS28Mtu59H7nOOQ7SxXroMou1zZSVRVi5Ou+A4gcq0TKFkP1bDW5/lkXZg6cftA5tvnOUqW+v27t6qr5fRutGgvxp8A+3yFEjoWZxbHKO/ViQpnaUzP7Vs10udh9vqNUoS/7DuBT1RVidLTpTb5ziByrWMwO+M5QdC4+O7Nv1VluqOY3vqNUkT+vW7v6bt8hfKq6Qox8xXcAkWNV7rNeHD9LZA6ccUW2p+E258j5TlMFqv7vYlUWYmtzw8OAZvWWspCqjfmO4FXu8OKrsoeW3OscXb6zVLBB4Bu+Q/hWlYUYucF3AJFjkaqthlMvJub6Zl+SPXjaXueqbH9q8fxw3drVB32H8K2aC/E7QJUORUk5SdXGTvKdoRS4obozMm0rUy4XPOg7SwWq6oNphlVtIbY2N3QB3/OdQ2QyiaQtBs0OAUAuMS+zb9VyN5S6w3eUCrIV+LXvEKWgagsxomFTKXlmFqu6Uy8mFKQyB5Y/Jdc7K+0cVXmJsTz7arVeqm20qi7E1uaG24HHfOcQmUxVnnoxieyhpU25w4vudo7KvQB64eWAr/oOUSqquhAjn/EdQGQyiVSgP/pjyPXOuSx78NRtzrHHd5YytW7d2tU7fIcoFSpE+BpQkXPOSeVI1QTmO0OpckPTVmXaVpjLBQ/5zlKGPuM7QCmp+kJsbW7oA1p85xCZSE1VzHpxAnLJBZl9q05xmeTvfEcpIw+sW7v6V75DlJKqL8TI59C0UFLCkjWx+b4zlL6gNrN/+WW5/plVO1vDFF3vO0CpUSECrc0NBwiHTkVKUiJpCwmvJiITMst2nHxV9vCCO5yj33eaErYL+LbvEKVGhXjEp0DXS5TSZGYx06kXxyzXM+8p2fZTNjqn4wPG8V/r1q7Wua2jqBAjrc0NG4Ef+s4hMp5YXKdeTIUbnH52Zv+KQedMp1YdrRP4gu8QpUiFeLRP+A4gMp5EMtB+7qnKJhdn9q1a5LKJe3xHKSGfW7d29WHfIUqRCnGE1uaGuwBdEkpKUqpWp14cFxebnmlbcVGuf3rad5QS0INOtRiXCvGJPuo7gMhYUrWxGb4zlC8Lsh2nNGW7Tvqtc1V9cNIX8zmrhZltNbMHzew+M7s3uq/BzG4xsw3R59njvPaDZrYreu19Zva86P6kmX01Wu79ZtaUr7yTUSGO0trc8HNAs3RLyUnVBvN8Zyh3ue75T812nPywc7T7zuLBAPDJAiz3ac6585xzF0X/fh9wq3PuDODW6N/j+XT02vOccz+P7lsD4Jw7B7gauN7MitJVKsSx/bPvACKjxRO2iPCPmpwANzDzSZn9y7uds02+sxTZ59etXV2MS9xdA9wY3b4ReNEUX38mYZHinGsjPAjooolekC8qxDG0NjfcAfzUdw6Rkcws0KkXeZJNNWb2rZrrsvF7fUcpkk7g3wuwXAf8ysz+YGZ/F9033zm3ByD6PNF8nm8xswfM7IYRQ6v3A9eYWdzMTgEuBJYWIPsTqBDH9350XqKUGJ16kUcuVp9pW3l+brDudt9RiuBj69auLsQw8VOccxcAzwX+wcyunMJrPw+cBpwH7OHIlXNuAHYC9xIeAHQnkMlT3gmpEMfR2tzwIPAt3zlERkqkAl19Ja8slj142pXZ7rm3O1ecP7oebAf+qxALds7tjj63AT8ALgH2mdlCgOhzW3T7q9HBMz+PXrPPOZd1zuWA1ui1OOcyzrl3RvsVrwFmARsKkX80FeLE/gVdLktKiGa9KIxc18Irs52NDzjHId9ZCuD/rVu7Ou9vpMxsmpnNGL4NPAv4M/Bj4NroadcCPwJwzr0uKrnho0kXjljci6PXYmZ10fIws6uBjHOuKDOZqBAn0NrcsAX4ku8cIsN06kXhuP76CzIHzmh3zrb5zpJH9wE3FWjZ84Hfmtn9wD3Az5xzvwSuA642sw2ER4leN87rPx6dWvEA8DTgndH9JwF/NLOHgfcCrylQ/icw51yx1lWW1rS0nwRsAqb7ziIyNJjbvf2x3kW+c1Q0y3TE5z221WLZ831HyYOr161d/WvfIcqFthAn0drc0EZ44W8R7+IJW4imKissF5+daVt1dm6wttzPR16nMpwaFeKxuQ7Y6juEiJmZTr0oBktkD55+Rban4TbnyvJo8xzwbt8hyo0K8Ri0Njf0AW/znUMEIBa3arzKihe5w4uvyh5a8gfn6PKdZYq+vm7t6gd9hyg3KsRj1Nrc8BOio6VEfEqkNOtFMbm+2RdnD56217my2TLvRFfbOi4qxKl5G+HV4kW8SdUGMd8Zqo0bqjsj07ayxuWCB3xnOQbvXrd29V7fIcqRCnEKWpsbtgP/5juHVLcanXrhRy4xN7Nv1Qo3lCrlKeLSwFd8hyhXKsSp+zTwF98hpHola4L5vjNUryCVObD8KbneWWnnKLVz1vqBv1u3dnWp5SobKsQpam1uGALe7DuHVK94whYAvb5zVLPsoaVNucOL7naupHah/Pu6tauLcomzSqVCPA6tzQ2/4cj0JiJFFZ16sct3jmqX651zWfbgqTucoxhTKk3mQeATvkOUOxXi8Xs3VOUko1ICYnHL26zncvzc0LSVmbaVgcsFRbnW5jhywBvXrV095DFDRVAhHqfW5ob9wJt855DqlNSsF6Ujl5if2bfqFJdJ/s5Tgs+tW7v6Hk/rrigqxBPQ2tzwP8A3fOeQ6qNTL0pNUJvZv/yyXN/MdJFXvB34QJHXWbFUiCfuLUAlXR1fykCqNjbTdwYZzSzbeXJT9vCCO52jWFvwb163dnV3kdZV8VSIJ6i1ueEw4fQk5Xi9QylTqdpgge8MMrZcz7wnZ9tP2eRcODFuAf3XurWrf17gdVQVFWIeREed6ggvKZp4IpiPrppUstzg9LMy+1dkXM4eLdAq/gS8p0DLrloqxPz5F8LJOEWKQrNelLhsclGmbdUSl0ncnecldwOvXLd29UCel1v1VIh50trcMAj8DRRt34FUuVhCs16UPBebltm/4uJc//R0Hpf6lnVrVz+Wx+VJRIWYR63NDQ8B7/WdQ6pDMhVoC6EsWJDtOKUp23XSb51j8AQXdtO6tat1UZACUSHm32eBX/gOIZUvVRuL+84gxy7XPf+p2Y5ljzjH8V5UYQO6bGRBqRDzrLW5wREOnW72nUUqW6o20KkXZcYNzDg3s395r3O2aYovHSTcb6hTLApIhVgArc0NHcCL0FGAUkCpGp16UZayqaWZfavmuWz83im86r3r1q7+Y8EyCaBCLJjW5oYHgTf4ziGVK54ITgK6fOeQ4+BiMzNtK8/PDUy77Rie/dN1a1d/ptCRRIVYUK3NDd8FrvedQyqXZr0oZxbLtp96VbZ73u3OkRnnSY8Cry5mqmqmQiy89wK3+g4hlSmesA7fGeTE5LoWXJntbHzAOTpHPdQJvHDd2tWHip+qOqkQC6y1uSELvALY6jmKVKCETr2oCK6//oLMgTM6nLOt0V1ZwoNodL5hEakQi6C1ueEg8BKgz3cWqSyp2ljCdwbJk0zNKZl9K2e5bOxPhAfRrPMdqdqoEIuktbnhT8Df+c4hlaWmNpjhO4PkkYvPyrSt+v26tat17IEHKsQiam1uuAn4d985pHIka4KFvjNIXt0C9g++Q1QrFWKRtTY3/AvwNd85pDLEE8E84LDvHJIXDwIvXd/SON4Rp1JgKkQ/1gDaPyB5YYFOvagAe4DV61sa9ebGIxWiB63NDRngpYRzmomckHjcOn1nkBPSAzx/fUvjDt9Bqp0K0ZPW5oZu4LnARt9ZpLxp1ouylgVeub6lUZdlKwEqRI9amxv2Ac8iHC4ROS6a9aJs5YA3rm9p/KnvIBJSIXrW2tywBXg2POEqFSLHJFUX1PvOIFOWA16/vqXxa76DyBEqxBIQXQh8NdDrO4uUn1RNsMh3BpmSHPDa9S2Nmui3xKgQS0Rrc8OdhPsUNd+ZTEksHswBdL3L8pAF/nZ9S+M3fAeRJ1IhlpDW5obbgeegKX1kinTqRVkYLsNv+g4iY1MhlpjW5oY7gKvRO36ZAp16UfKywKvXtzR+y3cQGV/FFqKZ1ZjZPWZ2v5n9xcw+FN3fYGa3mNmG6PPscV7/72b2gJndZ2a/MrNF0f1JM/uqmT0YLbsp39lbmxvuBp4JaGofOSbJmmDQdwYZVxb4m/Utjd/xHUQmVrGFCAwAT3fOPQk4D3iOmV0GvA+41Tl3BuE8he8b5/WfcM6d65w7D/gp8C/R/WsAnHPnEG7JXW9mef8+tjY33As8AziY72VL5dGsFyUrA/z1+pbG7/oOIpOr2EJ0oeEDVBLRhwOuAYaP7roReNE4rx95CaVp0WsBziSa8Nc510Z4usRFeYz+uGiGjKcD+wuxfKkcqdpglu8M8gQZ4FXrWxpv9h1Ejk3FFiKAmcXM7D6gDbjFOXc3MN85twcg+nzSBK//iJntAP6GI1uI9wPXmFnczE4BLgSWFupraG1ueAB4GrCvUOuQ8peqienUi9IyBLxifUvj93wHkWNX0YXonMtGQ55LgEvM7Owpvv4DzrmlwDeBt0R33wDsBO4FPgPcSfhOsGBamxv+AlwFbCnkeqR8xeI2G13coVQMl+H/+g4iU1PRhTjMOdcJpAlPadhnZgsBos9t0e2vRgfQ/HyMRXwL+KtoWRnn3Dudc+c5564BZgEbCv01tDY3PApcBtxd6HVJedKpFyWhC3jx+pbGH/gOIlNXsYVoZvPMbFZ0u5bwqM1HgB8D10ZPuxb4EYBz7nVRyT0ves0ZIxb3wui1mFmdmU2Lbl8NZJxzDxX+K4LW5oY2wuFT/WeTJ4gndOqFZ1uAJ69vafyZ7yByfMw5N/mzypCZnUt40EyMsPhvds79m5nNAW4GGoHtwMucc+1jvP77wArCyyxtA97knNtlZssI5zLMAbuANzjnthXhS3rcmpb2ALgeeEcx1yulbe/2/nTP4UyT7xxV6jbCyX0P+A4ix69iC7EarGlpfwvhfsyY5yhSAjr3D95xcN/gU3znqEJfAt6yvqVxyHcQOTEqxDK3pqX9BcC3CU8NkSrW1515aPfW/jN956giWeCd61saP+s7iORHxe5DrBatzQ0/ITwCda/vLOJXUqdeFFMH8ByVYWVRIVaA1uaGPwCXAvf4ziL+xOI2C3jC/nDJu0eBS9e3NP7adxDJLxVihWhtbtgOXAH8t+8s4k8QsNt3hgr3S8IyLPipVlJ82odYgda0tL8SaAWm+84ixbVjQ+8dgwM5HVhTGJ8G3r2+pTHrO4gUhgqxQq1paV8JfJ/w2qtSJfbt6E93H9KpF3k2CLxpfUvjV30HkcLSkGmFam1ueAS4BLjJdxYpnlRtkPSdocJsA56uMqwO2kKsAmta2v8e+E8g5TuLFFZfT/ah3Vv6NCqQH18hPK2iy3cQKQ4VYpVY09J+AeEVek7znUUKJ5t1h7Y+3FPvO0eZ2wusWd/S+FPfQaS4NGRaJVqbG/4InAt8jiNzO0qFicWsHtDlw47fzcDZKsPqpC3EKrSmpf1phNNYLfMcRQpgy0PdD+ZynOM7R5lpB/5hfUvjd3wHEX+0hViFWpsb/g84B/ii7yySf/FEcNh3hjLzc8KtQpVhldMWYpVb09J+NeHBA0t9Z5H80KkXx6wL+Mf1LY1f9h1ESoO2EKtca3PDLcDZhEOoUgF06sUxuQ04V2UoI2kLUR63pqX9ucDngZN9Z5Hj19eTfXj3lr5VvnOUqH7g/cBn1rc06o+fHEWFKEdZ09JeC7wPeA9Q4zmOHIdc1nVtebhnhu8cJWg94byFD/sOIqVJhShjWtPSvgy4HniJ5yhyHDb9uXs/MM93jhKxGXjX+pbGH/gOIqVNhSgTWtPS/kzCq9zo6idlRKdeANANfAT49PqWxgHfYaT0qRBlUmta2uPAW4F/BXQVlDKwY2Pvbwf7c0/1ncMTB3wd+Of1LY17fIeR8qFClGO2pqX9JOBjwOsA8xxHJlDFp16kgfesb2n8ve8gUn5UiDJla1razwU+BLzIcxQZx6GDg787sGfwct85iuh+wi3CX/gOIuVLhSjHLbpg+IeA5/vOIkfr780+umtz3wrfOYpgK7AW+KZOo5ATpUKUE7ampf1i4N+A5/jOIqFc1nVvebhnuu8cBbQf+DDwhfUtjYO+w0hlUCFK3qxpab+csBif6TuLwKY/d7cBJ/nOkWdbCI96vkHzFEq+qRAl79a0tF8BfBB4uucoVW3Lw93357I8yXeOPPkt8Gngh+tbGnO+w0hlUiFKwaxpaT8feBvwKiDlOU7V2bGx9zeD/bkrfOc4ARngfwjPI9RRo1JwKkQpuOh0jb8H3gws9BynarTt7E93dZblqRcdwJeAz61vadzpO4xUDxWiFM2alvYE8DLg7cAlnuNUvEMHh+46sGfgMt85pmAD4f7Br61vaezxHUaqjwpRvFjT0n4ZYTH+FZDwHKci9fdmH9u1uW+57xzHIA18CviZ9g+KTypE8WpNS/sC4NXAa4Gz/KapLLmc69nyUM803znG0Q58H2hZ39J4n+csIoAKUUrImpb2i4BrCQ/CmeM5TkXY9OfuvcAC3zkincAPge8Cv17f0pjxmgYwsxhwL7DLOfd8M2sgzLeM8KT/lzvnOiZ4/buATwDznHMHzCwJfBG4CMgBb3fOpQv6RUjeqBCl5ET7Gp9DWIwvBEp1K6fkbXm4+75clvM8RjgM/IiwZG4ptZPozewfCctrZlSIHwfanXPXmdn7gNnOufeO89qlwJeBlcCFUSH+A3CRc+51ZnYS8AvgYuechoLLQNx3AJHRWpsbhoCfAD9Z09I+jbAUX0l4wn+dz2zlJpEIugayRf9b3EX487sZ+GWpTr1kZkuA1YRTRP1jdPc1QFN0+0bC/ZtjFiLheZHvISz8YWcCtwI459rMrJOwcO/JX3IpFBWilLTW5oYe4NvAt9e0tNcATyP8I7aacFhLJpCsCbID/UUpxB7gp4Ql+PP1LY39xVjpCfoMYaHNGHHffOfcHgDn3J5oK+8JzOyFhMOs95sdNfHL/cA1ZvYdYClwYfRZhVgGVIhSNlqbG/oJh6B+AbxlTUv7WRwpxyej3+cnSNXGars6C7arbidwO+F+wZ+tb2nsLdSK8s3Mng+0Oef+YGZNU3xtHfAB4FljPHwDsIpwv+Q24E7CCwxIGdA+RKkIa1raZwHPJizHq4BGr4FKRH9vdsOuzX1n5GlxjwC/Gf5Y39K4NU/LLToz+xjwGsKyqgFmAv8LXAw0RVuHC4G0c26FmX0VOB/YTTiEeisw/AZgSXT/Jc65vaPWcyfwRufcQ0X4suQEqRClIq1paV9CuNX4lOjzeVThFmQu5/q2PNRTw9QndM4C93GkAH+7vqWxLc/xSkK0hfiu6KCaTwAHRxxU0+Cce88kr99KeCDNgWjr0ZxzPWZ2NbDWOXdlgb8EyRMVolSFNS3tdYRXxxkuyMuB2V5DFcmmP3fvYfJL5vUDd3OkAH9XLbNJjCrEOYT7QRuB7cDLnHPtk7x+K0cKcRmwjvCUi13AG5xz2wqXXvJJhShVaU1LuwGnER4VOPxxFuEh9BV1JOuWh7v/lMty/oi7dhAOfz4cfTwA3Ftqp0SIFJsKUWSEqCiXcXRRriI8UnA+EPMWbmragU3A5r3b+n7f05VtIyy/R9a3NHb7jSZSmlSIIsdoTUt7jLAUFwGLo8+LRv17HuGFBOrIf3l2Ec4UP97HAcKDOza3NjccyvO6RSqeClGkQNa0tKcIi3HaqM/DtwEGgaExPkbePwAcbG1uKMkT3EUqhQpRREQECHwHEBERKQUqRBEREVSIIiIigApRREQEUCGKiIgAKkQRERFAhSgiIgKoEEVERAAVooiICKBCFBERAVSIIiIigApRREQEUCGKiIgAKkQRERFAhSgiIgKoEEVERAAVooiICKBCFBERAVSIIiIigApRREQEUCGKiIgAKkQRERFAhSgiIgKoEEVERAAVooiICKBCFBERAVSIIiIigApRREQEUCGKiIgAKkQRERFAhSgiIgKoEEVERAAVooiICKBCFBERAVSIIiIigApRREQEUCGKiIgAKkQRERFAhSgiIgKoEEVERAAVooiICKBCFBERAVSIIiIigApRREQEUCGKiIgAKkQRERFAhSgiIgKoEEVERAAVooiICAD/H8JiyYnRf/hJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Divvy Riders based on Age, Subscribers Only- PIE CHART\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.pie(subscriber_age_totals, labels= group_names, colors = rainbow_colors)\n",
    "#startangle=90\n",
    "#autopct=\"%1.1f%%\",\n",
    "plt.title(\"Divvy Riders based on Age, Subscribers Only\")\n",
    "plt.savefig('Images/Age_Subscribers_Only.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}