exploration_template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ford GoBike System Data Analysis\n",
    "## by Mostafa Atef\n",
    "## Preliminary Wrangling\n",
    "\n",
    "> This data set includes information about individual bike rides made in a bike-sharing system covering the greater San Francisco Bay area for Q1, 2020 Year, this data is downloaded through this reference https://s3.amazonaws.com/baywheels-data/index.html."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import all the needed packages and set the plots to be embedded inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import os\n",
    "import glob\n",
    "from pathlib import Path\n",
    "import math\n",
    "from math import radians, sin, cos, acos\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gathering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading Q1, 2020 CSV files in df Dataframe\n",
    "\n",
    "df = pd.concat((pd.read_csv(path,parse_dates=True,low_memory=False) for path in Path('.').rglob('*.csv')), ignore_index = True)\n",
    "\n",
    "\n",
    "#Writing the concatenated data into one CSV file\n",
    "\n",
    "df.to_csv('baywheels_trips_Q1_2020.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Q1_2020 = pd.read_csv('baywheels_trips_Q1_2020.csv',parse_dates=True,low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3620028, 14)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3620028 entries, 0 to 3620027\n",
      "Data columns (total 14 columns):\n",
      " #   Column                   Dtype  \n",
      "---  ------                   -----  \n",
      " 0   duration_sec             int64  \n",
      " 1   start_time               object \n",
      " 2   end_time                 object \n",
      " 3   start_station_id         float64\n",
      " 4   start_station_name       object \n",
      " 5   start_station_latitude   float64\n",
      " 6   start_station_longitude  float64\n",
      " 7   end_station_id           float64\n",
      " 8   end_station_name         object \n",
      " 9   end_station_latitude     float64\n",
      " 10  end_station_longitude    float64\n",
      " 11  bike_id                  int64  \n",
      " 12  user_type                object \n",
      " 13  rental_access_method     object \n",
      "dtypes: float64(6), int64(2), object(6)\n",
      "memory usage: 386.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_Q1_2020.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83118</td>\n",
       "      <td>2020-01-31 15:23:47.7330</td>\n",
       "      <td>2020-02-01 14:29:06.2630</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>13052</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68239</td>\n",
       "      <td>2020-01-31 15:40:31.6160</td>\n",
       "      <td>2020-02-01 10:37:51.0000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>12235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55407</td>\n",
       "      <td>2020-01-31 17:48:16.6350</td>\n",
       "      <td>2020-02-01 09:11:44.3170</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>12822</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54929</td>\n",
       "      <td>2020-01-31 17:53:03.4130</td>\n",
       "      <td>2020-02-01 09:08:32.6340</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>11705</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55700</td>\n",
       "      <td>2020-01-31 17:12:33.4600</td>\n",
       "      <td>2020-02-01 08:40:53.6460</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>371.0</td>\n",
       "      <td>Lombard St at Columbus Ave</td>\n",
       "      <td>37.802746</td>\n",
       "      <td>-122.413579</td>\n",
       "      <td>3673</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time                  end_time  \\\n",
       "0         83118  2020-01-31 15:23:47.7330  2020-02-01 14:29:06.2630   \n",
       "1         68239  2020-01-31 15:40:31.6160  2020-02-01 10:37:51.0000   \n",
       "2         55407  2020-01-31 17:48:16.6350  2020-02-01 09:11:44.3170   \n",
       "3         54929  2020-01-31 17:53:03.4130  2020-02-01 09:08:32.6340   \n",
       "4         55700  2020-01-31 17:12:33.4600  2020-02-01 08:40:53.6460   \n",
       "\n",
       "   start_station_id             start_station_name  start_station_latitude  \\\n",
       "0             400.0  Buchanan St at North Point St               37.804272   \n",
       "1              99.0           Folsom St at 15th St               37.767037   \n",
       "2             197.0    El Embarcadero at Grand Ave               37.808848   \n",
       "3             197.0    El Embarcadero at Grand Ave               37.808848   \n",
       "4              12.0    Pier 1/2 at The Embarcadero               37.796389   \n",
       "\n",
       "   start_station_longitude  end_station_id               end_station_name  \\\n",
       "0              -122.433537           400.0  Buchanan St at North Point St   \n",
       "1              -122.415442            99.0           Folsom St at 15th St   \n",
       "2              -122.249680           197.0    El Embarcadero at Grand Ave   \n",
       "3              -122.249680           197.0    El Embarcadero at Grand Ave   \n",
       "4              -122.394586           371.0     Lombard St at Columbus Ave   \n",
       "\n",
       "   end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "0             37.804272            -122.433537    13052    Customer   \n",
       "1             37.767037            -122.415442    12235  Subscriber   \n",
       "2             37.808848            -122.249680    12822    Customer   \n",
       "3             37.808848            -122.249680    11705    Customer   \n",
       "4             37.802746            -122.413579     3673    Customer   \n",
       "\n",
       "  rental_access_method  \n",
       "0                  NaN  \n",
       "1                  NaN  \n",
       "2                  NaN  \n",
       "3                  NaN  \n",
       "4                  NaN  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>duration_sec</th>\n",
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       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2255671</th>\n",
       "      <td>574</td>\n",
       "      <td>2020-02-01 18:33:45</td>\n",
       "      <td>2020-02-01 18:43:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.749893</td>\n",
       "      <td>-122.421261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.749215</td>\n",
       "      <td>-122.420498</td>\n",
       "      <td>974864</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508107</th>\n",
       "      <td>637</td>\n",
       "      <td>2020-02-16 16:22:34</td>\n",
       "      <td>2020-02-16 16:33:12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.781168</td>\n",
       "      <td>-122.409254</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.767045</td>\n",
       "      <td>-122.410692</td>\n",
       "      <td>593379</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886652</th>\n",
       "      <td>261</td>\n",
       "      <td>2020-01-09 20:31:16.2430</td>\n",
       "      <td>2020-01-09 20:35:37.2480</td>\n",
       "      <td>104.0</td>\n",
       "      <td>4th St at 16th St</td>\n",
       "      <td>37.767045</td>\n",
       "      <td>-122.390833</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>10212</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661430</th>\n",
       "      <td>637</td>\n",
       "      <td>2020-02-20 08:34:12</td>\n",
       "      <td>2020-02-20 08:44:49</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.775744</td>\n",
       "      <td>-122.413836</td>\n",
       "      <td>311723</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881517</th>\n",
       "      <td>1229</td>\n",
       "      <td>2020-01-10 21:38:52.1400</td>\n",
       "      <td>2020-01-10 21:59:21.5100</td>\n",
       "      <td>460.0</td>\n",
       "      <td>Terry Francois Blvd at Warriors Way</td>\n",
       "      <td>37.769095</td>\n",
       "      <td>-122.386333</td>\n",
       "      <td>15.0</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>12008</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385838</th>\n",
       "      <td>326</td>\n",
       "      <td>2020-02-07 19:30:12.3100</td>\n",
       "      <td>2020-02-07 19:35:38.8070</td>\n",
       "      <td>133.0</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>122.0</td>\n",
       "      <td>19th St at Mission St</td>\n",
       "      <td>37.760299</td>\n",
       "      <td>-122.418892</td>\n",
       "      <td>12354</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59217</th>\n",
       "      <td>822</td>\n",
       "      <td>2020-01-14 15:44:54.1310</td>\n",
       "      <td>2020-01-14 15:58:36.9840</td>\n",
       "      <td>187.0</td>\n",
       "      <td>Jack London Square</td>\n",
       "      <td>37.796248</td>\n",
       "      <td>-122.279352</td>\n",
       "      <td>200.0</td>\n",
       "      <td>2nd Ave at E 18th St</td>\n",
       "      <td>37.800214</td>\n",
       "      <td>-122.253810</td>\n",
       "      <td>12690</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3394449</th>\n",
       "      <td>494</td>\n",
       "      <td>2020-02-28 13:48:30</td>\n",
       "      <td>2020-02-28 13:56:44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.792847</td>\n",
       "      <td>-122.412763</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.792361</td>\n",
       "      <td>-122.403032</td>\n",
       "      <td>376086</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1795964</th>\n",
       "      <td>780</td>\n",
       "      <td>2020-03-15 13:56:57</td>\n",
       "      <td>2020-03-15 14:09:58</td>\n",
       "      <td>384.0</td>\n",
       "      <td>Jackson St at Polk St</td>\n",
       "      <td>37.794160</td>\n",
       "      <td>-122.421568</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.789661</td>\n",
       "      <td>-122.440656</td>\n",
       "      <td>314751</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981546</th>\n",
       "      <td>310</td>\n",
       "      <td>2020-01-15 16:16:21</td>\n",
       "      <td>2020-01-15 16:21:32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.768217</td>\n",
       "      <td>-122.426926</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.763393</td>\n",
       "      <td>-122.420872</td>\n",
       "      <td>261260</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543196</th>\n",
       "      <td>380</td>\n",
       "      <td>2020-02-07 20:52:50</td>\n",
       "      <td>2020-02-07 20:59:10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.786864</td>\n",
       "      <td>-122.392387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784240</td>\n",
       "      <td>-122.403559</td>\n",
       "      <td>401322</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2926547</th>\n",
       "      <td>320</td>\n",
       "      <td>2020-01-18 20:55:58</td>\n",
       "      <td>2020-01-18 21:01:19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.777166</td>\n",
       "      <td>-122.443075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.771872</td>\n",
       "      <td>-122.431893</td>\n",
       "      <td>913161</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1563614</th>\n",
       "      <td>943</td>\n",
       "      <td>2020-02-04 16:43:52</td>\n",
       "      <td>2020-02-04 16:59:35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.789437</td>\n",
       "      <td>-122.390707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.761839</td>\n",
       "      <td>-122.397543</td>\n",
       "      <td>629047</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471612</th>\n",
       "      <td>71</td>\n",
       "      <td>2020-02-14 15:32:30</td>\n",
       "      <td>2020-02-14 15:33:41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.760490</td>\n",
       "      <td>-122.464009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.760497</td>\n",
       "      <td>-122.464010</td>\n",
       "      <td>322975</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771323</th>\n",
       "      <td>548</td>\n",
       "      <td>2020-03-04 09:39:21</td>\n",
       "      <td>2020-03-04 09:48:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.781258</td>\n",
       "      <td>-122.397035</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.794014</td>\n",
       "      <td>-122.403034</td>\n",
       "      <td>610701</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327166</th>\n",
       "      <td>138</td>\n",
       "      <td>2020-02-01 13:30:47</td>\n",
       "      <td>2020-02-01 13:33:06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.334036</td>\n",
       "      <td>-121.900877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.332460</td>\n",
       "      <td>-121.904741</td>\n",
       "      <td>338364</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840316</th>\n",
       "      <td>317</td>\n",
       "      <td>2020-01-23 17:08:53.0660</td>\n",
       "      <td>2020-01-23 17:14:10.2870</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Telegraph Ave at Ashby Ave</td>\n",
       "      <td>37.855956</td>\n",
       "      <td>-122.259795</td>\n",
       "      <td>168.0</td>\n",
       "      <td>Alcatraz Ave at Shattuck Ave</td>\n",
       "      <td>37.849595</td>\n",
       "      <td>-122.265569</td>\n",
       "      <td>12308</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498888</th>\n",
       "      <td>1887</td>\n",
       "      <td>2020-02-20 15:18:02</td>\n",
       "      <td>2020-02-20 15:49:29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.779508</td>\n",
       "      <td>-122.388507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.752521</td>\n",
       "      <td>-122.392504</td>\n",
       "      <td>410522</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3099981</th>\n",
       "      <td>851</td>\n",
       "      <td>2020-02-08 10:29:16.1710</td>\n",
       "      <td>2020-02-08 10:43:27.5230</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Esprit Park</td>\n",
       "      <td>37.761634</td>\n",
       "      <td>-122.390648</td>\n",
       "      <td>109.0</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>3006</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3109403</th>\n",
       "      <td>457</td>\n",
       "      <td>2020-02-05 19:36:20.1440</td>\n",
       "      <td>2020-02-05 19:43:57.7270</td>\n",
       "      <td>310.0</td>\n",
       "      <td>San Fernando St at 4th St</td>\n",
       "      <td>37.335885</td>\n",
       "      <td>-121.885660</td>\n",
       "      <td>305.0</td>\n",
       "      <td>Ryland Park</td>\n",
       "      <td>37.342725</td>\n",
       "      <td>-121.895617</td>\n",
       "      <td>10500</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592494</th>\n",
       "      <td>1054</td>\n",
       "      <td>2020-02-21 15:35:02</td>\n",
       "      <td>2020-02-21 15:52:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.779123</td>\n",
       "      <td>-122.436077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784847</td>\n",
       "      <td>-122.407127</td>\n",
       "      <td>803984</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847445</th>\n",
       "      <td>1247</td>\n",
       "      <td>2020-03-25 18:15:58</td>\n",
       "      <td>2020-03-25 18:36:46</td>\n",
       "      <td>78.0</td>\n",
       "      <td>Folsom St at 9th St</td>\n",
       "      <td>37.773717</td>\n",
       "      <td>-122.411647</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Valencia St at 16th St</td>\n",
       "      <td>37.765052</td>\n",
       "      <td>-122.421866</td>\n",
       "      <td>387845</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2402213</th>\n",
       "      <td>1142</td>\n",
       "      <td>2020-02-19 08:43:26</td>\n",
       "      <td>2020-02-19 09:02:28</td>\n",
       "      <td>380.0</td>\n",
       "      <td>Masonic Ave at Turk St</td>\n",
       "      <td>37.779047</td>\n",
       "      <td>-122.447291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.794349</td>\n",
       "      <td>-122.400015</td>\n",
       "      <td>404577</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2809299</th>\n",
       "      <td>600</td>\n",
       "      <td>2020-01-06 15:50:41.1540</td>\n",
       "      <td>2020-01-06 16:00:41.3160</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2nd St at Townsend St</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>37.789756</td>\n",
       "      <td>-122.394643</td>\n",
       "      <td>9879</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680704</th>\n",
       "      <td>505</td>\n",
       "      <td>2020-02-14 16:34:51</td>\n",
       "      <td>2020-02-14 16:43:16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.793176</td>\n",
       "      <td>-122.401232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.785660</td>\n",
       "      <td>-122.405812</td>\n",
       "      <td>336738</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2932276</th>\n",
       "      <td>299</td>\n",
       "      <td>2020-01-21 15:02:27</td>\n",
       "      <td>2020-01-21 15:07:26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.778738</td>\n",
       "      <td>-122.395657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.772978</td>\n",
       "      <td>-122.397041</td>\n",
       "      <td>394905</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395933</th>\n",
       "      <td>436</td>\n",
       "      <td>2020-02-21 11:56:58</td>\n",
       "      <td>2020-02-21 12:04:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.764494</td>\n",
       "      <td>-122.411326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.773259</td>\n",
       "      <td>-122.399988</td>\n",
       "      <td>104451</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2606814</th>\n",
       "      <td>2258</td>\n",
       "      <td>2020-03-04 16:33:39</td>\n",
       "      <td>2020-03-04 17:11:17</td>\n",
       "      <td>313.0</td>\n",
       "      <td>Almaden Blvd at San Fernando St</td>\n",
       "      <td>37.331415</td>\n",
       "      <td>-121.893200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.388166</td>\n",
       "      <td>-121.912978</td>\n",
       "      <td>895016</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316248</th>\n",
       "      <td>587</td>\n",
       "      <td>2020-02-25 18:41:26.6380</td>\n",
       "      <td>2020-02-25 18:51:14.2240</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>453.0</td>\n",
       "      <td>Brannan St at 4th St</td>\n",
       "      <td>37.777934</td>\n",
       "      <td>-122.396973</td>\n",
       "      <td>11360</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373844</th>\n",
       "      <td>217</td>\n",
       "      <td>2020-02-11 11:23:56.4690</td>\n",
       "      <td>2020-02-11 11:27:33.5880</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.783830</td>\n",
       "      <td>-122.398870</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Post St at Kearny St</td>\n",
       "      <td>37.788975</td>\n",
       "      <td>-122.403452</td>\n",
       "      <td>1788</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1946865</th>\n",
       "      <td>384</td>\n",
       "      <td>2020-01-11 18:49:57</td>\n",
       "      <td>2020-01-11 18:56:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.752650</td>\n",
       "      <td>-122.410719</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415442</td>\n",
       "      <td>560821</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3376342</th>\n",
       "      <td>469</td>\n",
       "      <td>2020-02-22 18:43:51</td>\n",
       "      <td>2020-02-22 18:51:40</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.792153</td>\n",
       "      <td>-122.401428</td>\n",
       "      <td>594988</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458156</th>\n",
       "      <td>358</td>\n",
       "      <td>2020-02-29 21:18:32</td>\n",
       "      <td>2020-02-29 21:24:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.756156</td>\n",
       "      <td>-122.418752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.757890</td>\n",
       "      <td>-122.409715</td>\n",
       "      <td>952719</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3439942</th>\n",
       "      <td>717</td>\n",
       "      <td>2020-02-01 18:43:35</td>\n",
       "      <td>2020-02-01 18:55:33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.806255</td>\n",
       "      <td>-122.422082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.787019</td>\n",
       "      <td>-122.420069</td>\n",
       "      <td>241255</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2624650</th>\n",
       "      <td>302</td>\n",
       "      <td>2020-03-03 19:03:53</td>\n",
       "      <td>2020-03-03 19:08:56</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>387.0</td>\n",
       "      <td>Randall St at Chenery St</td>\n",
       "      <td>37.739813</td>\n",
       "      <td>-122.425523</td>\n",
       "      <td>594196</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2342531</th>\n",
       "      <td>1797</td>\n",
       "      <td>2020-02-01 17:03:59</td>\n",
       "      <td>2020-02-01 17:33:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.771474</td>\n",
       "      <td>-122.510909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.773530</td>\n",
       "      <td>-122.441110</td>\n",
       "      <td>576439</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158957</th>\n",
       "      <td>1057</td>\n",
       "      <td>2020-01-10 07:10:41</td>\n",
       "      <td>2020-01-10 07:28:18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.764581</td>\n",
       "      <td>-122.429761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.757053</td>\n",
       "      <td>-122.388299</td>\n",
       "      <td>847223</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3123177</th>\n",
       "      <td>748</td>\n",
       "      <td>2020-02-01 20:06:27.1080</td>\n",
       "      <td>2020-02-01 20:18:55.2340</td>\n",
       "      <td>468.0</td>\n",
       "      <td>Grove St at Gough St</td>\n",
       "      <td>37.777870</td>\n",
       "      <td>-122.422953</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Howard St at 8th St</td>\n",
       "      <td>37.776513</td>\n",
       "      <td>-122.411306</td>\n",
       "      <td>2429</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954328</th>\n",
       "      <td>949</td>\n",
       "      <td>2020-01-17 08:57:48.7110</td>\n",
       "      <td>2020-01-17 09:13:38.6530</td>\n",
       "      <td>15.0</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>460.0</td>\n",
       "      <td>Terry Francois Blvd at Warriors Way</td>\n",
       "      <td>37.769095</td>\n",
       "      <td>-122.386333</td>\n",
       "      <td>13018</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3486120</th>\n",
       "      <td>925</td>\n",
       "      <td>2020-03-04 22:20:10.1070</td>\n",
       "      <td>2020-03-04 22:35:35.6750</td>\n",
       "      <td>465.0</td>\n",
       "      <td>San Francisco Caltrain Station (King St at 4th...</td>\n",
       "      <td>37.776329</td>\n",
       "      <td>-122.394438</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>9788</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310932</th>\n",
       "      <td>476</td>\n",
       "      <td>2020-02-26 17:53:04.1150</td>\n",
       "      <td>2020-02-26 18:01:00.4560</td>\n",
       "      <td>38.0</td>\n",
       "      <td>The Embarcadero at Pier 38</td>\n",
       "      <td>37.782926</td>\n",
       "      <td>-122.387921</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Market St at Steuart St</td>\n",
       "      <td>37.794497</td>\n",
       "      <td>-122.394838</td>\n",
       "      <td>3130</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77468</th>\n",
       "      <td>565</td>\n",
       "      <td>2020-01-09 18:14:39.6340</td>\n",
       "      <td>2020-01-09 18:24:04.8100</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Golden Gate Ave at Hyde St</td>\n",
       "      <td>37.781650</td>\n",
       "      <td>-122.415408</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>10213</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3183756</th>\n",
       "      <td>1055</td>\n",
       "      <td>2020-02-18 17:01:41</td>\n",
       "      <td>2020-02-18 17:19:16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.759913</td>\n",
       "      <td>-122.476500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.791783</td>\n",
       "      <td>-122.447811</td>\n",
       "      <td>548367</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924069</th>\n",
       "      <td>369</td>\n",
       "      <td>2020-01-12 14:02:51</td>\n",
       "      <td>2020-01-12 14:09:00</td>\n",
       "      <td>294.0</td>\n",
       "      <td>Pierce Ave at Market St</td>\n",
       "      <td>37.327581</td>\n",
       "      <td>-121.884559</td>\n",
       "      <td>208.0</td>\n",
       "      <td>4th St at San Carlos St</td>\n",
       "      <td>37.332800</td>\n",
       "      <td>-121.883850</td>\n",
       "      <td>155154</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051765</th>\n",
       "      <td>369</td>\n",
       "      <td>2020-01-27 07:38:42</td>\n",
       "      <td>2020-01-27 07:44:51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.759984</td>\n",
       "      <td>-122.434902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.764628</td>\n",
       "      <td>-122.432796</td>\n",
       "      <td>419445</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>clipper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133250</th>\n",
       "      <td>713</td>\n",
       "      <td>2020-01-30 07:18:14</td>\n",
       "      <td>2020-01-30 07:30:08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.749275</td>\n",
       "      <td>-122.421408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.764797</td>\n",
       "      <td>-122.401306</td>\n",
       "      <td>445474</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288466</th>\n",
       "      <td>626</td>\n",
       "      <td>2020-02-06 18:31:50</td>\n",
       "      <td>2020-02-06 18:42:17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.761871</td>\n",
       "      <td>-122.421623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.748882</td>\n",
       "      <td>-122.434969</td>\n",
       "      <td>302919</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743182</th>\n",
       "      <td>625</td>\n",
       "      <td>2020-03-14 21:36:35</td>\n",
       "      <td>2020-03-14 21:47:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.769880</td>\n",
       "      <td>-122.443610</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.777564</td>\n",
       "      <td>-122.438427</td>\n",
       "      <td>917276</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499318</th>\n",
       "      <td>705</td>\n",
       "      <td>2020-03-02 18:08:03.0370</td>\n",
       "      <td>2020-03-02 18:19:48.4540</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Folsom St at 2nd St</td>\n",
       "      <td>37.785377</td>\n",
       "      <td>-122.396906</td>\n",
       "      <td>482.0</td>\n",
       "      <td>Brannan St at 7th St</td>\n",
       "      <td>37.772658</td>\n",
       "      <td>-122.403681</td>\n",
       "      <td>1523</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680700</th>\n",
       "      <td>316</td>\n",
       "      <td>2020-03-04 08:44:44.9090</td>\n",
       "      <td>2020-03-04 08:50:01.0370</td>\n",
       "      <td>398.0</td>\n",
       "      <td>Leavenworth St at Broadway</td>\n",
       "      <td>37.796471</td>\n",
       "      <td>-122.416858</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>3564</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_sec                start_time                  end_time  \\\n",
       "2255671           574       2020-02-01 18:33:45       2020-02-01 18:43:20   \n",
       "1508107           637       2020-02-16 16:22:34       2020-02-16 16:33:12   \n",
       "1886652           261  2020-01-09 20:31:16.2430  2020-01-09 20:35:37.2480   \n",
       "661430            637       2020-02-20 08:34:12       2020-02-20 08:44:49   \n",
       "1881517          1229  2020-01-10 21:38:52.1400  2020-01-10 21:59:21.5100   \n",
       "385838            326  2020-02-07 19:30:12.3100  2020-02-07 19:35:38.8070   \n",
       "59217             822  2020-01-14 15:44:54.1310  2020-01-14 15:58:36.9840   \n",
       "3394449           494       2020-02-28 13:48:30       2020-02-28 13:56:44   \n",
       "1795964           780       2020-03-15 13:56:57       2020-03-15 14:09:58   \n",
       "1981546           310       2020-01-15 16:16:21       2020-01-15 16:21:32   \n",
       "1543196           380       2020-02-07 20:52:50       2020-02-07 20:59:10   \n",
       "2926547           320       2020-01-18 20:55:58       2020-01-18 21:01:19   \n",
       "1563614           943       2020-02-04 16:43:52       2020-02-04 16:59:35   \n",
       "471612             71       2020-02-14 15:32:30       2020-02-14 15:33:41   \n",
       "1771323           548       2020-03-04 09:39:21       2020-03-04 09:48:29   \n",
       "1327166           138       2020-02-01 13:30:47       2020-02-01 13:33:06   \n",
       "1840316           317  2020-01-23 17:08:53.0660  2020-01-23 17:14:10.2870   \n",
       "1498888          1887       2020-02-20 15:18:02       2020-02-20 15:49:29   \n",
       "3099981           851  2020-02-08 10:29:16.1710  2020-02-08 10:43:27.5230   \n",
       "3109403           457  2020-02-05 19:36:20.1440  2020-02-05 19:43:57.7270   \n",
       "592494           1054       2020-02-21 15:35:02       2020-02-21 15:52:37   \n",
       "847445           1247       2020-03-25 18:15:58       2020-03-25 18:36:46   \n",
       "2402213          1142       2020-02-19 08:43:26       2020-02-19 09:02:28   \n",
       "2809299           600  2020-01-06 15:50:41.1540  2020-01-06 16:00:41.3160   \n",
       "680704            505       2020-02-14 16:34:51       2020-02-14 16:43:16   \n",
       "2932276           299       2020-01-21 15:02:27       2020-01-21 15:07:26   \n",
       "1395933           436       2020-02-21 11:56:58       2020-02-21 12:04:15   \n",
       "2606814          2258       2020-03-04 16:33:39       2020-03-04 17:11:17   \n",
       "316248            587  2020-02-25 18:41:26.6380  2020-02-25 18:51:14.2240   \n",
       "373844            217  2020-02-11 11:23:56.4690  2020-02-11 11:27:33.5880   \n",
       "1946865           384       2020-01-11 18:49:57       2020-01-11 18:56:21   \n",
       "3376342           469       2020-02-22 18:43:51       2020-02-22 18:51:40   \n",
       "458156            358       2020-02-29 21:18:32       2020-02-29 21:24:30   \n",
       "3439942           717       2020-02-01 18:43:35       2020-02-01 18:55:33   \n",
       "2624650           302       2020-03-03 19:03:53       2020-03-03 19:08:56   \n",
       "2342531          1797       2020-02-01 17:03:59       2020-02-01 17:33:57   \n",
       "158957           1057       2020-01-10 07:10:41       2020-01-10 07:28:18   \n",
       "3123177           748  2020-02-01 20:06:27.1080  2020-02-01 20:18:55.2340   \n",
       "954328            949  2020-01-17 08:57:48.7110  2020-01-17 09:13:38.6530   \n",
       "3486120           925  2020-03-04 22:20:10.1070  2020-03-04 22:35:35.6750   \n",
       "310932            476  2020-02-26 17:53:04.1150  2020-02-26 18:01:00.4560   \n",
       "77468             565  2020-01-09 18:14:39.6340  2020-01-09 18:24:04.8100   \n",
       "3183756          1055       2020-02-18 17:01:41       2020-02-18 17:19:16   \n",
       "1924069           369       2020-01-12 14:02:51       2020-01-12 14:09:00   \n",
       "1051765           369       2020-01-27 07:38:42       2020-01-27 07:44:51   \n",
       "133250            713       2020-01-30 07:18:14       2020-01-30 07:30:08   \n",
       "2288466           626       2020-02-06 18:31:50       2020-02-06 18:42:17   \n",
       "1743182           625       2020-03-14 21:36:35       2020-03-14 21:47:00   \n",
       "3499318           705  2020-03-02 18:08:03.0370  2020-03-02 18:19:48.4540   \n",
       "1680700           316  2020-03-04 08:44:44.9090  2020-03-04 08:50:01.0370   \n",
       "\n",
       "         start_station_id                                 start_station_name  \\\n",
       "2255671               NaN                                                NaN   \n",
       "1508107               NaN                                                NaN   \n",
       "1886652             104.0                                  4th St at 16th St   \n",
       "661430               24.0                              Spear St at Folsom St   \n",
       "1881517             460.0                Terry Francois Blvd at Warriors Way   \n",
       "385838              133.0                             Valencia St at 22nd St   \n",
       "59217               187.0                                 Jack London Square   \n",
       "3394449               NaN                                                NaN   \n",
       "1795964             384.0                              Jackson St at Polk St   \n",
       "1981546               NaN                                                NaN   \n",
       "1543196               NaN                                                NaN   \n",
       "2926547               NaN                                                NaN   \n",
       "1563614               NaN                                                NaN   \n",
       "471612                NaN                                                NaN   \n",
       "1771323               NaN                                                NaN   \n",
       "1327166               NaN                                                NaN   \n",
       "1840316             248.0                         Telegraph Ave at Ashby Ave   \n",
       "1498888               NaN                                                NaN   \n",
       "3099981             126.0                                        Esprit Park   \n",
       "3109403             310.0                          San Fernando St at 4th St   \n",
       "592494                NaN                                                NaN   \n",
       "847445               78.0                                Folsom St at 9th St   \n",
       "2402213             380.0                             Masonic Ave at Turk St   \n",
       "2809299              50.0                              2nd St at Townsend St   \n",
       "680704                NaN                                                NaN   \n",
       "2932276               NaN                                                NaN   \n",
       "1395933               NaN                                                NaN   \n",
       "2606814             313.0                    Almaden Blvd at San Fernando St   \n",
       "316248               58.0                               Market St at 10th St   \n",
       "373844               36.0                                Folsom St at 3rd St   \n",
       "1946865               NaN                                                NaN   \n",
       "3376342              24.0                              Spear St at Folsom St   \n",
       "458156                NaN                                                NaN   \n",
       "3439942               NaN                                                NaN   \n",
       "2624650             134.0                             Valencia St at 24th St   \n",
       "2342531               NaN                                                NaN   \n",
       "158957                NaN                                                NaN   \n",
       "3123177             468.0                               Grove St at Gough St   \n",
       "954328               15.0  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "3486120             465.0  San Francisco Caltrain Station (King St at 4th...   \n",
       "310932               38.0                         The Embarcadero at Pier 38   \n",
       "77468                33.0                         Golden Gate Ave at Hyde St   \n",
       "3183756               NaN                                                NaN   \n",
       "1924069             294.0                            Pierce Ave at Market St   \n",
       "1051765               NaN                                                NaN   \n",
       "133250                NaN                                                NaN   \n",
       "2288466               NaN                                                NaN   \n",
       "1743182               NaN                                                NaN   \n",
       "3499318              37.0                                Folsom St at 2nd St   \n",
       "1680700             398.0                         Leavenworth St at Broadway   \n",
       "\n",
       "         start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "2255671               37.749893              -122.421261             NaN   \n",
       "1508107               37.781168              -122.409254             NaN   \n",
       "1886652               37.767045              -122.390833            81.0   \n",
       "661430                37.789677              -122.390428             NaN   \n",
       "1881517               37.769095              -122.386333            15.0   \n",
       "385838                37.755213              -122.420975           122.0   \n",
       "59217                 37.796248              -122.279352           200.0   \n",
       "3394449               37.792847              -122.412763             NaN   \n",
       "1795964               37.794160              -122.421568             NaN   \n",
       "1981546               37.768217              -122.426926             NaN   \n",
       "1543196               37.786864              -122.392387             NaN   \n",
       "2926547               37.777166              -122.443075             NaN   \n",
       "1563614               37.789437              -122.390707             NaN   \n",
       "471612                37.760490              -122.464009             NaN   \n",
       "1771323               37.781258              -122.397035             NaN   \n",
       "1327166               37.334036              -121.900877             NaN   \n",
       "1840316               37.855956              -122.259795           168.0   \n",
       "1498888               37.779508              -122.388507             NaN   \n",
       "3099981               37.761634              -122.390648           109.0   \n",
       "3109403               37.335885              -121.885660           305.0   \n",
       "592494                37.779123              -122.436077             NaN   \n",
       "847445                37.773717              -122.411647            98.0   \n",
       "2402213               37.779047              -122.447291             NaN   \n",
       "2809299               37.780526              -122.390288            22.0   \n",
       "680704                37.793176              -122.401232             NaN   \n",
       "2932276               37.778738              -122.395657             NaN   \n",
       "1395933               37.764494              -122.411326             NaN   \n",
       "2606814               37.331415              -121.893200             NaN   \n",
       "316248                37.776619              -122.417385           453.0   \n",
       "373844                37.783830              -122.398870            19.0   \n",
       "1946865               37.752650              -122.410719            99.0   \n",
       "3376342               37.789677              -122.390428             NaN   \n",
       "458156                37.756156              -122.418752             NaN   \n",
       "3439942               37.806255              -122.422082             NaN   \n",
       "2624650               37.752428              -122.420628           387.0   \n",
       "2342531               37.771474              -122.510909             NaN   \n",
       "158957                37.764581              -122.429761             NaN   \n",
       "3123177               37.777870              -122.422953            61.0   \n",
       "954328                37.795392              -122.394203           460.0   \n",
       "3486120               37.776329              -122.394438            13.0   \n",
       "310932                37.782926              -122.387921            16.0   \n",
       "77468                 37.781650              -122.415408            21.0   \n",
       "3183756               37.759913              -122.476500             NaN   \n",
       "1924069               37.327581              -121.884559           208.0   \n",
       "1051765               37.759984              -122.434902             NaN   \n",
       "133250                37.749275              -122.421408             NaN   \n",
       "2288466               37.761871              -122.421623             NaN   \n",
       "1743182               37.769880              -122.443610             NaN   \n",
       "3499318               37.785377              -122.396906           482.0   \n",
       "1680700               37.796471              -122.416858            13.0   \n",
       "\n",
       "                                          end_station_name  \\\n",
       "2255671                                                NaN   \n",
       "1508107                                                NaN   \n",
       "1886652                                 Berry St at 4th St   \n",
       "661430                                                 NaN   \n",
       "1881517  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "385838                               19th St at Mission St   \n",
       "59217                                 2nd Ave at E 18th St   \n",
       "3394449                                                NaN   \n",
       "1795964                                                NaN   \n",
       "1981546                                                NaN   \n",
       "1543196                                                NaN   \n",
       "2926547                                                NaN   \n",
       "1563614                                                NaN   \n",
       "471612                                                 NaN   \n",
       "1771323                                                NaN   \n",
       "1327166                                                NaN   \n",
       "1840316                       Alcatraz Ave at Shattuck Ave   \n",
       "1498888                                                NaN   \n",
       "3099981                             17th St at Valencia St   \n",
       "3109403                                        Ryland Park   \n",
       "592494                                                 NaN   \n",
       "847445                              Valencia St at 16th St   \n",
       "2402213                                                NaN   \n",
       "2809299                              Howard St at Beale St   \n",
       "680704                                                 NaN   \n",
       "2932276                                                NaN   \n",
       "1395933                                                NaN   \n",
       "2606814                                                NaN   \n",
       "316248                                Brannan St at 4th St   \n",
       "373844                                Post St at Kearny St   \n",
       "1946865                               Folsom St at 15th St   \n",
       "3376342                                                NaN   \n",
       "458156                                                 NaN   \n",
       "3439942                                                NaN   \n",
       "2624650                           Randall St at Chenery St   \n",
       "2342531                                                NaN   \n",
       "158957                                                 NaN   \n",
       "3123177                                Howard St at 8th St   \n",
       "954328                 Terry Francois Blvd at Warriors Way   \n",
       "3486120                     Commercial St at Montgomery St   \n",
       "310932                             Market St at Steuart St   \n",
       "77468     Montgomery St BART Station (Market St at 2nd St)   \n",
       "3183756                                                NaN   \n",
       "1924069                            4th St at San Carlos St   \n",
       "1051765                                                NaN   \n",
       "133250                                                 NaN   \n",
       "2288466                                                NaN   \n",
       "1743182                                                NaN   \n",
       "3499318                               Brannan St at 7th St   \n",
       "1680700                     Commercial St at Montgomery St   \n",
       "\n",
       "         end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "2255671             37.749215            -122.420498   974864  Subscriber   \n",
       "1508107             37.767045            -122.410692   593379  Subscriber   \n",
       "1886652             37.775880            -122.393170    10212    Customer   \n",
       "661430              37.775744            -122.413836   311723  Subscriber   \n",
       "1881517             37.795392            -122.394203    12008    Customer   \n",
       "385838              37.760299            -122.418892    12354    Customer   \n",
       "59217               37.800214            -122.253810    12690    Customer   \n",
       "3394449             37.792361            -122.403032   376086  Subscriber   \n",
       "1795964             37.789661            -122.440656   314751  Subscriber   \n",
       "1981546             37.763393            -122.420872   261260  Subscriber   \n",
       "1543196             37.784240            -122.403559   401322  Subscriber   \n",
       "2926547             37.771872            -122.431893   913161  Subscriber   \n",
       "1563614             37.761839            -122.397543   629047    Customer   \n",
       "471612              37.760497            -122.464010   322975  Subscriber   \n",
       "1771323             37.794014            -122.403034   610701  Subscriber   \n",
       "1327166             37.332460            -121.904741   338364  Subscriber   \n",
       "1840316             37.849595            -122.265569    12308    Customer   \n",
       "1498888             37.752521            -122.392504   410522  Subscriber   \n",
       "3099981             37.763316            -122.421904     3006    Customer   \n",
       "3109403             37.342725            -121.895617    10500    Customer   \n",
       "592494              37.784847            -122.407127   803984  Subscriber   \n",
       "847445              37.765052            -122.421866   387845  Subscriber   \n",
       "2402213             37.794349            -122.400015   404577  Subscriber   \n",
       "2809299             37.789756            -122.394643     9879  Subscriber   \n",
       "680704              37.785660            -122.405812   336738    Customer   \n",
       "2932276             37.772978            -122.397041   394905  Subscriber   \n",
       "1395933             37.773259            -122.399988   104451  Subscriber   \n",
       "2606814             37.388166            -121.912978   895016  Subscriber   \n",
       "316248              37.777934            -122.396973    11360    Customer   \n",
       "373844              37.788975            -122.403452     1788    Customer   \n",
       "1946865             37.767037            -122.415442   560821  Subscriber   \n",
       "3376342             37.792153            -122.401428   594988  Subscriber   \n",
       "458156              37.757890            -122.409715   952719  Subscriber   \n",
       "3439942             37.787019            -122.420069   241255  Subscriber   \n",
       "2624650             37.739813            -122.425523   594196  Subscriber   \n",
       "2342531             37.773530            -122.441110   576439  Subscriber   \n",
       "158957              37.757053            -122.388299   847223  Subscriber   \n",
       "3123177             37.776513            -122.411306     2429    Customer   \n",
       "954328              37.769095            -122.386333    13018    Customer   \n",
       "3486120             37.794231            -122.402923     9788    Customer   \n",
       "310932              37.794497            -122.394838     3130    Customer   \n",
       "77468               37.789625            -122.400811    10213    Customer   \n",
       "3183756             37.791783            -122.447811   548367  Subscriber   \n",
       "1924069             37.332800            -121.883850   155154  Subscriber   \n",
       "1051765             37.764628            -122.432796   419445  Subscriber   \n",
       "133250              37.764797            -122.401306   445474  Subscriber   \n",
       "2288466             37.748882            -122.434969   302919  Subscriber   \n",
       "1743182             37.777564            -122.438427   917276    Customer   \n",
       "3499318             37.772658            -122.403681     1523    Customer   \n",
       "1680700             37.794231            -122.402923     3564    Customer   \n",
       "\n",
       "        rental_access_method  \n",
       "2255671                  app  \n",
       "1508107                  app  \n",
       "1886652                  NaN  \n",
       "661430                   app  \n",
       "1881517                  NaN  \n",
       "385838                   NaN  \n",
       "59217                    NaN  \n",
       "3394449                  app  \n",
       "1795964                  app  \n",
       "1981546                  app  \n",
       "1543196                  app  \n",
       "2926547                  app  \n",
       "1563614                  app  \n",
       "471612                   app  \n",
       "1771323                  app  \n",
       "1327166                  app  \n",
       "1840316                  NaN  \n",
       "1498888                  app  \n",
       "3099981                  NaN  \n",
       "3109403                  NaN  \n",
       "592494                   app  \n",
       "847445                   app  \n",
       "2402213                  app  \n",
       "2809299                  NaN  \n",
       "680704                   app  \n",
       "2932276                  app  \n",
       "1395933                  app  \n",
       "2606814                  app  \n",
       "316248                   NaN  \n",
       "373844                   NaN  \n",
       "1946865                  app  \n",
       "3376342                  app  \n",
       "458156                   app  \n",
       "3439942                  app  \n",
       "2624650                  app  \n",
       "2342531                  app  \n",
       "158957                   app  \n",
       "3123177                  NaN  \n",
       "954328                   NaN  \n",
       "3486120                  NaN  \n",
       "310932                   NaN  \n",
       "77468                    NaN  \n",
       "3183756                  app  \n",
       "1924069                  app  \n",
       "1051765              clipper  \n",
       "133250                   app  \n",
       "2288466                  app  \n",
       "1743182                  app  \n",
       "3499318                  NaN  \n",
       "1680700                  NaN  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020.sample(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                     0\n",
       "start_time                       0\n",
       "end_time                         0\n",
       "start_station_id           1945208\n",
       "start_station_name         1940276\n",
       "start_station_latitude           0\n",
       "start_station_longitude          0\n",
       "end_station_id             1943128\n",
       "end_station_name           1938032\n",
       "end_station_latitude             0\n",
       "end_station_longitude            0\n",
       "bike_id                          0\n",
       "user_type                        0\n",
       "rental_access_method       1148596\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2378744"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    2178784\n",
       "Customer      1441244\n",
       "Name: user_type, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020.user_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "app        2281884\n",
       "clipper     189548\n",
       "Name: rental_access_method, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020.rental_access_method.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assessing Results\n",
    "\n",
    "> - There are many missing values in the dataset (start_station_id, start_station_name, end_station_id, end_station_name)\n",
    "> - Start and end times are not in the timestamp format\n",
    "> - The following columns are not in date format:\n",
    "    >> - start_time\n",
    "    >> - end_time\n",
    "> - Dataset does not has detailed start time values like hour of day and day of week\n",
    "> - the following columns are categorical type:\n",
    "    >> - user_type\n",
    "    >> - rental_access_method\n",
    "> - IDs (start_station_id, end_station_id, bike_id) not objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take a copy from original datafram before cleaning\n",
    "df_Q1_2020_clean = df_Q1_2020.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Duplicates\n",
    "df_Q1_2020_clean = df_Q1_2020_clean.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the bike_id, start_station_id, and end_station_id to object\n",
    "df_Q1_2020_clean['bike_id'] = df_Q1_2020_clean['bike_id'].apply(lambda x: \"{:.0f}\".format(x) if not pd.isnull(x) else x)\n",
    "df_Q1_2020_clean['start_station_id'] = df_Q1_2020_clean['start_station_id'].apply(lambda x: \"{:.0f}\".format(x) if not pd.isnull(x) else x)\n",
    "df_Q1_2020_clean['end_station_id'] = df_Q1_2020_clean['end_station_id'].apply(lambda x: \"{:.0f}\".format(x) if not pd.isnull(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert start and End times from object to datetime\n",
    "df_Q1_2020_clean['start_time'] = pd.to_datetime(df_Q1_2020_clean['start_time'])\n",
    "df_Q1_2020_clean['end_time'] = pd.to_datetime(df_Q1_2020_clean['end_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert user type and rental method to categorical columns\n",
    "df_Q1_2020_clean['user_type'] = df_Q1_2020_clean['user_type'].astype('category')\n",
    "df_Q1_2020_clean['rental_access_method'] = df_Q1_2020_clean['rental_access_method'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detail the duration column through spliting it into many usefull columns\n",
    "df_Q1_2020_clean['duration_minute'] = df_Q1_2020_clean['duration_sec']/60\n",
    "df_Q1_2020_clean['start_date'] = df_Q1_2020_clean['start_time'].dt.strftime('%Y-%m-%d')\n",
    "df_Q1_2020_clean['start_hourofday'] = df_Q1_2020_clean['start_time'].dt.strftime('%H')\n",
    "df_Q1_2020_clean['start_dayofweek'] = df_Q1_2020_clean['start_time'].dt.strftime('%A')\n",
    "df_Q1_2020_clean['start_month'] = df_Q1_2020_clean['start_time'].dt.strftime('%B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the start_dayofweek to ordinal variable.\n",
    "weekdays = ['Monday','Tuesday','Wednesday','Thursday','Friday', 'Saturday', 'Sunday']\n",
    "ordered_weekdays = pd.api.types.CategoricalDtype(ordered = True, categories = weekdays)\n",
    "df_Q1_2020_clean['start_dayofweek'] = df_Q1_2020_clean['start_dayofweek'].astype(ordered_weekdays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the start_hourofday to int variable.\n",
    "df_Q1_2020_clean['start_hourofday'] = df_Q1_2020_clean['start_hourofday'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Math fuction to calclate the geo distance\n",
    "\n",
    "def distance(origin, destination):\n",
    "\n",
    "    lat1, long1 = origin\n",
    "    lat2, long2 = destination\n",
    "    radius = 6371\n",
    "\n",
    "    dlat = math.radians(lat2 - lat1)\n",
    "    dlong = math.radians(long2 - long1)\n",
    "    \n",
    "    a = (math.sin(dlat / 2) * math.sin(dlat / 2) + math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) * math.sin(dlong / 2) * math.sin(dlong / 2))\n",
    "    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n",
    "    d = radius * c\n",
    "\n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using the implemented math function to make new distance column\n",
    "df_Q1_2020_clean['distance_km'] = df_Q1_2020_clean.apply(lambda x: distance((x['start_station_latitude'], x['start_station_longitude']), (x['end_station_latitude'], x['end_station_longitude'])), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows which contains null values\n",
    "df_Q1_2020_clean.drop(df_Q1_2020_clean[df_Q1_2020_clean['start_station_id'].isnull()].index, inplace = True)\n",
    "df_Q1_2020_clean.drop(df_Q1_2020_clean[df_Q1_2020_clean['end_station_id'].isnull()].index, inplace = True)\n",
    "df_Q1_2020_clean.drop(df_Q1_2020_clean[df_Q1_2020_clean['rental_access_method'].isnull()].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 82791 entries, 110669 to 1807944\n",
      "Data columns (total 20 columns):\n",
      " #   Column                   Non-Null Count  Dtype         \n",
      "---  ------                   --------------  -----         \n",
      " 0   duration_sec             82791 non-null  int64         \n",
      " 1   start_time               82791 non-null  datetime64[ns]\n",
      " 2   end_time                 82791 non-null  datetime64[ns]\n",
      " 3   start_station_id         82791 non-null  object        \n",
      " 4   start_station_name       82791 non-null  object        \n",
      " 5   start_station_latitude   82791 non-null  float64       \n",
      " 6   start_station_longitude  82791 non-null  float64       \n",
      " 7   end_station_id           82791 non-null  object        \n",
      " 8   end_station_name         82791 non-null  object        \n",
      " 9   end_station_latitude     82791 non-null  float64       \n",
      " 10  end_station_longitude    82791 non-null  float64       \n",
      " 11  bike_id                  82791 non-null  object        \n",
      " 12  user_type                82791 non-null  category      \n",
      " 13  rental_access_method     82791 non-null  category      \n",
      " 14  duration_minute          82791 non-null  float64       \n",
      " 15  start_date               82791 non-null  object        \n",
      " 16  start_hourofday          82791 non-null  int32         \n",
      " 17  start_dayofweek          82791 non-null  category      \n",
      " 18  start_month              82791 non-null  object        \n",
      " 19  distance_km              82791 non-null  float64       \n",
      "dtypes: category(3), datetime64[ns](2), float64(6), int32(1), int64(1), object(7)\n",
      "memory usage: 11.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_Q1_2020_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>duration_minute</th>\n",
       "      <th>start_date</th>\n",
       "      <th>start_hourofday</th>\n",
       "      <th>start_dayofweek</th>\n",
       "      <th>start_month</th>\n",
       "      <th>distance_km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>708734</th>\n",
       "      <td>2201</td>\n",
       "      <td>2020-02-23 16:25:59</td>\n",
       "      <td>2020-02-23 17:02:41</td>\n",
       "      <td>487</td>\n",
       "      <td>Powell St at Columbus Ave</td>\n",
       "      <td>37.800496</td>\n",
       "      <td>-122.410887</td>\n",
       "      <td>120</td>\n",
       "      <td>Mission Dolores Park</td>\n",
       "      <td>37.761420</td>\n",
       "      <td>-122.426435</td>\n",
       "      <td>303263</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>36.683333</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>16</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>February</td>\n",
       "      <td>4.554800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186907</th>\n",
       "      <td>178</td>\n",
       "      <td>2020-01-17 18:55:26</td>\n",
       "      <td>2020-01-17 18:58:25</td>\n",
       "      <td>482</td>\n",
       "      <td>Brannan St at 7th St</td>\n",
       "      <td>37.772658</td>\n",
       "      <td>-122.403681</td>\n",
       "      <td>63</td>\n",
       "      <td>Bryant St at 6th St</td>\n",
       "      <td>37.775910</td>\n",
       "      <td>-122.402575</td>\n",
       "      <td>771490</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>2.966667</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>18</td>\n",
       "      <td>Friday</td>\n",
       "      <td>January</td>\n",
       "      <td>0.374487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1196949</th>\n",
       "      <td>945</td>\n",
       "      <td>2020-01-01 18:04:55</td>\n",
       "      <td>2020-01-01 18:20:41</td>\n",
       "      <td>6</td>\n",
       "      <td>The Embarcadero at Sansome St</td>\n",
       "      <td>37.804770</td>\n",
       "      <td>-122.403234</td>\n",
       "      <td>364</td>\n",
       "      <td>China Basin St at 3rd St</td>\n",
       "      <td>37.772000</td>\n",
       "      <td>-122.389970</td>\n",
       "      <td>106280</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>18</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>3.825785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871413</th>\n",
       "      <td>523</td>\n",
       "      <td>2020-03-10 19:57:17</td>\n",
       "      <td>2020-03-10 20:06:01</td>\n",
       "      <td>5</td>\n",
       "      <td>Powell St BART Station (Market St at 5th St)</td>\n",
       "      <td>37.783899</td>\n",
       "      <td>-122.408445</td>\n",
       "      <td>365</td>\n",
       "      <td>Turk St at Fillmore St</td>\n",
       "      <td>37.780450</td>\n",
       "      <td>-122.431946</td>\n",
       "      <td>949835</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>8.716667</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>19</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>March</td>\n",
       "      <td>2.100681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1725746</th>\n",
       "      <td>818</td>\n",
       "      <td>2020-03-19 19:18:38</td>\n",
       "      <td>2020-03-19 19:32:17</td>\n",
       "      <td>122</td>\n",
       "      <td>19th St at Mission St</td>\n",
       "      <td>37.760299</td>\n",
       "      <td>-122.418892</td>\n",
       "      <td>50</td>\n",
       "      <td>2nd St at Townsend St</td>\n",
       "      <td>37.780526</td>\n",
       "      <td>-122.390288</td>\n",
       "      <td>577099</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>13.633333</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>March</td>\n",
       "      <td>3.373392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154745</th>\n",
       "      <td>680</td>\n",
       "      <td>2020-01-23 19:16:27</td>\n",
       "      <td>2020-01-23 19:27:48</td>\n",
       "      <td>109</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>468</td>\n",
       "      <td>Grove St at Gough St</td>\n",
       "      <td>37.777870</td>\n",
       "      <td>-122.422953</td>\n",
       "      <td>288560</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>19</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>January</td>\n",
       "      <td>1.620986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483907</th>\n",
       "      <td>292</td>\n",
       "      <td>2020-02-27 10:10:25</td>\n",
       "      <td>2020-02-27 10:15:18</td>\n",
       "      <td>109</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>109</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.763316</td>\n",
       "      <td>-122.421904</td>\n",
       "      <td>940290</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>4.866667</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>10</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>February</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1752312</th>\n",
       "      <td>891</td>\n",
       "      <td>2020-03-15 16:42:42</td>\n",
       "      <td>2020-03-15 16:57:34</td>\n",
       "      <td>78</td>\n",
       "      <td>Folsom St at 9th St</td>\n",
       "      <td>37.773717</td>\n",
       "      <td>-122.411647</td>\n",
       "      <td>285</td>\n",
       "      <td>Webster St at O'Farrell St</td>\n",
       "      <td>37.783521</td>\n",
       "      <td>-122.431158</td>\n",
       "      <td>925465</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>16</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>March</td>\n",
       "      <td>2.031939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239355</th>\n",
       "      <td>369</td>\n",
       "      <td>2020-01-29 08:25:01</td>\n",
       "      <td>2020-01-29 08:31:11</td>\n",
       "      <td>37</td>\n",
       "      <td>Folsom St at 2nd St</td>\n",
       "      <td>37.785377</td>\n",
       "      <td>-122.396906</td>\n",
       "      <td>30</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.776598</td>\n",
       "      <td>-122.395282</td>\n",
       "      <td>203237</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>6.150000</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>8</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>0.986514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1427677</th>\n",
       "      <td>637</td>\n",
       "      <td>2020-02-02 17:31:15</td>\n",
       "      <td>2020-02-02 17:41:52</td>\n",
       "      <td>88</td>\n",
       "      <td>11th St at Bryant St</td>\n",
       "      <td>37.770030</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>81</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>264509</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>10.616667</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>17</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>February</td>\n",
       "      <td>1.755878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880878</th>\n",
       "      <td>764</td>\n",
       "      <td>2020-03-07 17:02:34</td>\n",
       "      <td>2020-03-07 17:15:18</td>\n",
       "      <td>419</td>\n",
       "      <td>Fern St at Polk St</td>\n",
       "      <td>37.788197</td>\n",
       "      <td>-122.420373</td>\n",
       "      <td>106</td>\n",
       "      <td>Sanchez St at 17th St</td>\n",
       "      <td>37.763242</td>\n",
       "      <td>-122.430675</td>\n",
       "      <td>432267</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>12.733333</td>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>17</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>March</td>\n",
       "      <td>2.918878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259158</th>\n",
       "      <td>619</td>\n",
       "      <td>2020-01-29 13:58:26</td>\n",
       "      <td>2020-01-29 14:08:46</td>\n",
       "      <td>24</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>104</td>\n",
       "      <td>4th St at 16th St</td>\n",
       "      <td>37.767045</td>\n",
       "      <td>-122.390833</td>\n",
       "      <td>747052</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>10.316667</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>13</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>January</td>\n",
       "      <td>2.516829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692881</th>\n",
       "      <td>2012</td>\n",
       "      <td>2020-02-01 15:43:19</td>\n",
       "      <td>2020-02-01 16:16:52</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>15</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>516859</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>33.533333</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>15</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>February</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877691</th>\n",
       "      <td>316</td>\n",
       "      <td>2020-03-09 10:28:46</td>\n",
       "      <td>2020-03-09 10:34:03</td>\n",
       "      <td>28</td>\n",
       "      <td>The Embarcadero at Bryant St</td>\n",
       "      <td>37.787168</td>\n",
       "      <td>-122.388098</td>\n",
       "      <td>16</td>\n",
       "      <td>Market St at Steuart St</td>\n",
       "      <td>37.794497</td>\n",
       "      <td>-122.394838</td>\n",
       "      <td>457958</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.266667</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>10</td>\n",
       "      <td>Monday</td>\n",
       "      <td>March</td>\n",
       "      <td>1.007472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1764216</th>\n",
       "      <td>1580</td>\n",
       "      <td>2020-03-09 11:58:34</td>\n",
       "      <td>2020-03-09 12:24:55</td>\n",
       "      <td>471</td>\n",
       "      <td>Turk Blvd at Stanyan St</td>\n",
       "      <td>37.777609</td>\n",
       "      <td>-122.454622</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>213142</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>26.333333</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>11</td>\n",
       "      <td>Monday</td>\n",
       "      <td>March</td>\n",
       "      <td>3.274536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490859</th>\n",
       "      <td>1343</td>\n",
       "      <td>2020-02-14 17:52:34</td>\n",
       "      <td>2020-02-14 18:14:57</td>\n",
       "      <td>114</td>\n",
       "      <td>Rhode Island St at 17th St</td>\n",
       "      <td>37.764478</td>\n",
       "      <td>-122.402570</td>\n",
       "      <td>54</td>\n",
       "      <td>Alamo Square (Steiner St at Fulton St)</td>\n",
       "      <td>37.777547</td>\n",
       "      <td>-122.433274</td>\n",
       "      <td>265671</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>22.383333</td>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>17</td>\n",
       "      <td>Friday</td>\n",
       "      <td>February</td>\n",
       "      <td>3.065107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865039</th>\n",
       "      <td>1027</td>\n",
       "      <td>2020-03-06 17:27:33</td>\n",
       "      <td>2020-03-06 17:44:41</td>\n",
       "      <td>383</td>\n",
       "      <td>Golden Gate Ave at Franklin St</td>\n",
       "      <td>37.780787</td>\n",
       "      <td>-122.421934</td>\n",
       "      <td>24</td>\n",
       "      <td>Spear St at Folsom St</td>\n",
       "      <td>37.789677</td>\n",
       "      <td>-122.390428</td>\n",
       "      <td>110949</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>17.116667</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>17</td>\n",
       "      <td>Friday</td>\n",
       "      <td>March</td>\n",
       "      <td>2.939804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732952</th>\n",
       "      <td>138</td>\n",
       "      <td>2020-03-10 16:43:51</td>\n",
       "      <td>2020-03-10 16:46:10</td>\n",
       "      <td>113</td>\n",
       "      <td>Franklin Square</td>\n",
       "      <td>37.764555</td>\n",
       "      <td>-122.410345</td>\n",
       "      <td>125</td>\n",
       "      <td>20th St at Bryant St</td>\n",
       "      <td>37.759200</td>\n",
       "      <td>-122.409851</td>\n",
       "      <td>248411</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>16</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>March</td>\n",
       "      <td>0.596975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859937</th>\n",
       "      <td>404</td>\n",
       "      <td>2020-03-03 17:26:47</td>\n",
       "      <td>2020-03-03 17:33:31</td>\n",
       "      <td>468</td>\n",
       "      <td>Grove St at Gough St</td>\n",
       "      <td>37.777870</td>\n",
       "      <td>-122.422953</td>\n",
       "      <td>85</td>\n",
       "      <td>Church St at Duboce Ave</td>\n",
       "      <td>37.770083</td>\n",
       "      <td>-122.429156</td>\n",
       "      <td>114475</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>6.733333</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>17</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>March</td>\n",
       "      <td>1.023204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829490</th>\n",
       "      <td>394</td>\n",
       "      <td>2020-03-13 17:27:53</td>\n",
       "      <td>2020-03-13 17:34:27</td>\n",
       "      <td>98</td>\n",
       "      <td>Valencia St at 16th St</td>\n",
       "      <td>37.765052</td>\n",
       "      <td>-122.421866</td>\n",
       "      <td>58</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.776619</td>\n",
       "      <td>-122.417385</td>\n",
       "      <td>640363</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>6.566667</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>17</td>\n",
       "      <td>Friday</td>\n",
       "      <td>March</td>\n",
       "      <td>1.345148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_sec          start_time            end_time  \\\n",
       "708734           2201 2020-02-23 16:25:59 2020-02-23 17:02:41   \n",
       "186907            178 2020-01-17 18:55:26 2020-01-17 18:58:25   \n",
       "1196949           945 2020-01-01 18:04:55 2020-01-01 18:20:41   \n",
       "871413            523 2020-03-10 19:57:17 2020-03-10 20:06:01   \n",
       "1725746           818 2020-03-19 19:18:38 2020-03-19 19:32:17   \n",
       "154745            680 2020-01-23 19:16:27 2020-01-23 19:27:48   \n",
       "483907            292 2020-02-27 10:10:25 2020-02-27 10:15:18   \n",
       "1752312           891 2020-03-15 16:42:42 2020-03-15 16:57:34   \n",
       "239355            369 2020-01-29 08:25:01 2020-01-29 08:31:11   \n",
       "1427677           637 2020-02-02 17:31:15 2020-02-02 17:41:52   \n",
       "880878            764 2020-03-07 17:02:34 2020-03-07 17:15:18   \n",
       "259158            619 2020-01-29 13:58:26 2020-01-29 14:08:46   \n",
       "692881           2012 2020-02-01 15:43:19 2020-02-01 16:16:52   \n",
       "877691            316 2020-03-09 10:28:46 2020-03-09 10:34:03   \n",
       "1764216          1580 2020-03-09 11:58:34 2020-03-09 12:24:55   \n",
       "490859           1343 2020-02-14 17:52:34 2020-02-14 18:14:57   \n",
       "865039           1027 2020-03-06 17:27:33 2020-03-06 17:44:41   \n",
       "1732952           138 2020-03-10 16:43:51 2020-03-10 16:46:10   \n",
       "859937            404 2020-03-03 17:26:47 2020-03-03 17:33:31   \n",
       "829490            394 2020-03-13 17:27:53 2020-03-13 17:34:27   \n",
       "\n",
       "        start_station_id                                 start_station_name  \\\n",
       "708734               487                          Powell St at Columbus Ave   \n",
       "186907               482                               Brannan St at 7th St   \n",
       "1196949                6                      The Embarcadero at Sansome St   \n",
       "871413                 5       Powell St BART Station (Market St at 5th St)   \n",
       "1725746              122                              19th St at Mission St   \n",
       "154745               109                             17th St at Valencia St   \n",
       "483907               109                             17th St at Valencia St   \n",
       "1752312               78                                Folsom St at 9th St   \n",
       "239355                37                                Folsom St at 2nd St   \n",
       "1427677               88                               11th St at Bryant St   \n",
       "880878               419                                 Fern St at Polk St   \n",
       "259158                24                              Spear St at Folsom St   \n",
       "692881                15  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "877691                28                       The Embarcadero at Bryant St   \n",
       "1764216              471                            Turk Blvd at Stanyan St   \n",
       "490859               114                         Rhode Island St at 17th St   \n",
       "865039               383                     Golden Gate Ave at Franklin St   \n",
       "1732952              113                                    Franklin Square   \n",
       "859937               468                               Grove St at Gough St   \n",
       "829490                98                             Valencia St at 16th St   \n",
       "\n",
       "         start_station_latitude  start_station_longitude end_station_id  \\\n",
       "708734                37.800496              -122.410887            120   \n",
       "186907                37.772658              -122.403681             63   \n",
       "1196949               37.804770              -122.403234            364   \n",
       "871413                37.783899              -122.408445            365   \n",
       "1725746               37.760299              -122.418892             50   \n",
       "154745                37.763316              -122.421904            468   \n",
       "483907                37.763316              -122.421904            109   \n",
       "1752312               37.773717              -122.411647            285   \n",
       "239355                37.785377              -122.396906             30   \n",
       "1427677               37.770030              -122.411726             81   \n",
       "880878                37.788197              -122.420373            106   \n",
       "259158                37.789677              -122.390428            104   \n",
       "692881                37.795392              -122.394203             15   \n",
       "877691                37.787168              -122.388098             16   \n",
       "1764216               37.777609              -122.454622             58   \n",
       "490859                37.764478              -122.402570             54   \n",
       "865039                37.780787              -122.421934             24   \n",
       "1732952               37.764555              -122.410345            125   \n",
       "859937                37.777870              -122.422953             85   \n",
       "829490                37.765052              -122.421866             58   \n",
       "\n",
       "                                          end_station_name  \\\n",
       "708734                                Mission Dolores Park   \n",
       "186907                                 Bryant St at 6th St   \n",
       "1196949                           China Basin St at 3rd St   \n",
       "871413                              Turk St at Fillmore St   \n",
       "1725746                              2nd St at Townsend St   \n",
       "154745                                Grove St at Gough St   \n",
       "483907                              17th St at Valencia St   \n",
       "1752312                         Webster St at O'Farrell St   \n",
       "239355      San Francisco Caltrain (Townsend St at 4th St)   \n",
       "1427677                                 Berry St at 4th St   \n",
       "880878                               Sanchez St at 17th St   \n",
       "259158                                   4th St at 16th St   \n",
       "692881   San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "877691                             Market St at Steuart St   \n",
       "1764216                               Market St at 10th St   \n",
       "490859              Alamo Square (Steiner St at Fulton St)   \n",
       "865039                               Spear St at Folsom St   \n",
       "1732952                               20th St at Bryant St   \n",
       "859937                             Church St at Duboce Ave   \n",
       "829490                                Market St at 10th St   \n",
       "\n",
       "         end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "708734              37.761420            -122.426435  303263  Subscriber   \n",
       "186907              37.775910            -122.402575  771490  Subscriber   \n",
       "1196949             37.772000            -122.389970  106280  Subscriber   \n",
       "871413              37.780450            -122.431946  949835  Subscriber   \n",
       "1725746             37.780526            -122.390288  577099  Subscriber   \n",
       "154745              37.777870            -122.422953  288560  Subscriber   \n",
       "483907              37.763316            -122.421904  940290  Subscriber   \n",
       "1752312             37.783521            -122.431158  925465  Subscriber   \n",
       "239355              37.776598            -122.395282  203237  Subscriber   \n",
       "1427677             37.775880            -122.393170  264509  Subscriber   \n",
       "880878              37.763242            -122.430675  432267  Subscriber   \n",
       "259158              37.767045            -122.390833  747052  Subscriber   \n",
       "692881              37.795392            -122.394203  516859    Customer   \n",
       "877691              37.794497            -122.394838  457958  Subscriber   \n",
       "1764216             37.776619            -122.417385  213142    Customer   \n",
       "490859              37.777547            -122.433274  265671  Subscriber   \n",
       "865039              37.789677            -122.390428  110949  Subscriber   \n",
       "1732952             37.759200            -122.409851  248411  Subscriber   \n",
       "859937              37.770083            -122.429156  114475  Subscriber   \n",
       "829490              37.776619            -122.417385  640363  Subscriber   \n",
       "\n",
       "        rental_access_method  duration_minute  start_date  start_hourofday  \\\n",
       "708734                   app        36.683333  2020-02-23               16   \n",
       "186907                   app         2.966667  2020-01-17               18   \n",
       "1196949                  app        15.750000  2020-01-01               18   \n",
       "871413                   app         8.716667  2020-03-10               19   \n",
       "1725746                  app        13.633333  2020-03-19               19   \n",
       "154745                   app        11.333333  2020-01-23               19   \n",
       "483907                   app         4.866667  2020-02-27               10   \n",
       "1752312                  app        14.850000  2020-03-15               16   \n",
       "239355                   app         6.150000  2020-01-29                8   \n",
       "1427677                  app        10.616667  2020-02-02               17   \n",
       "880878                   app        12.733333  2020-03-07               17   \n",
       "259158                   app        10.316667  2020-01-29               13   \n",
       "692881                   app        33.533333  2020-02-01               15   \n",
       "877691                   app         5.266667  2020-03-09               10   \n",
       "1764216                  app        26.333333  2020-03-09               11   \n",
       "490859                   app        22.383333  2020-02-14               17   \n",
       "865039                   app        17.116667  2020-03-06               17   \n",
       "1732952                  app         2.300000  2020-03-10               16   \n",
       "859937                   app         6.733333  2020-03-03               17   \n",
       "829490                   app         6.566667  2020-03-13               17   \n",
       "\n",
       "        start_dayofweek start_month  distance_km  \n",
       "708734           Sunday    February     4.554800  \n",
       "186907           Friday     January     0.374487  \n",
       "1196949       Wednesday     January     3.825785  \n",
       "871413          Tuesday       March     2.100681  \n",
       "1725746        Thursday       March     3.373392  \n",
       "154745         Thursday     January     1.620986  \n",
       "483907         Thursday    February     0.000000  \n",
       "1752312          Sunday       March     2.031939  \n",
       "239355        Wednesday     January     0.986514  \n",
       "1427677          Sunday    February     1.755878  \n",
       "880878         Saturday       March     2.918878  \n",
       "259158        Wednesday     January     2.516829  \n",
       "692881         Saturday    February     0.000000  \n",
       "877691           Monday       March     1.007472  \n",
       "1764216          Monday       March     3.274536  \n",
       "490859           Friday    February     3.065107  \n",
       "865039           Friday       March     2.939804  \n",
       "1732952         Tuesday       March     0.596975  \n",
       "859937          Tuesday       March     1.023204  \n",
       "829490           Friday       March     1.345148  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020_clean.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the cleaned dataset\n",
    "\n",
    "df_Q1_2020_clean.to_csv('baywheels_trips_Q1_2020_cleaned.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "The dataset has 80000+ rides in Q1, 2020 in San Francisco Bay Area, here some basic structure for the dataset:\n",
    "> - trip duration: how long the trip took in seconds.\n",
    "> - start and end times: start and end date time for the bike rides.\n",
    "> - stations: start and end stations name and geolocation (latitude, longitude).\n",
    "> - user types: which is subscriber or customer.\n",
    "> - rental access: method which is through app or clipper.\n",
    "> - rented bike: bike id, which is the trip identifier.\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> - Trip Duration.\n",
    "> - Trip Distance.\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> - User Type.\n",
    "> - Start Time (hour of day, week day)\n",
    "> - Rental Access Methods\n",
    "> - Start and End Stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trip rates over day hours\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "sb.countplot(data=df_Q1_2020_clean, x='start_hourofday', color=base_color);\n",
    "plt.xlabel('Trip Start Hour of Day')\n",
    "plt.ylabel('Trips Count')\n",
    "plt.title('Baywheels Data Usage by Hour', y=1.05, fontsize=16, fontweight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    82791.000000\n",
       "mean        13.226015\n",
       "std          4.705778\n",
       "min          0.000000\n",
       "25%          9.000000\n",
       "50%         14.000000\n",
       "75%         17.000000\n",
       "max         23.000000\n",
       "Name: start_hourofday, dtype: float64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020_clean.start_hourofday.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: most of the trips are happened at (07-09) and (16-18), going to work and returing from work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "sb.countplot(data=df_Q1_2020_clean, x='start_dayofweek', color=base_color);\n",
    "plt.xlabel('Trip Start Day of Week');\n",
    "plt.ylabel('Trips Count');\n",
    "plt.title('Baywheels Trips Data Usage by Week Day', y=1.05, fontsize=16, fontweight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         82791\n",
       "unique            7\n",
       "top       Wednesday\n",
       "freq          14262\n",
       "Name: start_dayofweek, dtype: object"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020_clean.start_dayofweek.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: most of the trips are during working days, with special high rates on wdensday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Duration of the bike rides in minutes\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "bin_edges = np.arange(0, 60, 1)\n",
    "\n",
    "plt.hist(data = df_Q1_2020_clean, x = 'duration_minute', bins = bin_edges);\n",
    "\n",
    "plt.title(\"Baywheels Trips Data Duration in Mintues\", y=1.05, fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Duration (Min)')\n",
    "plt.ylabel('Number of Bike Trips')\n",
    "plt.xticks(bin_edges);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    82791.000000\n",
       "mean        12.221261\n",
       "std         12.742346\n",
       "min          1.000000\n",
       "25%          6.150000\n",
       "50%          9.400000\n",
       "75%         14.116667\n",
       "max        491.766667\n",
       "Name: duration_minute, dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020_clean.duration_minute.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: most of the trips duration are between 5 to 8 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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00Uc6d+6cZs+erddff71SjyNJR44ckc1ms4e60pPzXPw/Dn5fb0XLfv/4vXv31uLFiyVdOMusyWS67Ihgqcq+1qX/YwcAnIWRQQCohKNHj9pPONKoUSNJ0k8//STpQugICQnRrl279N1330kqO4ozcOBA+fn56dNPP1VRUdElUyDDw8PVvn17nTx5UseOHVNkZKT8/f0VGRmpAwcOKD8/Xx06dKjwOnyXU3ptttWrV9tPMjJmzBh16dLF/gO7dJu33npLFotF+fn5uvfee9WtWzf7WR1/LzU1VR07dlRiYqKaNm2q//3f/7WPdJYe7/hHVSZIrFmzRtnZ2bLZbHrttdckXfjR3qxZs6taq81m06pVq+zHwA0bNuyy2+3YsUNRUVHq0aOHTp06pf79+2vixIn2kbLS5y0NHfn5+TIMQzab7YrfU47UqlXLfuKRN998U8XFxcrLy1NycrImTpyo999/X61bt1bNmjWVn5+v5cuXS7pwTFznzp0VExNT5gyazhYUFGQfhWvYsKFGjx5tr6e8Y1cv55ZbbpHJZNKJEye0dOlSGYahI0eO2Efz+/Tp84fqLP28bNq0Sbt375Z04WRHnTp10sSJE+3blQb1/Px8nT9/vtKvdWXe/wBQFYwMAkAFUlNT9fe//13Sf6d/2Ww2+fv7Kz4+XtKFKaA///yz0tLS9P7775f5QXfxNeSuvfZadevWzX76+d+fLl+6MFV0586dkmQ/dujmm2+2nwn0clNEHUlISNCqVau0b98+9ezZUzVq1JDFYlFISIj9GMKxY8dq/fr1ysjIUFRUlMxmswoKCtSoUSNFR0df9nH79++v5cuX66uvvlLXrl1Vs2ZN+/S40uOvXKmgoED9+vVTzZo17cfVPfzwwzKbzX+41o8++kibN2+WYRjKz8+3P/7AgQPVs2fPy96nffv2uu6667Rnzx4NGDBAdevW1dmzZ2W1WnXDDTfYj1csPc5x/fr16tKli5YvX37F76krMXnyZI0dO1YbNmywXw6jqKhIDRo00NChQ1WnTh0lJCRo6dKlevrpp/Xiiy/ag2lUVJRq165dqee7mkaPHq2VK1fq+PHjmjt3rnr27Gk/+U5Frr32Wo0dO1ZLlizRiy++qCVLlqigoECGYahJkyZljterih49eqhTp0765ptvFBsbq9q1a9v30Z133mnf7rrrrtOBAwc0ZcoUtWzZUu+++261fa0B+CZGBgGgAmfPnrUf15OTk6OgoCB16tRJS5cu1U033SRJevLJJ9W/f3+FhITI399fMTExGjNmjCTp66+/LvN4f/7znyVdGLG5+PpspS4Oe6U/3C8+i2ZVwmCDBg20fPly3XbbbfZpl927d9eyZcvUrFkzSdKf/vQnLVu2TF27dpW/v78CAwPVp08fvfXWW+WORHbo0EHLli1TdHS0atWqpeLiYrVs2VJPPfWUHn744UrX+Uc9+uijGjBggAzDUMOGDTVt2jTdd999V6XWgoICHTt2TMePH5dhGGrdurVmzJhhvxD95fj7++v1119XQkKCGjdurPz8fDVs2FCDBw/Wm2++aZ8CGBsbq6ioKAUFBal27dqy2WyVek85Eh0draVLl6pjx46SpODgYPXt21dvvfWWfbrjlClT9Pjjj6tZs2YqLCxU48aNNWHCBM2YMaNSz3W11axZU1OmTJF04TjK0ks7XIlHH31Uqampatu2rWw2m8LCwjR48GCtWLHiqpyQZcmSJYqLi1P9+vVVVFSkVq1aaf78+WXC4JNPPmkP+6VBr7q+1gB8k8m40iO6AQB/yLlz5zRs2DD99NNPGjRokJ599ll3l+QVevfurcOHD2vevHn2SwAAAADHmCYKAC7Qr18/HTt2TOfOnZOfn1+VL6QNAABwtTBNFABcoH79+jp//ryaNWum559/3n5SDwAAAHdhmigAAAAA+CBGBgEAAADABxEGAQAAAMAHEQYBAAAAwAcRBgEAAADABxEGAQAAAMAHEQYBAAAAwAcRBgEAAADABxEGAQAAAMAHEQYBAAAAwAcRBgEAAADABxEGAQAAAMAHEQYBAAAAwAcRBgEAAADABxEGAQAAAMAHEQYBAAAAwAf5u7sAZ7LZbLJaDXeXcQmz2VQt66oKb+nFW/qQ6KW68pZevKUPiV6qK2/pxVv6kOilOvKWPiR6cYWAAHO567w6DFqthnJyzrm7jEuEhdWqlnVVhbf04i19SPRSXXlLL97Sh0Qv1ZW39OItfUj0Uh15Sx8SvbhC/fqh5a5jmigAAAAA+CDCIAAAAAD4IMIgAAAAAPggwiAAAAAA+CDCIAAAAAD4IMIgAAAAAPggp11aYteuXXr++eeVlpamKVOm6OTJk5Kkw4cPq0OHDnrxxRc1btw45eTkKCAgQDVq1NDrr7+uAwcOKCkpSSaTSTfeeKNmzpwpPz8/vfLKK/riiy/k7++v6dOnq3379s4qHQAAAAC8nlPC4GuvvaYPPvhANWvWlCS9+OKLkqSzZ89q5MiRmjZtmiTp4MGDWrdunUwmk/2+8+bN0+TJk9WtWzclJyfrs88+U+PGjZWRkaHVq1fr6NGjSkxM1Jo1a5xROgAAAAD4BKdME42IiNCCBQsuWb5gwQLdd999atCggU6ePKnc3FyNGzdOI0aM0IYNGyRJWVlZ6tq1qySpZ8+e2rx5szIzMxUdHS2TyaTGjRvLarXq9OnTzigdAAAAAHyCU0YGY2JidOjQoTLLTp06pS1btthHBUtKSjRq1CiNHDlSZ8+e1YgRI9S+fXsZhmEfKQwODlZeXp7y8/MVFhZmf6zS5eHh4RXWYTabFBZW6yp398eZzX7Vsq6q8JZevKUPiV6qK2/pxVv6kOiluvKWXrylD4leqiNv6UOiF3dz2jGDv/fRRx/p7rvvltlsliTVq1dPcXFx8vf31zXXXKObbrpJ+/fvl5/ffwcrLRaLateurZCQEFksljLLQ0NDHT6n1WooJ+fc1W/mDwoLq1Ut66oKb+nFW/qQ6KW68pZevKUPiV6qK2/pxVv6kOilOvKWPiR6cYX69cvPTS4Lg1u2bNH48ePttzdv3qzly5dr6dKlslgs+umnn9SiRQu1bt1aW7duVbdu3ZSenq6oqChFREToueee0+jRo/Xbb7/JZrM5HBUEKsOqij8onqSwxOruEgAAAOABXBYG9+/fr+uvv95+u1evXtq4caOGDRsmPz8/PfroowoPD9fUqVM1Y8YMzZ8/Xy1atFBMTIzMZrMiIyM1fPhw2Ww2JScnu6ps+IigALOaJa1zdxlXRXbqXcpzdxEAAACo9kyGYRjuLsJZSkqs1XKotroOIVeFt/RSv36oV4XBEye8Iw56y/tL8p5evKUPiV6qK2/pxVv6kOilOvKWPiR6cYWKZr9x0XkAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBTguDu3btUkJCgiQpKytLPXr0UEJCghISEvThhx9Kkl555RXFxsYqLi5O3377rSTpwIEDGjFihOLj4zVz5kzZbLZytwUAAAAAVI2/Mx70tdde0wcffKCaNWtKkr777js98MADGjVqlH2brKwsZWRkaPXq1Tp69KgSExO1Zs0azZs3T5MnT1a3bt2UnJyszz77TI0bN77stgAAAACAqnFKGIyIiNCCBQv05JNPSpL27Nmj/fv367PPPlPTpk01ffp0ZWZmKjo6WiaTSY0bN5bVatXp06eVlZWlrl27SpJ69uypTZs2qXnz5pfdNjw8vMI6zGaTwsJqOaPFP8Rs9quWdVWFN/XiTbxln3jT+8tbevGWPiR6qa68pRdv6UOil+rIW/qQ6MXdnBIGY2JidOjQIfvt9u3ba+jQoWrbtq0WL16shQsXKjQ0VGFhYfZtgoODlZeXJ8MwZDKZyizLz8+/7LaOwqDVaign59xV7u6PCwurVS3rqgpv6aV+/VB3l3BVecM+kbzn/SV5Ty/e0odEL9WVt/TiLX1I9FIdeUsfEr24QkW/c11yApm+ffuqbdu29r+/++47hYSEyGKx2LexWCwKDQ2Vn59fmWW1a9cud1sAAAAAQNW4JAyOHj3aftKXLVu2qE2bNurcubM2btwom82mI0eOyGazKTw8XK1bt9bWrVslSenp6YqMjCx3WwAAAABA1ThlmujvzZo1S3PmzFFAQIDq1aunOXPmKCQkRJGRkRo+fLhsNpuSk5MlSVOnTtWMGTM0f/58tWjRQjExMTKbzZfdFgAAAABQNSbDMAx3F+EsJSXWajlvt7rOJ64Kb+mlfv1QNUta5+4yrors1Lt04kSeu8u4Krzl/SV5Ty/e0odEL9WVt/TiLX1I9FIdeUsfEr24gtuPGQQAAAAAVC+EQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EH+7i4AwNVVWGJV/fqh7i7jqigssbq7BAAAAK9FGAS8TFCAWc2S1rm7jKsiO/Uu5bm7CAAAAC/FNFEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQYRBAAAAAPBBhEEAAAAA8EGEQQAAAADwQf7OeuBdu3bp+eefV1pamvbu3as5c+bIbDYrMDBQzz77rOrVq6eUlBTt2LFDwcHBkqRFixappKREjz/+uAoLC9WgQQPNmzdPNWvW1KpVq7RixQr5+/tr/Pjxuu2225xVOgAAAAB4PaeEwddee00ffPCBatasKUl65plnNGPGDN10001asWKFXnvtNU2bNk1ZWVl6/fXXFR4ebr9vSkqK7r77bg0ePFhLly7VypUrdddddyktLU1r1qxRUVGR4uPjdcsttygwMNAZ5QMAAACA13NKGIyIiNCCBQv05JNPSpLmz5+vBg0aSJKsVqtq1Kghm82mAwcOKDk5WSdPnlRsbKxiY2OVmZmphx56SJLUs2dPzZ8/X9dff706deqkwMBABQYGKiIiQt9//73at29fYR1ms0lhYbWc0eIfYjb7Vcu6qsKbekH15C3vL2/5rHhLHxK9VFfe0ou39CHRS3XkLX1I9OJuTgmDMTExOnTokP12aRDcsWOH3n77bS1fvlznzp3TfffdpwceeEBWq1UjR45U27ZtlZ+fr9DQUElScHCw8vLyyiwrXZ6fn++wDqvVUE7Ouavc3R8XFlarWtZVFd7SS/36oY43glt4w/tL8p7Pirf0IdFLdeUtvXhLHxK9VEfe0odEL65Q0e9cpx0z+HsffvihFi9erKVLlyo8PNweAEunkkZFRen7779XSEiILBaLgoKCZLFYVLt2bfuyUhaLpUw4BAAAAABUjkvOJvr+++/r7bffVlpamq6//npJUnZ2tuLj42W1WlVSUqIdO3aoTZs26ty5s7788ktJUnp6urp06aL27dsrMzNTRUVFysvL088//6yWLVu6onQAAAAA8EpOHxm0Wq165plndO211yoxMVGSdPPNN2vSpEkaMGCAhg0bpoCAAA0cOFA33nijxo8fr6lTp2rVqlWqW7euXnjhBdWqVUsJCQmKj4+XYRiaMmWKatSo4ezSAQAAAMBrOS0MNmnSRKtWrZIkZWRkXHabMWPGaMyYMWWW1atXT//4xz8u2XbYsGEaNmzY1S8UAAAAAHxQpaaJHj161Fl1AAAAAABcyOHI4FtvvaWgoCDl5uZq7dq16tGjh6ZNm+aK2gAAAAAATuJwZHDdunUaNGiQ0tPTtdjjFD8AACAASURBVG7dOu3du9cVdQEAAAAAnMhhGDSZTDpx4oTq1asnk8mks2fPuqIuAAAAAIATOZwm2q1bN91333164YUXNHfuXN1xxx2uqAsewiou2A4AAAB4IodhcMqUKZo0aZLOnDmjJ554QgEBAa6oCx4iKMCsZknr3F3GH5adepe7SwAAAABcyuE00fXr16tv37568MEHdeedd2rTpk2uqAsAAAAA4EQORwYXLlyo1atX65prrtHJkyc1btw43XLLLa6oDQAAAADgJA7DYFhYmK655hpJFy4IHxIS4vSiAECSCkusXnNMamGJ1d0lAAAAlOEwDIaEhGj06NG6+eabtWfPHhUWFmr+/PmSpEcffdTpBQLwXd5yTKp04bjUPHcXAQAAcBGHYbBPnz72vxs2bOjUYgAAAAAArlFuGNy9e7fatWun+vXrX7IuOjraqUUBAAAAAJyr3DC4ZcsWtWvXTuvWXTpFizAIAAAAAJ6t3DA4duxYSVJoaKimT5/usoIAAAAAAM7n8DqDv/zyi3Jzc11RCwAAAADARRyeQObnn39WVFSU6tatK5PJJEnauHGj0wsDAAAAADhPuWFwzZo1GjJkiDZs2ODKegAAAAAALlDuNNH333/flXUAAAAAAFyo3JHBwsJCZWdnyzCMS9Y1b97cqUUBAAAAAJyr3DC4f/9+JScnXxIGTSaT3nrrLacXBgAAAABwnnLD4J/+9CdCHwAAAAB4KYeXlgAAAAAAeJ9yw+BLL73kyjoAAAAAAC5UbhgMDw93ZR0AAAAAABdimigAAAAA+KByTyBTymq1au3atTp69Ki6deumG2+8kVFDAAAAAPBwDkcGk5OTdeTIEW3atEkWi0VTp051RV0AAAAAACdyGAYPHjyoRx55RDVq1FDv3r2Vl5fniroAAAAAAE7kMAxarVadPn1akpSfny8/Pw4zBAAAAABP5zDZTZ48WSNGjNCePXs0fPhwTZw48YoeeNeuXUpISJAkHThwQCNGjFB8fLxmzpwpm80mSXrllVcUGxuruLg4ffvtt5XeFgAAAABQNQ5PINOpUyd9/PHHOn36tOrWratff/3V4YO+9tpr+uCDD1SzZk1J0rx58zR58mR169ZNycnJ+uyzz9S4cWNlZGRo9erVOnr0qBITE7VmzZpKbQsAAAAAqBqHI4OPPfaYpAvXHVy5cqXGjBnj8EEjIiK0YMEC++2srCx17dpVktSzZ09t3rxZmZmZio6OlslkUuPGje3TUSuzLQAAAACgahyODHbv3l1PPPGE8vLyFBoaqlWrVjl80JiYGB06dMh+2zAMmUwmSVJwcLDy8vKUn5+vsLAw+zalyyuzraNLXJjNJoWF1XJYr6uZzX7Vsi4AzuUNn3tv+veLXqonb+nFW/qQ6KU68pY+JHpxt3LDYHFxsSRpyJAhOnfunLZs2aJnnnmmSk9y8UlnLBaLateurZCQEFksljLLQ0NDK7WtI1aroZycc1Wq2ZnCwmpVy7qqon59x/sBwAXe8Ln3pn+/6KV68pZevKUPiV6qI2/pQ6IXV6jo93q500T79eun/v37q1+/flq+fLl++eUX+7LKat26tbZu3SpJSk9PV2RkpDp37qyNGzfKZrPpyJEjstlsCg8Pr9S2AAAAAICqKXdk8PPPP79qTzJ16lTNmDFD8+fPV4sWLRQTEyOz2azIyEgNHz5cNptNycnJld4WAAAAAFA15YbBp59+WsnJyRo+fLj9GL5SK1ascPjATZo0sR9f2Lx5c7399tuXbJOYmKjExMQyyyqzLQAAAACgasoNgw8//LAkaf78+S4rBgAAAADgGuUeM1i3bl198sknOn36tOrUqaPFixdrwYIFMgzDlfUBAAAAAJyg3JHB2bNny2KxyGKx6PTp04qOjta1116r6dOn66233nJljQAAAACAq6zcMPjjjz9qxYoVslqtuvPOOzVp0iRJ0rp161xWHAAAAADAOcqdJhoYGChJMpvNatiwoX25zWZzflUAAAAAAKcqd2QwJydHGzdulGEYOnv2bJm/AQAAAACerdww2KZNG/uU0NatW5f5GwAAAADg2coNg/PmzXNlHQAAAAAAFyr3mEEAAAAAgPcqNwzm5eW5sg4AAAAAgAuVGwbHjRsnSZo5c6bLigEAAAAAuEa5xwwGBQVpyJAhOnDggH744QdJkmEYMplMWrFihcsKBAAAAABcfeWGwddee03Hjx9XcnKyZs2aJcMwXFkXAAAAAMCJyg2Dfn5+atSokRYtWqSVK1dq3759atasmUaMGOHK+gAAAAAATuDwbKLJyck6ePCgbrnlFh0+fFhPPfWUK+oCAAAAADhRuSODpQ4cOKDly5dLkm6//XbFxcU5vSgAAAAAgHM5HBksKipSQUGBJKmwsFBWq9XpRQEAAAAAnMvhyODIkSM1cOBA3Xjjjdq3b58mTZrkiroAAAAAAE7kMAzec8896tmzp3799Vc1adJEdevWdUVdAAAAAAAnchgGJSksLExhYWHOrgUAAAAA4CIOjxkEAAAAAHgfh2HwH//4hyvqAAAAAAC4kMMw+OWXX3IGUQAAAADwMg6PGTxz5ox69OihJk2ayGQyyWQyacWKFa6oDQAAAADgJA7D4KuvvuqKOgAAAAAALuQwDPr7++u5557TmTNnFBMTo1atWum6665zRW0A4DUKS6yqXz/U3WX8YYUlHDYAAIC3cBgGZ8yYoQceeECLFi1SZGSkkpKStGrVKlfUBgBeIyjArGZJ69xdxh+WnXqX8txdBAAAuCocnkCmqKhI3bt3l8lkUosWLVSjRg1X1AUAAAAAcCKHYTAwMFBfffWVbDabdu7cqcDAQFfUBQAAAABwIofTROfMmaNnn31WZ86c0RtvvKFZs2ZV6YnWrl2r9957T9KF0ca9e/fqhRde0N/+9jdde+21kqTExERFRkZq1qxZ+uGHHxQYGKiUlBQ1bdpUO3fu1DPPPCOz2azo6GhNnDixSnUAAAAAAK4gDDZq1EgPPfSQsrOzdeONN+r666+v0hMNHjxYgwcPliTNnj1bQ4YMUVZWlp544gnFxMTYt/vkk09UXFyslStXaufOnUpNTdXixYs1c+ZMLViwQNdff73Gjh2rrKwstWnTpkq1AAAAAICvczhNdNGiRZo9e7Z27Nihv/71r1q2bNkfesLdu3dr3759Gj58uLKysrRmzRrFx8crNTVV58+fV2Zmpnr06CFJ6tixo/bs2aP8/HwVFxcrIiJCJpNJ0dHR2rJlyx+qAwAAAAB8mcORwfT0dL3zzjvy8/PT+fPnFR8fr7/85S9VfsIlS5ZowoQJkqRbbrlFt99+u5o0aaKZM2dqxYoVys/PV0hIiH17s9l8ybLg4GD9+uuvDp/LbDYpLKxWlWt1FrPZr1rWBQBXwlv+/fKmf4vppfrxlj4keqmOvKUPiV7czWEYDA8PV0FBgYKDg1VSUqLw8PAqP1lubq5++eUXRUVFSZKGDBmi2rVrS5L69Omjjz/+WKGhobJYLPb72Gw2hYSElFlmsVjs96uI1WooJ+dclet1lrCwWtWyrqrwhuumAagcb/n3y5v+LaaX6sdb+pDopTrylj4kenGFin6vlztNdPjw4YqLi9NPP/2kmJgYjR49Wv3799dvv/1W5UK2bdumP//5z5IkwzB0zz332B9vy5YtatOmjTp37qz09HRJ0s6dO9WyZUuFhIQoICBABw8elGEY2rhxoyIjI6tcBwAAAAD4unJHBufPn3/Vn2z//v1q0qSJJMlkMiklJUUTJ05UUFCQbrjhBg0bNkxms1mbNm1SXFycDMPQ3LlzJV046czjjz8uq9Wq6OhodejQ4arXBwAAAAC+otwweN1110mSvv32W61bt05FRUX2dVW9vMSDDz5Y5nZ0dLSio6Mv2e7pp5++ZFnHjh21atWqKj0vAAAAAKAsh8cMTp06VWPGjLmiY/QAAAAAAJ7BYRhs2rSp/fqAAAAAAADv4DAMxsTEaMqUKbrhhhvsyyZOnOjUogAAAAAAzuUwDL7zzjvq27cv00QBAAAAwIs4DIN16tTR2LFjXVELAAAAAMBFHIbBunXrKjk5Wa1bt5bJZJJ04RqEAAAAAADPdUUnkJGkkydPOr0YAAAAAIBrOAyDnEkUAAAAALyPwzA4ZcoUmUwm2Ww2HTp0SE2bNtX/+3//zxW1AQAAAACcxGEYXLlypf3v3NxcJScnO7UgAAAAAIDz+VVm49DQUB08eNBZtQAAAAAAXMThyODw4cNlMplkGIZOnz6t7t27u6IuAAAAAIATOQyD8+fPt/9do0YN1atXz6kFAQAAAACcr9ww+M9//rPcOw0aNMgpxQAAAAAAXKPcMPjzzz+XuW0YhtauXaugoCDCIAAAAAB4uHLD4GOPPWb/+8CBA0pKStKtt96q6dOnu6QwAAAAAIDzODxmcPny5fq///s/TZs2TbfddpsragIAAAAAOFm5YfDYsWOaNm2a6tSpo9WrV6tOnTqurAsAAAAA4ETlhsG7775bAQEBioqK0tNPP11m3QsvvOD0wryZVVL9+qHuLgMAAACADys3DC5cuNCVdfiUoACzmiWtc3cZV0V26l3uLgEAAABAFZQbBrt27erKOgAAAAAALuTn7gIAAAAAAK5HGAQAAAAAH0QYBAAAAAAfRBgEAAAAAB9EGAQAAAAAH1Tu2UQBAPi9whKr11wntbDE6u4SAABwK8IgAOCKedt1UvPcXQQAAG7ENFEAAAAA8EGEQQAAAADwQS6dJjpo0CCFhl441qRJkyYaPny4nnnmGZnNZkVHR2vixImy2WyaNWuWfvjhBwUGBiolJUVNmzbVzp07L9kWAAAAAFA1LguDRUVFkqS0tDT7soEDB2rBggW6/vrrNXbsWGVlZenw4cMqLi7WypUrtXPnTqWmpmrx4sWaOXPmJdu2adPGVeUDAAAAgFdxWRj8/vvvVVBQoFGjRun8+fNKTExUcXGxIiIiJEnR0dHasmWLTpw4oR49ekiSOnbsqD179ig/P/+y2zoKg2azSWFhtZzbGADAY3nLd4TZ7Ecv1Yy39CHRS3XkLX1I9OJuLguDQUFBGj16tIYOHars7GyNGTNGtWvXtq8PDg7Wr7/+qvz8fIWEhNiXm83mS5aVbuuI1WooJ+fc1W3kKvCW07IDgKerjt8RVREWVoteqhlv6UOil+rIW/qQ6MUVKsoeLguDzZs3V9OmTWUymdS8eXOFhoYqJyfHvt5isah27doqLCyUxWKxL7fZbAoJCSmzrHRbAAAAAEDVuOxsou+++65SU1MlSceOHVNBQYFq1aqlgwcPyjAMbdy4UZGRkercubPS09MlSTt37lTLli0VEhKigICAS7YFAAAAAFSNy0YGY2NjNW3aNI0YMUImk0lz586Vn5+fHn/8cVmtVkVHR6tDhw5q166dNm3apLi4OBmGoblz50qSZs+efcm2AAAAAICqcVkYDAwM1AsvvHDJ8lWrVpW57efnp6effvqS7Tp27HjJtgAAAACAquGi8wAAAADggwiDAAAAAOCDCIMAAAAA4IMIgwAAAADggwiDAAAAAOCDCIMAAAAA4IMIgwAAAADggwiDAAAAAOCDCIMAAAAA4IMIgwAAAADgg/zdXQAAAO5QWGJV/fqh7i7jqigssbq7BACAByIMAgB8UlCAWc2S1rm7jKsiO/Uu5bm7CACAx2GaKAAAAAD4IMIgAAAAAPggwiAAAAAA+CDCIAAAAAD4IMIgAAAAAPggwiAAAAAA+CDCIAAAAAD4IMIgAAAAAPggwiAAAAAA+CDCIAAAAAD4IMIgAAAAAPggwiAAAAAA+CDCIAAAAAD4IMIgAAAAAPggwiAAAAAA+CDCIAAAAAD4IMIgAAAAAPggf1c9UUlJiaZPn67Dhw+ruLhY48ePV6NGjTRu3Dg1a9ZMkjRixAjdeeedeuWVV/TFF1/I399f06dPV/v27XXgwAElJSXJZDLpxhtv1MyZM+XnR5YFAAAAgKpwWRj84IMPFBYWpueee05nzpzRvffeqwkTJuiBBx7QqFGj7NtlZWUpIyNDq1ev1tGjR5WYmKg1a9Zo3rx5mjx5srp166bk5GR99tln6tu3r6vKBwAAAACv4rIw2K9fP8XExNhvm81m7dmzR/v379dnn32mpk2bavr06crMzFR0dLRMJpMaN24sq9Wq06dPKysrS127dpUk9ezZU5s2bSIMAgAAAEAVuSwMBgcHS5Ly8/M1adIkTZ48WcXFxRo6dKjatm2rxYsXa+HChQoNDVVYWFiZ++Xl5ckwDJlMpjLLHDGbTQoLq+WchgAAqEa85fvObPbzil68pQ+JXqojb+lDohd3c1kYlKSjR49qwoQJio+P14ABA5Sbm6vatWtLkvr27as5c+aoT58+slgs9vtYLBaFhoaWOT7QYrHY71cRq9VQTs65q9/IH1S/fqi7SwAAeJnq+H1XFWFhtbyiF2/pQ6KX6shb+pDoxRUqyh4uOwPLyZMnNWrUKD3xxBOKjY2VJI0ePVrffvutJGnLli1q06aNOnfurI0bN8pms+nIkSOy2WwKDw9X69attXXrVklSenq6IiMjXVU6AAAAAHgdl40Mvvrqq8rNzdWiRYu0aNEiSVJSUpLmzp2rgIAA1atXT3PmzFFISIgiIyM1fPhw2Ww2JScnS5KmTp2qGTNmaP78+WrRokWZ4w8BAPBlhSVWr5l1UlhidXcJAOAzXBYGn3rqKT311FOXLF+xYsUlyxITE5WYmFhmWfPmzfX22287rT4AADxVUIBZzZLWubuMqyI79S45PisAAOBq4EJ9AAAAAOCDCIMAAAAA4IMIgwAAAADggwiDAAAAAOCDCIMAAAAA4IMIgwAAAADggwiDAAAAAOCDCIMAAAAA4IMIgwAAAADggwiDAAAAAOCDCIMAAAAA4IMIgwAAAADggwiDAAAAAOCDCIMAAAAA4IMIgwAAAADggwiDAAAAAOCD/N1dAAAAQKnCEqvq1w91dxl/WGGJ1d0lAIBDhEEAAFBtBAWY1SxpnbvL+MOyU+9SnruLAAAHmCYKAAAAAD6IMAgAAAAAPogwCAAAAAA+iDAIAAAAAD6IMAgAAAAAPoiziQIAAFxl3nKJDInLZADejDAIAABwlXnLJTIkLpMBeDPCIAAAAMrFKCfgvQiDAAAAKBejnID34gQyAAAAAOCDGBkEAACAT/CWKa9Md8XV4lFh0GazadasWfrhhx8UGBiolJQUNW3a1N1lAQAAwAN4y5TX7+f084pQKxFs3c2jwuCnn36q4uJirVy5Ujt37lRqaqoWL17s7rIAAAAAl/GWUCtxHKe7eVQYzMzMVI8ePSRJHTt21J49e9xcEQAAAICq8papu5JnjnKaDMMw3F3ElfrrX/+qO+64Q7169ZIk3Xrrrfr000/l7+9RmRYAAAAA3M6jziYaEhIii8Viv22z2QiCAAAAAFAFHhUGO3furPT0dEnSzp071bJlSzdXBAAAAACeyaOmiZaeTfTHH3+UYRiaO3eubrjhBneXBQAAAAAex6PCIAAAAADg6vCoaaIAAAAAgKuDMAgAAAAAPogwCAAAAAA+iOsyOEnpyW5++OEHBQYGKiUlRU2bNrWvX7VqlVasWCF/f3+NHz9et912mxurrZijXlJSUrRjxw4FBwdLkhYtWqTQ0Op98dBdu3bp+eefV1paWpnln3/+uRYuXCh/f38NGTJEw4YNc1OFV6a8Pt588029++67Cg8PlyTNnj1bLVq0cEeJDpWUlGj69Ok6fPiwiouLNX78ePXp08e+3pP2iaNePGm/WK1WPfXUU9q/f7/MZrPmzZuniIgI+3pP2i+OevGk/SJJp06d0uDBg/XGG2+UOYmaJ+2TUuX14mn7ZNCgQfbvvSZNmmjevHn2dZ70fS9V3Isnfd8vWbJEn3/+uUpKSjRixAgNHTrUvs7TPisV9eJJn5W1a9fqvffekyQVFRVp79692rRpk2rXri3Jsz4rjnrxpM+KDDjFxx9/bEydOtUwDMP45ptvjHHjxtnXHT9+3Lj77ruNoqIiIzc31/53dVVRL4ZhGHFxccapU6fcUVqVLF261Lj77ruNoUOHllleXFxs3H777UZOTo5RVFRkDB482Dh+/LibqnSsvD4MwzAee+wxY/fu3W6oqvLeffddIyUlxTAMwzh9+rTRq1cv+zpP2ycV9WIYnrVf1q9fbyQlJRmGYRhff/11mc+9p+2XinoxDM/aL8XFxcbDDz9s3HHHHca+ffvKLPekfWIY5fdiGJ61TwoLC42BAwdedp2nfd9X1ItheM73/ddff2089NBDhtVqNfLz842XX37Zvs7TPisV9WIYnvVZudisWbOMFStW2G972mflYr/vxTA857NiGIbBNFEnyczMVI8ePSRJHTt21J49e+zrvv32W3Xq1EmBgYEKDQ1VRESEvv/+e3eV6lBFvdhsNh04cEDJycmKi4vTu+++664yr1hERIQWLFhwyfKff/5ZERERqlOnjgIDA9WlSxdt377dDRVemfL6kKSsrCwtXbpUI0aM0JIlS1xcWeX069dPjzzyiP222Wy2/+1p+6SiXiTP2i+333675syZI0k6cuSI6tWrZ1/nafulol4kz9ovzz77rOLi4tSgQYMyyz1tn0jl9yJ51j75/vvvVVBQoFGjRmnkyJHauXOnfZ2nfd9X1Isnfd9v3LhRLVu21IQJEzRu3Djdeuut9nWe9lmpqBfJsz4rpXbv3q19+/Zp+PDh9mWe9lkpdblePOmzIjFN1Gny8/MVEhJiv202m3X+/Hn5+/srPz+/zFBxcHCw8vPz3VHmFamol3Pnzum+++7TAw88IKvVqpEjR6pt27b605/+5MaKKxYTE6NDhw5dstzT9kt5fUjSXXfdpfj4eIWEhGjixInasGFDtZ1uUTqFIj8/X5MmTdLkyZPt6zxtn1TUi+RZ+0WS/P39NXXqVK1fv14vv/yyfbmn7Rep/F4kz9kva9euVXh4uHr06KGlS5eWWedp+6SiXiTP2SeSFBQUpNGjR2vo0KHKzs7WmDFj9NFHH3nk931FvXjS9/2ZM2d05MgRvfrqqzp06JDGjx+vjz76SCaTyeP2SUW9SJ71WSm1ZMkSTZgwocwyT9svpS7Xiyd9ViROIOM0ISEhslgs9ts2m03+/v6XXWexWKrvPGJV3EvNmjU1cuRI1axZUyEhIYqKivKI/5NzOZ62X8pjGIbuv/9+hYeHKzAwUL169dJ3333n7rIqdPToUY0cOVIDBw7UgAED7Ms9cZ+U14sn7hfpwujNxx9/rBkzZujcuXOSPHO/SJfvxZP2y5o1a7R582YlJCRo7969mjp1qk6cOCHJ8/ZJRb140j6RpObNm+uee+6RyWRS8+bNFRYW5rH7paJePOn7PiwsTNHR0QoMDFSLFi1Uo0YNnT59WpLn7ZOKevG0z4ok5ebm6pdfflFUVFSZ5Z62X6Tye/Gkz4pEGHSazp07Kz09XZK0c+dOtWzZ0r6uffv2yszMVFFRkfLy8vTzzz+XWV/dVNRLdna24uPjZbVaVVJSoh07dqhNmzbuKvUPueGGG3TgwAHl5OSouLhY27dvV6dOndxdVqXl5+fr7rvvlsVikWEY2rp1q9q2bevussp18uRJjRo1Sk888YRiY2PLrPO0fVJRL562X/75z3/apxzVrFlTJpPJPu3V0/ZLRb140n5Zvny53n77baWlpemmm27Ss88+q/r160vyvH1SUS+etE8k6d1331Vqaqok6dixY8rPz7f34mnf9xX14knf9126dNFXX30lwzB07NgxFRQUKCwsTJLnfVYq6sXTPiuStG3bNv35z3++ZLmnfVak8nvxpM+KxDRRp+nbt682bdqkuLg4GYahuXPn6s0331RERIT69OmjhIQExcfHyzAMTZkyRTVq1HB3yeVy1MuAAQM0bNgwBQQEaODAgbrxxhvdXXKl/Otf/9K5c+c0fPhwJSUlafTo0TIMQ0OGDFHDhg3dXd4Vu7iPKVOmaOTIkQoMDFT37t3Vq1cvd5dXrldffVW5ublatGiRFi1aJEkaOnSoCgoKPG6fOOrFk/bLHXfcoWnTpul///d/df78eU2fPl2ffPKJR35WHPXiSfvl97zl3y/Jc/8Ni42N1bRp0zRixAiZTCbNnTtXaWlpHvl976gXT/m+v+2227Rt2zbFxsbKMAwlJyfrww8/9MjPiqNePOmzIkn79+///+3dX0hTbQDH8e9mBi2FcM38E9FMsCCoq2xJdhVdJBW7KTDBjCAsCSJRghGCmoIWohaZUmBEMOpGjYgIxKJMupAYSmGYF5mTcNWJNDfPeyEdWL11UeH76vl97p6/59mBsf3Os3PG2rVrrfJi/W4Mv34ti+W9AuAwTdP8rxchIiIiIiIiC0s/ExUREREREbEhhUEREREREREbUhgUERERERGxIYVBERERERERG1IYFBERERERsSGFQRERWdL6+/vx+XwUFRVx+PBhDh06xN27dwEYGhqipaXlp2MHBgYW9M+CR0dHaWxsBCAvL8+qHxkZYc+ePTx58oQ7d+6Qk5PD4OCg1T47O0tubi7Nzc2YpklFRQXT09MLtm4REVmcFAZFRGTJ2759O52dndy4cYOOjg7a29sZGhpi06ZNnDx58qfjbt++TTgcXrB11tfXc+TIkbi6V69eceLECerq6vD5fABkZWXR3d1t9enr6yM5ORkAh8NBQUEB7e3tC7ZuERFZnPSn8yIiYisrV67k4MGD3Lt3j48fP3Lr1i0uXrxIZWUlY2NjzMzMcPToUdatW0dfXx+hUIjs7GwePnzI/fv3iUajJCcn09zcTHd3N729vUxPTzM2NsaxY8fw+/0MDg5SU1ODaZqsWbOGhoYG3rx5Q3V1NQCrVq2itrbWCnAAr1+/xjRNUlJSrLrh4WFOnTpFU1MTGzdutOrz8/N5nKtFgQAAAxtJREFU9OgRc3NzOJ1Oenp62Lt3r9W+Y8cO6urqKC0txenUdV8REfl3+oQQERHbcbvdTE1NWWXDMOjv76elpYWrV68Si8XYvHkzO3fupLy8nLS0NCKRCNevX+fmzZtEo1FevHhhjb1y5QqXL1+mra0NgEAgwPnz5wkGg/h8PkZGRggEApw7d47Ozk7y8/N/2LkbGBggJyfHKn/+/JnKykoSEhL49OlTXN/ExES2bt3Ks2fPMAwDwzBIS0uz2hMSEkhJSeHly5d//dyJiMjSoZ1BERGxnbdv38aFp6SkJAKBAIFAAMMw2LdvX1x/p9NJYmIip0+fxuVy8e7dO6LRKIC1Y5eens7Xr18BeP/+PRs2bACgsLAQmL/vr6qqCpi/x8/r9cYdY2pqCrfbbZUdDgetra1EIhHKysoIBoNx7QUFBfT09DA+Ps7u3buZnZ2Nmy81NZVIJPL7J0lERJY8hUEREbEVwzAIBoM0NTUxOTkJQDgcJhQK0drayszMDLt27WL//v04HA5M02R4eJgHDx4QDAb58uULfr8f0zSB+dD2vdTUVEZHR1m/fj1tbW14vV68Xi/19fVkZGTw/Plz69jfuN1uJiYmrLLL5SIzM5PMzEwKCws5c+YMHR0dVntubi61tbWEw2EaGxvp6uqKm+/Dhw9x4VFEROR7CoMiIrLkPX36lKKiIpxOJ7FYjLKyMrKysqxA5vF4mJyc5MCBA7hcLkpKSli2bBlbtmyhoaGBCxcusGLFCvx+P8uXL8fj8fzywTJVVVWcPXsWp9OJx+OhuLiY9PR0KioqiMViANTU1MSN2bZt2w9135SUlPD48WMuXbpERkYGML9bmZeXx/j4OElJSXH95+bmmJiYIDs7+7fPmYiILH0O89ulTREREflPHT9+nOrqalavXv1H8/T29hIKhSgtLf1LKxMRkaVID5ARERH5nygvL+fatWt/NIdpmnR1dVFcXPx3FiUiIkuWdgZFRERERERsSDuDIiIiIiIiNqQwKCIiIiIiYkMKgyIiIiIiIjakMCgiIiIiImJDCoMiIiIiIiI29A8CXQes5aWXOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distance of the bike rides in KMs\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "bin_edges = np.arange(0, 8, 0.5)\n",
    "\n",
    "plt.hist(data = df_Q1_2020_clean, x = 'distance_km', bins = bin_edges);\n",
    "\n",
    "plt.title(\"Baywheels Trips Distance in Kilometer\", y=1.05, fontsize=16, fontweight='bold')\n",
    "plt.xlabel('Distance (KM)')\n",
    "plt.ylabel('Number of Bike Trips')\n",
    "plt.xticks(bin_edges);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    82791.000000\n",
       "mean         2.856570\n",
       "std        108.911514\n",
       "min          0.000000\n",
       "25%          1.091548\n",
       "50%          1.699364\n",
       "75%          2.566812\n",
       "max      12796.827118\n",
       "Name: distance_km, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_Q1_2020_clean.distance_km.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: most of the deestinations traveled are between 1000 to 1500 meters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?\n",
    "\n",
    "> The number of rides peaked around 7-9am and 16-18pm during a day, \n",
    "\n",
    "> There are more rdies on workdays (Mon-Fri) compared to weekends,\n",
    "\n",
    "> Most rides duration are short between 5 to 8 minutes, though there were some long outliers like 8 hrs,\n",
    "\n",
    "> Most rides distance are short between 1 to 1.5 kilometer, though there were some long outliers like 12796 kilometer,\n",
    "\n",
    "> No transformation was needed.\n",
    "\n",
    "### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?\n",
    "\n",
    "> The distribution are normal to some extent with some outliers,tidiness was not big issues, we just splited/extracted new variables from existing ones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_vars = ['duration_sec', 'distance_km', 'start_hourofday']\n",
    "categoric_vars = [' user_type', 'rental_access_method', 'start_dayofweek']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'Baywheels Trips Numeric Correlation')"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lQkNDMZvNxMfH8/LLL1O/fv07Rg5kmDp1Kv7+/gwePJgnnniCbt26GZWa7Drp5UROPpjXrFnDqVOnSE9PNzoflixZkkqVKuVarK6urrzzzjt3nZfRzAYQFRUF3GrbP3HihNXbz07GexQZGcnBgweBW30iAgIC7vqleb/XLqMPzNq1a7lw4QLp6enMnj2bkSNH8v777991HTc3N4KDg4FbfS42b95Meno68fHxhISEGCNnnnvuOQCjaXDRokWcPXsWi8XCggULiI2NxdnZ+Y7E+V4x3z7dmnP6dsePH8diseDg4MBjjz1GcnIya9euBbL+P81IDOLj4+9Zlco4rv379/PVV18B8Ouvvxr9OIKCgu66nkhe0nU27MjUqVP58MMPgf+VqtPT03FycuLVV18FbjWRnDhxgqVLl/LVV19lGaqY+VdtuXLlqF+/vvGLOHMTSoamTZsSHR0NwDPPPGP8m9EGfLcmlPvp3r07X3zxBcePH6dJkya4urpiNpvx9PSkRYsWALz22mt8//337Nq1iwYNGuDo6EhiYiKPPfaY8cF6u9atW7N8+XK2bt3Ks88+i5ubm1FCz+iLkJcSExNp1aoVbm5uRhv8gAEDcHR0zNVYAwMDad68+R3t8p6entSqVYuDBw8yZswYFi1axOnTpyldurTVTS7Zady4MQEBAURFRdGpUyeKFi1qnGsvvvhijrfXq1cv1q1bx9mzZ2nevDlubm7Ex8fj5OTE66+/fs/1hg0bxuHDh9m1axdvvPEG7u7uJCcnG1/MY8aMMZqxBg0axObNm/n9998JCgrK8t4MHz7cqk6md2PNOX27mjVr4uzszM2bN2nRogVOTk4kJiYCWf+fli9fntOnT/P++++zYMGCuw4Frlu3Ln/729/45ptvGDlyJOPHjzeaHWvVqmX0zxDJT6ps2JFr164ZbcJXr16lSJEiBAQEsGDBAqpXrw7AyJEjad26NZ6enjg5OfHCCy/Qr18/AHbs2JFle88//zxwq3Pg3TomZk4m6tWrB5BlFMaDJBtlypRh+fLlNG/e3ChHP/fccyxZssTogPrUU0+xZMkSnn32WZycnHBxcSEoKIjQ0NB7VlJq167NkiVLaNSokfGFU61aNcaMGWN00MxLw4cPp23btlgsFsqWLcuoUaP4+9//bpNY33nnHaNvTGYzZszg2WefxdnZmdTUVCZOnGj0xckN8+fPJzg4mNKlS5OUlISvry8ffPDBAyUbZcqUYcWKFbRo0QI3NzcsFgsBAQHMnz8/2wusubq68tlnn/H2229TrVo10tLSKFq0KM899xz//ve/6dq1q7HsY489Rnh4uNEJNSUlherVqzNz5kx69+79QK9BRuz3O6dvV7FiRaZPn06lSpVwdHSkQoUKTJ48GW9vb27cuGF0iB40aBCVK1fGZDJRvHjxe1Y3pk+fzqhRo6hatSopKSmULl2aHj16sGTJkrueGyJ5zWTJaW8kKRASEhLo3Lkzx44do3379sYQOHk4GRdimjJlSparvYqIFGZqRimEWrVqxYULF0hISMDBweGhbmYmIiJyP2pGKYQyboJVqVIlZsyYkeUmTyIiIrlNzSgiIiJyh/379zNjxow7rkK7efNm5s6di5OTEx07dqRz58733ZaaUURERCSLhQsXsn79+iwXfoNbIyGnTJnC6tWrcXNzo2vXrjRv3vy+F+6zabJRZcBmW25eCqFfZjTI7xCkAElTYVdsoIiHR57u70G+a99rGpvlfjtdunShS5cuxvOKFSsa17rJ7MSJE1SsWNG4EF/dunXZs2ePcc+te1FlQ0REpJC5Pbm43QsvvGBchTaz+Pj4LLcR8PDwuOeViTNTB1ERERGxiqenZ5ab9pnN5mzvYZRByYaIiIgdczTl/PGgKleuzOnTp7l69SrJycns2bOHgICA+66nZhQRERHJ1tdff01CQgJdunQhJCSEPn36YLFY6Nixo1WX+rfp0Fd1EJXcpg6ikpvUQVRsIa87iNYYvCnH6/w8O29v0KdmFBEREbEpNaOIiIjYsYfpg5FXVNkQERERm1JlQ0RExI6psiEiIiKFnpINERERsSk1o4iIiNgxBzWjiIiISGGnyoaIiIgdUwdRERERKfRU2RAREbFj6rMhIiIihZ4qGyIiInZMfTZERESk0FNlQ0RExI6pz4aIiIgUeqpsiIiI2DFVNkRERKTQU2VDRETEjqmyISIiIoWeKhsiIiJ2TNfZEBERkUJPyYaIiIjYlJpRRERE7Jg6iIqIiEihp8qGiIiIHVNlQ0RERAo9VTZERETsmCobIiIiUuipsiEiImLHHOygbGAHIYqIiIg9U2VDRETEjqnPhoiIiBR6qmyIiIjYMVU2REREpNBTZUNERMSO2UNlQ8mGDdWuVJSR7SvT7cOoLNMDa5Vk0ItPkpZmYfVP5wiL/ANXZwdm9qpBSS8XzDdTGRn6C1fiU+66rBQ+6enpTJw8maNHj+Ls4sKEsWOpWLGiMX91eDhfrF6Nk5MTr/XtS7MmTYiLi2Pk6NEkJSVRunRpJo0bh5ubG1u3bePTBQsAqP7UU4wZNQqTyQ4+rSRXpaen896UKRw9ehQXFxf+9e67Wc6pNeHhrF6zBkdHR/r17UvTJk2MeXv37mXUmDF89+23WbY5YeJEinp7888hQ/LsOMQ+qBnFRvq1rMjkbk/h6pz1JXZyMPFOx6r0+jiaV2fto0ujxylV1IVujctz9A8zXT/Yx9qd5xnQutI9l5XCZ9MPP5CcnMzy0FCGDRnC9A8+MOZdunSJ5StXsmzJEubPnctHs2eTnJzMpwsW0KZ1a0IXLaK6ry9frlmD2Wxm5ocfMvejj1gRGkr5xx8nLi4uH49M8svmP8+ppZ9/ztDBg5k5a5Yx79KlS6xYtYrPFy/m07lz+XjOHJKTkwE4f/48ocuWkZqammV7X65ezbHjx/P0GOQWkynnj7ymZMNGfo9NZMCCg3dMr1zOndOxiVxPTCUlzcLe41d5prI3dat4E3H4MgBbDl+moW/xey4rhU9UVBQNn38egNp+fhz++Wdj3sFDh/CvXRsXFxe8vLzw8fHhyLFjREVH0+jPdRo1bMhPO3cSvX8/VatUYfoHH9Cjd29KlihBiRIl8uWYJH9FRUfz/J/nh99t59Shw4fvOKeOHjtGUlISkyZPZvSoUVm2tX//fg4cPEinjh3z9BjEfliVbPz+++988803AMycOZMzZ87YNKiC4L/RsaSmWe6Y7lnEiRs3//eLID4pDS83pyzTzXeZlnlZKXzizWa8PD2N5w6OjsYvy3izGS8vL2Oeh7s78TduEG824/nnOh4eHsTHxxN39Sq79uxh+NChzJszh6UrVnDq9Om8PRh5JJhvO6ccM59T8fHGuQN/nlPx8UyZNo0e3btTtkwZY15sbCyfzp/P6JCQvAte7I5VycbIkSMpXbo0AE2aNOGdd96xaVAFWfzNVDxcHY3nnq6OXE9MzTLd4y7TMi8rhY+nhwfmhATjuSU9HScnp//NM5uNeeaEBLy8vLKsY/4zISnm7U3Np5+mVKlSuLu7U7dOHX49ciRvD0YeCR63nTfpmc8pT08SMp1v5oQEnJ2c2BcVxfwFC+jTrx/Xrl1jZEgI323cyNWrVxk0ZAiLlizh2w0b+Gr9+jw/nsLMwZTzR57HaO2C9evXB+CZZ54hPT3dZgEVdCfOJVCpjDve7k44O5p4pmoxon67xr4T12hWsxQATZ8uye7jV++5rBQ+Af7+bN22DYD9Bw5QtUoVY16tmjXZFxVFUlISN27c4OTJk1StUoWA2rWNdbZFRlI3IIAa1atz/Phx4uLiSE1N5cDBg1T+y1/y5ZgkfwX4+7MtMhKAA7edUzWffvqOc6pmzZqsX7uWzxYu5LOFC/H29ub9qVPp1rUrq1as4LOFC+ndqxetW7XipXbt8uuw5BFlVU2+aNGihIWF4e/vz4EDB/Dw8LB1XAVO23plcXd1JCzyDyavOcbiwf44mGD19nNcuJbM8oizTO9Zg1XD65CSZmHY4sOkplvuuqwUPkGBgWzfsYNuPXuCxcLE8eP5fOlSKvr40LxZM7p17UqP3r2xWCwMGTgQV1dXXuvXj3fGjmV1eDjFixVj2pQpuLu5MXTwYF4fOBCAF1q2zPIlI4VHYPPm/LRjBz169cJisTBh3DhCly2joo8PzZo25dXgYP7Rpw/p6ekM/vOckkeTPQx9NVksljs7FtzmypUrfPrpp5w6dYrKlSvz2muvWdWprMqAzbkSpEiGX2Y0yO8QpABJu//Hn0iOFcnjH+SdJ2zK8TpfjA2yQST3ZlVlo0SJEjRv3pwzZ87g5+enyoaIiMgjwh4qG1YlGx988AHnz5/nxIkTODs7s2DBAj7INM5fRERE5F6s6iC6d+9e3n//fdzd3Xn55Zc19FVEROQR4WAy5fiRnfT0dMaOHUuXLl3o3r07p28bHv/ZZ5/RoUMHOnbsyPfff29VjFZVNtLS0khKSsJkMpGWloaDg64FJiIiUhBt3LiR5ORkwsLCiI6OZurUqXz66acAXL9+naVLl/Ldd9+RmJhI+/btadmy5X23aVWy0bNnTzp06MCVK1d45ZVX6NWr10MdiIiIiOSO3O6zsXfvXho3bgyAv78/hw4dMua5ubnx+OOPk5iYSGJiotX3VbIq2WjdujXPP/88p0+fxsfHh+LFiz9A+CIiIvIoCAsLIywszHjepUsXunTpAtx5BdmMq8tmXPStXLlytGnThrS0NF5//XWr9mdVsrF7924SExOxWCwMHz6coUOH0rZtW6sPSkRERGzjQW6sljm5uJ2np+c9ry4bERHBxYsX2bTp1nDbPn36UKdOHfz8/LLdn1WdL6ZPn06lSpUIDQ1l5cqVrFq1yqqDEREREftSp04dIiIiAIiOjqZatWrGPG9vb4oUKYKLiwuurq54eXlx/fr1+27TqsqGq6srJUuWxMnJidKlSxu3GhYREZH8ldt9Nlq2bElkZCTBwcFYLBYmT57M4sWLqVixIkFBQWzfvp3OnTvj4OBAnTp1aNiw4X23aVWy4enpyT/+8Q9effVVli9fTrly5R76YEREROTR4+DgwIQJE7JMq1y5svH3kCFDGDJkSI62aVWy8dFHH/H7779TpUoVjh49yiuvvALA/v37qV27do52KCIiIrnHHq4galWfDRcXF6r8ebOmatWq4eLiAsDMmTNtF5mIiIgUCA91dS4r7uEmIiIihZxVzSj3Yu3FPERERMQ2CkwzioiIiMiDeqjKhppRRERE8tf9bqz2KHioyoauIioiIiL3Y1VlY926dcyfP5/k5GQsFgsmk4lNmzbRuXNnW8cnIiIi2bCDwoZ1ycbChQuZN2+eLuYlIiIiOWZVsuHj48MTTzxh61hEREQkh+xhNIpVyUaRIkXo27cv1atXN4a7Dh8+3KaBiYiISMFgVbLRtGlTW8chIiIiD8AeKhtWjUZp27YtCQkJHDhwgOvXr9OmTRtbxyUiIiIFhFXJxtixY4mJiaFhw4acPXuWMWPG2DouERERsYLJZMrxI69Z1Yxy+vRpli9fDkCLFi0IDg62aVAiIiJScFiVbCQlJZGYmIibmxs3b94kLS3N1nGJiIiIFeyhz4ZVyUaPHj146aWXqFq1KsePH2fw4MG2jktEREQKCKuSjXbt2tGkSRNiYmKoUKECxYsXt3VcIiIiYgUHO7ilarbJxieffMKAAQMYPnz4HR1KZs6cadPAREREpGDINtkIDAwEUIdQERGRR5Q93Bsl2+JL1apVSU5OJjQ0lICAAPz9/fHz82POnDl5FZ+IiIjYuWwrG2vWrGHevHlcunSJVq1aYbFYcHR0pG7dunkVn4iIiNi5bJONzp0707lzZ1avXk2nTp3yKiYRERGxkoMdtKNYNRrlmWeeYf78+aSkpABw8eJFJkyYYNPAREREpGCwasDM22+/DcC+ffs4c+YMV69etWlQIiIiYh0HU84feR6jNQsVKVKE119/nbJlyzJ16lQuXbpk67hERESkgLCqGcVisRAbG0tCQgIJCQlcu3bN1nGJiIiIFezhcuVWVTYGDRrExo0badeuHUFBQTRp0sTWcYmIiEgBYVVl48CBA/Tp0weAoKAgmwcebWkAACAASURBVAYkIiIi1suPW8bnlFWVjS1btuhOryIiIvJArKpsxMXF0bhxYypUqIDJZMJkMrFq1SpbxyYiIiL3YQ99NqxKNubNm/dAG/9lRoMHWk/kXqqP2JHfIUgBos8okbxhVbKxdu3aO6YNGjQo14MRERGRnLGDLhvWJRulSpUCbg2B/fnnn0lPT7dpUCIiIlJwWJVs3H6L+b59+9okGBEREcmZAnNvlJMnTxp/X7x4kXPnztksIBERESlYrEo2xo4da4zjdXV1Ne6VIiIiIvnL7kejBAYGYjKZsFgsADg7O5OSksKUKVN0FVERERGxSrbJxoYNG7BYLIwfP57g4GD8/Pz4+eefWblyZV7FJyIiInYu22TDxcUFgJiYGPz8/ACoUaMGv/32m+0jExERkfuyg/6h1vXZ8PLy4sMPP8TPz4/o6GjKly9v67hERESkgLDq3igzZsygdOnSREREUKpUKaZMmWLruERERMQKDiZTjh95zarKhru7O926dbN1LCIiIlIAWZVsiIiIyKPJHoa+WtWMIiIiIvKgVNkQERGxYyY7GI6iyoaIiIjYlCobIiIidiy3+2ykp6czbtw4jhw5gouLC5MmTeKJJ54w5m/ZsoW5c+cCt6699a9//eu+1RVVNkRERMSwceNGkpOTCQsL480332Tq1KnGvPj4eKZPn868efP44osvKF++PHFxcffdpiobIiIidszhAcoGYWFhhIWFGc+7dOlCly5dANi7dy+NGzcGwN/fn0OHDhnLRUVFUa1aNaZNm0ZMTAyvvPIKJUqUuO/+lGyIiIgUMpmTi9vFx8fj6elpPHd0dCQ1NRUnJyfi4uLYuXMn69atM67B5e/vz5NPPpnt/tSMIiIiYsdMJlOOH9nx9PTEbDYbz9PT03FyulWbKFasGLVq1aJ06dJ4eHhQr149fvnll/vGqGRDREREDHXq1CEiIgKA6OhoqlWrZsyrWbMmR48e5cqVK6SmprJ//36qVKly322qGUVERMSO5fZolJYtWxIZGUlwcDAWi4XJkyezePFiKlasSFBQEG+++SZ9+/YFoFWrVlmSkXtRsiEiIiIGBwcHJkyYkGVa5cqVjb/btGlDmzZtcrbNXIlMRERE5B5U2RAREbFjuly5iIiIFHqqbIiIiNgx3WJeRERECj1VNkREROyYg/psiIiISGGnyoaIiIgds4PChiobIiIiYluqbIiIiNgx9dkQERGRQk+VDRERETtmB4UNVTZERETEtlTZEBERsWO6gqiIiIgUeqpsiIiI2DEHOyhtqLIhIiIiNqVkQ0RERGxKzSg2kp6ezsTJkzl69CjOLi5MGDuWihUrGvNXh4fzxerVODk58VrfvjRr0oS4uDhGjh5NUlISpUuXZtK4cbi5ubF12zY+XbAAgOpPPcWYUaMw2cNYJ8l1tSsVZWT7ynT7MCrL9MBaJRn04pOkpVlY/dM5wiL/wNXZgZm9alDSywXzzVRGhv7ClfiUuy4rhU9ufkYBXLlyhb/36sXaL7/E1dU1vw6rULKHrwNVNmxk0w8/kJyczPLQUIYNGcL0Dz4w5l26dInlK1eybMkS5s+dy0ezZ5OcnMynCxbQpnVrQhctorqvL1+uWYPZbGbmhx8y96OPWBEaSvnHHycuLi4fj0zyS7+WFZnc7SlcnbP+t3VyMPFOx6r0+jiaV2fto0ujxylV1IVujctz9A8zXT/Yx9qd5xnQutI9l5XCJ7c+owAit2/ntQEDuHzlSn4djjzilGzYSFRUFA2ffx6A2n5+HP75Z2PewUOH8K9dGxcXF7y8vPDx8eHIsWNERUfT6M91GjVsyE87dxK9fz9Vq1Rh+gcf0KN3b0qWKEGJEiXy5Zgkf/0em8iABQfvmF65nDunYxO5nphKSpqFvcev8kxlb+pW8Sbi8GUAthy+TEPf4vdcVgqf3PqMAjA5OPDvefPwLlo07w9EcDCZcvzIa1Y1o8yaNYvVq1dnKd1v27bNZkEVBPFmM16ensZzB0dHUlNTcXJyujXPy8uY5+HuTvyNG8SbzXj+uY6Hhwfx8fHEXb3Krj17WLNqFe7u7vTo3ZvatWtT6Ykn8vyYJH/9NzqW8iWK3DHds4gTN26mGs/jk9LwcnPKMt18l2mZl5XCJ7c+owCeb9Agb4MXu2PVp8yWLVv44YcfcHFRudVanh4emBMSjOeW9HScnJz+N89sNuaZExLw8vIy1ilSpAjmP/+zF/P2pubTT1OqVCkA6tapw69HjijZEEP8zVQ8XB2N556ujlxPTM0y3eMu0zIvK4VPbn1GSf6zhz58VjWjVK9enaSkJFvHUqAE+Puz9c/qz/4DB6hapYoxr1bNmuyLiiIpKYkbN25w8uRJqlapQkDt2sY62yIjqRsQQI3q1Tl+/DhxcXGkpqZy4OBBKv/lL/lyTPJoOnEugUpl3PF2d8LZ0cQzVYsR9ds19p24RrOat5LUpk+XZPfxq/dcVgqf3PqMErGGVZWNqlWr0qhRI0qVKoXFYsFkMrFp0yZbx2bXggID2b5jB9169gSLhYnjx/P50qVU9PGhebNmdOvalR69e2OxWBgycCCurq681q8f74wdy+rwcIoXK8a0KVNwd3Nj6ODBvD5wIAAvtGyZ5UNBCq+29cri7upIWOQfTF5zjMWD/XEwwert57hwLZnlEWeZ3rMGq4bXISXNwrDFh0lNt9x1WSl8cuszSvKfHVzTC5PFYrHcb6FOnToxb948imbq/GNNk0pKphKdSG6oPmJHfocgBcgvM9TXQHKfs7t7nu5v0bo9OV6nd/t6Nojk3qyqbDz++OO4ubmpz4aIiMgjJj9Gl+SUVcnG+fPnadmyJT4+PsCtziirVq2yaWAiIiJSMFg99DWzlJQUmwQjIiIiOWMHhQ3rRqN8++23lC9fnvLly2M2mxk2bJit4xIREZECwqrKxtGjR1m5ciUJCQmsW7eO8ePH2zouERERsUKB6bMxdepURowYwZUrV1izZo06ioqIiIjVsk02unTpYlyZLCUlhSNHjtCjRw8AdRAVERF5BJjs4C5n2SYbH2S6C+Dd7N+/n9q1a+dqQCIiIlKwZJtslC9fPtuVZ86cSWhoaK4GJCIiIgXLQ93u0YqLj4qIiIgN2UMH0Ydq6bGHO82JiIhI/nqoyoaIiIjkL3u4EdtDVTbUjCIiIiL3Y3WyER8fz5EjR0jIdCfXtm3b2iQoERERsY7JZMrxI69Z1YyyYcMG5s2bR1paGq1atcJkMjFgwAA6d+5s6/hERETEzllV2ViyZAlffPEFxYoVY8CAAWzcuNHWcYmIiIgVHEymHD/yPEarFnJwwMXFxSi/uLm52TouERERKSCsakapV68ew4cP58KFC4wdO5ZatWrZOi4RERGxgj1chcKqZGP48OFERERQo0YNKleuTPPmzW0dl4iIiBQQViUbmzdv5uDBgwwdOpQ+ffrg7OxMo0aNbB2biIiI3IeDHVxow6o+G7Nnz+bvf/87AB9++CFz5syxaVAiIiKSP9LT0xk7dixdunShe/funD59+q7L9O3bl5UrV1q1TauSDScnJ0qWLAmAl5cXDg52cD9bERGRQsDBlPNHdjZu3EhycjJhYWG8+eabTJ069Y5lPvzwQ65du2Z1jFY1o/j5+fHmm2/i7+/PgQMHqFGjhtU7EBEREfuxd+9eGjduDIC/vz+HDh3KMn/Dhg2YTCaaNGli9TatSjbGjBnDpk2b+O2332jdujWBgYE5CFtERERs5UGuCBoWFkZYWJjxvEuXLnTp0gW4dcVwT09PY56joyOpqak4OTlx9OhRvvnmGz7++GPmzp1r9f6sSjbMZjPJycmUKVOG69evs27dOtq3b2/1TkREROTRkTm5uJ2npydms9l4np6ejpPTrXRh3bp1XLhwgZ49e3L27FmcnZ0pX778fascViUbAwYMoEyZMpQrVw7QreVFREQKqjp16vDDDz/w4osvEh0dTbVq1Yx5I0eONP6ePXs2pUqVsqo5xapkw2KxMGPGjAcIWURERGwpty8/3rJlSyIjIwkODsZisTB58mQWL15MxYoVCQoKeqBtWpVs+Pr6sn//fqpXr25Mc3FxeaAdioiIyKPLwcGBCRMmZJlWuXLlO5YbPHiw1du0KtnYtWsXmzdvNp6bTCY2bdpk9U5ERETENuyhZ4NVycb69ettHYeIiIgUUFYlG5s2bWLFihWkpKRgsVi4evUqX3/9ta1jExERkfvIj1vG55RVlwKdO3cugwYNoly5crz88stZeqaKiIiIZMeqZKN48eIEBAQA0KFDBy5cuGDToERERMQ6Dg6mHD/yPEZrFnJ2dmb37t2kpqaydetWYmNjbR2XiIiIFBBWJRvjx48nNTWVN954gy+++IKhQ4faOi4RERGxgsmU80desyrZWLNmDc899xxVqlRh9uzZ/PLLL7aOS0RERAqIbEejfPnll6xevZoTJ04QEREB3LpGekpKCm+++WaeBCgiIiL3Zg+jUbJNNl566SWee+455s+fT//+/YFbVxYrWbJkngQnIiIi9i/bZhQXFxcqVKjAqFGjcHR0xNXVlfDwcHUQFREReUSYTKYcP/KaVX02RowYwaFDh3j//fdxdnZm7Nixto5LRERECgirriB6/fp1goKCCA0N5f3332fr1q1WbTzNYnmo4ERu98uMBvkdghQg1UfsyO8QpAA6/klgnu4vHy6bkWNWVTZSUlJYtGgRTz/9NMePH8dsNts6LhERESkgrEo23n77bS5fvswbb7zBzp07GTdunI3DEhERkYIi22aU8+fP89hjj1G8eHFeeeUVLl++zPPPP59XsYmIiMh95Mflx3Mq22Rj8eLFjBo1irFjx97RezU0NNSmgYmIiEjBkG2y8f3337Nx40Ysf3b0dHZ2JiUlBVdX1zwJTkRERLKXH0NZcyrbZGPDhg1YLBbGjx9PcHAwfn5+/Pzzz6xcuTKv4hMRERE7l22y4eLiAkBMTAx+fn4A1KhRg99++832kYmIiMh92f3lyjN4eXnx4Ycf4ufnR3R0NOXLl7d1XCIiIlJAWDX0dcaMGZQuXZqIiAhKlSrFlClTbB2XiIiIWMEebjFvVWXD3d2dbt262ToWERERKYCsSjZERETk0WQP19mwqhlFRERE5EGpsiEiImLH7GE0iiobIiIiYlOqbIiIiNgxe7iCqCobIiIiYlOqbIiIiNgxOxiMosqGiIiI2JaSDREREbEpNaOIiIjYMXUQFRERkUJPlQ0RERE7psuVi4iISKGnyoaIiIgd0+XKRUREpNBTZUNERMSO2UFhQ5UNERERsS1VNkREROyYRqOIiIhIoafKhoiIiB3TaBQREREp9FTZEBERsWO6N4qIiIgUeqpsiIiI2LHc7rORnp7OuHHjOHLkCC4uLkyaNIknnnjCmL9kyRL+85//ANC0aVMGDRp0/xhzNUIRERGxaxs3biQ5OZmwsDDefPNNpk6dasyLiYlh/fr1rFq1irCwMLZt28avv/56322qsiEiIiKGvXv30rhxYwD8/f05dOiQMe+xxx7j3//+N46OjgCkpqbi6up6320q2RAREbFjD3JRr7CwMMLCwoznXbp0oUuXLgDEx8fj6elpzHN0dCQ1NRUnJyecnZ0pUaIEFouF999/nxo1avDkk0/ed39KNkRERAqZzMnF7Tw9PTGbzcbz9PR0nJz+ly4kJSUxevRoPDw8+Ne//mXV/tRnQ0RExI6ZTDl/ZKdOnTpEREQAEB0dTbVq1Yx5FouFAQMG4Ovry4QJE4zmlPtRZUNEREQMLVu2JDIykuDgYCwWC5MnT2bx4sVUrFiR9PR0du3aRXJyMlu3bgVg+PDhBAQEZLtNJRsiIiJ2LLeHvjo4ODBhwoQs0ypXrmz8ffDgwZxv86Gjkjukp6cz8b336N6zJ3369eP333/PMn9NeDhdu3Xj7z16sOXPUlWGvXv38tfWre/Y5oSJE/nw449tGrc8utLT0xk/aRLdevSgV9++d5xTq8PD6fzqq7zaowc//nlOxcXF0e+NN+jRuzdvvv02iYmJxvJXrlzhxXbtSEpKytPjkEdP7UpFWf7PO3+VBtYqSfjb9fhyRF26NHwcAFdnB+b0q8nK4XX49wA/Sng633NZkcyUbNjA5h9+IDk5maWff87QwYOZOWuWMe/SpUusWLWKzxcv5tO5c/l4zhySk5MBOH/+PKHLlpGampple1+uXs2x48fz9Bjk0bLpz3NqeWgow4YMYfoHHxjzLl26xPKVK1m2ZAnz587lo9mzSU5O5tMFC2jTujWhixZR3deXL9esASBy+3ZeGzCAy1eu5NfhyCOiX8uKTO72FK7OWb8KnBxMvNOxKr0+jubVWfvo0uhxShV1oVvj8hz9w0zXD/axdud5BrSudM9lJe+YHEw5fuQ1JRs2EBUdzfPPPw+An58fh3/+2Zh36PBh/GvXxsXFBS8vL3x8fDh67BhJSUlMmjyZ0aNGZdnW/v37OXDwIJ06dszTY5BHS1RUFA3/PKdq33ZOHTx06I5z6sixY0RFR9Poz3UaNWzITzt3AmBycODf8+bhXbRo3h+IPFJ+j01kwII7S+KVy7lzOjaR64mppKRZ2Hv8Ks9U9qZuFW8iDl8GYMvhyzT0LX7PZUUysyrZuHTpkq3jKFDMZjNedxmjDHeOX/Zwdyc+Pp4p06bRo3t3ypYpY8yLjY3l0/nzGR0SknfByyMp/rZzyiHzOWU24+XlZczzcHcn/sYN4s1m41zz8PAgPj4egOcbNKBYsWJ5GL08qv4bHUtqmuWO6Z5FnLhx838V1vikNLzcnLJMN99lWuZlJe84mEw5fuQ1q86IwYMHU6JECTp16kTTpk1xcFBBJDseHh73HKPs6elJQkKCMc+ckICzkxP7oqKIiYlh/oIFXLt2jZEhIdSuXZurV68yaMgQLl2+zM2bN3myUiVeatcuz49J8penhwfmTOeNJfM5ddv5Zk5IwMvLy1inSJEitxLgTAmJSHbib6bi4fq/IY2ero5cT0zNMt3jLtMyLyuSmVVZw8qVKxk+fDi7du0iODiYWbNmERMTY+vY7FaAvz/bIiMBOHDgAFWrVDHm1Xz6afZFRZGUlMSNGzc4efIkNWvWZP3atXy2cCGfLVyIt7c370+dSreuXVm1YgWfLVxI7169aN2qlRKNQirA35+t27YBsP+2c6pWzZp3nFNVq1QhoHZtY51tkZHUvc/QNJEMJ84lUKmMO97uTjg7mnimajGifrvGvhPXaFazFABNny7J7uNX77ms5J0CU9kAKFOmDD4+Phw+fJijR4/y3nvvUb16dYYOHWrL+OxSYPPm/LRjBz169cJisTBh3DhCly2joo8PzZo25dXgYP7Rpw/p6ekMHjjQquvKS+EWFBjI9h076NazJ1gsTBw/ns+XLqWijw/NmzWjW9eu9OjdG4vFwpA/z6nX+vXjnbFjWR0eTvFixZg2ZUp+H4Y84trWK4u7qyNhkX8wec0xFg/2x8EEq7ef48K1ZJZHnGV6zxqsGl6HlDQLwxYfJjXdctdlRTIzWSyWOxvsbjN06FCOHTtGu3btePnllylbtiwAHTp0IDw8/J7r3cxU2hXJDY75kJFLwVV9xI78DkEKoOOfBObp/mLOxOZ4HZ8KpW0Qyb1ZVdno3LkzDRs2vGP6ypUrcz0gERERKVisSjY8PDwYO3YsKSkpAFy8eJHPPvtM5X8REZF89iB3fc1rVnUQnTRpEs8++yzx8fE8/vjjGjYnIiIiVrMq2ShatCh/+9vf8PT0ZPDgwVy4cMHWcYmIiEgBYVUzislk4tixYyQmJvLbb78RG5vzzigiIiKS+/JjKGtOWVXZCAkJ4dixY3Tv3p0RI0bQtWtXW8clIiIiBYRVlY2qVatStWpVgGyHuoqIiEjesoPCRvbJRqNGjQBISUkhMTGRcuXKceHCBUqUKMHmzZvzJEARERGxb9k2o2zbto1t27bRuHFj/vvf/xoPPz+/vIpPREREsuHgYMrxI89jtGahM2fOUK5cOQDKli3LuXPnbBqUiIiIFBxW9dn4y1/+wltvvYWfnx/R0dHUrVvX1nGJiIiIFex+NMru3bsBePfdd2nTpg03b97kxRdfZOTIkXkSnIiIiNi/bJONadOmkZCQwGuvvUbDhg3p2bMnjRs3JjlZd/QTERF5FJhMphw/8lq2zSgNGzakffv2nD9/nlatWpFxg1iTycSmTZvyJEARERGxb1bdYn7u3LkMHDgwxxvXLeYlt+kW85KbdIt5sYW8vsV83JVrOV6neAlvG0Ryb1aNRunQoQNDhgyhTZs2DBw4kLNnz9o6LhERESkgrEo23n33XV566SVWrlzJyy+/zOjRo20dl4iIiFjBHvpsWJVsJCUlERQURNGiRWnRogVpaWm2jktEREQKCKuSjbS0NI4cOQJg/CsiIiL5z8FkyvEjr1l1Ua93332X0aNHExsbS5kyZZg0aZKt4xIREZECwqpkY/v27axZs8bWsYiIiEgO2cMgPauaUbZs2aJ+GiIiIvJArKpsxMXF0bhxYypUqGD0ZF21apWtYxMREZECwKpkY968ebaOQ0RERB5AfgxlzSmrko21a9feMW3QoEG5HoyIiIgUPFYlG6VKlQLAYrHw888/k56ebtOgRERExDr2cIt5q5KN4ODgLM/79u1rk2BERESk4LEq2Th58qTxd2xsLOfOnbNZQCIiImI9OyhsWJdsjB071vjb1dWVkSNH2iwgERERKVisSjaWLl1KXFwcMTExVKhQgRIlStg6LhEREbGCPVQ2rLqo17fffktwcDDz5s2jS5cufPXVV7aOS0RERAoIqyobS5YsITw8HA8PD+Lj4+nZsycvvfSSrWMTERGR+zDx6Jc2rKpsmEwmPDw8APD09MTV1dWmQYmIiEjBYVVlo2LFikydOpV69eqxZ88eKlasaOu4RERExAoFps/G5MmT8fHxYfv27fj4+OgW8yIiImI1q5KNxMRESpYsiZ+fHx4eHnzzzTe2jktEREQKCKuaUQYOHEj58uWNy5bbw01fRERE5NFgVbJhsViYMmWKrWMRERGRHLKHn//ZNqMkJyeTnJyMj48PUVFRxvPk5OS8ik9ERETsXLaVjVatWmEymbBYLOzYscP422QysWnTpryKUUREROxYtsnG5s2bs1151apVd9wRVkRERCQzk8VisTzoyj169CA0NDQ34xEREZECxqqhr/fyEHmKiIiIFBIPlWxoCKyIiIjcz0MlGyIiIiL3o2YUERERsSmrko1PPvkky/OZM2cC8NZbb+V+RCIiIlKgZDsa5csvv2T16tWcOHGCKlWqAJCWlkZqaipr167NsyBFRETEfmWbbCQnJ3Px4kXmz59P//79AXBwcKBkyZK4uLjkWZAiIiJiv7JtRnFxcaFChQqcPXuW8uXLU758ecqVK6dEQ0QknyxbtszqZZOSkvjyyy+zXSYwMJCkpKSHDeu+Zs2aRYcOHdi5c2eW6Q0bNrT5viX/WdVnw8vLi40bN3LixAlOnjzJyZMnbR3XIyMpKYnAwMCH2kZYWBgpKSn88ssvzJkzJ5ciE3uVcU699957/PHHH/dc5n5fErZw5swZOnfunOf7Fet9+umnVi8bGxubL+fR3fzf//0foaGh1K9fP79DkXxg1V1fr1y5wueff248N5lMunJoDsyfP5/27dtTvXp1qlevnt/hyCPinXfeuee8jC+JV155JQ8jkkfNyZMnGTVqFE5OTjg6OtKgQQOuXbvGuHHjGDFiBO+88w43btwgLi6OV155hVdffZXu3btTvHhxrl+/ToUKFTh+/Dhz5sxh0KBB99zPuHHjOHPmDABz5szB3d2d0aNHExMTQ1paGv/4xz948cUX6d69O+PGjaNy5cqsXLmSS5cu8fLLL/PGG29QrFgxmjRpQsOGDZk4cSKOjo64uroyceJEwsPDOX/+PK+//joLFy5k0qRJHD9+HB8fH+PGnkePHmXq1Kmkp6dz/fp1xowZQ0JCAl988QUff/wxAMHBwXz88ceUKVPG9i++5Cqrko2lS5dmeV7Q7/pqNpsZMWIE169fp2LFigBW/SerXbu2Ubm4efMm06ZNY8+ePcTGxjJs2DB69uzJqlWrmDVrFuvXr+fzzz/HxcWFSpUqMWHCBL7++mu2bNnCzZs3+f333+nXrx8dOnS4a4xJSUkMHTqU+Ph4bt68yVtvvUX9+vX59ttvWbJkCQ4ODtStW5cRI0Zw+fJlQkJCuHHjBhaLhWnTplGpUqW8ejmF7M+pq1evMm3aNJycnChatCgzZsxg3rx5xpdEp06dGDduHElJSVy9epWBAwfSokUL2rZty7PPPsuRI0cwmUx88skneHh4MGnSJA4cOEBKSgqDBw+mRYsWzJw5k927d2OxWOjVqxetW7fONt60tDRCQkKoWrUqL774IsOGDaNcuXKcOXOGNm3acOzYMX7++WeaNWvG8OHD8+IlLJS2b9/O008/TUhICHv27KFkyZIsW7aMcePGcfjwYdq0acNf//pXLly4QPfu3Xn11VcBaNu2LS1btuTMmTMcPXo020QDoGPHjtSrV4+QkBAiIyO5cuUKxYsXZ/r06cTHx9OhQwcaNGhwz/VjY2NZs2YNLi4udOjQgffee4/q1auzceNGpk6dyscff0x4eDiLFi1i+/btJCUl8cUXX/DHH3/w3//+F4Djx4/z9ttv4+vry9dff014eDgTJ05k0qRJXLt2jdjYWIoXL65Ew05ZlWysWrWKxYsXk5qaisViwdnZ2ThBCqK1a9dSrVo1hg0bxv79++9oY8ws83+y5cuXM336dMqWLcu8efPYsGEDb7zxBp9++imzZs0iOjoagLi4OGbPns3atWvx9PRk8uTJhIWF4e7uTnx8PJ999hmnTp2if//+90w2fv/9dy5dusSSJUu4fPkyp06d4urVq8yePZs1i/almgAAB0tJREFUa9bg5ubGW2+9RWRkJD/88AOBgYF07dqVn376iQMHDijZyGPZnVMbN26kZcuW9OnTh82bN3P9+nX69+9vfEls376df/zjH9SvX599+/Yxe/ZsWrRogdlspk2bNrz77ru8+eabRERE4OLiQlxcHKtXryY2NpZly5bh7OzMmTNnWLVqFUlJSXTu3JmGDRtStGjRu8aamprKiBEjqFevHt26dePMmTPExMSwaNEibt68SVBQEBEREbi5udG8eXMlGzbUqVMnFi5cSN++ffHy8mLYsGHGvFKlSvH555/z3Xff4enpSWpqqjHvySefzNF+atasaWzz5s2bnDhxgueffx4AT09PKleuTExMTJZ1Mo8tqFChgtGX7+LFi0YF95lnnjEulZDh2LFj+Pn5AfD4449Trlw5AMqUKcMnn3xCkSJFMJvNeHp6YjKZaNeuHd988w1nzpyhU6dOOToueXRY1Wfjiy++YOnSpTRp0oQpU6ZQuXJlW8eVr44dO0atWrUAqF27Nk5OWXOye/0nK1u2LO+99x4hISHs3Lkzy3/+zGJiYqhSpQqenp7Arf+Qx44dA+Cpp54CoFy5ctlWkKpWrUq3bt0YPnw448ePJz09nd9//50rV67w2muv0b17d06cOEFMTAwnT54kICAAgOeee4527do9yMsiDyG7c6p///5cuXKFnj17smHDhjvOt9KlSxMWFsZbb73FqlWrspxXNWrUAG6dL0lJSf/f3v2FNPXGcRx/OxlGUZiKNJoSG3VRSUKjEVRSerERJBEKWzoSIyJLKEaWRMTMpRlkF1FZDNYQ1KuyAi+qu6i0qyJJIZLuLDIrlcCtfhex83P5p2k/6yd8Xldn5+zwPDvnPM/zfZ7znDPevHlDfn6+sd/Ro0fp7+/n5cuXlJeXs3//fqLR6LRzRQD6+vr48OEDY2NjxrqcnByWLl3KsmXLyMrKIj09nbS0NP1lwTx78OABGzduJBwO43K5uHHjhlH/hEIh8vPzuXDhAi6XK6Feip8Xk8nEt2/ffpnOz+fRbrfz7NkzAEZGRujv7zfquvfv3wPQ29trfN9k+rcpyc7O5tWrVwD09PRM6tjYbDaj4zU4OMjg4CAA9fX1VFdX09jYyJo1a4zfs2fPHrq6uujp6aGgoOCXv0X+n5IKNuJDV6OjozidTj59+jTf+fqrJhaG3t5eotFoUoXs1KlTBINBGhoayM7ONgpLSkpKQoG3Wq28fv3aqMy7u7uNnkiylXdfXx+jo6O0tLTQ0NBAXV0dVqsVi8VCKBQiEolQVlbGhg0bsNvtvHjxAvhR+JuamuZ6aGSOprqm4u7cucPu3buJRCKsXr2ajo6OhEbi0qVLFBcX09TUhNPpnLJRmZhO/Fx/+fKFyspKbDYbTqeTSCRCOBzG7XZjtVqnzeu6detoaWmhs7PTaDQUVPwd69evp7m5Ga/XS1tbG2VlZdjtdvx+P9u3b+fmzZt4PB7C4TCpqamTOiiZmZmMj4/PusyXlpYyPDyMx+PB5/Nx+PBhMjMz8fl8BAIBKisricViU+579uxZ6urq8Hq9hMNhamtrE7YXFRWxYsUKSkpKCAaDLF++HIBdu3Zx6NAhvF4vAwMDvHv3DvjRiVuyZAmbN2+eFIjLwpHUmYs/jZKSkkJbWxtDQ0Pzna+/au/evZw8eRKPx4PNZsNsNhuFzGKxTHvPsLi4mNLSUqP3Fy8sDoeDAwcOUFVVBUBGRgZHjhzB5/NhMpnIzc3F7/dz7969pPO4atUqLl++zK1btzCbzVRXV5ORkcG+ffsoLy8nFouxcuVK3G43Bw8epLa2ls7OTgCCweBvHiGZramuqbi8vDxOnDjB4sWLMZvNBAKBhEbC5XJRX1/PtWvXsFgsfPz4cdp0CgsLefz4MR6Ph1gsRlVVFdu2baO7uxuv18vY2BhFRUXGqNp0Fi1axJkzZ6ipqeHixYv/2XGQ2cnNzaW9vT1h3cQ5dF1dXZP2mbg9LS2N27dvz5jGw4cPjWW/328sNzY2TvpuQUHBlKMLHR0dxvLatWtpbW2dMZ2amppJ2ysqKqioqJgyj9+/f9ctlAVuxpd6xY2MjPD27VuysrIIhULs2LGDTZs2/Yn8iYjIb3r+/PmUoxtut9uYVPp/9PXrV7xeL1u3bk2YryILT1LBRiAQ4PTp08bn48ePc/78+XnNmPzQ3t7O3bt3J60/duyYMQ9DZLZ0XYnInzRjsNHa2sqVK1cYHh4mPT3dWG+32xPeuyEiIiIynaRGNq5evUphYSGpqalcv34dn8+nl1OJiIhIUpJ6GuXp06cMDQ3R3NzMli1bNMFQREREkpZUsBGNRnE4HHz+/JmdO3cm9dy2iIiICCQZbIyPj3Pu3DkcDgdPnjyZ9vlqERERkZ8lNWdjYGCAR48eUVJSwv3798nLyyMnJ+dP5E9EREQWuKSCDREREZG5Suo2ioiIiMhcKdgQERGReaVgQ0REROaVgg0RERGZV/8Ad647+eHh8hAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Numeric correlation plot\n",
    "\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "sb.heatmap(df_Q1_2020_clean[numeric_vars].corr(), annot = True, fmt = '.3f', cmap = 'vlag_r', center = 0)\n",
    "plt.title(\"Baywheels Trips Numeric Correlation\", y=1.05, fontsize=16, fontweight='bold')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: no clear correlation among dataset numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trip duration distribution vary between customers and subscribers\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(7,10))\n",
    "\n",
    "sb.violinplot(data=df_Q1_2020_clean[df_Q1_2020_clean['duration_minute']<=120], x='user_type', y='duration_minute', color=base_color, inner='quartile');\n",
    "plt.xlabel('User Type');\n",
    "plt.ylabel('Trip Duration in Minute');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: The trip duration distribution is much narrower for subscribers and shorter trip as welll. subscribers seem to have a more specific usage or targeted goal riding the bikes compared to customers who vary more and generally rented the bikes for longer time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trip distance distribution vary between customers and subscribers\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(7,10))\n",
    "\n",
    "sb.violinplot(data=df_Q1_2020_clean[df_Q1_2020_clean['distance_km']<=10], x='user_type', y='distance_km', color=base_color, inner='quartile');\n",
    "plt.xlabel('User Type');\n",
    "plt.ylabel('Trip Distance in Kilometer');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: subscribers take more short trips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Customers vs Subscribers for trip rides\n",
    "\n",
    "customer_count = df_Q1_2020_clean.query('user_type == \"Customer\"')['bike_id'].count()\n",
    "subscriber_count = df_Q1_2020_clean.query('user_type == \"Subscriber\"')['bike_id'].count()\n",
    "customer_prop = customer_count / df_Q1_2020_clean['bike_id'].count()\n",
    "subscriber_prop = subscriber_count / df_Q1_2020_clean['bike_id'].count()\n",
    "\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(5,10))\n",
    "\n",
    "labels = ['Customer', 'Subscriber']\n",
    "size = [customer_prop, subscriber_prop]\n",
    "explode = (0, 0.3)\n",
    "\n",
    "plt.pie(size, explode=explode, labels = labels, autopct='%1.1f%%', shadow=False, startangle=90, textprops={'color':'black'})\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.suptitle('Ford GoBike System - Customers vs. Subscribers', y=1.05, fontsize=18, fontweight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# App vs Dipper for trip rides\n",
    "\n",
    "app_count = df_Q1_2020_clean.query('rental_access_method == \"app\"')['bike_id'].count()\n",
    "dipper_count = df_Q1_2020_clean.query('rental_access_method == \"clipper\"')['bike_id'].count()\n",
    "app_prop = app_count / df_Q1_2020_clean['bike_id'].count()\n",
    "dipper_prop = dipper_count / df_Q1_2020_clean['bike_id'].count()\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(5,10))\n",
    "\n",
    "labels = ['App', 'clipper']\n",
    "size = [app_prop, dipper_prop]\n",
    "explode = (0, 0.3)\n",
    "\n",
    "plt.pie(size, explode=explode, labels = labels, autopct='%1.1f%%', shadow=False, startangle=90, textprops={'color':'black'})\n",
    "plt.axis('equal')\n",
    "\n",
    "plt.suptitle('Ford GoBike System - App vs. clipper', y=1.05, fontsize=18, fontweight='bold');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average Trip Duration on Weekdays\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "sb.barplot(data=df_Q1_2020_clean, x='start_dayofweek', y='duration_minute', color=base_color);\n",
    "plt.xlabel('Day of Week');\n",
    "plt.ylabel('Avg. Trip Duration in Minute');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: The riding trips are much shorter on workdays compared to weekends."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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woTw8PPTMM8/oo48+0rhx46zrpc+nc+fOatu2rf79739rx44d5x17oXkqPfTQQ3rttde0atUqRUdH6+GHHz7v/Y8cOVItW7bU1KlT5e3trcWLF6tp06ZKTEzUDz/8oFmzZunaa6+1uU2XLl307LPPqqCgQGPHjtWXX36ppKQk6/KE6kREROjOO+/Uhg0b9MILL+iWW26RpGrfK3PmzFF4eLhmzpxpXVZz9rIHX19fvfjii3Jzc9P48eP1xhtv6IEHHlBsbKyk32b9+/fvL0k2a3unTp2q0NBQTZ8+XSkpKQoNDVVqaqok6S9/+YuuuOIKvfDCC3J1db2g5+RSdOnSRTk5OXr11VfVt29fPf3003J3d9drr72mG264QSkpKcrPz1dycrI6duxY4/1d6s/Kxb7H/qh379667rrrtHjxYuvrOHDgQElSSEiI9cOelTp27KitW7dqxowZuv322zV16lRJF/ZaA7CPwVLT35ABOKUff/xRe/bsUXJysq688kp99NFHdi2duBhvvPGGduzYoS5duui6667T/Pnz9c033yg3N1f+/v4OyYC6d+TIEe3Zs0dTpkxRaWmpPv/882qXzTjSgQMHdOedd+rhhx8+5wwezm7r1q1as2aNVq9erfT0dN13333W64KDg3XnnXdaz/gCwLFYUw00Ulu3btVzzz2n66+/XtOnT3dYoZakXr16acOGDXrppZd04sQJXX/99Zo5cyaFupHZu3evxowZoxYtWuj5559vEIW6sUtOTtavv/6q6Oho618IADQMzFQDAAAAdmJNNQAAAGAnSjUAAABgJ0o1AAAAYCdKNQAAAGAnSjUAAABgJ0o1AAAAYCdKNQAAAGAnSjUAAABgJ0o1AAAAYCdKNQAAAGAnSjUAAABgJ0o1AAAAYCdKNQAAAGAnSjUAAABgJ0o1AAAAYCdKNQAAAGAnSjUAAABgJ0o1AAAAYCdKNRq9//u//9MTTzyh2NhYDR48WHPmzJHFYrng2x87dkzvvvtuHSYEgMZv4cKFGjFihEaOHKlRo0bpm2++qXLcgQMHNHTo0Fp5zJ9//lmTJ0+WJPXu3Vvl5eW1cr9AVdzqOwBQl44fP66nn35ac+fO1Y033iiTyaSnnnpKy5cv1/Dhwy/oPvbs2aN169ZpwIABdZwWABqnb7/9VuvWrdOyZctkMBi0a9cuJSYmas2aNXX6uC1atLCWaqCuGSwXM2UHOJm33npLBQUFSklJsW4zGo368ssvlZOTo5deekmS1K1bN23evFmffPKJFi1aJDc3N1133XV64YUXNGrUKO3evVvx8fHq1q2bJk6cqDNnzshgMCglJUU333yz7rrrLoWEhKiwsFBdunRRaWmpdu7cqcDAQKWnp6uoqEipqakqLy+Xh4eHpk6dKpPJpNGjR8vPz0+RkZF6/PHH6+tpAoA6VVxcrIEDByouLk6RkZH605/+pNOnT2vUqFGaPHmybrrpJi1btkxHjx7V/fffrxEjRqh169YqLi5Wjx49NGbMmCqPzyUlJUpKSlJpaaksFotmzZqld999V19++aVOnDih6dOna8KECVq5cqV69+6t22+/XQcPHtRVV12lWbNmydXVVZMmTVJhYaHMZrPi4+PVuXNn9e/fXzfeeKPc3d2VkZFR308fnAQz1WjUfvrpJ11//fU227y8vNSkSZMqx7/33nsaMWKE7r33Xr399tsqKyvT3/72Ny1fvlzR0dEaO3asYmNj1adPH+3atUvJyclavXq1Dh48qDfffFMtWrRQeHi4Vq1apdTUVN155506fvy4Zs2apdjYWPXo0UNbt27Viy++qISEBP3888/KycmRu7u7I54OAKgX/v7+evXVV5Wdna1XXnlFTZs2VUJCQrXjT5w4ofT0dHl6eurBBx/UnXfeWeXx+dVXX1Xv3r01fPhwbd26VTt37pQkBQUFKSUlRQcOHLC53+HDh6tjx4564YUXtHLlSrm5uenKK6/U888/r5KSEj300EN6//33deLECf39739Xu3bt6vR5QeNCqUajFhAQoP/9738223788Udt27bNZlvlH2wmTJigBQsWaNmyZQoKClKfPn1sxu3bt0+33367JOmWW27R4cOHJUl+fn4KCAiQJHl6eqp169aSJB8fH5WXl2vv3r1asGCBXnvtNVksFmupb9myJYUaQKNXWFgob29vzZgxQ5L03//+V0888YSaN29uHfPHP5zffPPN8vHxkSTdeuut+v7776s8Pn///fcaMmSIJKlr166SpLlz5yowMPCcDE2aNFHHjh0lSaGhodq8ebMsFot27NhhLeNnzpxRSUmJJFV5H8D58EFFNGq9evXSxo0b9cMPP0iSKioqNHPmTLm5uennn3+WJB08eFC//vqrJGnFihWKi4tTdna2JOnTTz+Vi4uLzGazJOmmm27S9u3bJUm7du2y/g/BYDCcN0dQUJCeeeYZZWVlacqUKYqKipIkubjwIwig8duzZ48mT55s/aBgYGCgfHx85OfnZz0W/3ECZN++fTIajTpz5ox27typNm3aVHl8vummm/Tf//5XkrRt2zalp6dLqvrYWlFRoV27dkmStm/frjZt2igoKEj33nuvsrKytGjRIvXt21fNmjWr9j6A82GmGo2at7e3Zs6cqZSUFFksFhmNRvXq1UujRo3S119/rQceeEA33XSTWrZsKUnq0KGDHn30Ufn5+cnLy0s9e/bU6dOntXfvXr3xxhsaP368UlNT9frrr+vMmTOaPn36BeVITEy0/g/l1KlTmjhxYl3uNgA0KHfffbf27dunBx54QJ6enrJYLBo/fryaNGmi5557Ttdee62uvvpq6/hmzZopISFBxcXF6tevn1q3bl3l8blHjx5KTk62fuDx+eef19tvv11lhiZNmigrK0uFhYUKCAjQuHHjZLFYlJKSooceekhlZWWKiYmhTOOS8UFFAAAAwE78OgYAAADYiVINAAAA2IlSDQAAANiJUg0AAADYqVGc/eP06TP69deT9R0DAGpdixY+9R3hknBcBtBYVXdcbhQz1TWdIxgALidff/21YmNjbba9++67io6Otl5euXKlBg0apKFDh+rzzz+X9NtXSY8cOVIxMTGKj4/XyZMnqx1bE47LAC43jWKmGgDwm0WLFmnNmjW64oorrNt27dqlf//739ZvrPv555+VlZWlnJwclZeXKyYmRt26ddO8efPUv39/DRo0SAsXLtSKFSusX4xx9li+CRQAbDWKmWoAwG9atWqluXPnWi+XlJToxRdfVHJysnXbzp07FRISInd3d/n4+KhVq1bavXu3duzYoe7du0uSIiMjtWXLlmrHAgBsMVMNAI1IVFSUDhw4IEkymUyaOHGikpOT5eHhYR1TVlYmH5/f1wR6eXmprKzMZruXl5dKS0urHVsTV1eD/Pw8a2u3AKDBo1QDQCNVUFCgwsJCTZ48WeXl5fr22281ffp0denSRUaj0TrOaDTKx8dH3t7eMhqNatq0qYxGo3x9fa3bzh5bE5PJomPHTtTJfgFAfWrUH1QEAJyrQ4cOev/995WVlaWMjAy1bt1aEydOVIcOHbRjxw6Vl5ertLRU+/btU9u2bRUaGqoNGzZIknJzc9WpU6dqxwIAbDFTDQCXmRYtWig2NlYxMTGyWCxKSEiQh4eHRo8ercTERK1cuVJXXnmlZs+eLU9PzyrHAgBsGSyVHwd3YhUVJv7MCKBRctbzVHNcBtBYsfwDAAAAqCOUagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpynGsBlzcu3qTw9mtR3DKsT5RUyHj9V3zHghHgvA/WLUg3gsubp0UTXjF1c3zGsDs8ZJaMoIrh4vJeB+sXyDwAAAMBOlGoAAADATnVWqr/++mvFxsZKkgoLCzV8+HDFxMRo0qRJMpvNkqTMzEwNGTJEw4YN086dO887FgAAAGio6qRUL1q0SCkpKSovL5ckzZgxQ/Hx8Vq6dKksFovWrl2rgoIC5efna9WqVcrIyNCUKVOqHQsAAAA0ZHVSqlu1aqW5c+daLxcUFCg8PFySFBkZqS1btmjHjh2KiIiQwWBQQECATCaTiouLqxwLAAAANGR1cvaPqKgoHThwwHrZYrHIYDBIkry8vFRaWqqysjL5+flZx1Rur2psTVxdDfLz86zlvQCA+sHxrGFoSKeo4/R0QMPnkFPqubj8PiFuNBrl6+srb29vGY1Gm+0+Pj5Vjq2JyWTRsWMnajc0gMtCixY+9R3hHH88njXEfJeLhnSKOk5PBzR8Djn7R7t27ZSXlydJys3NVVhYmEJDQ7Vp0yaZzWYdOnRIZrNZ/v7+VY4FAAAAGjKHzFQnJiYqNTVVGRkZCgoKUlRUlFxdXRUWFqbo6GiZzWalpaVVOxYAAABoyOqsVLds2VIrV66UJAUGBio7O/ucMXFxcYqLi7PZVt1YAAAAoKHiy18AAAAAO1GqAQAAADtRqgEAAAA7OeSDigAAAGfjXOBoTCjVAACgXnAucDQmLP8AAAAA7ESpBgAAAOxEqQYAAADsRKkGAAAA7ESpBgAAAOzE2T8AAAAuQEM6BaDEaQAbGko1AADABWhIpwCUOA1gQ0OpBlCrGtJMDrM4AABHoVQDqFUNaSaHWRwAgKPwQUUAAADATsxUAwDqXENaFiSxNAhA7aNUA0Aj8/XXX+vFF19UVlaWdu3apalTp8rV1VXu7u6aNWuWmjdvrpUrV2r58uVyc3PT6NGj1atXLxUXF+uZZ57RqVOndPXVV2vGjBm64oorqhx7sRrSsiCJpUEAah+lGgAakUWLFmnNmjW64oorJEnTp09XamqqbrnlFi1fvlyLFi3SY489pqysLOXk5Ki8vFwxMTHq1q2b5s2bp/79+2vQoEFauHChVqxYoXvvvbfKse7u7vW8pwDQsLCmGgAakVatWmnu3LnWyxkZGbrlllskSSaTSR4eHtq5c6dCQkLk7u4uHx8ftWrVSrt379aOHTvUvXt3SVJkZKS2bNlS7VgAgC1mqgGgEYmKitKBAwesl6+++mpJ0hdffKHs7GwtWbJEGzdulI+Pj3WMl5eXysrKVFZWZt3u5eWl0tJSm21/HFsTV1eD/Pw8a2u36kRDz3c2Z8srOV9mZ8srOWfmxopSDQCN3AcffKBXX31VCxculL+/v7y9vWU0Gq3XG41G+fj4WLc3bdpURqNRvr6+1Y6ticlk0bFjJ6yXW7So+TaO9sd8VWlomZ0tr+R8mZ0tr1RzZtS+6t4HLP8AgEbsnXfeUXZ2trKysnT99ddLkjp06KAdO3aovLxcpaWl2rdvn9q2bavQ0FBt2LBBkpSbm6tOnTpVOxYAYIuZagBopEwmk6ZPn65rr71WcXFxkqTbb79dY8eOVWxsrGJiYmSxWJSQkCAPDw+NHj1aiYmJWrlypa688krNnj1bnp6eVY4FANiiVANAI9OyZUutXLlSkpSfn1/lmKFDh2ro0KE225o3b67Fi8897V1VYwEAtlj+AQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANjJrb4DAKiel29TeXo0qe8YVifKK2Q8fqq+YwAA0OBQqoEGzNOjia4Zu7i+Y1gdnjNKRlGqAQA4G8s/AAAAADtRqgEAAAA7OWz5R0VFhZKSknTw4EG5uLho6tSpcnNzU1JSkgwGg9q0aaNJkybJxcVFmZmZWr9+vdzc3JScnKwOHTo4KiYAAABw0RxWqjds2KAzZ85o+fLl2rx5s15++WVVVFQoPj5enTt3VlpamtauXauAgADl5+dr1apVKioqUlxcnHJychwVEwAAALhoDlv+ERgYKJPJJLPZrLKyMrm5uamgoEDh4eGSpMjISG3ZskU7duxQRESEDAaDAgICZDKZVFxc7KiYAAAAwEVz2Ey1p6enDh48qHvuuUclJSWaP3++tm3bJoPBIEny8vJSaWmpysrK5OfnZ71d5XZ/f/9q79vV1SA/P8863+l28mMAACAASURBVAcAcrqfNWfLKzlnZgC43DmsVL/xxhuKiIjQuHHjVFRUpEceeUQVFRXW641Go3x9feXt7S2j0Wiz3cfH57z3bTJZdOzYiTrLDtSXFi3O/96vDzX9rDW0zM6WV7LN3BDzAQDO5bDlH76+vtZy3KxZM505c0bt2rVTXl6eJCk3N1dhYWEKDQ3Vpk2bZDabdejQIZnN5vPOUgMAAAD1zWEz1SNGjFBycrJiYmJUUVGhhIQEtW/fXqmpqcrIyFBQUJCioqLk6uqqsLAwRUdHy2w2Ky0tzVERAQAAgEvisFLt5eWlf/zjH+dsz87OPmdbXFyc4uLiHBELAAAAsBtf/gIAAADYiVINAAAA2IlSDQAAANiJUg0AjczXX3+t2NhYSVJhYaGGDx+umJgYTZo0SWazWZKUmZmpIUOGaNiwYdq5c+dFjwUA2KJUA0AjsmjRIqWkpKi8vFySNGPGDMXHx2vp0qWyWCxau3atCgoKlJ+fr1WrVikjI0NTpky56LEAAFuUagBoRFq1aqW5c+daLxcUFCg8PFySFBkZqS1btmjHjh2KiIiQwWBQQECATCaTiouLL2osAMCWw06pBwCoe1FRUTpw4ID1ssVikcFgkPTbqU1LS0tVVlYmPz8/65jK7RcztqYv5XJ1NTT4r1tv6PnO5mx5JefL7Gx5JefM3FhRqnHJvHybytOjSX3HsDpRXiHj8VP1HQNoUFxcfv+DpNFolK+vr7y9vWU0Gm22+/j4XNTYmphMlgb/devO9hX2zpZXcr7MzpZXqjkzal917wNKNS6Zp0cTXTN2cX3HsDo8Z5SMolQDf9SuXTvl5eWpc+fOys3NVZcuXdSqVSulp6dr1KhROnz4sMxms/z9/S9qLADAFqUaABqxxMREpaamKiMjQ0FBQYqKipKrq6vCwsIUHR0ts9mstLS0ix4LALBFqQaARqZly5ZauXKlJCkwMFDZ2dnnjImLi1NcXJzNtosZCwCwxdk/AAAAADtRqgEAAAA7UaoBAAAAO1GqAQAAADvxQUVcNjivNgAAqCuUalw2OK82AACoK5TqBoJZVAAAAOdFqW4gmEUFAABwXnxQEQAAALATpRoAAACwU6Nd/tGQ1iizPhkAAKBxa7SluiGtUWZ9MgAAQOPG8g8AAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOF1WqzWZzXeUAAAAAnFaNpfrDDz/U+++/r7feekvdunXT4sUN46u/AQAAgIaixlL9+uuv64477tCaNWu0YcMGff75547IBQAAADiNGku1h4eHJMnLy0vu7u4yGo11HgoAAABwJjWW6pYtW2rw4MEaPHiwMjMz1aFDB0fkAgAAAJyGW00DZs6cKaPRKC8vL916661q3ry5I3IBAAAATqPGmeovvvhCMTExioiI0BNPPKFdu3Y5IhcAAADgNGqcqZ42bZpmz56t1q1ba+/evUpLS9Py5csdkQ0AAABwCjXOVPv4+Kh169aSpLZt26pp06Z1HgoAAABwJjXOVF911VWaOHGiunTpooKCApnNZq1YsUKSFB0dXecBAQAAgIauxlIdFBQkSSosLJS3t7fCw8P1888/13kwAAAAwFlUW6oPHz6sa665Rvfee+851wUGBtZpKAAAAMCZVFuq//nPf2rChAlKS0uTwWCQJFksFhkMBv3rX/9yWEAAAACgoau2VE+YMEGS1KNHDz322GMOCwQAqF0VFRVKSkrSwYMH5eLioqlTp8rNzU1JSUkyGAxq06aNJk2aJBcXF2VmZmr9+vVyc3NTcnKyOnTooMLCwirHAgB+V+NRMTc3VyaTyRFZAAB1YMOGDTpz5oyWL1+uMWPG6OWXX9aMGTMUHx+vpUuXymKxaO3atSooKFB+fr5WrVqljIwMTZkyRZKqHAsAsFXjBxVLSkrUvXt3tWzZUgaDQQaD4ZLPU71gwQKtW7dOFRUVGj58uMLDwy94pgQAcGkCAwNlMplkNptVVlYmNzc3ffXVVwoPD5ckRUZGavPmzQoMDFRERIQMBoMCAgJkMplUXFysgoKCc8bedddd9blLANDgVFuqN2zYoB49emj+/Pm18kB5eXn68ssvtWzZMp08eVKvv/66dfajc+fOSktL09q1axUQEGCdKSkqKlJcXJxycnJqJQMAXI48PT118OBB3XPPPSopKdH8+fO1bds26+dlvLy8VFpaqrKyMvn5+VlvV7m98vM0f9xWE1dXg/z8POtmh2pJQ893NmfLKzlfZmfLKzln5saq2lK9ePFi9ejRQ9ddd12tPNCmTZvUtm1bjRkzRmVlZRo/frxWrlx5wTMl/v7+tZIDAC43b7zxhiIiIjRu3DgVFRXpkUceUUVFhfV6o9EoX19feXt7y2g02mz38fGxWT9dObYmJpNFx46dsF5u0cKnlvam9vwxX1UaWmZnyys5X2ZnyyvVnBm1r7r3QbWl2mKxqKKiQhaL5Zzr3N3dLzpASUmJDh06pPnz5+vAgQMaPXp0lbMf1c2UnK9UMyNSN8hc95wtr+R8mZ0tr1T7mX19fdWkSRNJUrNmzXTmzBm1a9dOeXl56ty5s3Jzc9WlSxe1atVK6enpGjVqlA4fPiyz2Sx/f/8qxwIAbFVbqr/++mv17dvXWqoNBoO1BF/Kh1T8/PwUFBQkd3d3BQUFycPDQ4cPH7ZeX9NMyfmcPSMiNbzfJhvjb7/OltnZ8kpkrg3OlldSrc/wjhgxQsnJyYqJiVFFRYUSEhLUvn17paamKiMjQ0FBQYqKipKrq6vCwsIUHR0ts9mstLQ0SVJiYuI5YwEAtqot1bfddpuysrJq7YE6deqkf/3rX3r00Uf1008/6eTJk+ratesFz5QAAC6Nl5eX/vGPf5yzPTs7+5xtcXFxiouLs9kWGBhY5VgAwO9qPPtHbenVq5e2bdumIUOGyGKxKC0tTS1btrzgmRIAAACgoaq2VKemptb6g40fP/6cbRc6UwIAAAA0VNV++Uvbtm0dmQMAAABwWtWW6gs5DykAAACA85Tqv/3tb5KkSZMmOSwMAAAA4IyqXVPdtGlTDR48WIWFhdqzZ48kWU+pd6lfUw4AAAA0RtWW6kWLFumnn35SWlqaJk+eXOWXwAAAAAA4T6l2cXHRNddco3nz5mnFihX69ttvdeONN2r48OGOzAcAAAA0eNWuqa6UlpamH374Qd26ddPBgweVkpLiiFwAAACA06jxy18KCwu1ZMkSSVKfPn00bNiwOg8FAAAAOJMaZ6rLy8t18uRJSdKpU6dkMpnqPBQAAADgTGqcqX744Yf1l7/8RW3atNG3336rsWPHOiIXAAAA4DRqLNX33XefIiMj9eOPP6ply5a68sorHZELAAAAcBo1lmpJ8vPzk5+fX11nAQAAAJxSjWuqAQAAAJxfjaV68eLFjsgBAAAAOK0aS/WGDRs44wcAAABwHjWuqS4pKVH37t3VsmVLGQwGGQwGLV++3BHZAAAAAKdQY6meP3++I3IAAAAATqvGUu3m5qb09HSVlJQoKipKwcHBuu666xyRDQAAAHAKNa6pTk1N1eDBg3X69GmFhYVp+vTpjsgFAAAAOI0L+pryrl27ymAwKCgoSB4eHo7IBQAAADiNGku1u7u7Nm7cKLPZrK+++kru7u6OyAUAAAA4jRpL9dSpU7V69WqVlJTo9ddf1+TJkx0QCwAAAHAeNX5Q8ZprrtFf//pX7d+/X23atNH111/viFwAAACA06ixVM+bN08bN27UrbfeqjfeeEN9+/bViBEjHBANAAAAlxsv36by9GhS3zEkSSfKK2Q8fuqCxtZYqnNzc7V06VK5uLjozJkziomJoVQDAAA4gYZUUKULK6meHk10zdjFDkp0fofnjJJRtVSq/f39dfLkSXl5eamiokL+/v52BwQAAEDda0gFVbq4kupsqi3V0dHRMhgM+uWXX6xf+rJv3z75+fk5Mh8AAADQ4FVbqjMyMhyZAwAAAHBa1Zbqyq8i37lzp95//32Vl5dbr+O0egAAAMDvalxTnZiYqMcff1y+vr6OyAMAAAA4nRpL9Q033KBBgwY5IgsAAADglGos1VFRUUpISNBNN91k3fbkk0/WaSgAQO1asGCB1q1bp4qKCg0fPlzh4eFKSkqSwWBQmzZtNGnSJLm4uCgzM1Pr16+Xm5ubkpOT1aFDBxUWFlY5FgDwuxqPikuXLtUtt9yi5s2bW/8DADiPvLw8ffnll1q2bJmysrJ0+PBhzZgxQ/Hx8Vq6dKksFovWrl2rgoIC5efna9WqVcrIyNCUKVMkqcqxAABbNc5UN2vWTE888YQjsgAA6sCmTZvUtm1bjRkzRmVlZRo/frxWrlyp8PBwSVJkZKQ2b96swMBARUREyGAwKCAgQCaTScXFxSooKDhn7F133VWfuwQADU6NpfrKK69UWlqa2rVrJ4PBIOm3c1gDAJxDSUmJDh06pPnz5+vAgQMaPXq0LBaL9Zju5eWl0tJSlZWV2XwXQeX2qsbWxNXVID8/z7rZoVrS0POdzdnySs6X2dnySmR2hAvNe0EfVJSko0eP2pcIAFAv/Pz8FBQUJHd3dwUFBcnDw0OHDx+2Xm80GuXr6ytvb28ZjUab7T4+PjbrpyvH1sRksujYsRPWyy1a+NTS3tSeP+arSkPL7Gx5JefL7Gx5JTI7wtl5q8tX45rqQYMGnfMfAMB5dOrUSRs3bpTFYtGRI0d08uRJde3aVXl5eZKk3NxchYWFKTQ0VJs2bZLZbNahQ4dkNpvl7++vdu3anTMWAGCrxpnqhIQEGQwGmc1mHThwQDfccIOWLVvmiGwAgFrQq1cvbdu2TUOGDJHFYlFaWppatmyp1NRUZWRkKCgoSFFRUXJ1dVVYWJiio6NlNpuVlpYm6bfvKzh7LADAVo2lesWKFdZ/Hz9+3HqQBQA4j/Hjx5+zLTs7+5xtcXFxiouLs9kWGBhY5VgAwO8u6kSjPj4++uGHH+oqCwAAAOCUapypjo6OlsFgkMViUXFxsbp27eqIXAAAAIDTqLFUZ2RkWP/t4eHBl78AAAAAZ6m2VL/99tvV3mjgwIF1EgYAAABwRtWW6n379tlctlgsWr16tZo2bUqpBgAAAP6g2lI9btw4678LCwuVlJSknj17Kjk52SHBAAAAAGdR45rqJUuW6M0339SECRPUq1cvR2QCAAAAnEq1pfrIkSOaMGGCmjVrplWrVqlZs2aOzAUAAAA4jWpLdf/+/dWkSRN16dJFzz33nM11s2fPvuQH/OWXXzRo0CC9/vrrcnNzU1JSkgwGg9q0aaNJkybJxcVFmZmZWr9+vdzc3JScnKwOHTpc8uMBAAAAda3aUv3KK6/U+oNVVFQoLS1NTZs2lSTNmDFD8fHx6ty5s9LS0rR27VoFBAQoPz9fq1atUlFRkeLi4pSTk1PrWQAAAIDaUm2pDg8Pr/UHmzVrloYNG6aFCxdKkgoKCqyPExkZqc2bNyswMFAREREyGAwKCAiQyWRScXGx/P39az0PAAAAUBtq/KBibVm9erX8/f3VvXt3a6m2WCwyGAySJC8vL5WWlqqsrEx+fn7W21VuP1+pdnU1yM/Ps253wE4NPV9VyFz3nC2v5HyZnS2v5JyZAeBy57BSnZOTI4PBoK1bt2rXrl1KTExUcXGx9Xqj0ShfX195e3vLaDTabPfx8TnvfZtMFh07dsJmW4sW57+No52d72wNLa/U+DI7W16JzLXB2fJKtpkbYj4AwLlcHPVAS5YsUXZ2trKysnTLLbdo1qxZioyMVF5eniQpNzdXYWFhCg0N1aZNm2Q2m3Xo0CGZzWaWfgAAAKBBc9hMdVUSExOVmpqqjIwMBQUFKSoqSq6urgoLC1N0dLTMZrPS0tLqMyIAAABQo3op1VlZWdZ/Z2dnn3N9XFyc4uLiHBkJAAAAuGQOW/4BAAAANFaUagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqALhM/PLLL+rRo4f27dunwsJCDR8+XDExMZo0aZLMZrMkKTMzU0OGDNGwYcO0c+dOSap2LADgd5RqALgMVFRUKC0tTU2bNpUkzZgxQ/Hx8Vq6dKksFovWrl2rgoIC5efna9WqVcrIyNCUKVOqHQsAsEWpBoDLwKxZszRs2DBdffXVkqSCggKFh4dLkiIjI7Vlyxbt2LFDERERMhgMCggIkMlkUnFxcZVjAQC23Oo7AACgbq1evVr+/v7q3r27Fi5cKEmyWCwyGAySJC8vL5WWlqqsrEx+fn7W21Vur2psTVxdDfLz86yDvak9DT3f2Zwtr+R8mZ0tr0RmR7jQvJRqAGjkcnJyZDAYtHXrVu3atUuJiYkqLi62Xm80GuXr6ytvb28ZjUab7T4+PnJxcTlnbE1MJouOHTthvdyihU8t7U3t+WO+qjS0zM6WV3K+zM6WVyKzI5ydt7p8LP8AgEZuyZIlys7OVlZWlm655RbNmjVLkZGRysvLkyTl5uYqLCxMoaGh2rRpk8xmsw4dOiSz2Sx/f3+1a9funLEAAFvMVAPAZSgxMVGpqanKyMhQUFCQoqKi5OrqqrCwMEVHR8tsNistLa3asQAAW5RqALiMZGVlWf+dnZ19zvVxcXGKi4uz2RYYGFjlWADA71j+AQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2IlSDQAAANiJUg0AAADYiVINAAAA2MnNUQ9UUVGh5ORkHTx4UKdPn9bo0aPVunVrJSUlyWAwqE2bNpo0aZJcXFyUmZmp9evXy83NTcnJyerQoYOjYgIAAAAXzWGles2aNfLz81N6erpKSkp0//336+abb1Z8fLw6d+6stLQ0rV27VgEBAcrPz9eqVatUVFSkuLg45eTkOComAAAAcNEcVqr79u2rqKgo62VXV1cVFBQoPDxckhQZGanNmzcrMDBQERERMhgMCggIkMlkUnFxsfz9/R0VFQAAALgoDivVXl5ekqSysjKNHTtW8fHxmjVrlgwGg/X60tJSlZWVyc/Pz+Z2paWl5y3Vrq4G+fl51u0O2Kmh56sKmeues+WVnC+zs+WVnDMzAFzuHFaqJamoqEhjxoxRTEyMBgwYoPT0dOt1RqNRvr6+8vb2ltFotNnu4+Nz3vs1mSw6duyEzbYWLc5/G0c7O9/ZGlpeqfFldra8Eplrg7PllWwzN8R8AIBzOezsH0ePHtXIkSP17LPPasiQIZKkdu3aKS8vT5KUm5ursLAwhYaGatOmTTKbzTp06JDMZjNLPwAAANCgOWymev78+Tp+/LjmzZunefPmSZImTpyoadOmKSMjQ0FBQYqKipKrq6vCwsIUHR0ts9mstLQ0R0UEAAAALonDSnVKSopSUlLO2Z6dnX3Otri4OMXFxTkiFgAAAGA3vvwFAAAAsBOlGgAAALATpRoAAACwE6UaAAAAsBOlGgAAALCTQ7/8BQDgeBUVFUpOTtbBgwd1+vRpjR49Wq1bt1ZSUpIMBoPatGmjSZMmycXFRZmZmVq/fr3c3NyUnJysDh06qLCwsMqxAIDfcVQEgEZuzZo18vPz09KlS7Vo0SJNnTpVM2bMUHx8vJYuXSqLxaK1a9eqoKBA+fn5WrVqlTIyMjRlyhRJqnIsAMAWpRoAGrm+ffvqqaeesl52dXVVQUGBwsPDJUmRkZHasmWLduzYoYiICBkMBgUEBMhkMqm4uLjKsQAAWyz/AIBGzsvLS5JUVlamsWPHKj4+XrNmzZLBYLBeX1paqrKyMvn5+dncrrS0VBaL5ZyxNXF1NcjPz7MO9qb2NPR8Z3O2vJLzZXa2vBKZHeFC81KqAeAyUFRUpDFjxigmJkYDBgxQenq69Tqj0ShfX195e3vLaDTabPfx8bFZP105tiYmk0XHjp2wXm7RwqeW9qT2/DFfVRpaZmfLKzlfZmfLK5HZEc7OW10+ln8AQCN39OhRjRw5Us8++6yGDBkiSWrXrp3y8vIkSbm5uQoLC1NoaKg2bdoks9msQ4cOyWw2y9/fv8qxAABbzFQDQCM3f/58HT9+XPPmzdO8efMkSRMnTtS0adOUkZGhoKAgRUVFydXVVWFhYYqOjpbZbFZaWpokKTExUampqTZjAQC2KNUA0MilpKQoJSXlnO3Z2dnnbIuLi1NcXJzNtsDAwCrHAgB+x/IPAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE6UagAAAMBOlGoAAADATpRqAAAAwE5u9R2gKmazWZMnT9aePXvk7u6uadOm6YYbbqjvWABw2eK4DADn1yBnqj/77DOdPn1aK1as0Lhx4zRz5sz6jgQAlzWOywBwfg2yVO/YsUPdu3eXJHXs2FHffPNNPScCgMsbx2UAOD+DxWKx1HeIs02cOFF33323evToIUnq2bOnPvvsM7m5NcjVKgDQ6HFcBoDza5Az1d7e3jIajdbLZrOZAzcA1COOywBwfg2yVIeGhio3N1eS9NVXX6lt27b1nAgALm8clwHg/Brk8o/KT5nv3btXFotFzz//vG666ab6jgUAly2OywBwfg2yVAMAAADOpEEu/wAAAACcCaUaAAAAsFOjLdV5eXkKDg7WBx98YLN9wIABSkpKuuT7XbZsmebOnWtvPBszZ85UbGys+vbtq549eyo2NlZjx46ttfsvLy9X7969L+o2Dz/8sHbu3ClJOn36tDp16qTFixdbr3/ooYe0e/fu897Hvn37FBsbe/GBa5Cbm3tRr2FVz2+XLl2UkJBQ69kuJd/FOnDggEJDQxUbG2v9LzMz02ZMQkKCTp8+7dBckrRw4UKNGDFCI0eO1KhRo857LuMVK1aooqLikh6nW7dulxqxWheTvSZ19d53dhyXf3c5H5c5Jjsml+Tcx2TJ+Y7Ljfp8SEFBQXrvvffUr18/SdKePXt08uTJek51rsofqtWrV+u7777TM888U8+JpIiICG3fvl0dOnTQjh07FBERofXr12vUqFEqLy9XUVGRbr755vqOeUGqen7z8vK0fPnyek526Vq3bq2srKxqr3/ppZccmOY33377rdatW6dly5bJYDBo165dSkxM1Jo1a6ocv2DBAg0cONDBKat2sdlx6TguX7rGclzmmOwYznxMlpzzuNyoS/XNN9+s/fv36/jx4/L19dWaNWs0YMAAFRUVac2aNXrzzTfl7u6uG2+8Uc8995zeffddbdiwQadOndIPP/ygxx9/XIMGDdL27f/f3n0HRXH/fxx/3k3EAIeIgqgRFewmChZsKCEOZmyoEO7Egho1jjpBByxYRzQiEsTYBtRY0EOxxR41iZkIlhGwJcZYAAdLULAhKhHluN8fjPuTbw6D0QDC+/EfM9zua9/7ufftfXZv9xQLFizA2toatVqNi4sLAJGRkfz+++88efKERo0aERYWhp+fH1999RVNmjQhPj6eI0eOMGfOnNfOPm3aNHr37o27uzsJCQkcOHCAhQsXcvDgQWJiYlCr1bRr147Jkydz+vRpwsPDee+996hWrRqLFi1CpVIxefJkcnJyqF+/vrLcpKQk5Rv006dPCQ8PJykpifT0dIKDgzEYDAwYMIB58+bx7bffMnLkSOLj49FqtSxatIhHjx5x4cIFOnToYDJLVlYWkydPxmg0Ymdnp6zXy8uLDh06cPnyZVQqFVFRUVhZWREZGUlycjJGo5ERI0bQq1cvNm3axO7du1Gr1bRt25bg4GDS0tKYMWMG5ubmmJubY21tDUBsbCw//vgj+fn5WFlZsXz5cqZPn46XlxceHh6kpaURHh7O6tWr/1bja9euMXr0aO7fv88nn3xCQEAA/v7+hISE0KhRI+Li4rh79y7e3t6MGzeO6tWr4+7ujoWFRankex2JiYksWrSIKlWqoNPpWLZsGQcPHuTmzZullqtGjRpkZGSwY8cO3N3dadGiBTt27DA55k6dOsWdO3cIDAxk+PDhbNmyRfnQcXNz4/jx40ybNo3s7Gyys7OJjo4mIiKC1NRUHBwclBmfK1eusHDhQgoKCsjJyWHWrFnk5uaybds2li1bBoCfnx/Lli2jVq1ar529uPEwadIkateuzY0bN2jVqhVz584tduwfOnSITZs2KX8vXbqUmJgY7O3tGTJkCA8fPuTzzz9n586dJa71u0z6svTlF/2lZ8+eReorPVl6cknyl+e+XGEv/3ihR48e/PTTTxiNRn777TfatGlDdnY2y5cvZ8OGDcTFxWFlZcXWrVsBePz4MatWrSI6OloZvGFhYURGRrJ+/Xrq1aun/F+1atVYv349W7Zs4dy5c2RmZqLVatm1axcA3333Hb6+vm9tW17kjomJIS4ujszMTI4fP87hw4fp0aMHsbGx+Pr6kpOTw65du2jatCmbNm3Cz89PWUZKSgoRERFs3LiR7t27c+jQIfr06cPPP/+MwWDg6NGjdOzYEWdnZ65exrw43wAADG5JREFUvYrRaCQ5OZkOHTrQuXNnTpw4QVJSEt26dTOZZf369fTt2xe9Xo+np6ey3idPntCnTx9iY2OpVasWCQkJxMfHc/PmTbZs2cLGjRtZuXIlOTk57Ny5k5kzZ7J161YcHBzIz89n6dKlTJgwgZiYGNq0aQMU3uIrOzubmJgYNm/eTH5+PufPny+yD3bs2FHsPsjLyyMqKopNmzYRGxv7ytrfuXOHtWvX8sUXX5RavldJTU0tcqoxMzOTvLw8Nm/eXGSmoTRz1ahRg+joaM6cOcPAgQPp2bMnv/zyi8kxp9VqsbOz+8fZm06dOinvr7y8PLZt28akSZOUmc3U1FSCg4OJiYlRGqCbmxtXrlzh4cOHpKamYmNjU6LmbSp7cdLT0wkNDWX79u0kJCRw586dYsd+eno6q1evRq/X4+joyLFjx9BqtezevRuA/fv34+XlVdIyVwjSl6Uvm9oH0pOlJ5ckf3HKQ1+u0DPVUPhNPCQkBAcHB9q3bw8UDuDGjRuj0WgAcHV15dixYzg7OyunzurUqaN888rMzMTR0REofADC9evXqVq1Kvfv3ycoKAgLCwtyc3N5/vw5vXv3xtvbm1GjRnH79m0+/PDDN96GF3c9vH79Ovfv32fMmDFAYUO8ceMGY8eOZeXKlQwfPhx7e3tat25NSkoK3bp1A8DZ2Vl58pm9vT2hoaFYWFiQmZlJ27Zt0Wg0Sg127tzJ+PHjUavVNG/enISEBOzs7DAzM8Pd3Z0jR45w6dIlPDw8TGZJSUmhf//+Sq3i4uKU7WjZsqVS27y8PDIyMrhw4YJyjVN+fj4ZGRmEhYWxbt06Fi1ahIuLC0ajkZSUFFq3bq0s9+rVq6jVaqpUqaLsg9u3b5Ofn0/Hjh0JDQ3l3r17HD9+nKCgIJN1bdKkCWZmZgAmnwz38t0m69Wrp/xvaeV7lf891ZiYmKiM0ZeVZq5r166h0WgICwsD4Pz584wZM4apU6f+bcy9yst1f7FNL29H3bp1qVOnDgC1atUiKiqK999/nydPnqDRaFCpVPTr14/9+/dz8+bNEn0QFZfd1tbWZK769esr/cPOzo68vLxix37NmjUJDg7G0tKSq1ev4uLigoODA5aWlqSmprJv3z6ioqL+MWNFIn1Z+nJQUBD79u0rUlPpydKTS5K/PPflCj9T7eDgQG5uLnq9nn79+gGgUqlIS0sjNzcXKDz19mKgqFSqvy3Dzs6OtLQ0oHCnQuEPDG7dusXixYsJCgri6dOnGI1GzM3NlTfCix35b5iZmXHnzh0A/vjjD6CwidSpU4d169ah1+sZOnQozs7O7Nu3D29vb/R6PU2aNGHbtm04OTlx7tw55fX5+fkAzJo1iwULFrBw4UJq1aqlDEidTsf27du5d++e8gHm5ubGqlWrlA+Bdu3a/WMWJycnzp49W6RWL/xvbZ2cnOjYsSN6vZ4NGzbQq1cv6tWrx7Zt25g7dy6xsbFcvHiRs2fPFlnuix8qXLp0icOHD7NkyRJmz55NQUEBRqMRlUqFl5cXoaGhuLm5UaVKFZM1NrWvTdUdQK3+/7dKaeV7XS9nfKE0c12+fJmQkBDy8vKAwuZrZWXFggULTI45lUpFQUEBVatWVWr+559/8vDhQ2WZL/bRy+M5MzOTzMxMAEJDQ5kwYQLh4eE0bdpUWfZnn33GoUOHSE5O5uOPP/7X2atXr25yPJgaO6bG/qNHj1i2bBnffPMN8+fPp2rVqkXec9HR0djb21OjRo1/LnAFIn1Z+rKp/iI9WXpySfKX575c4WeqAXr37s2ePXtwdHTkxo0b2NjY0LdvX4YNG4ZaraZ+/fpMnjyZ77//3uTrIyIilG80lpaWWFtb07p1a6KiotDpdJiZmeHg4EBWVhYODg7odDoGDRpESEjIv86s1WqZMWMG+/bto2HDhkDhqZARI0bg7++PwWDggw8+oFevXjx79oxp06ZhYWFBlSpVmDdvHnXq1GH69OkMGjQIJycn5c3Yv39/dDod1apVw9bWlqysLKBw1uTatWsMGTJEydClSxdmzZrF119/DRQ2NysrK1q2bFlslokTJxIYGMiBAweUU7LF6d69O0lJSQwePJjc3Fw8PT3RaDQ0a9YMX19fbGxssLe3x9nZmTlz5hAYGMjatWupUaMGVatWpUGDBpibm+Pj44OZmRl2dnbK9vj4+ODh4cGePXteq+7Dhg1T6lfc6amyzPe6SjPXp59+SlpaGlqtFgsLC4xGI1OnTiU5OdnkmGvfvj1jxoxh3bp1WFlZodVqadSokclx4+npyenTp9FqtdStWxcbGxsA+vXrx/jx46lZsya1a9fmwYMHQOHMn6WlJS4uLiZnvEqa/eX30z+drjQ19jUaDW3btsXb2xsLCwuqVaumbL+npyfz5s0jIiKi5EWuQKQvS18uCenJlbMnvyp/ue7LRvHW/frrr8YpU6aUdYzXYjAYjDqdzvjo0aOyjvJW3L592zhs2LCyjlGs8pqvvOb6N8aMGWNMT08v6xjFys3NNfr4+BgNBkNZR6kUpC+XvfLcX8prtvKa698o7z3ZaHzzvlzhL/8obbGxscyZM4eAgICyjlJiN27cwNvbm/79+yvXI73LfvjhB0aPHs2kSZPKOopJ5TVfec31up4+fYqPjw/NmzenQYMGZR3HpDNnzqDT6ZTrZMV/S/py2SvP/aW8ZiuvuV7Xu9CT4e30ZZXR+NJV3kIIIYQQQojXJlMkQgghhBBCvCE5qBZCCCGEEOINyUG1EEIIIYQQb0gOqsU7LzExkc6dO+Pv78/QoUPx8/PjwIED/8m6pk6dik6nU+6P++zZM9zc3DAYDACcPXuW5s2bK/fDzMvLo1u3bhQUFJR4HYmJiQQGBr798EIIUQqkJ4vKqlLcp1pUfJ06dVIer/rkyRP8/f1xdHSkRYsWb3U9x44d48SJE8rfZmZmNG/enIsXL/LRRx8RHx9Pz549iY+Pp1WrVpw9exZXV1e5w4MQolKRniwqIxlVosKxtLRk4MCBHDp0CIPBwMyZMxk1ahQ+Pj4sWbKEgoICevToQXZ2NgCbN29mzZo1RZZx/PhxtFotQ4cO5csvvyQnJ4eQkBBycnIYN25ckf91c3Pj1KlTAJw8eZJJkyaRkJAAFD4V7sWTzyIjI/Hz82PgwIEcPHgQKHxilL+/P/7+/gQEBPDo0SNluX/99RejRo1i7969/02hhBCiFEhPFpWFHFSLCqlmzZo8ePCAW7du4eLiwtq1a4mLiyMuLg61Wo2Xl5fypLa9e/cyYMAA5bVGo5HZs2ezYsUKYmNjcXV1JTo6mpCQEKytrYmOji6yri5dunD69Gnu3r2Lubk5Dg4OGI1G7t+/T3JyMl27diU+Pp6bN2+yZcsWNm7cyMqVK8nJyWH27NnMmTMHvV6Pu7u78kGSm5vL2LFjGTx4sPIYZyGEeFdJTxaVgVz+ISqkjIwMateuTfXq1Tl//jwnT55Eo9Hw7NkzAHx9fQkMDMTV1RVbW1tsbW2V1z548ACNRoO9vT0Arq6uLF68uNh1NWvWjPT0dI4eParMgHTt2pXExESePXuGnZ0dV65c4cKFC/j7+wOQn59PRkYGaWlpzJ07F4Dnz5/j6OgIFM6mNGvWTMkrhBDvMunJojKQmWpR4Tx+/Jjt27fTs2dPdu7ciZWVFZGRkYwcOZKnT59iNBqpW7cuVlZWrFy5El9f3yKvt7Gx4fHjx2RlZQGFzbRhw4bFrk+lUtGsWTO2b9+Ou7s7AO7u7mzcuJEOHToA4OTkRMeOHdHr9WzYsIFevXpRr149HB0dCQ8PR6/XM2XKFD7++GMAPDw8WLFiBUuWLCEzM/M/qJIQQpQO6cmispCZalEhnDx5En9/f9RqNQaDgYCAAJycnDAYDAQFBXH69GnMzc1p0KABWVlZ2Nvbo9PpmD9/PhEREUWWpVKpmD9/PgEBAahUKqytrQkLC3vl+t3c3Fi+fDmNGzcGoHXr1ly9elX5xXj37t1JSkpi8ODB5Obm4unpiUajISQkhODgYOWX6qGhocoHh62tLQEBAcyYMYM1a9agUqnedtmEEOI/IT1ZVEbymHJRaR04cICUlBQmTpxY1lGEEKLSk54s3nUyUy0qpcWLF3Pq1CmioqLKOooQQlR60pNFRSAz1UIIIYQQQrwh+aGiEEIIIYQQb0gOqoUQQgghhHhDclAthBBCCCHEG5KDaiGEEEIIId6QHFQLIYQQQgjxhuSgWgghhBBCiDf0fzPXEYcJV2h2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Analysis of the number of trips taken by different user types across days of week\n",
    "\n",
    "g = sb.catplot(data=df_Q1_2020_clean, x='start_dayofweek', col=\"user_type\", kind='count', sharey = False, color = base_color)\n",
    "g.set_axis_labels(\"Day of Week\", \"Number of Trips\")\n",
    "g.set_titles(\"{col_name}\")\n",
    "g.fig.suptitle('Daily usage of the bike share system per user type', y=1.05, fontsize=12, fontweight='semibold');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: subscribers usualy use the rides through the working days, while the customer tend to use more rides during weekends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The majority of users for the Ford Go Bike System are Subscribers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "> - User Type\n",
    ">> - subscribers are:\n",
    ">>> - more likey to take short length trips.\n",
    ">>> - more likey to take short duration trips.\n",
    ">>> - more likey to make rides during working days.\n",
    ">> - customers are:\n",
    ">>> - more likey to take long length trips.\n",
    ">>> - more likey to take long duration trips.\n",
    ">>> - more likey to make rides during weekend days.\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> No!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "> Create plots of three or more variables to investigate your data even\n",
    "further. Make sure that your investigations are justified, and follow from\n",
    "your work in the previous sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average trip duration vary in weekdays between customers and subscribers\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "sb.pointplot(data=df_Q1_2020_clean, x='start_dayofweek', y='duration_minute', hue='user_type', dodge=0.3, linestyles=\"\");\n",
    "plt.xlabel('Day of Week');\n",
    "plt.ylabel('Avg. Trip Duration in Minute');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: subscribers ride much shorter trips compared to customers on each day of the week. Both user types have an increase of trip duration on Saturdays and Sundays over working days, especially for customer riders. \n",
    "Subscribers usage seems to be more efficient than customers overall and maintained almost a very consistent average duration Monday through Friday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average trip distance vary in weekdays between customers and subscribers\n",
    "\n",
    "base_color = sb.color_palette('colorblind')[0]\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(10,7))\n",
    "\n",
    "sb.pointplot(data=df_Q1_2020_clean, x='start_dayofweek', y='distance_km', hue='user_type', dodge=0.3, linestyles=\"\");\n",
    "plt.xlabel('Day of Week');\n",
    "plt.ylabel('Avg. Trip Distance in Kilometer');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: Customer keep steady average distance on all weekdays, and subscribers vary on the avergae disatnce through the working days "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XcXFxio2NlWEYio+Pl7u7+01lOFWDZBiGJOnnn3/WyZMn9cILL0i6NIR28OBBvfjii3rnnXf0/PPPq3r16goJCdHevXvVvHlzSdL999+vSpUuPeTq1asrOTlZnp6eysrKUmhoqLy9vfXQQw9p3bp1Wrx4sXr37l02DxQAAABwUo64UGxgYKAWLFgg6dJMsKuJjo5WdHT0n84q9w2Sm5ubsrOzJUm7du2SdOkJqlGjht599125urpq8eLFatiwoZYtW6YOHTooISFB06ZN04IFCxQcHKzt27erVatW2rVrlwoLCyVJw4cP18qVK+Xt7a2EhARb8xUdHa0ZM2bo1KlTatCgQdk8aAAAAMBJ2XuZb7OV+wapY8eOSkxM1LJly3TnnXdKkvz9/dWtWzfFxcWpqKhId9xxh9q2bav8/HwNGTJEnp6ecnV11ejRo1WjRg0NHTpUnTt3VnBwsFxdXSVJTz/9tKKjo+Xr66tbb71Vx44dk3RplOnAgQPq0qVLWT1kAAAAwGk5+3WELMbloRNIkoqLi9W5c2fNnDlT3t7eN9y/4Ph+E6r6leIic/OKCs3Lcr25eaJOoyDP1LjiM8dMzSv6JNXUvMIffjY1z6WGv6l51pq3m5pnnD5ral7h7gOmZVW6L9i0LElSQYGpcUWZR0zNO//DBVPzpu0PNDWvg9Xc10JwnJepedaGd5map1uqmxpnqWpensWvhmlZl7nXe8T0zJsRW6tDqfafe+AjB1Vyc5y9wbOrgwcPqkOHDnr66af/UHMEAAAA4EpWWUq1lTflfoqdmYKCgrRkyZKyLgMAAABwWuWv5SkdGiQAAAAAdmN18HWQHI0GCQAAAIDduDj5GBINEgAAAAC7cfZFDmiQAAAAANiNIy4UayYaJAAAAAB2wwgSAAAAAJQoj0t3lwYNEgAAAAC7ce72iAYJAAAAgB0xggQAAAAAJTgH6S+u+MwxU/OsnlVMzZOLiT8ixUXmZUky8i+amiej2Nw8F1dT4yx16pqad3TmIVPzbmt20tQ8s1kb3mVqnstF815/Fm8v07IkqXDnXlPzzn6Xb2re5z8HmZoXYPInrWq1zpmaZ/GpZmqerM7+0fX6LB6+Jmb5mJblbFjFDgAAAABKOHsbToMEAAAAwG6ce/yIBgkAAACAHbFIAwAAAACUYIodAAAAAJRw7vEjGiQAAAAAdsQUOwAAAAAowRQ7AAAAACjh3ONHNEgAAAAA7IgpdgAAAABQwmqUdQV/jrNPEQQAAABQjlhLuf0R3333neLi4iRJBw4cUOfOnRUbG6tRo0apuLhYkvT2228rKipKnTp10vfff/+n6gcAAAAAu7CUcruRGTNmaPjw4crLy5MkjRs3Tv3799fcuXNlGIZWrVqljIwMbdq0SR9++KEmTZqkV1555abrp0ECAAAAYDf2HkGqWbOmUlJSbLczMjLUpEkTSVJ4eLg2bNigLVu2qFmzZrJYLLr99ttVVFSkkydP3lT9nIMEAAAAwG5Ku0hDWlqa0tLSbLdjYmIUExNjux0REaFDhw7ZbhuGIYvlUoaXl5fOnTunnJwc+fn52fa5fL+/v3+p66dBAgAAAGA3pV3D7rcN0Y1Yrf837pSbmytfX195e3srNzf3ivt9fHxKWUnJ8W/qb11H165dbSdF5efnq1GjRpo5c6bt+88995z27Nlz3WNkZmbaTsKyp/T0dA0ZMsTuxwUAAABwiSMWafi1u+++Wxs3bpR06fN948aNFRoaqnXr1qm4uFhHjhxRcXHxTY0eXa7frpo1a6Zvv/1WkmxzAb/88ktJUl5enn755Rc1aNDA3rEAAAAAygGrUbqttBISEpSSkqKYmBgVFBQoIiJC9957rxo3bqyYmBj16dNHI0eOvOn67T7F7pFHHtGUKVPUvXt3rV27Vh07dtRrr72mc+fO2U6o+s9//qNZs2bJarWqUaNGGjRokI4dO6ZBgwbJMAxVq1bNdrz27durSZMm+uGHH2SxWDRlyhT5+Pjo9ddf1+bNm2UYhrp166a2bdtqzpw5+vjjj2W1WhUaGqqEhARlZmYqMTFRHh4e8vDwUJUqVSRJs2fP1ueff67CwkL5+PgoJSVFQ4cOVfv27dWiRQtlZmZqwoQJmj59ur2fIgAAAKDCcsRlYgMDA7VgwQJJUu3atTV79uzf7dOnTx/16dPnT2fZfQTp7rvv1v79+2UYhjZv3qwmTZrob3/7mzZs2KBNmzapefPmSklJ0axZszRv3jxlZWVp/fr1eu+999SuXTulpqaqVatWtuPl5ubqySef1OzZsxUQEKD09HStXbtWhw4d0vz58/XBBx/onXfe0dmzZ7V48WINGzZMaWlpCgoKUmFhoSZPnqy+fftq1qxZevDBByVJxcXFOn36tGbNmqW5c+eqsLBQO3bsUMeOHfXRRx9JkhYuXKioqCh7Pz0AAABAhVaplFt5Y/cGyWq1qkGDBkpPT1e1atXk5uam8PBwbd26VVu2bFFgYKBOnjypF154QXFxccrMzNTBgwe1d+9ehYSESJJCQ0OvOObdd98tSapRo4by8vL0448/KiMjQ3FxcerZs6cKCwt15MgRjRs3TvPnz9dzzz2nI0eOyDCMqx7XarXK1dVVAwYMUGJioo4eParCwkKFhYVp//79OnHihNavX6/HHnvM3k8PAAAAUKE5eoqdozmkaWvatKmmTZumJ598UpLUqFEjTZkyRVarVYGBgapRo4beffddubq6avHixWrYsKH279+vbdu2qUGDBtqxY8cVx7u8jN9lwcHBCgsL05gxY1RcXKwpU6YoMDBQb775pl555RW5u7urR48e2rZtm4KDg7Vt2zaFh4dr586dkqQ9e/Zo5cqV+vDDD3XhwgVFRkbalgts3769kpOT1bRpU7m6ujri6QEAAAAqLEdMsTOTQxqkRx55RMOHD9e//vUvSZKbm5t8fHx09913y9/fX926dVNcXJyKiop0xx13qG3bturXr5/i4+O1fPlyBQYGXvf4LVu21KZNmxQbG6vz58+rVatW8vb21l133aWoqChVrVpV1atX1/33369Ro0YpPj5eM2fOlL+/v9zd3VWrVi15eHgoMjJSbm5uqlatmo4dOyZJioyMVIsWLbRkyRJHPDUAAABAhWb3KWomsxiGUQ4HtspOVlaWBg8erPfff/8P7Z+X+Y2DK7qS1bOKqXlyKY8zQ+3DyL9ocmCxuXEXc2+8kx0V70w3Ne9g4pem5t3WzNx/P5caN7c06c2yNrzL1LzizEzTsqy3BZiWJUmFO/eamnd2o7mv9c9/vt3UvDyTP2k9U/vQjXeyI9/2wabmWWrWMjVPVavdeB87st5ez7Qsi99tpmVd5hZ4n+mZN+OdoOdKtf+LB3+/4EJZcvYGz65WrFihnj17auDAgWVdCgAAAOCULEbptvKm4g4P3ISIiAhFRESUdRkAAACA03L2ERgaJAAAAAB24+wN0g3r/+STT2xfZ2dnq2fPng4tCAAAAIDzqvDLfH/88cfy8vJSXl6e3njjDfXt29eMugAAAAA4oQq/zPfbb7+tF198UXl5eZo3b578/c1dWQkAAACA83D2KXbXbJAGDBhgu0Br5cqV9f333ys5OVmS9Prrr5tTHQAAAACnUh6nzZXGNRukTp06XXG7e/fuDi8GAAAAgHOrsFPsmjRpIknKycnRjBkzlJ2drRYtWuiuu8y9mCAAAAAA52GVcw8h3XCKYGJiooKCgvTTTz/p1ltv1bBhw8yoCwAAAIATcvZV7G7YIJ0+fVpRUVGqVKmSQkNDZRjl8FEAAAAAKBespdzKmz90odjMzExJ0tGjR2W1lseHAQAAAKA8qLDnIF02fPhwJSYmKjMzU3379tWoUaPMqMt5XMw1N69KgKlxxoVzpmVZPHxMy5IkS2UvU/NUkGdqXPH5w6bmma16kwJT8yxenubm3V7d1LzCjVvNzTts3nuLe+XKpmVJ0un1503N829fw9S8v80+aWreD2f9TM0ruOBiap5xwdzfDRbfqubm3Rpoap5RZN7vBkuhuf92zsTq5DPObtgg1a9fX//+9791+PBhBQUFycvL5A+VAAAAAJyGs883u2GDtGLFCk2dOlVFRUVq06aNLBaLevfubUZtAAAAAJyMs0+xu2GD995772nBggXy8/NT7969tXLlSjPqAgAAAOCErDJKtZU3NxxBslqtcnNzk8VikcVikYeHhxl1AQAAAHBC5XHp7tK4YYPUuHFjDRw4UFlZWRo5cqTuu+8+M+oCAAAA4IQs5XBUqDRu2CANGDBA6enpatiwoYKDg9WyZUsz6gIAAADghCrsIg3h4eF66KGH1Lx5czVv3lzh4eFm1gUAAADACVXYBmnlypXatm2bNm3apPj4eOXn5yssLEzNmzdX48aNzawRAAAAgJOw5xS7goICDRkyRIcPH5bVatWYMWNUqVIlDRkyRBaLRfXq1dOoUaNktdqvLbtmg+Tm5qawsDCFhYXp5MmT2rRpkz744AOlpaXpm2++sVsBAAAAACoOe44grV27VoWFhZo/f77Wr1+vN998UwUFBerfv7/CwsI0cuRIrVq1Sq1bt7Zb5jUbpIyMDH355ZdKT0+XJDVr1kyDBw9WSEiI3cIBAAAAVCylHUFKS0tTWlqa7XZMTIxiYmIkSbVr11ZRUZGKi4uVk5OjSpUqafv27WrSpImkS6cFrV+/3pwGKSoqSm3bttXrr7+uwMBAuwUCAAAAqLgqWUrXIP26IfotT09PHT58WG3bttWpU6f0zjvvaPPmzbJYLl2O1svLS+fOnfvTNf/aNRukuXPnKj09XYMGDZKXl5dtsYY6derYtQAAAAAAFYc9z0GaNWuWmjVrpoEDB+qXX37R888/r4KCAtv3c3Nz5evra7c86ToN0oMPPqgHH3xQ/fr104kTJ/TVV19p5MiROnr0qFatWmXXIgAAAABUDPY8B8nX11eurq6SpCpVqqiwsFB33323Nm7cqLCwMKWnp+vhhx+2Y+J1GiTDMLR79259++23+vbbb/XTTz/prrvuUseOHe1aAAAAAICKw1LKKXbX061bNyUmJio2NlYFBQWKj4/XvffeqxEjRmjSpEkKDg5WRESE3fKk6zRIzZs3V8OGDfXII4/opZde0l133WXXYEkaP368MjIylJ2drYsXLyooKEh79+7V3/72N73xxht2z0tPT9fy5cs1fvx4ux8bAAAAgGS1Y4Pk5eWlyZMn/+7+2bNn2y3jt67ZIK1evVpubm4OC5akIUOGSJIWL16s/fv3a9CgQdq4caPmz5/v0FwAAAAAjmEp6wL+pOteB6msHDhwQD179tTJkyf12GOPqU+fPoqLi1NSUpLq1KmjefPm6fjx4+rQoYN69eolPz8/hYeHy9PTUx9//LGsVqtCQ0OVkJCgzMxMJSYmysPDQx4eHqpSpYqkS13n559/rsLCQvn4+CglJUVDhw5V+/bt1aJFC2VmZmrChAmaPn16mT0PAAAAgLOx5whSWbjmOVT//e9/zazjCnl5eZoyZYrmzJlzw+Gz7OxszZw5U//4xz+0ePFiDRs2TGlpaQoKClJhYaEmT56svn37atasWXrwwQclScXFxTp9+rRmzZqluXPnqrCwUDt27FDHjh310UcfSZIWLlyoqKgohz9WAAAAoCKxWIxSbeXNNRukoUOHSpJeeukl04q5rF69enJzc5OHh4cqVfr9IJdh/N8TGRgYaBvtGjdunObPn6/nnntOR44ckWEY2rt3r+3itqGhoZIkq9UqV1dXDRgwQImJiTp69KgKCwsVFham/fv368SJE1q/fr0ee+wxEx4tAAAAUHFYLUaptvLmmlPsatasqaZNm+rMmTNq1qzZFd9bt26dQ4u6fOGnX3Nzc1N2drbq1KmjXbt2qXr16pIuNTuXLViwQK+88orc3d3Vo0cPbdu2TcHBwdq2bZvCw8O1c+dOSdKePXu0cuVKffjhh7pw4YIiIyNlGIYsFovat2+v5ORkNW3a1LakIAAAAIA/5iof5Z3KNRukf/3rX5KkV155RaNGjTKtoGvp2rWrRo8erRo1aiggIOCq+9x1112KiopS1apVVb16dd1///0aNWqU4uPjNXPmTPn7+8vd3V21ars+zTcAACAASURBVNWSh4eHIiMj5ebmpmrVqunYsWOSpMjISLVo0UJLliwx8+EBAAAAFUJ5nDZXGhbj1/PVruLcuXOaOnWq9u3bpzvvvFO9e/eWn5+fWfWZLisrS4MHD9b777//h/bPyzD3ornW6rVNzTMunDMty+LhY1pWmSjIMzWu6Ji55xEa+783NS//4y9MzbNW9TQ1z6VBsKl5RXv2m5pXeNi89xb3sLqmZUnSiTRzX3v+7W8zNe/g7FOm5v1w1tzPHKFBWabmmf3vZ30ozNQ8S/U7Tc2TyzX/79/urFWu/h/2juR2Z2PTM2/G93e2L9X+IT8tc1AlN+eGF7odNmyYbrvtNsXHx+uOO+6wLc1dEa1YsUI9e/bUwIEDy7oUAAAAwClZLKXbypsbttmnTp1S165dJUkNGzbUihUrHF5UWYmIiLD7lXgBAACAvxJnn2J3wxGkvLw8ZWdnS5KOHz+u4uJihxcFAAAAwDlV2FXsLuvXr586deokHx8f5eTkaMyYMWbUBQAAAMAJWazlr+kpjRs2SE2bNtWqVat08uRJ+fv7m1ETAAAAACdVHs8rKo0/vNQHzREAAACAG6nwI0gAAAAA8EdV+EUa3n33XZ08edKMWgAAAAA4uQq/SIOHh4d69+6tgIAAPfvsswoPD5fF2ScWAgAAAHAIyw2HYMq3G5bfuXNnzZ8/X3369NHSpUv12GOPKSUlRWfPnjWjPgAAAABOxGIxSrWVNzccQTp79qw+/fRTLVmyRD4+Pho2bJgKCwvVu3dvzZ4924waAQAAADiJCr9IQ1RUlJ566im98cYbqlGjhu3+PXv2OLQwAAAAAM7H2c/GuWGDtGLFiivOOTp27JgCAgIUHx/v0MKcRmUvU+OMC+dMzbN4+JiaZ6riorKuwLGKCs3NczF3Ucy9q8z92awfkWtqni5eNDXO6lXZ3DzP86ZlZc/9ybQsSbqQ425qntuaQ6bmVatnapy+21rV1LyfD5qbd2vQHabm6USWuXl+1UyNM078YlpWsWlJzqfCjyClpKRo7ty5Kigo0MWLF3XnnXfq008/NaM2AAAAAE7G2RukGy7SkJ6ervT0dLVv317Lly9X9erVzagLAAAAgBOyWEq3lTc3HEHy8/OTm5ubcnNzVatWLV24cMGMugAAAAA4IWcfQbphg3Tbbbdp4cKF8vDw0Ouvv66cnBwz6gIAAADghKwuFbxBGj16tI4ePao2bdroo48+0htvvGFGXQAAAACckLNfKPa6DdKePXu0YsUKnTp1SrfddpvatGmjO++806TSAAAAADgbe0+xmzZtmlavXq2CggJ17txZTZo00ZAhQ2SxWFSvXj2NGjVKVqv9urJrHuk///mPEhMTVaNGDTVv3lxeXl7q27evVq5cabdwAAAAABWLxVq67Xo2btyobdu2ad68eUpNTdXRo0c1btw49e/fX3PnzpVhGFq1apVd67/mCNIHH3yg2bNny9PT03Zfhw4d1KtXL7Vq1cquRQAAAACoICylG0FKS0tTWlqa7XZMTIxiYmIkSevWrVP9+vX10ksvKScnR4MHD9aCBQvUpEkTSVJ4eLjWr1+v1q1b2638azZIlSpVuqI5kiRvb2+5uLjYLRwAAABAxVLac5B+3RD91qlTp3TkyBG98847OnTokHr16iXDMGQpWR/cy8tL586d+7MlX+GaDZLlGouSFxdz3WAAAAAAV2fPRRr8/PwUHBwsNzc3BQcHy93dXUePHrV9Pzc3V76+vvYL1HUapH379mngwIFX3GcYhjIzM+1aAAAAAICKw54NUqNGjfTBBx/o73//u44dO6YLFy7ob3/7mzZu3KiwsDClp6fr4Ycftl+grtMgvfnmm1e9v1OnTnYtAAAAAEAFYscG6bHHHtPmzZsVFRUlwzA0cuRIBQYGasSIEZo0aZKCg4MVERFhv0Bdp0G6fOITAAAAAPxR9r4O0uDBg3933+zZs+0b8is3vFAsAAAAAPxhFflCsQAAAABQGvYeQTKb05Z/6NAhhYaGKi4uzra9/fbbV+wTHx+v/Pz8K+5LT0/XkCFDzCwVAAAA+OuwlnIrZ5x6BKlu3bpKTU295vffeOMNE6sBAAAAYLFe/XJBzqIc9mw3b+PGjerYsaNiY2P18ccfq2XLlsrLy1NmZqZiYmLUrVs3zZs3z7b/7Nmz1bVrV8XGxuqf//yn8vPzNXDgQH355ZeSpMzMTL3wwgtl9GgAAAAAJ+TkI0jlsKQ/bt++fVdMscvKylJeXp7mzp2rZ555xrbf5MmT1bdvX82aNUsPPvigpEsXvD19+rRmzZqluXPnqrCwUDt27FDHjh310UcfSZIWLlyoqKioMnlsAAAAgDOyWC2l2sqbCjXFbuPGjapdu/bv9tu7d69CQkIkSaGhodq/f7+sVqtcXV01YMAAeXp66ujRoyosLFRYWJiSk5N14sQJrV+/XgMGDDDt8QAAAABOz6mHYJy8Qboaq/X3/yLBwcHatm2bwsPDtXPnTknSnj17tHLlSn344Ye6cOGCIiMjZRiGLBaL2rdvr+TkZDVt2lSurq5mPwQAAADAeZXDUaHSqHAN0tWMGjVK8fHxmjlzpvz9/eXu7q5atWrJw8NDkZGRcnNzU7Vq1XTs2DFJUmRkpFq0aKElS5aUceUAAACAcymP0+ZKw2kbpMDAQC1YsOCK+8LCwhQWFma7vXr1aklSQECA5syZ87tjfPDBB1c9dlFRkRo1aqQ6derYsWIAAADgL8DJp9g5efn2t2LFCvXs2VMDBw4s61IAAAAA52O1lG4rZ5x2BMlRIiIiFBERUdZlAAAAAE6JKXYAAAAAcBkNEgAAAACUoEECAAAAgEssFhokAAAAALiEESQAAAAAKEGDBAAAAAAlrM59JSEaJAAAAAB2Y6lEgwQAAAAAlzDFDgAAAABKWBhB+kuzuJj7FBpnjpmbl3vatCyLt79pWZJkqexlap6Rf9HUPJca9UzNU2BDU+PuSfUzNU+Zu0yNMwqLTM0z2+KVNUzL2u5aaFqWJO0sPGlqXsT+IFPzVhSZ+3soLeSQqXnF5v64qHifuf/Tbg2519Q849wpU/Pka95nCYuLq2lZTocRJAAAAAAoQYMEAAAAAJdYnHwVO+euHgAAAED5YrWUbvsDTpw4oUcffVSZmZk6cOCAOnfurNjYWI0aNUrFxcX2Ld+uRwMAAADw12axlm67gYKCAo0cOVKVK1eWJI0bN079+/fX3LlzZRiGVq1aZdfyaZAAAAAA2I+dR5AmTJigTp06KSAgQJKUkZGhJk2aSJLCw8O1YcMG+5Zv16MBAAAA+GuzWku1paWlKTIy0ralpaXZDrV48WL5+/urefPmtvsMw5DFcqmx8vLy0rlz5+xaPos0AAAAALCfUq5iFxMTo5iYmKt+b9GiRbJYLPr666+1e/duJSQk6OTJ/7ucQm5urnx9ff9Uub9FgwQAAADAfux4odg5c+bYvo6Li1NSUpImTpyojRs3KiwsTOnp6Xr44YftlicxxQ4AAACAPTlgFbtfS0hIUEpKimJiYlRQUKCIiAi7ls8IEgAAAAC7cdR1kFJTU21fz5492yEZEg0SAAAAAHu6iVGh8oQGCQAAAID92PEcpLJAgwQAAADAfhhBAgAAAIASDjoHySw0SAAAAADsx+LcI0jlpr2bPn26unXrpu7du6tHjx7auXPnNfdNS0tTQUHBTeU0bdr0ZksEAAAAcCNWa+m2cqZcjCDt27dPq1ev1rx582SxWGxXyV26dOlV9582bZqeeeYZk6sEAAAAcEPlsOkpjXLRIPn7++vIkSNauHChwsPD1bBhQy1cuFCbNm3S22+/LUm6ePGiJkyYoG+//VbZ2dmKj4/X888/r/nz5+uNN96QdGl0aP369RoyZIhOnz6t06dPa+rUqZo4caL27dunoKAg5efnS5J+/PFHjR8/XsXFxTp79qyGDx+u8+fPa8GCBXrrrbckSZ06ddJbb72lgICAsnliAAAAAGfj5Is0lIv2zt/fX1OnTtXWrVsVExOjNm3aaM2aNdq7d68mTpyoDz74QC1bttRnn32mjh07qlq1aram6FoefvhhzZ8/X9u3b1deXp4WLFiggQMH6sKFC5IujVolJCRo1qxZ+vvf/67FixeradOm+vHHH3XmzBnt27dPVatWpTkCAAAASsNiLd1WzpSLEaQDBw7I29tb48aNkyTt2LFDL7zwggYPHqzk5GR5enoqKytLoaGh1z2OYRi2r2vXri1J2rt3r0JCQiRJt99+u2rUqCFJCggI0JQpU1S5cmXl5ubK29tbFotFTz31lD755BMdOnRIUVFRjni4AAAAQMXl5FPsykX1P/zwg5KSkpSXlyfpUnPj4+OjsWPHauzYsRo/frwCAgJsDZDFYlFxcbHc3d2VnZ0tSTp8+LDOnDljO6alZPWM4OBgbd++XZKUlZWlrKwsSVJycrL69u2rCRMmqH79+rZjP/vss/rss8+0efNmPfroo+Y8AQAAAEAFYbFaSrWVN+ViBOnxxx9XZmamOnbsKE9PTxmGocGDB2vz5s2Kjo6Wr6+vbr31Vh07dkyS1LhxY73wwgt699135ePjo44dO6pOnToKDAz83bFbtWqlLVu2qGPHjrr99ttVtWpVSdJTTz2l3r1765ZbbtFtt92mU6dOSZKqV68uLy8vPfDAA6pUqVw8PQAAAIDzKIfT5krDYvx6XhokSf/85z+VmJioWrVq3XDf/J++NaGi/2PknDI1T5XcTIuyePubliVJlspepuYZF3NNzbO4mNzgm5xXmLHW1Dxl7jI1zigsMjfv4BFT8xbMcjcta7troWlZkrSz8KSpeREu5p4ru6LomKl5affkmZpXbO6Pi7weNvffzxpyr6l5qlrN3LzKnqZFWf3vMC3rMvd7W5ueeTMuvDe4VPt7/P1fDqrk5jh3e2dnFy9eVGRkpBo0aPCHmiMAAAAAv+HiUrqtnGEO2a9UrlxZixcvLusyAAAAAOfl5Is00CABAAAAsB8nPweJBgkAAACA/TCCBAAAAAAlaJAAAAAAoISl/F3bqDRokAAAAADYDyNIAAAAAFCCBgkAAAAASrCKHQAAAACUYAQJAAAAAEowgvTXZlzMNTXPUiXA3DwPH1PzjAvnzM0qKjAtT5Is3v6m5qm4yNw8E1kqe5maV3Qky9S8gt1HTM07+I23qXmbXZ17haPruauSn6YfWW9a3lpl6NGAe0zLk6SmlaqZlnVwd45pWZJUq9FpU/Pk7Wlunoe5751mjyRYbwkyNU8m/y5yGowgAfZhZnMkieYIgEOY2RxJqtDNEVCu0Rxdmx1HkAoKCpSYmKjDhw8rPz9fvXr1Ut26dTVkyBBZLBbVq1dPo0aNktWOTRkNEgAAAAD7sWOzsnTpUvn5+WnixIk6deqUOnTooAYNGqh///4KCwvTyJEjtWrVKrVu3dpumc49/gUAAACgfLFaS7ddR5s2bdSvXz/bbRcXF2VkZKhJkyaSpPDwcG3YsMG+5dv1aAAAAAD+2izWUm1paWmKjIy0bWlpabZDeXl5ydvbWzk5Oerbt6/69+8vwzBksVhs3z93zr6naTDFDgAAAID9lHKKXUxMjGJiYq75/V9++UUvvfSSYmNj1b59e02cONH2vdzcXPn6+t50qVfDCBIAAAAA+ynlCNL1HD9+XN27d9fLL7+sqKgoSdLdd9+tjRs3SpLS09PVuHFju5bPCBIAAAAA+7HjIg3vvPOOzp49qylTpmjKlCmSpGHDhunVV1/VpEmTFBwcrIiICLvlSTRIAAAAAOzJjst8Dx8+XMOHD//d/bNnz7Zbxm/RIAEAAACwHy4UCwAAAACXWCwuZV3Cn0KDBAAAAMB+GEECAAAAgBI0SAAAAABQwo6LNJQFGiQAAAAA9sMIEgAAAACUYATJ8aZPn64NGzbIarXKYrEoPj5e9957700dKzMzU0lJSUpNTbVzlQAAAABkZRU7h9q3b59Wr16tefPmyWKxaPfu3UpISNDSpUvLujQAAAAAv+VS7luM6yr31fv7++vIkSNauHChwsPD1bBhQy1cuFBxcXFKSkpSnTp1NG/ePB0/flwdOnTQwIEDddttt+ngwYO677779Morr+jYsWMaNGiQDMNQtWrVbMf+7LPPNGfOHNvtyZMna9asWapevbq6dOmiM2fO6O9//7sWL15cFg8dAAAAcD5OPsWu3Ffv7++vqVOnauvWrYqJiVGbNm20Zs2aa+7/008/KTk5WR9++KHS09OVnZ2t9957T+3atVNqaqpatWp1xb7Tp09XamqqateurXXr1qljx476+OOPJUmffPKJ2rdv7/DHCAAAAFQUFotLqbbyptyPIB04cEDe3t4aN26cJGnHjh164YUXdOutt9r2MQzD9nXNmjXl7e0tSapWrZry8vK0d+9ePf3005Kk0NBQzZs3T5J0yy23KCEhQV5eXtq/f78eeOABBQUFycvLS/v27dOyZcs0ZcoUsx4qAAAA4PycfBW7cl/9Dz/8oKSkJOXl5UmSateuLR8fH/n5+Sk7O1uStGvXLtv+Fovld8cIDg7Wtm3bJF1qsCTp3Llzeuutt/TGG2/o1Vdflbu7u63Rio6O1tSpU1W9enX5+/s79PEBAAAAFYrFWrqtnCn3I0iPP/64MjMz1bFjR3l6esowDA0ePFiurq4aPXq0atSooYCAgOseo1+/foqPj9fy5csVGBgoSfL29lZoaKg6dOggT09P+fr66tixY5KkVq1aafTo0Zo4caLDHx8AAABQoTj5CFK5b5AkqVevXurVq9fv7n/00Ud/d9+CBQuu+vX06dN/t+/kyZOvmldUVKQ77rhDTZs2vZlyAQAAgL+ucjgqVBrOXb0DbN26VdHR0erdu7esTt79AgAAAKazupRuK2ecYgTJTKGhoVq2bFlZlwEAAAA4JycfQaJBAgAAAGA/Tj4LiwYJAAAAgN1YGEECAAAAgBKMIAEAAABACUaQAAAAAKBEOVyZrjRokAAAAADYDyNIAAAAAFCCc5AAAAAA4BJWsQMAAACAy+w4glRcXKykpCT98MMPcnNz06uvvqpatWrZ7fhX49ztHQAAAIDyxWIt3XYdK1euVH5+vtLS0jRw4ECNHz/e8eUbhmE4PAUAAAAAriItLU1paWm22zExMYqJiZEkjRs3TiEhIXryySclSc2bN9dXX33l0HqYYgcAAACgzPy6IfqtnJwceXt72267uLiosLBQlSo5ro1hih0AAACAcsnb21u5ubm228XFxQ5tjiQaJAAAAADlVGhoqNLT0yVJ27dvV/369R2eyTlIAAAAAMqly6vY/fjjjzIMQ2PHjlWdOnUcmkmDBAAAAAAlmGIHAAAAACVokAAAAACgBA2SyYqLizVy5EjFxMQoLi5OBw4ccHjmd999p7i4OIfnFBQU6OWXX1ZsbKyioqK0atUqh+YVFRVp6NCh6tSpk7p06aKff/7ZoXmXnThxQo8++qgyMzMdnvXMM88oLi5OcXFxGjp0qMPzpk2bppiYGEVGRurDDz90aNbixYttjy06Olr33Xefzp4967C8goICDRw4UJ06dVJsbKzD//3y8/M1cOBARUdHq3v37vrpp58clvXr1/iBAwfUuXNnxcbGatSoUSouLnZoniR98cUXGjhwoN1zfpu1e/duxcbGKi4uTj169NDx48cdmrdv3z517txZnTp1UlJSkoqKihyad9myZcuuudytPfMyMjLUvHlz2+tw+fLlDs07ceKEevXqpS5duqhTp04Oec/+dV58fLztsbVs2VLx8fEOzdu9e7eio6PVuXNnDR061OGvvYyMDEVFRSk2NlZjxoyxa97Vfp878r3lep8fxo4dq3nz5tkt61p5jnx/uVqeGe8v+BMMmGrFihVGQkKCYRiGsW3bNuPFF190aN706dONdu3aGR07dnRojmEYxsKFC41XX33VMAzDOHnypPHoo486NO+LL74whgwZYhiGYXzzzTcOfy4NwzDy8/ON3r17G48//rixb98+h2ZdvHjRePrppx2a8WvffPON8c9//tMoKioycnJyjLfeesu07KSkJGP+/PkOzfjiiy+Mvn37GoZhGOvWrTP+3//7fw7NS01NNYYPH24YhmFkZmYa3bt3d0jOb1/j//znP41vvvnGMAzDGDFihPH55587NG/MmDFGRESE0b9/f7vmXC2rS5cuxq5duwzDMIx58+YZY8eOdWher169jE2bNhmGYRgJCQkOfy4NwzB27dpldO3a1SHv2b/NW7BggTFz5ky751wrLyEhwfj0008NwzCMr7/+2lizZo1D8y47ffq08dRTTxlZWVkOzevdu7fx5ZdfGoZhGAMGDDBWrVrl0LwOHToYW7ZsMQzDMCZNmmR8/PHHdsu62u9zR763XC3vxIkTRo8ePYz/+Z//MebOnWu3rGvlOfL95Wp5jn5/wZ/DCJLJtmzZoubNm0uSHnjgAe3cudOheTVr1lRKSopDMy5r06aN+vXrZ7vt4uLi0LxWrVppzJgxkqQjR47o1ltvdWieJE2YMEGdOnVSQECAw7P27NmjCxcuqHv37uratau2b9/u0Lx169apfv36eumll/Tiiy+qRYsWDs27bMeOHdq3b5/D/sf8stq1a6uoqEjFxcXKyclx+DUU9u3bp/DwcElScHCww0asfvsaz8jIUJMmTSRJ4eHh2rBhg0PzQkNDlZSUZNeMa2VNmjRJDRs2lHRpBNnd3d2heSkpKXrooYeUn5+v7Oxs3XLLLQ7NO3XqlF577TUlJibaNedaeTt37tSXX36pLl26KDExUTk5OQ7N27p1q7KystStWzctW7bM9nPqqLzLUlJS9Nxzz9n9ffu3eQ0bNtTp06dlGIZyc3Pt/h7z27ysrCyFhoZKuvQ63LJli92yrvb73JHvLVfLy83NVZ8+ffT000/bLed6eY58f7lanqPfX/Dn0CCZ7FpXA3aUiIgIh38QvMzLy0ve3t7KyclR37591b9/f4dnVqpUSQkJCRozZowiIiIcmrV48WL5+/vbGlxHq1y5snr06KGZM2fqlVde0aBBgxz6s3Lq1Cnt3LlTkydPtuUZJixyOW3aNL300ksOz/H09NThw4fVtm1bjRgxwuHTThs2bKg1a9bIMAxt375dWVlZDplC8dvXuGEYslgski69Js+dO+fQvCeeeMKWZ2+/zbr8AXfr1q2aPXu2unXr5tA8FxcXHT58WO3atdOpU6dUu3Zth+UVFRVp2LBhSkxMlJeXl11zrpYnSSEhIRo8eLDmzJmjoKAg/e///q9D8w4fPixfX1/NmjVLNWrU0IwZMxyaJ12a1vf1118rMjLSrllXy7vzzjuVnJystm3b6sSJEwoLC3NoXlBQkDZt2iRJWrNmjS5cuGC3rKv9Pnfke8vV8oKCgnT//ffbLeNGeY58f7lanqPfX/Dn0CCZrCyuBmymX375RV27dtXTTz+t9u3bm5I5YcIErVixQiNGjND58+cdlrNo0SJt2LBBcXFx2r17txISEpSdne2wvNq1a+upp56SxWJR7dq15efn59A8Pz8/NWvWTG5ubgoODpa7u7tOnjzpsDxJOnv2rPbv36+HH37YoTmSNGvWLDVr1kwrVqzQkiVLNGTIEOXl5Tks79lnn5W3t7e6du2qNWvW6J577nH4qKokWa3/97aem5srX19fh2eaafny5Ro1apSmT58uf39/h+fdcccd+vzzz9W5c2eNHz/eYTkZGRk6cOCAkpKSNGDAAO3bt0/JyckOy5Ok1q1b695777V9vWvXLofm+fn5qWXLlpKkli1bOnwGhSR99tlnateunSmvveTkZM2ZM0efffaZnnnmGYf+vEiXzs2ZNm2aXnjhBd1yyy2qWrWqXY//29/njn5vMfvzw9XyHPn+crU8s95fUHo0SCYri6sBm+X48ePq3r27Xn75ZUVFRTk87+OPP9a0adMkSR4eHrJYLA79JThnzhzNnj1bqampatiwoSZMmKBq1ao5LG/hwoW2N8ysrCzl5OQ4NK9Ro0b66quvZBiGsrKydOHCBfn5+TksT5I2b96sRx55xKEZl/n6+srHx0eSVKVKFRUWFjr0pNgdO3aoUaNGSk1NVatWrRQUFOSwrF+7++67tXHjRklSenq6GjdubEquGZYsWWJ7DZrxfL744ou2xTW8vLyu+IBobyEhIfr000+VmpqqSZMmqW7duho2bJjD8iSpR48e+v777yVJX3/9te655x6H5jVq1Ehr166VdOm1X7duXYfmSZce1+Wpro5WpUoV2wyRgIAAhy46I0lr167V2LFjNX36dJ0+fVpNmza127Gv9vvcke8tZn9+uFqeI99frpZn5vsLSq/iDF04idatW2v9+vXq1KmT7WrAFcU777yjs2fPasqUKZoyZYokacaMGapcubJD8h5//HENHTpUXbp0UWFhoRITE+1+TkJZioqK0tChQ9W5c2dZLBaNHTvWoaONjz32mDZv3qyoqCgZhqGRI0c6/H9d//vf/yowMNChGZd169ZNiYmJio2NVUFBgeLj4+Xp6emwvFq1amny5Mn/n707j4uq3v84/jrDJrKk5JJecMHMrayQpFJpuXqxEk0CUROvW1aW5pYi5pKKy3W7iUnprSw1xYVSy/KmZly1K+VSSlmKS+6SG4sKwszvD2l+mQviHY4MvZ89zuPhHL5zPp8zwDCfPt/zPbz77rv4+PiUeDfgN0OHDmXEiBFMmzaNwMDAEp96apaCggLi4+OpVq0affv2BeCBBx6gX79+JRazd+/exMbG4ubmhqenJ+PGjSuxWLfC6NGjGTt2LG5ublSqVMl+TWdJGTp0KK+99hqLFi3C29ubqVOnlmg8uPQeY9b/nBg3bhwDBgzA1dUVNze3En89a9asSe/evfH09CQkJIRHHnnEYce+2t/z4cOHM27cuBJ5bzH788Mf4xUUd07klQAAIABJREFUFLB7926qV69eIu8vVzu//v37l+n3F2dn2My4yEBERERERMQJqJ8nIiIiIiJSSAWSiIiIiIhIIRVIIiIiIiIihVQgiYiIiIiIFFKBJCIiIiIiUkgFkoiIALB582YGDBhw2b4pU6aQnJxc4rGHDBlChw4dSE9Pt+9LSEggLCyMmJgYOnfuTI8ePUr8ZqYiIiK6D5KIiNxyGzZsYNOmTVfs79atG506dQIgPT2dl156ieXLl5epe56JiEjpog6SiIjckIkTJxIVFUVUVBTvv/8+ALGxsaSkpACQkpJCbGwscOnGwz179rziBrkbN24kKiqKLl268PLLL5OZmcno0aPJzMzkxRdfvG78OnXq0KhRI7Zs2cKxY8d44YUX6N69O+3bt2fNmjXs27fPfpd6uHQjxu+//96RL4GIiPwJqIMkIiJ2//3vf4mJibE/PnjwIP369ePLL7/k0KFDLF68mPz8fDp37syDDz54zeMcPXqU5ORkKlasaN9ns9kYMWIECxcupGrVqrz//vskJiYyevRovvjiCxITE4vM7/bbb+f06dPs3buX7t27ExISwtatW0lISOC9996jXLly7Nmzh0qVKnHo0CEaN278v70gIiLyp6MCSURE7B588EGmT59ufzxlyhTg0vS24OBgDMPAzc2Ne++997LrheBSAfSbihUrXlYcAZw+fRpvb2+qVq0KwAMPPMC0adOKld+RI0f429/+xm233UZiYiJLly7FMAzy8/MBiIqKIjk5merVq9O2bdtiHVtERAQ0xU5ERG5AnTp12LJlCwAXL15k27Zt1KxZE3d3dzIyMgAuW0DBYrnyz0vFihXJzs7mxIkTAKSmplKrVq0bzuHnn39mz5493Hfffbzxxhu0a9eOyZMnExISYi/OWrduzcaNG/niiy9UIImIyE1RB0lERIr02GOPkZqaSnR0NBcvXqR169Y0atSIqKgo4uLiWLlyZZHFjmEYjBs3jr59+2IYBrfddhsTJky47nPmzp3LqlWrsFgsuLq6MmPGDFxdXWndujXx8fG8/fbbVKtWjdOnTwPg4eHBAw88wKlTp6hQoYKjTl9ERP5EDNvv50SIiIg4udGjRxMWFsZDDz10q1MREREnpCl2IiJSZvTo0YMLFy6oOBIRkZumDpKIiIiIiEghdZBEREREREQKqUASEREREREppAJJRERERESkkAokERERERGRQiqQRERERERECqlAEhERERERKaQCyUG2b99OTEwM4eHhtGnThl69erF79+7rPichIYExY8aUSD7PPfcce/bsYfPmzbRp06ZEYoiIiIiIlDWutzqBsiAvL4/nn3+ed999l0aNGgGwfPlynnvuOdauXYuLi4vpOc2ZMweAkydPmh5bRERERMRZqYPkAOfPnycrK4tz587Z97Vt25YRI0bw9ddfX9bB+WNHJz09nWeffZY2bdrw6quvkp2dDcCHH35I27ZteeaZZ+jcuTN79uwBYN++fcTExPDUU08RHh7OqlWrAHj88cfp378/TzzxBF988QWPP/44O3bsAODcuXP069ePdu3aERMTw759+4BLhd348eNp3749bdu2JTY21h7/j8cTEREREfkzUIHkALfddhuvvvoqvXr14q9//Suvvvoqy5Yt4+GHH8bNze26z/3ll19ISEhg5cqV2Gw2EhMTKSgoYPz48fzrX/9i2bJldOjQgS1btgAwcOBAWrduzaeffsrs2bOZNm2avaipW7cun332Ga1atbosxtGjR+nWrRvLly+nTZs2DBkyBIDZs2fj4uJCcnIyK1asoEqVKkyZMsX+vGsdT0RERESkrFKB5CDdu3dn48aNvPbaa1SuXJk5c+bw9NNPk5WVdd3ntWrVCj8/PwzD4JlnnmHTpk24uLjQunVrOnbsyJgxY/D19SUyMpIzZ86wa9cuoqKiAKhWrRpr1qzB29sbgODg4KvGqFevHkFBQQC0b9+enTt3kpWVxfr161m3bh1PP/007dq1Y82aNaSnp9ufd63jiYiIiIiUVboGyQG2bNnCtm3b6NWrF4899hiPPfYYAwcOpE2bNuzatQubzWYfe/Hixcue+/vrk6xWK66ul74lU6ZM4eeff2bTpk3Mnj2b5cuXEx8fD4BhGPbn7N27l+rVqwNQvnz5q+ZnsVxeBxuGgaurK1arlbi4OB555BEAcnJyyM3NtY+71vFERERERMoqdZAcwM/Pj8TERL799lv7voyMDLKzs2nZsiVHjhzh5MmT2Gw2Pv3008ueu27dOs6ePUtBQQGLFy8mNDSUU6dO8cgjj1ChQgW6detG//792bFjB97e3jRq1IiPP/4YuDR1rlOnTkV2qX766Sd+/PFHAJKSkmjSpAmenp40b96cBQsWkJeXh9VqZcSIEUybNs3Br46IiIiIiPNQB8kBateuzZtvvsn06dM5duwYHh4e+Pj4MH78eOrXr0/Hjh155plnqFy5Mo8++qh98QSAOnXq8Pzzz5OZmUmTJk3o3bs3Hh4evPjii3Tr1o1y5crh4uLCuHHjAJg6dSqvv/468+bNwzAM4uPjqVy58nXzCwwMZObMmRw8eJDbb7+diRMnAtCnTx8mTZpE+/btKSgooEGDBsTGxpbcCyUiIiIiUsoZtt/P/xIREREREfkT0xQ7ERERERGRQiqQRERERERECqlAEhERERERKaRFGkREREREpFS6ePEicXFxHD58mLy8PF588UXuvPNOYmNjMQyDunXrMmrUKCwWCzNnzmT9+vW4uroSFxdH48aNOXDgwFXHXo8KpP9R3pE0U+MZ7p6mxivTrAXmhjt1xNx4339larz9w/9raryOWWdMjfcvl6qmxrt7Qn1T4+Fi8p+DvNyixzhKQb55sQBbvrnvLQU7d5sab8sSL1PjTfc4b2q8frnmnl/wkydNjefeuoWp8ahwu6nhLNXqmhbLuO36qwiXBPeaQabHvBmu7n8p1vj8vMPX/NqKFSuoUKECkydP5vTp07Rv35769evTv39/QkJCGDlyJGvXrqV69eqkpqayZMkSjh49St++fVm2bBkTJky4YmyrVq2um4+m2ImIiIiIiMMYxdyup3Xr1rzyyiv2xy4uLqSlpdG0aVMAQkND2bRpE1u2bKF58+YYhkH16tUpKCjg1KlTVx1bFBVIIiIiIiLiMIZhFGtLSkoiIiLCviUlJdmP5eXlhbe3N9nZ2fTr14/+/ftjs9kwDMP+9aysLLKzs/H29r7seVlZWVcdWxRNsRMREREREYf5rSC5UdHR0URHR1/z60ePHuWll16ic+fOhIeHM3nyZPvXcnJy8PX1xdvbm5ycnMv2+/j4XHa90W9ji6IOkoiIiIiIOIxRzP+u59dff6VHjx68+uqrREZGAtCwYUM2b94MQEpKCsHBwQQFBbFhwwasVitHjhzBarXi5+d31bFFUQdJREREREQcprgdpOt56623yMzMZNasWcyaNQuA4cOHM27cOKZNm0ZgYCBhYWG4uLgQHBxMdHQ0VquVkSNHAjB06FBGjBhx2dgi87fZbDaHncGfkFaxc2Jaxc6htIqdY2kVOwfSKnYOpVXsHEur2DmWVrErHbzK1yrW+Jxz+0skj5ulDpKIiIiIiDhMUdPmSjsVSCIiIiIi4jAWB06xuxVUIImIiIiIiMM48hqkW0EFkoiIiIiIOIxFU+xEREREREQuUQdJRERERESkkK5BEhERERERKaQOkoiIiIiISCFnvwbJcqsTuJbNmzdTr149Vq1addn+8PBwYmNjb/q4CxcuJCEh4X9NT0RERERErsIwjGJtpU2pLZAAAgMD+eSTT+yPf/rpJ86fN/eO2iIiIiIicuOMYv5X2pTqKXb169dn//79ZGZm4uvry4oVKwgPD+fo0aOsWLGC999/H3d3d2rVqsWYMWNYuXIlX331FRcuXOCXX37hueeeIyIigm+//Zbx48dz2223YbFYuO+++wCYOnUqO3fuJCcnhzp16jBhwgQ6duzI2LFjqVu3Ll999RXr169n1KhRt/iVEBERERFxDs6+SEOp7iABtGrVii+++AKbzcb333/P/fffz5kzZ0hISOD9999n4cKF+Pj4kJSUBEB2djZvv/02iYmJzJ49G4AJEyYwdepU3nvvPfz9/e3jfH19ee+991i0aBHbt2/n+PHjREVF8dFHHwGwbNkyIiMjb82Ji4iIiIg4IU2xK2Hh4eGsWrWKb775huDgYACsVit33nkn3t7eADzwwAPs3r0buNR1AqhWrRp5eXkAHD9+nNq1awMQFBQEgIeHB6dOnWLgwIGMHDmSc+fOcfHiRZ588knWrVvHyZMnOXbsGI0aNTL1fEVEREREnJkFo1hbaVPqC6SAgADOnTvHvHnzaNu2LXCpKk1PT+fcuXMApKam2gugq1WhlStXJj09HYAdO3YAkJKSwtGjR5k2bRoDBw7kwoUL2Gw2PD09CQkJIT4+nnbt2plxiiIiIiIiZYaLYSnWVtqUvoyu4sknn+To0aP2IqhixYr07duXrl270qFDB06fPk2nTp2u+fzJkyczdOhQ/v73v3PkyBEAGjduzMGDB+nQoQP9+vUjICCAEydOANChQwfWrFlDeHh4yZ+ciIiIiEgZYhiWYm2lTaldpCEkJISQkBAAYmJiiImJASA0NJTQ0FCAKwqYiIgI+789PDxYt24dAHfeeSdLly69IsayZcuuGrugoIDWrVvj6+v7v5+IiIiIiMifSGlcma44Sm2BdKvMnz+fZcuWMWPGjFudioiIiIiI03H2VexUIP1Bly5d6NKly61OQ0RERETEKamDJCIiIiIiUkgdJBERERERkULqIImIiIiIiBRSB0lERERERKSQOkgiIiIiIiKF1EESEREREREpVBIdpO+++44pU6Ywb948BgwYwK+//grA4cOHuffee5k+fTovvPACZ86cwc3NDQ8PD/71r39x4MABYmNjMQyDunXrMmrUKCyW69+cVgWSiIiIiIg4jMXBBdKcOXNYsWIFnp6eAEyfPh2As2fP0rVrV4YNGwbAL7/8wqefforxuw7WhAkT6N+/PyEhIYwcOZK1a9fSqlWrIvIXERERERFxEMMwirUlJSURERFh35KSki47Xo0aNUhISLgiTkJCAl26dKFKlSr8+uuvZGZm8sILL9CpUye+/PJLANLS0mjatCkAoaGhbNq0qcj81UESERERERGHKW4HKTo6mujo6Gt+PSwsjEOHDl227+TJk3z99df27tHFixfp0aMHXbt25ezZs3Tq1InGjRtjs9nsHSUvLy+ysrKKzEcFkoiIiIiIOIxhwiINn3/+OW3atMHFxQWASpUq0bFjR1xdXbn99ttp0KAB+/btu+x6o5ycHHx9fYs8tgqk/5FhmDxL0VpgbjyLi7nxzGT2ueXnmRsv84yp4X7MqmBqvLRT35ka77BfLVPjNTp1ytR4RrXqpsaz5WSbFsuwmLuaklE4R968eB6mxjP55STbau57Z7ZR9IcnR7Jm55sajwvnTY53ztRwtlwT31tst5sWy9mYscz3119/zYsvvmh/vGnTJhYsWMDs2bPJyclh9+7dBAYG0rBhQzZv3kxISAgpKSk8+OCDRR5bBZKIiIiIiDiMoxdpuJp9+/YREBBgf/zII4+wYcMGOnTogMViYeDAgfj5+TF06FBGjBjBtGnTCAwMJCwsrMhjq0ASERERERGHKYkpdv7+/ixevNj++NNPP71izPDhw6/YV7t2bebPn1+sWCqQRERERETEYczoIJUkFUgiIiIiIuIwKpBEREREREQKOXd5pAJJREREREQcyGLCMt8lSQWSiIiIiIg4jBnLfJckFUgiIiIiIuIwugZJRERERESkUEks820mFUgiIiIiIuIw6iCJiIiIiIgU0jVIIiIiIiIihSy3OoH/kQokERERERFxGBfDuUskFUgiIiIiIuIwmmLnQBMnTiQtLY2MjAwuXLhAQEAAFStWZMaMGQ45fm5uLk888QTr1q1zyPFERERERORyzt0/KmUFUmxsLADJycns3buXwYMH3+KMRERERESkOLSKXQmLjY3lySefJDQ0lJSUFFatWsXEiRP57LPPmDt3LhaLhSZNmjB48GC2bNnCpEmTcHV1xdfXlylTpmAYBoMHDyYzM5MaNWrYj5uamsrMmTMBuHDhApMmTSI1NZX9+/czdOhQCgoKePrpp1m2bBnu7u636vRFRERERJyKs98HySk7YGfOnCEhIYG5c+eycOFCjh8/zsaNG1mzZg2tWrVi/vz5REZGkpmZyUcffcRdd93FggUL6Nixo/0Yu3fvZvLkyXzwwQc8/vjjfP755zz11FOsXbuWgoIC/vOf/xASEqLiSERERESkGCzF3EqbUt9B+j2bzQbAL7/8wqlTp+jduzcAOTk5HDx4kBdeeIG33nqLv//971StWpXGjRuze/duWrRoAcC9996Lq+ulU65atSrx8fGUL1+e48ePExQUhLe3Nw888AAbNmwgOTmZPn363JoTFRERERFxUlqkoYS5u7uTkZEBwA8//ACAv78/1apV491338XNzY3k5GQaNGjAypUrad++PUOHDuXtt99m8eLFBAYGsn37dlq2bMkPP/xAfn4+AK+99hpr1qzB29uboUOH2ouvDh06MGfOHE6fPk39+vVvzUmLiIiIiDgpXYNUwqKiooiLi2PlypXUqlULAD8/P7p160ZMTAwFBQX85S9/4YknniAvL4/Y2FjKly+Pm5sbY8aMoVq1agwbNoxOnToRGBiIm5sbAO3ataNDhw74+vpSqVIlTpw4AVzqMh04cIBnn332Vp2yiIiIiIjTKo3T5oqjVBZIERER9n/fc889rFy58oox7dq1o127dpftu/fee0lOTr5i7OTJk6/YN2zYMIYNG3bFfqvVSvny5WnTps3NpC4iIiIi8qfm7FPsnL3Ac6iDBw/Svn172rVrh7e3961OR0RERETE6VgwirWVNqWyg3SrBAQEsHz58ludhoiIiIiI0yp9JU/xqEASERERERGHseg+SCIiIiIiIpeUxH2QvvvuO2JiYgBIS0ujRYsWxMTEEBMTw6pVqwCYOXMmkZGRdOzYke+//x6AAwcO0KlTJzp37syoUaOwWq1FxlIHSUREREREHMbRizTMmTOHFStW4OnpCVy69U/37t3p0aOHfUxaWhqpqaksWbKEo0eP0rdvX5YtW8aECRPo378/ISEhjBw5krVr19KqVavrxlMHSUREREREHKa4HaSkpCQiIiLsW1JS0mXHq1GjBgkJCfbHO3fuZP369Tz77LPExcWRnZ3Nli1baN68OYZhUL16dQoKCjh16hRpaWk0bdoUgNDQUDZt2lRk/uogiYiIiIiIwxS3gxQdHU10dPQ1vx4WFsahQ4fsjxs3bkxUVBR33303iYmJvPnmm/j4+FChQgX7GC8vL7KysrDZbBiF10T9tq8o6iCJiIiIiIjDlMQ1SL/XqlUr7r77bvu/f/jhB7y9vcnJybGPycnJwcfHB4vFctk+X1/fG8pfRERERETEIUr6Pkg9e/a0L8Lw9ddf06hRI4KCgtiwYQNWq5UjR45gtVrx8/OjYcOGbN68GYCUlBSCg4OLPL6m2ImIiIiIiMOU9CLfo0ePZuzYsbi5uVGpUiXGjh2Lt7c3wcHBREdHY7VaGTlyJABDhw5lxIgRTJs2jcDAQMLCwoo8vgokERERERFxmJvpChXF39+fxYsXA9CoUSMWLVp0xZi+ffvSt2/fy/bVrl2b+fPnFyuWCiQREREREXEYZ7+GRwWSiFncPW91BmVKhXJepsY742Ly272Li7nxzuUUPcaRsrNNC2UzLVIhs793BQWmhjuFue9lHhZzP6qcNfl3Pf+M6T+h5nJ1NzeeiX9rbQX5psVyNo6+D5LZVCCJiIiIiIjDqIMkIiIiIiJSyLn7RyqQRERERETEgUpikQYzqUASERERERGH0RQ7ERERERGRQi7qIImIiIiIiFyiDpKIiIiIiEgh5+4fqUASEREREREH0iINIiIiIiIihSxOfv9jFUgiIiIiIuIwugZJRERERESkkHNPsFOBJCIiIiIiDqQOkoiIiIiISCEt0iAiIiIiIlLIucujEuiAde3ale+//x6AvLw8mjRpwjvvvGP/epcuXdi1a9d1j5Genk5MTIyjUyMlJYXY2FiHH1dERERERC6xFHMrbRyeU/Pmzfn2228B2LJlC82bN2f9+vUA5ObmcvToUerXr+/osCIiIiIiUgpYbMXbShuHT7F7+OGHmTVrFj169OCrr74iKiqKKVOmkJWVRVpaGk2bNuWzzz5j7ty5WCwWmjRpwuDBgzlx4gSDBw/GZrNRuXJl+/HCw8Np2rQpP/30E4ZhMGvWLHx8fJg6dSrffPMNNpuNbt268cQTT7BgwQI+/vhjLBYLQUFBDB06lPT0dOLi4vD09MTT05PbbrsNgPnz5/Pvf/+b/Px8fHx8SEhIYNiwYYSHh/Poo4+Snp7OpEmTmD17tqNfIhERERGRMktT7P6gYcOG7N27F5vNxjfffEPTpk156KGH2LRpE6mpqbRo0YKEhATmzp3LwoULOX78OBs3buS9996jTZs2zJs3j5YtW9qPl5OTw1NPPcX8+fOpUqUKKSkpfPXVVxw6dIhFixbxwQcf8NZbb5GZmUlycjLDhw8nKSmJgIAA8vPzeeONN+jXrx9z587l/vvvB8BqtXLmzBnmzp3Lhx9+SH5+Pjt27CAqKoqPPvoIgKVLlxIZGenol0dEREREpEzTFLs/HtBioX79+qSkpFC5cmXc3d0JDQ1l69atbNmyBX9/f06dOkXv3r2JiYkhPT2dgwcPsnv3bho3bgxAUFDQZcds2LAhANWqVSM3N5eff/6ZtLQ0YmJi6NWrF/n5+Rw5coQJEyawaNEiunTpwpEjR7DZbFc9rsViwc3NjYEDBxIXF8exY8fIz88nJCSEvXv3cvLkSTZu3Mhjjz3m6JdHRERERKRMc/YCqURWsWvWrBlvv/02Tz31FABNmjRh1qxZWCwW/P39qVatGu+++y5ubm4kJyfToEED9u7dy7Zt26hfvz47duy47HiGcXmjLjAwkJCQEMaOHYvVamXWrFn4+/vzz3/+k9dffx0PDw969uzJtm3bCAwMZNu2bYSGhrJz504Adu3axZo1a1iyZAnnz58nIiICm82GYRiEh4cTHx9Ps2bNcHNzK4mXR0RERESkzCqN1xUVR4kUSA8//DCvvfYa//jHPwBwd3fHx8eHhg0b4ufnR7du3YiJiaGgoIC//OUvPPHEE7zyyisMGDCAVatW4e/vf93jP/7446SmptK5c2fOnTtHy5Yt8fb2pl69ekRGRlKxYkWqVq3Kvffey6hRoxgwYADvvPMOfn5+eHh4ULNmTTw9PYmIiMDd3Z3KlStz4sQJACIiInj00UdZvnx5Sbw0IiIiIiJlWklcg/Tdd98xZcoU5s2bx48//sjYsWNxcXHB3d2dSZMmUalSJcaNG8fWrVvx8vICYNasWVy8eJHBgwdz4cIFqlSpwoQJE/D09Lx+/jabzclrPMc6fvw4Q4YM4f3337+h8ReP/ljCGf2Bi8m3rrK4mBuvDLOeOmJuvJRkU+N99toxU+P1PvetqfGm+DQ1NV6HWF9T4xne3qbGs50+bWo8U7mY+75pPWDue8uaD839WZntfsbUeB0L/EyN90TDg6bG8+7ykKnxqHSHqeEM/7vMi+Vt7s8KgEedB02PeTNmBXQp1vg+B+df9+tz5sxhxYoVeHp6snjxYrp06cLw4cNp0KABixYtYt++fQwbNoxOnTrx5ptv4uf3/9+bcePG0bBhQyIiIpg9ezbu7u5069btuvFK47S/W2b16tX06tWLQYMG3epURERERESckqOvQapRowYJCQn2x9OmTaNBgwYAFBQU4OHhgdVq5cCBA4wcOZKOHTuydOlS4NJth1q0aAFAaGgomzZtKjKeye2I0i0sLIywsLBbnYaIiIiIiNMyijk/LSkpiaSkJPvj6OhooqOj7Y/DwsI4dOiQ/XGVKlUA2Lp1K/Pnz2fBggWcO3eOLl260L17dwoKCujatSt333032dnZ+Pj4AODl5UVWVlaR+ahAEhERERERhynuFLU/FkQ3YtWqVSQmJjJ79mz8/PzsRdFv1xc9+OCD7Nq1C29vb3JycihXrhw5OTn4+hY9hV1T7ERERERExGFKepnv5cuXM3/+fObNm0dAQAAA+/fvp3PnzhQUFHDx4kW2bt1Ko0aNCAoK4quvvgIgJSWFJk2a3FD+1/XJJ5/Y/52RkUGvXr1u4jREREREROTPwGIr3lYcBQUFxMfHk5OTQ9++fYmJiWHGjBnUqVOH8PBwOnToQExMDO3ataNu3bq8+OKLfPrpp3Ts2JFt27bRpUvRC0gUOcXu448/xsvLi9zcXKZPn06/fv2KdxYiIiIiIvKnURLLfPv7+7N48WIAUlNTrzrmueee47nnnrtsX6VKlXjnnXeKFavIAmnmzJm88MIL5ObmsnDhwsuWzRMREREREfk9Z7+G55oF0sCBAzGMS/VfuXLl+P7774mPjwdg6tSp5mQnIiIiIiJOpbjT5kqbaxZIHTt2vOxxjx49SjwZERERERFxbiUxxc5M1yyQmja9dNf47Oxs5syZQ0ZGBo8++ij16tUzLTkREREREXEuFpy7hVTkFMG4uDgCAgLYv38/lSpVYvjw4WbkJSIiIiIiTsjVVryttCmyQDpz5gyRkZG4uroSFBSEzVYKz0JEREREREoFo5hbaVPkKnYA6enpABw7dgyLxdnXpXAsW0G+qfEMlxv6ljmOtcC8WBYX82LdCmZ/78p5mhvPZOfz80yNd3uBib8Lt4LVanI8E/9nm5fJvwv55v6s2PLN/d752sz9u9fQcpup8e7KP2dqvHJ1y5saz/S/DSZ/TsJm8nuZXJXFyRsSFAyZAAAgAElEQVQqRX5ie+2114iLiyM9PZ1+/foxatQoM/ISEREREREn5OztlCILpLvuuot//etfHD58mICAALy8vMzIS0REREREnFBpnDZXHEUWSKtXryYxMZGCggJat26NYRj06dPHjNxERERERMTJlPlV7N577z0WL15MhQoV6NOnD2vWrDEjLxERERERcUIWW/G20qbIDpLFYsHd3R3DMDAMA0/Psn3ht4iIiIiI3DzDyTtIRRZIwcHBDBo0iOPHjzNy5EjuueceM/ISEREREREnVOYXaRg4cCApKSk0aNCAwMBAHn/8cTPyEhERERERJ1RmC6TQ0FAeeOABWrRoQYsWLQgNDTUzLxERERERcUJldordmjVr2LZtG6mpqQwYMIC8vDxCQkJo0aIFwcHBZuYoIiIiIiJOosx2kNzd3QkJCSEkJIRTp06RmprKBx98QFJSEv/973/NzFFERERERJxEme0gpaWlsX79elJSUgBo3rw5Q4YMoXHjxqYlJyIiIiIizsXZ74N0zQIpMjKSJ554gqlTp+Lv729mTiIiIiIi4qQsxq3O4H9zzQLpww8/JCUlhcGDB+Pl5WVfrKFOnTpm5iciIiIiIk6kzE6xu//++7n//vt55ZVXOHnyJP/5z38YOXIkx44dY+3atWbmKCIiIiIiTqLMLtJgs9n48ccf+fbbb/n222/Zv38/9erVIyoqysz8RERERETEiRhGGe0gtWjRggYNGvDwww/z0ksvUa9ePYcHnzhxImlpaWRkZHDhwgUCAgLYvXs3Dz30ENOnT3d4vJSUFFatWsXEiRMdfmwREREREQFLWS2Q1q1bh7u7e4kGj42NBSA5OZm9e/cyePBgNm/ezKJFi0o0roiIiIiIlIySWKPhu+++Y8qUKcybN48DBw4QGxuLYRjUrVuXUaNGYbFYmDlzJuvXr8fV1ZW4uDgaN258zbHXc82vlnRxdD0HDhygV69eREREkJCQAEBMTAzp6ekALFy4kISEBA4dOkR4eDgxMTHMmTOHBQsWEBUVRXR0NJMmTQIgPT2d6OhounXrxsKFC+0x5s+fT9euXencuTPPP/88eXl5DBo0iPXr19uf17t3b3NPXERERETEyVkMW7G2osyZM4fXXnuN3NxcACZMmED//v358MMPsdlsrF27lrS0NFJTU1myZAnTpk3j9ddfv+bYIvO/1hf27dt3o6+Bw+Xm5jJr1iwWLFjA/Pnzrzs2IyODd955h+eee47k5GSGDx9OUlISAQEB5Ofn88Ybb9CvXz/mzp3L/fffD4DVauXMmTPMnTuXDz/8kPz8fHbs2EFUVBQfffQRAEuXLiUyMrLEz1VEREREpCwxDFuxtqLUqFHD3jSBS/drbdq0KQChoaFs2rSJLVu20Lx5cwzDoHr16hQUFHDq1Kmrji3KNQukYcOGAfDSSy8VeRBHq1u3Lu7u7nh6euLqeuUsQJvt/19If39/e7drwoQJLFq0iC5dunDkyBFsNhu7d++239w2KCgIAIvFgpubGwMHDiQuLo5jx46Rn59PSEgIe/fu5eTJk2zcuJHHHnvMhLMVERERESk7ittBSkpKIiIiwr4lJSVddrywsLDLagKbzYZhXJrI5+XlRVZWFtnZ2Xh7e9vH/Lb/amOLcs1rkGrUqEGzZs04e/YszZs3v+xrGzZsuIGX5ub9dhK/5+7uTkZGBnXq1OGHH36gatWqAJfNIVy8eDGvv/46Hh4e9OzZk23bthEYGMi2bdsIDQ1l586dAOzatYs1a9awZMkSzp8/T0REhP3FCw8PJz4+nmbNmuHm5lai5ykiIiIiUtZc5aP8dUVHRxMdHX3D43//+T8nJwdfX1+8vb3Jycm5bL+Pj89VxxblmgXSP/7xDwBef/11Ro0adcMJl5SuXbsyZswYqlWrRpUqVa46pl69ekRGRlKxYkWqVq3Kvffey6hRoxgwYADvvPMOfn5+eHh4ULNmTTw9PYmIiMDd3Z3KlStz4sQJACIiInj00UdZvny5macnIiIiIlImlPQy3w0bNmTz5s2EhISQkpLCgw8+SI0aNZg8eTI9e/bk2LFjWK1W/Pz8rjq2yPxtv5+vdhVZWVkkJiayZ88eatWqRZ8+fahQoYLDTrC0OX78OEOGDOH999+/ofF5h3aUcEaXM9zLmRrPVBaXW51BibKePWFuvK9Xmhrvs8HmXrcYk1n0HGJHWuT7kKnx/jq8oqnxjPLlTY1nO33GvGBenubFAsgvMDVcwe5fTI33TZK5PyurPM392xCZl2tqvEZPnzc1nusjRX84dChPL1PDGTXqmxfLp5JpsX7jUcfk799N+r5WeLHGN95f9GeWQ4cOMXDgQBYvXsy+ffsYMWIEFy9eJDAwkHHjxuHi4kJCQgIpKSlYrVaGDRtGcHDwNcdezzU7SL8ZPnw4wcHBhIeHk5qaSmxsLG+99daNn7ETWb16NTNnziQ+Pv5WpyIiIiIi4pSKO8XuRvj7+7N48WIAateufdWF3Pr27Uvfvn0v23etsddTZIF0+vRpunbtCkCDBg1YvXp1sQI4k7CwMMLCwm51GiIiIiIiTqukp9iVtCILpNzcXDIyMqhcuTK//vorVqvVjLxERERERMQJubg4d71QZIH0yiuv0LFjR3x8fMjOzmbs2LFm5CUiIiIiIk6oJKbYmanIAqlZs2asXbuWU6dO4efnZ0ZOIiIiIiLipAxLGZ9i9xsVRyIiIiIiUpQyfw2SiIiIiIjIjbI4eYFkKWrAu+++y6lTp8zIRUREREREnJxhKd5W2hTZQfL09KRPnz5UqVKFZ555htDQUAxnv/JKRERERERKhLNPsSuyZuvUqROLFi2ib9++rFixgscee4yEhAQyMzPNyE9ERERERJyIYbEVayttiuwgZWZm8umnn7J8+XJ8fHwYPnw4+fn59OnTp9h3pRURERERkbLN2SebFVkgRUZG0rZtW6ZPn061atXs+3ft2lWiiTkLw8XkdS5sJt94y8yJodYC82IBWFxMDWf6z0p5L1PD3V81w9R4nufcTY132M3cnxfOnTc3Xvnypoaz5eaZF8zs19JkBSfPmRrPgqep8Uz+zaOir7k/Ly53+psaD+/bTA1nVP6LqfGwmvg5qSDfvFhOpjR2hYqjyE9sq1evvuyaoxMnTlClShUGDBhQoomJiIiIiIjzKfMFUkJCAh9++CEXL17kwoUL1KpVi08//dSM3ERERERExMk4+xS7IudPpaSkkJKSQnh4OKtWraJq1apm5CUiIiIiIk6ozC/SUKFCBdzd3cnJyaFmzZqcP1+253KLiIiIiMjNK433NiqOIgukO+64g6VLl+Lp6cnUqVPJzs42Iy8REREREXFCzn4fpCILpDFjxnDs2DFat27NRx99xPTp083IS0REREREnFCZ7iDt2rWL1atXc/r0ae644w5at25NrVq1TEpNREREREScTWm8rqg4rlnfffbZZ8TFxVGtWjVatGiBl5cX/fr1Y82aNWbmJyIiIiIiTsSwFG8rba7ZQfrggw+YP38+5X9388D27dvz4osv0rJlS1OSExERERERJ1NWr0FydXW9rDgC8Pb2xsXF7Htai4iIiIiIsyiNXaHiuGaBZFzjDk9Wq7XEkhEREREREedWZgukPXv2MGjQoMv22Ww20tPTSzwpERERERFxTmW2QPrnP/951f0dO3YssWRERERERMTJObhASk5O5qOPPgIgNzeXH3/8kalTp/KPf/yDatWqAdC3b1+Cg4MZPXo0P/30E+7u7owbN46aNWsWO941C6SmTZve5CmIiIiIiMiflaM7SBEREURERADw+uuv88wzz5CWlsarr75KWFiYfdy///1v8vLySEpKYvv27UycOJHExMRix3PyBpiIiIiIiJQqluJtSUlJ9iIoIiKCpKSkqx52x44d7Nmzh+joaNLS0li2bBmdO3dm4sSJ5Ofns2XLFlq0aAHAfffdx86dO28q/eveKLY0O3ToEG3btqVRo0b2fSEhIbz88sv2xwMGDGDSpEm4u7vb96WkpLBq1SomTpxoar4iIiIiIn8Gxe0gRUdHEx0dXeS4t99+m5deegmAZs2a0bJlS/z9/Rk1ahSLFi0iOzsbb29v+3gXFxfy8/NxdS1eyeO0BRLAnXfeybx586759enTp5uYjYiIiIiIlMQctczMTPbu3cuDDz4IwDPPPIOvry8Af/3rX1m9ejU+Pj7k5OTYn2O1WotdHEEZm2K3efNmoqKi6Ny5Mx9//DGPP/44ubm5pKenEx0dTbdu3Vi4cKF9/Pz58+natSudO3fm+eefJy8vj0GDBrF+/XoA0tPT6d279y06GxERERER52O4GsXabsQ333zDww8/DFxaWbtt27YcO3YMgK+//ppGjRoRFBRESkoKANu3b+euu+66qfyduoO0Z88eYmJi7I+joqLIzc1lyZIlAMyYMQOAN954g379+tGsWTNmz57N3r17sVqtnDlzhrlz52KxWOjZsyc7duwgKiqKhQsX8uijj7J06VIiIyNvybmJiIiIiDgjw3JjRU9x7Nu3D39//0vHNwzGjRvHyy+/TLly5ahTpw4dOnTAxcWFjRs30rFjR2w2G+PHj7+pWE5dIP1xit3mzZupXbv2FeN2795N48aNAQgKCmLv3r1YLBbc3NwYOHAg5cuX59ixY+Tn5xMSEkJ8fDwnT55k48aNDBw40LTzERERERFxeiUwR61Xr16XPW7evDnNmze/YtyYMWP+51hOXSBdjcVy5XckMDCQbdu2ERoaal/NYteuXaxZs4YlS5Zw/vx5IiIisNlsGIZBeHg48fHxNGvWDDc3N7NPQURERETEeZVAB8lMZa5AuppRo0YxYMAA3nnnHfz8/PDw8KBmzZp4enoSERGBu7s7lStX5sSJE8CltdYfffRRli9ffoszFxERERFxLiUxxc5MTlsg+fv7s3jx4sv2hYSEEBISYn+8bt06AKpUqcKCBQuuOMYHH3xw1WMXFBTQpEkT6tSp48CMRURERET+BJx8GTgnT9/xVq9eTa9evRg0aNCtTkVERERExPlYjOJtpYzTdpBKSlhYGGFhYbc6DRERERERp6QpdiIiIiIiIr9RgSQiIiIiIlJIBZKIiIiIiMglhqECSURERERE5BJ1kERERERERAqpQBIRERERESlkce47CalAEhERERERx1EHSURERERE5BLdB0lEREREROQ3KpBEREREREQKGboG6U/Neu6sqfEMDy9z47m5mxjMuX+ZimJ4+5kaz/Wh9qbG+8tHLU2Nd+joblPj2U4eNTUep381NZzt5ElT4xmeHqbFsuXmmRYLgHPnTQ3nUrGcqfEe6GVqOB5wMzeeUb2+yfECTI2Hq8kf/VxM/gaauThAwUXzYjkbdZBEREREREQKqUASERERERG5xNAy3yIiIiIiIoXUQRIRERERESnk5NeVq0ASERERERHHUQdJRERERESkkK5BEhERERERKaQOkoiIiIiISCEHX4P09NNP4+PjA4C/vz/R0dHEx8fj4uJC8+bNefnll7FarYwePZqffvoJd3d3xo0bR82aNW8qngokERERERFxHAd2kHJzcwGYN2+efV+7du1ISEggICCA3r17k5aWxuHDh8nLyyMpKYnt27czceJEEhMTbyqmCiQREREREXEYw9WlWOOTkpJISkqyP46OjiY6OhqAXbt2cf78eXr06EF+fj59+/YlLy+PGjVqANC8eXO+/vprMjIyaNGiBQD33XcfO3fuvOn8VSCJiIiIiIjjFHOK3e8Loj8qV64cPXv2JCoqiv379/Pcc8/h6+tr/7qXlxcHDx4kOzsbb29v+34XFxfy8/NxdS1+uaMCSUREREREHMeBU+xq165NzZo1MQyD2rVr4+Pjw5kzZ+xfz8nJwdfXlwsXLpCTk2Pfb7Vab6o4AnDuNfhERERERKR0sViKt13H0qVLmThxIgDHjx/n/PnzlC9fnl9++QWbzcaGDRsIDg4mKCiIlJQUALZv385dd9110+mrgyQiIiIiIo5jOK6DFBkZybBhw+jUqROGYTB+/HgsFguDBw+moKCA5s2bc++993LPPfewceNGOnbsiM1mY/z48Tcds9QUSLNnz2bTpk1YLBYMw2DAgAHcfffdVx2blJREREQEbm5uxY7TrFkzNm7c+L+mKyIiIiIiV+PAG8W6u7szderUK/YvXrz4DyEtjBkzxiExS0WBtGfPHtatW8fChQsxDIMff/yRoUOHsmLFiquOf/vtt3n66adNzlJERERERIrkwALpVigVBZKfnx9Hjhxh6dKlhIaG0qBBA5YuXUpqaiozZ84E4MKFC0yaNIlvv/2WjIwMBgwYwN///ncWLVrE9OnTgf/vDsXGxnLmzBnOnDlDYmIikydPZs+ePQQEBJCXlwfAzz//zMSJE7FarWRmZvLaa69x7tw5Fi9ezIwZMwDo2LEjM2bMoEqVKrfmhRERERERcTYOXKThVigV5Z2fnx+JiYls3bqV6OhoWrduzZdffsnu3buZPHkyH3zwAY8//jiff/45UVFRVK5c2V4UXcuDDz7IokWL2L59O7m5uSxevJhBgwZx/vx54FLXaujQocydO5fu3buTnJxMs2bN+Pnnnzl79ix79uyhYsWKKo5ERERERIrDsBRvK2VKRQfpwIEDeHt7M2HCBAB27NhB7969GTJkCPHx8ZQvX57jx48TFBR03ePYbDb7v2vXrg3A7t27ady4MQDVq1enWrVqAFSpUoVZs2ZRrlw5cnJy8Pb2xjAM2rZtyyeffMKhQ4eIjIwsidMVERERESm7nHyKXanI/qeffmL06NHk5uYC2Nc4Hz9+POPHj2fixIlUqVLFXgAZhoHVasXDw4OMjAwADh8+zNmzZ+3HNApXzwgMDGT79u3ApaUBjx8/DkB8fDz9+vVj0qRJ3HXXXfZjP/PMM3z++ed88803PPLII+a8ACIiIiIiZYRhMYq1lTalooP0t7/9jfT0dKKioihfvjw2m40hQ4bwzTff0KFDB3x9falUqRInTpwAIDg4mN69e/Puu+/i4+NDVFQUderUwd/f/4pjt2zZki1bthAVFUX16tWpWLEiAG3btqVPnz7cfvvt3HHHHZw+fRqAqlWr4uXlxX333XfTN5cSEREREfnTKoXT5orDsP1+XpoA8PzzzxMXF0fNmjWLHJub/l8TMvp/hoeXufHc3E0MZvIvk8XF3Hhmu5hrajjb+SxT41mP7jY1nu3kUVPjcfpXU8PZTp40NZ6ZbLl55gY8d97UcNbT2abGs9xm7t8h3Mz9n5VG9TtMjhdgajzM/LsOGJWu/J/XJcrFvJ8Xsz+TAXg0eMz0mDfj/HtDijXes/s/SiiTm+Pc5Z2DXbhwgYiICOrXr39DxZGIiIiIiPyBxVK8rZTRHLLfKVeuHMnJybc6DRERERER52WUvuuKikMFkoiIiIiIOE4p7AoVhwokERERERFxHCdfpEEFkoiIiIiIOI46SCIiIiIiIoVUIImIiIiIiBTSIg0iIiIiIiKF1EESEREREREppAJJRERERESkkFaxExERERERKaQOkoiIiIiISCF1kP7krFZz4+XnmhvPxcQfEReTf5msBebGs7iYHM+535yKVM7L3Hge5cyNZ/YKQFabqeFsp8+aFsuadd60WACGu7m/64aPp6nxbOdN/juUl29qOIvF5N+9c9mmhjMC6poaz+y/tUaFO8yL5WHy3yFn4uSfQVQgiYiIiIiI46iDJCIiIiIiUkgdJBERERERkUIOvETj4sWLxMXFcfjwYfLy8njxxRe54447eOGFF6hVqxYAnTp14sknn2TmzJmsX78eV1dX4uLiaNy48U3FVIEkIiIiIiKO48AO0ooVK6hQoQKTJ0/m9OnTtG/fnpdeeonu3bvTo0cP+7i0tDRSU1NZsmQJR48epW/fvixbtuymYqpAEhERERERx3HgNUitW7cmLCzM/tjFxYWdO3eyb98+1q5dS82aNYmLi2PLli00b94cwzCoXr06BQUFnDp1Cj8/v2LHVIEkIiIiIiKOU8wOUlJSEklJSfbH0dHRREdHA+DldWm1wOzsbPr160f//v3Jy8sjKiqKu+++m8TERN588018fHyoUKGC/RheXl5kZWWpQBIRERERkVusmB2k3xdEV3P06FFeeuklOnfuTHh4OJmZmfj6+gLQqlUrxo4dy1//+ldycnLsz8nJycHHx+em0nfuJSZERERERKR0sViKt13Hr7/+So8ePXj11VeJjIwEoGfPnnz//fcAfP311zRq1IigoCA2bNiA1WrlyJEjWK3Wm+oegTpIIiIiIiLiQIbhuBtmv/XWW2RmZjJr1ixmzZoFQGxsLOPHj8fNzY1KlSoxduxYvL29CQ4OJjo6GqvVysiRI286pmGz2cy9fXoZk7t7k6nxDDd3c+OZeZdoBy4JWSpZHPdmcUMKLpoazpZz1tR41tNHTI1n+/WQqfE4ftjUcLYTGebGO23ez4s167xpsQAMd5N/191Mfu/MNfe9BRdzX0+XerVNjYdvhaLHOJARUNfUeLia/LnF7y/mxTLzM1Ihd/97TI95My5sXFCs8eWaPVtCmdycMv6JVERERERETKUbxYqIiIiIiBRy4DLft4IKJBERERERcRx1kERERERERAqpgyQiIiIiIlLI7IWpHMwpCqTZs2ezadMmLBYLhmEwYMAA7r777ps6Vnp6OqNHj2bevHkOzlJERERERNRBKmF79uxh3bp1LFy4EMMw+PHHHxk6dCgrVqy41amJiIiIiMgf6RqkkuXn58eRI0dYunQpoaGhNGjQgKVLlxITE8Po0aOpU6cOCxcu5Ndff6V9+/YMGjSIO+64g4MHD3LPPffw+uuvc+LECQYPHozNZqNy5cr2Y3/++ecsWPD/67S/8cYbzJ07l6pVq/Lss89y9uxZunfvTnJy8q04dRERERER5+PkHaRSn72fnx+JiYls3bqV6OhoWrduzZdffnnN8fv37yc+Pp4lS5aQkpJCRkYG7733Hm3atGHevHm0bNnysrGzZ89m3rx51K5dmw0bNhAVFcXHH38MwCeffEJ4eHiJn6OIiIiISFlhGC7F2kqbUt9BOnDgAN7e3kyYMAGAHTt20Lt3bypVqmQfY7PZ7P+uUaMG3t7eAFSuXJnc3Fx2795Nu3btAAgKCmLhwoUA3H777QwdOhQvLy/27t3LfffdR0BAAF5eXuzZs4eVK1cya9Yss05VRERERMT5OfkUu1Kf/U8//cTo0aPJzc0FoHbt2vj4+FChQgUyMjIA+OGHH+zjDcO44hiBgYFs27YNuFRgAWRlZTFjxgymT5/OuHHj8PDwsBdaHTp0IDExkapVq+Ln51ei5yciIiIiUqYYluJtpUyp7yD97W9/Iz09naioKMqXL4/NZmPIkCG4ubkxZswYqlWrRpUqVa57jFdeeYUBAwawatUq/P39AfD29iYoKIj27dtTvnx5fH19OXHiBAAtW7ZkzJgxTJ48ucTPT0RERESkTHHyDpJh+/38NAHg/PnzdOnShSVLlmAp4hucu3uTSVldYri5mxvPw8u8YC6lvl7/35h9T4CCi6aGs+WcNTWe9fQRU+PZfj1kajyOHzY1nO1EhrnxTpv382LNOm9aLADD3eTfdTeT3ztzzX1vwcXc19OlXm1T4+FbwdRwRkBdU+PhavLnFr+/mBfLzM9Ihdz97zE95s3I3fVVscZ71H+khDK5Oc5d3pWArVu30qFDB/r06VNkcSQiIiIiIn9gcSneVsqU8f9lX3xBQUGsXLnyVqchIiIiIuKcSuF1RcWhAklERERERBzHyWdhqUCS/2vv3mOqrh8/jr+OgqKigJpOixR1bkSpUwkrlSQVbCZqXA8ehrApiBBIeriInsYlWURTlobO0iHhzJiMyVRyKMlFHGgq0uqgWV4i5TI6hAGH9+8PDuendKz8et7HrNfjL8/ZPM/POfB5f86b9+ecDxERERGR2Si4gkRERERERGTAFSQiIiIiIiIDriAREREREREZ/AO/me5RcIJERERERETm85Rf2/Lp3noiIiIiIvpH4Zc0EBERERER9eGXNBARERERERlwBYmIiIiIiMjgKf+SBoUQQjzpjSAiIiIiIvoneLrXv4iIiIiIiMyIEyQiIiIiIiIDTpCIiIiIiIgMOEEiIiIiIiIy4ASJiIiIiIjIgBMkIiIiIiIiA06QiIiIiIiIDDhBsrCenh5s2bIF/v7+UKlUuH79uvTmN998A5VKJb3T1dWFjRs3QqlUwsfHBydPnpTa0+v1SEhIQEBAAIKCgvDjjz9K7fVpamqCu7s7GhoapLeWL18OlUoFlUqFhIQE6b2cnBz4+/tj5cqV+OKLL6S2CgoKjM/Nz88PL730Etra2qT1urq6EBcXh4CAACiVSuk/v87OTsTFxcHPzw+hoaH44YcfpLXu38evX7+OwMBAKJVKbN26FT09PVJ7AFBSUoK4uDizd/q36uvroVQqoVKpEBYWhrt370rtabVaBAYGIiAgABqNBnq9XmqvT1FREfz9/c3e6t+rq6vDvHnzjPthcXGx1F5TUxMiIiIQFBSEgIAAKWP2/b3Y2Fjjc/Pw8EBsbKzUXn19Pfz8/BAYGIiEhATp+15dXR18fHygVCqRkpJi1p6p47nMseXP3j+kp6cjPz/fbK2H9WSOL6Z6lhhf6DEIsqjjx48LtVothBDi/PnzIjw8XGpv9+7dYunSpcLX11dqRwghDh8+LFJTU4UQQjQ3Nwt3d3epvZKSEhEfHy+EEKKqqkr6aymEEJ2dnWLdunVi8eLFQqvVSm3du3dPeHt7S23cr6qqSqxdu1bo9Xqh0+nEjh07LNbWaDTi4MGDUhslJSUiOjpaCCHEmTNnxPr166X2cnNzxebNm4UQQjQ0NIjQ0FApnf77+Nq1a0VVVZUQQojk5GRx4sQJqSJ1EK0AAAsSSURBVL2UlBTh6ekpYmJizNox1QoKChJXrlwRQgiRn58v0tPTpfYiIiJEdXW1EEIItVot/bUUQogrV66I4OBgKWN2/96hQ4fE3r17zd55WE+tVoujR48KIYSorKwUpaWlUnt9WltbxbJly0RjY6PU3rp168SpU6eEEEJs2LBBnDx5UmpvxYoVoqamRgghRFZWljhy5IjZWqaO5zLHFlO9pqYmERYWJt544w3x+eefm631sJ7M8cVUT/b4Qo+HK0gWVlNTg3nz5gEAZsyYgcuXL0vtPf/888jOzpba6OPl5YV33nnHeHvgwIFSewsXLkRKSgoA4NatWxg9erTUHgBkZGQgICAAY8aMkd769ttv0dHRgdDQUAQHB+PChQtSe2fOnMHUqVMRGRmJ8PBwvP7661J7fS5dugStVivtL+Z9nJycoNfr0dPTA51OBysrK6k9rVaL+fPnAwAmTZokbcWq/z5eV1eHl19+GQAwf/58VFRUSO3NnDkTGo3GrI2HtbKysuDs7AygdwV58ODBUnvZ2dlwdXVFZ2cn7ty5g1GjRknttbS0IDMzE4mJiWbtPKx3+fJlnDp1CkFBQUhMTIROp5Paq62tRWNjI0JCQlBUVGT8PZXV65OdnY1Vq1aZfdzu33N2dkZrayuEEGhvbzf7GNO/19jYiJkzZwLo3Q9ramrM1jJ1PJc5tpjqtbe3IyoqCt7e3mbr/FlP5vhiqid7fKHHwwmShel0Otja2hpvDxw4EN3d3dJ6np6e0t8I9hk2bBhsbW2h0+kQHR2NmJgY6U0rKyuo1WqkpKTA09NTaqugoAAjR440TnBls7GxQVhYGPbu3Yv33nsP7777rtTflZaWFly+fBnbt2839oQQ0np9cnJyEBkZKb0zdOhQ3Lx5E0uWLEFycrL0006dnZ1RWloKIQQuXLiAxsZGKadQ9N/HhRBQKBQAevfJX3/9VWrvzTffNPbMrX+r7w1ubW0tDhw4gJCQEKm9gQMH4ubNm1i6dClaWlrg5OQkrafX65GUlITExEQMGzbMrB1TPQCYNm0aNm3ahLy8PDg6OuLjjz+W2rt58yZGjBiBffv2Ydy4cdizZ4/UHtB7Wl9lZSVWrlxp1pap3sSJE5GWloYlS5agqakJbm5uUnuOjo6orq4GAJSWlqKjo8NsLVPHc5lji6meo6Mjpk+fbrbGX/Vkji+merLHF3o8nCBZmK2tLdrb2423e3p6LDaBsYTbt28jODgY3t7eeOuttyzSzMjIwPHjx5GcnIzffvtNWufLL79ERUUFVCoV6uvroVarcefOHWk9JycnLFu2DAqFAk5OTrC3t5fas7e3x9y5czFo0CBMmjQJgwcPRnNzs7QeALS1teHq1auYM2eO1A4A7Nu3D3PnzsXx48dRWFiI+Ph4/P7779J6b7/9NmxtbREcHIzS0lK4uLhIX1UFgAED/n9Yb29vx4gRI6Q3Lam4uBhbt27F7t27MXLkSOm9Z599FidOnEBgYCC2bdsmrVNXV4fr169Do9Fgw4YN0Gq1SEtLk9YDgEWLFuHFF180/vvKlStSe/b29vDw8AAAeHh4SD+DAgCOHTuGpUuXWmTfS0tLQ15eHo4dO4bly5dL/X0Bej+bk5OTgzVr1mDUqFFwcHAw6+P3P57LHlss/f7BVE/m+GKqZ6nxhR4dJ0gWNnPmTJSVlQEALly4gKlTpz7hLTKfu3fvIjQ0FBs3boSPj4/03pEjR5CTkwMAGDJkCBQKhdSDYF5eHg4cOIDc3Fw4OzsjIyMDzzzzjLTe4cOHjQNmY2MjdDqd1N6sWbPw9ddfQwiBxsZGdHR0wN7eXloPAM6dO4dXX31VaqPPiBEjMHz4cACAnZ0duru7pX4o9tKlS5g1axZyc3OxcOFCODo6Smvd74UXXsDZs2cBAGVlZZg9e7ZFupZQWFho3Act8XqGh4cbv1xj2LBhD7xBNLdp06bh6NGjyM3NRVZWFqZMmYKkpCRpPQAICwvDxYsXAQCVlZVwcXGR2ps1axZOnz4NoHffnzJlitQe0Pu8+k51lc3Ozs54hsiYMWOkfukMAJw+fRrp6enYvXs3Wltb8dprr5ntsU0dz2WOLZZ+/2CqJ3N8MdWz5PhCj+7fs3TxlFi0aBHKy8sREBAAIQTS09Of9CaZzSeffIK2tjbs3LkTO3fuBADs2bMHNjY2UnqLFy9GQkICgoKC0N3djcTERLN/JuFJ8vHxQUJCAgIDA6FQKJCeni51tXHBggU4d+4cfHx8IITAli1bpP/V9dq1a3juueekNvqEhIQgMTERSqUSXV1diI2NxdChQ6X1JkyYgO3bt+PTTz/F8OHDpa8G9FGr1UhOTkZWVhYmTZok/dRTS9Hr9UhLS8O4ceMQFRUFAHB1dUV0dLS05po1axAfHw9ra2sMGTIEqamp0lpPgkajQUpKCqytrTF69GjjZzplUavV2Lx5Mw4ePAhbW1t8+OGHUntA7xhjqT9OpKamIjY2FlZWVrC2tpb+ek6YMAFr1qzBkCFD4ObmBnd3d7M9tqnjeVJSElJTU6WMLZZ+/9C/p9fr8f3332P8+PFSxhdTzy8mJuZfPb487RTCEh8yICIiIiIiegpwPY+IiIiIiMiAEyQiIiIiIiIDTpCIiIiIiIgMOEEiIiIiIiIy4ASJiIiIiIjIgBMkIiICAJw9exaxsbEP3JeZmYmCggLp7U2bNsHPzw8NDQ3G+7Kzs+Hp6QmVSgWlUonQ0FDpFzMlIiLidZCIiOiJO3PmDCoqKv5wf0hICAIDAwEADQ0NiIyMRGFh4b/qmmdERPTPwhUkIiL6W7Zt2wZfX1/4+vpi//79AID4+HiUlZUBAMrKyhAfHw+g98LDYWFhf7hAbnl5OXx9fbFq1SqsX78ebW1t0Gg0aGtrQ0RExJ/2J0+eDBcXF9TU1ODnn39GeHg4Vq9ejRUrVuCrr77CtWvXjFepB3ovxHjx4kVzvgRERPQfwBUkIiIyqqqqgkqlMt7+6aefEB0djdLSUty4cQOHDh1Cd3c3lEol5syZ89DHuX37NgoKCuDg4GC8TwiB5ORk5OfnY+zYsdi/fz927doFjUaDkpIS7Nq16y+3b9SoUWhpacHVq1exevVquLm5oba2FtnZ2fjss89gY2MDrVaL0aNH48aNG5g2bdrjvSBERPSfwwkSEREZzZkzBx999JHxdmZmJoDe09tmz54NhUIBa2trTJ8+/YHPCwG9E6A+Dg4OD0yOAKClpQW2trYYO3YsAMDV1RVZWVmPtH23bt3C4sWLYWdnh127duHw4cNQKBTo7u4GAPj6+qKgoADjx4/HsmXLHumxiYiIAJ5iR0REf8PkyZNRU1MDAOjq6sL58+cxYcIEDBo0CHfu3AGAB75AYcCAPx5eHBwcoNPp8MsvvwAAqqurMXHixL+9Dd999x20Wi1mzJiB7du3w9vbGx988AHc3NyMkzMvLy+Ul5ejpKSEEyQiIvqfcAWJiIj+0oIFC1BdXQ1/f390dXXBy8sLLi4u8PX1RWJiIoqKiv5ysqNQKJCamoqoqCgoFArY2dnh/fff/9P/s2/fPhQXF2PAgAGwsrLCjh07YGVlBS8vL6SlpSEnJwfjxo1DS0sLAGDw4MFwdXVFc3Mz7O3tzfX0iYjoP0Qh7j8ngoiI6Cmn0Wjg6emJV1555UlvChERPYV4ih0REf1rhIaG4t69e5wcERHR/4wrSERERERERAZcQSIiIiIiIjLgBImIiIiIiMiAEyQiIiIiIiIDTpCIiIiIiIgMOEEiIiIiIiIy+D/L2szzI+PuaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The hourly usage vary during weekdays for customers and subscribers\n",
    "\n",
    "plt.suptitle('Hourly Usage during Weekdays for Customers and Subscribers')\n",
    "\n",
    "sb.set_style('darkgrid')\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "\n",
    "customers = df_Q1_2020_clean.query('user_type == \"Customer\"')\n",
    "ct_counts = customers.groupby(['start_dayofweek', 'start_hourofday']).size()\n",
    "ct_counts = ct_counts.reset_index(name='count')\n",
    "ct_counts = ct_counts.pivot(index='start_dayofweek', columns='start_hourofday', values='count')\n",
    "sb.heatmap(ct_counts, cmap='rocket_r');\n",
    "plt.title('Customer', loc='left');\n",
    "plt.xlabel('Hour of Day');\n",
    "plt.ylabel('Day of Week');\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "subscribers = df_Q1_2020_clean.query('user_type == \"Subscriber\"')\n",
    "st_counts = subscribers.groupby(['start_dayofweek', 'start_hourofday']).size()\n",
    "st_counts = st_counts.reset_index(name='count')\n",
    "st_counts = st_counts.pivot(index='start_dayofweek', columns='start_hourofday', values='count')\n",
    "sb.heatmap(st_counts, cmap='rocket_r');\n",
    "plt.title('Subscriber', loc='left');\n",
    "plt.xlabel('Hour of Day');\n",
    "plt.ylabel('Day of Week');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "observation: Subscribers use the system heavily on work days i.e. Monday through Friday whereas customers ride a lot on weekends, especially in the afternoon. Many trips concentrated around 7-9am and 16-18pm on work days for subscribers when used for work commute, yet customers tend to use more in the late afternoon around 12-16pm Saturday to Sunday, weekend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize = [8,6]);\n",
    "\n",
    "ax = sb.pointplot(data = df_Q1_2020_clean, x = 'rental_access_method', y = 'duration_minute', hue = 'user_type', \n",
    "                  palette = 'Greens', linestyles = '', dodge = 0.4);\n",
    "\n",
    "plt.title('Biking durations for rental access method and user type');\n",
    "plt.ylabel('Avg. Duration (min)');\n",
    "plt.xlabel('Rental Access Method');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "subscribers have steady duration for their ride while the customer vary in duration for their rides, dipper seems to have vary in duration over the app "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "\n",
    "> - Almost the same discovered insights in the previous bivariate and univariate exploration.\n",
    "> - The short period of usage for subscribers corresponds to their high concentration on rush hours from Monday through Friday, indicating the use is primarily for the work commute.\n",
    "> - The flexible pattern of customer use shows that they are taking advantage of the bike sharing system quite differently from the subscribers, and heavily over weekends and in the afternoon, for city tour or leisure purpose probably.\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> not too much as the current dataset for 2020 doesnot contain two must intersecting varaibles there are only two categroical variable user type and rental access with very far percentage between their values."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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