data-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ford GoBike System Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This document explores a data set that includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area in (2019)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminary Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import glob\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/m/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (14) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/Users/m/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (13,14) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n",
      "/Users/m/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (13) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# load in the datasets (01/2019 -> 12/2019) into a pandas dataframe\n",
    "df = None\n",
    "\n",
    "for filename in sorted(glob.glob('data/*.csv')):\n",
    "    if df is None:\n",
    "        df = pd.read_csv(filename)\n",
    "    else:\n",
    "        temp = pd.read_csv(filename)\n",
    "        df = df.append(temp, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2506983, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
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       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>rental_access_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80825</td>\n",
       "      <td>2019-01-31 17:57:44.6130</td>\n",
       "      <td>2019-02-01 16:24:49.8640</td>\n",
       "      <td>229.0</td>\n",
       "      <td>Foothill Blvd at 42nd Ave</td>\n",
       "      <td>37.775745</td>\n",
       "      <td>-122.213037</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Grand Ave at Perkins St</td>\n",
       "      <td>37.808894</td>\n",
       "      <td>-122.256460</td>\n",
       "      <td>4861</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65900</td>\n",
       "      <td>2019-01-31 20:58:33.8860</td>\n",
       "      <td>2019-02-01 15:16:54.1730</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Cyril Magnin St at Ellis St</td>\n",
       "      <td>37.785881</td>\n",
       "      <td>-122.408915</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>5506</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62633</td>\n",
       "      <td>2019-01-31 18:06:52.9240</td>\n",
       "      <td>2019-02-01 11:30:46.5300</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Downtown Berkeley BART</td>\n",
       "      <td>37.870139</td>\n",
       "      <td>-122.268422</td>\n",
       "      <td>157.0</td>\n",
       "      <td>65th St at Hollis St</td>\n",
       "      <td>37.846784</td>\n",
       "      <td>-122.291376</td>\n",
       "      <td>2717</td>\n",
       "      <td>Customer</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>44680</td>\n",
       "      <td>2019-01-31 19:46:09.7190</td>\n",
       "      <td>2019-02-01 08:10:50.3180</td>\n",
       "      <td>85.0</td>\n",
       "      <td>Church St at Duboce Ave</td>\n",
       "      <td>37.770083</td>\n",
       "      <td>-122.429156</td>\n",
       "      <td>53.0</td>\n",
       "      <td>Grove St at Divisadero</td>\n",
       "      <td>37.775946</td>\n",
       "      <td>-122.437777</td>\n",
       "      <td>4557</td>\n",
       "      <td>Customer</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60709</td>\n",
       "      <td>2019-01-31 14:19:01.5410</td>\n",
       "      <td>2019-02-01 07:10:51.0650</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>37.794130</td>\n",
       "      <td>-122.394430</td>\n",
       "      <td>28.0</td>\n",
       "      <td>The Embarcadero at Bryant St</td>\n",
       "      <td>37.787168</td>\n",
       "      <td>-122.388098</td>\n",
       "      <td>2100</td>\n",
       "      <td>Customer</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2506978</th>\n",
       "      <td>731</td>\n",
       "      <td>2019-12-28 18:37:29</td>\n",
       "      <td>2019-12-28 18:49:40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809731</td>\n",
       "      <td>-122.409855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.807573</td>\n",
       "      <td>-122.421681</td>\n",
       "      <td>719651</td>\n",
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       "      <th>2506979</th>\n",
       "      <td>748</td>\n",
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       "      <td>2019-12-28 18:49:53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809768</td>\n",
       "      <td>-122.409797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.807558</td>\n",
       "      <td>-122.421638</td>\n",
       "      <td>655411</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>app</td>\n",
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       "      <th>2506980</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810183</td>\n",
       "      <td>-122.410271</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>37.807225</td>\n",
       "      <td>-122.408850</td>\n",
       "      <td>936881</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>app</td>\n",
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       "      <th>2506981</th>\n",
       "      <td>326</td>\n",
       "      <td>2019-12-30 20:35:43</td>\n",
       "      <td>2019-12-30 20:41:09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810471</td>\n",
       "      <td>-122.417386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.803143</td>\n",
       "      <td>-122.414373</td>\n",
       "      <td>397539</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>app</td>\n",
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       "    <tr>\n",
       "      <th>2506982</th>\n",
       "      <td>4023</td>\n",
       "      <td>2019-12-31 19:26:48</td>\n",
       "      <td>2019-12-31 20:33:52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.811042</td>\n",
       "      <td>-122.418149</td>\n",
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       "      <td>-122.418054</td>\n",
       "      <td>662304</td>\n",
       "      <td>Subscriber</td>\n",
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       "         duration_sec                start_time                  end_time  \\\n",
       "0               80825  2019-01-31 17:57:44.6130  2019-02-01 16:24:49.8640   \n",
       "1               65900  2019-01-31 20:58:33.8860  2019-02-01 15:16:54.1730   \n",
       "2               62633  2019-01-31 18:06:52.9240  2019-02-01 11:30:46.5300   \n",
       "3               44680  2019-01-31 19:46:09.7190  2019-02-01 08:10:50.3180   \n",
       "4               60709  2019-01-31 14:19:01.5410  2019-02-01 07:10:51.0650   \n",
       "...               ...                       ...                       ...   \n",
       "2506978           731       2019-12-28 18:37:29       2019-12-28 18:49:40   \n",
       "2506979           748       2019-12-28 18:37:24       2019-12-28 18:49:53   \n",
       "2506980          1044       2019-12-26 14:25:37       2019-12-26 14:43:02   \n",
       "2506981           326       2019-12-30 20:35:43       2019-12-30 20:41:09   \n",
       "2506982          4023       2019-12-31 19:26:48       2019-12-31 20:33:52   \n",
       "\n",
       "         start_station_id           start_station_name  \\\n",
       "0                   229.0    Foothill Blvd at 42nd Ave   \n",
       "1                     4.0  Cyril Magnin St at Ellis St   \n",
       "2                   245.0       Downtown Berkeley BART   \n",
       "3                    85.0      Church St at Duboce Ave   \n",
       "4                    16.0      Steuart St at Market St   \n",
       "...                   ...                          ...   \n",
       "2506978               NaN                          NaN   \n",
       "2506979               NaN                          NaN   \n",
       "2506980               NaN                          NaN   \n",
       "2506981               NaN                          NaN   \n",
       "2506982               NaN                          NaN   \n",
       "\n",
       "         start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0                     37.775745              -122.213037           196.0   \n",
       "1                     37.785881              -122.408915           134.0   \n",
       "2                     37.870139              -122.268422           157.0   \n",
       "3                     37.770083              -122.429156            53.0   \n",
       "4                     37.794130              -122.394430            28.0   \n",
       "...                         ...                      ...             ...   \n",
       "2506978               37.809731              -122.409855             NaN   \n",
       "2506979               37.809768              -122.409797             NaN   \n",
       "2506980               37.810183              -122.410271             NaN   \n",
       "2506981               37.810471              -122.417386             NaN   \n",
       "2506982               37.811042              -122.418149             NaN   \n",
       "\n",
       "                     end_station_name  end_station_latitude  \\\n",
       "0             Grand Ave at Perkins St             37.808894   \n",
       "1              Valencia St at 24th St             37.752428   \n",
       "2                65th St at Hollis St             37.846784   \n",
       "3              Grove St at Divisadero             37.775946   \n",
       "4        The Embarcadero at Bryant St             37.787168   \n",
       "...                               ...                   ...   \n",
       "2506978                           NaN             37.807573   \n",
       "2506979                           NaN             37.807558   \n",
       "2506980                           NaN             37.807225   \n",
       "2506981                           NaN             37.803143   \n",
       "2506982                           NaN             37.810970   \n",
       "\n",
       "         end_station_longitude  bike_id   user_type bike_share_for_all_trip  \\\n",
       "0                  -122.256460     4861  Subscriber                      No   \n",
       "1                  -122.420628     5506  Subscriber                      No   \n",
       "2                  -122.291376     2717    Customer                      No   \n",
       "3                  -122.437777     4557    Customer                      No   \n",
       "4                  -122.388098     2100    Customer                      No   \n",
       "...                        ...      ...         ...                     ...   \n",
       "2506978            -122.421681   719651  Subscriber                     NaN   \n",
       "2506979            -122.421638   655411  Subscriber                     NaN   \n",
       "2506980            -122.408850   936881  Subscriber                     NaN   \n",
       "2506981            -122.414373   397539  Subscriber                     NaN   \n",
       "2506982            -122.418054   662304  Subscriber                     NaN   \n",
       "\n",
       "        rental_access_method  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "...                      ...  \n",
       "2506978                  app  \n",
       "2506979                  app  \n",
       "2506980                  app  \n",
       "2506981                  app  \n",
       "2506982                  app  \n",
       "\n",
       "[2506983 rows x 15 columns]"
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    {
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       "  <thead>\n",
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       "      <th>1506664</th>\n",
       "      <td>1388</td>\n",
       "      <td>2019-08-31 04:52:58.6120</td>\n",
       "      <td>2019-08-31 05:16:07.2990</td>\n",
       "      <td>213.0</td>\n",
       "      <td>32nd St at Adeline St</td>\n",
       "      <td>37.823847</td>\n",
       "      <td>-122.281193</td>\n",
       "      <td>220.0</td>\n",
       "      <td>San Pablo Ave at MLK Jr Way</td>\n",
       "      <td>37.811351</td>\n",
       "      <td>-122.273422</td>\n",
       "      <td>2807</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1852426</th>\n",
       "      <td>187</td>\n",
       "      <td>2019-09-12 10:09:45.3340</td>\n",
       "      <td>2019-09-12 10:12:52.6900</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Rhode Island St at 17th St</td>\n",
       "      <td>37.764478</td>\n",
       "      <td>-122.402570</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Irwin St at 8th St</td>\n",
       "      <td>37.766883</td>\n",
       "      <td>-122.399579</td>\n",
       "      <td>2359</td>\n",
       "      <td>Customer</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587764</th>\n",
       "      <td>437</td>\n",
       "      <td>2019-03-07 13:30:05.4500</td>\n",
       "      <td>2019-03-07 13:37:22.9720</td>\n",
       "      <td>245.0</td>\n",
       "      <td>Downtown Berkeley BART</td>\n",
       "      <td>37.870139</td>\n",
       "      <td>-122.268422</td>\n",
       "      <td>256.0</td>\n",
       "      <td>Hearst Ave at Euclid Ave</td>\n",
       "      <td>37.875112</td>\n",
       "      <td>-122.260553</td>\n",
       "      <td>6439</td>\n",
       "      <td>Customer</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285367</th>\n",
       "      <td>1270</td>\n",
       "      <td>2019-02-15 17:35:11.6360</td>\n",
       "      <td>2019-02-15 17:56:22.3780</td>\n",
       "      <td>67.0</td>\n",
       "      <td>San Francisco Caltrain Station 2  (Townsend St...</td>\n",
       "      <td>37.776639</td>\n",
       "      <td>-122.395526</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>5242</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716671</th>\n",
       "      <td>223</td>\n",
       "      <td>2019-09-30 16:55:21.4160</td>\n",
       "      <td>2019-09-30 16:59:04.5460</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19th Street BART Station</td>\n",
       "      <td>37.809013</td>\n",
       "      <td>-122.268247</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19th Street BART Station</td>\n",
       "      <td>37.809013</td>\n",
       "      <td>-122.268247</td>\n",
       "      <td>1956</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568540</th>\n",
       "      <td>903</td>\n",
       "      <td>2019-08-22 10:42:16.4770</td>\n",
       "      <td>2019-08-22 10:57:19.5780</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Market St at Franklin St</td>\n",
       "      <td>37.773793</td>\n",
       "      <td>-122.421239</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Clay St at Battery St</td>\n",
       "      <td>37.795001</td>\n",
       "      <td>-122.399970</td>\n",
       "      <td>3256</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507993</th>\n",
       "      <td>177</td>\n",
       "      <td>2019-03-16 16:21:35.7250</td>\n",
       "      <td>2019-03-16 16:24:33.5220</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>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>1102</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383417</th>\n",
       "      <td>5304</td>\n",
       "      <td>2019-07-07 12:47:58.7390</td>\n",
       "      <td>2019-07-07 14:16:23.0420</td>\n",
       "      <td>377.0</td>\n",
       "      <td>Fell St at Stanyan St</td>\n",
       "      <td>37.771951</td>\n",
       "      <td>-122.453705</td>\n",
       "      <td>377.0</td>\n",
       "      <td>Fell St at Stanyan St</td>\n",
       "      <td>37.771951</td>\n",
       "      <td>-122.453705</td>\n",
       "      <td>1654</td>\n",
       "      <td>Customer</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         duration_sec                start_time                  end_time  \\\n",
       "1506664          1388  2019-08-31 04:52:58.6120  2019-08-31 05:16:07.2990   \n",
       "1852426           187  2019-09-12 10:09:45.3340  2019-09-12 10:12:52.6900   \n",
       "587764            437  2019-03-07 13:30:05.4500  2019-03-07 13:37:22.9720   \n",
       "285367           1270  2019-02-15 17:35:11.6360  2019-02-15 17:56:22.3780   \n",
       "1716671           223  2019-09-30 16:55:21.4160  2019-09-30 16:59:04.5460   \n",
       "1568540           903  2019-08-22 10:42:16.4770  2019-08-22 10:57:19.5780   \n",
       "507993            177  2019-03-16 16:21:35.7250  2019-03-16 16:24:33.5220   \n",
       "1383417          5304  2019-07-07 12:47:58.7390  2019-07-07 14:16:23.0420   \n",
       "\n",
       "         start_station_id                                 start_station_name  \\\n",
       "1506664             213.0                              32nd St at Adeline St   \n",
       "1852426             114.0                         Rhode Island St at 17th St   \n",
       "587764              245.0                             Downtown Berkeley BART   \n",
       "285367               67.0  San Francisco Caltrain Station 2  (Townsend St...   \n",
       "1716671             182.0                           19th Street BART Station   \n",
       "1568540              75.0                           Market St at Franklin St   \n",
       "507993               50.0                              2nd St at Townsend St   \n",
       "1383417             377.0                              Fell St at Stanyan St   \n",
       "\n",
       "         start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "1506664               37.823847              -122.281193           220.0   \n",
       "1852426               37.764478              -122.402570           102.0   \n",
       "587764                37.870139              -122.268422           256.0   \n",
       "285367                37.776639              -122.395526            70.0   \n",
       "1716671               37.809013              -122.268247           182.0   \n",
       "1568540               37.773793              -122.421239            14.0   \n",
       "507993                37.780526              -122.390288            81.0   \n",
       "1383417               37.771951              -122.453705           377.0   \n",
       "\n",
       "                    end_station_name  end_station_latitude  \\\n",
       "1506664  San Pablo Ave at MLK Jr Way             37.811351   \n",
       "1852426           Irwin St at 8th St             37.766883   \n",
       "587764      Hearst Ave at Euclid Ave             37.875112   \n",
       "285367        Central Ave at Fell St             37.773311   \n",
       "1716671     19th Street BART Station             37.809013   \n",
       "1568540        Clay St at Battery St             37.795001   \n",
       "507993            Berry St at 4th St             37.775880   \n",
       "1383417        Fell St at Stanyan St             37.771951   \n",
       "\n",
       "         end_station_longitude  bike_id   user_type bike_share_for_all_trip  \\\n",
       "1506664            -122.273422     2807  Subscriber                     Yes   \n",
       "1852426            -122.399579     2359    Customer                      No   \n",
       "587764             -122.260553     6439    Customer                      No   \n",
       "285367             -122.444293     5242  Subscriber                      No   \n",
       "1716671            -122.268247     1956  Subscriber                      No   \n",
       "1568540            -122.399970     3256  Subscriber                      No   \n",
       "507993             -122.393170     1102  Subscriber                      No   \n",
       "1383417            -122.453705     1654    Customer                      No   \n",
       "\n",
       "        rental_access_method  \n",
       "1506664                  NaN  \n",
       "1852426                  NaN  \n",
       "587764                   NaN  \n",
       "285367                   NaN  \n",
       "1716671                  NaN  \n",
       "1568540                  NaN  \n",
       "507993                   NaN  \n",
       "1383417                  NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random sample\n",
    "df.sample(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2506983 entries, 0 to 2506982\n",
      "Data columns (total 15 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  bike_share_for_all_trip  object \n",
      " 14  rental_access_method     object \n",
      "dtypes: float64(6), int64(2), object(7)\n",
      "memory usage: 286.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                     0\n",
       "start_time                       0\n",
       "end_time                         0\n",
       "start_station_id             80734\n",
       "start_station_name           80133\n",
       "start_station_latitude           0\n",
       "start_station_longitude          0\n",
       "end_station_id               82902\n",
       "end_station_name             82350\n",
       "end_station_latitude             0\n",
       "end_station_longitude            0\n",
       "bike_id                          0\n",
       "user_type                        0\n",
       "bike_share_for_all_trip     243259\n",
       "rental_access_method       2386145\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicated rows\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count   2506983.000000\n",
       "mean        807.648330\n",
       "std        1974.713981\n",
       "min          60.000000\n",
       "25%         359.000000\n",
       "50%         571.000000\n",
       "75%         887.000000\n",
       "max      912110.000000\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duration_sec summary statistics\n",
    "pd.set_option('float_format', '{:f}'.format)\n",
    "df.duration_sec.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2481276    912110\n",
       "2385537     86230\n",
       "1265667     86217\n",
       "644460      86114\n",
       "1741971     86114\n",
       "            ...  \n",
       "1486204        60\n",
       "1415886        60\n",
       "1415832        60\n",
       "1449824        60\n",
       "1421086        60\n",
       "Name: duration_sec, Length: 2506983, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duration_sec sorted in descending order\n",
    "df.duration_sec.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "912,110 is a very high value compared to the other values, so it will be removed later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Subscriber', 'Customer'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# unique user types\n",
    "df.user_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "446"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique start stations\n",
    "df.start_station_name.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "446"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique end stations\n",
    "df.end_station_name.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11070"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique bikes\n",
    "df.bike_id.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quality\n",
    "Erroneous data types (*start_time, end_time, bike_id, and user_type*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Not needed columns\n",
    "*bike_share_for_all_trip, rental_access_method, start_station_id, end_station_id, start_station_latitude, start_station_longitude, end_station_latitude, end_station_longitude*. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a copy of the data\n",
    "df_clean = df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Not needed columns: *bike_share_for_all_trip, rental_access_method, start_station_id, end_station_id, start_station_latitude, start_station_longitude, end_station_latitude, end_station_longitude*.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop (*bike_share_for_all_trip, rental_access_method, start_station_id, end_station_id, start_station_latitude, start_station_longitude, end_station_latitude, end_station_longitude*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean.drop(['bike_share_for_all_trip', 'rental_access_method', 'start_station_id', 'end_station_id',\n",
    "              'start_station_latitude', 'start_station_longitude', 'end_station_latitude', \n",
    "               'end_station_longitude'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['duration_sec',\n",
       " 'start_time',\n",
       " 'end_time',\n",
       " 'start_station_name',\n",
       " 'end_station_name',\n",
       " 'bike_id',\n",
       " 'user_type']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_clean.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Erroneous data types (*start_time, end_time, bike_id, and user_type*).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert (*start_time, end_time*) to datetime data type, (*bike_id*) to object/string data type, and (*user_type*) to categorical data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to datetime\n",
    "df_clean.start_time = pd.to_datetime(df_clean.start_time)\n",
    "df_clean.end_time = pd.to_datetime(df_clean.end_time)\n",
    "\n",
    "# to object/string\n",
    "df_clean.bike_id = df_clean.bike_id.astype('str')\n",
    "\n",
    "# to category\n",
    "df_clean.user_type = df_clean.user_type.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                   int64\n",
       "start_time            datetime64[ns]\n",
       "end_time              datetime64[ns]\n",
       "start_station_name            object\n",
       "end_station_name              object\n",
       "bike_id                       object\n",
       "user_type                   category\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2506983, 7)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "After a simple cleaning, the dataset contains 2,506,983 rows and 7 columns:\n",
    "- duration_sec (int).\n",
    "- start_time (datetime).\n",
    "- end_time (datetime).\n",
    "- start_station_name (object) - *446 unique stations*.\n",
    "- end_station_name (object) - *446 unique stations*.\n",
    "- bike_id (object) - *11,070 unique bikes*.\n",
    "- user_type (category) - *Subscriber, and Customer*.\n",
    "\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "- *start_time*, to find bikes usage by (month/week day/hour).\n",
    "- *user_type*, to find the differences in bikes usage between subscribers and customers.\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "All remaining  features, since not neeeded columns were dropped during the cleaning process. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86230"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove 912,110\n",
    "df_clean = df_clean[df_clean.duration_sec != df_clean.duration_sec.max()]\n",
    "\n",
    "# test (not 912,110)\n",
    "df_clean.duration_sec.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                   int64\n",
       "start_time            datetime64[ns]\n",
       "end_time              datetime64[ns]\n",
       "start_station_name            object\n",
       "end_station_name              object\n",
       "bike_id                       object\n",
       "user_type                   category\n",
       "start_month                    int64\n",
       "start_weekday                  int64\n",
       "start_hour                     int64\n",
       "end_hour                       int64\n",
       "duration_minutes             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new useful columns\n",
    "\n",
    "# from start_time\n",
    "df_clean['start_month'] = df_clean.start_time.dt.month\n",
    "df_clean['start_weekday'] = df_clean.start_time.dt.weekday \n",
    "df_clean['start_hour'] = df_clean.start_time.dt.hour\n",
    "\n",
    "# from end_time\n",
    "df_clean['end_hour'] = df_clean.end_time.dt.hour\n",
    "\n",
    "# from duration_sec\n",
    "df_clean['duration_minutes'] = df_clean['duration_sec']/60\n",
    "\n",
    "# test\n",
    "df_clean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_weekday</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>duration_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80825</td>\n",
       "      <td>2019-01-31 17:57:44.613</td>\n",
       "      <td>2019-02-01 16:24:49.864</td>\n",
       "      <td>Foothill Blvd at 42nd Ave</td>\n",
       "      <td>Grand Ave at Perkins St</td>\n",
       "      <td>4861</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>1347.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65900</td>\n",
       "      <td>2019-01-31 20:58:33.886</td>\n",
       "      <td>2019-02-01 15:16:54.173</td>\n",
       "      <td>Cyril Magnin St at Ellis St</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>5506</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>1098.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62633</td>\n",
       "      <td>2019-01-31 18:06:52.924</td>\n",
       "      <td>2019-02-01 11:30:46.530</td>\n",
       "      <td>Downtown Berkeley BART</td>\n",
       "      <td>65th St at Hollis St</td>\n",
       "      <td>2717</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1043.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44680</td>\n",
       "      <td>2019-01-31 19:46:09.719</td>\n",
       "      <td>2019-02-01 08:10:50.318</td>\n",
       "      <td>Church St at Duboce Ave</td>\n",
       "      <td>Grove St at Divisadero</td>\n",
       "      <td>4557</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>744.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60709</td>\n",
       "      <td>2019-01-31 14:19:01.541</td>\n",
       "      <td>2019-02-01 07:10:51.065</td>\n",
       "      <td>Steuart St at Market St</td>\n",
       "      <td>The Embarcadero at Bryant St</td>\n",
       "      <td>2100</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>1011.816667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time                end_time  \\\n",
       "0         80825 2019-01-31 17:57:44.613 2019-02-01 16:24:49.864   \n",
       "1         65900 2019-01-31 20:58:33.886 2019-02-01 15:16:54.173   \n",
       "2         62633 2019-01-31 18:06:52.924 2019-02-01 11:30:46.530   \n",
       "3         44680 2019-01-31 19:46:09.719 2019-02-01 08:10:50.318   \n",
       "4         60709 2019-01-31 14:19:01.541 2019-02-01 07:10:51.065   \n",
       "\n",
       "            start_station_name              end_station_name bike_id  \\\n",
       "0    Foothill Blvd at 42nd Ave       Grand Ave at Perkins St    4861   \n",
       "1  Cyril Magnin St at Ellis St        Valencia St at 24th St    5506   \n",
       "2       Downtown Berkeley BART          65th St at Hollis St    2717   \n",
       "3      Church St at Duboce Ave        Grove St at Divisadero    4557   \n",
       "4      Steuart St at Market St  The Embarcadero at Bryant St    2100   \n",
       "\n",
       "    user_type  start_month  start_weekday  start_hour  end_hour  \\\n",
       "0  Subscriber            1              3          17        16   \n",
       "1  Subscriber            1              3          20        15   \n",
       "2    Customer            1              3          18        11   \n",
       "3    Customer            1              3          19         8   \n",
       "4    Customer            1              3          14         7   \n",
       "\n",
       "   duration_minutes  \n",
       "0       1347.083333  \n",
       "1       1098.333333  \n",
       "2       1043.883333  \n",
       "3        744.666667  \n",
       "4       1011.816667  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# labels to use in plots\n",
    "month_labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "weekday_labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
    "\n",
    "# color to use in plots \n",
    "base_color = sb.color_palette()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### duration_minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will use *duration_minutes* instead of *duration_sec* because minutes are more clear than seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a histogram \n",
    "plt.figure(figsize=[10,5])\n",
    "bin_edges = np.arange(0, df_clean.duration_minutes.max()+10, 10)\n",
    "plt.hist(data = df_clean, x = 'duration_minutes', bins = bin_edges)\n",
    "plt.xlabel('duration_minutes');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data follows an approximately log-normal distribution, lots of points with low values, with a very long tail of data points with large values. Thus, a logarithmic scale transformation will be applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a histogram with logarithmic scale transformation\n",
    "plt.figure(figsize=[15,5])\n",
    "bin_edges = 10 ** np.arange(0.0, np.log10(df_clean.duration_minutes.max())+0.1, 0.1)\n",
    "plt.hist(data = df_clean, x = 'duration_minutes', bins = bin_edges)\n",
    "plt.xscale('log')\n",
    "tick_locs = [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]\n",
    "plt.xticks(tick_locs, tick_locs)\n",
    "plt.xlabel('duration_minutes');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the histogram that most rides took about (8-12) minutes. And very few rides lasted more than one hour (60 minutes). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### start_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a histogram with gaps between bars\n",
    "plt.figure(figsize=[10,5])\n",
    "bin_edges = np.arange(0.5, 12.5+1, 1)\n",
    "plt.hist(data = df_clean, x = 'start_month', bins = bin_edges, rwidth = 0.7)\n",
    "plt.xticks(np.arange(1, 12+1, 1), month_labels)\n",
    "plt.xlabel('start_month');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the plot, most rides in 2019 were in March and July, with about 250,000 rides for each.<br> On the other hand, December had the lowest number of rides with about 150,000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### start_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a histogram with gaps between bars\n",
    "plt.figure(figsize=[10,5])\n",
    "bin_edges = np.arange(-0.5, 6.5+1, 1)\n",
    "plt.hist(data = df_clean, x = 'start_weekday', bins = bin_edges, rwidth = 0.7)\n",
    "plt.xticks(np.arange(0, 6+1, 1), weekday_labels)\n",
    "plt.xlabel('start_weekday');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's clear from the chart, bikes were mostly used on work days (Monday-Friday)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### start_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a histogram with gaps between bars\n",
    "plt.figure(figsize=[15,5])\n",
    "bin_edges = np.arange(-0.5, 23.5+1, 1)\n",
    "plt.hist(data = df_clean, x = 'start_hour', bins = bin_edges, rwidth = 0.7)\n",
    "plt.xticks(np.arange(0, 23+1, 1))\n",
    "plt.xlabel('start_hour');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the distribution is roughly bimodal (has two peaks) at 8 and 17. This might be because most work days start and end around this time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### end_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a histogram with gaps between bars\n",
    "plt.figure(figsize=[15,5])\n",
    "bin_edges = np.arange(-0.5, 23.5+1, 1)\n",
    "plt.hist(data = df_clean, x = 'end_hour', bins = bin_edges, rwidth = 0.7)\n",
    "plt.xticks(np.arange(0, 23+1, 1))\n",
    "plt.xlabel('end_hour');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot looks a lot like the plot above (start_hour), since we saw before that most rides didn't exceed an hour, meaning that the ride started and ended in the same hour."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### user_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a bar chart\n",
    "user_cat_order = df_clean['user_type'].value_counts().index\n",
    "sb.countplot(data = df_clean, x = 'user_type', color = base_color, order = user_cat_order);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a pie chart\n",
    "sorted_counts = df_clean['user_type'].value_counts()\n",
    "plt.pie(sorted_counts, labels = sorted_counts.index, startangle = 90,\n",
    "        counterclock = False, autopct='%1.1f%%');\n",
    "plt.axis('square');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's pretty clear, most rides in 2019 were made by subscribers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### start_station_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are a lot of start stations (446), I will just check the top 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a horizontal bar chart\n",
    "cat_order = df_clean['start_station_name'].value_counts().index[:10]\n",
    "plt.figure(figsize=[15,5])\n",
    "sb.countplot(data = df_clean, y = 'start_station_name', color = base_color, order = cat_order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that (Market St at 10th St) and (Berry St at 4th St) were the most common start stations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### end_station_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are a lot of end stations (446), I will just check the top 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a horizontal bar chart\n",
    "cat_order = df_clean['end_station_name'].value_counts().index[:10]\n",
    "plt.figure(figsize=[15,5])\n",
    "sb.countplot(data = df_clean, y = 'end_station_name', color = base_color, order = cat_order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(San Francisco Caltrain Station 2 - Townsend St at 4th St) and (San Francisco Caltrain - Townsend St at 4th St) were the most common end stations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### bike_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are a lot of bikes (11,070), I will just check the top 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a bar chart\n",
    "cat_order = df_clean['bike_id'].value_counts().index[:15]\n",
    "plt.figure(figsize=[15,4])\n",
    "sb.countplot(data = df_clean, x = 'bike_id', color = base_color, order = cat_order);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the maximum number of uses for a single bike was around 850."
   ]
  },
  {
   "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",
    "most rides took about (8-12) minutes. And very few rides lasted more than one hour.\n",
    "\n",
    "*start_time*\n",
    "- Based on the month: most rides were in March and July. On the other hand, December had the lowest number of rides.\n",
    "- Based on week day: bikes were mostly used on work days (Monday-Friday).\n",
    "- Based on starting hour: the distribution is roughly bimodal (has two peaks) at 8 and 17.\n",
    "\n",
    "*user_type*\n",
    "- The majority of bike users in 2019 were subscribers (80.6%).\n",
    "\n",
    "A logarithmic scale transformation was applied on *duration_minutes* hist plot, because the data follows an approximately log-normal distribution, lots of points with low values, with a very long tail of data points with large values.\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",
    "Nothing seemed unusual, just that most rides didn't last for a long period.\n",
    "\n",
    "New columns were created:\n",
    "- *start_month*: to store the month number from *start_time*.\n",
    "- *start_weekday*: to store week day number from *start_time*.\n",
    "- *start_hour*: to store hour number from *start_time*.\n",
    "- *end_hour*: to store hour number from *end_time*.\n",
    "- *duration_minutes*: to store the duration in minutes. (was created because I believe that minutes are more clear than seconds)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### user_type vs. duration_minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# facetting histograms of duration_minutes against user_type\n",
    "bin_edges = 10 ** np.arange(0.0, np.log10(df_clean.duration_minutes.max())+0.1, 0.1)\n",
    "g = sb.FacetGrid(data = df_clean, col = 'user_type', height=4, aspect=2, \n",
    "                 col_order = user_cat_order, sharey=False)\n",
    "g.map(plt.hist, \"duration_minutes\", bins = bin_edges)\n",
    "plt.xscale('log')\n",
    "tick_locs = [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]\n",
    "plt.xticks(tick_locs, tick_locs);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like subscribers mostly rode bikes for (8-12) minutes, while customers mostly rode them for (10-19) minutes.<br>Also, customers rides were more likely to last more than one hour compared to subscribers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### user_type vs. start_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# facetting histograms of start_month against user_type\n",
    "bin_edges = np.arange(0.5, 12.5+1, 1)\n",
    "g = sb.FacetGrid(data = df_clean, col = 'user_type', height=4, aspect=2, \n",
    "                 col_order = user_cat_order, sharey=False)\n",
    "g.map(plt.hist, \"start_month\", bins = bin_edges, rwidth = 0.7);\n",
    "plt.xticks(np.arange(1, 12+1, 1), month_labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subscribers used bikes mostly in March and July, while customers mostly used them in December."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### user_type vs. start_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# facetting histograms of start_weekday against user_type\n",
    "bin_edges = np.arange(-0.5, 6.5+1, 1)\n",
    "g = sb.FacetGrid(data = df_clean, col = 'user_type', height=4, aspect=2, \n",
    "                 col_order = user_cat_order, sharey=False)\n",
    "g.map(plt.hist, 'start_weekday', bins = bin_edges, rwidth = 0.7);\n",
    "plt.xticks(np.arange(0, 6+1, 1), weekday_labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subscribers mostly used bikes on workdays (Monday - Friday), while customers bikes usage was about the same for the whole week with a slight increment on Friday and Saturday. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### user_type vs. start_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# facetting histograms of start_hour against user_type\n",
    "bin_edges = np.arange(-0.5, 23.5+1, 1)\n",
    "g = sb.FacetGrid(data = df_clean, col = 'user_type', height=4, aspect=2, \n",
    "                 col_order = user_cat_order, sharey=False)\n",
    "g.map(plt.hist, 'start_hour', bins = bin_edges, rwidth = 0.7);\n",
    "plt.xticks(np.arange(0, 23+1, 1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most rides for subscribers started at 8 or 17. While most customer rides started at 17 or 18."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  duration_minutes vs. start_month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since most rides didn't exceed an hour, I will just keep the rides with duration_minutes <= 60 in this part, to get a more accurate duration_minutes mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean_60_or_less = df_clean[df_clean.duration_minutes <= 60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_avg_mint = df_clean_60_or_less.groupby('start_month').duration_minutes.mean()\n",
    "\n",
    "# plot a line plot\n",
    "plt.figure(figsize=[15,5])\n",
    "sb.lineplot(x=month_avg_mint.index, y=month_avg_mint, marker='o')\n",
    "plt.xticks(np.arange(1,13), month_labels)\n",
    "plt.ylabel('Average Duration (min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average rides from June to October lasted longer than the other months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  duration_minutes vs. start_weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rddqBk3TMzMqgywEySjIzfmdLmuXuHakuBgAAANnrmvuWKhwyXX767KBLATJOMhdwXyUpN9WFAAAAIHs9vmyDHn9to770vpmaVFYYdDlAxklmxq9V0mIz+6uk3lk/d/9iyqoCAABA1mjviuia+5dq5oQx+vTRNHQBUiGZ4Hdv/AMAAAAYdr98YqXWNrTpjovfo9xwMgvSAOyqnQY/d//dSBQCAACA7LN6U4t+/dQqnTVvso7YZ1zQ5QAZa9DgZ2b/6+4fNbNXFevi2Ye7H5TSygAAAJDR3F1X3btUeTkhfftUGroAqTTUjN+X4p9PH4lCAAAAkF0eWbpeT62o1xWnz9GE0oKgywEy2qDBz93fjX9eI0lmVjrU8QAAAECyWju7de19y7T/XiW68IjpQZcDZLxkLuD+WUnXSmrT9iWfLmnvFNYFAACADPbzv63Uuq3t+tl5hyiHhi5AyiUzg/dVSQe4+6ZUFwMAAIDMt3LjNt30zCqdc2iV5ldXBF0OkBWSeXvlTcWu5QcAAADskVhDlyUqyA3rslP3D7ocIGskM+N3maRnzex5cQF3AAAA7IH7X3lX/1i5WdeedYDGj8kPuhwgayQT/H4j6W+SXpUUTW05AAAAyFTbOrr1nQeWae6UUp1/OA1dgJGUTPDrdvd/T3klAAAAyGg/e3yFNjR16NefOEzhkAVdDpBVktnj94SZXWJmk8ysoucj5ZUBAAAgY7y+vlm3/GO1zp0/VYdMKw+6HCDrJDPj9/H458sSxricAwAAAJLi7rriniUqKcjR10+hoQsQhJ0GP3efMRKFAAAAIDPdvfgdvfBWg77/oQNVUZwXdDlAVhp0qaeZHT3UiWZWamZzh3j8FjPbaGZLEsauN7PlZvaKmd1lZmMHOXe1mb1qZovNrDaZbwQAAADpZ2tbl777wHIdPHWsPlYzNehygKw11B6/c8zsWTO70sxOM7MFZnasmX3azG6TdL+kwiHO/62kU/qNPSZprrsfJGmF+i4f7e94d5/n7jVJfB8AAABIQz95bIU2t3ToO2fNVYiGLkBgBl3q6e5fMbNySR+W9BFJkyS1SXpN0m/c/e9DPbG7P21m1f3GHk24+8/4cwMAACADLV23Vbc+t1qfOHy6DqwqC7ocIKsNucfP3Rsl3Rj/GG6flvSnwb60pEfNzBULmTek4OsDAAAgRaJR1xV3L1F5UZ6+etKsoMsBsl4yXT2HnZl9W1K3pNsHOeQod19nZhMkPWZmy9396UGe6xJJl0jStGnTUlIvAAAAds2dL9bpxbe36PoPH6SyotygywGyXjLX8RtWZnahpNMlne/uPtAx7r4u/nmjpLskLRjs+dz9BnevcfeaysrKVJQMAACAXbCltVM/eGi5aqaX65xDq4IuB4BGOPiZ2SmSviHpTHdvHeSYYjMr6bkt6SRJSwY6FgAAAOnn+kde15bWTl1LQxcgbSS11NPMjpRUnXi8u9+6k3PukHScpPFmVifpKsW6eOYrtnxTkv7p7p8zs8mSbnL3UyVNlHRX/PEcSX9w94d37dsCAABAEF6p26I/vPC2PnlkteZMLg26HABxOw1+8Us37CNpsaRIfNglDRn83P28AYZvHuTYdZJOjd9eJengndUFAACA9BKJN3QZPyZfXzlxv6DLAZAgmRm/GklzBtuPBwAAAEjSHxe+rZfrtuqnH5un0gIaugDpJJk9fksk7ZXqQgAAADB6NbR06kcPv67DZ1TorHmTgy4HQD/JzPiNl7TMzF6Q1NEz6O5npqwqAAAAjCo/fGi5Wjq6dd3ZcxXv1QAgjSQT/K5OdREAAAAYvV58u1F/ql2rS47dW/tNLAm6HAAD2Gnwc/enzGyipPnxoRfi19cDAABAlutp6LJXaYG++L6ZQZcDYBA73eNnZh+V9IKkj0j6qKTnzezDqS4MAAAA6e/3/1yjpeuadPnpszUmP6krhQEIQDL/Or8taX7PLJ+ZVUp6XNKdqSwMAAAA6a2+uUM/fvR1Hb3veJ124KSgywEwhGS6eob6Le3cnOR5AAAAyGDff+g1tXdFdM1ZB9DQBUhzycz4PWxmj0i6I37/Y5IeTF1JAAAASHcvvNWgv7z4ji49bh/tUzkm6HIA7EQyzV2+ZmbnSDpKkkm6wd3vSnllAAAASEtdkaiuuHuJpowt1BdO2DfocgAkIakduO7+Z0l/TnEtAAAAGAV+9+xqvb6hWb/5l8NUlEdDF2A0GPRfqpn93d2PNrNmSZ74kCR399KUVwcAAIC0sqGpXT99/A0dN6tSJ82ZGHQ5AJI0aPBz96Pjn7kKJwAAACRJ333gNXVGorrmTBq6AKNJMtfxuy2ZMQAAAGS2Z1du0r0vr9O/vncfTR9XHHQ5AHZBMpdlOCDxjpnlSDosNeUAAAAgHXV2R3XFPUs0taJQ/3rcPkGXA2AXDRr8zOyy+P6+g8ysKf7RLGmDpHtGrEIAAAAE7pZ/vKU361t0zZkHqCA3HHQ5AHbRoMHP3b8f3993vbuXxj9K3H2cu182gjUCAAAgQOu2tOlnj7+h98+eqBP2p6ELMBolcx2/y8ysXNJMSQUJ40+nsjAAAACkh+vuXyaX66oz5gRdCoDdtNPgZ2YXSfqSpCpJiyW9R9Jzkk5IbWkAAAAI2lMr6vXQkvX66kn7aWpFUdDlANhNyTR3+ZKk+ZLWuPvxkg6RVJ/SqgAAABC4ju6Irr53qWaML9bFx+4ddDkA9kAywa/d3dslyczy3X25pFmpLQsAAABBu/HpVXprU6yhS34ODV2A0WynSz0l1ZnZWEl3S3rMzBolrUttWQAAAAjS2oZW/eKJlTr1wL107H6VQZcDYA8l09zlg/GbV5vZE5LKJD2c0qoAAAAQqGvuW6aQmS4/jYYuQCYYMviZWUjSK+4+V5Lc/akRqQoAAACB+etrG/T4axv0zQ/sr8ljC4MuB8AwGHKPn7tHJb1sZtNGqB4AAAAEqL0roqvvW6p9J4zRp4+aEXQ5AIZJMnv8JklaamYvSGrpGXT3M1NWFQAAAALxyyff1NqGNv3h4sOVl5NMH0AAo0Eywe+alFcBAACAwK3e1KJfP/Wmzjx4so7cZ3zQ5QAYRsk0d2FfHwAAQIZzd11931LlhUP69mmzgy4HwDDbafAzs2ZJHr+bJylXUou7l6ayMAAAAIycR5Zu0JOv1+vy02ZrYmlB0OUAGGbJzPiVJN43s7MlLUhZRQAAABhRrZ3duu7+Zdp/rxJ98sjqoMsBkAK7vGPX3e+WdEIKagEAAEAAfvG3lXpnS5uuPWuucsI0dAEyUTJLPT+UcDckqUbbl34CAABgFFu5cZtufGaVPnToFC2YURF0OQBSJJmunmck3O6WtFrSWSmpBgAAACPG3XX1vUtVkBvWZR+goQuQyZLZ4/epkSgEAAAAI+uBV9/V31du0jVnHqDKkvygywGQQkMu4jaz483sz2a2NP5xp5kdN0K1AQAAIEW2dcQauhwwuVSfeM/0oMsBkGKDBj8zO03SLZLul/RxSedLelDSLWZ26siUBwAAgFT4r7++oQ1NHbru7LkKhyzocgCk2FBLPb8m6Wx3fzlhbLGZ1Ur6uWIhEAAAAKPMig3NuuXvb+ljNVN16LTyoMsBMAKGWuq5V7/QJ0ly91ckTUxdSQAAAEgVd9cVdy/RmIIcfeMD+wddDoARMlTwa9nNxwAAAJCm7lm8Ts+/1aCvn7y/Korzgi4HwAgZaqnnPmZ27wDjJmnvFNUDAACAFGlq79J3HnhNB1eV6WPzpwZdDoARNFTwG+pafT/e2ROb2S2STpe00d3nxseuV+y6gJ2S3pT0KXffMsC5p0j6maSwpJvc/Qc7+3oAAAAY2k8eW6HNLR265ZM1NHQBssygwc/dn9rD5/6tpF9IujVh7DFJl7l7t5n9UNJlkr6ReJKZhSX9t6QTJdVJWmhm97r7sj2sBwAAIGstW9ek3z27WucfPk0HVY0NuhwAI2zI6/jtCXd/WlJDv7FH3b07fvefkqoGOHWBpJXuvsrdOyX9UUPPPgIAAGAI0ajrinuWaGxRnr560qygywEQgJQFvyR8WtJDA4xPkbQ24X5dfAwAAAC74c8v1mnRmkZ98wP7a2wRDV2AbJR08DOz4uH6omb2bUndkm4f6OEBxnyI57rEzGrNrLa+vn64SgQAAMgIW1u79IOHluuw6eX68KEDLbYCkA12GvzM7EgzWybptfj9g83sl7v7Bc3sQsWavpzv7gMFujpJiW2mqiStG+z53P0Gd69x95rKysrdLQsAACAjXf/ocjW2durasw5QiIYuQNZKZsbvJ5JOlrRZkuIXdT92d75YvFvnNySd6e6tgxy2UNJMM5thZnmSzpU00GUlAAAAMIRX67bq9uff1gVHVOuAyWVBlwMgQEkt9XT3tf2GIjs7x8zukPScpFlmVmdmn1Gsy2eJpMfMbLGZ/Tp+7GQzezD+tbolfUHSI4rNMv6vuy9N9hsCAABArKHL5fcs0bjifP37SfsFXQ6AgA11Hb8ea83sSEken4H7ouLLPofi7ucNMHzzIMeuk3Rqwv0HJT2YRG0AAAAYwB8XrtXLa7foJx87WKUFuUGXAyBgycz4fU7S5xXrrFknaV78PgAAANJQQ0unfvTIci2YUaGz59EcHUASM37uvknS+SNQCwAAAIbBjx5erub2bl131lyZ0dAFQBLBz8z+a4DhrZJq3f2e4S8JAAAAu+vFtxv1x4VrdfExMzRrr5KgywGQJpJZ6lmg2PLON+IfB0mqkPQZM/tpCmsDAADALohEXVfcvUQTS/P1pffT0AXAdsk0d9lX0gnxbpsys19JelTSiZJeTWFtAAAA2AW3P79GS9c16efnHaIx+cn8mgcgWyQz4zdFUnHC/WJJk909IqkjJVUBAABgl2za1qHrH3ldR+87XqcfNCnocgCkmWTeCvqRpMVm9qQkU+zi7d8zs2JJj6ewNgAAACTp+w8uV3tXRFefeQANXQDsIJmunjfHL66+QLHg9634dfck6WupLA4AAAA7t3B1g/78Yp3+9bh9tO+EMUGXAyANJbPUU5LaJb0rqUHSvmZ2bOpKAgAAQLK6I1FdcfcSTRlbqH87Yd+gywGQppK5nMNFkr4kqUrSYknvkfScpBNSWxoAAAB25nfPrdHy9c369ScOU1EeDV0ADCyZGb8vSZovaY27Hy/pEEn1Ka0KAAAAO7WhqV0/eWyF3rtfpU4+YGLQ5QBIY8kEv3Z3b5ckM8t39+WSZqW2LAAAAOzM9x58TZ2RqK6hoQuAnUhmPUCdmY2VdLekx8ysUdK6nZwDAACAFHr2zU26Z/E6ffF9M1U9vnjnJwDIasl09fxg/ObVZvaEpDJJD6e0KgAAAAyqszuqK+9ZqqkVhbr0uH2CLgfAKDBk8DOzkKRX3H2uJLn7UyNSFQAAAAb1P/94Sys3btPNF9aoIDccdDkARoEh9/i5e1TSy2Y2bYTqAQAAwBDe3dqmn/31Db1/9gS9bzYNXQAkJ5k9fpMkLTWzFyS19Ay6+5kpqwoAAAADuu7+ZYpEXVedcUDQpQAYRZIJftekvAoAAADs1NMr6vXgq+v1Hyfup6kVRUGXA2AUSaa5y1NmNl3STHd/3MyKJLGYHAAAYAR1dEd01b1LVT2uSBcfu3fQ5QAYZXZ6HT8zu1jSnZJ+Ex+aotilHQAAADBCbnx6ld7a1KJrzppLQxcAuyyZC7h/XtJRkpokyd3fkDQhlUUBAABgu7UNrfrFEyv1gbl76b37VQZdDoBRKJng1+HunT13zCxHkqeuJAAAACS69v5lMpmuOH1O0KUAGKWSCX5Pmdm3JBWa2YmS/k/SfaktCwAAAJL0t+Ub9NiyDfri+2Zq8tjCoMsBMEolE/y+Kale0quSPivpQUmXp7IoAAAASO1dEV197zLtU1mszxw9I+hyAIxiyVzO4SxJt7r7jakuBgAAANv96sk39XZDq/5w0eHKy0nm/XoAGFgyP0HOlLTCzG4zs9Pie/wAAACQQms2t+hXT72pMw6erCP3HR90OQBGuZ0GP3f/lKR9Fdvb93FJb5rZTakuDAAAIFu5u66+d6lyQ6bLT5sddDkAMkBSs3fu3mVmDynWzbNQseWfF6WyMAAAgGz16L8ydVkAAB13SURBVLINeuL1el1+2mxNLC0IuhwAGSCZC7ifYma/lbRS0ocl3SRpUorrAgAAyEptnRFde98yzZpYoguPrA66HAAZIpkZv09K+qOkz7p7R2rLAQAAyG6/eOINvbOlTf/72SOUG6ahC4DhsdPg5+7nJt43s6MkfdzdP5+yqgAAALLQm/XbdMPTq/ShQ6ZowYyKoMsBkEGS2uNnZvMUa+zyUUlvSfpLKosCAADINj0NXQpywrrsVBq6ABhegwY/M9tP0rmSzpO0WdKfJJm7Hz9CtQEAAGSNB19dr2fe2KSrz5ijypL8oMsBkGGGmvFbLukZSWe4+0pJMrOvjEhVAAAAWWRbR7euu3+Z5kwq1SfeMz3ocgBkoKF2DJ8jab2kJ8zsRjN7nyQbmbIAAACyx8//+obWN7XrurPnKoeGLgBSYNCfLO5+l7t/TNL+kp6U9BVJE83sV2Z20gjVBwAAkNFWbGjWzX9/Sx+tqdJh08uDLgdAhtrpW0ru3uLut7v76ZKqJC2W9M2UVwYAAJDh3F1X3L1Exfk5+sYp+wddDoAMtktrCdy9wd1/4+4npKogAACAbHHvy+v0/FsN+vopszRuDA1dAKQOi8gBAAAC0NTepe888JoOqirTufOnBV0OgAyX1HX8AAAAMLx+8tgKbdrWoZsvrFE4RP88AKmVshk/M7vFzDaa2ZKEsY+Y2VIzi5pZzRDnrjazV81ssZnVpqpGAACAICxb16TfPbtaH18wTQdVjQ26HABZIJVLPX8r6ZR+Y0skfUjS00mcf7y7z3P3QQMiAADAaBONuq68Z4nGFuXpayfPCrocAFkiZUs93f1pM6vuN/aaJJmxnAEAAGSnv7z0jmrXNOpH5xyksUV5QZcDIEuka3MXl/SomS0ys0uCLgYAAGA4bG3t0vcffE2HThurDx9WFXQ5ALJIujZ3Ocrd15nZBEmPmdlydx9weWg8GF4iSdOm0RELAACkrx8/+roaWzt162cWKERDFwAjKC1n/Nx9XfzzRkl3SVowxLE3uHuNu9dUVlaOVIkAAAC75NW6rfr982t0wRHVOmByWdDlAMgyaRf8zKzYzEp6bks6SbGmMAAAAKNSNOq64p4lGlecr6+cuF/Q5QDIQqm8nMMdkp6TNMvM6szsM2b2QTOrk3SEpAfM7JH4sZPN7MH4qRMl/d3MXpb0gqQH3P3hVNUJAACQan+qXavFa7foW6fur7LC3KDLAZCFUtnV87xBHrprgGPXSTo1fnuVpINTVRcAAMBIamzp1A8fXq4F1RX64CFTgi4HQJZKu6WeAAAAmeRHjyxXc3u3rj37AC5pBSAwBD8AAIAUeentRv1x4Vp96shq7b9XadDlAMhiBD8AAIAUiMQbukwoydeXaegCIGAEPwAAgBT4w/NrtOSdJn37tDkak5+ul04GkC0IfgAAAMNs07YOXf/I6zpyn3E646BJQZcDAAQ/AACA4faDh5arrSuia8+aS0MXAGmB4AcAADCMalc36M5FdfrM0Xtr3wljgi4HACQR/AAAAIZNdySqy+9eosllBfri+/YNuhwA6EXwAwAAGCa3PrdGy9c368oz5qgoj4YuANIHwQ8AAGAYbGxq138+tkLH7lepkw/YK+hyAKAPgh8AAMAw+N6Dr6mzO6przjyAhi4A0g5rEAAAAHZTNOra3NKppvYufeDASVpQXa4Z44uDLgsAdkDwAwAA2A3RqOv1Dc26+NZa1TW2qaq8UDf8S42iUVcoxIwfgPTCUk8AAIDdsLmlszf0SVJdY5suua1Wm1s6A64MAHbEjB8AAEAS3F11jW1auLpBC1c36sOHVfWGvh51jW3q7I4EVCEADI7gBwAAMIBI1LV8fZNqVzdq4eoG1a5u1PqmdklSSUGOzjx4kqrKC/uEv6ryQuXlhIMqGQAGRfADAACQ1NYZ0eK1W1S7ukEL1zTqxTWN2tbRLUmaVFagBTMqNL+6XDXVFdpvYolM0o0X1PTZ43fjBTUaV5wX7DcCAAMg+AEAgKy0eVuHatc0xoLe6kYteWeruqMuM2nWxBKdfchkza+uUE11haaMLRzwOWZNLNFdlx6lzu6I8nLCGlecR2MXAGmJ4AcAADKeu+vthla98FZsyebCNQ1aVd8iScoLh3Tw1DJdcuzeml9doUOnlausKDep5w2FTJUl+aksHQCGBcEPAABknO5IVK+92xzbm7cmNqNX39whSSorzFXN9HJ95LCpml9drrlTylSQy748AJmN4AcAAEa9lo5uLV67pbcJy4tvN6q1M9Zds6q8UEfvO1411eWaX12hfSvHsBwTQNYh+AEAgFFnY3O7Fq1u1MLVjapd06Cl65oUie/Pm71XqT5yWJVqqitUU12uSWUD788DgGxC8AMAAGnN3bVqU0tvE5ba1Q1avblVkpSfE9K8qWP1r+/dRzXV5Tp0erlKC5LbnwcA2YTgBwAA0kpXJKql65riQS+2dHNzS6ckqbwoVzXVFfr44dNUU12huZPLlJcTCrhiAEh/BD8AABCo5vYuvfT2lt4ZvZfWNqq9KypJmj6uSMfNmtB7/bx9Kotlxv48ANhVBD8AADCiNjS1987kLVzdoNfebVLUpZBJcyaX6tz507RgRoVqppdrQmlB0OUCQEYg+AEAgJSJRl1v1m/r3Zu3cE2D1ja0SZIKc8M6ZNpYfeGEmZpfXa5DppVrTD6/mgBAKvDTFQAADJuO7oiWvNPUu2xz0ZoGNbZ2SZLGj8lTzfQKXXhEteZXV2jO5FLlhtmfBwAjgeAHAAB229a2Lr34dmNv0Ht57RZ1dMf2580YX6wT50xUTXWF5ldXqHpcEfvzACAgBD8AAJC0dVva+uzPe31Ds9ylcMg0d3KpPvGe6ZpfXa7DpleosiQ/6HIBAHEEPwAAMKBo1PXGxm1amHBZhXe2xPbnFeeFdej0cn1g7iTNry7XvGljVZTHrxUAkK74CQ0AACRJ7V0RvfrO1t6QV7u6QU3t3ZKkypJ8Laiu0EXHzND86grtv1eJctifBwCjBsEPAIAstaW1U4vWNPZ23Hylbqs6I7H9eftUFuvUAyepprpCC6orNLWikP15ADCKEfwAAMgC7q66xjbVrmnoDXorNmyTJOWETAdWlemTR1WrZnq5DpternFj2J8HAJmE4AcAQAaKRF2vr2/usz9vfVO7JKkkP0eHTi/XmQdPVk11hQ6uGqvCvHDAFQMAUongBwBABmjvimjx2i29l1V4cU2jmjti+/MmluZrfnWFFsyoUM30Cs3aq0ThEMs2ASCbEPxSKBp1bW7pVGd3RHk5YY0rzlOI/2gBAMOgoaVTtasbVLsmdlmFJe9sVVfEJUn7TRyjM+ZN1vzqctVMr1BVOfvzACDbEfxSJBp1vb6hWRffWqu6xjZVlRfqxgtqNGtiCeEPALBL3F1vN7T27s1buLpBb9a3SJLywiEdVFWmzxy9d/z6eeUaW5QXcMUAgHRD8EuRzS2dvaFPkuoa23TxrbW66cIabWru1ITSfE0oyVdZYS7vwgIA+uiORPXau7H9eT3NWOqbOyRJpQU5qqmu0DmHVWl+dYUOnFKmglz25wEAhkbwS5HO7khv6OtR19imra1d+sTNz/eO5eWENKEkP/5RoIml+ZpQWqDKknxNLC3ofay8iGWiAJCpWju7tfjtLbEZvTUNenFNo1o6I5KkKWMLdeQ+41RTXaH51eXabwIrRwAAuy5lwc/MbpF0uqSN7j43PvYRSVdLmi1pgbvXDnLuKZJ+Jiks6SZ3/0Gq6kyVvJywqsoL+4S/qvJCTR5bqD9d8h5tbO7QhqZ21Td39N5eWb9Nz765qfdiuYlyw6bKMfmqjIfBiaWxoDghHhArS/I1oTRf44rz2bAPAGmuvrlDixIuq7BkXZMiUZeZNGtiiT50aJVqqstVU12hKWMLgy4XAJABzN1T88Rmx0raJunWhOA3W1JU0m8kfXWg4GdmYUkrJJ0oqU7SQknnufuynX3Nmpoar60dMEuOuD3Z49feFdHGpg5tbG7vDYUbmzu2j8U/N7Z27XBuOGQaPyavNxRO6Jk1LM3XxJKC+BLTAo0fk6eccChV3z4AZJWhmnm5u97a1KLa1Y3xpZuNemtTfH9eTkjzpo6NNWGprtCh08pVVpgb5LcCABjlzGyRu9f0H0/ZjJ+7P21m1f3GXosXM9SpCyStdPdV8WP/KOksSTsNfukkFDLNmliiuy49ape7ehbkhjVtXJGmjSsa8riO7kjvjGH/ULihqUPrtrbr5bot2tzSqf753kwaV5w/QChMDIsFqhyTr7wcAiIADGagN/p+df5hWlXfrAeXrFft6kZtbumUJI0tylXN9HJ9bP5Uza+u0NwppcrPYX8eACD10nGP3xRJaxPu10k6PKBa9kgoZKosyU/Z8+fnhFVVXqSq8qEDYlckqk3besLh9hnE+nhQ3NDcrmXrmrRpW4eiA0wAVxTnaUJJ/g77DieWbp9BrCzJp7kAgFHN3dUVcbV1RdTeFVFbZ0Tt3bHPPWPtXdE+99s6Izpmv/H6wh9e6tPM619vX6QrTp+jZe826b37Vfbuz9uncgz78wAAgUjH4DfQ/4iDrkc1s0skXSJJ06ZNS1VNo1puOKRJZYWaVDb0PpFI1LV5W3wGMT5rmLjkdGNTu1Zu3Kb65g51D5AQywpz+8wgVvbbh9jzWFFeOv61A5Cu3F0d3dHtwaurfxiL9I71OabPWPx+V1TticGtT8iLKjLQu187sWBGxYDNvGbvVaJnvn7CcP0xAACwR9LxN/A6SVMT7ldJWjfYwe5+g6QbpNgev9SWltnCIYst8ywtkFQ26HHRqKuhtXOH5aU9S043NLfr+bcaVN/coc5IdIfzS/Jz4qEwcQZx++xhz5LTMfk5XOoCSGM9gawnhPUErY7uiNo6t4ev9n4zZ21dEXV09T1vxwAX7RPOdmc7el44pPzckApzwyrMC6swN6z83LAKc0MaPyZPhXlhFeTGPgp7PvLCys8J9R5fmBtWQV5YBTnhfmOh3vO2tHYN2MyrkDe5AABpJB3/V1ooaaaZzZD0jqRzJX082JKQKBQyjR+Tr/Fj8jVHpYMe5+7a0trVdwYxHhTr40tOX3p7izY2t6u9a8eAWJQXHjQUJgbG0kICItLDUA0+RrqOvksUo31CVZ+g1RmfBesXvAY6r+/MWWTAf7fJyMsJbQ9QufEAFQ9VpQW528NY3vbj8hOCWZ/z+oyFe5+rICc0Yg2sxhXn6cYLanZo5jWumIuoAwDSRyq7et4h6ThJ4yVtkHSVpAZJP5dUKWmLpMXufrKZTVbssg2nxs89VdJPFbucwy3u/t1kvmY6dfVE8txdTe3dvXsO+3QyTbjsxYamdrXGr2uVKD8n1BsMJybsOey/D7G8KJeAiJRJppNvJOoDh6j4MsOe8e1jkd5liT1Bq2cGLXGsvavvbFpn9+4Fsj5hKjFExWfNEoNW35myUMKxCcfkxMJb4lh+TjgjLzmTLqEfAIDBunqmLPgFgeCX+bZ1dGtjQijsvd2UMKPY3KHmAa6FmBcOqbK3SU1+wiUvEjqZlhTwC9soEom6uiJRdUdd3ZGoOiNRdUdc3RFXVzR2uysS7T2mq+fxaFRdke33E5+jK+F+7+N9nit2XJ/HI1FddMwMfe3OV3ZY7nftmQfoy39arPau6IBLn3fGTH2WGfaf6SrI3T7esyyxT0jrMxbqE+gSj8vPCfH3HgCADDDil3MAUmFMfo7GVI7R3pVjhjyurTPSd99hb1iMzSq+talFz7/VoC0DXAsxJ76Utf/y0sQZxQml+RpXPPC1EEfDO/893Qt7AlBPkOns7huAdghI0Z5gFVVX1NXVHd3hObYfHzumz3N1JzxHwnldA4Sv/l97h+Oj0d3a97WrQiblhEPKDZlyc0LKCYWUGzblhE25oVDsczik/NzwgA0+JpQW6EOHVvWZAeuzLHGgAJcQyvJzQsxUAwCAPUbwQ0YqzAtr+rhiTR9XPORx7V3br4VY328f4obmDtU1tuqlt7dfgytRyKRxY/L7XN7ioCmlmj25TP92x0u9y/1+ef6h6opEtaW1qzfQ9Jk92iE4RRNmkvoHnu1hKhbEEsb7PXd3PJj1Pb4nRI3MTH9u2JQTD0d54djn7cEppJyQKS8n9jknHFJBbkg5+TnKjYepnsCVEz8+N35cbjjU57l7bufmJB6T/NfO7fd4z/3cUPKzYPXNHQM2+JhYWqCrzzwgVX/EAAAASWGpJ5CEzu74tRB7lpU2d6g+PouYuB/xO2fP1XX3L9vhl/8rTp+jz962KOmvFw7ZDoEkryewJMw05YTj4wkzTz3n9QSc3H6P5w4QgHLjYapPiBogTPWf5crtF8h6Q1bIFA5ZVs1UJbPHDwAAINVY6gnsgbyckCaPLdTksUNfC7GusXXA5X4zJ4zRPZ8/aoBw1j/I7dosE9JHKGSaNbFEd116VFov8wUAANmJ4AcMo/yc8IDL/UoKcne6LxGjXyhkqizJD7oMAACAHYzMRY6ALNFzPa+q8tjMINfzAgAAQDpgxg8YRiz3AwAAQDoi+AHDjOV+AAAASDcs9QQAAACADEfwAwAAAIAMR/ADAAAAgAxH8AMAAACADEfwAwAAAIAMR/ADAAAAgAxH8AMAAACADGfuHnQNw8bM6iWtCbqOAYyXtCnoIjDieN2zF6999uK1z1689tmL1z57petrP93dK/sPZlTwS1dmVuvuNUHXgZHF6569eO2zF6999uK1z1689tlrtL32LPUEAAAAgAxH8AMAAACADEfwGxk3BF0AAsHrnr147bMXr3324rXPXrz22WtUvfbs8QMAAACADMeMHwAAAABkOILfHjAzN7PbEu7nmFm9md0fZF1IPTMbZ2aL4x/rzeydhPt5QdeH4WdmPzGzLyfcf8TMbkq4///M7N+TfK6rzeyrqagTqTPEv/stZrYs6PowcswskvB3YbGZVQ9wzGQzu3Pkq0MqmNm3zWypmb0Sf80PH+LYT5rZ5JGsD6mxK6/7aJATdAGjXIukuWZW6O5tkk6U9E7ANWEEuPtmSfOk2C/xkra5+48DLQqp9qykj0j6qZmFFLt2T2nC40dK+vJAJyIzDPbvPv5LP2/4ZZc2d5832INmluPu6yR9eARrQoqY2RGSTpd0qLt3mNl4SUO9yftJSUskrRuB8pAiu/G6pz1m/PbcQ5JOi98+T9IdPQ+YWYWZ3R1/l+CfZnZQfPxqM7vFzJ40s1Vm9sUA6kYKmNm+ZrY44f43zezy+O2Z8VmiRWb2tJntF1yl2A3/UCzcSdIBiv2n3mxm5WaWL2m2pJfM7GtmtjD+7/6anpPj7xq+bmaPS5o14tUj1cJmdmP8neFHzaxQkuI/52vit8eb2epAq0TKxGd5/s/M7pP0qJlVm9mSoOvCsJgkaZO7d0iSu29y93VmdmX85/0SM7vBYj4sqUbS7fEZosJAK8eeGOx1Xx0PgTKzGjN7Mn477X+/J/jtuT9KOtfMCiQdJOn5hMeukfSSux8k6VuSbk14bH9JJ0taIOkqM8sdoXoRnBskXeruh0m6TNIvAq4HuyD+7n23mU1TLAA+p9i/9yMU+0/+FUnHSZqp2L/reZIOM7NjzewwSedKOkTShyTNH/FvAKk2U9J/u/sBkrZIOifgepBahQnLPO9KGD9C0oXufkJQhSElHpU01cxWmNkvzey98fFfuPt8d58rqVDS6e5+p6RaSee7+7z4ijCMToO97kNJ69/vWeq5h9z9lfgyn/MkPdjv4aMV/8/f3f8W3x9SFn/sgfg7CB1mtlHSREl1I1M1RpqZjZX0Hkl/NrOeYf79jT49s35HSvpPSVPit7cqthT0pPjHS/HjxygWCEok3eXurZJkZveObNkYAW+5e89s/yJJ1QHWgtQbbKnnY+7eMOLVIKXcfVv8DbxjJB0v6U9m9k3FVn18XVKRpApJSyXdF1ylGE5DvO5DSevf7/nFc3jcK+nHir3bPy5h3AY4tuf6GR0JYxHxWmSKbvWdSS+Ij5liywUG3ROCUeFZxYLegYot9Vwr6T8kNUm6RbGfAd93998knmSxpjBcOyez9f+Z3rO8K/FnQsGIVoQgtARdAFLD3SOSnpT0pJm9Kumziq30qnH3tfF9v/wbzzADvO4Xauif62n9+z1LPYfHLZKudfdX+40/Lel8STKz4xT7xb9phGvDyFovaXJ831eB4vs/3b1R0rtm9kFJMrOQmR0cYJ3YPf9QbKN3g7tH4u/sj1Vseddzkh6R9GkzGyNJZjbFzCYo9rPgg2ZWaGYlks4IpnwEYLWkw+K3afQBjEJmNsvMZiYMzZP0evz2pvjP/MR/382KrfTAKDbI675GfX+uj6pl/WmVQkcrd6+T9LMBHrpa0v+Y2SuSWhV7lwAZzN3bzex7khZKWiUpscX7uZJ+FX9XME/S7yW9POJFYk+8qlg3zz/0Gxvj7psUa+gwW9Jz8SW92yR9wt1fNLM/SVqs2H8az4xs2QjQjyX9r5n9i6S/BV0MgN0yRtLP49s2uiWtlHSJYvt5X1UsCCxMOP63kn5tZm2SjmCf36g12Os+W9LNZvYt9e3tkfbMndVHAAAAAJDJWOoJAAAAABmO4AcAAAAAGY7gBwAAAAAZjuAHAAAAABmO4AcAAAAAGY7gBwAAAAAZjuAHAMgoZvZlMyvajfM+aWaTU1HTEF/zajP76k6OqTazJSNVEwAgMxH8AACZ5suSdin4mVlY0icljWjwAwBgpBD8AACjlpkVm9kDZvaymS0xs6sUC29PmNkT8WN+ZWa1ZrbUzK5JOHe1mV1pZn+XdJ6kGkm3m9liMysc4GstMLO/xG+fZWZtZpZnZgVmtio+vo+ZPWxmi8zsGTPbPz5eaWZ/NrOF8Y+jBnj+i83sITMrNLPD4t/Tc5I+n3BMdfx5X4x/HBkfv83Mzko47nYzO3M4/owBAJkhJ+gCAADYA6dIWufup0mSmZVJ+pSk4919U/yYb7t7Q3xW769mdpC7vxJ/rN3dj46fe5Gkr7p77SBf60VJh8RvHyNpiaT5iv1f+nx8/AZJn3P3N8zscEm/lHSCpJ9J+om7/93Mpkl6RNLsnic2sy9IOknS2e7eYWb/I+nf3P0pM7s+oYaNkk5093YzmynpDsUC602SviLpnvifwZGSLkz+jxEAkOkIfgCA0exVST82sx9Kut/dnzGz/sd81MwuUez/vEmS5kjqCX5/SvYLuXu3ma00s9mSFkj6T0nHSgpLesbMxigWuP4voYb8+Of3S5qTMF5qZiXx2/8iqU6x0NcVD25j3f2p+OO3SfpA/HaupF+Y2TxJEUn7xWt7ysz+28wmSPqQpD+7e3ey3xsAIPMR/AAAo5a7rzCzwySdKun7ZvZo4uNmNkPSVyXNd/dGM/utpIKEQ1p28Us+o1gI65L0uKTfKhb8vqrY9okt7j5vgPNCko5w97Z+9UmxmcN5kqokvSXJJPkgX/8rkjZIOjj+nO0Jj90m6XxJ50r69C5+XwCADMcePwDAqBXvwtnq7r+X9GNJh0pqltQzm1aqWLjbamYTtX3mbCCJ5w3macWaxzz3/9u7f5auojiO4+8PPgCHBKHNrUWCaHT0KTQ5ODToEK4NrT0DKZzKZyAEIrq4JGRLqfyqzVHCpQTXvg73SBcpGxzsd32/xvvn3O+ZLh++555bVafAPeABMKqqM+A4yZNWW5I8bPftAM96dffD4SdgCXiX5H5V/Wj1zrXzC71rJ4GTqvpF1ymc6J1bb7VRVaN/zEOSdMcY/CRJ42wW+JjkM/ACeEn3nd1Wkt2qOqALViPgDbB3zVjrwNrfNndp9oFpugAI3ZLRw6q67NAtAE+THLRnXm64sgI8TnKY5Auw3B+0qt7TdQ03k0zRfaf4qm3u0u8SvgYWk3ygW+Z53hvjO/AVeHvNHCVJd1R+v6skSdK4av8uPAIeVdXP265HkvR/seMnSdKYSzIPfANWDX2SpD+x4ydJ0hVJNoCZK4efV9X2bdQjSdJNGfwkSZIkaeBc6ilJkiRJA2fwkyRJkqSBM/hJkiRJ0sAZ/CRJkiRp4Ax+kiRJkjRwFy6/IxFa6t8vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_avg_mint = df_clean_60_or_less.groupby('start_weekday').duration_minutes.mean()\n",
    "\n",
    "# plot a line plot\n",
    "plt.figure(figsize=[15,5])\n",
    "sb.lineplot(x=weekday_avg_mint.index, y=weekday_avg_mint, marker='o')\n",
    "plt.xticks(np.arange(0,7), weekday_labels)\n",
    "plt.ylabel('Average Duration (min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average rides on the weekend (Saturday and Sunday) lasted longer than the other days."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### bike_id vs. start_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_of_bikes = df_clean.groupby('start_month').bike_id.nunique()\n",
    "\n",
    "# plot a line plot\n",
    "plt.figure(figsize=[15,5])\n",
    "sb.lineplot(x=number_of_bikes.index, y=number_of_bikes, marker='o')\n",
    "plt.xticks(np.arange(1,13), month_labels)\n",
    "plt.ylabel('Total Number of Unique Bikes');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "December had the highest number of unique bikes used, and May had the lowest."
   ]
  },
  {
   "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",
    "**Subscribers and customers bikes usage was different:**\n",
    "- Subscribers mostly rode bikes for (8-12) minutes, while customers mostly rode them for (10-19) minutes.\n",
    "- Customers rides were more likely to last more than one hour compared to subscribers.\n",
    "- Subscribers used bikes mostly in March and July, while customers mostly used them in December.\n",
    "- Subscribers mostly used bikes on workdays (Monday - Friday), while customers bikes usage was about the same for the whole week with a slight increment on Friday and Saturday.\n",
    "- Most rides for subscribers started at 8 or 17. While most customer rides started at 17 or 18.\n",
    "\n",
    "**Average duration in minutes:**\n",
    "- Based on the month: on average rides from June to October lasted longer than the other months.\n",
    "- Based on week day: on average rides on the weekend (Saturday and Sunday) lasted longer than the other days.\n",
    "\n",
    "**Total number of unique bikes:**\n",
    "- December had the highest number of unique bikes used, and May had the lowest.\n",
    "\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "No."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  duration_minutes vs. start_month (for each user_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since most rides didn't exceed an hour, I will just keep the rides with duration_minutes <= 60 in this part, to get a more accurate duration_minutes mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_month_avg_mint = df_clean_60_or_less.groupby(['user_type','start_month']).duration_minutes.mean()\n",
    "user_month_avg_mint = user_month_avg_mint.reset_index()\n",
    "\n",
    "# plot a line plot with colors based on user_type\n",
    "plt.figure(figsize=[10,5])\n",
    "sb.lineplot(data = user_month_avg_mint, x='start_month', y='duration_minutes', hue='user_type', marker='o')\n",
    "plt.xticks(np.arange(1,13), month_labels)\n",
    "plt.ylabel('Average Duration (min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average customers rides lasted longer than subscribers rides in every month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  duration_minutes vs. start_weekday (for each user_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_weekday_avg_mint = df_clean_60_or_less.groupby(['user_type','start_weekday']).duration_minutes.mean()\n",
    "user_weekday_avg_mint = user_weekday_avg_mint.reset_index()\n",
    "\n",
    "# plot a line plot with colors based on user_type\n",
    "plt.figure(figsize=[10,5])\n",
    "sb.lineplot(data = user_weekday_avg_mint, x='start_weekday', y='duration_minutes', hue='user_type', marker='o')\n",
    "plt.xticks(np.arange(0,7), weekday_labels)\n",
    "plt.ylabel('Average Duration (min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On average customers rides lasted longer than subscribers rides on every week day."
   ]
  },
  {
   "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",
    "- On average customers rides lasted longer than subscribers rides in every month.\n",
    "- On average customers rides lasted longer than subscribers rides on every week day.\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "Yes, customers were more likely to use bikes for a longer period than subscribers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save df_clean to a file named 2019-baywheels-tripdata.csv with no index column\n",
    "df_clean.to_csv('2019-baywheels-tripdata.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}