exploration.ipynb

{
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   "cell_type": "markdown",
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    "id": "TcNHbLYRUugt"
   },
   "source": [
    "# Ford GoBike System Data Exploration\n",
    "## by Ayanwoye Gideon Ayandele\n",
    "\n",
    "## Preliminary Wrangling\n",
    "\n",
    "> This document explores a dataset with information about individual rides made\n",
    "in a bike-sharing system covering the greater San Francisco\n",
    "Bay area in 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "id": "cer_tVDEUuhp"
   },
   "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 sns\n",
    "from IPython.display import display\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "id": "-TjMlcVxUuhy"
   },
   "outputs": [],
   "source": [
    "# load in the dataset into a pandas dataframe, print statistics\n",
    "data_path = '201902-fordgobike-tripdata.csv'\n",
    "df = pd.read_csv(data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 690
    },
    "id": "EhO9C2VdUuhz",
    "outputId": "7eaee2e2-af29-411d-fc9d-73b68091e481"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(183412, 16) \n",
      "\n",
      "duration_sec                 int64\n",
      "start_time                  object\n",
      "end_time                    object\n",
      "start_station_id           float64\n",
      "start_station_name          object\n",
      "start_station_latitude     float64\n",
      "start_station_longitude    float64\n",
      "end_station_id             float64\n",
      "end_station_name            object\n",
      "end_station_latitude       float64\n",
      "end_station_longitude      float64\n",
      "bike_id                      int64\n",
      "user_type                   object\n",
      "member_birth_year          float64\n",
      "member_gender               object\n",
      "bike_share_for_all_trip     object\n",
      "dtype: object \n",
      "\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.1450</td>\n",
       "      <td>2019-03-01 08:01:55.9750</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>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.7890</td>\n",
       "      <td>2019-03-01 06:42:03.0560</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>2535</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.2180</td>\n",
       "      <td>2019-03-01 05:24:08.1460</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>5905</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.0100</td>\n",
       "      <td>2019-03-01 04:02:36.8420</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</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>6638</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.5490</td>\n",
       "      <td>2019-03-01 00:20:44.0740</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.792714</td>\n",
       "      <td>-122.248780</td>\n",
       "      <td>4898</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time  ... member_gender  bike_share_for_all_trip\n",
       "0         52185  2019-02-28 17:32:10.1450  ...          Male                       No\n",
       "1         42521  2019-02-28 18:53:21.7890  ...           NaN                       No\n",
       "2         61854  2019-02-28 12:13:13.2180  ...          Male                       No\n",
       "3         36490  2019-02-28 17:54:26.0100  ...         Other                       No\n",
       "4          1585  2019-02-28 23:54:18.5490  ...          Male                      Yes\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# high-level overview of data shape and composition\n",
    "print(df.shape,\"\\n\")\n",
    "print(df.dtypes,\"\\n\")\n",
    "display(df.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "qHOYSLWPUuh1",
    "outputId": "35352819-1b6c-43e3-db4a-24940dd18fac"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "        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_station_id</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>member_birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183215.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183215.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>175147.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>726.078435</td>\n",
       "      <td>138.590427</td>\n",
       "      <td>37.771223</td>\n",
       "      <td>-122.352664</td>\n",
       "      <td>136.249123</td>\n",
       "      <td>37.771427</td>\n",
       "      <td>-122.352250</td>\n",
       "      <td>4472.906375</td>\n",
       "      <td>1984.806437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1794.389780</td>\n",
       "      <td>111.778864</td>\n",
       "      <td>0.099581</td>\n",
       "      <td>0.117097</td>\n",
       "      <td>111.515131</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.116673</td>\n",
       "      <td>1664.383394</td>\n",
       "      <td>10.116689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>37.770083</td>\n",
       "      <td>-122.412408</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>3777.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.398285</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>37.781010</td>\n",
       "      <td>-122.398279</td>\n",
       "      <td>4958.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>796.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>37.797280</td>\n",
       "      <td>-122.286533</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.288045</td>\n",
       "      <td>5502.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85444.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>6645.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec  start_station_id  ...        bike_id  member_birth_year\n",
       "count  183412.000000     183215.000000  ...  183412.000000      175147.000000\n",
       "mean      726.078435        138.590427  ...    4472.906375        1984.806437\n",
       "std      1794.389780        111.778864  ...    1664.383394          10.116689\n",
       "min        61.000000          3.000000  ...      11.000000        1878.000000\n",
       "25%       325.000000         47.000000  ...    3777.000000        1980.000000\n",
       "50%       514.000000        104.000000  ...    4958.000000        1987.000000\n",
       "75%       796.000000        239.000000  ...    5502.000000        1992.000000\n",
       "max     85444.000000        398.000000  ...    6645.000000        2001.000000\n",
       "\n",
       "[8 rows x 9 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# descriptive statistics for numeric variables\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aM0RrfsDUuh4",
    "outputId": "60180a8a-adbd-4f2c-9aa7-5a378aa06195"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# chek for duplicates\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "GwONuXHGUuh5",
    "outputId": "cae898af-b735-4c3f-c4e1-ed1985dd5560"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec               0.000000\n",
       "start_time                 0.000000\n",
       "end_time                   0.000000\n",
       "start_station_id           0.107408\n",
       "start_station_name         0.107408\n",
       "start_station_latitude     0.000000\n",
       "start_station_longitude    0.000000\n",
       "end_station_id             0.107408\n",
       "end_station_name           0.107408\n",
       "end_station_latitude       0.000000\n",
       "end_station_longitude      0.000000\n",
       "bike_id                    0.000000\n",
       "user_type                  0.000000\n",
       "member_birth_year          4.506248\n",
       "member_gender              4.506248\n",
       "bike_share_for_all_trip    0.000000\n",
       "dtype: float64"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check for the percentage of missing values\n",
    "missing_val_summary = (df.isnull().sum()/df.isnull().count())*100\n",
    "\n",
    "display(missing_val_summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VFFHYmJKUuh-"
   },
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> There are 183,412 rides made in the dataset with 16 features  (`'duration_sec', 'start_time', 'end_time', 'start_station_id', 'start_station_name', 'start_station_latitude','start_station_longitude', 'end_station_id', 'end_station_name', 'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type','member_birth_year', 'member_gender', 'bike_share_for_all_trip'`). Most variables are numeric in nature while some variables like the `start_time` and `end_time` have the wrong data types instead of the datetime.\n",
    "\n",
    "> Also, the variables `start_station_id`, `start_station_name`, `end_station_id`, `end_station_name`, `member_birth_year` and  `member_gender` contained missing values with the following percentages:\n",
    "\n",
    ">`start_station_id`:         0.107 <br>\n",
    "`start_station_name`:        0.107 <br>\n",
    "`end_station_id`:            0.107 <br>\n",
    "`end_station_name`:          0.107 <br>\n",
    "`member_birth_year`:         4.506 <br>\n",
    "`member_gender`:             4.506 <br>\n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> Among several other informations that would warrant investigation along the data exploration, I am most interested in figuring out:\n",
    "1. The time of day, day of the week that most trips are taken\n",
    "2. How long does the average trip take?\n",
    "3. If the answer in 1 & 2 above depend on if a user is a subscriber or a customer? \n",
    "\n",
    "> The main features of interest would be the `duration_sec`, `start_time`, and `user_type`. \n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> With Additional feature engineering from the main features of interest, I believe extracting the time of the day and day of the week from the start_time will help answer the number one objective listed above. It could also be expected that  the duration_sec would be able to help in answering the number two objective and then the user_type with the number three objective. Also features like `member_birth_year` and `member_gender` would greatly help support my investigations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "id": "rcXbU6X4UuiA"
   },
   "outputs": [],
   "source": [
    "# make a copy of the original dataset to clean\n",
    "df_new = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "R_NTguQ5UuiB",
    "outputId": "3de08e23-2ce0-4774-a47e-d07f9e685a76"
   },
   "outputs": [
    {
     "data": {
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       "    }\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>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.1450</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.7890</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.2180</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.0100</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.5490</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time  ... member_birth_year member_gender\n",
       "0         52185  2019-02-28 17:32:10.1450  ...            1984.0          Male\n",
       "1         42521  2019-02-28 18:53:21.7890  ...               NaN           NaN\n",
       "2         61854  2019-02-28 12:13:13.2180  ...            1972.0          Male\n",
       "3         36490  2019-02-28 17:54:26.0100  ...            1989.0         Other\n",
       "4          1585  2019-02-28 23:54:18.5490  ...            1974.0          Male\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop variables that are irrelevant to my analysis\n",
    "df_new.drop(['end_time', 'start_station_id', 'start_station_latitude', 'start_station_longitude', 'end_station_id','end_station_latitude', 'end_station_longitude', 'bike_id', \n",
    "        'bike_share_for_all_trip'], axis=1, inplace=True)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "id": "ok5NLfVlUuiD"
   },
   "outputs": [],
   "source": [
    "# assign proper data types to the start_time\n",
    "df_new['start_time'] = pd.to_datetime(df_new['start_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 416
    },
    "id": "U5Grs-XcUuiE",
    "outputId": "e7eb411b-414c-4a1e-b95c-b7c6dfaf12d2"
   },
   "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>day_of_week</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>time_of_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>18</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>12</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183407</th>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183408</th>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183409</th>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183410</th>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183411</th>\n",
       "      <td>Friday</td>\n",
       "      <td>0</td>\n",
       "      <td>Morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183412 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       day_of_week  start_hour time_of_day\n",
       "0         Thursday          17   Afternoon\n",
       "1         Thursday          18       Night\n",
       "2         Thursday          12   Afternoon\n",
       "3         Thursday          17   Afternoon\n",
       "4         Thursday          23       Night\n",
       "...            ...         ...         ...\n",
       "183407      Friday           0     Morning\n",
       "183408      Friday           0     Morning\n",
       "183409      Friday           0     Morning\n",
       "183410      Friday           0     Morning\n",
       "183411      Friday           0     Morning\n",
       "\n",
       "[183412 rows x 3 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract day of the week\n",
    "df_new['day_of_week'] = pd.DatetimeIndex(df_new['start_time']).strftime('%A')\n",
    "# extract the time of the day\n",
    "df_new['start_hour'] = df_new['start_time'].apply(lambda time: time.hour)\n",
    "df_new['time_of_day'] = 'Morning'\n",
    "df_new['time_of_day'][(df_new['start_hour'] >= 12) & (df_new['start_hour'] <= 17)] = 'Afternoon'\n",
    "df_new['time_of_day'][(df_new['start_hour'] >= 18) & (df_new['start_hour'] <= 23)] = 'Night'\n",
    "\n",
    "# display the added columns\n",
    "display(df_new[['day_of_week','start_hour','time_of_day']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "id": "eb5Xev4jUuiG",
    "outputId": "1d5c9aec-d831-4832-a6fa-e893e54832b8"
   },
   "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>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>time_of_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.789</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>18</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.218</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>12</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.010</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>Other</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>Afternoon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.549</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time  ... start_hour time_of_day\n",
       "0         52185 2019-02-28 17:32:10.145  ...         17   Afternoon\n",
       "1         42521 2019-02-28 18:53:21.789  ...         18       Night\n",
       "2         61854 2019-02-28 12:13:13.218  ...         12   Afternoon\n",
       "3         36490 2019-02-28 17:54:26.010  ...         17   Afternoon\n",
       "4          1585 2019-02-28 23:54:18.549  ...         23       Night\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the resulting dataframe\n",
    "display(df_new.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q20Oq2jFUuiI"
   },
   "source": [
    "## Univariate Exploration\n",
    "\n",
    "> In this section, investigate distributions of individual variables. If\n",
    "you see unusual points or outliers, take a deeper look to clean things up\n",
    "and prepare yourself to look at relationships between variables.\n",
    "\n",
    "I'll start by looking at the distribution of the main variables of interest: `day_of_week`, `time_of_day`, `user_type` and `duration_sec`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 489
    },
    "id": "r4ldQzX2VvZd",
    "outputId": "6bfc6c3a-26eb-45f0-f4d0-ea05f99fee09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Thursday     35197\n",
       "Tuesday      31813\n",
       "Wednesday    29641\n",
       "Friday       28981\n",
       "Monday       26852\n",
       "Sunday       15523\n",
       "Saturday     15405\n",
       "Name: day_of_week, dtype: int64"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the count of trips made in each day of the week\n",
    "display(df_new['day_of_week'].value_counts().sort_values(ascending=False))\n",
    "weekday = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "# plot distribution of day of week\n",
    "plt.figure(figsize=[8, 5])\n",
    "\n",
    "# base_color = sns.color_palette()[0]\n",
    "sns.countplot(data = df_new, x = 'day_of_week', order=weekday)\n",
    "plt.title('Trips by Days of the Week')\n",
    "plt.xlabel('Days')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oC6aCzrccSd_"
   },
   "source": [
    "It happened that the day of the week with the most trips is **Thursday** followed by Tuesday and wednesday. It could be seen that Saturday and Sunday have the least number of trips which is quite reasonable with the fact that they are not working days. Therefore, I would be investigating how the user type and gender are distributed among the days of the week. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "id": "76hwpNdKe-bS",
    "outputId": "fcfc2182-e1d5-4162-8cd4-30cbaea1607b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage count of trips made in terms of times of the day(%)\n",
      "Afternoon    38.51\n",
      "Morning      38.42\n",
      "Night        23.07\n",
      "Name: time_of_day, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the percentage count of trips made in terms of times of the day\n",
    "time_of_day_count = (df_new['time_of_day'].value_counts()/df_new['time_of_day'].count())*100\n",
    "print(\"The percentage count of trips made in terms of times of the day(%)\")\n",
    "print(time_of_day_count.round(2).sort_values(ascending=False))\n",
    "\n",
    "# plot distribution of day of week\n",
    "plt.figure(figsize=[8, 5])\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data = df_new, x = 'time_of_day', color = base_color)\n",
    "n_points = df_new.shape[0]\n",
    "user_counts = df_new['time_of_day'].value_counts()\n",
    "locs, labels = plt.xticks()\n",
    "\n",
    "# print frequency on the bar chart\n",
    "for loc, label in zip(locs, labels):\n",
    "    count = user_counts[label.get_text()]\n",
    "    pct_string = '{:0.1f}%'.format(100*count/n_points)\n",
    "    plt.text(loc, count-8, pct_string, ha='center')\n",
    "\n",
    "plt.title('Trips by Times of the Day')\n",
    "plt.xlabel('Times of the Day')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YTZha8yMRlkr"
   },
   "source": [
    "It could be seen from the plot that the time of the day with the most trips is **Afternoon** (12:00 - 17:59 hours of the day) followed by Morning (00:00 - 11:59 hours of the day). However, the differences between the number of trips taken in the two times is very little.This is really reasonable. Noone expects so much rides in the night time. Further analysis will be done to see if this is probably influenced by user types(subscriber and customer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 686
    },
    "collapsed": true,
    "id": "Y-CrOKL8UuiO",
    "outputId": "f94eb8f1-a465-461f-fdde-42f2dbf69c33"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User Types Percentage Count (%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Subscriber    89.17\n",
       "Customer      10.83\n",
       "Name: user_type, dtype: float64"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Member Gender Percentage Count (%)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Male      74.60\n",
       "Female    23.32\n",
       "Other      2.09\n",
       "Name: member_gender, dtype: float64"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the percentage count of user types and gender\n",
    "user_type_count = (df_new['user_type'].value_counts()/df_new['user_type'].count())*100\n",
    "member_gender_count = (df_new['member_gender'].value_counts()/df_new['member_gender'].count())*100\n",
    "\n",
    "print(\"User Types Percentage Count (%)\")\n",
    "display(user_type_count.round(2).sort_values(ascending=False))\n",
    "print(\"\\nMember Gender Percentage Count (%)\")\n",
    "display(member_gender_count.round(2).sort_values(ascending=False))\n",
    "\n",
    "# Attributes of the Riders: the distributions of user type and gender\n",
    "fig, ax = plt.subplots(nrows=2, figsize = [8,8])\n",
    "default_color = sns.color_palette()[1]\n",
    "sns.countplot(data = df_new, x = 'user_type', color = default_color, ax = ax[0])\n",
    "sns.countplot(data = df_new, x = 'member_gender', color = default_color, ax = ax[1])\n",
    "ax[0].set_xlabel('User Type')\n",
    "ax[0].set_ylabel('Count')\n",
    "ax[1].set_xlabel('Member Gender')\n",
    "ax[1].set_ylabel('Count')\n",
    "ax[0].set_title('Distributions of User Types and Gender');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jUk2G-WqXxUr"
   },
   "source": [
    "- As it would be expected in most cases, most trips (about 90%) are taken by the **subscribers** with the remaining 10% being just casual riders. In the case of larger percentage of trips by subscribers; who wouldn't want to make efficient use of their subscription?. Or in another view, the Subscribers might be using the bikes for daily commute while customers just use it for leisure activities\n",
    "\n",
    "\n",
    "\n",
    "-  Also, almost 75% of the riders that's taken trips are **males** which is like 3 times that of the females with the remaining 2% regarded as other.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LtCVM9xagpTB"
   },
   "source": [
    "Now unto the last main variables of interest: duration_sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "OTJIqMS4UuiK",
    "outputId": "3898ec9c-bbbd-4617-ee90-e0ae112d9ac8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start with a standard-scaled plot\n",
    "binsize = 100\n",
    "bins = np.arange(0, df_new['duration_sec'].max()+binsize, binsize)\n",
    "\n",
    "plt.figure(figsize=[8, 5])\n",
    "plt.hist(data = df_new, x = 'duration_sec', bins=bins)\n",
    "plt.xlim(0, 3000)\n",
    "plt.xlabel('Trip Duration (sec)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Trip Duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oQLKJX9_UuiM"
   },
   "source": [
    "The Trip duration in seconds distribution is highly right-skewed with a really long tail that it was very difficult to view the plot. I had to resolve into binning of size 100 and also set the plot's x_axis limit to 3000. I will change the trip duration variable to minute instead of second and also get to know how long the average trip take."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 416
    },
    "id": "dkUaBPgbsEif",
    "outputId": "03b5b06b-fc18-499d-baba-14cdef4ffa62"
   },
   "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_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>869.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>708.683333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1030.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>608.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183407</th>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183408</th>\n",
       "      <td>5.216667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183409</th>\n",
       "      <td>2.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183410</th>\n",
       "      <td>2.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183411</th>\n",
       "      <td>4.516667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183412 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_min\n",
       "0         869.750000\n",
       "1         708.683333\n",
       "2        1030.900000\n",
       "3         608.166667\n",
       "4          26.416667\n",
       "...              ...\n",
       "183407      8.000000\n",
       "183408      5.216667\n",
       "183409      2.350000\n",
       "183410      2.316667\n",
       "183411      4.516667\n",
       "\n",
       "[183412 rows x 1 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# standardizing the trip duration variable into minutes\n",
    "df_new['duration_min'] = df_new['duration_sec'] / 60\n",
    "# drop the 'duration_sec\" column\n",
    "df_new.drop('duration_sec' , axis = 1 , inplace = True)\n",
    "\n",
    "# show\n",
    "display(df_new[['duration_min']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "id": "xT5xnDNarzil",
    "outputId": "cd0ac346-2306-4010-8e02-dda76e3d68b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The time duration of average trip taken is 12.1 minutes.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print the average trip duration\n",
    "average_trip_duration = np.round(df_new['duration_min'].mean(),2)\n",
    "print(\"The time duration of average trip taken is %s minutes.\"%(average_trip_duration))\n",
    "\n",
    "# trip duration distribution in log scale\n",
    "log_binsize = 0.1\n",
    "bins = 10 ** np.arange(0, np.log10(df_new['duration_min'].max())+log_binsize, log_binsize)\n",
    "ticks =  [ 0.1 , 0.3 , 1 , 3, 10, 30, 100, 300]\n",
    "labels = ['{}'.format(v) for v in ticks]\n",
    "\n",
    "plt.figure(figsize=[8, 5])\n",
    "plt.hist(data = df_new, x = 'duration_min', bins = bins)\n",
    "plt.xscale('log')\n",
    "plt.xticks(ticks, labels)\n",
    "plt.xlabel('Trip Duration (min)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Log Distribution of Trip Duration')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ATZG7JIYxCry"
   },
   "source": [
    "When the trip duration is converted to minutes and plotted on a log-scale, the duration distribution looks highly unimodal, with the peak around 10 minutes. Interestingly, the average trip duration is around 12 minutes. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "55Fde6d791-v"
   },
   "source": [
    "I'll now look at the other features in the data to see if any of them hold interesting properties: `start_hour`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 801
    },
    "id": "50my77yE-6Fa",
    "outputId": "da4e35e4-cf62-4611-cda2-42978c357941"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage count of trip starting hours (%)\n",
      "17    11.92\n",
      "8     11.48\n",
      "18     9.17\n",
      "9      8.67\n",
      "16     7.73\n",
      "7      5.79\n",
      "19     5.39\n",
      "15     5.00\n",
      "12     4.76\n",
      "13     4.66\n",
      "10     4.56\n",
      "14     4.44\n",
      "11     4.30\n",
      "20     3.53\n",
      "21     2.49\n",
      "6      1.90\n",
      "22     1.59\n",
      "23     0.90\n",
      "0      0.50\n",
      "5      0.49\n",
      "1      0.30\n",
      "2      0.21\n",
      "4      0.13\n",
      "3      0.09\n",
      "Name: start_hour, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the percentage count of trips made in terms of times of the day\n",
    "start_hour_count = (df_new['start_hour'].value_counts()/df_new['start_hour'].count())*100\n",
    "print(\"The percentage count of trip starting hours (%)\")\n",
    "print(start_hour_count.round(2).sort_values(ascending=False))\n",
    "\n",
    "# plot distribution of day of week\n",
    "plt.figure(figsize=[8, 5])\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data = df_new, x = 'start_hour', color = base_color)\n",
    "plt.title('Starting Hours of the trips')\n",
    "plt.xlabel('Start Hour')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m7r2wHwFDJkV"
   },
   "source": [
    "From the above barchart, it could be seen that trips are started mostly between 8-9am and 5-6pm (17-18). This is highly justifiable as these hours correspond to the hours people leave for work and home respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Feq0Kba8pto"
   },
   "source": [
    "Before closing this section, I'll take a look at the age distribution of the riders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "YxtPgcGN59Ph",
    "outputId": "0cab8156-7843-44b3-f9a4-0bef9c80ff16"
   },
   "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>user_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_age\n",
       "0      35.0\n",
       "1       NaN\n",
       "2      47.0\n",
       "3      30.0\n",
       "4      45.0"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the age column taking the year 2019 as the current year\n",
    "df_new['user_age'] = df_new['member_birth_year'].apply(lambda x: 2019 - x)\n",
    "# drop the 'member_birth_year\" column\n",
    "df_new.drop('member_birth_year' , axis = 1 , inplace = True)\n",
    "# test\n",
    "display(df_new[['user_age']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "ddIK09LYjyz0",
    "outputId": "d6e55ba6-4325-40b2-d27f-783fee74bb8c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# start with a standard-scaled plot\n",
    "binsize = 1\n",
    "bins = np.arange(0, df_new['user_age'].max()+binsize, binsize)\n",
    "\n",
    "plt.figure(figsize=[8, 6])\n",
    "plt.hist(data = df_new, x = 'user_age', bins=bins)\n",
    "plt.xlabel('Users age (years)')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of users age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LNw7cc3rpDn6"
   },
   "source": [
    "The users age distribution is highly right-skewed with as well a quite long tail. i.e most users ages ranges between 18 and 60 while there are some suprisingly old agesranging from 80 to 140. There might have been an error in data input or it could just be a case of an exception in the general expectations. \n",
    "To have a better understanding of the distibution, I would bin the ages into groups to see how age ranges are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "id": "O_x5d6Z9uKBL",
    "outputId": "6276f090-4e69-46dd-9bb6-015367401807"
   },
   "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>user_age_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31 - 40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41 - 50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21 - 30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41 - 50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  user_age_bin\n",
       "0      31 - 40\n",
       "1          NaN\n",
       "2      41 - 50\n",
       "3      21 - 30\n",
       "4      41 - 50"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a new column user_age_bin from user_age group\n",
    "max_age = str(int(df_new.user_age.max()))\n",
    "df_new['user_age_bin'] = df_new['user_age'].apply(lambda x: '10 - 20' if 10<x<=20 else \n",
    "                                                  '21 - 30' if 20<x<=30 else\n",
    "                                                  '31 - 40' if 30<x<=40 else \n",
    "                                                  '41 - 50' if 40<x<=50 else\n",
    "                                                  '51 - 60' if 50<x<=60 else\n",
    "                                                  '61 - 70' if 60<x<=70 else\n",
    "                                                  '71 - 80' if 70<x<=80 else\n",
    "                                                  '81 - '+max_age if 80<x<=float(max_age) else x)\n",
    "\n",
    "# test\n",
    "display(df_new[['user_age_bin']].head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "id": "MH5DhF1r0j_g",
    "outputId": "77bd26a4-2d20-4b71-9b81-a0630773c2ad"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distribution of day of week\n",
    "plt.figure(figsize=[8, 6])\n",
    "base_color = sns.color_palette()[8]\n",
    "sns.countplot(data = df_new.sort_values(['user_age_bin']), x = 'user_age_bin', color = base_color)\n",
    "n_points = df_new.shape[0]\n",
    "user_counts = df_new['user_age_bin'].value_counts()\n",
    "locs, labels = plt.xticks()\n",
    "\n",
    "# print frequency on the bar chart\n",
    "for loc, label in zip(locs, labels):\n",
    "    count = user_counts[label.get_text()]\n",
    "    pct_string = '{:0.1f}%'.format(100*count/n_points)\n",
    "    plt.text(loc, count-8, pct_string, ha='center')\n",
    "\n",
    "plt.title('Distribution of age groups')\n",
    "plt.xlabel('age group (years)')\n",
    "plt.ylabel(\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EZkT83FL4MfD"
   },
   "source": [
    "Not less than 70% of the riders ranges from age 21 to 40. i.e people of age 21 - 40 took more rides compared to all other age groups. However, I would like to investigate further on the relationship between the age groups and the duration of trips."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wVwLSmbTUuiP"
   },
   "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 variables `duration_sec` and `user_age` (gotten from subtracting `member_birth_year` from the year the data was recorded - 2019) look very right skewed. To treat the duration_sec, I needed to convert the duration from second to minute and then used a log transform which then made the resulting distribution to be slightly unimodal with the peak around 10 minutes while the mean of the distribution was around 12 minutes. \n",
    "For the transformation of age, since the supposed outliers in the age distribution are realized to be valid years. I was able to bin/group the ages which resulted into a distribution with less skewness.\n",
    "\n",
    ">I also checked variables like `user_type`, `member_genders` and so many other derived variables that are critical to understand the dataset and communicate the data findings. The following summarizes my findings in general:\n",
    "\n",
    " * The day of the week with the most trips is Thursday followed by Tuesday and wednesday. Most of the people take rides during Weekdays from Monday to Friday  compared to weekends\n",
    " * The time of the day with the most trips is Afternoon (12:00 - 17:59 hours of the day) followed by Morning (00:00 - 11:59 hours of the day)\n",
    " * Most trips (about 90%) are taken by the subscribers and would like to further investigate if this also influences the trip duration\n",
    " * Almost 75% of the riders that's taken trips are males which was like 3 times that of the females.\n",
    " * Trips are started mostly between 8-9am and 5-6pm (17-18)\n",
    " * Not less than 70% of the riders ranges from age 21 to 40, quite a youthful population, but could this relate to longer trip duration?\n",
    "\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 age distribution looked unusual, very old ages are present that seemed like outliers but I thought it was fair to let it be since they are valid. Data types of date related variables were also changed to the appropriate ones.\n",
    "Also, feature engineering was performed to create features that are critical and important to my analysis. It was this feature engineering that gave birth to features like `'day_of_week', 'start_hour', 'time_of_day', 'duration_min', 'user_age'` and `'user_age_bin'`.\n",
    "I as well dropped variables that aren't of interest in the dataset to tidy up the dataset for exploration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V72KIRkRUuiQ"
   },
   "source": [
    "## Bivariate Exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ovz9B-u9NQGb"
   },
   "source": [
    "**To start off with, I want to look at the pairwise correlations present between features in the data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "id": "GV14uQ9nO0iy"
   },
   "outputs": [],
   "source": [
    "numeric_vars = ['start_hour', 'duration_min', 'user_age' ]\n",
    "categoric_vars = ['user_type', 'member_gender', 'day_of_week', 'time_of_day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 325
    },
    "id": "OgsvdeH3NPHL",
    "outputId": "eb58eb8c-0590-4f9c-dc0b-7a6896550b92"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation plot\n",
    "plt.figure(figsize = [8, 5])\n",
    "sns.heatmap(df_new[numeric_vars].corr(), annot = True, fmt = '.3f',\n",
    "           cmap = 'vlag_r', center = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "p8F6o7g5UMs4",
    "outputId": "ecb61d76-8ae3-47be-cae3-58b7ee731b3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7fd4a14ccc50>"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot matrix: sample 1834 trips so that plots are clearer and\n",
    "# they render faster\n",
    "samples = np.random.choice(df_new.shape[0], 1834, replace = False)\n",
    "df_new_samp = df_new.loc[samples,:]\n",
    "\n",
    "g = sns.PairGrid(data = df_new_samp, vars = numeric_vars)\n",
    "g = g.map_diag(plt.hist, bins = 20);\n",
    "g.map_offdiag(plt.scatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6LFvWkrGVhT2"
   },
   "source": [
    "As it could be seen from the plots, 'start_hour', 'duration_min' and 'user_age' are in no way correlated with one another. However, a slightly negative correlation was found between user age and trip duration.  I would further investigate if the age groups (binned age) would have any relationship with the trip duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 582
    },
    "id": "nk8PR1FNWDuu",
    "outputId": "d518de0b-4431-4eac-9028-183a26a82b5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'trip duration (minute)')"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_color=['orange', 'deepskyblue']\n",
    "df_new.groupby('user_age_bin')['duration_min'].mean().plot(kind='bar', color=new_color, figsize=(10,8))\n",
    "plt.title('Average trip duration per age group', fontsize=16, y=1.015)\n",
    "plt.xlabel('User age group', labelpad=16)\n",
    "plt.ylabel('trip duration (minute)', labelpad=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tCGNgFWbbYCs"
   },
   "source": [
    "I expected that the young and youthful age groups should have the highest average trip duration because of the strength and agility they would possess but the graph above has proved me wrong. It could be compounded that, trips duration aren't based on how strong and agile the user is. It would however be better and right to say the trip duration is not affected by the user age not considering the extreme age groups (81-141)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "ILkiDv4kb-al",
    "outputId": "08066af2-a6c3-4c36-8909-ce55117d9cef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot matrix of numeric features against categorical features.\n",
    "# can use a larger sample since there are fewer plots and they're simpler in nature.\n",
    "\n",
    "samples = np.random.choice(df_new.shape[0], 2000, replace = False)\n",
    "df_new_samp = df_new.loc[samples,:]\n",
    "\n",
    "def boxgrid(x, y, **kwargs):\n",
    "    \"\"\" Quick hack for creating box plots with seaborn's PairGrid. \"\"\"\n",
    "    default_color = sns.color_palette()[0]\n",
    "    sns.boxplot(x, y, color = default_color)\n",
    "    plt.xticks(rotation = 30)\n",
    "\n",
    "plt.figure(figsize = [10, 10])\n",
    "g = sns.PairGrid(data = df_new_samp, y_vars = ['duration_min', 'user_age'], x_vars = categoric_vars,\n",
    "                size = 3, aspect = 1.5)\n",
    "g.map(boxgrid)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x_F-f3-QUZlt"
   },
   "source": [
    " It appears that there is a relationship between the user type variable and the trip duration variable. The customer user type seem to spend longer time on a trip compared to the subscriber counterpart. No other tangible correlation could be seen from any of the above plots. And with this preliminary look at bivariate relationships out of the way, I want to dig into some of the relationships more. I want to see how the day of the week, time of the day and the average trip duration relates to user_types."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lUWn4w7AAEcT"
   },
   "source": [
    "#### Does the day of the week that most trips are taken dependent on if a user is a subscriber or a customer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 460
    },
    "collapsed": true,
    "id": "8ECPvw3mUuiR",
    "outputId": "bae0e964-1d76-493e-f193-d14eac0108cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd4a0e59bd0>"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=df_new, x='day_of_week', col=\"user_type\", kind='count', sharey = False,\n",
    "            color = 'deepskyblue', order = weekday)\n",
    "g.set_axis_labels(\"Days of the Week\", \"Bike Rides\")\n",
    "g.set_titles(\"{col_name}\")\n",
    "g.fig.suptitle('Days of the Week Usage Trends by User Type', y=1.03, fontsize=14)\n",
    "g.set_xticklabels(rotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_V8p0LNgARUm"
   },
   "source": [
    "From the above bar graph, the day of the week that most trips are taken (Thursday) is **NOT** dependent on if a user is a subscriber or a customer.\n",
    "From the bar graphs, the trends shows that Customers often use the service during weekends where as Subscribers mostly use the service during regular weekdays from Monday to Friday. We could deduce from this that subscribers might be regularly using the bikes for work and home commute purposes and customers use it for recreational activities during weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G2NkwR_ClMBY"
   },
   "source": [
    "#### Does the time of the day that most trips are taken dependent on if a user is a subscriber or a customer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 453
    },
    "id": "LG7FzvmWACh9",
    "outputId": "b99d5f3d-a336-4001-deca-0228497f2d82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd4a066d590>"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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29vP1rc6mSb7d2s4fJnluK5+X5PtJfpDk7Fb23iSfSfI/wGeS7DbY7o91/Lbf3ya5oLXv/7eVbZnkyiTHAT8Etpiwd0rTmj3WmumeDly4spWTPBJ4EfCUqqokG1TVr5KcCpxWVSe1epcAf1VV30ryPuAQ4O3tMHdX1dwkbwNOAbYHlgE/SXIY8GjgFcBzqup3ST4J/AVwHPAw4PyqekcPr12SpoqvA/8nyY+BbwCfr6pvrWCfZwA70bWLFyX5KvAq4MyqOrR1SDw0ycbAkcCuVXVNko0GjrENsEtV3Zlkt5U4/tOBrYEdgACnJtkV+Fkrn19V563me6BZwMRaur9bgd8CR7WejdNGV0iyPrDBwH8KC4EvDFQ5tT1fClxWVTe0/a6m6+XYhS7ZvqC7osi6wM1tn3uBL/b5giRp2Krq9iTbA8+lu5L4+SQHr2C3U6rqTuDOJOfQJbsXAEcneTBwclVd3BLmb1fVNe1cywaOcWo7xsoefxdgL+CiVufhdAn1z4CfmlRrRUysNdNdBrx0jPJ7uP9QqHUAquqeJDsAe7T93gLsvornvKs9/35geWR9Dl0vyMKqes8Y+/7WcdWSZqLWtp0LnJvkUmA+92+L1xm9ywMPUd9uPch/Chyb5CPALeOc9jfjhTTGeoB/rqr/GNyQZMsVHEsCHGOtme+bwEOSLBgpSPIMusZzmyQPSbIBXSJNkocD61fV6cBfA9u23X4NPAKgqm4FbhkZ2we8BljRJc1BZwMvTfLods6Nkjx+dV+gJE11SZ6cZOuBomcCPwWupbuCB/CSUbvtm2SdNkRvN7qrfI8HbqqqI4FPA9sB5wG7JtmqnWsjVs4Djg+cCbyu/V9Aks1G2mppZdhjrRmtjZN+EfDRJO+mG+ZxLd146BPpbkK5hvsu+z0COCXJOnTJ99+08hOAI5O8la4nez7wqSQPBa4GDliFmC5P8g/A15M8CPgdcBDdfzKSNBM9HPi31pFxD7AYWAA8lW7o3fvperMHXQKcAzwKeH+72XE+8LdJfgfcDuxfVUtb58mXWpt6M7DnSsT0gOMDP0/yVOC7baje7cCr6YbpSSuUqtFXQiRJkiStKoeCSJIkST0wsZYkSZJ6YGItSZIk9cDEWpIkSerBjJwVZN68eXXGGWcMOwxJ6kuGHcCasl2WNMOM2S7PyB7rX/ziF8MOQZI0wHZZ0mwwIxNrSZIkabKZWEuSJEk9MLGWJEmSemBiLUmSJPXAxFqSJEnqgYm1JEmS1AMTa0mSJKkHJtaSJElSD0ysJUmSpB6YWEuSJEk9MLGWJEmSemBiLUmSJPVgzrADkMay1cnXDjuEGeOa/bYcdgjSSvPffn/8ty9NPnusJUmSpB6YWEuSJEk9MLGWJEmSemBiLUmSJPXAxFqSJEnqgYm1JEmS1AMTa0mSJKkHJtaSJElSD0ysJUmSpB6YWEuSJEk9MLGWJEmSemBiLUmSJPVgzrADkCRJU99WJ1877BBmhGv223LYIWgC2WMtSZIk9cDEWpJmmCQbJDkpyY+SXJFk5yQbJTkryVXtecNWN0k+nmRxkkuSbDdwnPmt/lVJ5g+Ub5/k0rbPx5NkGK9TkqYaE2tJmnk+BpxRVU8BtgWuAA4Gzq6qrYGz2zrAPsDW7bEAOBwgyUbAIcCOwA7AISPJeKvz+oH95k3Ca5KkKc/EWpJmkCTrA7sCRwFU1d1V9StgX2Bhq7YQ2K8t7wscV53zgA2SbArsDZxVVcuq6hbgLGBe27ZeVZ1XVQUcN3AsSZrVTKwlaWbZClgKHJPkoiSfTvIwYJOquqHVuRHYpC1vBlw3sP+SVjZe+ZIxyh8gyYIki5IsWrp06Rq+LEma+kysJWlmmQNsBxxeVc8CfsN9wz4AaD3NNdGBVNURVTW3quZuvPHGE306SRo6E2tJmlmWAEuq6vy2fhJdon1TG8ZBe765bb8e2GJg/81b2Xjlm49RLkmznom1JM0gVXUjcF2SJ7eiPYDLgVOBkZk95gOntOVTgf3b7CA7Abe2ISNnAnsl2bDdtLgXcGbbdluSndpsIPsPHEuSZjV/IEaSZp6/Ao5PsjZwNXAAXUfKiUkOBH4KvLzVPR14PrAYuKPVpaqWJXk/cEGr976qWtaW3wwcC6wLfK09JGnWM7GWpBmmqi4G5o6xaY8x6hZw0HKOczRw9Bjli4Cnr2GYkjTjOBREkiRJ6oGJtSRJktQDE2tJkiSpBybWkiRJUg9MrCVJkqQemFhLkiRJPTCxliRJknpgYi1JkiT1wMRakiRJ6oGJtSRJktQDE2tJkiSpBybWkiRJUg9MrCVJkqQemFhLkiRJPTCxliRJknpgYi1JkiT1wMRakiRJ6oGJtSRJktQDE2tJkiSpBxOaWCf56ySXJflhks8lWSfJVknOT7I4yeeTrN3qPqStL27btxw4znta+ZVJ9p7ImCVJkqTVMWGJdZLNgLcCc6vq6cBawCuBDwGHVdWTgFuAA9suBwK3tPLDWj2SbNP2exowD/hkkrUmKm5JkiRpdUz0UJA5wLpJ5gAPBW4AdgdOatsXAvu15X3bOm37HknSyk+oqruq6hpgMbDDBMctSZIkrZIJS6yr6nrgw8DP6BLqW4ELgV9V1T2t2hJgs7a8GXBd2/eeVv+Rg+Vj7PMHSRYkWZRk0dKlS/t/QZIkSdI4JnIoyIZ0vc1bAY8FHkY3lGNCVNURVTW3quZuvPHGE3UaSZIkaUwTORTkT4BrqmppVf0O+BLwHGCDNjQEYHPg+rZ8PbAFQNu+PvDLwfIx9pEkSZKmhIlMrH8G7JTkoW2s9B7A5cA5wEtbnfnAKW351LZO2/7NqqpW/so2a8hWwNbA9yYwbkmSJGmVzVlxldVTVecnOQn4PnAPcBFwBPBV4IQkH2hlR7VdjgI+k2QxsIxuJhCq6rIkJ9Il5fcAB1XVvRMVtyRJkrQ6JiyxBqiqQ4BDRhVfzRizelTVb4GXLec4hwKH9h6gJEmS1BN/eVGSJEnqgYm1JEmS1AMTa0mSJKkHJtaSJElSD0ysJUmSpB6YWEuSJEk9MLGWJEmSemBiLUmSJPXAxFqSJEnqgYm1JM0wSa5NcmmSi5MsamUbJTkryVXtecNWniQfT7I4ySVJths4zvxW/6ok8wfKt2/HX9z2zeS/SkmaekysJWlm+t9V9cyqmtvWDwbOrqqtgbPbOsA+wNbtsQA4HLpEHDgE2BHYAThkJBlvdV4/sN+8iX85kjT1mVhL0uywL7CwLS8E9hsoP6465wEbJNkU2Bs4q6qWVdUtwFnAvLZtvao6r6oKOG7gWJI0q5lYS9LMU8DXk1yYZEEr26SqbmjLNwKbtOXNgOsG9l3SysYrXzJG+QMkWZBkUZJFS5cuXZPXI0nTwpxhByBJ6t0uVXV9kkcDZyX50eDGqqokNdFBVNURwBEAc+fOnfDzSdKw2WMtSTNMVV3fnm8Gvkw3RvqmNoyD9nxzq349sMXA7pu3svHKNx+jXJJmPRNrSZpBkjwsySNGloG9gB8CpwIjM3vMB05py6cC+7fZQXYCbm1DRs4E9kqyYbtpcS/gzLbttiQ7tdlA9h84liTNag4FkaSZZRPgy20GvDnAZ6vqjCQXACcmORD4KfDyVv904PnAYuAO4ACAqlqW5P3ABa3e+6pqWVt+M3AssC7wtfaQpFnPxFqSZpCquhrYdozyXwJ7jFFewEHLOdbRwNFjlC8Cnr7GwUrSDONQEEmSJKkHJtaSJElSD0ysJUmSpB6YWEuSJEk9MLGWJEmSemBiLUmSJPXAxFqSJEnqgYm1JEmS1AMTa0mSJKkHJtaSJElSD0ysJUmSpB6YWEuSJEk9MLGWJEmSemBiLUmSJPXAxFqSJEnqgYm1JEmS1AMTa0mSJKkHJtaSJElSD0ysJUmSpB6YWEuSJEk9MLGWJEmSejBn2AEM21YnXzvsEGaEa/bbctghSJIkDZU91pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9WBCE+skGyQ5KcmPklyRZOckGyU5K8lV7XnDVjdJPp5kcZJLkmw3cJz5rf5VSeZPZMySJEnS6pjoHuuPAWdU1VOAbYErgIOBs6tqa+Dstg6wD7B1eywADgdIshFwCLAjsANwyEgyLkmSJE0VE5ZYJ1kf2BU4CqCq7q6qXwH7AgtbtYXAfm15X+C46pwHbJBkU2Bv4KyqWlZVtwBnAfMmKm5JmgmSrJXkoiSntfWtkpzfrgp+PsnarfwhbX1x277lwDHe08qvTLL3QPm8VrY4ycGjzy1Js9VE9lhvBSwFjmmN+6eTPAzYpKpuaHVuBDZpy5sB1w3sv6SVLa/8fpIsSLIoyaKlS5f2/FIkadp5G91VwhEfAg6rqicBtwAHtvIDgVta+WGtHkm2AV4JPI2uM+OTLVlfC/h3uquM2wCvanUladabyMR6DrAdcHhVPQv4DfcN+wCgqgqoPk5WVUdU1dyqmrvxxhv3cUhJmpaSbA78KfDpth5gd+CkVmX01cKRq4gnAXu0+vsCJ1TVXVV1DbCYbjjeDsDiqrq6qu4GTmh1JWnWm8jEegmwpKrOb+sn0SXaN7UhHrTnm9v264EtBvbfvJUtr1ySNLaPAu8Cft/WHwn8qqruaeuDV/7+cFWwbb+11V+jq4jglURJs8+EJdZVdSNwXZInt6I9gMuBU4GRmT3mA6e05VOB/dvsIDsBt7YhI2cCeyXZsN20uFcrkySNkuQFwM1VdeGwY/FKoqTZZs4EH/+vgOPbTTJXAwfQJfMnJjkQ+Cnw8lb3dOD5dJcb72h1qaplSd4PXNDqva+qlk1w3JI0XT0HeGGS5wPrAOvRzdC0QZI5rVd68MrfyFXBJUnmAOsDv2T8q4VeRZSkMUxoYl1VFwNzx9i0xxh1CzhoOcc5Gji63+gkaeapqvcA7wFIshvwzqr6iyRfAF5KNyZ69NXC+cB32/ZvVlUlORX4bJKPAI+lmwr1e0CArZNsRZdQvxL480l6eZI0pU10j7UkaWp4N3BCkg8AF9GmQm3Pn0myGFhGlyhTVZclOZFuCN89wEFVdS9AkrfQDclbCzi6qi6b1FciSVPUChPrNkXenVX1+yR/BDwF+FpV/W7Co5MkrbaqOhc4ty1fTTejx+g6vwVetpz9DwUOHaP8dLrhe5KkAStz8+K3gXWSbAZ8HXgNcOxEBiVJkiRNNyszFCRVdUe72fCTVfUvSS6e6MAkSZK0YludfO2wQ5gRrtlvyzU+xsr0WCfJzsBfAF9tZWut8ZklSZKkGWRlEuu3091h/uV2M8sTgHMmNixJkiRpelnhUJCq+hbwrSQPbetXA2+d6MAkSZKk6WSFPdZJdk5yOfCjtr5tkk9OeGSSJEnSNLIyQ0E+CuxN90tcVNUPgF0nMihJkiRpulmZxJqqum5U0b0TEIskSZI0ba3MdHvXJXk2UEkeDLwNuGJiw5IkSZKml5XpsX4jcBCwGXA98My2LkmSJKlZmVlBfkE3h7UkSZKk5VhuYp3k34Ba3vaqcso9SZIkqRlvKMgi4EJgHWA74Kr2eCaw9sSHJkmSJE0fy+2xrqqFAEneBOxSVfe09U8B/zU54UmSJEnTw8rcvLghsN7A+sNbmSRJkqRmZabb+yBwUZJzgND9OMx7JzIoSZIkabpZmVlBjknyNWDHVvTuqrpxYsOSJEmSppflDgVJ8pT2vB3wWOC69nhsK5MkSZLUjNdj/TfAAuD/jbGtgN0nJCJJkiRpGhpvVpAF7fl/T144kiRJ0vS0MrOC3E+SPZOcNRHBSJIkSdPVeGOsd0/y4yS3J/nPJH+cZBHdLCGHT16IkiRJ0tQ3Xo/1/6MbY/1I4CTgu8CxVbV9VX1pMoKTJEmSpovxbl6sqjq3LZ+c5Pqq+sQkxCRJkiRNO+Ml1hskefFg3cF1e60lSZKk+4yXWH8L+LOB9W8PrBdgYi1JkiQ14023d8BkBiJJkiRNZ6s83Z4kSZKkBzKxliRJknpgYi1JkiT1YIWJdZKHJvnHJEe29a2TvGDiQ5MkSZKmj5XpsT4GuAvYua1fDxJ4w7oAAB5MSURBVHxgwiKSJEmSpqGVSayfWFX/AvwOoKruADKhUUmSJEnTzMok1ncnWZdu7mqSPJGuB1uSJElSM94PxIw4BDgD2CLJ8cBzgNdOZFCSJEnSdLMyifWFwIuBneiGgLwNeMREBiVJkiRNNyszFOQrwO+q6qtVdRqwcSuTJEmS1KxMYv1PwFeSPCzJ9sBJwKsnNixJ0upKsk6S7yX5QZLLkvzfVr5VkvOTLE7y+SRrt/KHtPXFbfuWA8d6Tyu/MsneA+XzWtniJAdP9muUpKlohYl1VX0VOAw4CzgWeFFVXTzBcUmSVt9dwO5VtS3wTGBekp2ADwGHVdWTgFuAA1v9A4FbWvlhrR5JtgFeCTwNmAd8MslaSdYC/h3YB9gGeFWrK0mz2nLHWCf5N9pMIM36wE+AtyShqt460cFJklZdVRVwe1t9cHsUsDvw5618IfBe4HBg37YM3VXJTyRJKz+hqu4CrkmyGNih1VtcVVcDJDmh1b184l6VJE194928uGjU+oUTGYgkqT+tV/lC4El0vcs/AX5VVfe0KkuAzdryZsB1AFV1T5JbgUe28vMGDju4z3WjynccI4YFwAKAxz3ucWv+oiRpiltuYl1VCyczEElSf6rqXuCZSTYAvgw8ZQgxHAEcATB37txaQXVJmvbGGwpyYlW9PMml3H9ICABV9YwJjUyStMaq6ldJzgF2BjZIMqf1Wm8OXN+qXQ9sASxJModu6N8vB8pHDO6zvHJJmrXGGwrytvb8gskIRJLUjyQb002T+qv2y7l70t2QeA7wUuAEYD5wStvl1Lb+3bb9m1VVSU4FPpvkI8Bjga2B79H9psHWSbaiS6hfyX1jtyVp1hpvKMgN7fmnI2VJHgX8st0YI0mamjYFFrZx1g8CTqyq05JcDpyQ5APARcBRrf5RwGfazYnL6BJlquqyJCfS3ZR4D3BQG2JCkrcAZwJrAUdX1WWT9/IkaWoabyjITsAH6RrZ9wOfAR4FPCjJ/lV1xuSEKElaFVV1CfCsMcqv5r5ZPQbLfwu8bDnHOhQ4dIzy04HT1zhYSZpBxhsK8gng7+jG2n0T2KeqzkvyFOBzgIm1JEmS1Iz3AzFzqurrVfUF4MaqOg+gqn40OaFJkiRJ08d4ifXvB5bvHLXNMdaSJEnSgPGGgmyb5Da6u7/Xbcu09XUmPDJJkiRpGhlvVpC1JjMQSZIkaTobbyhIL5KsleSiJKe19a2SnJ9kcZLPJ1m7lT+krS9u27ccOMZ7WvmVSfae6JglSZKkVTXhiTXdD81cMbD+IeCwqnoScAtwYCs/ELillR/W6pFkG7o5VZ8GzAM+2eZmlSRJkqaMCU2sk2wO/Cnw6bYeYHfgpFZlIbBfW963rdO279Hq7wucUFV3VdU1wGLGmIdVkiRJGqaJ7rH+KPAu7pth5JHAr6rqnra+BNisLW8GXAfQtt/a6v+hfIx9/iDJgiSLkixaunRp369DkiRJGteEJdZJXgDcXFUXTtQ5BlXVEVU1t6rmbrzxxpNxSkmSJOkPxptub009B3hhkufTTc+3HvAxYIMkc1qv9ObA9a3+9cAWwJIkc+h+8fGXA+UjBveRJEmSpoQJ67GuqvdU1eZVtSXdzYffrKq/AM4BXtqqzQdOacuntnXa9m9WVbXyV7ZZQ7YCtga+N1FxS5IkSatjInusl+fdwAlJPgBcBBzVyo8CPpNkMbCMLhmnqi5LciJwOXAPcFBV3Tv5YUuSJEnLNymJdVWdC5zblq9mjFk9quq3wMuWs/+hwKETF6EkSZK0ZobRYy1pGtvq5GuHHcKMcc1+Ww47BElSjybjB2IkSZKkGc/EWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS9IMkmSLJOckuTzJZUne1so3SnJWkqva84atPEk+nmRxkkuSbDdwrPmt/lVJ5g+Ub5/k0rbPx5Nk8l+pJE09JtaSNLPcA7yjqrYBdgIOSrINcDBwdlVtDZzd1gH2AbZujwXA4dAl4sAhwI7ADsAhI8l4q/P6gf3mTcLrkqQpz8RakmaQqrqhqr7fln8NXAFsBuwLLGzVFgL7teV9geOqcx6wQZJNgb2Bs6pqWVXdApwFzGvb1quq86qqgOMGjiVJs5qJtSTNUEm2BJ4FnA9sUlU3tE03Apu05c2A6wZ2W9LKxitfMkb5WOdfkGRRkkVLly5do9ciSdOBibUkzUBJHg58EXh7Vd02uK31NNdEx1BVR1TV3Kqau/HGG0/06SRp6EysJWmGSfJguqT6+Kr6Uiu+qQ3joD3f3MqvB7YY2H3zVjZe+eZjlEvSrGdiLUkzSJuh4yjgiqr6yMCmU4GRmT3mA6cMlO/fZgfZCbi1DRk5E9gryYbtpsW9gDPbttuS7NTOtf/AsSRpVpsz7AAkSb16DvAa4NIkF7eyvwM+CJyY5EDgp8DL27bTgecDi4E7gAMAqmpZkvcDF7R676uqZW35zcCxwLrA19pDkmY9E2tJmkGq6r+B5c0rvccY9Qs4aDnHOho4eozyRcDT1yBMSZqRHAoiSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcTllgn2SLJOUkuT3JZkre18o2SnJXkqva8YStPko8nWZzkkiTbDRxrfqt/VZL5ExWzJEmStLomssf6HuAdVbUNsBNwUJJtgIOBs6tqa+Dstg6wD7B1eywADocuEQcOAXYEdgAOGUnGJUmSpKliwhLrqrqhqr7fln8NXAFsBuwLLGzVFgL7teV9geOqcx6wQZJNgb2Bs6pqWVXdApwFzJuouCVJkqTVMSljrJNsCTwLOB/YpKpuaJtuBDZpy5sB1w3stqSVLa9ckiRJmjImPLFO8nDgi8Dbq+q2wW1VVUD1dJ4FSRYlWbR06dI+DilJkiSttAlNrJM8mC6pPr6qvtSKb2pDPGjPN7fy64EtBnbfvJUtr/x+quqIqppbVXM33njjfl+IJEmStAITOStIgKOAK6rqIwObTgVGZvaYD5wyUL5/mx1kJ+DWNmTkTGCvJBu2mxb3amWSJEnSlDFnAo/9HOA1wKVJLm5lfwd8EDgxyYHAT4GXt22nA88HFgN3AAcAVNWyJO8HLmj13ldVyyYwbkmSJGmVTVhiXVX/DWQ5m/cYo34BBy3nWEcDR/cXnSRJktQvf3lRkiRJ6oGJtSRJktQDE2tJkiSpBybWkiRJUg9MrCVJkqQemFhLkiRJPTCxliRJknpgYi1JM0ySo5PcnOSHA2UbJTkryVXtecNWniQfT7I4ySVJthvYZ36rf1WS+QPl2ye5tO3z8fZLu5I065lYS9LMcywwb1TZwcDZVbU1cHZbB9gH2Lo9FgCHQ5eIA4cAOwI7AIeMJOOtzusH9ht9LkmalUysJWmGqapvA8tGFe8LLGzLC4H9BsqPq855wAZJNgX2Bs6qqmVVdQtwFjCvbVuvqs5rv5h73MCxJGlWM7GWpNlhk6q6oS3fCGzSljcDrhuot6SVjVe+ZIzyB0iyIMmiJIuWLl265q9AkqY4E2tJmmVaT3NNwnmOqKq5VTV34403nujTSdLQmVhL0uxwUxvGQXu+uZVfD2wxUG/zVjZe+eZjlEvSrGdiLUmzw6nAyMwe84FTBsr3b7OD7ATc2oaMnAnslWTDdtPiXsCZbdttSXZqs4HsP3AsSZrV5gw7AElSv5J8DtgNeFSSJXSze3wQODHJgcBPgZe36qcDzwcWA3cABwBU1bIk7wcuaPXeV1UjN0S+mW7mkXWBr7WHJM16JtaSNMNU1auWs2mPMeoWcNByjnM0cPQY5YuAp69JjJI0EzkURJIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5IkST0wsZYkSZJ6YGItSZIk9cDEWpIkSeqBibUkSZLUAxNrSZIkqQcm1pIkSVIPTKwlSZKkHkybxDrJvCRXJlmc5OBhxyNJs5ltsiQ90LRIrJOsBfw7sA+wDfCqJNsMNypJmp1skyVpbNMisQZ2ABZX1dVVdTdwArDvkGOSpNnKNlmSxjBn2AGspM2A6wbWlwA7DlZIsgBY0FZvT3LlJMU2GR4F/GLYQYwnww5gePxspqYp/7nAKn02Z1TVvImLZJWtsE2GGd0uz7Tv10wy5T+bWfq5wMz7bMZsl6dLYr1CVXUEcMSw45gISRZV1dxhx6EH8rOZmvxcpoaZ2i77/Zq6/Gymrtny2UyXoSDXA1sMrG/eyiRJk882WZLGMF0S6wuArZNslWRt4JXAqUOOSZJmK9tkSRrDtBgKUlX3JHkLcCawFnB0VV025LAm04y7lDqD+NlMTX4uE8g22e/XFOZnM3XNis8mVTXsGCRJkqRpb7oMBZEkSZKmNBNrSZIkqQcm1pIkSVIPTKwlSZKkHphYS5MgyQP+rY1Vpv4leXiSndvyTkmeMeyYJA2XbfJwzeR22S/RDJLkWUn+dthx6IGq6vcASXZNsmOSDarq9zbkk+JBwIeSnAQchu2eJpHt8tRkmzx0M7ZdnjEvZLZKkva8C/A64IAkbx1uVBox8vm05TcC/wm8AfhaksfYkE+sJKmq24B/AeYBP66qi9u2tQY/H6kvtstTl23y8M30dtkvzzRXVZVkN+CzwLeAE4BdkrxnqIEJ6D4fgPYZPR3YuapeB5xD15BvYkM+MZKs1f59PAi4FHgRsHuSQwGq6l7gEcOMUTOT7fLUZZs8XLOhXZ4Wv7yoFXoC8JGqOinJesD2wP9N8uuq+sSQY5uV2l/klWQtYD3gA23TIwCq6u/aH+XnJ9mhqm4eUqgzTpJHAndV1e1J/gTYE/jvqvpKkmcD30nya+ArwD8meQNw28h/uFJPbJenENvk4ZpN7bJ/kU1DY1wmuQd4Q7uMdRvwXeB6ur8CXzzpAc5yIw14W31wVd0CvBxYBuyXZEPoGnLgGODhw4l05kmyLvDXwN+1xvvDwG+ADyZ5e1VdB+wEvBj4D+CEqrp1Ojbemlpsl6cu2+Thmm3tsj9pPk21sXtzgdOAa4F3A9sBbwMeSTd26VLguqr62JDCnNWS/CXd+LGfA98A/hs4HjgXOLKqlg0vupmpJTf7AM8DngosrKovth6RdwPnVNVHk6wDbFBVN476T1dabbbLU5tt8nDMtnbZoSDTSJIHtbFfuwCHA1cCuwGfB06i+zy/DATYH9gReE679PX76folnY6SvAZ4M12j8UjgH4CPA68FTgF+m+TfRu5M15prY/fuBU5PcgvwNODVSb5RVd9J8s/AoW1s38eq6ka4b8yltDpsl6cH2+ThmI3tsj3W00CS9dqlRJI8HfgY8I6qujjJ64FnAd+uqhOSjAz6n0t3SWW/qrp8KIHPYkneDNxaVce39e2B9wMvBR4H3FFVPxtiiDNSunlRX1VVb23v+WuBX9KNdb2t9ZDcXVWLhhmnpj/b5enFNnl4Zlu77BjrKS7JQ4HTkmzSiraku5P5RQBVdSSwCHhBklcDdwLr0l1ysfGeBMuZGijAOwfWfwj8GnhsVf3IBrw/I+9/kl3p/l28IslHqupCusu8G9CN7Vuvqr4zUxpvDY/t8tRmmzx8s7lddijIFFdVdyR5IbBZkt2q6vNJFgALkiyoqiOq6uh2WfHiqroHuDnJP1fVXcONfnYYmL7pVcAWdH+JHws8Psm3gT8HdgUeD9wxpDBnnJFLjO1O/53ppjR7LfA9YH6ST1XVG5M8GHglsAlw2/Ai1kxhuzy12SYPj+2yQ0GmjSR7AmcCr6iqLyT5M7ofHjh7cOqmkfF+w4pztkryFuDVwBeBJ9LdoPEy4B10lxkfDbytqn44tCBnkNZTuBfw+aq6O8newK5V9fctmXkM3bRN36iqdyV5aFX5H6h6Zbs8ddkmTz7b5Y491lPUyB2xSdanu8HlrNaIf7ltOzHJHOD1SU4BllTHxns4ngm8se779agPAP+nqt6SZG1graq6c6gRziyPAS4A1mvv7210vSELq+rHwPVJvkF3k9jfVtW/DjNYzQy2y9OKbfLks13GxHrKao33vsBfAY9I8taqOjvJfsAXkjy4qo5P8j/lRPaTauQ/14H1tekalD8BLm7FpwCvB6iquyc9yBkqyaOBF9BdXrwT+DfgRuCDdD/4cGqSA4B16G4eOwHYeDjRaqaxXZ6abJOHy3b5/kysp6gkzwDeTnfZ6nnAp5K8o6q+2caNnZzkm3RfXk2SwQa8XeZaSjcv7cHAl5LcUlVHAU8BnpDkEVX16+FFPOM8uz3WoZtd4UvAvnT/Vo4EfgccAqxNN3fwk4E9082Petd0nsJJw2e7PPXYJk8JtssDHGM9BSXZGngTsEVVvayVLQAWAH9fVWcm2aiczH5o2l/f/0A3Z+3lwEK6P1S/QPejA9sDr6yqy4YW5AwyckNMG6e3H7A73V39R9A16C8HrgEOr6o7kzyklX8KeInjKLWmbJenNtvkyWe7PDYT6ymmjdd7BfAT4LnAUVX1xbbtLcAb6XpKbqnuRwmm7a8TTSejekVeAewJvAXYlO7z2gT4d+AG4BF0V41vGlK4M0qSJwN/CXydbl7gu5LsA+wNXEXXQ7IzMB/4Md2PPjwEeAnwnar60VAC14xhuzz12CYPl+3y8plYTyFJtgE+Cbyuqq5O8k66RuK/q+rLrc4WVXXdMOOcbUY14E+imwv15cATqupXSZ5K99f6E4Cjq+q7w4t25knyPOAcusb6RLr3+V/p/iOdAyytqk8n2Q24udocwbnvF7+k1Wa7PPXYJg+f7fLyOcZ6imjT1LwVWA/4TSs+hu4ncPdsX8aTgOuHFOKsNdCA7w+8hm7Kpg2BzybZr6quaJfC9qLr0VKPqupb6X5k4DTgP+l6PP6C7kcHlgBPSnJPVR0L9/2nO9Mbb0082+WpyTZ5+GyXl88e6yEa407m3YCDgO8An62qm5I8im5e1K9U1RXDiVRJXg4cCLylqq5K9xPFH6G73PjS6ubsXNu7zSdOu8z4L8Czq+rXrcfkj+nGuP5NVX1jqAFqRrBdnh5sk6cG2+UHMrEekpHGO8lewI50d9N+sC3vC/wI+GJV3Zj8//buP9bqsoDj+PuDyvJHiDNilZmtPyAxxdQGOjZAXSSWDZ2ziblMl26p/FNZrWIWf2hoplnOkeaSBTE2NUrMCH9VOKeBTJFZ02aaZmYYSv6AT388zx1XdlWUc8/33PP9vLazfc/3Pvd7nnvuPZ/77Pk+P7S7y85d0SVD/HM9C1hE3Qiintu7ntvN9qkZVzn8JJ1AWcrpqIFJYoM+S3n/Y5ckl3tXMrl3JZdfL0NBGlL/4KYDl9XHsZRJACdRlqb5PLC7pJ/U59ElQ4zfe9Zle+KtwMWS/mn7TtsvSvoisC9svz0Zw8f2byRtAx6RNMH28wPve97/2FXJ5d6UTO5tyeXXS491F0k6kLJU0x/q8wXAC7Yvqc+/Dxxj+2hJJwMbBgb8R/fVSUozKLtHPUBZj/OzlDGXX7N9e4PVazVJs4EXbd/RdF1iZEsujxzJ5N6WXC5GNV2BNlAxBrgX+KnKIvZQlgHad6Cc7a8AT0ja3/byhHdz6u9olu3ZlKWaJgGb6kSMa4DvSNpTkhqsZmvZ/rXtO/L+xzuVXB5Zksm9L7lcZChIF9RbIS9I+hlwJHBhnbF8M/ArSRuAlZTlaiZRQuO5hqrbSipbEQ++tWtghaQLKZ+TL9XbxAfbvlbSUttbmqltDGjjbcbojORyb0smj1xtz+U0rIfZDuFwBzAWuJUyY/ZS4HOUsXzHAx8FLrL9ePdr2l6SxgKnSlpM+T2MBR4ETgG2AjNcNn2YB0yVdKbtTc3VOCJ2RXK5tyWTYyTLGOthJGki8A3getur6+2RxcDjwH3AOcACyjJO+wH72f5rG2fRNqXOZh5Lef/nA08DhwGjge8Br1HW5HyZsuTWXPfpNqwRbZBc7m3J5Bjp0mM9vN4LzAUmSboG2AZ8ndIbsoaylNMC4Oq6XNC/IbdRukXShymTX6YDBwIPsf0f6XOSfgRMoWzLKuB02w81VN2I6Izkco9KJkc/SMN6GNm+S2VnotuAp4CjgSXAB4BVwDJKODzWWCXb7UlgKfBNyu3eTwJnAyslnWH7EUljbC+RNMr2tiYrGxG7Lrnc05LJMeKlYT3MbN8jaQ5wBXAoZWLMLGCM7dck/dLZZKCrJI23/YzLzlwGTgUut/0fYKGkdwHLJd0IzJR0mu1MWoroE8nl3pJMjn6SMdZdUtd3XAhMsb1piBnP0QV1fOXDlF2i1gPrgEMoM/83D1q79lxgGrAgy2tF9KfkcvOSydFv0rDuIkmzgBuAibafb7o+bSTpAOAXwApgJvBEPd4f+BBlXdTLatnRtl9pqq4RMfySy81KJke/yQYxXWR7JXAWZYZzNMD23ykz/z8OzAZWA6cB5wHPAp+SNLcWT89VRJ9LLjcrmRz9Jg3rLsvORM0Z9J5fRNls4D2UyTJTgD8BJwATKTuxZRWAiJZILjcjmRz9KJMXG5KA6L66S9dAkD9K2QDiCGCe7ZskfRB4KZNiItopudxdyeToRxljHa0kaQJwJ2Wt2u82XZ+IiDZLJke/yFCQaCXbGym3H3eTtFfT9YmIaLNkcvSLNKyjzdZQJsxERETzkskx4mUoSLSapL1sv9R0PSIiIpkcI18a1hERERERHZChIBERERERHZCGdUREREREB6RhHRERERHRAWlYR0RERER0QBrW0dMk7S9pbX08LenJerxZ0o+7WI9xku6V9GdJ03b42rzB665K2rwLrzNd0qb6Ohsl3SXpxF2pe0REpySTk8nx5rKlefS0upXtZABJ84HNthc2UJVjgfW2zx7ia/OAG4FOLRF1t+0TASRNBm6StMX2qg5dPyLiHUkmJ5PjzaXHOkak2ouwoh7Pl3SDpLsl/U3SHEmXSlovaaWkPWq5IyTdKel+SbdJet8Q1z1I0u8lPShplaQDa5BeCpxUe2b2HFT+AuD9wGpJqwedXyBpnaQ1ksbXc+MkLZd0X30c81Y/p+21wMXAl+s1Pj2ol+Z3ksZLGiXpUUnjaplRkv4y8DwiYrglk5PJUaRhHf3iI8BM4DOUnorVtj8GbAFm1yC/CjjF9hHAdcCCIa5zFXCD7UOBxcCVNUi/DSy1Pdn2loHCtq8EngJm2J5RT+8NrLF9GHAXcE49/0PgB7aPAk4GFu3kz/YAMLEe3wNMsX04sAT4qu1t9Wc+vZY5Dlhn+9mdvH5ERKclk5PJrZShINEvbrX9qqT1wG7Aynp+PXAQMAE4BLhdErXMP4a4zlRgTj3+OaVX5O16BVhRj+8Hjq/HxwEH19cHGCNpH9tvNf5Pg44PAJbWnp3RwGP1/HXAzcAVwFnA9e+g3hERnZJMTia3UhrW0S9eBrC9TdKr3r6l6DbK37mAh2xP7UJdBr/+VrZ/zkZRejb+9zavdziwoR5fBVxu+xZJ04H5ALafkPSMpJnAJ9jeUxIR0YRkcjK5lTIUJNpiIzBO0lQASXtImjREuT8Cp9Xj04G7d+La/wXevRPlfgucP/CkjhN8U5IOBb4FXF1P7Qs8WY/P3KH4Isrtx2W2t+5EfSIimpJMjr6UhnW0gu1XgFOASyStA9YCRw9R9HzgC5IeBM4ALtyJy18LrBw8UeYNXAAcWSfhPAyc+wblptWJMBsp4X3BoNnn84Flku4H/rXD990C7ENuOUZEj0smR7/S9rsjETGSSTqSMhFn2lsWjoiIYZVMbqeMsY7oA5IuAs4j4/giIhqXTG6v9FhHRERERHRAxlhHRERERHRAGtYRERERER2QhnVERERERAekYR0RERER0QFpWEdEREREdMD/AeGcTONi+dSEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_time = ['Morning', 'Afternoon', 'Night']\n",
    "g = sns.catplot(data=df_new, x='time_of_day', col=\"user_type\", kind='count', sharey = False,\n",
    "            color = 'deepskyblue', order = day_time)\n",
    "g.set_axis_labels(\"Time of the Day\", \"Bike Rides\")\n",
    "g.set_titles(\"{col_name}\")\n",
    "g.fig.suptitle('Time of the Day Usage Trends by User Type', y=1.03, fontsize=14)\n",
    "g.set_xticklabels(rotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8CXycOHgmSYH"
   },
   "source": [
    "From the above bar graph, the time of the day that most trips are taken (Afternoon and Morning) is slightly dependent on if a user is a subscriber or a customer. From the bar graphs, the trends shows that Customers often start the trip in the afternoon where as Subscribers mostly start the trip in the morning. This further proved that subscribers might be using the bikes for work and home commute purposes and customers use it for recreational activities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ocnpgwETopDd"
   },
   "source": [
    "Does the average trip duration taken dependent on if a user is a subscriber or a customer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 389
    },
    "id": "gWKFimIEooR5",
    "outputId": "80d95628-19be-4658-d6d3-b6bfb8568bfc"
   },
   "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>user_type</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Customer</td>\n",
       "      <td>23.874417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Subscriber</td>\n",
       "      <td>10.671061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    user_type  duration_min\n",
       "0    Customer     23.874417\n",
       "1  Subscriber     10.671061"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average trip duration mean plot\n",
    "grouped_user_type_duration = df_new[['user_type','duration_min']].groupby(['user_type'],as_index= False).mean()\n",
    "# show analysis\n",
    "display(grouped_user_type_duration)\n",
    "\n",
    "sns.barplot(x='user_type', y='duration_min', data=grouped_user_type_duration)\n",
    "plt.title(\"Average trip duration by User Types\")\n",
    "plt.ylabel(\"Average Trip Duration (minute)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "id": "TEO6CerOVob-",
    "outputId": "3d7340d9-5cee-4943-9a60-1146034711ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Duration Against User Type')"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between user type and duration\n",
    "samples = np.random.choice(df_new.shape[0], 1834, replace = False)\n",
    "df_new_samp = df_new.loc[samples,:]\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.violinplot(data=df_new_samp, x='user_type', y='duration_min', color=base_color, inner='quartile')\n",
    "plt.xlabel('User Type')\n",
    "plt.ylabel('Duration (min)')\n",
    "plt.title('Duration Against User Type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a_2ZUIIzwhLx"
   },
   "source": [
    "Subscriber users tend to ride the bikes in lesser duration than customer users. This might be because the subscriber users ride the bikes for regular commutes - work and school, but the customer users might be tourists that use the bikes for traveling around town. The average trip duration **IS** highly dependent on if a user is a subscriber or just a customer. Customers have two times trip duration lenght of subscribers. \n",
    "Since Customers mostly go on trips by weekends, one would expect that average trip duration should then be the highest on wekends. This would be checked further for confirmation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q_a-Xr5Cnde6"
   },
   "source": [
    "Average trip duration based on The Day of The Week: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "QpH9MGe2ncxZ",
    "outputId": "af71a1a0-a9e1-47ec-9f29-77bd79773d4e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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700idiIhEpYpKx9qCUnI3FweW/JKgfxfzjzP221nQVcnLL6GsvMKnxCIiIjVTUSciIs2Sc4784h07i7TczSWs3FxMXn6gcFu1pYQdFbvOkTODDi2SyMlK4YhebUhOiCU7M3m3wi47M5mEuFg/Ho6IiEitVNSJiEjEKi4rJ88bYVu5OVC45XpFW15+CUXby3e7f1ZqAjmZyQzolMGogR3onJVCTlYyOZkpdGyZTELcrrMSKisdj/5m+G7n1N03YSitUhMa+2GKiIjUSUWdiIg0WeUVlazZWuoVbD8fcdtYtPv5bcnxsTuLtIO7tyInK4WczOTAf7NSSEsM/X97MTFGn3bpvPKnwygo2kbBDuPG1xYyaewAhuS0bOiHKiIistdU1ImIiG+cc2wsKtttWmRV0ZabX8yaraVUBF1GIDbG6NgyiZzMFI7t146crBSyvaKtc1YKrVITMGu4zpQxMUab9EQWzJrBsIMPI29LCROnzueVPx2mDpgiItJkqKgTEZGwKizdsdu0yOCmJHn5JZTs2L3xSOu0RDpnJTOsSyY5mbumR+ZkpdAhI4m4WH8aN6cnxXPt6H5c8vxspszM5YwDO/uSQ0REpDoVdSIisk/KyitZtSXovLb8YvKCirj84h273T8tMY6crBS6tU7lyN5tyMlMpnOrFHIyU8jOTCE5oek2Ihk3tCPPfLWS299ezKiB7WmZovPrRETEfyrqRESkTpWVjvWF28nNL2blpl3ntVUVbWsLSnG7ZkiSEBtDp8xksjOTGTjIa0YSNOLWMiW+QadINiYzY/K4AYy5/zPuencJN58yyO9IIiIiKupERAS2Fu/YOcpW1ZBk5eYS8jYXk7elhLLyyp33NYN26UnkZCVzSFUzkqCGJO1aJBHbjM8369ehBWcf3IWnZqzgjAM6M7BTht+RREQkyqmoExGJAqU7KrxGJLtG2IIvAVBYunvr/4zkeHKykunbIZ3j+rcj2yvaOmel0CkzmcQov1bbpcf15vW5q5k4dT4vXnComqaIiIivVNSJiDQDFZWONVtLam1Isr5w+273T4yL2Tm6NrxrUEMSb9StRVK8T48kMmQkx3P1qL5c+eJcXvo2j18Oz/E7koiIRDEVdSIiEcA5x+ZtZeTml+y8Zlte0DXbVm8poTyo9X+MQYeMZHKykjmqd5udLf+rzmtrnZao0aV99Iv9s3n260DTlOMHtCcjWYWwiIj4Q0WdiEgTsW17+a4mJEEX2q76d3HZ7q3/W6UmkJ2VwuDsDMYM7uCNvAUKt44tk4n3qfV/tIiJMW4cN5CTH/iMe9/7nkljB/gdSUREopSKOhGRRrKjopLVW0p+fl5bfqAhyaZtZbvdPyUhduf12Q7t2Wrnv6tG21IT9Rbut4GdMjjroM48PWMFEw7IoV+HFn5HEhGRKKRPBCIiIaisdGzaVkZOn8FsKNxOq9SEn01fdM6xwWv9XzUtMnjEbc3WEoJmSBIXY3RsGWg+cvyAdmR7RVtn71y3rNSEiG39H02uOL4Pb8xdww1TF/D8Hw/WMRMRkUanok5EpB6VlY4l6wo57+mZ5OWXkJ2ZzIO/2p8fNxTxbe6W3RqSbA9q/Q/QNj2RnKwUDuiaSU5Wp91G29q3SCJOUyQjXsuUBK4a1Ze/vjyPqbNXc8p+nfyOJCIiUSZsRZ2ZPQGMAdY75wZ66yYB5wEbvLtd45x7M1wZREQawqZtZTsLOoC8/BIufOZbrh/Tn1e+W0VOZgo92qQyoncbOrfadV5bdmYKSfHR3fo/WkwYnsNzX6/kljcXcUy/tqSre6iIiDSicI7UPQk8ADxdbf29zrm7wrhfEZEGtb28YmdBVyUvv4T+HVowb9IJPqWSpqSqacopD33OPz5YyrUn9fc7koiIRJGwzftxzn0CbA7X9kVEGsuW4h1kZybvti47M1mjcLKbITktmTA8h39/voKl6wr9jiMiIlHEnHP132tvN27WFXi92vTLc4ACYCZwuXMuv5bfPR84H6Bdu3bDnnvuubDl3FtFRUWkpaX5HUN8oGMfPXILK3l1ZSJXndiHy1+Yu/OcuofOHELZhhUUFerDezQI9W++sMzxl0+L6Zwew1UHJKlpSjOg9/vopWMfvZrqsR85cuQs59zwmm5r7KKuHbARcMBNQAfn3O/r287w4cPdzJkzw5Zzb02fPp0RI0b4HUN8oGMfHbaXVzDugc/ZWFTGe5ceQXklFBRto0Vaao3dL6X52pO/+f98+RPXvzqfB361H2MGdwxvMAk7vd9HLx376NVUj72Z1VrUNWrbNefcOudchXOuEngUOLAx9y8isifuefd7Fq8t5I7TB5GZmkib9ERyl8ylTXqiCjqp1a8O7MyAji24+fVFbNte7nccERGJAo1a1JlZh6AfTwXmN+b+RURC9eXyTTzy6XJ+dVBnju7bzu84EkFivaYpawtKuf/DH/yOIyIiUSBsRZ2ZPQvMAPqYWZ6ZnQvcYWbzzGwuMBK4NFz7FxHZW4WlO7h8yhw6Z6Vw7eh+fseRCDSsSyanD8vm8c+Ws2xDkd9xRESkmQvbJQ2cc2fWsPrxcO1PRKShTH5tIWu2lvDCBYeSmhjOK79Ic3b1qL68s2Atk6Yt4OnfH6imKSIiEjaNOv1SRKSpe3v+Gl6clceFI3syrEum33EkgrVJT+Sy43rz6dKNvLNgrd9xRESkGVNRJyLiWV9Yyl9fnsegThn8+ZhefseRZuDsg7vQt306N72+iJKyCr/jiIhIM6WiTkQEcM5x9YtzKS6r4N4JQ4iP1duj7Lu42Bgmjx3Aqi0lPDRdTVNERCQ89KlFRAR49utcPlqygb+c2JeebdP9jiPNyEHdW3HK0I48/PFyVmzc5nccERFphlTUiUjUW7FxGze9vpDDe7bmt4d09TuONEPXjO5HfKxx4+sL/Y4iIiLNkIo6EYlq5RWVXDplNvGxxp2/HKyLiktYtG2RxCXH9ubDxet5f+E6v+OIiEgzo6JORKLaP6cv47uVW7j51EF0yEj2O440Y+cc1pVebdOY/PoCSneoaYqIiDQcFXUiErXm5W3l7x8sZeyQjowd0tHvONLMxXtNU3I3l/Dwx8v9jiMiIs2IijoRiUqlOyq45PnvaJ2WyE3jBvodR6LEoT1bc9LgDjw0/QdyNxf7HUdERJoJFXUiEpVue2sxyzZs485fDiYjJd7vOBJFrjupH7ExapoiIiINR0WdiESdT5du4MkvVnDOoV05olcbv+NIlOmQkczFR/fivYXr+GjJer/jiIhIM6CiTkSiypbiMq54YQ492qTylxP7+h1HotS5h3eje+tUJk9bwPZyNU0REZF9o6JORKLK9VMXsKmojPsm7EdSfKzfcSRKJcTFMGnsAFZsKuaxT3/0O46IiEQ4FXUiEjWmzl7Fa3NW8/+O6cWg7Ay/40iUO7J3G0YNaM/9Hy5l1ZYSv+OIiEgEU1EnIlFhzdYSrn91Pvt1bsn/jejhdxwRAK4b0w+AW95Q0xQREdl7KupEpNmrrHRc8cIcdlQ47h0/lLhYvfVJ05CdmcKFI3ry5ry1fLZ0o99xREQkQumTjYg0e0/NWMHnP2zi+jH96do61e84Irs578judGmVwg3T5lNWXul3HBERiUAq6kSkWfthfSG3vbWYo/u25cwDc/yOI/IzSfGx3HByf5Zt2Ma/P1fTFBER2XMq6kSk2Sorr+SS52eTkhDLbb8YhJn5HUmkRkf3bcex/dry9w+WsnZrqd9xREQkwqioE5Fm6/4PlzJ/VQG3njaItulJfscRqdPEMQMor3T87c1FfkcREZEIo6JORJqlWT/l8+BHP3D6sGxGDezgdxyRenVulcIFR/Vg2pzVzFi2ye84IiISQVTUiUizs217OZdNmU2HjGRuOLm/33FEQvanET3Izkzmhmnz2VGhpikiIhIaFXUi0uzc/MYiVm4u5p7xQ0hPivc7jkjIkuJjmTimP9+vK+LpGT/5HUdERCKEijoRaVY+XLyOZ79eyflHdOeg7q38jiOyx47r346jerfhvve+Z32hmqaIiEj9VNSJSLOxqWg7V704j77t07ns+N5+xxHZK2bGpLED2F5eyW1vLvY7joiIRAAVdSLSLDjn+OvL8ygo2cG9E4aSGBfrdySRvdatdSrnHdmNl79bxTcrNvsdR0REmjgVdSLSLLw4K493F67jihN6069DC7/jiOyzC0f2pGNGEhOnLqBcTVNERKQOKupEJOLlbi5m8msLOahbFuce3t3vOCINIiUhjuvG9GfRmgL+99VKv+OIiEgTpqJORCJaRaXj8ilzALh7/BBiY8znRCIN58SB7Tm8Z2vufncJG4u2+x1HRESaKBV1IhLRHvt0OV+v2MyksQPIzkzxO45Igwo0TelPcVkFd7ytpikiIlIzFXUiErEWri7grneXMGpAe36xfye/44iERc+26Zx7eDemzMzj25X5fscREZEmSEWdiESk0h0VXDZlNhnJCfzttEGYadqlNF8XH9OLdi0SuWHqAioqnd9xRESkiVFRJyIR6Z73vmfx2kLuPH0wWakJfscRCau0xDiuGd2Peau28tw3apoiIiK7U1EnIhHny+WbePTT5Zx1UGdG9m3rdxyRRjF2SEcO6pbFne8sIX9bmd9xRESkCVFRJyIRpaB0B5dPmUOXrBSuPamf33FEGo2ZceO4gRSWlnPnu0v8jiMiIk2IijoRiSiTpy1kzdYS7pkwlJSEOL/jiDSqPu3T+e0hXXn265XMzdvidxwREWkiVNSJSMR4a94aXvo2j4tG9mT/zpl+xxHxxSXH9aJVaiITpy6gUk1TREQEFXUiEiHWF5RyzSvzGNQpg4uP6eV3HBHftEiK55rRfZmdu4UXZ+X5HUdERJoAFXUi0uQ557j6pbkUl1Vw74ShxMfqrUui26n7dWJ4l0xue3sxW4t3+B1HRER8pk9GItLkPfP1Sj5asoG/ntiXnm3T/I4j4ruqpilbisu4+z01TRERiXYq6kSkSftx4zZufn0RR/RqzW8O6ep3HJEmo3/HFpx9cBf+++VPLFi91e84IiLiIxV1ItJklVdUcunzs0mIi+HO04cQE2N+RxJpUi47rg+ZKQncMHUBzqlpiohItKq3qDOz4WZ2qZndaWY3mtl4M1PbOREJu4emL2N27hZuPmUg7TOS/I4j0uRkpMRz9ai+zPwpn5e/XeV3HBER8UmtRZ2Z/c7MvgX+CiQDS4D1wOHA+2b2lJl1bpyYIhJt5uZt4R8fLGXc0I6cPKSj33FEmqzTh2UzNKclt761mIJSNU0REYlGdV25NwU4zDlXUtONZjYU6AWsDEcwEYleJWUVXPr8bFqnJXLj2IF+xxFp0mJijBvHDWDcg59z33tLmXhyf78jiYhII6t1pM4592BtBZ13+2zn3AfhiSUi0ez2txezbMM27vrlEDJS4v2OI9LkDc5uyZkHduapGStYvLbA7zgiItLIQjmnro2ZXWNmj5jZE1VLY4QTkejzyfcbePKLFfzusK4c3qu133FEIsaVx/chPSlOTVNERKJQKN0vpwIZwPvAG0GLiEiD2lJcxpUvzqFn2zSuHtXX7zgiESUzNYErT+jDVz9uZtqc1X7HERGRRhRKUZfinLvaOTfFOfdS1VLfL3kjeuvNbH4Nt11uZs7M9DW8iOx0/dQFbCoq474JQ0mKj/U7jkjEOeOAzgzqlMHf3lxE0fZyv+OIiEgjCaWoe93MRu/Ftp8ERlVfaWY5wPGowYqIBJk6exWvzVnNJcf2YmCnDL/jiESk2Bhj8rgBrCvYzv0fLPU7joiINJJQirr/R6CwKzGzAjMrNLN6z8J2zn0CbK7hpnuBqwBN+BcRAFZvKeH6V+ezf+eWXHBUD7/jiES0/TtnMn54No9/9iM/rC/0O46IiDSCeos651y6cy7GOZfsnGvh/dxib3ZmZuOAVc65OXvz+yLS/FRWOq58cQ7llY57xg8lLjaU75pEpC5Xj+pLSkIsN0xT0xQRkWhgtb3Zm1lf59xiM9u/ptudc9/Wu3GzrsDrzrmBZpYCfAQc75zbamYrgOHOuY21/O75wPkA7dq1G/bcc8+F8HAaV1FREWlpaX7HEB/o2Decd1fs4JnFZZwzIIEROU3/8pER1/YAACAASURBVAU69tEpEo/7+z/t4L+LyrhwaCIHtK/rsrRSl0g89tIwdOyjV1M99iNHjpzlnBte0211FXWPOOfON7OParjZOeeOrm/H1Yq6QcAHQLF3czawGjjQObe2ru0MHz7czZw5s77dNbrp06czYsQIv2OID3TsG8bSdYWMuf8zDu/Zmsd+Oxwz8ztSvXTso1MkHvfyikpOfuBzthaX8f7lR5GSoMJub0TisZeGoWMfvZrqsTezWou6ui4+fr7335E1LPUWdDVsb55zrq1zrqtzriuQB+xfX0EnIs1TWXkll06ZTWpiHLf9YnBEFHQikSQuNoabxg1g9dZSHvjwB7/jiIhIGNX7tZ2ZxQInAV2D7++cu6ee33sWGAG0NrM84Abn3OP7ElZEmo9/fLCU+asK+Nevh9EmPdHvOCLN0vCuWZy2Xyce/XQ5pw/LpnubpjedSERE9l0oHQleA84BWgHpQUudnHNnOuc6OOfinXPZ1Qs6b8SuxvPpRKR5m/XTZh6a/gO/HJbNqIHt/Y4j0qz9ZXRfkuJimfzaQjVNERFppkKZYJ/tnBsc9iQiEhW2bS/n0ufn0LFlMhNP7u93HJFmr216Epcc15ubXl/IuwvXccIAfZEiItLchDJS95aZHR/2JCISFW5+YyG5+cXcM34o6UlNv9ulSHPwm0O60LtdGje+tpDSHRV+xxERkQYWSlH3JfDKnl58XESkug8WrePZr3M5/8juHNgty+84IlEjPjaGG8cNZNWWEh6avszvOCIi0sBCKeruAQ4BUvb14uMiEr02FW3n6pfm0rd9Opcd19vvOCJR5+DurRg7pCP/+ngZP23a5nccERFpQKEUdbnAfKezq0VkLznn+OvL8ygoKee+M4aSGBfrdySRqHTN6H7Exxg3vb7Q7ygiItKAQmmUshyYbmZvAdurVtZ3SQMRkSovzMrj3YXruHZ0P/q210C/iF/aZyTx52N6cetbi/lw8TqO7tvO70giItIAQhmp+xH4AEhgDy5pICICkLu5mMnTFnBw9yzOPbyb33FEot7vDutGjzapTJqmpikiIs1FvSN1zrnJjRFERJqfikrH5VPmEGPGXb8cQkyM+R1JJOolxMUweexAfv34Vzz6yXIuPqaX35FERGQf1TpSZ2aPmtmgWm5LNbPfm9lZ4YsmIpHu0U+X8/WKzUwaO4DszBS/44iI5/BerRk9qD0PTv+BvPxiv+OIiMg+qmv65YPA9Wa2yMxeMLOHzOwJM/sU+ILAFMwXGyWliESchasLuPvdJZw4sD2n7d/J7zgiUs21J/XHUNMUEZHmoNbpl8652cB4M0sDhgMdgBJgkXNuSSPlE5EIVLqjgkufn03LlARuOXUQZpp2KdLUdGqZzEVH9+TOd5bw8fcbOKp3G78jiYjIXqq3UYpzrsg5N90596xz7lUVdCJSn7vfXcKSdYXccfpgslIT/I4jIrX4wxHd6NY6lcnTFrC9XE1TREQiVSjdL0VEQjZj2SYe++xHzjqoMyP7tPU7jojUITEulhtO7s/yjdt4/LMf/Y4jIiJ7SUWdiDSYgtIdXPHCHLpkpXDtSf38jiMiIRjRpy3H9W/H/R/8wJqtJX7HERGRvaCiTkQazKRpC1hbUMq9E4aSklDvFVNEpImYOKY/lc5x8xuL/I4iIiJ7od6izsx6e5c3eNfMPqxaGiOciESON+et4eVvV3HhyJ7s1znT7zgisgdyslL404ievDF3DZ//sNHvOCIisodCGal7AfgWuA64MmgREQFgfUEp17wyj8HZGVx8dE+/44jIXvjjUd3JyUrmhmkL2FFR6XccERHZA6EUdeXOuX865752zs2qWsKeTEQignOOq16aS0lZBfeMH0p8rGZ1i0SipPhYbhgzgB/WF/Hk5yv8jiMiInsglE9fr5nZn8ysg5llVS1hTyYiEeF/X61k+pINXDO6Hz3bpvkdR0T2wbH923F037bc9/73rCso9TuOiIiEKJSi7rcEplt+AczylpnhDCUikWH5hiJueWMRR/RqzdkHd/E7jog0gIlj+rOjwnHrm2qaIiISKUK5+Hi3GpbujRFORJqu8opKLp0yh4S4GO48fQgxMeZ3JBFpAF1bp/LHo7rz6uzVfLV8k99xREQkBKF0v4w3sz+b2YvecpGZxTdGOBFpuh78aBlzcrdwy6kDaZ+R5HccEWlAfxrRk04tA01TytU0RUSkyQtl+uU/gWHAQ94yzFsnIlFqTu4W/vHhUsYN7ciYwR39jiMiDSw5IZbrx/Rj8dpC/vPlT37HERGReoRydeADnHNDgn7+0MzmhCuQiDRtJWUVXDplNm3TE7lx7EC/44hImJwwoD1H9GrNPe9+z5jBHWmTnuh3JBERqUUoI3UVZtaj6gcz6w5UhC+SiDRlt721iOUbtnHXL4eQkaKZ2CLNlZkxaewASssruO2txX7HERGROoRS1F0JfGRm083sY+BD4PLwxhKRpujj7zfw1Iyf+P1h3TisZ2u/44hImPVok8a5h3fnpW/zmPXTZr/jiIhILULpfvkB0Av4M3Ax0Mc591G4g4lI07KluIwrX5hDr7ZpXDWqj99xRKSRXHx0TzpkJDFx6gIqKp3fcUREpAa1FnVmdrT339OAk4Ce3nKSt05EooRzjmtfnc/mbWXcO2EoSfGxfkcSkUaSmhjHtSf1Y8HqAp75Sk1TRESaoroapRxFYKrlyTXc5oCXw5JIRJqcaXNW88bcNVx5Qh8GdsrwO46INLKTBnXgmR4rufOdJYwe1IFWaWqaIiLSlNQ6Uuecu8H7543Oud8FL8BNjRNPRPy2eksJ1706n2FdMvnjkd39jiMiPjAzJo8dQHFZBXe+s8TvOCIiUk0ojVJeqmHdiw0dRESanspKxxUvzKGi0nHP+CHExYbyliEizVGvdun87rCuPD8zl9m5W/yOIyIiQeo6p66vmf0CyDCz04KWc4CkRksoIr759xcr+GLZJiaO6U+XVql+xxERn/35mF60Tktk4tT5VKppiohIk1HX1+59gDFASwLn1VUt+wPnhT+aiPhp6bpCbn97Mcf2a8uEA3L8jiMiTUB6UjzXju7H3LytPD8z1+84IiLiqbVRinNuKjDVzA5xzs1oxEwi4rOy8koueX42aYlx3HraYMzM70gi0kSMG9qRZ75ayR1vL+bEge1pmZLgdyQRkagXygky35nZhWb2kJk9UbWEPZmI+ObvH3zPgtUF3HraINqkq8udiOxiZkweN4CC0nLueldNU0REmoJQirr/AO2BE4CPgWygMJyhRMQ/M1ds5p/TlzF+eDYnDGjvdxwRaYL6dWjB2Qd34X9frWT+qq1+xxERiXqhFHU9nXPXA9ucc08RuBD5QeGNJSJ+KNpezmVT5tApM5mJJw/wO46INGGXHtebVqkJXK+mKSIivgulqNvh/XeLmQ0EMoC24YskIn655Y2F5OYXc/cvh5KWWOsptyIiZCTHc/Wovny3cgsvfZvndxwRkagWSlH3iJllAtcB04CFwO1hTSUije79het49utc/nhkDw7sluV3HBGJAL/YP5v9O7fktrcWs7VkR/2/ICIiYVFnUWdmMUCBcy7fOfeJc667c66tc+7hRsonIo1gY9F2/vLyXPp1aMGlx/XyO46IRIiYGOPGcQPZXFzGve9973ccEZGoVWdR55yrBK5qpCwi4gPnHH99eR4FJeXcN2EoiXGxfkcSkQgysFMGZx3UmadnrGDRmgK/44iIRKVQpl++b2ZXmFmOmWVVLWFPJiKN4oWZeby3cB1XjepDn/bpfscRkQh0xfF9yEiOZ+LU+TinpikiIo0tlKJuAnAh8Akwy1tmhjOUiDSO3M3FTH5tAQd3z+L3h3XzO46IRKiWKQlcPaov36zI59XZq/yOIyISdeot6pxz3WpYujdGOBEJn4pKx2VTZhNjxl2/HEJMjPkdSUQi2PjhOQzJzuBvby6msFRNU0REGlO9RZ2Z/aampTHCiUj4PPLJcr5Zkc/kcQPIzkzxO46IRLiqpikbi7bz9/eX+h1HRCSqhDL98oCg5QhgEjA2jJlEJMwWrN7KPe8tYfSg9py6Xye/44hIMzEkpyVnHJDDv79YwffrCv2OIyISNUKZfnlx0HIesD+QFv5oIhIOpTsquPT52WSmJHDLKYMw07RLEWk4V57Ql7TEOG6YukBNU0REGkkoI3XVbQPUUUEkQt397hK+X1fE7acPJjM1we84ItLMZKUmcMUJfZixfBOvz13jdxwRkagQV98dzOw1oOqrthigPzAlnKFEJDy+WLaRxz77kV8f3JmRfdr6HUdEmqlfHdiZ575eyS1vLOLovm1JTaz344aIiOyDUEbq7gLu9pZbgSOdc3+p75fM7AkzW29m84PW3WRmc81stpm9a2Yd9zq5iOyRgtIdXDFlDl1bpXLN6H5+xxGRZizWa5qytqCU+z/8we84IiLNXihF3Ryg2FvmO+fyQtz2k8CoauvudM4Nds4NBV4HJoYaVET2zaSpC1hXuJ17xg8hJUHfmotIeA3rksnpw7J5/LPlLNtQ5HccEZFmrdaizswSzexJ4EfgYeBRYIU3AlfviTjOuU+AzdXWFQT9mMquaZ0iEkZvzlvDy9+t4qKRPdmvc6bfcUQkSlw9qi9J8bFMmqamKSIi4VTXSN21QDzQ2Tm3vze61pnAeXjX7+0OzewWM8sFzkIjdSJht76glGtemceQ7AwuOrqn33FEJIq0SU/ksuN68+nSjbyzYK3fcUREmi2r7Zsz71y4A51zxdXWpwFfOucG1rtxs67A6zXd18z+CiQ5526o5XfPB84HaNeu3bDnnnuuvt01uqKiItLSdHWHaBQpx945xz2ztrNkcwWTD02mQ9reNLyVYJFy7KVh6bjvvYpKx6QZpRTvcPztiGQSYyPrMio69tFLxz56NdVjP3LkyFnOueE13VbXiTWV1Qs6AOdckZk1xByK/wFvAjUWdc65R4BHAIYPH+5GjBjRALtsWNOnT6cp5pLwi5Rj/58vf2LexvncOG4AZx7S1e84zUKkHHtpWDru+ya922bGPzyDeeUdueKYPn7H2SM69tFLxz56ReKxr+tre2dmmWaWVX0BKvdmZ2bWK+jHccDivdmOiNRv+YYibnljIUf2bsPZB3fxO46IRLEDu2VxytCOPPLJclZs3OZ3HBGRZqeukboMYBZQ0zyJekfqzOxZYATQ2szyCIzIjTazPgSKwp+AC/Y0sIjUr7yikkunzCExLpY7Tx+MWWRNdxKR5uea0f14f9F6Jr+2gCfOOUDvSyIiDajWos4513VfNuycO7OG1Y/vyzZFJDQPfrSMOblbePBX+9OuRZLfcUREaNsiiUuO7cXNbyzig0XrObZ/O78jiYg0G+qaINLMzMndwj8+XMopQzty0uAOfscREdnpt4d2pVfbNCa/voDSHRV+xxERaTZU1Ik0IyVlFVz6/GzapicyeVy9DWpFRBpVfGwMk8cNIHdzCf/6eJnfcUREmg0VdSLNyK1vLWL5xm3c/cshZCTH+x1HRORnDu3RmjGDO/DP6cvI3fyzJtsiIrIXQirqzOxwM/ud9+82ZtYtvLFEZE99/P0Gnp7xE+ce3o1De7b2O46ISK2uPakfsTHGja8v9DuKiEizUG9RZ2Y3AFcDf/VWxQP/DWcoEdkz+dvKuPKFOfRqm8aVJ0TWNaBEJPp0yEjm4qN78d7CdXy0ZL3fcUREIl4oI3WnAmOBbQDOudVAejhDiUjonHNc9+p88ovLuHfCUJLiY/2OJCJSr3MP70b31qlMnraA7eVqmiIisi9CKerKnHMO79p0ZpYa3kgisiemzl7NG/PWcMmxvRnYKcPvOCIiIUmIi2HS2AGs2FTMY5/+6HccEZGIFkpRN8XMHgZamtl5wPvAo+GNJSKhWLWlhOunzmdYl0wuOKqH33FERPbIkb3bMGpAe+7/cCmrtpT4HUdEJGLVW9Q55+4CXgReAvoAE51z94c7mIjUrbLSccWUOVRWOu4dP5TYGPM7kojIHrtuTD8AbnlDTVNERPZWXCh3cs69B7wX5iwisgf+/cUKZizfxO2/GETnVil+xxER2SvZmSlcOKInd7/3PZ8t3cjhvdS9V0RkT4XS/bLQzAqqLblm9oqZdW+MkCKyu+/XFXL724s5tl9bxg/P8TuOiMg+Oe/I7nRplcLEafMpK6/0O46ISMQJ5Zy6+4ArgU5ANnAF8AzwHPBE+KKJSE3Kyiu55LnZpCfGcetpgzHTtEsRiWxJ8bFMOnkAyzds49+fq2mKiMieCqWoG+uce9g5V+icK3DOPQKc4Jx7HsgMcz4Rqea+979n4ZoCbj1tEG3SE/2OIyLSIEb2bcux/dry9w+WsnZrqd9xREQiSihFXbGZjTezGG8ZD1S927owZhORamau2My/Pl7GhOE5HD+gvd9xREQa1MQxAyivdNzy5iK/o4iIRJRQirqzgLOB9cA679+/NrNk4KIwZhORIEXby7lsyhw6ZSZz/cn9/Y4jItLgOrdK4f+O6sFrc1YzY9kmv+OIiESMUC5psNw5d7JzrrVzro337x+ccyXOuc8aI6SIwM2vLyQ3v5h7xg8lLTGkxrUiIhHn/0b0IDszmRumzWdHhZqmiIiEIpTul0lmdqGZPWRmT1QtjRFORALeW7iO577J5YKjenBA1yy/44iIhE1SfCwTx/Tn+3VFPPXFCr/jiIhEhFCmX/4HaA+cAHxMoANmYThDicguG4u285eX5tKvQwsuPba333FERMLuuP7tGNGnDfe9v5T1hWqaIiJSn1CKup7OueuBbc65p4CTgIPCG0tEAJxz/OWleRRuL+e+CUNJiAvlT1ZEJLKZGTecPICy8kpue3Ox33FERJq8UD4h7vD+u8XMBgIZQNvwRRKRKi/MzOP9Reu46oQ+9Gmf7nccEZFG0611Kucd2Y2Xv1vFNys2+x1HRKRJC6Woe8TMMoHrgGnAQuD2sKYSEVZuKmbyaws4pHsrfn9YN7/jiIg0ugtH9qRjRhLXvzqfcjVNERGpVZ1FnZnFAAXOuXzn3CfOue7OubbOuYcbKZ9IVKqodFw2ZTYxZtw1fggxMeZ3JBGRRpeSEMd1Y/qzeG0h//tqpd9xRESarDqLOudcJXBVI2UREc/Dnyxj5k/53HjKADq1TPY7joiIb04c2J7De7bmrneXsLFou99xRESapFCmX75vZleYWY6ZZVUtYU8mEqXmr9rKve99z0mDOnDK0E5+xxER8ZWZMWnsAErKKrjjbTVNERGpSShF3QTgQuATYJa3zAxnKJFoVbqjgkufn01mSgI3nzIQM027FBHp2TaNcw/vxpSZeXy7Mt/vOCIiTU69RZ1zrlsNS/fGCCcSbe56ZwlL1xdxx+mDyUxN8DuOiEiTcfExvWjXIpGJU+dTUen8jiMi0qTUW9SZWYqZXWdmj3g/9zKzMeGPJhJdvvhhI4999iNnH9yFEX101RARkWBpiXFcM7of81cV8Nw3apoiIhIslOmX/wbKgEO9n1cBN4ctkUgU2lqygytemEP31qn8dXRfv+OIiDRJY4d05KBuWdz5zhLyt5X5HUdEpMkIpajr4Zy7A+8i5M65YkAn+og0oEnTFrCucDv3TBhKSkKc33FERJokM+PGcQMpLC3njneW+B1HRKTJCKWoKzOzZMABmFkPQD2FRRrIG3PX8Mp3q7j46J4MzWnpdxwRkSatT/t0fntIV577ZiVz87b4HUdEpEkIpaibBLwN5JjZ/4AP0LXrRBrEuoJSrn11HkOyM7hwZE+/44iIRIRLjutFq9REJk5dQKWapoiIhNT98l3gNOAc4FlguHNuenhjiTR/zjmufHEupTsquGfCUOJjQ/mORUREWiTFc83ovszO3cILs3L9jiMi4rtQul++BhwPTHfOve6c2xj+WCLN33+//IlPvt/AtaP70aNNmt9xREQiyqn7dWJ4l0xuf3sJW4t3+B1HRMRXoQwN3AUcASw0sxfN7HQzSwpzLpFmbdmGIm55cxFH9m7Drw/u4nccEZGIU9U0ZUtxGXe/p6YpIhLdQpl++bFz7k9Ad+BhYDywPtzBRJqrHRWVXPb8bJLiY7nz9MGYqZmsiMje6N+xBWcf3IX/fvkTC1Zv9TuOiIhvQjqJx+t++QvgAuAA4KlwhhJpzh786Afm5G3lllMG0a6FBr1FRPbFZcf1ITMlgYlTF+CcmqaISHQK5Zy6KcAi4GjgAQLXrbs43MFEmqPZuVu4/8MfOHW/Tpw0uIPfcUREIl5GSjxXj+rLrJ/yefnbVX7HERHxRSgjdY8TKOQucM59BBxqZg+GOZdIs1NcVs6lz8+mXXoik8YO8DuOiEizcfqwbIbmtOTWtxZTUKqmKSISfUI5p+4dYLCZ3WFmK4CbgMXhDibS3Nz65mJ+3LiNu8YPISM53u84IiLNRkyMceO4AWzatp373lvqdxwRkUZXa1FnZr3N7AYzWwzcD+QC5pwb6Zy7v9ESijQD05es5z9f/sQfDu/GoT1a+x1HRKTZGZzdkjMP7MxTM1aweG2B33FERBpVXSN1iwmcRzfGOXe4V8hVNE4skeYjf1sZV704l97t0rjihD5+xxERabauPL4P6UlxapoiIlGnrqLuNGAN8JGZPWpmxwDqvS6yB5xzXPvqPPKLy7hn/FCS4mP9jiQi0mxlpiZw5Ql9+PrHzUybs9rvOCIijabWos4596pz7gygL/ARcAnQ1sz+aWbHN1ZAkUj26uxVvDlvLZce15uBnTL8jiMi0uydcUBnBnXK4G9vLqJoe7nfcUREGkUojVK2Oeeecc6dDGQD3wFXhz2ZSIRbtaWEia8uYHiXTP54ZA+/44iIRIVYr2nKuoLt/OMDNU0RkegQ0sXHqzjn8p1zjzjnjglXIJHmoLLSccWUOVQ6xz3jhxIbo5nLIiKNZb/OmYwfns0Tn/3ID+sL/Y4jIhJ2e1TUiUhonvj8R2Ys38TEk/vTuVWK33FERKLO1aP6kpIQyw3T1DRFRJo/FXUiDWzJ2kLueGcJx/Zrx/jhOX7HERGJSq3SErnihD58/sMm3py31u84IiJhpaJOpAFtL6/gkudnk54Yx22/GISZpl2KiPjlVwd2pl+HFtz8xkKKy9Q0RUSaLxV1Ig3ovveXsmhNAbf9YjCt0xL9jiMiEtXiYmO4adwA1mwt5YEPf/A7johI2IStqDOzJ8xsvZnND1p3p5ktNrO5ZvaKmbUM1/5FGts3Kzbzr4+XccYBORzXv53fcUREBBjeNYvT9u/Eo58uZ/mGIr/jiIiERThH6p4ERlVb9x4w0Dk3GPge+GsY9y/SaIq2l3PZlNlkZyZz3Zj+fscREZEgfzmxL0lxsUx6baGapohIsxS2os459wmwudq6d51zVZPavyRw3TuRiHfTawtZlV/CveOHkpYY53ccEREJ0jY9iUuO680n32/g3YXr/I4jItLg/Dyn7vfAWz7uX6RBvLtgLc/PzOWCo3owvGuW33FERKQGvz2kC33apXPjawspKavwO46ISIOycE5DMLOuwOvOuYHV1l8LDAdOc7UEMLPzgfMB2rVrN+y5554LW869VVRURFpamt8xxAdVx37rdsd1nxeTmRjDxEOSiNNFxps9/d1HJx335mHx5gpu+7qUcT3iObVXQki/o2MfvXTso1dTPfYjR46c5ZwbXtNtjT5PzMzOAcYAx9RW0AE45x4BHgEYPny4GzFiRKPk2xPTp0+nKeaS8Js+fTpHHXUU5z09k+2VpTz2h8Pp3S7d71jSCPR3H5103JuHEcDC7d/x1oK1XHbaAXRplVrv7+jYRy8d++gVice+Uadfmtko4CpgrHOuuDH3LdLQpszM5f1F67nqhD4q6EREIsS1J/UjPsa48bWFfkcREWkw4bykwbPADKCPmeWZ2bnAA0A68J6ZzTazf4Vr/yLhUFnp2FC4nY69B5OVmsCvD+rM7w/r5ncsEREJUbsWSfz5mF58sHg9HyxS0xQRaR7CNv3SOXdmDasfD9f+RMKtstKxZF0h5z09k7z8ErIzk3n418P8jiUiInvod4d1Y8rMXCa/tpDDerYmKT7W70giIvvEz+6XIhFlfeH2nQUdQF5+CX/87yw2bSvzOZmIiOyJhLgYJo8dyMrNxTzyyXK/44iI7DNdUEvEU1HpWLO1hJWbi8ndXMzKzcWs3FxCrvfzQ2ftv7Ogq5KXX0JZuVpji4hEmsN7tWb0oPY8+NEPnLpfJ3KyUvyOJCKy11TUSVTZWrzDK9YCS27+rgJuVX4J5ZW7GrLGxhidWiaTk5XM8QPakZIYS3Zm8m6FXXZmMglxmrYjIhKJrjupPx8t3sDNbyzk4bNr7BIuIhIRVNRJs1JWXsmqLbtG23KDC7jNxRSUlu92/6zUBHIykxnUKYOTBnWgc1YKOVkpdM5KoUNGEnGxu2YoV1Y6Hv3N8N3OqXv0N8NplRratY5ERKRp6dgymYuO7smd7yzh4+83cFTvNn5HEhHZKyrqJKI459hYVLZrhG1T8IhbCWu2lhA02EZCbAzZWcl0zkphWJdMcjJ3FW05WcmkJ8WHvO+YGKNPu3Re+dNhFBRto0VaKq1SE4jRBcdFRCLWH47oxouz8pg0bQFvX3IEiZp9ISIRSEWdNDklZRXk5QePsO1+nlvJjt3PYWubnkjnrBQO7Ja1s2CrWtqmJzZo0RUTY7RJT2TBrBkRd1FKERH5ucS4WG44uT/n/PsbHv/sR/40oqffkURE9piKOml0lZWOdYWlrNxUTG7+7gVb7uZi1hdu3+3+KQmxO6dFHvb/27vz+LrqOv/jr0/2Pc3WdElXErqwtbSUpS20goiILIqiogiOoMOMzIyjM/obFfi5/FQYUWEcEUSsKCCDKGORRbB2oSAUKGtpCrRQypJ0oUvaJE0+vz++35vcpEnpkvTmNu/n45FH7z333HO+t99z7j2fB97WFAAAHupJREFU8/2cz6mtZHR5PqMrChhVVkBNWQH5OTqrKiIi+27OhKGcOrmaax9cxdlTRjJiSH6qmyQislcU1Em/2LKjdZcRttfi6NvaDdtpaWvvmDfDYHhpKEgyZ0JVRwCXGHWrKMzBTCmOIiLSf75+xmRO+cFf+fY9L/Bfnzg61c0REdkrCupkn+xsa+eNd3Z0KUKS/O/GptYu8xfnZTGmooCJw4p576TqLmmSI4bkk5OlWyaKiEjqjCov4NI5tVzz55V8YkYjM2srU90kEZE9pqBOeuTubIrl/xMjbMmVJNdt2kFbUkWSrAyjpiyfUeUFvD9WkUz8jSoroLRgzwuSiIiIpMLnThrPnU+s5fK7n+Oey2brhKOIpA0FdYNY8842Xt/Y/WbboTDJaxua2NLctfx/ZVEONWUFTB1VxllHhYAtUVlyeGk+maoCKSIiaSwvO5NvnDGZz857nF8+vJqLTxyf6iaJiOwRBXUHMXenYWtzZ8C2fnuXUbc3N+/Ak8r/52ZldKRFzhhXTk1ZCNgSRUkKc7W5iIjIwe2UydW8Z+JQfvjnlZw5ZUSqmyMiskd0lJ7mmlp27lqQJKkwyY7W9i7zDyvJY3R5ASccUsmoOMqW+Kss6tvy/yIiIuno8g9O5ku/Xc6a9U2MmnAkDVuadV9SERnQFNQNcG3tzpubd+wasG1o4tUN22nc2rX8f2FOJqMrChlXWchJh1aFUbbyRPn/fPKyVf5fRERkd0aVFfDV0ydx2W1PsnbjdmrK8rnhgulMqC5WYCciA5KCugFg847WcM+2LqX/w3Vtazc20drWmSOZmWEMLw2jbadMGtqRLpn4t6wgW+X/RURE9sP6bS0dAR3A2o3buXje49x16QlUFeeluHUiIrtSULcP2tud9dta9jglo7WtnXWbtncUIemeIrmpW/n/IQXZjC4vYPKIEk47fBijyjpTJIcPySM7U9W4RERE+kvLzraOgC5h7cbtrF7fxPfvfZFZdZXMqq2koig3RS0UEelKQd1eam93XnxrCxfPe7xLSsawklzWJAds6ztvBbBu03aSqv+Tk5nRUf7/qFGlnaX/419Jnsr/i4iIpEpOViY1ZfldAruasnza2537nnuTO5atBeCwESXMrqtidl0l08aU6RIHEUkZBXV7af22lo6ADjpTMr5+xmQ+96tlHfNVFecyqiyf6WPKGD11ZEfANrq8gOqSPJX/FxERGaAqCnO44YLpu5zAnVBdzJPfOJVnXn+HRSsbWLSqkRsXvcxP//oSedkZzBhXwYl1lcyqq2RCdbEuhxCRA0ZB3V7qLSVjTEUBN14wndEVoSBJQY7+a0VERNJRRoYxobqYuy6dyeat2ygpKuxyqcWUUUOYMmoIXzi5jq3NO3n05fUsqm9kUX0D35r/AhBO7s6urWT2oZXMrK1kqK7FE5F+pMhjL/WWklFRmMvEYSUpbJmIiIj0lYwMo6o4l+eWLWXOnDm9zleUm8XJk6o5eVI1AOs2bWdxfSML6xv4y4tv87snXwdg4rBiZtdVMruuihnjypWqKSJ9SkHdXuotJaOiMCfVTRMREZEUGzEkn48eM4qPHjOK9nbnuXWbWbSqgUUrG/nlw2u4YdEr5GRlMGNsObPqKpldV8mkYSW6VYKI7BcFdXvp3VIyRERERCAcMxxRU8oRNaVcOqeWppadPPrKBhbHVM3v/mkF3/1TOGGcqKg5u66KYaVK1RSRvaOgbh/saUqGiIiISEJBThZzJwxl7oShALz5zg4Wr2pkcX0Di1c18oen1gFQN7Soo6rmsePLdZ2+iLwrfUuIiIiIpMCw0jzOnVbDudNqaG93Vry5hUUxwLvl0TXctOQVsjONaWPKOoK8w0eUKjtIRHahoE5EREQkxTIyjMkjSpg8ooTPnXQIO1rbeGz1hlhVs5Gr7nuRq+57kbKCbE6orYy3Tqhi5JD8VDddRAYABXUiIiIiA0xedmYcnasCoGFLM0tWhaqai+sbmf/0GwCMryoMt06oq+K4QyooytWhnchgpD1fREREZICrKs7l7KkjOXvqSNydlW9tZVF9A4vqG7n98df45dI1ZGUYR48u66iqeWTNEDKVqikyKCioExEREUkjZsaEYcVMGFbMZ2ePp3lnG8tWb2TRqlBV8wcPrOQHD6ykJC+LmbWVzKqr5MS6KkaVF6S66SLSTxTUiYiIiKSx3KxMTqit5ITaSv79tIms39rMkpfWsziO5P3p2TcBGFNRwOy6SmbVVnFCbQUledkpbrmI9BUFdSIiIiIHkYqiXM48agRnHjUCd+elhm2hqmZ9I7974nVueeRVMjOMKaOGMKu2khMPreSomiFkZWakuukiso8U1ImIiIgcpMyM2qFF1A4t4qKZ42jZ2c6Tr24MVTVXNfLjh+r50YP1FOdmcfwhFcyuC0VXxlQUYKbr8UTShYI6ERERkUEiJyuDY8dXcOz4Cr70vglsamrh4ZfWs6i+gYUrG7n/+bcAqCnL77g33gmHVDCkICfFLReR3VFQJyIiIjJIDSnI4fQjhnP6EcNxd1avb2JxfQML6xv54/J13Pq3V8kwOKJmSLg3Xm0lU0eXkZOlVE2RgURBnYiIiIhgZoyrLGRcZSGfOn4srW3tLH9tU7wBegM/WfAS1z60isKcTI4bH1I1Z9VVcUhVoVI1RVJMQZ2IiIiI7CI7M4PpY8uZPracf3nvobyzvZWlL61n8apQVfPBFW8DMKI0L94br4qZtZWUFypVU+RAU1AnIiIiIu+qND+b0w4fxmmHDwPg1fVNLFoVqmre++yb/PbxtZjB4SNKO26APm1MGblZmSluucjBT0GdiIiIiOy10RUFnF8xhvOPHUNbu/P02pCqubi+kRsWvsx/L3iJ/OxMjh1fHm+dUEXd0CKlasqA1d7urN/WwqgJR9KwpZmKwhwyMtJje1VQJyIiIiL7JTPDmDq6jKmjy7js5Dq2Nu/kkVhVc9GqRr41/wWY/wLVJbnMqg1VNWfWVlJVnJvqposAIaB78a0tXDzvcdZu3E5NWT43XDCdCdXFaRHYKagTERERkT5VlJvFKZOrOWVyNQCvb9reUVXzwRVvcecTawGYNLwkVNWsq+SYseXkZStVUw68Ha1tbNja0hHQAazduJ2L5z3OXZfOTIuTDwrqRERERKRfjRySz3nHjOa8Y0bT1u48t+6djqqaNy15hesXvkxuVgYzxpWHqpq1VUwaXqxUTdlrbe3OpqYWNja1sH5r+HfDttb4bwsbt7WwIT5OPN/W0sbtlxzXEdAlrN24nZadbSn6JHtHQZ2IiIiIHDCZGcaRNUM4smYI/zC3lm3NO/nbKxs6grzv3LMCWEFlUS6zais6boI+tCQv1U2XA8zd2dK8MwRiSX8dgVpSgJZ4/M72Vtx7Xl5hTiZlhTmUF+ZQVpDDIVVFlBXkUFGUQ0l+NjVl+V0Cu5qyfHLSpNCPgjoRERERSZnC3CzmThzK3IlDAXjznR0sqm9g8apGFtU38vun1gEwobo43huvkmPHVZCfkx4H29JpR2tbD8FZZ0C2cVsr67c1s3Fba3zews72niO07EzrCM7KC3OYNKKE8vi4vDAnBG8FOZQVZnfMt7v03vZ254YLpu9yTV1FmtyiQ0GdiIiIiAwYw0rz+Mj0UXxk+ija250X3tzcUVVz3iNruHHxK+RkZjB9bFnHKN7k4SVpUcziYNLa1s6mps60xuR0xkRAtqGplQ2JIG1bC9tbe05lNIOyghzKCkIANqaigKmjh3QGaAW7BmpFuVl9mp6bkWFMqC7mrktnsnnrNkqKClX9UkRERERkf2VkGIeNKOWwEaV8/qRD2N7SxmOrN4SqmvWNfO/eFXzvXigvzGFmbbg33uy6SoaX5qe66Wmlvd3ZsmPnLteadQRncVRt/bbO55t37Ox1ecW5WZTFIKyqKJdDq4up6BKUdQZrFYUh9TFzAARPGRlGVXEuzy1bypw5c1LdnL2ioE5ERERE0kJ+TiYnHlrFiYdWAfD2lh0sWdXIopWNLFrVyP8uD6matUOL4r3xQqpmYe7gOeR1d7bHNMeOdMZu16Bt7JYCubGplbZe0hxzsjJCQBZHy2rKCpKeZ3cJ1CoKcxhSkENOVsYB/tQyeLZwERERETmoDC3O45ypNZwztQb3cJ+xxfWNLKxv5LbHXuXmh1eTnWkcPbosjuJVcfjI0gExKrSnWna2s6lp14qNXSo6dqn02ELzzvYel5WZYZQVZIdUx8JQKKS8KHn0LLsz1TEWEMnPzlQV0jSgoE5ERERE0p6ZMXFYCROHlfDZ2ePZ0drGsjUbO6pqXn3/Sq6+fyWl+dnMqg0FV2bXVVJTVtBlOe3tzvptLYyacCQNW5r79Lqq9nbnne2dRUDW75Lm2PUatY3bWtjS3HuaY0leVse1ZsNL8zhsREm3a886A7WKwlyK87LS5hox2TsK6kRERETkoJOXncnM2kpm1lbylfdPpHFrM0tWhYIri+obmf/MGwCMqyyM98ar5IRDKngt3nQ6uQLihOriXYIhd2dbS1tnKmNTCxuSRss6KzsmUiBb2dTUQi9ZjuRnZ8aALARhYysKerwGLVE8ZEhBNtmZSnOUQEGdiIiIiBz0KotyOWvKSM6aMhJ356WGrSxc2cjiVY38z7K1zFu6hus/NY1v/vH5jnuVrY0B3o8/NpWblrzScW1aoqJjS1vPaY5ZGdalUuPEYSWhtH4vAVpZQY5u0SD7pd+COjO7CTgDeNvdD4/TPgJcAUwCZrj74/21fhERERGRnpgZtUOLqR1azGdmjaNlZztPvLqRkrzsLjefhhDYtbU7z6/bTFlhDjVl+Rw5snTXa9BioZCywhyK+7jcvsi76c+RupuB64B5SdOeBT4EXN+P6xURERER2WM5WRkcN76Chi3N1JTldwnsasryGVtZyENfmpO6Boq8i35LxHX3hcCGbtNecPcX+2udIiIiIiL7qqIwhxsumE5NWbjPXeKauorCnBS3TGT3dE2diIiIiAjh5tMTqou569KZbN66jZKiwj6tfinSX8y9lxI8fbFws7HAHxPX1CVNXwB8aXfX1JnZJcAlANXV1dNuu+22fmvnvtq6dStFRUWpboakgPp+8FLfD07q98FLfT94qe8Hr4Ha93Pnzl3m7tN7em3AjtS5+8+AnwFMnz7d58yZk9oG9WDBggUMxHZJ/1PfD17q+8FJ/T54qe8HL/X94JWOfa+bW4iIiIiIiKSxfgvqzOxWYCkwwczWmtnfmdk5ZrYWOB6Yb2b39df6RUREREREBoN+S79094/38tJd/bVOERERERGRwUbplyIiIiIiImlMQZ2IiIiIiEgaU1AnIiIiIiKSxhTUiYiIiIiIpLF+vfl4XzGzBmBNqtvRg0qgMdWNkJRQ3w9e6vvBSf0+eKnvBy/1/eA1UPt+jLtX9fRCWgR1A5WZPd7bXd3l4Ka+H7zU94OT+n3wUt8PXur7wSsd+17plyIiIiIiImlMQZ2IiIiIiEgaU1C3f36W6gZIyqjvBy/1/eCkfh+81PeDl/p+8Eq7vtc1dSIiIiIiImlMI3UiIiIiIiJpTEEdYGZtZvZU0t/YHua5x8yG9DD9CjP70oFop+wfM3MzuyXpeZaZNZjZH/to+doWBhgz+w8ze87Mno779rG7mfdCMxvRB+tcbWaV+7sc6Tt7sx3sxTK1vw8gZlaR9Bv+ppm9Hh9vMrPnD8D6LzSz6/p7PbJ7u9kOnjKznD5cz5y+OnaQd2dm15jZPyc9v8/Mbkx6/p9m9sU9WM5YM3u2v9oZ17G1P5e/O1mpWvEAs93dp/T0gpkZIU319APcJul724DDzSzf3bcD7wVeT3GbpJ+Y2fHAGcDR7t4cA63d/ahfCDwLrNuLdWS5+879aqj0q33YDiQNuft6YAqEgBvY6u5Xx5O0+3zwrX08vfS2HaS0UdIXlgAfBX5oZhmEe8iVJL1+AvAvqWjYQKKRuh7ESP5FM5tHOMgblXz2PZ71XWlmi4EJSe+72MweM7PlZnanmRWYWbGZvWJm2XGekuTncsDdA3wgPv44cGviBTMrN7Pfx7P5j5jZkXH6FWZ2k5ktMLOXzeyypPdoWxi4hgON7t4M4O6N7r7OzL4R++ZZM/uZBecC04FfxzO6+d32+elmtiA+vsLMfmVmS4BfxTPD98eRoBsBSzQgbk/L4muXxGmfMbMfJs1zsZldc6D+Uwah3raD3fWv9veDS6aZ3RD3w/vNLB8g9vH0+LjSzFbHxxea2d1m9hDwoJkNN7OF8bvhWTObHee7KG4PfwNmJlZmZh80s0fN7Ekz+7OZVZtZhpnVm1lVnCfDzFYlnkv/MbOb43d84vnWpMdfjvvu02Z2ZZxWaGbz4/78rJmdF6efZmYrzOwJ4ENJy5hhZktjfz9sZhPi9IVmNiVpvsVmdtQB+MgHo4eB4+PjwwjH5lvMrMzMcoFJgJvZX+Nv7n1mNhzAzKbFvlwO/ENigXE//52Z3Rv3ze8nvXZq7NMnzOwOMyuK079rZs/H7eXqOG1cnPcZM/tW0jKKzOzBuIxnzOysOP3/WtdRx2+b2T/1xX+Sgrog3zqH5++K0+qAn7j7Ye6+JjGjmU0DPkY4E3Q6cEzScn7n7se4+1HAC8DfufsWYAGdgcTH4nyt/fuRpBe3AR8zszzgSODRpNeuBJ509yOB/wPMS3ptIvA+YAZwuZlla1sY8O4nnJBZaWY/MbOT4vTrYt8cDuQDZ7j7/wCPA+e7+5Q4krs7k4FT3P3jwOXAYnc/DLgLGJ0032fcfRohYLzMzCqA3wIfTDq4vwi4qQ8+r/Sst+1gd7S/H1zqgP+K++gm4MN78J6jgXPd/STgE8B9MaPnKOCpeMB4JSGYm0X4TkhYDBzn7lMJvzn/5u7twC3A+XGeU4Dl7t6w359O9omZnUrYNmYQ9utpZnYicBqwzt2Pir8T98ZjhhuADwLTgGFJi1oBzI79/Q3gO3H6zwkZIJjZoUCeuy/v9w92EHL3dcBOMxtNGJVbSjh+O57w+/oCcA1hn51G+E39dnz7L4AvxO/n7qYA5wFHAOeZ2SgLJ/u+RviNP5pwbPDF+Pt9DnBYPE5MBHA/Av7b3Y8A3kha9g7gnLiMucB/mpnFtl0A4eQO4bfhFvqAgrpgezyQm+Lu58Rpa9z9kR7mnQ3c5e5N7r4ZuDvptcPNbJGZPUP44j4sTr+RcOBG/PcX/fAZZA+4+9PAWMIo3T3dXp4F/CrO9xBQYWaJ4f357t7s7o3A20A12hYGNHffSvjxvQRoAG43swuBufEs+jPAe+jsm71xd1LgdyLxC9nd5wMbk+a7LJ4dfAQYBdTFdj0EnGFmE4Fsd39mH9oge2A328HuaH8/uLzi7k/Fx8sIvwHv5gF33xAfPwZcZCGd74gYsB8LLHD3BndvAW5Pem8NcF/cHr5M5/bQcTAHfAZtD6l2avx7EniCcDKnDngGeK+Zfc/MZrv7O/G1V9y93kPZ+OSD8FLgDgvXal1DZ3/fQfiezyb0980H4DMdzB4mBHSJoG5p0vPXgcOBB8zsKUJQVmOhFsYQd18Yl/Grbst80N3fcfcdwPPAGOA4wkmaJXFZn47T3yEEaj83sw8BTXEZM+nM+kpevgHfMbOngT8DI4Fqd18NrDezqcTtL6YN7zddU9e7bfvwnpuBs919eTxomAPg7ksspHTOATLdvV8v0pR3dTdwNaF/KvbwPc1Jj9t4933nZrQtpJy7txFGSxbEA6zPEUZop7v7a/EgLa+Xt++k88RX93ne9fsh9vEpwPHu3mQhvS+xnBsJo8Er0IFdv+thO/g0u+9f7e8Hl+79mR8f79E+7u4L4wjOB4CbzewHwObdrO9a4Afufnfs+yvicl4zs7fM7D2E0aHze1+E9KGOfo4jI4lrag34f+5+ffc3mNnRhBH5b5nZg3Q9idPdN4G/uPs5Fq7hXAAQv/cfAM4iXA82rS8+zCC2hBDAHUFIv3wN+FfCvrgAGOnuxye/wXoocNhNT9/1Rjip8/HuM5vZDOBk4FzgHwknhgF6uj/c+UAVMM3dWy2kdycfA1xIGPHts0wdjdTtvYXA2RauuSkmDMUnFANvxLMy3b+s5wG/QQdwA8FNwJU9jI4sIvZb/CFujGfke6NtYQAzswlmVpc0aQrwYnzcGHPkz016fQuh3xJW0/kjvLt0rYWE9CzM7P1AWZxeCmyMP+wTCWf/AHD3Rwkjd58g6bpO6Xu9bAdr2PP+TdD+fvBZTec2cG5vM5nZGOAtd7+BcDB2NCH16yQL19RmAx9JekspnUW4Pt1tcTcSRnnuiCcbpP+tprOfzwQSqe/3AZ9Jul5qpJkNtVAFucndbwGuIvT3CmCsmR0S35t8wJ/c3xd2W/eNwI+Bx9x9I7I/HiYUvdrg7m1xJH0IIQXzVqDKQmEsYsr8Ye6+CdhkZrPiMvbkRMojwEwzq43LKjSzQ+N2Uuru9xCKsiTSOZcQUii7L78UeDsGdHMJo30JdxHSfI8hbId9QiN1e8ndnzCz24HlhLScx5Je/jrhi74h/pt8gPhrQv6tDuBSzN3XEr5ku7sCuCkOlTex649x9+VoWxjYioBr45m6ncAqQgreJsJZvjfp2mc3Az81s+2EH4krCWkW3ySeee3FlcCtZvYc4Ufn1Tj9XuDzZvYCIZjsns79W2CKfuj7XW/bwST2rH8B7e8HqauB31ooYjR/N/PNAb5sZq3AVuACd38jjvQvJXynPJU0/xWEdLyNhFTrcUmv3U0I8BXkHzg3AH+IqfD3Ekdh3f1+M5sELA2XOrEV+CRQC1xlZu1AK/D37r4jsZ2YWRPhJHBiP/8+8Esz+xrdtiN3X2Zmm1F/94VnCFUvf9NtWpG7v22hGM6PzayUEN/8EHiOeN26mTnhGuvdcveGmHFxq4UiLBDSObcQtqM8wmhe4hYK/wT8xsz+HfhD0qJ+DfxvzA55nHBiILGOFjP7C7CpL0/uWEgNlv4WN7az3P1TqW6LpJa2BQGwcI+ja9z9wVS3RfqP9ndJZqHa5jXuPjvVbZH+F0f9FgATY7EckUQa8BPAR9y9vq+Wq5G6A8DMrgXeT8jPlkFM24LEEaO/ESrfKaA7iGl/l2Rm9hXg79G1dIOCmV1AqMD4RQV0kmBmkwn3zryrLwM60EidiIiIiIhIWlOhFBERERERkTSmoE5ERERERCSNKagTERERERFJYwrqREQkpcyszcyeMrPnzGy5mf1rrA7Wn+u8Kq7vqqRpF8V2PGVmLWb2THz8XTO7wsy+tI/rOsvMfp/0/Ktmtirp+QfNbHc3N+5tuRea2XX70iYRETm4qPqliIik2nZ3nwJgZkMJ9yEqAS7vx3VeApQn3yPI3TvuH2Zmq4G57t4Yn1+xH+t6GLg+6fnxwGYzG+rubwMnxHlERET2iUbqRERkwIhBziXAP1ow1swWmdkT8e8EADObZ2ZnJ95nZr82s7OSlxXff5WZPRtH3c6L0+8m3JR8WWLaHppsZgvM7GUzuyxpPZ80s7/FUb3rzSyz22dqIARxtXHSSOBOQjBH/HeJmVWZ2Z1m9lj8mxmXX2hmN8V1PNn9c8Z5PmBmS82sci8+j4iIHCQU1ImIyIDi7i8DmcBQ4G3gve5+NHAe8OM428+BCwHMrJQQGM3vtqgPAVOAo4BTgKvMbLi7n0kcHXT32/eiaROB9wEzgMvNLNvMJsV2zYyjjW30fB+yJcAJZjYBqAceic+zYvseA35EuDH1McCHgRvje/8DeMjdZwBz4+coTCzYzM4BvgKcnhhZFBGRwUXplyIiMpBlA9eZWSJgOhTA3f9qZj8xsypCAHSnu+/s9t5ZwK0xxfItM/srcAyw19evRfPdvRloNrO3gWrgZGAa8JiZAeQTAtHuHiYEnpnAUsIN6L8BTAVWuPsOMzuFMBqYeE+JmRUBpwJnJl3TlweMjo/fA0wHTnX3zfv4uUREJM0pqBMRkQHFzMYTAri3CdfVvUUYzcoAdiTNOg/4JPAx4KID0LTmpMdthN9QA37p7l99l/cuAb5ACOpucPctZpYHzKHzeroM4Dh3T/6MWIjyPuzuL3abfizwEjCeEOw+vi8fSkRE0p/SL0VEZMCII28/Ba5zdwdKgTfcvR34FCEoSrgZ+GcAd3++h8UtAs4zs8y43BMJI2R96UHg3FjgBTMrN7MxPcz3AjCCMHr4ZJz2FPB5QsAHcD8h8CMua0p8eB/whRjcYWZTk5a7hjBSOc/MDuuTTyQiImlHQZ2IiKRafuKWBsCfCcHNlfG1nwCfNrPlhGvatiXe5O5vEYKlX/Sy3LuAp4HlwEPAv7n7m33Z8BhMfg2438yeBh4AhvcwnwOPAuvdvTVOXkoYZUuM1F0GTDezp83seULAB/BNQhrq0/H/6Jvdlr2CcB3fHWZ2SF9+PhERSQ8WfmdERETSi5kVAM8AR7v7O6luj4iISKpopE5ERNJOLCryAnCtAjoRERnsNFInIiIiIiKSxjRSJyIiIiIiksYU1ImIiIiIiKQxBXUiIiIiIiJpTEGdiIiIiIhIGlNQJyIiIiIiksYU1ImIiIiIiKSx/w+uV1DrjcMukAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekday_avg_min = df_new[['day_of_week','duration_min']].groupby(['day_of_week'],as_index= False).mean()\n",
    "# plot a line plot\n",
    "plt.figure(figsize=[15,5])\n",
    "sns.lineplot(data=weekday_avg_min, x='day_of_week', y='duration_min',marker='o')\n",
    "plt.ylabel('Average Duration (min)');\n",
    "plt.xlabel('Day of The Week')\n",
    "plt.grid()\n",
    "plt.title('Average Trip Duration Based on The Day of The Week')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LAXjp57CsgnE"
   },
   "source": [
    "As expected, Saturdays and Sundays have the longest trip duration since the two days are majorly used by the customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J-JdMrkXbCRV"
   },
   "source": [
    "Relationship between the user types and the trip starting hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 730
    },
    "id": "aY1FPpy6bNI6",
    "outputId": "be376cb8-2fac-4822-bf3c-ccd2b91361c5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7fd4a0990f10>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0990490>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a1216ed0>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0bcc990>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0bcc210>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a07fd810>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a08f9410>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a08ef990>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0738250>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a063c450>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0d96c90>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0c83ad0>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a13f6210>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a13f63d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a13f6690>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0b70f50>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a13f61d0>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a07fd750>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a08f9290>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0defe10>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a063c790>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0bfe090>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0b70a90>,\n",
       "  <matplotlib.axis.XTick at 0x7fd4a0610cd0>],\n",
       " <a list of 24 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# facetting histograms of start_hour against user_type\n",
    "user_cat_order = df_new['user_type'].value_counts().index\n",
    "bin_edges = np.arange(-0.5, 23.5+1, 1)\n",
    "g = sns.FacetGrid(data = df_new, 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",
    "for ax in g.axes.flat:\n",
    "    ax.set_ylabel('count')\n",
    "plt.xticks(np.arange(0, 23+1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wQr61h2yb8ZO"
   },
   "source": [
    "Most rides for subscribers started at 8 or 17. While most customer rides started at 17 or 18. This has just confirmed that subscribers could be using the bikes for daily commute while the customers just for leisure rides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ExHaWiugfL4H"
   },
   "source": [
    "Does the average trip duration taken dependent on if a user is a subscriber or a customer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 420
    },
    "id": "Bb0yVBd_ACoB",
    "outputId": "14d0e6d2-397d-467f-82d2-a10dfbf59ad7"
   },
   "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>member_gender</th>\n",
       "      <th>duration_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>12.984506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>11.210651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Other</td>\n",
       "      <td>16.608589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  member_gender  duration_min\n",
       "0        Female     12.984506\n",
       "1          Male     11.210651\n",
       "2         Other     16.608589"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Average trip duration mean plot\n",
    "grouped_gender_type_duration = df_new[['member_gender','duration_min']].groupby(['member_gender'],as_index= False).mean()\n",
    "# show analysis\n",
    "display(grouped_gender_type_duration)\n",
    "\n",
    "sns.barplot(x='member_gender', y='duration_min', data=grouped_gender_type_duration)\n",
    "plt.title(\"Average trip duration by user gender\")\n",
    "plt.ylabel(\"Average Trip Duration (minute)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tVxHSn8uk-A-"
   },
   "source": [
    "Interestingly, eventhough the larger percentage of the users is male, the female tends to go on a longer ride compared to me. Unexpectedly, the \"Other\" type in gender has the highest trip duration on average. I wouldn't investigate this further as no information is known about the other gender."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W37CGNI0UuiS"
   },
   "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",
    "Subscribers and customers bikes usage was different:\n",
    "-  The day of the week that most trips are taken (Thursday) is NOT dependent on if a user is a subscriber or a customer. However, it was discovered that Subscribers mostly used bikes on workdays (Monday - Friday), while customers bikes usage was about the same for the whole week with a huge increment on Saturday and Sunday\n",
    "-  On average, Subscribers' trips mostly last for (10-11) minutes, while customers mostly last for (23-24) minutes.\n",
    "-  Customers rides were more likely to last more than one hour compared to subscribers.\n",
    "- Customers often start the trip in the afternoon where as Subscribers starts mostly in the morning followed by afternoon.\n",
    "-  Most rides for subscribers started at 8 or 17. While most customer rides started at 17.\n",
    "\n",
    "Average trip duration in minutes:\n",
    "-  On average, rides on the weekend (Saturday and Sunday) lasted longer than the other days.\n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "-  The user gender named \"other\" had the highest ride duration, followed by the female with the male having the least trip duration. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DWLd-rHmUuiT"
   },
   "source": [
    "## Multivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9MsTVw2j0gSC"
   },
   "source": [
    "Daily average trip duration for each user_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "gUs7uePTUuiV",
    "outputId": "e67fd0ed-607c-41b3-b159-fb30c6e91dbe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_weekday_avg_min = df_new.groupby(['user_type','day_of_week']).duration_min.mean()\n",
    "user_weekday_avg_min = user_weekday_avg_min.reset_index()\n",
    "\n",
    "# plot a line plot with colors based on user_type\n",
    "plt.figure(figsize=[10,5])\n",
    "sns.lineplot(data = user_weekday_avg_min, x='day_of_week', y='duration_min', hue='user_type', marker='o')\n",
    "plt.title(\"Daily average trip duration for each user_type\")\n",
    "plt.ylabel('Average Trip Duration (min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vi_AXQ8P1lhR"
   },
   "source": [
    "On average,  customers rides lasted longer than subscribers rides on every day of the week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y3tA4Sw_2pJN"
   },
   "source": [
    "Time of the day average trip duration for each user_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "2iYqUPr-4O5y",
    "outputId": "51674912-41fb-417b-9445-fb791c666763"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_daytime_avg_min = df_new.groupby(['user_type','time_of_day']).duration_min.mean()\n",
    "user_daytime_avg_min = user_daytime_avg_min.reset_index()\n",
    "\n",
    "# plot a line plot with colors based on user_type\n",
    "plt.figure(figsize=[10,5])\n",
    "sns.lineplot(data = user_daytime_avg_min, x='time_of_day', y='duration_min', hue='user_type', marker='o')\n",
    "plt.title(\"Time of the day average trip duration for each user_type\")\n",
    "plt.ylabel('Average Trip Duration (min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ImgOqUwm_Slb"
   },
   "source": [
    "It could be seen that the plots maintained a slighly constant trip durations for both the morning and night time for the two user types groups while there is a huge rise/increase in the average trip for the customers in the afternoon. This is because, customers mostly start their trip during the afternoon time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6MouZ_gZUuiW"
   },
   "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",
    "> I extended my investigation of the dependency of the user_type on the average trip duration, days of the week and the time of the day in this section by looking at the relationship between the features. The multivariate exploration here showed that there indeed is a dependency of the average trip duration on if a user is a subscriber or a customer. On average, customers rides lasted longer than subscribers rides on every day of the week and time of the day.\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> Not did I discover. Maybe I should have explored more, but then, I feel satisfied with the findings so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "id": "fuJ3dHAjUuiY"
   },
   "outputs": [],
   "source": [
    "# # export the new dataset\n",
    "# df_new.to_csv(\"engineered-201902-fordgobike-tripdata.csv\", index=False)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "exploration.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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