Ford_Gobike_Project_part_2.ipynb

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   "source": [
    "# Part I - (Ford Gobike  Dataset)\n",
    "## by (Gideon Oba)\n",
    "\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#faults\">Quality Issues Discovered</a></li>\n",
    "<li><a href=\"#cleaning\">Data Cleaning</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "<li><a href=\"#limitation\">Project Limitation</a></li> \n",
    "<li><a href=\"#ref\">References</a></li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af9160d",
   "metadata": {
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   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "This data set includes information about 183,400 individual rides made in a bike-sharing system covering the greater San Francisco Bay area.\n",
    "The data set contains information regarding:\n",
    "+ Duration – Duration of trip\n",
    "+ Start Date – Includes start date and time\n",
    "+ End Date – Includes end date and time\n",
    "+ Start Station – Includes starting station name and number\n",
    "+ End Station – Includes ending station name and number\n",
    "+ Bike Number – Includes ID number of bike used for the trip\n",
    "+ Member Type – Indicates whether user was a \"registered\" member  or a \"casual\" rider.\n",
    "+ Start and End Coordinates - Includes the longitude and latitude of each start and end station.\n",
    "+ Bike id.\n",
    "\n",
    "From a similar dataset website [Capital Bikeshare](https://ride.capitalbikeshare.com/system-data) these are some question posed about the bike sharing system.\n",
    "\n",
    "+ Which stations are most popular?\n",
    "+ What days of the week are most rides taken on?\n",
    "+ How long does the average trip take?\n",
    "\n",
    "These Project would try to answer these questions and more."
   ]
  },
  {
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   "id": "ee9ed035",
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   "source": [
    "# Packeages for Data Wrangling and Analysis\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Packages for Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "# Package for working with dates and times\n",
    "import datetime as dt\n",
    "\n",
    "# Package for accessing files on local device from the Jupyter Notebook\n",
    "import os\n",
    "\n",
    "# Package for visualization of maps\n",
    "# pip install folium to access this module\n",
    "import folium\n",
    "from folium import plugins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8bf7fef4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gobike shape: (183412, 16)\n",
      "\n"
     ]
    },
    {
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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>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                  end_time  \\\n",
       "0         52185  2019-02-28 17:32:10.1450  2019-03-01 08:01:55.9750   \n",
       "1         42521  2019-02-28 18:53:21.7890  2019-03-01 06:42:03.0560   \n",
       "2         61854  2019-02-28 12:13:13.2180  2019-03-01 05:24:08.1460   \n",
       "3         36490  2019-02-28 17:54:26.0100  2019-03-01 04:02:36.8420   \n",
       "4          1585  2019-02-28 23:54:18.5490  2019-03-01 00:20:44.0740   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1              23.0                     The Embarcadero at Steuart St   \n",
       "2              86.0                           Market St at Dolores St   \n",
       "3             375.0                           Grove St at Masonic Ave   \n",
       "4               7.0                               Frank H Ogawa Plaza   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "1               37.791464              -122.391034            81.0   \n",
       "2               37.769305              -122.426826             3.0   \n",
       "3               37.774836              -122.446546            70.0   \n",
       "4               37.804562              -122.271738           222.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "1                            Berry St at 4th St             37.775880   \n",
       "2  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "3                        Central Ave at Fell St             37.773311   \n",
       "4                         10th Ave at E 15th St             37.792714   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.402923     4902    Customer             1984.0   \n",
       "1            -122.393170     2535    Customer                NaN   \n",
       "2            -122.404904     5905    Customer             1972.0   \n",
       "3            -122.444293     6638  Subscriber             1989.0   \n",
       "4            -122.248780     4898  Subscriber             1974.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip  \n",
       "0          Male                      No  \n",
       "1           NaN                      No  \n",
       "2          Male                      No  \n",
       "3         Other                      No  \n",
       "4          Male                     Yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loads the dataset into a DataFrame\n",
    "gobike = pd.read_csv('201902-fordgobike-tripdata.csv')\n",
    "print('gobike shape:', gobike.shape, end='\\n\\n')\n",
    "gobike.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "269d13a2",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 183412 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   duration_sec             183412 non-null  int64  \n",
      " 1   start_time               183412 non-null  object \n",
      " 2   end_time                 183412 non-null  object \n",
      " 3   start_station_id         183215 non-null  float64\n",
      " 4   start_station_name       183215 non-null  object \n",
      " 5   start_station_latitude   183412 non-null  float64\n",
      " 6   start_station_longitude  183412 non-null  float64\n",
      " 7   end_station_id           183215 non-null  float64\n",
      " 8   end_station_name         183215 non-null  object \n",
      " 9   end_station_latitude     183412 non-null  float64\n",
      " 10  end_station_longitude    183412 non-null  float64\n",
      " 11  bike_id                  183412 non-null  int64  \n",
      " 12  user_type                183412 non-null  object \n",
      " 13  member_birth_year        175147 non-null  float64\n",
      " 14  member_gender            175147 non-null  object \n",
      " 15  bike_share_for_all_trip  183412 non-null  object \n",
      "dtypes: float64(7), int64(2), object(7)\n",
      "memory usage: 22.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Summary info of the DataFrame\n",
    "gobike.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "806dd2ea",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station_id      0.11%\n",
       "start_station_name    0.11%\n",
       "end_station_id        0.11%\n",
       "end_station_name      0.11%\n",
       "member_birth_year     4.51%\n",
       "member_gender         4.51%\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks the percentage of missing data in the data set\n",
    "missing_data = gobike.isnull().sum()* 100/gobike.shape[0]\n",
    "missing_data[missing_data > 0].map(lambda x: '{:.2f}%'.format(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79252609",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_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  start_station_latitude  \\\n",
       "count  183412.000000     183215.000000           183412.000000   \n",
       "mean      726.078435        138.590427               37.771223   \n",
       "std      1794.389780        111.778864                0.099581   \n",
       "min        61.000000          3.000000               37.317298   \n",
       "25%       325.000000         47.000000               37.770083   \n",
       "50%       514.000000        104.000000               37.780760   \n",
       "75%       796.000000        239.000000               37.797280   \n",
       "max     85444.000000        398.000000               37.880222   \n",
       "\n",
       "       start_station_longitude  end_station_id  end_station_latitude  \\\n",
       "count            183412.000000   183215.000000         183412.000000   \n",
       "mean               -122.352664      136.249123             37.771427   \n",
       "std                   0.117097      111.515131              0.099490   \n",
       "min                -122.453704        3.000000             37.317298   \n",
       "25%                -122.412408       44.000000             37.770407   \n",
       "50%                -122.398285      100.000000             37.781010   \n",
       "75%                -122.286533      235.000000             37.797320   \n",
       "max                -121.874119      398.000000             37.880222   \n",
       "\n",
       "       end_station_longitude        bike_id  member_birth_year  \n",
       "count          183412.000000  183412.000000      175147.000000  \n",
       "mean             -122.352250    4472.906375        1984.806437  \n",
       "std                 0.116673    1664.383394          10.116689  \n",
       "min              -122.453704      11.000000        1878.000000  \n",
       "25%              -122.411726    3777.000000        1980.000000  \n",
       "50%              -122.398279    4958.000000        1987.000000  \n",
       "75%              -122.288045    5502.000000        1992.000000  \n",
       "max              -121.874119    6645.000000        2001.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics for numeric variables\n",
    "gobike.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3bc2cb6",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id=\"faults\"></a>\n",
    "## Quality Issues Discovered\n",
    "\n",
    "These are some of the quality issues discovered when exploring the `gobike_clean` data set:\n",
    "1. `start_time` and `end_time` data type are formatted as an object.\n",
    "2. `start_station_id`, `start_station_name`, `end_station_id`, and `end_station_name` have 0.11 percent data missing.\n",
    "3. `gobike_clean` table: `member_birth_year` has 4.51 percent  data missing.\n",
    "4. `member_gender` has 4.51 percent  data missing.\n",
    "5. `gobike_clean` table: `member_birth_year` has either outliers or incorrect data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c01ac4c4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<a id=\"cleaning\"></a>\n",
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd989653",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <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                  end_time  \\\n",
       "0         52185  2019-02-28 17:32:10.1450  2019-03-01 08:01:55.9750   \n",
       "1         42521  2019-02-28 18:53:21.7890  2019-03-01 06:42:03.0560   \n",
       "2         61854  2019-02-28 12:13:13.2180  2019-03-01 05:24:08.1460   \n",
       "3         36490  2019-02-28 17:54:26.0100  2019-03-01 04:02:36.8420   \n",
       "4          1585  2019-02-28 23:54:18.5490  2019-03-01 00:20:44.0740   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1              23.0                     The Embarcadero at Steuart St   \n",
       "2              86.0                           Market St at Dolores St   \n",
       "3             375.0                           Grove St at Masonic Ave   \n",
       "4               7.0                               Frank H Ogawa Plaza   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "1               37.791464              -122.391034            81.0   \n",
       "2               37.769305              -122.426826             3.0   \n",
       "3               37.774836              -122.446546            70.0   \n",
       "4               37.804562              -122.271738           222.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "1                            Berry St at 4th St             37.775880   \n",
       "2  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "3                        Central Ave at Fell St             37.773311   \n",
       "4                         10th Ave at E 15th St             37.792714   \n",
       "\n",
       "   end_station_longitude  bike_id   user_type  member_birth_year  \\\n",
       "0            -122.402923     4902    Customer             1984.0   \n",
       "1            -122.393170     2535    Customer                NaN   \n",
       "2            -122.404904     5905    Customer             1972.0   \n",
       "3            -122.444293     6638  Subscriber             1989.0   \n",
       "4            -122.248780     4898  Subscriber             1974.0   \n",
       "\n",
       "  member_gender bike_share_for_all_trip  \n",
       "0          Male                      No  \n",
       "1           NaN                      No  \n",
       "2          Male                      No  \n",
       "3         Other                      No  \n",
       "4          Male                     Yes  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creates a copy of the original dataset\n",
    "gobike_clean = gobike.copy()\n",
    "gobike_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b2b20af",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### 1. `gobike_clean` table: `start_time` and `end_time` data type are formatted as an object\n",
    "\n",
    "Convert the `start_time` and `end_time` form an object data type to datatime data type using the pandas method `pd.to_datetime`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aaf207bb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                        int64\n",
       "start_time                 datetime64[ns]\n",
       "end_time                   datetime64[ns]\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"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['start_time'] = pd.to_datetime(gobike_clean['start_time'])\n",
    "gobike_clean['end_time'] = pd.to_datetime(gobike_clean['end_time'])\n",
    "gobike_clean.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607a08eb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "After converting the columns `start_time` and `end_time` to date time data type, I want to check if the values in column `duration_sec` are correct with the difference between the `end_time` and `start_time`.\n",
    "```\n",
    "delta_time = gobike_clean['end_time'] - gobike_clean['start_time']\n",
    "compare = (gobike_clean['duration_sec'] != delta_time.seconds)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "daf34c76",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def time_checker(dataframe):\n",
    "    \"\"\"\n",
    "    Check dataframe column `duration_sec` for correct values.\n",
    "    \n",
    "    This function simply checks if `duration_sec` is equal to the extract duration in seconds\n",
    "    between the `end_time` and `start_time`.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    dataframe : DataFrame\n",
    "        Pandas DataFrame having `end_time` and `start_time` as a date time data type.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    affrim : bool\n",
    "    \"\"\"\n",
    "    boolean_list = []\n",
    "    affrim = np.array(boolean_list).any()\n",
    "    for i, row in dataframe.iterrows():\n",
    "        delta_time = row['end_time'] - row['start_time']\n",
    "        check = row['duration_sec'] != delta_time.seconds\n",
    "        boolean_list.append(check)\n",
    "    return affrim\n",
    "time_checker(gobike_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8800c9",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Report\n",
    "Using the custom function `time_checker` the data in `duration_sec` is accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cfd618b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### 2. `gobike_clean` table: `start_station_id`, `start_station_name`, `end_station_id`, and `end_station_name` have 0.11 percent data missing.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "81db23f3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>1709</td>\n",
       "      <td>2019-02-28 20:55:53.932</td>\n",
       "      <td>2019-02-28 21:24:23.738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4211</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1733</th>\n",
       "      <td>1272</td>\n",
       "      <td>2019-02-28 18:32:34.273</td>\n",
       "      <td>2019-02-28 18:53:46.727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>4174</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3625</th>\n",
       "      <td>142</td>\n",
       "      <td>2019-02-28 17:10:46.529</td>\n",
       "      <td>2019-02-28 17:13:09.431</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.41</td>\n",
       "      <td>-121.96</td>\n",
       "      <td>4283</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4070</th>\n",
       "      <td>585</td>\n",
       "      <td>2019-02-28 16:28:45.934</td>\n",
       "      <td>2019-02-28 16:38:31.332</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>4089</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5654</th>\n",
       "      <td>509</td>\n",
       "      <td>2019-02-28 12:30:17.131</td>\n",
       "      <td>2019-02-28 12:38:46.329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.40</td>\n",
       "      <td>-121.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.39</td>\n",
       "      <td>-121.93</td>\n",
       "      <td>4089</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      duration_sec              start_time                end_time  \\\n",
       "475           1709 2019-02-28 20:55:53.932 2019-02-28 21:24:23.738   \n",
       "1733          1272 2019-02-28 18:32:34.273 2019-02-28 18:53:46.727   \n",
       "3625           142 2019-02-28 17:10:46.529 2019-02-28 17:13:09.431   \n",
       "4070           585 2019-02-28 16:28:45.934 2019-02-28 16:38:31.332   \n",
       "5654           509 2019-02-28 12:30:17.131 2019-02-28 12:38:46.329   \n",
       "\n",
       "      start_station_id start_station_name  start_station_latitude  \\\n",
       "475                NaN                NaN                   37.40   \n",
       "1733               NaN                NaN                   37.40   \n",
       "3625               NaN                NaN                   37.41   \n",
       "4070               NaN                NaN                   37.39   \n",
       "5654               NaN                NaN                   37.40   \n",
       "\n",
       "      start_station_longitude  end_station_id end_station_name  \\\n",
       "475                   -121.94             NaN              NaN   \n",
       "1733                  -121.94             NaN              NaN   \n",
       "3625                  -121.95             NaN              NaN   \n",
       "4070                  -121.93             NaN              NaN   \n",
       "5654                  -121.92             NaN              NaN   \n",
       "\n",
       "      end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "475                  37.40                -121.93     4211    Customer   \n",
       "1733                 37.41                -121.96     4174  Subscriber   \n",
       "3625                 37.41                -121.96     4283  Subscriber   \n",
       "4070                 37.40                -121.92     4089  Subscriber   \n",
       "5654                 37.39                -121.93     4089  Subscriber   \n",
       "\n",
       "      member_birth_year member_gender bike_share_for_all_trip  \n",
       "475              1991.0        Female                      No  \n",
       "1733             1980.0          Male                      No  \n",
       "3625             1988.0          Male                      No  \n",
       "4070             1984.0          Male                     Yes  \n",
       "5654             1984.0          Male                     Yes  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_missing = gobike_clean[gobike_clean['start_station_id'].isnull()]\n",
    "station_missing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d06a0737",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start_station_id      197\n",
       "start_station_name    197\n",
       "end_station_id        197\n",
       "end_station_name      197\n",
       "member_birth_year       2\n",
       "member_gender           2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_missing.isnull().sum()[station_missing.isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e596f2d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean[gobike_clean['start_station_id'].isnull()].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "378e9992",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "gobike_clean.dropna(subset=['start_station_id'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfb5fffc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                  0\n",
       "start_time                    0\n",
       "end_time                      0\n",
       "start_station_id              0\n",
       "start_station_name            0\n",
       "start_station_latitude        0\n",
       "start_station_longitude       0\n",
       "end_station_id                0\n",
       "end_station_name              0\n",
       "end_station_latitude          0\n",
       "end_station_longitude         0\n",
       "bike_id                       0\n",
       "user_type                     0\n",
       "member_birth_year          8263\n",
       "member_gender              8263\n",
       "bike_share_for_all_trip       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71fa89fc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### 3. `gobike_clean` table: `member_birth_year` has 4.51 percent  data missing\n",
    "\n",
    "From the summary statistics of the DataFrame `member_birth_year` has **mean** and **median** of **1984** and **1987**, i'll be using the median as replacement for the missing data since the data is slightly skew to the left."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a443f13",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean      1984.803135\n",
       "median    1987.000000\n",
       "Name: member_birth_year, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['member_birth_year'].agg(['mean', 'median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9ccb9374",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8263"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['member_birth_year'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "13e2c0b5",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Replace missing values with the median `member_birth_year`\n",
    "gobike_clean['member_birth_year'].fillna(gobike_clean['member_birth_year'].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "898179c3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8107e65b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['member_birth_year'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8b224d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### 4. `gobike_clean` table: `member_gender` has 4.51 percent  data missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "82566b39",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      130500\n",
       "Female     40805\n",
       "NaN         8263\n",
       "Other       3647\n",
       "Name: member_gender, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['member_gender'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd6d814",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "The decision to drop or replace the data is quite challenging since:\n",
    "1. Missing values can be a proption of either male or female users.\n",
    "2. The Unique value `Other` might represent users that might not identify as either Male or Female, or might have made the decision not to answer the question.\n",
    "I made the decision to include the missing data as `Other`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07e58de7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Replace missing values with `member_gender` Other.\n",
    "gobike_clean['member_gender'].fillna('Other', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1b6725",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c80c38b0",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Other', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['member_gender'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839b8c11",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### 5. `gobike_clean` table: `member_birth_year` has either outliers or incorrect data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ebcc4b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "`member_birth_year` clearly has outliers or incorrect data since the minimum birth year is about 141 years old during 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "845c0fcc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min    1878.0\n",
       "max    2001.0\n",
       "Name: member_birth_year, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['member_birth_year'].agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64f41a7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "I'll take care of the outliers when performing feature engineering on the `member_birth_year` column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6164806",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id=\"eda\"></a>\n",
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87a05705",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Visualizaton of Quantative columns in the `gobike_clean` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1748a189",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJIybJPkn+Lcl1Sa5J8spWvmuSC5Pc0P7uMlDnxCTrk1yf5JkD5U9OcnWb984kaeXbJjmrlV+aZM2iBypJWnQmgJIkjZ7NwPFV9VPAocDLkxwAnABcVFX7Axe157R5RwEHAocD70myVVvXe4HjgP3b4/BWfixwV1XtB7wdePNiBCZJWlomgJIkjZiqurWqvtim7wGuA/YGjgROb4udDjyvTR8JnFlV91XVjcB64JAkewI7VtXFVVXAByfUGV/XOcBh42cHJUkr19YzLZDk/cBzgNur6qBWtitwFrAG2AD8RlXd1eadSHdU8QHgFVX1qVb+ZOA0YHvgfOCVVVVJtqXbIT0Z+A7wwqra0OocDbyuNeWvqmp8RyVJUi+0rplPAi4FVlXVrdAliUn2aIvtDVwyUG1jK7u/TU8sH69zc1vX5iTfBXYD7pikDcfRnUVk1apVjI2NzUdoP7Jp06Z5X+coMK7lxbiWj2FjOv7gzfPXmEnMtW2L9V7NmADSJW3vokvSxo13QTk5yQnt+WsmdEHZC/hMksdV1QP8uAvKJXQJ4OHABQx0QUlyFF0XlBe2JPMvgLVAAVckOXc80ZQkaaVL8kjgY8Crqup705ygm2xGTVM+XZ2HFladApwCsHbt2lq3bt00rd5yY2NjzPc6R4FxLS/GtXwMG9MxJ5w3f42ZxIYXrZtTvcV6r2ZMAKvqc5MMDD8SWNemTwfGgNcw0AUFuDHJeBeUDbQuKABJxrugXNDqvL6t6xzgXa0LyjOBC6vqzlbnQrqk8aNbHqYkSctLkm3okr8PV9U/teLbkuzZzv7tCdzeyjcC+wxUXw3c0spXT1I+WGdjkq2BnYA7FyQYSVokaxY4uVsJZnMGcDKL0QXlR+WT1HmQ+e6asmr76U8Nr7TT6PNhJXYvWAxuty3nNpsbt9vy0g6EngpcV1VvG5h1LnA0cHL7+4mB8o8keRtdD5z9gcuq6oEk9yQ5lK4L6UuBv5uwrouB5wOfbeMEJUkr2FwTwKnMZxeUJeua8ncf/gRvvXrqTTPX07or2UrsXrAY3G5bzm02N263ZecpwEuAq5Nc2cpeS5f4nZ3kWOAm4AUAVXVNkrOBa+muIPryNvwC4GX8eAz+Be0BXYJ5RuutcyfdEA5J0go31wRwMbqgbOTH3UzH64zNsb2SJC0bVfV5Jj8QCnDYFHVOAk6apPxy4KBJyr9PSyAlSf0x19tAjHcbgYd2QTmq3Vx2X37cBeVW4J4kh7ZuLS+dUGd8XYNdUD4FPCPJLu1Gt89oZZIkSZKkOZjNbSA+SncmbvckG+muzLngXVCq6s4kfwl8oS33xvELwkiSJEmSttxsrgL6m1PMWvAuKFX1fuD9M7VRkiRJkjSzuXYBlSRJkiQtMyaAkiRJktQTJoCSJEmS1BMmgJIkSZLUEyaAkiRJktQTJoCSJEmS1BMmgJIkSZLUEyaAkiRJktQTJoCSJEmS1BNbL3UDJEmSJGmlWHPCeTMus+HkIxahJZPzDKAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQXgZEkSZK0LKw54TyOP3gzx8ziQiuanGcAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJGkFJ3p/k9iRfGSjbNcmFSW5of3cZmHdikvVJrk/yzIHyJye5us17Z5K08m2TnNXKL02yZlEDlCQtCRNASZJG02nA4RPKTgAuqqr9gYvac5IcABwFHNjqvCfJVq3Oe4HjgP3bY3ydxwJ3VdV+wNuBNy9YJJKkkTFUAphkQzuqeGWSy1uZRyclSRpSVX0OuHNC8ZHA6W36dOB5A+VnVtV9VXUjsB44JMmewI5VdXFVFfDBCXXG13UOcNj4/leStHJtPQ/r+OWqumPg+fjRyZOTnNCev2bC0cm9gM8keVxVPcCPj05eApxPd3TyAgaOTiY5iu7o5Avnoc2SJC1Hq6rqVoCqujXJHq18b7p96LiNrez+Nj2xfLzOzW1dm5N8F9gNGNynA5DkOLr9NKtWrWJsbGy+4gFg06ZN877OUWBcy4txLQ/HH7yZVdt3f5ezyd6TxXqv5iMBnOhIYF2bPh0YA17DwNFJ4MYk40cnN9COTgIkGT86eUGr8/q2rnOAdyVJO4opSZI6k525q2nKp6vz0MKqU4BTANauXVvr1q2bQxOnNjY2xnyvcxQY1/JiXMvDMSecx/EHb+atVy9EGrN4Nrxo3UPKFuu9GnbLFfDpJAX8Q9tBLPrRyfk+MjnTUYWVdBRlvqy0o0uLxe225dxmc+N2WzFuS7Jn27/uCdzeyjcC+wwstxq4pZWvnqR8sM7GJFsDO/HQLqeSpBVm2ATwKVV1S0vyLkzy1WmWXbCjk/N9ZPLvPvyJaY8qTJax991KO7q0WNxuW85tNjdutxXjXOBo4OT29xMD5R9J8ja6YRb7A5dV1QNJ7klyKHAp8FLg7yas62Lg+cBn7WEjaamtOeG8pW7CijdUAlhVt7S/tyf5OHAIHp2UJGloST5KN6Ri9yQbgb+gS/zOTnIscBPwAoCquibJ2cC1wGbg5W2MPcDL6K4ouj3d8IoLWvmpwBltSMaddOP0JUkr3JwTwCQ7AA+rqnva9DOAN+LRSUmShlZVvznFrMOmWP4k4KRJyi8HDpqk/Pu0BFKS1B/DnAFcBXy8XTF6a+AjVfWvSb6ARyclSZIkaeTMOQGsqq8DT5ik/Dt4dFKSJEmSRs5QN4KXJEmSJC0fJoCSJEmS1BMmgJIkSZLUE8PeB1CSJEmSZuQ9/n5ssm1x/MGbOaaVbzj5iAV7bc8ASpIkSVJPmABKkiRJUk+YAEqSJElST5gASpIkSVJPmABKkiRJUk+YAEqSJElST3gbCEmSJElD8zYPy4NnACVJkiSpJ0wAJUmSJKknTAAlSZIkqSccAyhJkiRpRo7xWxk8AyhJkiRJPWECKEmSJEk9YQIoSZIkST3hGEBJkiRJjvHrCc8ASpIkSVJPeAZQkiRJWuE8u6dxngGUJEmSpJ4wAZQkSZKknrALqCRJkrTMTdXF8/iDN3OM3T81wDOAkiRJktQTJoCSJEmS1BMmgJIkSZLUEyaAkiRJktQTJoCSJEmS1BNeBVSSJEkacd7IXfPFM4CSJEmS1BPL4gxgksOBvwW2At5XVScvcZMkSVoR3MdKo8EzfFosI58AJtkKeDfwdGAj8IUk51bVtUvVptl8QTecfMQitESSpLkbxX2stBKZ3GmUjHwCCBwCrK+qrwMkORM4EnDnJEnScNzHSvPABE/LyXJIAPcGbh54vhH42cEFkhwHHNeebkpy/ZCvuTtwxzAryJuHbMHyM/Q26ym325Zzm83NKG63xy51AzTzPhYWZD870Sh+PueDcS0vKzKuV6zAuFZiTPDguOYhl5hyH7scEsBMUlYPelJ1CnDKvL1gcnlVrZ2v9fWB22xu3G5bzm02N243TWHGfSzM/372IY1YoZ9P41pejGv5WIkxweLFtRyuAroR2Gfg+WrgliVqiyRJK4n7WEnqmeWQAH4B2D/JvkkeDhwFnLvEbZIkaSVwHytJPTPyXUCranOSPwQ+RXeJ6vdX1TUL/LIL1s1lBXObzY3bbcu5zebG7aaHWKJ97GRW6ufTuJYX41o+VmJMsEhxpeohXf0lSZIkSSvQcugCKkmSJEmaByaAkiRJktQTJoADkhye5Pok65OcsNTtWWxJ9knyb0muS3JNkle28l2TXJjkhvZ3l4E6J7btdX2SZw6UPznJ1W3eO5OklW+b5KxWfmmSNYse6AJIslWSLyX5ZHvuNptBkp2TnJPkq+0z93Nut+kl+aP23fxKko8m2c5tplGV5P1Jbk/ylYGyJya5JMmVSS5Pckgr3ybJ6e1zeV2SEwfqTPp5XSpTxPWEJBe3dv5Lkh0H5m3R93ApbElMSZ6e5IpWfkWSpw7UGZmYWnu26L1q8x+TZFOSPxkoW9ZxJfnvbd41bf52rXzZxrVc/mdkEX5bz0lV+ejGQW4FfA34SeDhwFXAAUvdrkXeBnsCP92mHwX8H+AA4C3ACa38BODNbfqAtp22BfZt22+rNu8y4Ofo7jF1AfCsVv7/An/fpo8CzlrquOdp2/0x8BHgk+2522zmbXY68Ltt+uHAzm63abfX3sCNwPbt+dnAMW4zH6P6AH4J+GngKwNlnx74vD0bGGvTvwWc2aYfAWwA1rTnk35eRyyuLwD/o03/DvCXbXqLv4fLIKYnAXu16YOAbw7UGZmYtjSugfkfA/4R+JOVEBfdBR+/DDyhPd9tFD+Dc4hrWfzPYBF+W8/l4RnAHzsEWF9VX6+qHwBnAkcucZsWVVXdWlVfbNP3ANfR/eg8ku7HOu3v89r0kXRfvvuq6kZgPXBIkj2BHavq4uo+sR+cUGd8XecAhy31EadhJVkNHAG8b6DYbTaNdgTvl4BTAarqB1V1N263mWwNbJ9ka7od3i24zTSiqupzwJ0Ti4HxMxM78eN7DhawQ/tsbw/8APjeDJ/XJTFFXI8HPtemLwR+vU3P5Xu46LYkpqr6UlWNv2/XANul6z0wUjHBFr9XJHke8HW6uMbLlntczwC+XFVXtbrfqaoHVkBcy+J/xiL9tt5iJoA/tjdw88Dzja2sl9J1/XoScCmwqqpuhe6DDOzRFptqm+3dpieWP6hOVW0Gvkt3NGo5ewfwauCHA2Vus+n9JPBt4APpus6+L8kOuN2mVFXfBP4GuAm4FfhuVX0at5mWl1cB/zPJzXSf5/FuW+cA99J9tm8C/qaq7mT6z+so+Qrw3Db9AmCfNj2X7+GomCqmQb8OfKmq7mN5xARTxNX2Qa8B3jBh+WUdF/A4oJJ8KskXk7y6lS/3uJbd/4wF/G29xUwAf2yyo9y9vEdGkkfSdYF4VVV9b7pFJymracqnq7MsJXkOcHtVXTHbKpOU9WqbNVvTdfN4b1U9ie6f+HTjbnu/3dr4gCPpuoTsRXfk88XTVZmkrFfbTCPpZcAfVdU+wB/RegHQ9cJ5gO6zvS9wfJKfZPl8Jn8HeHmSK+i6ef2glc/lezgqpooJgCQHAm8Gfn+8aJJ1jFpMMHVcbwDeXlWbJiy/3OPaGvgF4EXt768mOYzlH9ey+p+xwL+tt9jI3wh+EW3kwUe3VvPjrim9kWQbug/oh6vqn1rxbUn2rKpb2yno21v5VNtsY5ueWD5YZ2M7bb8TDz3dv5w8BXhukmcD2wE7JvkQbrOZbAQ2VtWl7fk5dAmg221qTwNurKpvAyT5J+DncZtpeTkaeGWb/kd+3HX+t4B/rar7gduT/AewFvh3pv68joyq+ipdVzuSPI5uWADM7Xs4EqaJaXzow8eBl1bV11rxyMcE08b1s8Dzk7yFbkz6D5N8n+430XKOayPwv6vqjjbvfLoDsB9iece1bP5nLMJv6y3mGcAf+wKwf5J9kzyc7gII5y5xmxZVG+tzKnBdVb1tYNa5dDtt2t9PDJQf1fr+7wvsD1zWTmXfk+TQts6XTqgzvq7nA59tfZmXpao6sapWV9Uaus/MZ6vqxbjNplVV3wJuTvL4VnQYcC1ut+ncBBya5BEt1sPoxhK4zbSc3AL8jzb9VOCGNn0T8NR0dgAOBb46w+d1ZCTZo/19GPA64O/brLl8D0fCVDEl2Rk4Dzixqv5jfPnlEBNMHVdV/WJVrWn783cAb6qqdy33uIBPAf+97Tu2pvv+XbsC4loW/zMW6bf1lqslvNrPqD3orkj2f+iuuPNnS92eJYj/F+hOJ38ZuLI9nk03Bugiuh31RcCuA3X+rG2v6xm4GhHdUZivtHnvAtLKt6M76rue7mpGP7nUcc/j9lvHj68C6jabeXs9Ebi8fd7+GdjF7TbjNnsD8NUW7xl0Vwlzm/kYyQfwUbrxOffTHb0+tu1nrqC7yt2lwJPbso9sn71r6A4G/enAeib9vI5YXK+k+/3wf4CTB9u4pd/DUY+J7kf4vfz4d8KVwB6jFtNc3quBeq/nwVcBXdZxAS9u362vAG9ZCXEtl/8ZLMJv67k8xjeiJEmSJGmFswuoJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZJWlCSPSbIpyVYL+BobkjxtodY/8DqbkvzkPK6vkuw3X+vT8mMCKI2QUd9hJfnFJNfPY1vWJdk4X+uTJM1dktcn+dCQ6xj6/3qSNS1J2XoL6jxo31ZVN1XVI6vqgWHaMgpaHF8HSHJakr9a6jZpeTMB1LLnDmvhTDxKWFX/XlWPH5i/KEc/JUmjb0v2f5KWjgmges8dliSpb5K8Jsk3k9yT5PokRwCvBV7YeqJc1Zb77STXteW+nuT3B9axLsnGtq5vAR8FLgD2auvYlGSvadpwSJLLk3wvyW1J3tZmfa79vbut4+eS/Lckn03ynSR3JPlwkp3bes4AHgP8S1v+1RMPyibZK8m5Se5Msj7J7w204/VJzk7ywRbnNUnWbuH23DbJO5Lc0h7vSLLthO10fJLbk9ya5LcH6u6W5F/advhCkr9K8vmB+ZVkvyTHAS8CXt3i/JfB+QPLP+gsYZI/ba95S5LfmaTdf5PkpvYe/H2S7bckdi0/JoBaVtxhzfsO65AkFye5u+0c3pXk4W3eeDxXtfa9MANnSqdo/0POpGbgLGGS7duO6a4k1wI/M2HZvZJ8LMm3k9yY5BVbEo8kaWZJHg/8IfAzVfUo4JnAV4E3AWe1nihPaIvfDjwH2BH4beDtSX56YHU/AewKPBZ4KfAs4Ja2jkdW1S3TNOVvgb+tqh2B/wac3cp/qf3dua3jYiDAXwN7AT8F7AO8HqCqXgLcBPxKW/4tk7zWR4GNrf7zgTclOWxg/nOBM4GdgXOBd03T7sn8GXAo8ETgCcAhwOsG5v8EsBOwN3As8O4ku7R57wbubcsc3R4PUVWnAB8G3tLi/JWZGpXkcOBPgKcD+wMTe+28GXhca/d+rX1/PtN6tbyZAGrZcIe1IDusB4A/AnYHfg44DPh/W/vG43lCa99ZgxVn2f6J/oJum/03uvfvRzu5JA8D/gW4im4HdBjwqiTP3MKYJEnTewDYFjggyTZVtaGqvjbZglV1XlV9rTr/G/g08IsDi/wQ+Iuquq+q/u8WtuN+YL8ku1fVpqq6ZKoFq2p9VV3YXufbwNuA/zGbF0myD/ALwGuq6vtVdSXwPuAlA4t9vqrOb0MwzqBL4rbEi4A3VtXtrX1vmLD++9v8+6vqfGAT8Ph0Y/5/nW4b/ldVXQucvoWvPZ3fAD5QVV+pqntpv0EAkgT4PeCPqurOqrqH7jfVUfP4+hpBJoBaTtxhzfMOq6quqKpLqmpzVW0A/mG27Zuj3wBOajuam4F3Dsz7GeDRVfXGqvpBG/D+v3BHJEnzqqrWA6+iSwZuT3LmVD1fkjwrySWtJ8rdwLPpDhqO+3ZVfX+OTTmW7uzTV1vXx+dMtWCSPVo7v5nke8CHJrRjOnsB4wnOuG/QHWwc962B6f8CtsuWDRHZq61zcP2D2/Q7VbV5wms8Eng0sDVw88C8welh7TVhfYNtfDTwCOCK1hPobuBfW7lWMBNALRvusIB53mEleVySTyb5Vmvfm7agfXMx3Y7osXTdcO8e2BG9Fli1gO2RpF6qqo9U1S/Q/e8tuq6ANbhMujFsHwP+BlhVVTsD59P1bvnRqiauegvacENV/SawR3v9c5LsMMU6/rqV//fWA+fFM7Rj0C3ArkkeNVD2GOCbs23rLNxCty0H1z9db6Jx3wY2A6sHyvaZZvnJ4vwvukRu3E8MTN86YX2PGZi+A/i/wIFVtXN77FRVj5xFu7WMmQBqWXGHNe87rPfSdaPdv7XvtRPaN5OJ7b+XgZ1Q69oyeCRxuh3RzcCNAzuhnavqUVX17C1ojyRpBkken+SpbX/5fbok4AHgNmBN65IP8HC6njffBjYneRbwjBlWfxuwW5KdZtGOFyd5dFX9ELi7FT/QXu+HwOC97x5F123y7iR7A386yetOeq+81uPkP4G/TrJdkv9OdzD3wzO1cQt8FHhdkkcn2Z1uHN2MVyhvPXj+CXh9kkck+X/ohqZMZbI4rwR+K8lWbczfYE+es4FjkhyQ5BF0QzHGX/uHdD1t3p5kD4Akezv0YuUzAdSy4Q5rQXZYjwK+B2xqO52XzbZ9U8z/P3RnIY9Isg3dAPhtB+afDZyYZJckq4H/38C8y4Dvpbs4z/ZtR3ZQkgddKEaSNLRtgZPpzgB9i+6A5muBf2zzv5Pki60Hyivo/nffBfwW3XjzKVXVV+mSoa+33hxTXlQNOBy4JskmuvH1R7UhD/8FnAT8R1vHoXRj6n4a+C5wHl3SNOiv6RKwu5P8ySSv9ZvAGrqDqx+nGwZy4XSxbKG/Ai4HvgxcDXyxlc3GH9JdIOZbdMM5PgrcN8Wyp9INhbk7yT+3slcCv0L3m+RFwHg5VXUB8A7gs8D69nfQa1r5Ja0n0GeAx6MVLVWzPvEhLamWAL2P7mIq99MlR8fR/ZP8BHAg3Rmkn07ycrqjb9vSXVhkG2B9Vb0uyTrgQ1W1esL63w8cCWwFHDDVhWDS3XPwGXRnur4B/FlV/XOb90a6JGobuh3bPcAH6f6Zrqf7x/5H46+d5Ejg7+guVvNXwDnAjcA2VbW5JUl/D/w83c73f1bV37e6rwf2q6oXt+drButOsx03AL9bVZ9J8kvAKXRdT74E/Bvw1HaWlSR/QHe0cPu2rW8f3HYT219Vf5PkGLod8VbAW+h2bOOv94gWz3PpdsIfAF45sL69gLcCv0z33l0PvK6qPjNVPJIkrSRJ3gz8RFVNejVQaVgmgJIkSdISaT1wHk535vBn6Iat/O74wWVpvtkFVJIkSQsiyQX58T12Bx+vXeq2zSTJY6Zo+6Ykj5l5DbP2KLourffSdbd9K13PJmlBeAZQmkSSC3jwbSPGvamq3rTY7dkSbad07RSzD6iqmxazPZIkSRodJoCSJEmS1BN2AZUkSZKknpj1DaOXi913373WrFkz6bx7772XHXbYYXEbtET6Emtf4gRjXan6EutkcV5xxRV3VNWjp6iiETXdfnbU9eH7ZowrgzGuDEsZ43T72BWXAK5Zs4bLL7980nljY2OsW7ducRu0RPoSa1/iBGNdqfoS62RxJvnG0rRGw5huPzvq+vB9M8aVwRhXhqWMcbp9rF1AJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdW3FVAJWmhrTnhvGnnbzj5iEVqiaTJTPUdPf7gzRxzwnl+RyX1mmcAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSRpRSbZK8qUkn2zPd01yYZIb2t9dBpY9Mcn6JNcneeZA+ZOTXN3mvTNJWvm2Sc5q5ZcmWbPoAUqSFp0JoCRJo+uVwHUDz08ALqqq/YGL2nOSHAAcBRwIHA68J8lWrc57geOA/dvj8FZ+LHBXVe0HvB1488KGIkkaBSaAkiSNoCSrgSOA9w0UHwmc3qZPB543UH5mVd1XVTcC64FDkuwJ7FhVF1dVAR+cUGd8XecAh42fHZQkrVxbL3UDJEnSpN4BvBp41EDZqqq6FaCqbk2yRyvfG7hkYLmNrez+Nj2xfLzOzW1dm5N8F9gNuGNiQ5IcR3cWkVWrVjE2NjZMXAvu+IM3T1q+avtu3qi3fxibNm1a0fGBMa4Uxrh0TAAlSRoxSZ4D3F5VVyRZN5sqk5TVNOXT1XloYdUpwCkAa9eurXXrZtOkpXPMCedNWn78wZt569Vbs+FF6xa3QYtobGyMUX9/hmWMK4MxLp2huoAm2dAGll+Z5PJW5gB1SZKG8xTguUk2AGcCT03yIeC21q2T9vf2tvxGYJ+B+quBW1r56knKH1QnydbATsCdCxGMJGl0zMcYwF+uqidW1dr23AHqkiQNoapOrKrVVbWGbt/52ap6MXAucHRb7GjgE236XOCoduB0X7p96WWtu+g9SQ5tB1dfOqHO+Lqe315j0jOAkqSVYyEuAuMAdUmSFsbJwNOT3AA8vT2nqq4BzgauBf4VeHlVPdDqvIzuQjLrga8BF7TyU4HdkqwH/ph2wFaStLINOwawgE8nKeAf2hiBRR+gPtvB6aM6EHMh9CXWvsQJxjpKprrAxLgtafuoxzpf+hLnQqiqMWCsTX8HOGyK5U4CTpqk/HLgoEnKvw+8YB6bKklaBoZNAJ9SVbe0JO/CJF+dZtkFG6A+28HpozoQcyH0Jda+xAnGOkqmusDEuC25wMSoxzpf+hKnJEmjbqguoFV1S/t7O/Bx4BAcoC5JkiRJI2nOCWCSHZI8anwaeAbwFRygLkmSJEkjaZguoKuAj7drsmwNfKSq/jXJF4CzkxwL3EQbX1BV1yQZH6C+mYcOUD8N2J5ucPrgAPUz2gD1O+muhCZJkiRJmoM5J4BV9XXgCZOUO0BdkiSNrDUzjeM9+YhFaokkLb6FuA2EJEmSJGkEmQBKkiRJUk+YAEqSJElST5gASpIkSVJPDHsjeEmSpEU100VcJElT8wygJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1xNAJYJKtknwpySfb812TXJjkhvZ3l4FlT0yyPsn1SZ45UP7kJFe3ee9Mkla+bZKzWvmlSdYM215JkiRJ6qv5OAP4SuC6gecnABdV1f7ARe05SQ4AjgIOBA4H3pNkq1bnvcBxwP7tcXgrPxa4q6r2A94OvHke2itJkiRJvTRUAphkNXAE8L6B4iOB09v06cDzBsrPrKr7qupGYD1wSJI9gR2r6uKqKuCDE+qMr+sc4LDxs4OSJK1USbZLclmSq5Jck+QNrdxeNpKkoWw9ZP13AK8GHjVQtqqqbgWoqluT7NHK9wYuGVhuYyu7v01PLB+vc3Nb1+Yk3wV2A+4YbESS4+jOILJq1SrGxsYmbeymTZumnLfS9CXWvsQJxjpKjj9487Tzt6Ttox7rfOlLnPPoPuCpVbUpyTbA55NcAPwaXS+bk5OcQNfL5jUTetnsBXwmyeOq6gF+3MvmEuB8ul42FzDQyybJUXS9bF64uGFKkhbbnBPAJM8Bbq+qK5Ksm02VScpqmvLp6jy4oOoU4BSAtWvX1rp1kzdnbGyMqeatNH2JtS9xgrGOkmNOOG/6Ba6+d9rZG04+4kfTox7rfOlLnPOl9YjZ1J5u0x5F1zNmXSs/HRgDXsNALxvgxiTjvWw20HrZACQZ72VzQavz+rauc4B3JUl7bUnSCjXMGcCnAM9N8mxgO2DHJB8CbkuyZzv7tydwe1t+I7DPQP3VwC2tfPUk5YN1NibZGtgJuHOINkuStCy0cfJXAPsB766qS5Msei+b1pZZ9bRZLDOdhZ/Kqu1nV3ep4xtGH862G+PKYIxLZ84JYFWdCJwI0M4A/klVvTjJ/wSOBk5ufz/RqpwLfCTJ2+i6p+wPXFZVDyS5J8mhwKXAS4G/G6hzNHAx8Hzgsx6ZlCT1Qeu++cQkOwMfT3LQNIsvWC+b1pZZ9bRZLDOehZ/C8Qdv5q1Xz/zTZ8OL1s1p/aOgD2fbjXFlMMalM+wYwMmcDJyd5FjgJuAFAFV1TZKzgWuBzcDL284N4GXAacD2dN1SLmjlpwJntK4sd9KNb5AkqTeq6u4kY3Rj9+xlI0kayrwkgFU1RjcOgar6DnDYFMudBJw0SfnlwEOObFbV92kJpCRJfZHk0cD9LfnbHnga3UVaxnvG2MtGkjQnC3EGUJIkDWdP4PQ2DvBhwNlV9ckkF2MvG0nSEEwAJUkaMVX1ZeBJk5Tby0aSNJShbgQvSZIkSVo+TAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSfmnAAm2S7JZUmuSnJNkje08l2TXJjkhvZ3l4E6JyZZn+T6JM8cKH9ykqvbvHcmSSvfNslZrfzSJGuGiFWSJEmSem2YM4D3AU+tqicATwQOT3IocAJwUVXtD1zUnpPkAOAo4EDgcOA9SbZq63ovcBywf3sc3sqPBe6qqv2AtwNvHqK9kiRJktRrc04Aq7OpPd2mPQo4Eji9lZ8OPK9NHwmcWVX3VdWNwHrgkCR7AjtW1cVVVcAHJ9QZX9c5wGHjZwclSZIkSVtm62EqtzN4VwD7Ae+uqkuTrKqqWwGq6tYke7TF9wYuGai+sZXd36Ynlo/Xubmta3OS7wK7AXdMaMdxdGcQWbVqFWNjY5O2d9OmTVPOW2n6Emtf4gRjHSXHH7x5qPqDsY16rPOlL3FKkjTqhkoAq+oB4IlJdgY+nuSgaRaf7MxdTVM+XZ2J7TgFOAVg7dq1tW7dukkbMDY2xlTzVpq+xNqXOMFYR8kxJ5w3VP0NL1r3o+lRj3W+9CVOSZJG3bxcBbSq7gbG6Mbu3da6ddL+3t4W2wjsM1BtNXBLK189SfmD6iTZGtgJuHM+2ixJkiRJfTPMVUAf3c78kWR74GnAV4FzgaPbYkcDn2jT5wJHtSt77kt3sZfLWnfRe5Ic2sb3vXRCnfF1PR/4bBsnKEmSJEnaQsN0Ad0TOL2NA3wYcHZVfTLJxcDZSY4FbgJeAFBV1yQ5G7gW2Ay8vHUhBXgZcBqwPXBBewCcCpyRZD3dmb+jhmivJEmSJPXanBPAqvoy8KRJyr8DHDZFnZOAkyYpvxx4yPjBqvo+LYGUJEmSJA1nXsYASpIkSZJGnwmgJEkjJsk+Sf4tyXVJrknyyla+a5ILk9zQ/u4yUOfEJOuTXJ/kmQPlT05ydZv3zvH76bYx+We18kuTrFn0QCVJi84EUJKk0bMZOL6qfgo4FHh5kgOAE4CLqmp/4KL2nDbvKOBAuityv6eN0Qd4L929cvdvj8Nb+bHAXVW1H/B24M2LEZgkaWmZAEqSNGKq6taq+mKbvge4DtgbOBI4vS12OvC8Nn0kcGZV3VdVNwLrgUPa7Zh2rKqL21W0Pzihzvi6zgEOGz87KElauYa6EbwkSVpYrWvmk4BLgVXt9klU1a1J9miL7Q1cMlBtYyu7v01PLB+vc3Nb1+Yk3wV2A+6YpA3H0Z1FZNWqVYyNjc1HaHN2/MGb51Rv1fazq7vU8Q1j06ZNy7r9s2GMK4MxLh0TQEkasOaE85a6CdKPJHkk8DHgVVX1vWlO0E02o6Ypn67OQwurTgFOAVi7dm2tW7dumlYvvGPm+D09/uDNvPXqmX/6bHjRujmtfxSMjY2x1O/PQjPGlcEYl45dQCVJGkFJtqFL/j5cVf/Uim9r3Tppf29v5RuBfQaqrwZuaeWrJyl/UJ0kWwM70d1zV5K0gpkASpI0YtpYvFOB66rqbQOzzgWObtNHA58YKD+qXdlzX7qLvVzWuovek+TQts6XTqgzvq7nA59t4wQlSSuYXUAlSRo9TwFeAlyd5MpW9lrgZODsJMcCNwEvAKiqa5KcDVxLdwXRl1fVA63ey4DTgO2BC9oDugTzjCTr6c78HbXAMUmSRoAJoCRJI6aqPs/kY/QADpuizknASZOUXw4cNEn592kJpDSVmcZFbzj5iEVqiaT5YhdQSZIkSeoJE0BJkiRJ6gkTQEmSJEnqCRNASZIkSeoJE0BJkiRJ6gkTQEmSJEnqCRNASZIkSeqJOSeASfZJ8m9JrktyTZJXtvJdk1yY5Ib2d5eBOicmWZ/k+iTPHCh/cpKr27x3Jkkr3zbJWa380iRrhohVkiRJknptmBvBbwaOr6ovJnkUcEWSC4FjgIuq6uQkJwAnAK9JcgBwFHAgsBfwmSSPq6oHgPcCxwGXAOcDhwMXAMcCd1XVfkmOAt4MvHCINkuSJImZb/IuaWWa8xnAqrq1qr7Ypu8BrgP2Bo4ETm+LnQ48r00fCZxZVfdV1Y3AeuCQJHsCO1bVxVVVwAcn1Blf1znAYeNnByVJkiRJW2aYM4A/0rpmPgm4FFhVVbdClyQm2aMttjfdGb5xG1vZ/W16Yvl4nZvbujYn+S6wG3DHhNc/ju4MIqtWrWJsbGzSdm7atGnKeStNX2LtS5xgrIvl+IM3L/hrDMbWl/e1L3FKkjTqhk4AkzwS+Bjwqqr63jQn6CabUdOUT1fnwQVVpwCnAKxdu7bWrVs3aQPGxsaYat5K05dY+xInGOtiOWYRukRteNG6H0335X3tS5want0SJWlhDXUV0CTb0CV/H66qf2rFt7VunbS/t7fyjcA+A9VXA7e08tWTlD+oTpKtgZ2AO4dpsyRJkiT11TBXAQ1wKnBdVb1tYNa5wNFt+mjgEwPlR7Ure+4L7A9c1rqL3pPk0LbOl06oM76u5wOfbeMEJUmSJElbaJguoE8BXgJcneTKVvZa4GTg7CTHAjcBLwCoqmuSnA1cS3cF0Ze3K4ACvAw4Ddie7uqfF7TyU4EzkqynO/N31BDtlSRJkqRem3MCWFWfZ/IxegCHTVHnJOCkScovBw6apPz7tARSkiRJkjScocYASpIkSZKWDxNASZIkSeoJE0BJkiRJ6gkTQEmSJEnqCRNASZIkSeqJYW4DIUmagzUnnPej6eMP3swxA88BNpx8xGI3SZIk9YRnACVJkiSpJ0wAJUmSJKknTAAlSRpBSd6f5PYkXxko2zXJhUluaH93GZh3YpL1Sa5P8syB8icnubrNe2eStPJtk5zVyi9NsmZRA5QkLQkTQEmSRtNpwOETyk4ALqqq/YGL2nOSHAAcBRzY6rwnyVatznuB44D922N8nccCd1XVfsDbgTcvWCSSpJFhAihJ0giqqs8Bd04oPhI4vU2fDjxvoPzMqrqvqm4E1gOHJNkT2LGqLq6qAj44oc74us4BDhs/OyhJWrm8CqgkScvHqqq6FaCqbk2yRyvfG7hkYLmNrez+Nj2xfLzOzW1dm5N8F9gNuGPiiyY5ju4sIqtWrWJsbGy+4nmI4w/evGDrXrX97Na/kPEttE2bNs26/fOxrZdiW21JjMuVMa4MoxqjCaAkScvfZGfuapry6eo8tLDqFOAUgLVr19a6devm0MTZmXhblPl0/MGbeevVM//02fCidQvWhoU2NjbGbN+f+djWS7GttiTG5coYV4ZRjdEuoJIkLR+3tW6dtL+3t/KNwD4Dy60Gbmnlqycpf1CdJFsDO/HQLqeSpBXGM4CSpEmtmeHsgDesXxLnAkcDJ7e/nxgo/0iStwF70V3s5bKqeiDJPUkOBS4FXgr83YR1XQw8H/hsGycoSVrBTAAlSRpBST4KrAN2T7IR+Au6xO/sJMcCNwEvAKiqa5KcDVwLbAZeXlUPtFW9jO6KotsDF7QHwKnAGUnW0535O2oRwpIkLbGhEsAk7weeA9xeVQe1sl2Bs4A1wAbgN6rqrjbvRLrLTj8AvKKqPtXKn8yPd07nA6+sqkqyLd0Vy54MfAd4YVVtGKbNkiQtB1X1m1PMOmyK5U8CTpqk/HLgoEnKv09LICVJ/THsGMDT8B5FkiRJkrQsDJUAeo8iSZIkSVo+FmIM4KLfo2i29yca1XtxLIS+xNqXOMFYF8tC3oNsMpPdl2xU3ueZtsWWtLNPn19JkkbZYl4EZsHuUTTb+xON6r04FkJfYu1LnGCsi2Uh70E2mcnuSzYq9yCbaVtsSTv79PmVJGmULcR9AL1HkSRJkiSNoIVIAMfvKwQPvUfRUUm2TbIvP75H0a3APUkObeP7Xjqhzvi6vEeRJEmSJA1h2NtAeI8iSZIkSVomhkoAvUeRJEmSJC0fC9EFVJIkSZI0gkwAJUmSJKknTAAlSZIkqScW8z6AkrTk1izyff5GldtBkqR+8gygJEmSJPWEZwDnYDZHzjecfMQitESSJEnScjNTPrGQuYQJoCRJ0gpkV29Jk7ELqCRJkiT1hGcAJWnE2M1ckiQtFBNASVqG7Nql5crPriQtLbuASpIkSVJPeAZQkiRJkubJeE+H4w/ezDEj2OvBM4CSJEmS1BMmgJIkSZLUEyaAkiRJktQTjgGchFcokyRJo26y3yuLPeZopt9M3rJGGj3LIgFMcjjwt8BWwPuq6uQlbpIkSSuC+1hJ2jLL/WTRyHcBTbIV8G7gWcABwG8mOWBpWyVJ0vLnPlaS+mfkE0DgEGB9VX29qn4AnAkcucRtkiRpJXAfK0k9sxy6gO4N3DzwfCPws0vUllmzT7y0NJZ7twxpkS3LfayWj/n4n+xvJml+LYcEMJOU1YMWSI4DjmtPNyW5fop17Q7cMY9tm7O8ecFfYmRiXWB9iROMdUV6xTKOdQv/j00W52PnrTGaqxn3sbBF+9mRNtvv2yLsoxfMcv6fMpVJ3o8VF+MkjHEFGOb7OA//h6bcxy6HBHAjsM/A89XALYMLVNUpwCkzrSjJ5VW1dn6bN5r6Emtf4gRjXan6Emtf4lyGZtzHwuz3s6OuD59DY1wZjHFlGNUYl8MYwC8A+yfZN8nDgaOAc5e4TZIkrQTuYyWpZ0b+DGBVbU7yh8Cn6C5R/f6qumaJmyVJ0rLnPlaS+mfkE0CAqjofOH8eVrXsu69sgb7E2pc4wVhXqr7E2pc4l5153McuB334HBrjymCMK8NIxpiqh4z1liRJkiStQMthDKAkSZIkaR4s+wQwyXZJLktyVZJrkryhlZ+V5Mr22JDkytnWHVXDxDqwjq2SfCnJJxet4XMwbKxt3tVtucsXtfFbaB5i3TnJOUm+muS6JD+3qAHM0pDf1ccPLHNlku8ledVixzBb8/Ce/lGr95UkH02y3aIGsAXmIdZXtjivGeX3VKMryQva5+eHSdYOlD89yRVtX3BFkqe28kckOa/9z7wmyckzrP8xSTYl+ZOFjmWaNixIjEkOGfieXpXkVxcrpknaslAxTlp/qSxgnLsl+bf2WX3XYsUzRVsW7DuZ5MQk65Ncn+SZixHPFO3YohjbvJOS3Jxk0zTrfXiSD7T6VyVZtyABVNWyftDdw+iRbXob4FLg0AnLvBX487nUHaXHMLEOzP9j4CPAJ5c6noWMFdgA7L7UcSxSrKcDv9umHw7svNQxLUScA8tsBXwLeOxSx7QQsdLdmPtGYPv2/GzgmKWOaYFiPQj4CvAIujHpnwH2X+qYfCyvB/BTwOOBMWDtQPmTgL3a9EHAN9v0I4BfbtMPB/4deNY06/8Y8I/An6y0GMe/e216T+D28ecrKMZJ66/A93IH4BeAPwDetUJjPAC4CtgW2Bf4GrDVcoixPT+0fc82TbPelwMfaNN7AFcAD5vv9i+Li8BMp7otNJ5Jb9MePxrYmCTAbwAPOeIzU91RM0ysbf5q4AjgJLpEcGQNG+tyMkysSXYEfgk4pq3rB8APFrbFczOP7+lhwNeq6hsL0c75MA+xbg1sn+R+uh3jQ+7LNiqGjPWngEuq6r/asv8b+FXgLQvZZq0sVXUdQPdRe1D5lwaeXgNsl2Tb9nn7t7bMD5J8ke7+hw+R5HnA14F757/ls7dQMY5/95rtWMLfQAsY41T175vnEGZlAeO8F/h8kv0Wqu2ztYDfySOBM9t7d2OS9cAhwMXzH8X05hDjfVV1yWR1JjgAuKit6/YkdwNrgcvmrfGsgC6g8KNujVfSHbm6sKouHZj9i8BtVXXDHOqOnGFiBd4BvBr44YI2cp4MGWsBn26n349b4KYObYhYfxL4NvCBdF1735dkh4Vv8dwM+Z6OOwr46AI1cd7MNdaq+ibwN8BNwK3Ad6vq04vQ5Dkb4n39CvBLrevSI4Bn8+Cbkkvz5deBL0380Z9kZ+BXaD+4JszbAXgNMNLDQwZscYxt/s8muQa4GviDqtq80A0dwpxinKn+CBo2zuVgLjHuDdw88HxjKxtVc/m8XQUcmWTrJPsCT2YB9osrIgGsqgeq6ol0RwsOSXLQwOzfZJofizPUHTlzjTXJc4Dbq+qKhW/l/BjmfQWeUlU/DTwLeHmSX1q4lg5viFi3Bn4aeG9VPYnuKPUJC9nWYQz5npLuRtXPpeuONdKG+K7uQneUc19gL2CHJC9e4OYOZa6xtiOobwYuBP6Vbsc3yj8+tUSSfCbdWNGJjyNnUfdAus/Z708o35rus/nOqvr6JFXfALy9qqYcrzOflihGqurSqjoQ+BngxCzgmOOlinG6+gthKeNcLEsU42SnzhbsrPVCxDgL76dLbC+nO3HznyzAfnHZdwEdVFV3JxkDDge+0j5Iv0aXPW9R3YVs53yYQ6xPAZ6b5Nl03Tx2TPKhqhrpH5Ywt/e1qm5pf29P8nG6LgKfW4TmDmUOsW4ENg6ccTmHEU4Axw3xXX0W8MWqum2Bmzhv5hDr04Abq+rbAEn+Cfh54EOL0NyhzPG7eipwKkCSN9F9pqUHqaqnzaVeuqEPHwdeWlVfmzD7FOCGqnrHFNV/Fnh+krcAOwM/TPL9qlqQC2wsUYyDr39dknvpxi0tyMXTlirGGerPu6V+LxfDEsW4kQefDVvNAg6RWKAYZ3rNzcAfDazrP4GZekZtsWV/BjDJo9vpYpJsT/fj6att9tOAr1bVpD8oZqg7coaJtapOrKrVVbWGrgvdZ0c5+Rvyfd0hyaPGp4FnMMJJ/ZDv67eAm5M8vhUdBly7sC2em2HiHDDjWcJRMGSsNwGHprsqWuje0+sWuMlzNuz7mmSP9vcxdMniyL+/Wh7a5/I84MSq+o8J8/4K2Al41VT1q+oXq2pN22++A3jTQiV/czVsjEn2bQdqSPJYuotabFig5s7JPMQ4Zf1RMmycy8E8xHgucFSSbdN1j9yfeR4bN6xhP29t379Dm346sLmq5v93XS3hVYLm4wH8d+BLwJfpfuT/+cC80+j6sw8uvxdw/kx1R/ExTKwTytcx+lcBHeZ9/Um6rmRX0Q3A/bOljmch31fgiXRHa78M/DOwy1LHtEBxPgL4DrDTUseyCLG+gS6J+gpwBrDtUse0gLH+O91Bi6uAw5Y6Hh/L70F34aCNwH3AbcCnWvnr6LrFXznw2IPurEHRHVgZLx+/kvJzgTdO8hqvZ2mvArogMQIvodtPXgl8EXjeCoxx0vorLc72fANwJ92FuTYCB6zAGP+M7uqf1zPN1XtHLcY27y2tzg/b39dP8nld02K7ju7K2I9diPanvZgkSZIkaYVb9l1AJUmSJEmzYwIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkqShJTktyV8tcRuuSbJuAde/KDEmuSDJ0fO4vrEkvztf69PyZgKo3nOHNav6m5L85Dy2p5LsN1/rkyQtf0k2JHnaFiz/kH1bVR1YVWPz3rhFVlXPqqrTAZIck+TzS90mrRwmgNKQVtoOa7KjhFX1yKr6epu/5AmzJEmS5sYEUJIkqceS7JXkY0m+neTGJK9o5a9PcnaSDya5p/VWWTtQ70lJvtjmnQVsN4vX2j3JJ5PcneTOJP+e5GFJzgAeA/xL63Xy6rb8Pyb5VpLvJvlckgNb+XHAi4BXt+X/pZX/6KBskm2TvCPJLe3xjiTbtnnrkmxMcnyS25PcmuS357Dtfi/J+hbLuUn2GphXSf4gyQ1J7kry7iRp87ZK8tYkd7Rt/odt+a3b/LEkv5vkp4C/B36uxXn34PyB13rQWcIkT0/y1bbd3gVkQrt/J8l1rV2fSvLYLY1dy5cJoJYtd1hz22El2aXF8u32j/+TSVa3eScBvwi8q7XvXa28kuw3Tfsf1KUzE84SJvnT1tZbkvzOhPZsm+RvktyU5LYkf59k+y2JSZI0N0keBvwLcBWwN3AY8Kokz2yLPBc4E9gZOBcY3y88HPhn4AxgV+AfgV+fxUseD2wEHg2sAl4LVFW9BLgJ+JXW6+QtbfkLgP2BPYAvAh+mq3BKm35LW/5XJnmtPwMOBZ4IPAE4BHjdwPyfAHZqcR8LvDvJLrOIAYAkTwX+GvgNYE/gG3TbatBzgJ9pr/8bwPh2/T3gWa1tPw08b7LXqKrrgD8ALm5x7jyLdu0OfIwu1t2BrwFPGZj/PLrt/mt078O/Ax+dab1aOUwAtSy5w5r7Dovue/8B4LF0yev/pW2fqvozuh3BH7b2/eFgxVm2/0GSHA78CfB0um0ysbvsm4HHtXj3a3H9+RbEI0mau58BHl1Vb6yqH7Tu/v8LOKrN/3xVnV9VD9DtO5/Qyg8FtgHeUVX3V9U5wBdm8Xr30yVLj231/r2qaqqFq+r9VXVPVd0HvB54QpKdZhnbi4A3VtXtVfVt4A3ASya05Y2tHecDm4DHz3Ld4+t/f1V9sbXvRLozdWsGljm5qu6uqpuAf6Pb10GXDP5tVW2sqruAk7fgdWfybODaqjqnqu4H3gF8a2D+7wN/XVXXVdVm4E3AEz0L2B8mgFqu3GHNcYdVVd+pqo9V1X9V1T3AScD/mG39OfgN4ANV9ZWqupduewDQusL8HvBHVXVna8+b+PH7KElaWI8F9mo9XO5uXQxfS3ewEx6cOPwXsF3rprgX8M0J+8JvzOL1/iewHvh0kq8nOWGqBVs3yZOTfC3J94ANbdbuswmstXGwTd9oZeO+0xKgcf8FPHKW637I+qtqE/AdugOZ4yZuv/H17wXcPDBvcHpYD1p3e48G1/9Y4G8H3u876bqIDrZbK5gJoJYrd1g/tkU7rCSPSPIPSb7R2vc5YOckW812HVto4k5uMLZHA48Arhh4H/+1lUuSFt7NwI1VtfPA41FV9ewZ6t0K7N0O5I17zEwv1g6OHl9VPwn8CvDHSQ4bnz1h8d8CjqTrObITsKaVZ4rlJ7qF7vfCYPtumamNW+BB60+yA7Ab8M1Z1L0VWD3wfJ9plp0sznvp9p/jfmLCun+0vvYeDa7/ZuD3J7zn21fVf86i3VoBTAC1XLnDmrvj6c4Y/mxV7Qj80ha2b7L5/8Usd0Q8eHvfQdcF9cCB93GnqtqSI7CSpLm7DPhektck2b4dxDwoyc/MUO9iYDPwiiRbJ/k1uiEL00rynDamPMD3gAfaA+A2YPCWQ48C7qM7q/YIuh4igyYuP9FHgdcleXQbF/fnwIdmauMW+Ajw20me2Mbqvwm4tKo2zKLu2cArk+ydZGfgNdMsexuwug1jGXcl8GvtoO5+dENCxp0HHJjk19rB71fw4P3y3wMnDlyfYKckL5hFm7VCmABquXKHNXePoku67k6yK/AXW9i+yeZfCfxWex8O58FdSs8GjklyQJJHDL5eVf2Qruvu25PsAdB2hs9EkrTg2lCJX6Ebm3Yj3YG599EdwJyu3g/oLiJyDHAX8ELgn2bxkvsDn6EbvnAx8J6B2yD9Nd3+7+4kfwJ8kK7XyDeBa4FLJqzrVOCAtvw/T/JafwVcDnwZuJpuTP683caoqi4C/j+6C67cCvw3Zj+E4X8Bn25t+xJwPt3vkwcmWfazwDXAt5Lc0creDvyAbp98Ou1aA61ddwAvoBtX+B26bf4fA/M/Tjf+/szWE+grdBekUU9kmmFM0khLd6nltwK/DGwLXE93sZRfAParqhe35dbQ7dS2qarN6a4I+r/oLjhyflvdDVX1OqaQ5I+AV9J1TbwL+Ieq+ss270jg74Ad6XYsf0/3j/ipdP3q/z+6f877V9X6JPvTXXxmDTBWVc9LsgH43ar6TJLtgLfQ/fOmLfvqqvp+upvFf6iqftRtZLDuNO0/DdhYVa9r2+0jwFq6M4tvbW0e3z4/19r7aOCMqnpFkpqh/WtbncfQXWRna+Br49u0dZl9FfDD9h6dOrC+7eiS3KPousl+E3hvVb1zqngkSVpJkjwL+Puq8kIsWnAmgJIkSdIiSne7o1+mOwu4iu4s4iVV9aqlbJf6wS6gkiRJmjdJXpvuXrETHxcsddtmI939gydr/4vm82XorvJ9F10X0OvwFkhaJJ4BlJokr6W7kuhE/15VI983Psk1PPjiMeN+v6o+PEm5JEmSesYEUJIkSZJ6YuulbsB823333WvNmjWTzrv33nvZYYcdFrdBS6QvsfYlTjDWlaovsU4W5xVXXHFHVXnPx2Vmuv3sqOvD980YVwZjXBmWMsbp9rErLgFcs2YNl19++aTzxsbGWLdu3eI2aIn0Jda+xAnGulL1JdbJ4kzyjaVpjYYx3X521PXh+2aMK4MxrgxLGeN0+1gvAiNJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9seKuAipJC23NCedNO3/DyUcsUkskTWaq7+jxB2/mmBPO8zsqqdc8AyhJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST0xVAKYZEOSq5NcmeTyVrZrkguT3ND+7jKw/IlJ1ie5PskzB8qf3NazPsk7k6SVb5vkrFZ+aZI1w7RXkqTlJMlWSb6U5JPtuftYSdJQ5uMM4C9X1ROram17fgJwUVXtD1zUnpPkAOAo4EDgcOA9SbZqdd4LHAfs3x6Ht/Jjgbuqaj/g7cCb56G9kiQtF68Erht47j5WkjSUhegCeiRweps+HXjeQPmZVXVfVd0IrAcOSbInsGNVXVxVBXxwQp3xdZ0DHDZ+5FKSpJUsyWrgCOB9A8XuYyVJQ9l6yPoFfDpJAf9QVacAq6rqVoCqujXJHm3ZvYFLBupubGX3t+mJ5eN1bm7r2pzku8BuwB2DjUhyHN3RTVatWsXY2Nikjd20adOU81aavsTalzjBWEfJ8Qdvnnb+lrR91GOdL32Jc569A3g18KiBskXfx8Ls97OjYqrv6Krtu3mj3v5h9OG7ZowrgzEunWETwKdU1S1tB3Rhkq9Os+xkRxVrmvLp6jy4oEs8TwFYu3ZtrVu3btIGjI2NMdW8laYvsfYlTjDWUXLMCedNO3/Di9bNel2jHut86Uuc8yXJc4Dbq+qKJOtmU2WSsnnZx8Ls97OjYqrv6PEHb+atV2+9Rd/R5aYP3zVjXBmMcekM1QW0qm5pf28HPg4cAtzWupzQ/t7eFt8I7DNQfTVwSytfPUn5g+ok2RrYCbhzmDZLkrQMPAV4bpINwJnAU5N8CPexkqQhzTkBTLJDkkeNTwPPAL4CnAsc3RY7GvhEmz4XOKpddWxfuoHol7WuLPckObSNPXjphDrj63o+8Nk2hkGSpBWrqk6sqtVVtYbu4i6fraoX4z5WkjSkYbqArgI+3saLbw18pKr+NckXgLOTHAvcBLwAoKquSXI2cC2wGXh5VT3Q1vUy4DRge+CC9gA4FTgjyXq6o5JHDdFeSZKWu5NxHytJGsKcE8Cq+jrwhEnKvwMcNkWdk4CTJim/HDhokvLv03ZukiT1UVWNAWNt2n3sPFgz0zjek49YpJZI0uJbiNtASJIkSZJGkAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9YQJoCRJkiT1hAmgJEmSJPWECaAkSZIk9cTWS90ASZKkLbHmhPOWugmStGx5BlCSJEmSesIEUJIkSZJ6wgRQkiRJknrCBFCSJEmSesIEUJIkSZJ6wgRQkiRJknpi6AQwyVZJvpTkk+35rkkuTHJD+7vLwLInJlmf5Pokzxwof3KSq9u8dyZJK982yVmt/NIka4ZtryRJkiT11XycAXwlcN3A8xOAi6pqf+Ci9pwkBwBHAQcChwPvSbJVq/Ne4Dhg//Y4vJUfC9xVVfsBbwfePA/tlSRJkqReGupG8ElWA0cAJwF/3IqPBNa16dOBMeA1rfzMqroPuDHJeuCQJBuAHavq4rbODwLPAy5odV7f1nUO8K4kqaoapt2StJBmukn1hpOPWKSWSJIkPdhQCSDwDuDVwKMGylZV1a0AVXVrkj1a+d7AJQPLbWxl97fpieXjdW5u69qc5LvAbsAdg41IchzdGURWrVrF2NjYpI3dtGnTlPNWmr7E2pc4wVhHyfEHbx6q/mBsox7rfOlLnJIkjbo5J4BJngPcXlVXJFk3myqTlNU05dPVeXBB1SnAKQBr166tdesmb87Y2BhTzVtp+hJrX+IEYx0lx8xwhm8mG1607kfTox7rfOlLnJIkjbphzgA+BXhukmcD2wE7JvkQcFuSPdvZvz2B29vyG4F9BuqvBm5p5asnKR+sszHJ1sBOwJ1DtFmSJEmSemvOF4GpqhOranVVraG7uMtnq+rFwLnA0W2xo4FPtOlzgaPalT33pbvYy2Wtu+g9SQ5tV/986YQ64+t6fnsNx/9JkiRJ0hwsxH0ATwaenuQG4OntOVV1DXA2cC3wr8DLq+qBVudlwPuA9cDX6C4AA3AqsFu7YMwf064oKknSSpZkuySXJbkqyTVJ3tDKvdWSJGkow14EBoCqGqO72idV9R3gsCmWO4nuiqETyy8HDpqk/PvAC+ajjZIkLSP3AU+tqk1JtgE+n+QC4NfobrV0cpIT6A6MvmbCrZb2Aj6T5HHtQOv4rZYuAc6nu9XSBQzcainJUXS3Wnrh4oYpSVpsC3EGUJIkDaE6m9rTbdqj6G6PdHorP53utkkwcKulqrqRrkfNIW0s/o5VdXEbQvHBCXXG13UOcNj42UFJ0so1L2cAJUnS/EqyFXAFsB/w7qq6NMmi32qptWVWt1taLHO9Fcuq7WdXd6njG0YfbrlijCuDMS4dE0BJkkZQ6775xCQ7Ax9P8pChEgMW7FZLrS2zut3SYpnrrViOP3gzb7165p8+g7dqWW76cMsVY1wZjHHp2AVUkqQRVlV3042zP5x2qyWAebzVEt5qSZL6wwRQkqQRk+TR7cwfSbYHngZ8FW+1JEkakl1AJUkaPXsCp7dxgA8Dzq6qTya5GDg7ybHATbQrZVfVNUnGb7W0mYfeauk0YHu6q38O3mrpjHarpTvpriIqSVrhTAAlSRoxVfVl4EmTlHurJUnSUOwCKkmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9YQIoSZIkST1hAihJkiRJPWECKEmSJEk9MecEMMl2SS5LclWSa5K8oZXvmuTCJDe0v7sM1Dkxyfok1yd55kD5k5Nc3ea9M0la+bZJzmrllyZZM0SskiRJktRrw5wBvA94alU9AXgicHiSQ4ETgIuqan/govacJAcARwEHAocD70myVVvXe4HjgP3b4/BWfixwV1XtB7wdePMQ7ZUkSZKkXptzAlidTe3pNu1RwJHA6a38dOB5bfpI4Myquq+qbgTWA4ck2RPYsaourqoCPjihzvi6zgEOGz87KEmSJEnaMlsPU7mdwbsC2A94d1VdmmRVVd0KUFW3JtmjLb43cMlA9Y2t7P42PbF8vM7NbV2bk3wX2A24Y0I7jqM7g8iqVasYGxubtL2bNm2act5K05dY+xInGOsoOf7gzUPVH4xt1GOdL32JU5KkUTdUAlhVDwBPTLIz8PEkB02z+GRn7mqa8unqTGzHKcApAGvXrq1169ZN2oCxsTGmmrfS9CXWvsQJxjpKjjnhvKHqb3jRuh9Nj3qs86UvcUqSNOrm5SqgVXU3MEY3du+21q2T9vf2tthGYJ+BaquBW1r56knKH1QnydbATsCd89FmSZIkSeqbYa4C+uh25o8k2wNPA74KnAsc3RY7GvhEmz4XOKpd2XNfuou9XNa6i96T5NA2vu+lE+qMr+v5wGfbOEFJkiRJ0hYapgvonsDpbRzgw4Czq+qTSS4Gzk5yLHAT8AKAqromydnAtcBm4OWtCynAy4DTgO2BC9oD4FTgjCTr6c78HTVEeyVJkiSp1+acAFbVl4EnTVL+HeCwKeqcBJw0SfnlwEPGD1bV92kJpCRJkiRpOPMyBlCSJEmSNPpMACVJkiSpJ0wAJUmSJKknTAAlSZIkqSdMACVJGjFJ9knyb0muS3JNkle28l2TXJjkhvZ3l4E6JyZZn+T6JM8cKH9ykqvbvHe2Wy7Rbst0Viu/NMmaRQ9UkrToTAAlSRo9m4Hjq+qngEOBlyc5ADgBuKiq9gcuas9p844CDgQOB97TbtME8F7gOLr77+7f5gMcC9xVVfsBbwfevBiBSZKW1jD3AZSkFWfNCectdRMkqupW4NY2fU+S64C9gSOBdW2x04Ex4DWt/Myqug+4sd0/95AkG4Adq+pigCQfBJ5Hd7/dI4HXt3WdA7wrSaqqFjg8SdISMgGUJGmEta6ZTwIuBVa15JCqujXJHm2xvYFLBqptbGX3t+mJ5eN1bm7r2pzku8BuwB2TtOE4urOIrFq1irGxsfkIbc6OP3jznOqt2n52dZc6vmFs2rRpWbd/NoxxZTDGpWMCKEnSiErySOBjwKuq6ntt+N6ki05SVtOUT1fnoYVVpwCnAKxdu7bWrVs3TasX3jFzPFN//MGbeevVM//02fCidXNa/ygYGxtjqd+fhWaMK4MxLh3HAEqSNIKSbEOX/H24qv6pFd+WZM82f0/g9la+EdhnoPpq4JZWvnqS8gfVSbI1sBNw5/xHIkkaJSaAkiSNmHalzlOB66rqbQOzzgWObtNHA58YKD+qXdlzX7qLvVzWuovek+TQts6XTqgzvq7nA591/J8krXx2AZUkafQ8BXgJcHWSK1vZa4GTgbOTHAvcBLwAoKquSXI2cC3dFURfXlUPtHovA04Dtqe7+MsFrfxU4Ix2wZg76a4iKkla4UwAJUkaMVX1eSYfowdw2BR1TgJOmqT8cuCgScq/T0sgJUn9YRdQSZIkSeoJE0BJkiRJ6gkTQEmSJEnqCccASpIkaVJrZrjn4oaTj1iklkiaL3M+A5hknyT/luS6JNckeWUr3zXJhUluaH93GahzYpL1Sa5P8syB8icnubrNe2e7VDXtctZntfJLk6wZIlZJkiRJ6rVhzgBuBo6vqi8meRRwRZILgWOAi6rq5CQnACcAr0lyAN0lpg8E9gI+k+Rx7TLV7wWOAy4BzgcOp7tM9bHAXVW1X5KjgDcDLxyizZIkSWLms3uSVqY5nwGsqlur6ott+h7gOmBv4Ejg9LbY6cDz2vSRwJlVdV9V3QisBw5JsiewY1Vd3G5A+8EJdcbXdQ5w2PjZQUmSJEnSlpmXMYCta+aTgEuBVVV1K3RJYpI92mJ7053hG7exld3fpieWj9e5ua1rc5LvArsBd0x4/ePoziCyatUqxsbGJm3npk2bppy30vQl1r7ECca6WI4/ePOCv8ZgbH15X/sSpyRJo27oBDDJI4GPAa+qqu9Nc4Jushk1Tfl0dR5cUHUKcArA2rVra926dZM2YGxsjKnmrTR9ibUvcYKxLpZjFqFL1IYXrfvRdF/e177EKUnSqBvqNhBJtqFL/j5cVf/Uim9r3Tppf29v5RuBfQaqrwZuaeWrJyl/UJ0kWwM7AXcO02ZJkiRJ6qthrgIa4FTguqp628Csc4Gj2/TRwCcGyo9qV/bcF9gfuKx1F70nyaFtnS+dUGd8Xc8HPtvGCUqSJEmSttAwXUCfArwEuDrJla3stcDJwNlJjgVuAl4AUFXXJDkbuJbuCqIvb1cABXgZcBqwPd3VPy9o5acCZyRZT3fm76gh2itJkiRJvTbnBLCqPs/kY/QADpuizknASZOUXw4cNEn592kJpCRJkiRpOEONAZQkSZIkLR8mgJIkSZLUEyaAkiRJktQT83IjeEnS7K0ZuNfg8Qdvfsi9BzecfMRiN0mSJPWECaAkSRoZayYcEJEkzS+7gEqSJElST5gASpIkSVJPmABKkiRJUk+YAEqSJElST5gASpIkSVJPmABKkjSCkrw/ye1JvjJQtmuSC5Pc0P7uMjDvxCTrk1yf5JkD5U9OcnWb984kaeXbJjmrlV+aZM2iBihJWhImgJIkjabTgMMnlJ0AXFRV+wMXteckOQA4Cjiw1XlPkq1anfcCxwH7t8f4Oo8F7qqq/YC3A29esEgkSSPDBFCSpBFUVZ8D7pxQfCRweps+HXjeQPmZVXVfVd0IrAcOSbInsGNVXVxVBXxwQp3xdZ0DHDZ+dlCStHJ5I3hJkpaPVVV1K0BV3Zpkj1a+N3DJwHIbW9n9bXpi+Xidm9u6Nif5LrAbcMfEF01yHN1ZRFatWsXY2Nh8xfMQxx+8ecHWvWr72a1/IeNbaJs2bZp1++djWy/FttqSGJcrY1wZRjVGE0BJkpa/yc7c1TTl09V5aGHVKcApAGvXrq1169bNoYmzc8wJ5y3Yuo8/eDNvvXrmnz4bXrRuwdqw0MbGxpjt+zMf23opttWWxLhcGePKMKox2gVUkqTl47bWrZP29/ZWvhHYZ2C51cAtrXz1JOUPqpNka2AnHtrlVJK0wgx1BjDJ+4HnALdX1UGtbFfgLGANsAH4jaq6q807kW7Q+QPAK6rqU638yXSD3bcHzgdeWVWVZFu68QpPBr4DvLCqNgzTZknS7KyZ4ezAhpOPWKSWaMC5wNHAye3vJwbKP5LkbcBedBd7uayqHkhyT5JDgUuBlwJ/N2FdFwPPBz7bxglKklawYc8AnoZXKJMkad4l+Shdcvb4JBuTHEuX+D09yQ3A09tzquoa4GzgWuBfgZdX1QNtVS8D3kd3YZivARe08lOB3ZKsB/6Ytr+WJK1sQ50BrKrPTXLfoCOBdW36dGAMeA0DVygDbmw7nEOSbKBdoQwgyfgVyi5odV7f1nUO8K4k8QilJGmlq6rfnGLWYVMsfxJw0iTllwMHTVL+feAFw7RRkrT8LMRFYBb9CmWzvTrZqF6JZyH0Jda+xAnGulgW8gqEk5nsqoSj8j7PtC22pJ19+vxKkjTKFvMqoAt2hbLZXp1sVK/EsxD6Emtf4gRjXSwLeQXCyUx2VcJRuQLhTNtiS9rZp8+vJEmjbCGuAuoVyiRJkiRpBC1EAjh+VTF46BXKjkqybZJ9+fEVym4F7klyaJLQXaHsE5OsyyuUSZIkSdIQhr0NxEfpLviye5KNwF/QXZHs7Ha1sptoA8yr6pok41co28xDr1B2Gt1tIC7gwVcoO6NdMOZOuquISpIkSZLmYNirgHqFMkmSJElaJhaiC6gkSZIkaQSZAEqSJElSTyzmbSAkSSNizSLf7kKSJI0GzwBKkiRJUk+YAEqSJElST9gFdA5m03Vqw8lHLEJLJG0puz5K6gv/30majAmgJEmSJC2imQ7QLOTJJLuASpIkSVJPeAZQkkaM3cwlSdJCMQGUpGXIsT2SJGku7AIqSZIkST3hGUBJkrRoPHstSUvLBFCSJEmS5sn4ga7jD97MMSN40MsuoJIkSZLUEyaAkiRJktQTdgGdhOMTJEnSqJvs98pidzlbyptZS5obE0BJkiRJmqXlfrJoWXQBTXJ4kuuTrE9ywlK3R5KklcJ9rCT1y8gngEm2At4NPAs4APjNJAcsbaskSVr+3MdKUv8shy6ghwDrq+rrAEnOBI4Erl3SVs3APvHS0lju3TKkRbYs97FaPvw9JI2eVNVSt2FaSZ4PHF5Vv9uevwT42ar6w4FljgOOa08fD1w/xep2B+5YwOaOkr7E2pc4wVhXqr7EOlmcj62qRy9FY9SZzT62lc92Pzvq+vB9M8aVwRhXhqWMccp97HI4A5hJyh6UtVbVKcApM64oubyq1s5Xw0ZZX2LtS5xgrCtVX2LtS5zL0Iz7WJj9fnbU9eFzaIwrgzGuDKMa48iPAQQ2AvsMPF8N3LJEbZEkaSVxHytJPbMcEsAvAPsn2TfJw4GjgHOXuE2SJK0E7mMlqWdGvgtoVW1O8ofAp4CtgPdX1TVzXN2y776yBfoSa1/iBGNdqfoSa1/iXFbmeR+7HPThc2iMK4MxrgwjGePIXwRGkiRJkjQ/lkMXUEmSJEnSPDABlCRJkqSeWPYJYJLtklyW5Kok1yR5Qys/K8mV7bEhyZWzrTuqhol1YB1bJflSkk8uWsPnYNhY27yr23KXL2rjt9A8xLpzknOSfDXJdUl+blEDmKUhv6uPH1jmyiTfS/KqxY5htubhPf2jVu8rST6aZLtFDWALzEOsr2xxXjPK76lGV5IXtM/PD5OsHSh/epIr2r7giiRPbeWPSHJe+595TZKTZ1j/Y5JsSvInCx3LNG1YkBiTHDLwPb0qya8uVkyTtGWhYpy0/lJZwDh3S/Jv7bP6rsWKZ4q2LNh3MsmJSdYnuT7JMxcjninasUUxtnknJbk5yaZp1vvwJB9o9a9Ksm5BAqiqZf2gu4fRI9v0NsClwKETlnkr8OdzqTtKj2FiHZj/x8BHgE8udTwLGSuwAdh9qeNYpFhPB363TT8c2HmpY1qIOAeW2Qr4Ft0NTpc8rvmOFdgbuBHYvj0/GzhmqWNaoFgPAr4CPILuomSfAfZf6ph8LK8H8FN0N6cfA9YOlD8J2KtNHwR8s00/AvjlNv1w4N+BZ02z/o8B/wj8yUqLcfy716b3BG4ff76CYpy0/gp8L3cAfgH4A+BdKzTGA4CrgG2BfYGvAVsthxjb80Pb92zTNOt9OfCBNr0HcAXwsPlu/8hfBXQm1W2h8Ux6m/b40ZVtkgT4DeAhR3xmqjtqhom1zV8NHAGcRJcIjqxhY11Ohok1yY7ALwHHtHX9APjBwrZ4bubxPT0M+FpVfWMh2jkf5iHWrYHtk9xPt2Mc2fuyDRnrTwGXVNV/tWX/N/CrwFsWss1aWarqOoDuo/ag8i8NPL0G2C7Jtu3z9m9tmR8k+SLd/Q8fIsnzgK8D985/y2dvoWIc/+4127GEv4EWMMap6t83zyHMygLGeS/w+ST7LVTbZ2sBv5NHAme29+7GJOuBQ4CL5z+K6c0hxvuq6pLJ6kxwAHBRW9ftSe4G1gKXzVvjWQFdQOFH3RqvpDtydWFVXTow+xeB26rqhjnUHTnDxAq8A3g18MMFbeQ8GTLWAj7dTr8ft8BNHdoQsf4k8G3gA+m69r4vyQ4L3+K5GfI9HXcU8NEFauK8mWusVfVN4G+Am4Bbge9W1acXoclzNsT7+hXgl1rXpUcAz+bBNyWX5suvA1+a+KM/yc7Ar9B+cE2YtwPwGmCkh4cM2OIY2/yfTXINcDXwB1W1eaEbOoQ5xThT/RE0bJzLwVxi3Bu4eeD5xlY2qubyebsKODLJ1kn2BZ7MAuwXV0QCWFUPVNUT6Y4WHJLkoIHZv8k0PxZnqDty5hprkucAt1fVFQvfyvkxzPsKPKWqfhp4FvDyJL+0cC0d3hCxbg38NPDeqnoS3VHqExayrcMY8j0l3Y2qn0vXHWukDfFd3YXuKOe+wF7ADklevMDNHcpcY21HUN8MXAj8K92Ob5R/fGqJJPlMurGiEx9HzqLugXSfs9+fUL413WfznVX19UmqvgF4e1VNOV5nPi1RjFTVpVV1IPAzwIlZwDHHSxXjdPUXwlLGuViWKMbJTp0t2FnrhYhxFt5Pl9heTnfi5j9ZgP3isu8COqiq7k4yBhwOfKV9kH6NLnveoroL2c75MIdYnwI8N8mz6bp57JjkQ1U10j8sYW7va1Xd0v7enuTjdF0EPrcIzR3KHGLdCGwcOONyDiOcAI4b4rv6LOCLVXXbAjdx3swh1qcBN1bVtwGS/BPw88CHFqG5Q5njd/VU4FSAJG+i+0xLD1JVT5tLvXRDHz4OvLSqvjZh9inADVX1jimq/yzw/CRvAXYGfpjk+1W1IBfYWKIYB1//uiT30o1bWpCLpy1VjDPUn3dL/V4uhiWKcSMPPhu2mgUcIrFAMc70mpuBPxpY138CM/WM2mLL/gxgkke308Uk2Z7ux9NX2+ynAV+tqkl/UMxQd+QME2tVnVhVq6tqDV0Xus+OcvI35Pu6Q5JHjU8Dz2CEk/oh39dvATcneXwrOgy4dmFbPDfDxDlgxrOEo2DIWG8CDk13VbTQvafXLXCT52zY9zXJHu3vY+iSxZF/f7U8tM/lecCJVfUfE+b9FbAT8Kqp6lfVL1bVmrbffAfwpoVK/uZq2BiT7NsO1JDksXQXtdiwQM2dk3mIccr6o2TYOJeDeYjxXOCoJNum6x65P/M8Nm5Yw37e2r5/hzb9dGBzVc3/77pawqsEzccD+O/Al4Av0/3I//OBeafR9WcfXH4v4PyZ6o7iY5hYJ5SvY/SvAjrM+/qTdF3JrqIbgPtnSx3PQr6vwBPpjtZ+GfhnYJeljmmB4nwE8B1gp6WOZRFifQNdEvUV4Axg26WOaQFj/Xe6gxZXAYctdTw+lt+D7sJBG4H7gNuAT7Xy19F1i79y4LEH3VmDojuwMl4+fiXl5wJvnOQ1Xs/SXgV0QWIEXkK3n7wS+CLwvBUY46T1V1qc7fkG4E66C3NtBA5YgTH+Gd3VP69nmqv3jlqMbd5bWp0ftr+vn+TzuqbFdh3dlbEfuxDtT3sxSZIkSdIKt+y7gEqSJEmSZscEUJIkSZJ6wgRQkiRJknrCBFCSJEmSesIEUJIkSZJ6wgRQkiRJknrCBFCSJEmSeuL/D4/X86k/HTQ3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot of some numeric columns\n",
    "gobike_clean[[\n",
    "    'duration_sec','member_birth_year',\n",
    "    'start_station_latitude','start_station_longitude',\n",
    "    'end_station_latitude','end_station_longitude'\n",
    "]].hist(bins=40, figsize=(15,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1d50878e",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 12, 5\n",
    "\n",
    "fig, ax = plt.subplots(1,2)\n",
    "d = sns.histplot(data=gobike_clean, x='duration_sec', ax=ax[0])\n",
    "d.set_xlabel(\"Duration (sec)\");\n",
    "d.set_ylabel(\"Count\")\n",
    "\n",
    "v = sns.histplot(data=gobike_clean, x='duration_sec', ax=ax[1])\n",
    "v.set_xlabel('Duration (sec)')\n",
    "v.set_ylabel('Count')\n",
    "plt.xlim(0,2000);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dc455021",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_step = np.log10(3)\n",
    "bin_edges = 10 ** np.arange(1.78, 4.9+1, bin_step)\n",
    "plt.figure(figsize =(10,5))\n",
    "plt.hist(data=gobike_clean, x='duration_sec', bins=bin_edges)\n",
    "plt.xscale('log')\n",
    "plt.xticks([100, 300, 600, 8000],['100', '300', '600', '8000'])\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Duration (sec)')\n",
    "plt.title('Log Transformation of Duration (sec)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34fd3d3a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize'] = 12, 5\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.boxplot(data=gobike_clean, x='duration_sec', vert=False)\n",
    "plt.xlabel('Duration (sec)')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.boxplot(data=gobike_clean, x='duration_sec', vert=False)\n",
    "plt.xlim(0,2000)\n",
    "plt.xlabel('Duration (sec)');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537aae60",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "From the histogram visualization of the selected columns it is noted that columns:\n",
    "1. `duration_sec`:\n",
    "    + Appears to be very skewed to the right.\n",
    "    + Majority of the recorded observation have a bicycle ride duration between 0 to 1500 seconds.\n",
    "2. `member_birth_year`:\n",
    "    + Majority of riders are born either in 80's or 90's.\n",
    "    + The Minimum and Maximum year of birth of the riders are 1878 and 2001. \n",
    "    + But it is noted that `member_birth_year` clearly has outliers or false data, since we can not have somebody born 1878 having the strength to ride a bike in 2019, he or she would be about  141 years old.\n",
    "3. `start_station_longitude` and `end_station_longitude`: From the histogram it's noted that the longitude plotted has 3 distinct distribution. These distinct distribution might indicate that our data contains 3 distinct location concentration, meaning that stations in the data set might be located in 3 different settlement.\n",
    "4. `start_station_latitude` and `end_station_latitude`: For the distribution of latitude it's noted that there are 2 distinct distribution. These might indicate that 2 out of the 3 settlements might be closer to each other. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cfbfb43",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c93ab46",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "I'll create some new columns that will be used to discover features that are closly related.\n",
    "First a column i'll create contains the days of the week, this  is done by formating the datatime object to day of the week. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e93e9492",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Thursday', 'Wednesday', 'Tuesday', 'Monday', 'Sunday', 'Saturday',\n",
       "       'Friday'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['day_of_week'] = gobike_clean['start_time'].dt.strftime('%A')\n",
    "gobike_clean['day_of_week'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe5d6e20",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Next, a column containing the period of day each riders starts there ride is created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7679c0aa",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def period_of_day(x):\n",
    "    \"\"\"\n",
    "    Create Period of day\n",
    "    \n",
    "    The Period of day is the grouping of the 24 hour clock into 6 unique periods. The period of the day are\n",
    "    late night, early morning, morning, noon, evening, and night.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    x : pd.Series.datetime.datetime\n",
    "        Pandas Series containing datetime data type.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    output : {Late Night, Early Morning, Morning, Noon, Evening, Night}\n",
    "    \"\"\"\n",
    "    hour = x.hour\n",
    "    period = hour // 4\n",
    "    period_hour = {\n",
    "        0:'Late Night', 1:'Early Morning',\n",
    "        2:'Morning', 3:'Noon',\n",
    "        4:'Evening', 5:'Night'\n",
    "    }\n",
    "    output = period_hour[period]\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0bdab118",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Evening', 'Noon', 'Night', 'Morning', 'Late Night',\n",
       "       'Early Morning'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new feature `period_of_day` when the function `period_of_day` is applied to the Pandas Series `start_time`\n",
    "gobike_clean['period_of_day'] = gobike_clean['start_time'].apply(period_of_day)\n",
    "gobike_clean['period_of_day'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7327bf04",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Convert day_of_week, and period_of_day into ordered categorical types\n",
    "ordinal_var_dict = {'day_of_week':['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'],\n",
    "                    'period_of_day':['Early Morning', 'Morning', 'Noon', 'Evening', 'Night', 'Late Night']}\n",
    "for var in ordinal_var_dict:\n",
    "    ordered_var = pd.api.types.CategoricalDtype(ordered = True,\n",
    "                                               categories = ordinal_var_dict[var])\n",
    "    gobike_clean[var] = gobike_clean[var].astype(ordered_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "381f6ee7",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "49890edf",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Evening', 'Noon', 'Night', 'Morning', 'Late Night', 'Early Morning']\n",
      "Categories (6, object): ['Early Morning' < 'Morning' < 'Noon' < 'Evening' < 'Night' < 'Late Night']\n",
      "\n",
      "['Thursday', 'Wednesday', 'Tuesday', 'Monday', 'Sunday', 'Saturday', 'Friday']\n",
      "Categories (7, object): ['Monday' < 'Tuesday' < 'Wednesday' < 'Thursday' < 'Friday' < 'Saturday' < 'Sunday']\n"
     ]
    }
   ],
   "source": [
    "print(gobike_clean['period_of_day'].unique(), end='\\n\\n')\n",
    "print(gobike_clean['day_of_week'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a985e550",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "I'll create two columns containing the hour of day in which each rider starts and ends there ride."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c7395671",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Create two new columns `Hour_of_day_start` and `Hour_of_day_end`\n",
    "gobike_clean['Hour_of_day_start'] = gobike_clean['start_time'].dt.hour\n",
    "gobike_clean['Hour_of_day_end'] = gobike_clean['end_time'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "87c46afe",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min   2019-02-01 00:04:52.058\n",
       "max   2019-03-01 08:01:55.975\n",
       "Name: end_time, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['end_time'].agg(['min', 'max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a172804",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "From the code above we can see that the minimum and maximum end time for bike riders falls within the year 2019. Therefore, we can create a new column called `Age`, which caputures the riders age by subtracting 2019 from `member_birth_year`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "11800abb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# Create columns called `Age`\n",
    "gobike_clean['Age'] = 2019 - gobike_clean['member_birth_year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "72406e69",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max    141.0\n",
       "min     18.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['Age'].agg(['max', 'min'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0bb71bb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "Since both `member_birth_year` and `Age` are skew to the left. I decided to create a new column that captures the age groups. According to the [Statistics of Canada](https://www.statcan.gc.ca/en/concepts/definitions/age2) age groups or Categories can be broken down into:\n",
    "1. Children (00-14 years).\n",
    "2. Youth (15-24 years).\n",
    "3. Adults (25-64 years).\n",
    "4. Seniors (65 years and over).\n",
    "\n",
    "For my project i decided to capture the age range of 25 to 59 years as adults, and age range 60 and over as seniors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7bf6b751",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create bins (18, 24] as Youth, (24, 60] as Adult, and (60, 141] as Seniors\n",
    "bins = [18, 24, 60, 141]\n",
    "labels = ['Youth', 'Adult', 'Seniors']\n",
    "gobike_clean['Age_class'] = pd.cut(gobike_clean['Age'], bins=bins, labels=labels, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6f4fec9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Adult      156731\n",
       "Youth       22954\n",
       "Seniors      3530\n",
       "Name: Age_class, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['Age_class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d7cfe0",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "480ed2ce",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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>Hour_of_day_start</th>\n",
       "      <th>Hour_of_day_end</th>\n",
       "      <th>Age</th>\n",
       "      <th>Age_class</th>\n",
       "      <th>period_of_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Noon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Night</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hour_of_day_start  Hour_of_day_end   Age Age_class period_of_day\n",
       "0                 17                8  35.0     Adult       Evening\n",
       "1                 18                6  32.0     Adult       Evening\n",
       "2                 12                5  47.0     Adult          Noon\n",
       "3                 17                4  30.0     Adult       Evening\n",
       "4                 23                0  45.0     Adult         Night"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean[['Hour_of_day_start', 'Hour_of_day_end', 'Age', 'Age_class','period_of_day']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "563b8432",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Evening', 'Noon', 'Night', 'Morning', 'Late Night', 'Early Morning']\n",
       "Categories (6, object): ['Early Morning' < 'Morning' < 'Noon' < 'Evening' < 'Night' < 'Late Night']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean['period_of_day'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5b5a1a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data Analysis\n",
    "\n",
    "1. What is the percentage of riders that are customers and Subscribers?\n",
    "2. Which gender type is most common among riders?.\n",
    "3. What is the percentage of riders that share bike?\n",
    "4. When are most trips taken in terms of time of day, period of the day, day of the week?\n",
    "5. Which Age group is the most common among riders?\n",
    "6. How long does the average trip take?\n",
    "7. Does the average trip taken depend on if a user is a subscriber or customer?\n",
    "8. Which stations are most popular?\n",
    "9. At what point do we need to check or service bike in the sharing system?\n",
    "10. Heatmap visualization of each cooridinate of bike stations?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b20400a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 1. What is the percentage of riders that are customers and Subscribers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ffcdcf0b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def pie_plot(col_name, title, color=['#157fa1','#a4daeb']):\n",
    "    \"\"\"\n",
    "    Create pie plot.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    col_name : str\n",
    "        Takes in a string containing the name of the column in a DataFrame to be plotted.\n",
    "    title : str\n",
    "        Takes in a string containing the title of the plot.\n",
    "    color : str\n",
    "        Takes a string corespounding to the valid colors given in Matplotlib libary.\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=[8,8])\n",
    "    sort_count = gobike_clean[col_name].value_counts()\n",
    "    plt.pie(sort_count, labels=sort_count.index,autopct=\"%.1f%%\", \n",
    "            pctdistance=0.77, startangle=90, \n",
    "            counterclock=False, wedgeprops={'width': 0.4},\n",
    "           textprops={'fontsize': 12, 'family':'cursive','va':'baseline'}, colors=color)\n",
    "    plt.title(title, fontdict={'fontsize': 14});"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1992def7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Why is finding the percentage of user type important, This is because it indicates whether users are \"registered\" member or a \"casual\" rider.\n",
    "The user type column has two unique values which are:\n",
    "1. Subscriber: These are registered members.\n",
    "2. Customer: These are casual riders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "96c6d91d",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie_plot('user_type', title=\"Percentage Distribution of User Type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a32e07e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report:\n",
    "\n",
    "From the plot above we can see that Subsribers make up the majority of the dataset with 89.2%, while customers make up 10.8% of the data. Therefore, we can say that majority of riders are registered. To illustrate 17 out of 20 riders are registered members."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "068064ef",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 2. Which gender type is most common among riders?."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1d092e4a",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie_plot('member_gender', title=\"Percentage Distribution of Gender Types\", color=['#157fa1','#a4daeb', '#ccefff'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "153471f0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "I was expecting an even distribution of male and female riders, but from the visualization of the data, we can see that male riders make up 71.2% of the entire data set. These means that out of every 10 riders about 7 are male, 2 are female, and 1 is other. Since Men form the majority of riders in the data set, does this mean that they also ride on an average longer time duration then Women and Others?."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192fe954",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 3. What is the percentage of riders that share bike?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5638cad9",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie_plot('bike_share_for_all_trip', title=\"Percentage Distribution of Riders Who Share Bike\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5104ed48",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "From the pie plot we can see that majority of the dataset with about 90.5% of riders do not share bike, while the manority of 9.5% share bikes. This raises the question are customers allowed to share bikes, or is the right to share bike only given to subcribers?. We have already seen that majority of the data set are Subcribers so does that also influence the result here?."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5ac8f3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 4. When are most trips taken in terms of time of day, period of day, and day of the week?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b391148a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def bar_plot(col, xlabel, title, ylabel='Count', figsize=(12,5), text_fontsize=11):\n",
    "    \"\"\"\n",
    "    Create Bar Plot.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    col : str\n",
    "        Column name from the dataset.\n",
    "    xlabel : str\n",
    "        Label of the x axis.\n",
    "    ylabel : str default {Count}\n",
    "        Label of the y axis.\n",
    "    figsize : tuple of (int, int)\n",
    "        The size of the x and y axis of the plot.\n",
    "    text_fontsize : int\n",
    "       The text font size.  \n",
    "    \"\"\"\n",
    "    plt.rcParams['axes.spines.top'] = False\n",
    "    plt.rcParams['axes.spines.right'] = False\n",
    "    plt.rcParams['figure.figsize'] = figsize\n",
    "    sns.countplot(data=gobike_clean, x=col, color='#267ea3')\n",
    "    loc_y,labels_y = plt.yticks()\n",
    "    y_labels = [\"{:,}\".format(round(loc)) for loc in loc_y]\n",
    "    plt.yticks(loc_y, y_labels, fontfamily='cursive', fontsize=11)\n",
    "    plt.xticks(fontfamily='cursive', fontsize=11)\n",
    "    loc_x, labels_x = plt.xticks()\n",
    "    type_count = gobike_clean[col].value_counts()\n",
    "    \n",
    "    for loc, label in zip(loc_x, labels_x):\n",
    "        index = label.get_text()\n",
    "        confirm = type_count.index[0]\n",
    "        if type(confirm) == str:\n",
    "            value = type_count[index]\n",
    "            total = type_count.sum()\n",
    "            text_str = \"{:.2f}%\".format(value*100 / total)\n",
    "            plt.text(loc, value+1000, text_str,\n",
    "                     fontfamily='cursive', ha='center', \n",
    "                     fontsize=text_fontsize,\n",
    "                     fontweight='semibold')\n",
    "        else:\n",
    "            index = int(index)\n",
    "            value = type_count[index]\n",
    "            total = type_count.sum()\n",
    "            text_str = \"{:.2f}%\".format(value*100 / total)\n",
    "            plt.text(loc, value+1000, text_str, \n",
    "                     fontfamily='cursive', ha='center', \n",
    "                     fontsize=text_fontsize, fontweight='semibold')\n",
    "            \n",
    "    plt.ylabel(ylabel, fontdict={'fontsize': 11})\n",
    "    plt.xlabel(xlabel, fontdict={'fontsize': 11})\n",
    "    plt.title(title, fontdict={'fontsize': 14, 'fontweight':'roman'});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "aa4f93aa",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('Hour_of_day_start', xlabel='Hour of Day [24 hour clock]', \n",
    "         title='Bar Plot of the Hour of Day Riders Start Ride', \n",
    "         figsize=(15,5),text_fontsize=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ea2f6e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "From the plot above we can see that the hour most riders start there riders is at 5pm with 11.92%, and closely followed by 8 am with 11.48%. Close to this two peak times come the 3rd, 4th, 5th, and 6th highest hours in which riders are more like to start a ride. The least time in which riders take a bike ride are at the late hours of the night. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ac655b8c",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('Hour_of_day_end', xlabel='Hour of Day [24 hour clock]', \n",
    "         title='Bar Plot of the Hour of Day Riders End Ride', \n",
    "         figsize=(15,5),text_fontsize=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125a5eca",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "The bar plot of the hour of the day riders end there ride looks similar to the bar plot of the hour riders start there ride with only minor differences. What does this mean?. These might have resulted because of the short duration of time in which riders start and end there ride."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cc4903ba",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot('period_of_day', xlabel='Period of Day', title='Bar Plot of the Period of Day', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54212e44",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "I'm not suprised that late nights have the least representation in the data set with 1.11%, most bike riders would be at sleep by this time. But from the visualization the highest bike riders count falls in the evening with 34.21%, next is morning with 29.01%. At these times we expect that most bikes would be in use and common paths taken during riders would be in active use. Also people who work 9 am to 5 pm jobs would more likely take ride early in the morning before work and during the evening after work, These would only account for working days.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "90f7eb96",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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86U9/AqBOnTrcd999/OUvf/lO/Zo1a3LFFVdwwgknsPXWW3PXXXdxzjnnMG/ePIYNG0a1atUYO3YsjRs35sQTT6Rdu3Y8++yztGvXjgEDBjBz5kyKiooYNGgQjRs3pnPnzpW272ZmVZmDcjOzjcgVV1xB8+bN6dKlS2law4YNeeaZZ+jQoQNff/01EyZMYP78+eXWl1Q65WTMmDH07t2bNm3a8KMf/YhXX32Vo446isaNGwOw3377IYl33nmHXr16MWnSJD755BN23313WrRowdVXX02NGjXyv9NmZhsAT18xM9vIDRw4kPfee4/99tuP9u3b8+qrr7LjjjtWWH7JkiX88pe/5LDDDmOPPfbgySefRBJfffUVDRo0KC1XvXp1qlevzuLFiwFo3bo1Rx55JIMHD6ZGjRp07dqVb7/9Nu/7Z2a2IXBQbma2kTvyyCP5+OOPGTVqFL/73e8AOPbYYyss361bN26//XZuuukmnnvuOZo2bQpAgwYN+PLLL0vLffXVV3z77bc0bNiwNG3evHn069ePnj17cuaZZ1KrVi1at27N1KlT87R3ZmYbBgflZmZGvXr12Gyzzbj99tvZfPPN6dWrV7nlFi5cyL333ss555zD8ccfz3//+1++/vprAPbaay9eeuklvvnmGwCefPJJANq2bVtaf8CAARQXF9OqVStGjBjBAw88wIIFC7juuuvyu4NmZlWcg3Izs43IsmXLmDt3LkDpvPF33nmHQw45hIMOOogFCxbw8MMPl45+A4wYMYK33noLSEF5cXExQ4YMYeedd2bnnXemXr16vP7665x11ll8/vnnHHnkkVx44YVccMEFtG3blr322gtI01769+9P9+7dqVOnDsXFxYwbN465c+euMO3FzGxj5As9zcw2Ip07d+bBBx8EoGXLlsyePZt///vf/Pvf/+b888/nsssuY9ttt12hzpVXXkn79u1p06YNjRs3ZvTo0StMU6lduzZ77LEHNWvW5K677uL3v/89r732GgcccABDhgwpLTd9+nSqVatGjx49aNKkCZ06deKOO+5g33335ZJLLqmcA2BmVkUpIgrdh4IrKiqK8ePHF7obZmZ5N336dCZNmkRE0KBBA/bbb79Cd8nMbGOj8hI9Um5mthFp3rw5zZs3L3Q3zMysDM8pNzMzMzMrMAflZmZmZmYFltegXFInSf+VNEfSfZI2lXSxpCjzOKucultKelDSQknvSzoyJ6+VpHGSFkt6SdIOOXknS/ogq3ePpNr53EczMzMzs3WV75Hyk4E+wKFAU+BCYDbwCNAie2wbEXeXU3cQsCXQDvgTcL+k+pJqAE8ALwD7AuOAewEktQTuAvoCh2TbvCJP+2ZmZmZmtl7k9ULPiDi15LmkMcB2QDHwUURUePs2SU2BE4DtI2I28Iak9kAn4HPg04j4bVZ2EvChpN2A7sCgiLgny/sF8BpweT72z8zMzMxsfcj76iuSTgXOII14nwCcBuwhaQqwDfAscHZEzMupticwLgvIS0wEdgWaAyNLEiMissB8V6CINBpfkvdfSbUl1Y+Ir/Kxf2Zm+bTPpQML3YUNzmvXXlDoLpiZrbHKuNCzKWmEfCbwGbAMCKAXcCBQC7i4TJ2tgLll0hZnZdc2bwWSukkaL2n8zJkz12yPzMzMzMzWo7wH5RExICL2Al4GfhcRv4mIQyPi6YiYCAwF9i5T7SugYZm0JsCsNcmTVJM0L/07o+QRMTgiiiKiqFGjRmu/g2ZmZmZm6yhvQbmkapJ2zkn6B9BIUrUyRZsCX5ZJmwQUlVk55VBgAmkaS7uc7dQF2mZ1VsgDDgKmRsSSddgVMzMzM7O8yuec8q2B1yV1A6aR5nr/HRgs6WvgNtLc8auBn+VWjIhPJb0ODJV0A3AUsAvwZNbnWyT1AUYBlwEvRMTnku4HRkh6hzRV5mZgWB730czMzMxsneVtpDwipgO/Af4C/BOYDAwA/gC0Bv5FCqh7RcTTAJI2kXRS1sTZQDPStJdOwIkRsTAi5gKnAGcBrwJ1gXOzbY4FrgRuAZ4CngduzNc+mpmZmZmtD/leEnEoac54rg+BgyuocjJwKvC3LKhvX0G7Y4CWFeT1B/qvTX/NzMzMzAqhMlZfWRPHkEbWzczMzMw2GlUqKI+IcyPi9UL3w77/hg8fTrNmzVi0aBGLFi2iSZMmSFrhUb16dYYPH15u/TfeeIMDDzyQzTbbjLZt2zJ69GgAHn30UapXr75CO40bN2bp0qVMmzaNvffem3r16nHjjWlW1UUXXcT5559fafttZmZmVVPebx5kVtUMHTqUrl27UlxczKJFi2jYsCF9+/Zl1qxZAMyePZsbb7yRbbbZhvbty51BxcUXX8zMmTO58MILeeaZZ/jJT37C559/zhZbbMGyZcs477zzqFGjBvXr12efffahevXq9OvXj48++ogWLVrQp08fOnTowIABAxg8eHBl7r6ZmZlVQQ7KbaMzY8YMWrRowbvvvlua9otf/KL0eZcuXahevToPP/wwDRuWXRI/efzxx6lduzY1atRg55135pxzzuGTTz5h9ux0E9pHHnmEOXPmULt2bW699VYA2rVrx4ABA5g5cyZFRUUMGjSIxo0b07lz5zzurZmZmW0IqtT0FbPKcMUVV9CnT59y86ZMmcI999xDt27d2GeffSpso27dujzxxBMcfPDBdO3alTZt2rDTTjvxySefANCsWTNuu+02DjvsMHr27Mn8+fM55ZRTmDRpEqNGjWLkyJEMGTKE3r17U6NGjbzsp5mZmW04PFJuluOGG26gWrVqXH755ass++GHHzJ16lSWLVvG1ltvDUC9evXYZZddGDt2LA0bNqSoqIiioiLefPNNDj74YFq3bk3r1q25+uqrqVGjBl27duXbb791YG5mZraR80i5WWb+/Pk88MADnH766aVB9sr06tWLGTNm0LdvX5544gnGjRvH2Wefzbvvvls67aVWrVoALFmy/Kay8+bNo1+/fvTs2ZMzzzyTWrVq0bp1a6ZOnZqfHTMzM7Mqz0G5Wea+++5j4cKF/PKXv1xl2WuvvZaPPvoIgA4dOgCwcOFCvvjiC958883Scn/729+QRMuWy5fVHzBgAMXFxbRq1YoRI0bwwAMPsGDBAq677rr1vEdmZma2ofD0FdvoLFu2jLlz5wJpdLxkVHvYsGHssMMO7Lvvvt+pM2LECJo3b06bNm349ttv6devH8OGDaNjx46MGDGCRo0asffeezNw4ED69u1Lly5dWLp0KcOGDeP000+nefPmQBox79+/P927d6dOnToUFxczbtw45s6dS4MGDSrvIJiZmVmV4qDcNjqdO3fmwQcfBKBly5bMnj2bTTfdlK+//prevXuXW+fKK6+kffv2tGnThho1avD444/Ts2dPbrzxRlq1asXIkSPZcsst6dWrF++99x73338/m2yyCWeddRY333xzaTvTp0+nWrVq9OjRgyZNmtCpUyfuuOMO9t13Xy655JJK2X8zMzOrehQRhe5DwRUVFcX48eML3Q2rJNOnT2fSpElEBA0aNGC//fYrdJfMKrTPpQML3YUNzmvXXlDoLpiZrYzKS/RIuW10mjdvXjqdxMzMzKwq8IWeZmZmZmYF5qDczMzMzKzAHJSbmZmZmRWYg3Izq3TDhw+nWbNmLFq0aIX0iRMnssMOO/Dcc8+tso3//Oc/7LLLLowZM+Y7eXPmzOGggw7i6quvLk2bNm0ae++9N/Xq1ePGG28E4KKLLuL8889fx70xMzNbd77Q0zYIXoFizVXVFSiGDh1K165dKS4uZtGiRWy22WYATJkyhSOOOIJWrVqtckWcCRMm0L59e+bMmcOXX365Qt7ChQs55phjmD59Oqeffnpper9+/fjoo49o0aIFffr0oUOHDgwYMIDBgwev/500MzNbQx4pN7NKNWPGDFq0aLFC2tKlSznttNPYY489ePrpp0sD9Yp8/vnn7LDDDuXm/eY3v2H69Om8/PLL7LLLLqXp7dq1Y9asWYwfP5499tiDQYMG0bhxYzp37rzuO2VmZraOHJSbWaW64oor6NOnzwppjzzyCG+//TZvvvkmjRs35oQTTmDGjBkVtnHUUUfx2GOPfSf9k08+4Y477mDhwoW0bNmSoqIiXn75ZQBOOeUUJk2axKhRoxg5ciRDhgyhd+/e1KhRY/3uoJmZ2VpwUG5mBff0009To0YNjjjiCDp16sSYMWPo3r37GrfzzDPPUFxcTJs2bejatSszZ87k5JNPZvHixQC0bt2aI488ksGDB1OjRg26du3Kt99+u753x8zMbI05KDezgvv000859NBDefDBB7n99tv59a9/zQsvvLBW7dSqVYvRo0dz8803M2TIEL744gumTJlSWmbevHn069ePnj17cuaZZ1KrVi1at27N1KlT1+cumZWr7EXOF154IZJWeJx00kkV1p82bRqHH344m2++OXvuuSevv/76StsvqeOLnM2qPgflZlZwDRs25P333y99PWvWLOrWrbtW7SxZsoSPP/64tB1ghbYGDBhAcXExrVq1YsSIETzwwAMsWLCA6667bh33wmzlhg4dyumnn85nn31WGjRvttlmVKtWjd/97nd069aNa665hiuvvLLCNn76058ydepUevXqxeLFi+natetK24fvXuQ8depUBgwYwL777pu/nTWzNebVV8ysUi1btoy5c+cCMH/+fBo2bEiHDh24//77Oemkk2jatClDhw7lgguWrx4zYsQImjdvTps2bUrTvvrqq9I2ShxzzDFUr16dk046icMOO4xhw4ax1157sdNOOwGwZMkS+vfvT/fu3alTpw7FxcWMGzeOuXPn0qBBg8rYfduIlVzk/O6775amzZ49G0ncdNNNLFy4kGbNmnHMMceUW3/JkiW8/vrr9O/fnx49elCtWjVuuOGGlbYP6SLnAQMGMHPmTIqKinyRs1kV5ZFyM6tUnTt3plevXgC0bNmSxYsXc9ppp3HxxRfzwgsv8Oijj9K1a1euueaa0jpXXnkl99xzT+nr22+/nbZt2wJwzjnnMHr0aAB+8IMfcM899zBr1ixuvfVW2rZty2OPPYYkAKZPn061atXo0aMHRx99NJ06deKOO+6gbdu2XHLJJZV0BGxjVd5Fzp988glLly7lkEMOYciQIWy++eZcdNFF5davVasWe+yxB3/84x8566yzuOGGGzj66KNX2j74ImezDUVeR8oldQKuAeoDTwDnRcRiSd2B3wGbA0OA3hERZerWBG4Cfg7MAy6NiL9medsAQ4F2wHtAl4h4M8s7CLgF2Bl4DjgrIlZcyNjMCub666+nc+fORAQNGjRg0003LU2//vrry60zYcKEFV6fccYZbLfddixdupSaNWty4IEHluZ16tSJTp06ldvOzjvvzCeffFL6+r777lvHvTFbNw0aNOCkk07i0UcfRRJz5szht7/9LcuWLaNatWrfKb/bbrsxceJEnn/+eRYuXEjNmjWJiNIfnhVp3bo1rVu35uqrr17hImcH5mZVR75Hyk8G+gCHAk2BCyUdDlwNdAeOyfLOK6fulcCPgaOAHsCNklpmeQ8BnwL7AfcCj0mqLqkh8DgpYD8AmA30z8+umdnaaN68OccddxzHH3/8Km8SVJG6dety9NFHc/zxx3PkkUdSs2bN9dxLs8px5513rnA2p1atWhQXF5e7KtAbb7zB/fffXzpH/Oabb+aBBx7glVdeWa1t+SJns6otr0F5RJwaEQ9no9hjgO2AXsDlETEiIl4BfgN0y62XjZL/Avh5RLwaEX8DbgXOkVQEbEsadZ8QEX8GPiYF72cDT0fETdk2uwPHS6qXz/00MzNbG2+//XbpmvwRwd///nd22mknateu/Z2yJRdDd+jQAYD27dsDrHD2Z2V8kbNZ1Zb3Cz0lnQqcQZpqcgLwGNA1p8hEYNcy1XYEvoyIKWXKnQUUAU9FxNJy2igijZQDEBELJH2ctffmetkhMzOztVDeRc4XXXQRb7/9Nj//+c+ZOHEizz33HLfffntpndyLnFu3bo0kTj31VI444giee+45atWqxd57711h+yV8kbNZ1VcZF3o2JY2QzwQ+A7YC5ubkLwZqlalTtkxuubXNW4GkbpLGSxo/c+bM1d4ZMzOztVHeRc633HILu+66K/3792fy5Mlce+21dOu2/ORx7kXOrVu3ZsiQIcyZM4d+/frx9ddf88gjj7D99ttX2H4JX+RsVvWpzPWV+duQdCcg0jzyfSLi4yx9B+CliNgmp+yupNHwHXPSugCHA2Oz+ufk5N0NvESan/5URNyTk/cRcHhELF8EuYyioqIYP378+tlRy4t9Lh1Y6C5scF679oJVF7Iqz5/9NVdVP/vTp09n0qRJpRc5r+01FYVq38zWm3KvzM7b9BVJ1YAdIqLkCpJ/AOeQppq0I12gCSmQnlCm+jSggaTmETG9TLmJwMU529kEOBgYCDTI2r4ny9sJqJe1Z2ZmVjDNmzenefPmG2z7ZpZf+ZxTvjXwuqRupKD4QuDvpGks10r6PCt3Neniz1IR8Y2kR4C7JF0O/Ag4EbgMmAGEpJuAu4FzgfnAeOArYKKkcaQA/gbg3jLzz81sDXm0ds1U1ZFaMzOruvIWlEfEdEm/Af4CbAk8AAwAlgItgYeBZUD/iBheUk/SKRHxKGlVlrtIq7Z8CvysZNRc0imkZQ+7A28BHbN1zj+QdC5wPdCIdNHnb/O1j2ZmZmZm60NeV1+JiKGk4Lmsy7LHCrLlDi8GHo2IOcBJFbQ7ibTSSnl5D5HWMTczMzMz2yBUxuora+I44M+F7kRlGT58OM2aNWPRokXfyevZsyeNGjXio48+WmkbEUGPHj04/PDDV0i///772WWXXdhiiy049thjS9uZNm0ae++9N/Xq1ePGG28E4KKLLuL8889fT3tlZmZmZmsq7+uUr4mI6FvoPlSWoUOH0rVrV4qLi1m0aBGbbbZZad69997LgAEDAJg5cybbbbddhe307NmTwYMH8+CDD5amLVmyhHPPPZcDDjiAjh07MmzYMLp06cLo0aNL7wTXokUL+vTpQ4cOHRgwYACDBw/O386amW2gfD3FmvM1FWZrp0oF5RuTGTNm0KJFC959990V0j/++GMuuOACtt12Wz7++OOVtvHggw9y2223MXLkSI4++ujS9Fq1ajFjxgzq169fuq3nnnsOgHbt2jFgwABmzpxJUVERgwYNonHjxnTu3Hk976GZmZmZra6qNn1lo3HFFVfQp0+fFdIigrPPPhuAa6+9dpVt9O3blxo1atCxY0d23HFH7rjjjtK8+vXrc80119C6dWvuueceOnbsCMApp5zCpEmTGDVqFCNHjmTIkCH07t2bGjVqrL+dMzMzM7M14qC8CrnuuusYM2YMF1xwASU3dfriiy8o7wZPH374IVOmTGH77benS5cuNGnShPPPP5/cmyC98847TJ+elnnfeuutS9Nbt27NkUceyeDBg6lRowZdu3bl22+/zfPemZmZmVlFHJRXEfPnz+eKK64A4JprrimdTnLccccxcuTI75T/9NNPgTQ3fcCAAYwePZpNNtmEF198sbTMvffey+eff86xxx5L3759Wbp0+XLt8+bNo1+/fvTs2ZMzzzyTWrVq0bp1a6ZOnfqdbZmZmZlZfjkoryLq1q3LhAkTeOyxxxg+fDhXXXUVALfccgvHHHPMd8o3bNgQgPfeew+A2bNns2zZMurWrcuXX35J3759+eabb6hduzZHHHEES5YsYdmyZaX1BwwYQHFxMa1atWLEiBE88MADLFiwgOuuuy7/O2tmZmZmK/CFngWybNky5s6dC6RR8oYNG9K6dWtat24NQKNGjQDYe++9S+d7jxgxgubNm9OmTRtatGhBy5YtueSSS5g8eTKjRo2iTp06HHvssUyfPp2rrrqKF198kaKiIoYMGcJhhx1GrVq1gLQ6S//+/enevTt16tShuLiYcePGMXfuXBo0aFCAo2FmZma2cfNIeYF07tyZXr16AdCyZUsWL168Qv4OO+xAkyZNaNasWWnalVdeyT333FP6+v7772ebbbahf//+SGLkyJFsvfXW7LnnngwdOpRp06YxcOBA9t9/f4YNG1Zab/r06VSrVo0ePXpw9NFH06lTJ+644w7atm3LJZdcktf9NjMzM7Pv8kh5gVx//fV07tyZiKBBgwZsuummK+Rvu+22fPbZZyukTZgwYYXXe+65J2+88Ua57Xfp0oUuXbqUm7fzzjvzySeflL6+77771mIPzMzMzGx9cVBeIM2bN6d58+aF7oaZmZmZVQGevmJmZmZmVmAOys3MzMzMCsxBuZmZmZlZgTkoNzMzMzMrMAflZmZmZmYF5tVX1sA+lw4sdBc2KK9de0Ghu2BmZma2QfBIuZmZmZlZgTkoNzMzMzMrMAflZmZmZmYF5qDczMzMzKzAHJSbmZmZmRWYg3IzMzMzswJzUG5mZmZmVmB5DcolbSvpaUkLJb0sqYWkUyVFmcfvy6lbU9IgSfMk/U/Sz3PytpE0StJiSRMl7ZWTd5Ckt7O8f0hqkM99NDMzMzNbV3kLyiXVAF4APgD2z/69BZgNjANaZI8dgD+U08SVwI+Bo4AewI2SWmZ5DwGfAvsB9wKPSaouqSHwODAUOCDbVv987J+ZmZmZ2fqStzt6RsS3ks6MiBcBJD0OXAE0BGZExNSK6kqqCfwCODAipmRpbYBzJD0EbAscEhFLgQmSTiAF7y2BpyPipqxOd2CGpHoRMSdf+2pmZmZmti7yOn0lJyDfFLgYGAM0BbaT9KakBZJGS9q6TNUdgS9LAvLMRGBXoAh4KgvIy8sbmbP9BcDHWXtmZmZmZlVS3i/0lLQD8DJQhzRNpRiolj3fD5gG/F+ZalsBc8ukLQZqrUNe2X51kzRe0viZM2eu4V6ZmZmZma0/+b7Qsy3wOvAfYL+I+Coi+kfEXhHxt4h4mzTPfO8yVb8iTXPJ1QSYtQ55K4iIwRFRFBFFjRo1Wou9MzMzMzNbP/I9Un47MCQiOmdTSZBUdptNgS/LpE0DGkhqnpN2KDCBNFWlXUli1t7BFeTtBNTL2jMzMzMzq5LydqGnpLrA7sCBZbJ+J2kP4GrSyiv9gGtzC0TEN5IeAe6SdDnwI+BE4DJgBhCSbgLuBs4F5gPjSSPlEyWNIwXpNwD3lpl/bmZmZmZWpeRzpLw2IOCjbM3whZKGAwOBGqRlEW/MXg8uqSTplOzpb0jB9hjgUuBnETE9IoqBU4CDsjaKgI6RfEAK0q8AxgKfAL/N4z6amZmZma2zfC6JOFNSC1JwXmJ2RHwFdCivjqQi0iotj2ZLGJ5UQduTSMF4eXkPkdYxNzMzMzPbIOQtKAeIiA/XsMpxwJ/z0BUzMzMzsyorr0H5moqIvoXug5mZmZlZZcv7OuVmZmZmZrZyDsrNzMzMzArMQbmZmZmZWYE5KDczMzMzKzAH5WZmZmZmBeag3MzMzMyswByUm5mZmZkVmINyMzMzM7MCc1BuZmZmZlZgDsrNzMzMzArMQbmZmZmZWYGtVlAu6dDVSTMzMzMzszW3uiPlfykn7c/rsyNmZmZmZhur6ivLlLQzsAuwhaRjc7K2BDbLZ8fMzMzMzDYWKw3KgQOAs4EmwMU56fOA3nnqk5mZmZnZRmWlQXlE3A3cLensiBhWOV0yMzMzM9u4rGqkHICIGCZpJ2Cn3DoR8WS+OmZmZmZmtrFYraBc0p+ArsA7wLIsOQAH5WZmZmZm62i1gnLgNGCniJiXz86YmZmZmW2MVndJxE8dkJuZmZmZ5cfqjpS/KukBYDjwdUmi55SbmZmZma271Q3K987+7ZGT5jnlZmZmZmbrwWpNX4mI9uU8Dl1VPUnbSnpa0kJJL0tqkaWfLOmDLP0eSbUrqH+VpFnZ4+Kc9C0lPZjVf1/SkTl5rSSNk7RY0kuSdlidfTQzMzMzK5TVCsolHVveYxV1agAvAB8A+2f/3iKpJXAX0Bc4BGgKXFFO/fOAn5MuMj0duEDSEVn2INJdRdsBfwLul1Q/2+YT2Xb3BcYB967OPpqZmZmZFcrqTl/JvZtnbWBP4E1WMn0lIr6VdGZEvAgg6XFS8P0rYFBE3JOl/wJ4Dbi8TBO9gO4RMTor1xfoJult4ARg+4iYDbwhqT3QCficdFHqb7M6k4APJe0WEZNXc1/NzMzMzCrV2kxf2Q9oS1qzfFX1SgLyTUmB/RigCBiZU+a/QG1J9UvSsvI7As/mNDcR2JX0g2BcFpCXzSvbdgCTsjwzMzMzsyppdZdEXEFE/AfYfXXKZnO6XwbqAH8AtgLmlim2GKiV87oesCALqsuWWVn91Wm7pF/dJI2XNH7mzJmrsytmZmZmZnmxunf0zJ0/vglpNZZVBvSS2gKjgKeBX0TEAklfAQ1zytQkzQ//KqfqV8BWkjaJiOIsrQkwK8tryIpK8jZZSd4KImIwMBigqKgoyuabmZmZmVWWtZlTvpR00eapq1HvdmBIRPTJSZtIukBzbPb6IGBqRCwpKRARSySVXCD6UpZ8KDCBNB2lSFLtiPg6J68/UA04r6QdSXVJU20mrUZfzczMzMwKYrWC8ohov6YNZwHx7sCBZbLuB0ZIegf4DLgZGFZOE/cBgyT9ijTa3RM4IiI+lfQ6MFTSDcBRwC6ki06rk1Z46UMaob8MeCEiPl/T/puZmZmZVZbVXRJRks6XNFzSw5K6StIqqtUGBHyUrRm+UNLwiBgLXAncAjwFPA/cmLOtE7KlDa8H/kWa+tIfuDgi/pUVOxtoRpqr3gk4MSIWRsRc4BTgLOBVoC5w7urso5mZmZlZoazu9JXrgTak9cUhBb0tgEsqqhARM7ObBeXeGGh2ltefFGivQNLWpJHzXbLpLF2zR9m2pwPljt5HxBig5ap3yczMzMysaljdoPwoYK+IWAog6WHgDVYSlANExIdr2J8jgYEl2zEzMzMz2xisblAuIHeFksjS1quIGLa+2zQzMzMzq+pWNygfBTwlaRgpID87SzMzMzMzs3W00qBcUjXSjXcuAboBJ5NGyEeQrfFtZmZmZmbrZlWrr1wHnBERxRFxW0R0jIhTSOuBX5P/7pmZmZmZff+tKig/luUrruQamOWZmZmZmdk6WlVQXhwRy8omRkQxUJyfLpmZmZmZbVxWFZTXlLRZ2URJm5PmmpuZmZmZ2TpaVVD+EHC3pC1KEiRtCQwBhuezY2ZmZmZmG4tVBeVXA0uAGZLelPQm8D9gGXBVnvtmZmZmZt8z//znP2ndujV169bluOOO44svvlhp+QULFtCuXTvatGmzQnpE0KNHDw4//PDStCVLlvDTn/6UzTffnNNPP53i4mIee+wxdtttt7zsy/q00qA8IpZGRGegDXAtaTWWNhHxM99108zMzDZEqxsUfvHFF3Tq1InNN9+cHXbYgYEDB5bmjRw5kr322os6derQpk0bXn75ZWDDDgorwxdffMFJJ51E/fr1ueCCCxg7dix/+tOfVlqnV69evPjii7z//vsrpPfs2ZPBgwfzq1/9qjTtySef5JFHHuHAAw/koYce4oUXXuDSSy+lqKgoL/uzPq1qpByAiJgaEcMj4uGImJrvTpmZmZnlw5oEhbfddhtPP/003bp1o23btvTo0YOxY8cyefJkOnbsSJ06dfj1r3/NggULOO6445gxY8YGHRRWhrfeeosFCxYwaNAgrr32Wvbaay+mTq04tBwxYgR33nkn22677QrpDz74ILfddhuPP/44J510Uml627Zt2WyzzRg1ahRbbbUVkydP5v333+fiiy/O2z6tL6t7R08zMzOzDV5uUPijH/2IV155pcKg8PLLL6dXr15sscUWTJs2jUcffZT33nuPRYsWsXTpUp599llq165Njx49+MEPfsA//vEPjjnmmHKDwkcffbSS97Rqat26NZttthmdO3dmp512YuzYsQwYMKDcsl988QXnnXce++yzD0ceeSQ33nhjaV7fvn2pUaMGHTt2pHHjxlx66aV07dqVbbfdlilTpjBx4kR+/OMfc+KJJ3LCCSfQunXrytrFtbZaI+VmZmZm3we5QeHJJ5/M2LFjOfroo8stW61aNaZPn86JJ57I7rvvzlZbbcVhhx1Gw4YNKS4u5vbbb+ftt9/moYceIiKYP39+aVD4xBNP8N577/Hggw9uMEFhZdhiiy1o3rw5kydP5rXXXkMS1at/d4y4uLiYs88+m1mzZnHppZcyZ84cIoIvvviCDz/8kClTprD99tvTpUsXmjRpwvnnn8/48eMB2GabbTj22GP5z3/+w8svv0yfPn1YurTqz7r2SLmZmZltNHKDwlmzZlUYFJb44osveP/991m4cCHbbbcdderUoWPHjjzwwAP06tVrhbI77rgjkILCbbbZhrFjx/Lyyy/zyiuvsHTp0pVuZ2MxePBgPvjgA8aNG8eee+7JGWecQe/evTnnnHOoWbNmablRo0bx1FNPAXDiiSeWpjdr1oyXXnoJgKFDh7LvvvuyePFi6taty4svvrjCNKG+ffvSvn17XnrpJQ455BC23HJL7rzzTk444YTK2dk15JFyMzMz22jkBoUfffQRHTt2pHfv3nzzzTfllm/fvj2TJ09m9OjRfPTRR9x5553UrFmTkSNH8tZbb/H444+z0047UatWLQ477LAV6uYGhXXq1KFx48aMHDmyMnazynr//ffZeuutKSoqonr16hx00EEsXLiQuXPnrlDuqKOO4oUXXuCRRx5h+PDhnHrqqdSuXZtXXnmFhg0bAvDee+8BMHv2bJYtW0bdunVL67/88suMHj2a3/zmN1x22WVcfvnlFBUVceGFF1bezq4h/2QzMzOzjUZuUAhw0EEHMXz4cObOnUujRo1WKPvwww/TsGFDDj30UA4++GDq1avHwoULS/N32WUXBg4cyAcffEDv3r3ZcsstS/NKgsJ//OMfnHjiiVxxxRWMGzeOCy+8sMqO1FaG3Xffndtvv50TTzyRH/7wh/z1r39ll112oVGjRkyYMIGPP/6YDh06sMkmm9CuXbvSepMnT+bJJ59kn332AaBly5ZccsklTJ48mVGjRlGnTh2OPfbY0vLXX389++67L+3ataO4uJjJkyczffp0GjRoUOn7vLo8Um5mZmYbjd13352PP/6YE088kT59+nDdddetEBSOGDGitOwDDzzAySefzMUXX8zxxx/PnDlzSgO/P/zhD2y33XbceeedHH300fzxj39cYTsbYlBYGc4//3x+/etf88orrzBw4EBatWrF448/DqTpKFdddVW59Vq1asUPf/jD0tf3338/22yzDf3790cSI0eOZOutty7Nf/fdd7n88supW7cuf/nLXxg1ahTffvstgwYNyuv+rQuPlJuZmdlG4/zzz+e9997j/vvv55///Cf77bdf6eofQ4cO5aWXXqJDhw4A3HrrrXTv3p1bb72Vhg0bcuedd7L//vsD8Oijj7L99tvTr18/zjjjDCStsJ13332Xv/zlL6VBYd++fWnatGmVDgorQ7Vq1ejXrx/9+vX7Tl7//v0rrHfqqady6qmnlr7ec889eeONNyosP2XKlNLnF154YZWetlLCQbmZmZltNNYkKGzatCl/+9vfym1nwoQJK93OhhgUWmF5+oqZmZmZWYE5KDczMzMzKzAH5WZmZmZmBeag3MzMzMyswCrlQk9JmwMPACOzx3SgWk6R5yOifTn1ugO/AzYHhgC9IyIk1QRuAn4OzAMujYi/ZnW2AYYC7YD3gC4R8Wa+9s3MzMzs+2afSwcWugsbnNeuvWCd6uc9KJdUDRgDtAUeB2YCAlpkRYqBT8updzhwNXAuMAsYBJwH3AFcCfwYOApoCgyWND4i3gEeAqYC+wFHAI9J2jkiluZrH83MzGz9c2C4ZtY1KLTCyntQHhHLJA0AjicF4A2B2RExdRVVewGXR8QIAEm/Af5P0t3AL4ADI2JKltcGOEfSQ8C2wCFZED5B0gmk4P2J9b93ZmZmZmbrrlLmlEfEPcBmwALSyHaxpCclzZM0RdKB5VQrIk11KTER2BXYEfiyJCAvk1cEPFVmVLwkz8zMzMysSqrMCz3rk6ahCPiGNHJ9ANAfuE9lb4UFWwFzc14vBmqVk766eSuQ1E3SeEnjZ86cuVY7ZGZmZma2PlRmUL4VMCsi3oqIH0TEoIh4G7gVqAc0KVP+K9JUlxJNSEF92fTVzVtBRAyOiKKIKGrUqNFa7pKZmZmZ2bqrzKC8JrAESi/+LLFlljenTPmJpBVUShwKTACmAQ0kNS8nb4U6kjYBDs7yzMzMzMyqpMoMyucDm0tqB7wt6YBsLvljwN8j4usy5e8DrpV0hKQjSCuxDIuIb4BHgLsk7SPpPOBE4EHgX0BIuknSnsDN2XbHV8L+mZmZmZmtlcpYErEmMAnYCXgWOAx4kjSnfAlpmcTeOeXbk0a27wVaAg8Dy4D+ETE8K/Yb4C7SUoufAj+LiOlZ/VNI65R3B94COkZE5HUnzczMzMzWQWUsifhNNiJeGyiOiE9IwXLvsmUlbQoMA9pkgfRl2aNsm3OAkyrY3iTSKixmZmZmZhuESrmjZ0R850LLCuwLPB4RX+WzP2ZmZmZmVUmlBOWrKyLGkKakmJmZmZltNCrzQk8zMzMzMyuHg3IzMzMzswJzUG5mZmZmVmAOys3MzMzMCsxBuZmZmZlZgTkoNzMzMzMrMAflZmZmZmYF5qDczMzMzKzAHJSbmZmZmRWYg3IzMzMzswJzUG5mZmZmVmAOys3MzMzMCsxBuZmZmZlZgTkoNzMzMzMrMAflZmZmZmYF5qDczMzMzKzAHJSbmZmZmRWYg3IzMzMzswJzUG5mZmZmVmAOys3MzMzMCsxBuZmZmZlZgTkoNzMzMzMrsEoJyiVtLmmkpG7Z61aSxklaLOklSTtUUO9kSR9IWijpHkm1c/KukjQre1yck76lpAezOu9LOjL/e2hmZmZmtvbyHpRLqgaMAY4DiiXVAJ4AXgD2BcYB95ZTryVwF9AXOARoClyR5Z0H/Bw4DTgduEDSEVnVQcCWQDvgT8D9kurnaffMzMzMzNZZ9XxvICKWSRoAHA8UAz8BPo2I3wJImgR8KGm3iJicU/VXwKCIuCcr9wvgNeByoBfQPSJGZ3l9gW6S3gZOALaPiNnAG5LaA51IwbqZmZmZWZVTKdNXssB6M2ABUASMzMkLYBKwa5lqZcv9F6gtaRtgR+DZnLITs/p7AuOygLxsnpmZmZlZlVSZF3rWB2YBWwFzy+QtBmqVSauoXDGwIAvmy9Zf3baR1E3SeEnjZ86cuSb7YWZmZma2XlVmUL4VKSj/CmhYJq9JlpdrhXKSapLmin8FbCUpt+8l9Ve3bSJicEQURURRo0aN1nxvzMzMzMzWk8oMymsCS0jTSdqVJEqqC7QlTWHJtUI54CBgakQsAT4A9s/JOxSYkLVRlLtKS06emZmZmVmVlPcLPXPMBzYHngJukdQHGAVcBrwQEZ+XKX8/MELSO8BnwM3AsCzvPmCQpF+RRsJ7AkdExKeSXgeGSroBOArYBXgyr3tmZmZmZrYOKmNJxJqSpgC7kS7O3BE4BTgLeBWoC5ybU76tpO0iYixwJXALKZB/HrgxK3Y98C/gaaA/cHFE/CvLOxtoBrxMWnXlxIhYmMddNDMzMzNbJ5WxJOI3kg4EagPFEfFJltWygip3kdYeJyL6k4Lusm0uAbpmj7J504H266HrZmZmZmaVolKmr0TEdy60LI+knYD/RsR/8twlMzMzM7MqozLnlK9SRHwAnFjofpiZmZmZVabKXH3FzMzMzMzK4aDczMzMzKzAHJSbmZmZmRWYg3IzMzMzswJzUG5mZmZmVmAOys3MzMzMCsxBuZmZmZlZgTkoNzMzMzMrMAflZmZmZmYF5qDczMzMzKzAHJSbmZmZmRWYg3IzMzMzswJzUG5mZmZmVmAOys3MzMzMCsxBuZmZmZlZgTkoNzMzMzMrMAflZmZmZmYF5qDczMzMzKzAHJSbmZmZmRWYg3IzMzMzswJzUG5mZmZmVmAOys3MzMzMCqwgQbmkZpKWSoqcx5hyynWXNEPSXEk3SFKWXlPSIEnzJP1P0s9z6mwjaZSkxZImStqrMvfNzMzMzGxNVS/QdmcCAlpkr4uBT3MLSDocuBo4F5gFDALOA+4ArgR+DBwFNAUGSxofEe8ADwFTgf2AI4DHJO0cEUvzvVNmZmZmZmujUEF5Q2B2RExdSZlewOURMQJA0m+A/5N0N/AL4MCImJLltQHOkfQQsC1wSBaET5B0Ail4fyJve2NmZmZmtg4KNae8KVAs6clsCsoUSQeWKVMEjMx5PRHYFdgR+LIkIC+TVwQ8VWZUvCTPzMzMzKxKKlRQLuAb0uj1AUB/4L6SOeOZrYC5Oa8XA7XKSV/dvBU7IHWTNF7S+JkzZ67DrpiZmZmZrZuCBOUR8VZE/CAiBkXE28CtQD2gSU6xr0jTXEo0Ic0tL5u+unll+zA4IooioqhRo0brsjtmZmZmZuukYEsiSqqW83JLoCYwJydtItAu5/WhwARgGtBAUvNy8laoI2kT4OAsz8zMzMysSirUkojtgLclHZDNJX8M+HtEfJ1T7D7gWklHSDqCtBLLsIj4BngEuEvSPpLOA04EHgT+BYSkmyTtCdwMzAfGV9a+mZmZmZmtqUKtvvIi8CRpTvkS4HGgN4Ck9qSR7XuBlsDDwDKgf0QMz+r/BrgLGENaSvFnETE9q38KMBToDrwFdIyIqJS9MjMzMzNbCwUJyrMguXf2KCVpU2AY0CYrc1n2KFt/DnBSBW1PIq3CYmZmZma2QSjYnPIK7As8HhFfFbojZmZmZmaVpVDTV8oVEWNIU1LMzMzMzDYaVW2k3MzMzMxso+Og3MzMzMyswByUm5mZmZkVmINyMzMzM7MCc1BuZmZmZlZgDsrNzMzMzArMQbmZmZmZWYE5KDczMzMzKzAH5WZmZmZmBeag3MzMzMyswByUm5mZmZkVmINyMzMzM7MCc1BuZmZmZlZgDsrNzMzMzArMQbmZmZmZWYE5KDczMzMzKzAH5WZmZmZmBeag3MzMzMyswByUm5mZmZkVmINyMzMzM7MCc1BuZmZmZlZg38ugXFIrSeMkLZb0kqQdCt0nMzMzM7OKfO+Cckk1gCeAF4B9gXHAvQXtlJmZmZnZSlQvdAfy4CfApxHxWwBJk4APJe0WEZML2zUzMzMzs+/63o2UA0XAyJIXERHAJGDXgvXIzMzMzGwlvo9B+VbA3DJpi4FaBeiLmZmZmdkqKQ0kf39IuhZYHBFX56S9APwxIp7NSesGdMte7gq8W6kdXb8aArMK3YmNlI99Yfn4F5aPf+H42BeWj39hbejHf1ZEHF028fsYlJ8OnBcRh2ev6wKfAjtFxOcF7VyeSBofEUWF7sfGyMe+sHz8C8vHv3B87AvLx7+wvq/H//s4feUpYC9JfSS1AYYCL3xfA3IzMzMz2/B974LyiJgLnAKcBbwK1AXOLWinzMzMzMxW4vu4JCIRMQZoWeh+VKLBhe7ARszHvrB8/AvLx79wfOwLy8e/sL6Xx/97N6fczMzMzGxD872bvmJmZmZmtqFxUF5gku6W1DPndVdJ96yntkdkF7taRtLBkoolRTmPv+Zhe71z39/vM0m7SJosaZOctKsk/V+Zcm9I2nM12xwoqcN67mpJ29tIejUfbVcVq/q8Szq0EvrwvT/Oa0vSR5LmZe/HbEnjJO27kvJvSfrOjfAk7SNpeH57u+GStL2kv0v6QtK/JB1Widv23+FyrO/3RFINSR+sr/4VioPyApJUAzgVOC4nuSNw0nraRAug5npq63shIl4g3WBqB+Ah4Pek49QC+FUeNrkTsGke2q2KpgFbA/sBSKoF9ALalxSQ1BrYGZiymm3m8/j9AKiXp7arhFV83jcHtq+Ebnzvj/M62BboAuwGHAj8CfhoJeUPjojy7qmxPen9tDIkbU1a9OE14BjgRmDOKurcJ2nn9dQF/x0uY23ek9VQn/T3Z4P2vbzQcwOyGyng2B1AUk1gf2BzSc0i4tN1bL8W37276UYvW6FnrqQPgSURMXVt2slGhH8CdAIOIa2FP79MsY3mPYiIbyU9BRwNvEwKxmuTAsISRwFPRMTXq9nsWh8/Sa1IqzCdDvw+Ioatr7Y3JBV93iXNZT0MzPg4r7MXIqLkJiiTV1YwIuZVkOVjXLHuwOiIuDZ7/cZq1NkG2A5Yq78NZfi9+a61eU9W5XtxnD1SXlg/At4GGktqRApiFmZpuwJIukLSV5Lek7RHlvZnSd0kjcnyjs/Sm0h6XNJ8Sf8CtgRmS9pE0u8k/S87XdpN0jWSepd0RNL+kp5l4/IJ0LTkhaQHJO2TPT9A0oPZ8xbZ6bUlkiZmQQjAPcAAYBIwG2ianUK7QdJMSf8D2mZ5SPqJpP9I+lLS7ZKOkjQiZ/tbSJoqabPK2Pk8+QdwcPb8mOz15ko38YIUuA2T1FjSs9kxfV9SOwBJm0saJmmOpHdJfxhnSzpe0s2Sbs0+33/Iym+STXFZKOlzZVOFJB0NvAk0Bv5JGq1C0tHZe7AIuIrl78022WnmOZJeUZqK8x9JO5XsmKRbJJ2Vx2OXbyt83jNNJP0zO6Z9ACT9UtLF2fNakkqC+Icl/SY7Pp9L2s/Heb14XtLc7DvjXoDsO+T32XvzmaTTJDWQ9HJJJUlnSfqvpHnAeSw/xq0klbT5jKTtJL0raaucuv+Q1L5sR76nNgUa5XwHASCptqR+StMn3pPUQVK77DO7PzAy+35qJulFSdtk9TpJul5SI0mvZ+/TZEmTsnz/HV61it6T/5N0avZ825LPu6SXsvdnqqQPJG2bpe8tabykxcAQlv8fqKc0Pe8rpb/Z+0kaLemAnG1dIun3lbS/q81BeWEdBDwHvA/sCZwAjAD+DbSUdCxpjfWTgEdIASBAo+z568CfWb4O+xBgBtAOeJp0Omc+0Bs4GzgfGAQcBjwB9FU6jQTptPbYvOxl1fUp0CTndStgafZ8W5YHMMOA0cDewHTSOvhk/14J/AX4UUS8D/QEfgwcTzruLYD5SvNE/wr0A04DTiSdvttf0k+y9n4FfBQRi9bjPla2Z4G2kqqTPs+PA+8Bu0naBWiQlbmJFCT+GHieNNIKcC3p3gKHAv1Jp+Xnk26p/AtgR+CnwAVZ+XNJ02Xakz7DJfP3f0L6P3AZKWC5StIPSD+k+mbt1ya9NzWAJ0mnTw8HRJo28xLpvSX7I9AFeGHdD1HBlP28Q/r8vkYauTo7S9sVKM6e1ycdC0iBd2fSD6sRpPfCx3ndXUGaunIQaboXpP8n52avB5C+R+qSfqQiqYg01eUC0vTHpqRjXI/04+h10v+JXYDNgA9J7zWS9su293p+d6vKuJV0HOZmP1RGSfoRacrEAaTv46eAAyJiLOnM3gjg/4CdszPWuwMlZ/e2J/1fqAMUkQLMNsBOkoT/Dq+Oit6TH7L8u2eb7AFpCtxfgauBt4CTJNUB/gbcQfq/s4h0nAHuJ8VJx5LOduxJ+n8xQEkd4Lekv+tVS0T4UaAH6cPSEbib9GH7kPQhupQUCP4V6JaVrQ8szp4/BIzJnh8OjCGdulkI1MjSqwNB+uE1Dfhxln42MCx7PooU+LQifaAbFvqYVPLxPwp4Ouf1+8Au2fOzSMHidqSgsgPwIulLoH5W5vrsuM0Cjs7SXgba5bT5EikwuQu4JEvbHvgwe34pMBGoQfoiP67Qx2U9HNf3SQHFAtIfrvtIAfXVpOCtenbMjiKNpI8Bts/qzgC2zWnrf6TgrzvwFWl+dPX01RVkdc8EbiGd+j88Sz+YNDd3GTAoS+sKDM1p+zxS8Hgw6YfwJln686TpSNsCS4C9svd6eKGP7Xr+vA8jTSUq+UxOz57fwfLvne1yjvXzwPHZ87pANR/ndX5PAqheTvow4ILs+aak7/ftgf9ladcAV+aU/2P2/+ss4Mmc9A+zevsCi0lzbh8G/lzofa/k41yN9APwUGB49j2zGGiW5V8FXFXm+J+X8/pboGb2/PdZ/vbATJYvLV0P/x1el/fkf6QBmyOz/INZ/nfyc9LUuJLP+u+BI0hTYEraO5z0g2Y70t+XzXLey7NJ11zMAk4Gfgm8XuhjUN7Dc8oLJPtlvBMpiNuSNKpUjzRyvgw4B3iX9GGE9KWwJHtelxTIl6TXJAU/y7JHruqk0bF3stezSQEgpA/2c6Rf/X+N5fMaNxbLSMetxDyWXyyl7N/GpJGTn5H+SE4sKRwRl0i6khSA3kb6kq5L+rLPVfIePJa9zn0PBgC/AW7Ptv/kuu5UFTCe9MX5UEQslPQ2aUTpYNKIxlakH5kXAtdExIs5dSs6fnWBERExOxuNKs5GXhsDF5NGtXpExDIovcBxO0l7AY8prUyxsvfm3YgoGaGZTfqj+rGkYaT/JweQRv43ZGU/77D8M/kNyy+oLe//QYmvAWL5tRM+zvlTcqwXA6SPfam6fPfCuLLf9bD8GI+TNBa4jnQW7zsruHxfSaqbfV4/AD6QNBn4DCiO5ddtzSZ9l1Sk5P/EV6z4f2JJZJFfRMyRVB//HV6llbwn71L+d095Mc/KvmdyzziX/B9YIOkG0gh5HdIP2yrH01cKZ0/g8+xL4QXSaZpnI2IJKajZmXR6safSckp/IZ0Kg/RrfGb2fBZpZPEr0i/xAUrLzV2Z5W9BGkG8KGvnULILSyNiHGkk9zTSdIKNjUjHssSHwNHZqeGiLG0K6Yv4JQBJJyotS3awpMtJp5Vnko4zpJHbG7O5bj8nHeuS9+A8pesCTiHN5W0UEQuAG0in7PuVfMFv4P5FOp1+X/Z6EmnKw5SI+CwiZgL/BV4hnXI/KpszuCPp+N0mqY2kHqQv2C3I+cxnx+gr0gjrv0h/6KYCP5Z0j6SfK809P4A0GjM7a+N5oLOkU7NT+F1Y/t4cIqm9pENIP5Z3z/p+DenC1fcjYkNf1q/s510sPyX/BbBldlr3Q+AgSbuTRqPKbyxdP+HjXBhjgAuz/zuHkb5TSo7xT5SWSDyWdAq/5Bj/Hvg58HhETC9EpytbNiXiv5JOkdRS0lGkqQ3PA7OUliAuIk2B271M9dzv4g+BE7JpiLtVtD3/HV61VbwnE4DDsmN3YE6178Q8pGl3BynNzS8iTeXagvS3YFela7j2A/Zg+Xs7gPS9swVpdL7qKfRQ/cb6IJ1KfjDn9Xiy0zbZ6+dJo0YPkn6lPwM0zvLuBA7LntcBpmbPdyVNsViY1f8XKajZl3TaeC5pLttrOW1dRs7pzo3pQRqxPTrn9cGkU2hzSacU/5alH0r6slhM+mLtTPrRNIY0h+0j4PSsbF1SMDqXFCz+g3RNQJPsPZxPmkd4H3BSVmd/0hfNpoU+JuvpuLYmneHZInu9efb6iJwye5LOEi0ijZZclKU3y47Pguz/xBjSxbJnAX1z6j+Tbac+6XT8HNKX9qDsPRhAOkU9nzR1qOR08rnAx6QA8m6yqRKkP55fkL7QryaN8pds613gtEIf1zx83u8F9s15/TLpbE8T0moIi0jzMKeTBnCGA0Vl2vRxXrf3ZArZ9Icy6TeWfD/kpDUAXs15/bvs/9XnpKmON2Xv062kH60TSWeQbsrK1yL9Lflxofe7ko9xD9IUxK9JgwHXZt9JJ2TfPV+SziC8xfKpVTcAR+W08VPSd/Qs0qj2zaQfPP8qZ3v+O7z270nLLH0BaRByfFZ+MrB59vwQ4P7s+fFZ+XnZ99mLWfp5pL/ln2TfM8/nbPsZsqmkVfFRMhfKNkLZNID3Saf9nyp0fzZWku4EZkXEbwvdF1uRpINIP4y3i4ilqypva8fHOf+yM3cXRMQ+he6LLee/w5VH0nbAf4AfRMTsQvenPJ5TvnE7kLQywqhCd2RjJWlT0mnLolWVtYI4G7jbgWLenY2Pc76dTTrLalWL/w5XnjNJ07eqZEAOnlO+sWsJ3BPLL7yyyrcD8FaUf5c+K7ydWT433vLHxzn/fgA8WuhO2Hf473Dl2ZW0ElSV5ekrZmZmZmYF5pFyMzMzM7MCc1BuZmZmZlZgDsrNzMzMzArMQbmZ2QZC0oeSpkiaKOnfkk5fizaKJK3xRZWSzpb0yFrU6571+S1JdcvkXSXpiyzvPUmvS+opqdqabsfMbEPnJRHNzDYsHSPi39mdAV+R9M9YzVtzS6oeEeOBn+W3iyvoCfw8Il6vIP+eiOgNkN3V9V7Saiw9K6l/ZmZVgkfKzcw2QBHxFulOmjtIaibpEUn/kvS2pMtKymWj61dIGgPcLukQSeNz8s/M6kyS9DdJjbP0mpJul/SupNHAjyvqi6S9Jb2atfGqpL2z9IdIt7X+6+qMzkfEf4FzgO6StpRUXdIoSeMlTZZ0l6SaWdv/LtlO9voiSYPX7CiamVUdDsrNzDZAktqTbjryPmnt3f4R8WOgLXCMpCNyijeLiPYRcW6ZNlqTbjF+ZETsTroN+IAs+3zSOvqtSbezLjcoz4LkR4ErsjZ+BzwqqWZE/JR0q+uOEbFao/MRMQVYRFpTeBlwRkQUZf2oRgraAQYCv8z6IKA76fblZmYbJE9fMTPbsDwi6WtgHnAK8C1wCNAoxaYA1CXdlOTZ7HVFN8xoDzwZEZ9mr28HJubk3R0R3wLfSrqXdPfBsnYFvomIfwJExHOSvsnS316rPVxuE6C3pGNIAflWpIC9ZJ+ulFSf9IPh84iYWH4zZmZVn4NyM7MNS8eI+HfJi+ziyQD2zgLo8iyoIF1Z3VyRk7c6ymuDCtJW3Zi0K7AZMAU4g/RD4KCImJ9Ny9kFICIWSbof6EL6UeJRcjPboHn6ipnZBiwi5gMvAn1K0iQ1l9R0Nao/BxybU7Yr8M+cvJ9n87o3JQXI5ZkC1Mqm05RMq6kBvLem+yJpe+BO4NaImAfUA2ZlAfmW5fRhENCLNGXHt5A3sw2aR8rNzDZ8PwNuklQyXWQ+ae71ZyurFBGTJV0KPCspgP+S5pIDDAZ2ByYD/wNeIM0xL9vGN5JOAfpLqgMsJI3mf7OafT9T0mGk0fF5wH0sn9d+D/ATSZOBGaQfH5vmbHuapCnAa2uwPTOzKkkRa3WG0czMrKAkbUEaqf9xRPyv0P0xM1sXnr5iZmYbHEm/AP4D3OCA3My+DzxSbmZmZmZWYB4pNzMzMzMrMAflZmZmZmYF5qDczMzMzKzAHJSbmZmZmRWYg3IzMzMzswJzUG5mZmZmVmD/D4EGs7l0yIhTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot(col='day_of_week', xlabel='Period of Day', title='Bar Plot of the Day of the Week')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15811685",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "The output of these plot is not what i expected, I expected that at the begining of the week like Monday would have the least number of ride count, and that the end of the week like Saturday and Sunday would have the most ride count. From the bar plot of the day of the week we can see that Thursday and Tuesday have the most number of rider counts, while the end of the week like Saturday and Sunday have the least ride count with an average of 8.42%. So we can make the assumption that most people take rides during working days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e0389e74",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "sns.countplot(data=gobike_clean, x='day_of_week', hue='period_of_day', palette='Blues_d')\n",
    "plt.xlabel('Day of Week')\n",
    "plt.ylabel('Count')\n",
    "plt.legend(title='Period of Day')\n",
    "plt.title('Relation between Day of the Week and Period of Day');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ddf10b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "I went futher to explore the relationship between the period of day and day of the week. This what i discovered, I saw similar trend starting from Monday to Friday, were the 1st and 2nd highest counts are in the Evening and Morning and the 3rd is at Noon just as we saw in the visualization of Period of Day. But when it comes to the week end Saturday and Sunday, the trend changes as majority of rides start at Noon. I have an assumtion that the trends we see at the end of the week might be  because people tend to sleep or wake up late at the end of week."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d582c7bf",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 5. Which Age group is the most common among riders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "44492c5b",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot(col='Age_class', xlabel='Period of Day', title='Bar Plot of the Period of Day')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983bbee7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "I expected that Adult would make the majority of the data set. From the visualization youths made up 12.5% of the data, Adults made 85.54% of the data, and Seniors made 1.93% of the data set. These means Majority of our data set are Adults and Seniors from age 60 and above only make 1.93%. What might have influence the results we visualizied?, Well i think that the energy levels of Adults and Youth far exceed that of Seniors, Another reason would be hidden in population density of people living in greater San Francisco Bay areas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91e7fc47",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 6. How long does the average trip take?\n",
    "To answer this question we would divide the question to sub groups. How long does the average trip take for:\n",
    " + all riders?\n",
    " + customers and subscribes?\n",
    " + the different member gender?\n",
    " + different age class?\n",
    " + for those who share bike for all trip?\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ad0b93c6",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_duration = gobike_clean['duration_sec'].mean()\n",
    "median_duration = gobike_clean['duration_sec'].median()\n",
    "mode_duration = gobike_clean['duration_sec'].mode().values[0]\n",
    "plt.figure(figsize=[10,5])\n",
    "sns.histplot(data=gobike_clean, x='duration_sec')\n",
    "plt.xlim(0, 2000)\n",
    "plt.axvline(mean_duration, color='r', linestyle='--', linewidth=1, label='mean')\n",
    "plt.axvline(median_duration, color='black', linestyle='-.', linewidth=1, label='median')\n",
    "plt.axvline(mode_duration, color='green', linestyle='--', linewidth=1, label='mode')\n",
    "plt.legend(title='Histogram Statistics')\n",
    "min_lim_y, max_lim_y = plt.ylim()\n",
    "labels =['mean','median','mode']\n",
    "x_location = [mean_duration, median_duration, mode_duration]\n",
    "position = [50, 30, 50]\n",
    "for label , loc, position in zip(labels, x_location, position):\n",
    "    plt.text(loc+position, max_lim_y*0.9, '{0}:\\n {1:.2f}'.format(label, loc), fontweight='semibold')\n",
    "plt.xlabel('Duration [sec]')\n",
    "plt.title('Histogram Representation of The Duration in Seconds');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0414d96a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "From the histogram visualiztion the **mean** and **median** time is **725.9** and **514** seconds, so on an average riders have an average ride ranging from 726 seconds to 514 seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3603b7b0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### How long does the average trip take for customers and subscribes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9075c49d",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6,6])\n",
    "sns.barplot(data=gobike_clean, x='user_type', y='duration_sec', palette=['#157fa1','#a4daeb'])\n",
    "plt.xlabel('User Type')\n",
    "plt.ylabel('Duration [sec]')\n",
    "plt.title('Average Duration in Seconds for Customers and Subscribes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "afe32ccc",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "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>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Customer</th>\n",
       "      <td>1433.475734</td>\n",
       "      <td>791.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subscriber</th>\n",
       "      <td>640.164766</td>\n",
       "      <td>490.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   mean  median\n",
       "user_type                      \n",
       "Customer    1433.475734   791.0\n",
       "Subscriber   640.164766   490.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean.groupby('user_type')['duration_sec'].agg(['mean', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3a49608",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "On an average customers or casual riders have more rider time than subscriber, with customers having an average of 1433 seconds and subscribers having an average of 490 seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b953c3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### How long does the average trip the different member gender?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "dba3a022",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6,6])\n",
    "sns.barplot(data=gobike_clean, x='member_gender', y='duration_sec', palette=['#157fa1','#a4daeb'])\n",
    "plt.xlabel('Member Gender')\n",
    "plt.ylabel('Duration [sec]')\n",
    "plt.title('Average Time Taken by Different Member Gender');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b382903a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "member_gender\n",
       "Female     778.872761\n",
       "Male       672.394851\n",
       "Other     1130.705877\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean.groupby('member_gender')['duration_sec'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b8d5531",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "Earlier i posed a question since men have the highest ride count does it mean they also spend more time taken riders?. From the visualization the answer is No. We can see that generally spend an average of 672 seconds riding bikes, while women spend about 779 seconds, and Other spend the most time on average with 1131 seconds take rides."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877d7c5f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Let's Explore some Relationship between `user_type`, `member_gender`,  `period_of_day`,  `day_of_week`, and `bike_share_for_all_trip`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554a4406",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Relationship between `user_type`, `Age_class` and `duration_sec`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "30680b02",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:670: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x273a8856df0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=gobike_clean, col='user_type', height=5)\n",
    "g.map(sns.pointplot, 'Age_class','duration_sec')\n",
    "g.set_xlabels('Age Group')\n",
    "g.set_ylabels('Duration [sec]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d39eefcf",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_type   Age_class\n",
       "Customer    Youth        1588.160034\n",
       "            Adult        1413.104806\n",
       "            Seniors      1383.520619\n",
       "Subscriber  Youth         596.995198\n",
       "            Adult         645.010383\n",
       "            Seniors       704.389089\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean.groupby(['user_type','Age_class'])['duration_sec'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ebf19d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "Woe i instantly noticed the linear relationship of Subscriber age group with duration. Form the age group youth there is a gradual increase in duration down to the seniors age group."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a46621c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Relationship between `user_type`, `period_of_day` and `duration_sec`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c9451914",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:670: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x273acb4d4c0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=gobike_clean, col='user_type', height=6)\n",
    "g.map(sns.pointplot, 'period_of_day','duration_sec')\n",
    "g.set_xlabels('Period of Day')\n",
    "g.set_ylabels('Duration [sec]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c4a7c2e4",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_type   period_of_day\n",
       "Customer    Early Morning     987.992330\n",
       "            Morning          1293.154459\n",
       "            Noon             1782.766506\n",
       "            Evening          1287.924852\n",
       "            Night            1543.550554\n",
       "            Late Night       1834.197479\n",
       "Subscriber  Early Morning     570.733042\n",
       "            Morning           636.160767\n",
       "            Noon              684.642534\n",
       "            Evening           638.832267\n",
       "            Night             618.509613\n",
       "            Late Night        786.334824\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean.groupby(['user_type', 'period_of_day'])['duration_sec'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d22da19a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "The visualization of `user_type`, `period_of_day`, and `duration` i also noticed a trend between `user_type` Customer and `user_tye` Subscriber, that been that the highest average duration of bike ride fell at noon and late night. Notice that even though late night had the least representation by popularity, it has the highest average duration with 1834 for customers and 786 for subscribers. The trend am noticing is that the least represented population have higher average duration of time spent riding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b654287c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Relationship between `member_gender`, `day_of_week` and `duration_sec`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3f123064",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:670: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x273aa496d60>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nPqpvHzN7TtKxkrLMLFfSH51zj9Vx/IVm9qKkRZIqJF3vnAv4q38kb+bjVEnv+Q8AAAAADXh6+p6JSC5jIhIAAFCLRiUH/W7EnzrnPjKzQZIGmVmic668rn2ccxfXd0y/9WDo69sk3VbLdrMkDW1MnAAAAAA867cX65NvN0uSDurSRqN6ZUY5IgAA0Bw1tlvxZEnJZtZdXpfiq+S15gMAAADQDD03Y62C/lR+l43tJW+UHgAAgOoamxw051yRpHMl3e+cO0fS4PCFBQAAAGB/lVUE9fxX6yRJ6UnxOvvQ7lGOCAAANFeNTg6a2ThJl0p6xy9rVJdkAAAAAJH1wcJNyt9dKkk697AcZSTz1R0AANSuscnBmyT9RtJr/uQhfSV9Fr6wAAAAAOyviUxEAgAAGqmxsxVPljfuYOXrlZJ+Eq6gAAAAAOyfpZt3aeaqbZKk0b07aFCXNlGOCAAANGf1thw0s1sbOkBjtgEAAAAQGU+HthocR6tBAABQv4ZaDn7fzHbWs94kXSTp1iaLCAAAAMB+KSyt0KvfrJckZWUk6dQhXaIcEQAAaO4aSg4+IqmhfgiPNFEsAAAAAA7A63PWa3dphSTpe4f3UFJCY4cYBwAAsare5KBz7k+RCgQAAADA/nPOaeI0r0txnEkXj+4Z5YgAAEBL0KgJSQAAAIB9NeGxGcotKFZOZqomXjMm2uG0el+vKdDiTbskSccf1Fk5mWlRjggAALQEJAcBAAAQFrkFxVqVXxjtMGJGtYlIxtJqEAAANA6DkAAAAAAtXP7uUr07f5MkqWeHNB09IDvKEQEAgJaiUS0HzSxb0rWSeofu45y7OjxhAQAAAGisF2etU1kgKMlrNRgXZ1GOCAAAtBSN7Vb8hqQpkj6WFAhfOAAAAAD2RSDo9OyMtZKkpIQ4XTCyR5QjAgAALUljk4NpzrlbwhoJAAAAgH32+dItyi0oliR9d1hXZaYnRTkiAADQkjR2zMG3zey0sEYCAAAAYJ9NnLZnIpIJY3tFMRIAANASNTY5eJO8BGGJme3yHzvDGRgAAACA+q3bVqRJS/MkSUO6tdWIHu2jGxAAAGhxGtWt2DnXJtyBAAAAANg3z8xYK+e85Qlje8ms6SciyclMrfYMAABal8aOOSgzO1PS0f7LSc65t8MTEgAAAICGlFYE9OKsdZKkNikJOnNEt7CcZ+I1Y8JyXAAA0Dw0qluxmf1DXtfiRf7jJr8MAAAAQBS8N3+TthWWSZLOOyxHaUmNvu8PAABQpbHfIE6TNMI5F5QkM3tS0mxJvw5XYAAAAADqNnH6nolILmMiEgAAsJ8aOyGJJLUPWW7XxHEAAAAAaKRFG3bq6zUFkqTx/Tqqf6eMKEcEAABaqsa2HPy7pNlm9pkkkzf24G/CFhUAAACAOj09g1aDAACgaTR2tuLnzGySpMPlJQdvcc5tCmdgAAAAAPa2s6Rcr89eL0nq1CZZJw3uHOWIAABAS1Zvt2IzO8h/PkxSV0m5ktZJ6uaXAQAAAIig175Zr6KygCTp4tE9lRi/LyMFAQAAVNdQy8GfSbpO0l21rHOSjm/yiAAAAADUyjlXNRFJfJzp4tE9oxwRAABo6epNDjrnrvMXv+OcKwldZ2YpYYsKAAAAwF5mrNqm5Vt2S5JOOrizurTjKzkAADgwje2DMLWRZQAAAADCpLLVoCRNGMdEJAAA4MDV23LQzLpI6i4p1cwOlTcZiSS1lZQW5tgAAAAA+LbsLNEHC7w5AftmpWt8v45RjggAALQGDY05eIqkKyXlSPpXSPkuSb8NU0wAAAAAanjhq3WqCDpJ0qVje8nMGtgDAACgYQ2NOfikpCfN7Dzn3CsRigkAAABAiIpAUM/OXCtJSkmM0/mH5UQ5IgAA0Fo01HJQkuSce8XMTpc0RFJKSPmfwxUYAAAAAM8ni7do4w5vfsAzh3dTu7TEKEcEAABai0ZNSGJm/5X0PUk3yht38AJJjIAMAAAARMDToRORjO0dvUAAAECr09jZisc75y6XVOCc+5OkcZJ6hC8sAAAAAJK0Kr9QU5blS5KG92ivQ3LaRTkiAADQmjQ2OVjiPxeZWTdJ5ZL6hCckAAAAAJWeCWk1eNmYnlGMBAAAtEaNGnNQ0ltm1l7SHZK+keQkPRKuoAAAAABIJeUBvfR1riSpXWqizhjeLcoRAQCA1qbB5KCZxUn6xDm3XdIrZva2pBTn3I5wBwcAAADEsrfmbtCO4nJJ0oWjcpSSGB/liAAAQGvTYLdi51xQ0l0hr0tJDAIAAADhFzoRySVjmA8QAAA0vcaOOfihmZ1nZhbWaAAAAABIkublbtfcXO+e/FEDstQnKz3KEQEAgNaoscnBn0l6SVKpme00s11mtrO+HczscTPbYmYLQsruMLPFZjbPzF7zxzGsXPcbM1tuZkvM7JSQ8pFmNt9fdx8JSgAAAMSC0FaDE8bSahAAAIRHo5KDzrk2zrk451ySc66t/7ptA7s9IenUGmUfSRrqnBsmaamk30iSmQ2WdJGkIf4+D5pZ5YAq/5F0naQB/qPmMQEAAIBWZUdRud6cu0GS1LVdio4/qFOUIwIAAK1Vo2YrNrOjayt3zk2uax/n3GQz612j7MOQl9Mlne8vnyXpeedcqaRVZrZc0mgzWy2prXNumh/HU5LOlvReY+IGAAAAWqKXv8lVSXlQknTJ6J5KiG9shx8AAIB906jkoKRfhiynSBot6WtJxx/Aua+W9IK/3F1esrBSrl9W7i/XLK+VmV0nr5WhevbseQChAQBQP+ocAOESDLqqLsUJcabvje4R5YjQHFDvAADCpVHJQefcGaGvzayHpNv396Rm9jtJFZKeqSyq7bT1lNfKOfewpIcladSoUXVuBwDAgYpmnTPhsRnKLShWTmaqJl4zJpKnBhABU1ds1ar8QknSKUO7qFOblChHhOaA/3UAAOHS2JaDNeVKGro/O5rZFZK+K+kE51xlpZYrKfSWaI6kDX55Ti3lAADErNyC4qrEAYDWh4lIAABAJDV2zMH7tafFXpykEZLm7uvJzOxUSbdIOsY5VxSy6k1Jz5rZvyR1kzfxyEznXMCfGXmspBmSLpd0/76eFwAAAGgJNu4o1kffbpYkDeiUoTF9OkQ5IgAA0No1tuXgrJDlCknPOee+rG8HM3tO0rGSsswsV9If5c1OnCzpIzOTpOnOuR865xaa2YuSFvnHv945F/AP9SN5Mx+nypuIhMlIAAAA0Co9N3OdAkHvnvxlY3vJ/84MAAAQNo0dc/BJM8v2l/Mauc/FtRQ/Vs/2t0m6rZbyWdrPLswAAABAS1EeCOr5mWslSWlJ8TrnsDrn4QMAAGgycfWtNM+tZpYvabGkpWaWZ2Z/iEx4AAAAQGz4aNFmbdlVKkk6+9DuapuSGOWIAABALKg3OSjpZklHSDrcOdfROZcpaYykI8zsp+EODgAAAIgVE6ftmYjksjFMRAIAACKjoeTg5ZIuds6tqixwzq2UdJm/DgAAAMABWr5ll6at3CpJGtkrU4O7tY1yRAAAIFY0lBxMdM7l1yz0xx2knwMAAADqFHTexBrOf0bdnp6+tmp5wlhaDQIAgMhpKDlYtp/rAAAAEKPm5+7QFY/P1JqtRZKk3IJiPfHlKpKEdSgqq9ArX+dKkjqkJ+k7h3SJckQAACCWNDRb8XAz21lLuUlKCUM8AAAAaMG+WVugSx6ZrpLyYFVZRdDp1rcWafXWIt165pAoRtc8vTlng3aVVkiSLhzVQ8kJ8VGOCAAAxJJ6Ww465+Kdc21rebRxztGtGAAAANX89e1F1RKDoZ6YulpLN++KcETNm3NOT/kTkZhJl47pGeWIAABArGmoWzEAAADQKLkFRfpm7fZ6t3lr7obIBNNCzF63XYs2eh11jh2YrR4d0qIcEQAAiDUkBwEAANAkdhSXN8k2seTp6WuqlieMYyISAAAQeSQHAQAA0CR6dUxXcoLVu01FkElJKm0rLNPb8zZKknIyU3XMwE5RjggAAMQikoMAAABoEiapbWpSvds8O2Otbnl5ngr9CThi2Uuz1qmswhuf8ZIxPRUfV39iFQAAIBxIDgIAAOCAlZQH9P0nZylvV2mt6xNDEl8vzFqn0++bojnrtkcouuYnGHR6ZsZaSVJSfJwuHNUjyhEBAIBYRXIQAAAAB6S0IqAfTPxa01ZulSR1aZuiv509VBnJ8ZKkzLRETf/tCXr8ylHKyvBaFq7eWqTz/jNV932yTBWB2mc3bs0mL8vT2m1FkqTTDumirIzkKEcEAABiFclBAAAA7LfyQFA3PDtbny/NkyRlZSTr2WvH6JKxvZTdJkWS1D4tSR0zknX8QZ313k1H6/iDvLH1AkGnf320VBc9PF3r/ERZrAidiOSysUxEAgAAoofkIAAAAPZLIOj00xfm6KNFmyV5LQSf+f4Y9c3OqHOf7DbJeuyKUfrL2UOVkuh9FZ21pkDfuXeKXvk6V861/glLcguK9OniLZKkg7q00chemVGOCAAAxDKSgwAAANhnwaDTL1+eWzXbbtuUBE28ZowGdWnT4L5mpglje+ntG4/S0O5tJUm7Syv085fm6obnZmtHUXlYY4+252auVeWkzRPG9ZIZE5EAAIDoITkIAACAfeKc0/+9sUCvfrNekpSRnKAnrx6tod3b7dNx+nfK0Ks/OkI/PKafKvNj78zbqFPvnaypK/KbOuxmoawiqBe+WifJe9/OHtE9yhEBAIBYR3IQAAAAjeac05/fXqRn/Zl2UxPj9fiVh+vQnvvXNTYpIU6//s5Bevb7Y9WtnTdG4cYdJbr00Rn6+7vfqrQi0GSxh8OEx2bouDsnacJjMxq1/fsLNyl/d5kk6dzDuis9OSGc4QEAADSI5CAAAAAaxTmn2z9Yov99uVqSl9h79IpRGt2nwwEfe1y/jnrvpqN1xvBu/rmkhyav1DkPTNXyLbsO+PjhkltQrFX5hcotKG7U9k9PYyISAADQvJAcBAAAQKPc98ly/WfSCklSYrzpoctG6oj+WU12/HZpibrvohG6+3vD1cZvUbdo406dft8Xemra6hY/WcniTTs1c/U2SdKYPh00sHPD4zMCAACEG8lBAAAANOihz1fo7o+XSpLi40z3X3yYjjuoU5Ofx8x0zqE5evemo3R4b6+rcmlFUH94Y6GufuIr5e0qbfJzRsrT02k1CAAAmh+SgwAAAKjXE1+u0t/fWyxJMpP+deFwnTq0S1jP2aNDmp6/bpx+cfJAJcR5s5V8tiRPp94zWZ98uzms5w6H3aUVes2fwCUrI1mnDAnv+wcAANBYJAcBAABQp+dmrtWtby2qen37ecN0VoRm2I2PM91w/AC98qPx6pOVLknaWlima56cpd+9Nl/FZc17spJQr81er0I/3otH91BSAl/DAQBA88C3EgAAANTqtdm5+u1r86te/+XsobpgVI+IxzG8R3u9feORunj0nnM/M2OtTr9/iubn7oh4PPvKOVc1EUmcSReP7hnliAAAAPYgOQgAAIC9vDNvo37+4lxVzgHyf6cfrAlRHCcvPTlBfz93mB6eMFKZaYmSpJV5hTrnwS/14KTlCgSb72Qls9YUaMlmb8blEw7urG7tU6McEQAAwB4kBwEAAFDNR4s266bnZ6sy3/bLUwbp+0f1jW5QvpOHdNEHNx+towdmS5Iqgk63v79EFz8yXeu3F0c5utpNnLZnIpJoJlgBAABqQ3IQAAAAVT5fmqfrn/lGFX5m8Mbj++v64/pHOarqOrVN0RNXHq4/njG4auy+mau26dR7JuuNOeujHF11ebtK9d6CjZKkXh3TdGT/rChHBAAAUB3JQQAAgCiY8NgMHXfnJE14bEa0Q6kybcVWXffULJUFgpKka4/qo5+dNDDKUdUuLs501RF99NYNR+qgLm0kSbtKKnTT83N08/OztbOkPMoRel6ctU7lAS/RetmYXorzZ14GAABoLkgOAgAAREFuQbFW5Rcqt6B5dIX9es02XfPkVyqt8BKDl4/rpd+edrDMmncya1CXNnrjhiP0/SP7VJW9PmeDvnPPFM1ctS2KkUmBoNOzM9ZKkpIT4nT+yJyoxgMAAJqH5naTmOQgAAAtyKINO7WjyGsRVeYncYADNS93u658/CsVlQUkSd8b1UO3njGk2ScGKyUnxOv/vjtYT18zRp3bJkuS1m8v1kUPT9MdHyxWeSA6vyufLd5SNQ7id4d1U2Z6UlTiAAAAzUtzu0lMchAAgBagsLRC1z41S6fdN0XbisokecmPa5+apaKyiihHh5bs2407NeGxmdpV6v0cnTWim/527iEtsvvrkQOy9P5NR+s7Q7tIkoJOeuCzFTrvP1O1Mm93xON5ekbIRCTjmIgEAAA0TyQHAQBoAX71yjx9tGjzXuUfLdqsW16ZH4WI0Bos37JLlz06QzuKvdao3xnaRXddMFzxLTAxWCkzPUkPXnqYbj9/mNKT4iVJ83J36PT7vtCzM9bKOReRONZsLdTnS/MkSYd0b6fhOe0icl4AAIB9RXIQAIBmbs3WQr0zb2Od69+eu0FrtxZFMCK0BqvzC3XJIzO0tdBriXrCQZ1070WHKiG+5X89NDNdOKqH3r3pKB3as70kqbg8oN++Nl/XTfxaW3eXhj0GLxHpLV82tmeL6aINAABiT8v/9gcAQCv31eqCetc7SV+tju7EC02tuQ3S3NrkFhTp0kdnaMsuL0l21IAsPXDpYUpKaF1fDXt1TNdLPxinm08cUNUa8qNFm3XqvVM0acmWsJ23pDygF2etkyS1SUnQmcO7h+1cAAAAB6p1fQMEAKAVSoxvuMVRRbB1TU7S3AZpbk027SjRJY/MqJooY3SfDnp4wiilJMZHObLwSIiP080nDtSLPxinnh3SJEl5u0p15f++0h/fWKCS8kCTn/Pd+RtV4E8cdMHIHkpNap3vLQAAaB1IDgIA0Mwd0T+rwQTh7R8s0Rtz1kdsPDW0THm7SnXJo9O1dpvXDf3Qnu31+JWHx0TyamSvTL1701G6YGROVdmT09bojPu/0MINO5r0XBOn75mI5NKxPZv02AAAAE2N5CAAAM1cVkayrjqiT73bbN1dppuen6OLH5muZZt3RSgytCQFhWWa8NgMrcwrlCQN7d5WT1w1WhnJCVGOLHIykhN0xwXD9eClh6ldaqIkadmW3Trngal6ePIKBYMHnlxfsH6HZq/dLkk6on9H9cvOOOBjAgAAhBPJQQAAWoBbTj1INx7fX2khLbxM0veP6qOLD++hyrkOpq/cpu/cO0V/e/db7S6tiE6waHZ2FJdrwuMztHiTlzge1LmNJl49pipBFmtOO6Sr3r/5KB3Rv6MkqSwQ1N/eXazLHpuhjTsOrCv7MzP2tBqcMLbXAR0LAAAgEkgOAgDQAsTHmX5+8iDN+O0J6tw2WZLUs0Oa/u/0wfr7ecP02o+P0CHd20mSKoJOD09eqRPv+lxvz9tAV+MYt7u0Qlf+b6YWrN8pSeqbna6nvz9GmelJUY4surq2S9XEq8fod6cdrCR/huapK7bq1Hum6N35dc8OXp+dJeV6ffYGSVLntsk68eDOTRYvAABAuIQtOWhmj5vZFjNbEFLWwcw+MrNl/nNmyLrfmNlyM1tiZqeElI80s/n+uvvMrOFR2QEAaKXapCQqLcnrBhoXt6dKHNGjvV6//gj99eyhVa3BNu0s0Q3PztaEx2Zq+ZbdUYkX0VVcFtDVT3xV1c21Z4c0Pfv9scpukxzdwJqJuDjTtUf31evXH6EBnbzuvzuKy/XjZ77RL16au8+tb1/9OlfF/gQnF4/uqYR47sMDAIDmL5zfWJ6QdGqNsl9L+sQ5N0DSJ/5rmdlgSRdJGuLv86CZVfab+o+k6yQN8B81jwkAAOS1LrxsbC99+vNjdOGoPZMufLE8X9+5d7L++f5iFZXR1ThWlJQHdN3EWZq5apskqVu7FD177Rh1aZcS5cian8Hd2uqtG4/UleN7V5W9/HWuTrt3ir5eU9CoYzjnqiYiiY8zXTyaiUgAAEDLELbkoHNusqRtNYrPkvSkv/ykpLNDyp93zpU651ZJWi5ptJl1ldTWOTfNeX2ingrZBwAA1KJjRrJuP3+4XvnReA3u2laSVB5w+s+kFTrxrs/1/oKNdDVu5coqgrr+mW80ZVm+JKlTm2Q9e+1Y5WSmRTmy5islMV63njlET1x1eFXLyrXbinThQ9N090dLVREI1rt/SXlQK/zJXk4e3Fmd25KEBQAALUOk+zp0ds5tlCT/uZNf3l3SupDtcv2y7v5yzfJamdl1ZjbLzGbl5eU1aeAAAIRqCXXOyF6ZevOGI/SnM4eoTYrXFXnDjhL98OlvdMX/vtKq/MIoR4hwqAgEddPzs/XJ4i2SpI7pSXr22jHqnZUe5chahmMHddL7Nx2lkwZ74wUGgk73frJM5/93mlbX8zuzq6S8apmJSBAOLaHeAQA0TtC/Ud9cbtg3l4FQahtH0NVTXivn3MPOuVHOuVHZ2dlNFhwAADW1lDonIT5OV4zvrU9/fqzOO2xPV+PJS/N0yt2TddeHS1RcFohihGhKgaDTL16aq/cWbJIktUtN1MRrxqh/pzZRjqxl6ZiRrIcnjNTfzz1EqYneSDdz1m3XafdN0Ytfrav1i3yh/3vUNztd4/p1jGi8iA0tpd4BANRtzrrtuvzxmVqztUiStL6gWM/NXBv1JGGkk4Ob/a7C8p+3+OW5knqEbJcjaYNfnlNLOQAA2AfZbZJ114XD9eIPxumgLl6iqCwQ1P2fLteJ//pcHy7cFPUvJTgwwaDTb1+dr9fneF+V2iQnaOI1ozW4W9soR9YymXnjBr7zkyM1LMebCbyoLKBfvTJPP3r6G+XtKtFbczdo886SavtdNqaXmD8PAADU9NXqbbrwoWmavHRP6+/yoNNvXp2vOz5YEsXIIp8cfFPSFf7yFZLeCCm/yMySzayPvIlHZvpdj3eZ2Vh/luLLQ/YBAAD7aHSfDnr7xiP1++8OVkay19V4/fZiXTfxa13z5Cyt2UpX45bIOadb31qoF2Z5o7SkJcXriasP17Cc9tENrBXom52hV340Xjcc11+VE4S/v3CTxv/jU9343GwV1Wh5W9mFHwAAoJJzTre+uVBlFbWPYfyfSSu01m9NGA1hSw6a2XOSpkkaZGa5ZnaNpH9IOsnMlkk6yX8t59xCSS9KWiTpfUnXO+cqv2n9SNKj8iYpWSHpvXDFDABALEiIj9M1R/bRpz8/RmeP6FZV/uniLTrp7sm6+6OlKimnq3FL4ZzT399brKemeTPlJifE6bErDtfIXh2iHFnrkRgfp1+cMkjPXzdO3dunSvIm+anN715foE07SmpdBwAAYtPK/EIt3LCzzvVO0lvzotdRNpyzFV/snOvqnEt0zuU45x5zzm11zp3gnBvgP28L2f4251w/59wg59x7IeWznHND/XU3OPo8AQDQJDq1TdE9Fx2q564dqwGdMiR5s9ze+8kynXz3ZH26eHOUI0Rj3P3RUj08eaUkKSk+Tg9fPoox78JkdJ8OeuOG8UqIq7vbcFlFUC/OWlfnegAAEHvmrdve4DY7issb3CZcmsuEJAAAIErG9euod286Sr877WClJ3mTL6zdVqSrn5ila5+apXXbotfFAfV74LPluu/T5ZKkhDjTA5cepmMGMlFBOJWUB1URrP9e9Yq83RGKBgAANFeBoNMHCzfpkkem66cvzm1w+0GdozeBHIOiAAAAJcbH6dqj++qM4d3013cW6e15GyVJHy3arMlL83TDcf117dF9leLP3Iroe+yLVVWDV8eZdO9Fh+qkwZ2jHFXr1y41UXEm1ZcfzExLilxAAACgWdlRVK4XZq3VU9PWKLeguFH7dMxI0unDuoY5srrRchAAAFTp0i5F/77kMD3z/THql50uSSqtCOquj5bq1Hsm6/OQ2dUQPU9PX6O/vL1IkmQm3XnB8Kh+oYwlbVISdeLB9Sdhzzm0e4SiAQAAzcWSTbv0m1fna8zfP9bf3l1cLTF4WM/2+ud5h2h4j3Z77dc2JUEPTxgV1ZvwtBwEAAB7OaJ/lt676Wg99sUq3ffJMhWXB7R6a5GueHymTh3SRb8/Y3DVxAyIrJdmrdP/vb6g6vXfzjlE5x6WE8WI6paTmVrtubX4zWkHa9aaAm0rLNtr3YSxvTS8R/vIBwUAACIuEHT6+NvNeuLL1Zq2cmu1dUnxcfru8K66cnxvDctpL0k697Acfbhws3718lwVlgWUmZaoj392jDpmJEch+j1IDgIAgFolJcTpR8f201kjvK7G787fJEl6f+Emfb40Tzee0F/fP7KvkhLoiBApb87doFtemVf1+tYzBuvi0T2jGFH9Jl4zJtohhEWfrHS9/uMjdO8ny/TqN7lykhLjTX88Y4guHdN8Pw8AANA0theV6YWv1mni9L27Dndqk6wJY3vp4jE9lVUj6ZcYH6fTh3XVnR8u0ar8QrVPS4p6YlAiOQgAABrQrX2qHrx0pD5fmqdb31yoVfmFKi4P6Pb3l+jlr3P15zOH6sgBWdEOs9V7f8Em/fSFOVVj3f36OwfpyiP6RDeoGNazY5ruunC4vl6zTau3FiknM02Xje0V7bAAAEAYLd60U09OXa3XZq9XSXmw2rqRvTJ1xfje+s7QLkqMb1k3z0kOAgCARjlmYLbev/koPTplle7/dJlKyoNamVeoyx6bodOHddX/nX6wurZrXd1Hm4vPFm/Rjc99o4CfGbz5xAH64TH9ohwVJMnMoh0CAAAIo0DQ6aNFm/Xk1MZ1HW6JSA4CAIBGS06I1/XH9deZw7vpL28v0oeLNkuS3pm3UZ8t3qKbThigq4/s0+LuljZnXy7P1w+e/lrlAS8x+MNj+ummEwZEOSoAAIDWrbLr8FPT1mj99updhzu3TdZlY2rvOtwSkRwEAAD7rEeHND18+Sh9tniLbn1rodZsLVJRWUB/f2+xXv46V386a4jG96Or8YH6avU2ff/JWSqr8LqtXDm+t245dRCt1QAAAMKkoa7DV47vrVNbYNfh+pAcBAAA++24gzppXL+OeujzlXpw0nKVVgS1bMtuXfLIDJ05vJt+d/rB6tw2Jdphtkhz1m3XVf/7SsXlAUnSxaN76o9nDCYxCAAA0MQqAkF9/O0WPTF1laav3FZtXVJ8nM4Y3k1Xju+tQ3LaRSnC8CI5CAAADkhKYrxuOnGAzjm0u/701kJ9sniLJG9m3U8Xb9HNJw7QFeN7t6q7q+G2cMMOXf7YDO0urZAknXtYd9129lASgwAAoNWb8NgM5RYUKyczVROvGRPWc20vKtPzX63TxDq6Dk8Y20sXjW4dXYfrQ3IQAAA0iZ4d0/TYlYfr40WbdetbC5VbUKzdpRX66zvferManzVUo/t0iHaYzd7Szbs04bGZ2lniJQZPH9ZVt583THFxJAYBAEDrl1tQrFX5hWE9R31dh0f5sw63tq7D9SE5CAAAmtSJgzvriP5Z+s+k5frv5ytVFghq8aZduvChaTr30O769WkHqVMbuhrXZlV+oS59dIa2FZZJkk4a3Fn3fG+EEmLkiykAAEC4eF2HN+uJqatr7Tp85giv6/DQ7q2z63B9SA4CAIAml5oUr5+dPEjnHpajP765UJ8vzZMkvTp7vT5atFk/P3mgLhvbi6RXiHXbinTJI9OVt6tUknTMwGz9+5JDY+aONQAAQDjU13W4S9sUXTa2Z0x0Ha4PyUEAABA2vbPS9cRVh+uDhZv1l7cXaf32Yu0qrdCtby3SC7Ny9dezh2hkL7oab9xRrEsena6NO0okSeP6dtRDE0YqOSE+ypEBAAC0TN9u9LoOvz6n9q7DVx7RW6cMiZ2uw/UhOQgAAMLKzHTq0C46emCWHvhsuR6evFLlAadvN+7Uef+ZpgtG5uiW7xxU7W5t0LkoRhxea7cW6b+TV2jt1iJJXmLw3AenViUGR/XK1KNXjFJKIolBAACAfVHZdfh/X67WjFU1ug4nxOnM4c2j63BOZmq152gjOQgAACIiLSlBvzzlIJ17WI5ufXOhpizLlyS99HWuPli4Sb88ZZD6dEzXfZ8t1xo/cba+oFjvzt+o0w7pGs3Qm8y3G3fqooena0dxeVVZSXmwKjE4LKedHr/qcKUn8xUNAACgsQoKva7DT0+vvevwhHG9dNHhPdSxmXQdDvcszPuKb54AACCi+mVn6KmrR+u9BZv057cWadPOEu0sqdDv31i417ZlgaB+/Mw3+tOZQ3TF+N6RD7aJ/fa1+dUSg6FM0n0XHaq2KYmRDQoAAKCFquw6/Nrs9SqtqN51+PDembpyfB+dPKQzXYcbQHIQAABEnJnptEO66piB2brv02V6bMoqVQTr7kr8t3cXaVzfjkpLjlcg6FQRdN5zwH8OBhV0oa9Dn4NVr/daFwhWex2sZ19ve6eAq7F+r3MGFQxKFcFgtfKisoDWbiuq8xqdpCnL89U7Kz0M7zgAAEDr0FDX4bOGd9MVzaDrcEtCchAAAERNenKCfvOdgzUgu41+8fLcOrcrrXA6+Z7JEYwsOrbsLIl2CAAAAFERCDp9uniL8neVSpJ2lZSruCyg1CRvHObKrsMTp63Whh3VvzM1x67DLQnJQQAAWpjmNoBxU8hIaflfSeJMSoiLU3ycKSHOFB/vP8eZ4s3kpKqxBevSIzMtMsECAAA0IzuKynXVEzP1zdrtVWX5u8t04r8+1x/PGKxPvt2i1+fs3XV4dO8OumJ8b7oOH6CW/00cAIAY09wGMG4KAztnNLjNcYOy1aNDmuKsZvItrioJV5WMi6tlXXz18vg41bpvVYIv3vacq9r+cXudK95McXHW4DVMeGxG1UQsNbVJSdBpw1rHxCsAAAD74revz6+WGKy0fnuxrpv4dbUyug43PZKDANACTXhshnILipWTmdoqE0WIPX2zM3TUgKw6E2d9s9P12BWHNyoB15z97ZxD9L2Hpu3VFSYx3nTP90Yog1mKAQBAjNm4o1jvzd/Y4HZd26XosrG9dPHonuqQnhSByGIHbS4BoAXKLSjWqvxC5RYURzsUoMncecFw9e+0dwvCTm2S9d/LRrb4xKAk9eiQpnd+cpR+ftJAJSV4X8PapCTovZuO0gkHd45ydAAAAJG3eNMu1TMvnSTpzOHdNOVXx+n64/qTGAwDkoMAAKBZ6Nw2RW/feKRuP3+Y0vyBpzukJ+njnx+jgZ3bRDm6ppOZnqQbTxig7u29MSOzMpLVv1PruT4AAIDGCgadZq8paHC7Ub0zlcCYgmHDOwsAAJqNlMR4XTiqhzq3TZEktUtNVNuUxChHBQAAgKbknNPHizbrtPum6L5Pl9e7bUKc6ZQhXSIUWWxiYBsAAAAAAABExPSVW3XHB0v0dUiLQZNUV8/iHx/br+rGMcKD5CAAAACwn3IyU6s9AwCA2i1Yv0O3f7BEk5fmVSs/4aBO+sUpg7RxR7Hu+nCpFm7YKclrMfjHM4fosjE9oxFuTCE5CAAAAOwnZowHAKB+K/N2666PluqdedVnJB7dp4N+dcogjerdQZJ0cNe2Om5QJx19x2dat61YOZmpmjC2VzRCjjkkBwGgBSksrdC78zeqoLBMkhRoaFovAAAAAIiCjTuKde/Hy/TS17nV/m8Z0q2tfnnKIB0zMFtmVm0fM1NCXFzVMiKD5CAAtBCfLdmim56brZ0lFVVl67YV6cWv1unCw3tEMTIAAAAA8GwrLNODny3XU9PXqKwiWFXeJytdPztpoE4/pKvi4kj8NSckBwGgBViRt1s/mPh1tcpV8gbtveWVecrpkKrx/bKiExwAAACAmLe7tEKPTlmpR6es0u7SPQ0aurRN0U0nDtD5I3OUGB8XxQhRF5KDANACPDl19V6JwUpO0sOfr2h1ycEJj81QboE31ghjegEAAADNU0l5QE9PX6MHJ63QNn/4I0nKTEvUj4/trwnjeiklMT6KEaIhJAcBoAWYtbqg3vWTlubrpH99rp4d0tSjQ5p6dkhTr47ec05mmlKTWl5lnFtQrFX5hdEOAwCAmMcNOwC1qQgE9co3ubr342XasKOkqjw9KV7XHNVX1x7VR21SEqMYIRqL5CAAtACJ8Q2PybFsy24t27K71nWd2iSrp580rEwe9vSTh9kZyYz5AQAA6sQNOwChnHN6b8Em3fnhEq3M2/O3ISk+TpeN7aXrj+unjhnJUYwQ+4rkIAA0c5t3llRrnl+b7IxkxceZNu0sqXX9ll2l2rKrVLPW7N0CMTkhbk/CMDR52CFNPTqkKi2JqgIAAACIdc45TVmWrzs+WKL563dUlceZdP7IHN104kB1b596wOfJyUyt9ozw4z8+AGjGpizL083Pz9HWepKDbVMS9NIPx6l3VrpKygPKLSjWum1FWhvyqHxdVBbYa//SiqCWb9mt5XW0OszKSFbPDqm1tjzs3CaFVocAAABAK/fN2gLd/v5iTV+5rVr5aYd00c9OGqT+nTKa7FwMXxB5JAcBoBkKBJ3u/Xip7v9suZzzyvpkpatDeqK+XrO9arukhDg9e+1Y9c5KlySlJMarf6eMWitn55y2FpbtSRZurZ483LizpOpcofJ3lyp/d6m+Wbt9r3VJ8XHKCUkchiYPe3RIU0byvlUzzjm9Pme9np6+Vmu2el0UtheVaXdpxT4fCwAAAMCBWbJpl+74YIk+/nZztfKjBmTpl6cM0rCc9tEJDE2K/7QAoJnZsrNEP3l+drW7cheOytGfzhyq1KR4rdtWpAv+O02bdpaoe/tUDe3erlHHNTNlZSQrKyNZh/XM3Gt9aUVA6wuKtaaO5GFhLa0OywJBrcwrrDbWSKiO6UnVuilXJQ87pqlL2xTFh7Q6dM7pt68t0HMz11Y7RkFRub730DQ9f91YBjQGAAAAImDdtiLd/dFSvTZnfbUGBCN6tNevTh2k8f2yohccmlxUkoNm9lNJ35fkJM2XdJWkNEkvSOotabWkC51zBf72v5F0jaSApJ845z6IfNQAEH5fLs/XTc/PUf7uUklSamK8/nr2UJ03Mqdqmx4dwjP7cHJCvPpmZ6hvdu2tDrf5rQ5Duyl7y8XasKO41laHWwvLtLWwTHPWbd9rXWK8KSezsqVhqiqCTs/PXFdrbAs37NQDn63Qr79z0IFeJgAAAIA6bNlVon9/ulzPzVyr8sCeL/gDO2foFycP0kmDO8uMYYVam4gnB82su6SfSBrsnCs2sxclXSRpsKRPnHP/MLNfS/q1pFvMbLC/foikbpI+NrOBzrm9m7AAQAsVCDrd98ky3ffpsqok24BOGXrw0sM0oHOb6AYnr9Vhx4xkdcxI1qF1tDrcsL2kevIwpOXh7tKKvfYpDzityi9s9OyHL3+9juQgAAAAEAY7isv10Ocr9L8vV6u4fE+6JSczVT87aaDOGtG9Wq8ftC7R6lacICnVzMrltRjcIOk3ko711z8paZKkWySdJel551yppFVmtlzSaEnTIhwzAITFll0luvn5OZq6YmtV2fkjc/Tns4a0mJmCkxPi1ScrXX38sQ9DOee0vai81glS1m4r0obtxQrW0uqwpvzdZaoIBJUQHxeGKwAAADV9tmSLHv9iVdU4wNsKy5S/u1RZGclRjgyIXRMem6HcgmLlZKY2ycQdxWUB/W/qKv130grtLNlzQz8rI1k/OaG/Ljq8p5IS+P7d2kX8v07n3Hozu1PSWknFkj50zn1oZp2dcxv9bTaaWSd/l+6SpoccItcv24uZXSfpOknq2bNnuC4BAJrM1OX5+klIN+KUxDj95ayhumBUjyhH1nTMTJnpScpMT9LwHu33Wl8eCGrD9mL97rUF+mJ5fp3H6doupVklBqlzAACRFOl657+fr9A/3ltcrWxHcbnO+veXevlH49S1XWrYYwCwt9yC4kb3vKlPWUVQL3y1Vvd9ulx5u0qrytukJOiHx/TTVUf0bjENFXDgotGtOFNea8A+krZLesnMLqtvl1rKam1j4px7WNLDkjRq1KhGtEMBgOgIBJ3u/3SZ7v1kTzfi/n434oHNoBtxJCXGx6lXx3TdfOKAepODFx3evBJw1DkAgEiKZL2zKr9Q/6yRGKy0fnux/vrOt3rgksPCGQLCoKlbnKFlCgSd3py7Xnd/tExrtxVVlackxunK8X30w2P6qn1aUhQjRDREIw18oqRVzrk8STKzVyWNl7TZzLr6rQa7Strib58rKbQJTY68bsgA0CLl7SrVzS/M1pfL93QjPvew7vrr2UNj+u7cqN4ddMNx/fXvz5bvtW5s3w76wTF9oxAVAACx55Wvc2tvjeF7f/5G7SguV7vUxIjFhAPXVC3OmjMSoHVzzumTb7fojg+WaMnmXVXlCXGmi0b30E+OH6BObVOiGCGiKRr/ha6VNNbM0uR1Kz5B0ixJhZKukPQP//kNf/s3JT1rZv+SNyHJAEkzIx00ADSFqSu82Ygrm+6nJMbpz2cN1QUjc/Zp1q+czNRqz63FL04ZpMP7dNDT09do0pItKg84dUxP0lNXj2GsEwAAImTTzpJ61wec9JPnZuvkIZ11VP9s9eyYFqHIgPrFQgJ0f0xfuVW3v79Y36zdXlVmJp01vJt+etJA9eq497jhiC3RGHNwhpm9LOkbSRWSZstrHp8h6UUzu0ZeAvECf/uF/ozGi/ztr2emYgAtTSDo9MBny3XPx0urJt/ol52uBy8dqUFd9r0bcWu+E3rMwGwdMzBbx905SavyC9U2NZHEIAAAEdSYm4+fL83T50vzJEk9OqTqyP7ZOrJ/lsb366jMdLokAs3BgvU7dPsHSzTZ/12tdOLBnfTzkwfp4K5toxQZmpuo9F9zzv1R0h9rFJfKa0VY2/a3Sbot3HEBQDjk7y7VT1+YoynL9oynd86hXjfi9OTY7UYMAACap/NH5uj+T5crEKy9c3F6crwKS/e011i3rVjPzVyr52aulZk0tFs7HdE/S0cNyNLIXplKSYyPVOgAJK3I261/fbhU78zfWK18dJ8OuuXUQRrZq0OUIkNzxX+lABBG01Zs1U3Pz9YWvxtxckKc/nzWEF04qsc+dSMGAACIlJzMNP3lrKH63Wvz9xp7cECnDD1/3VgVlQX0xfJ8fbE8X1OX56ugqFyS5Jw0f/0OzV+/Q//9fIWSE+I0uk8HHdE/S0f2z9Lgrm0VF8d3ICAcNmwv1r0fL9PL3+RWS+4P6dZWvzxlkI4ZmM3/IKgVyUEACIOg34347pBuxH2z0/XgpYfpoC403wcAAM3bJWN66qCubfTk1NV6d/5GlQecMtMS9eqPx6tNSqI6Srp4dE9dPLqngkGnRRt3asqyfH25PF8zV29TWUVQklRaEdSUZflVPSgy0xI1vn+WjuqfpSP6Z6lHB8YrxIELBJ0mLdmirbu9G/LFZQE551pVIsw5pznrtqugqEySVFK+5xq3FZbpwc+W66npa6p+9ySpb1a6fnbyQJ02tCtJedSL5CAANLHauhGfPaKbbjvnELoR74PWOukKGofPHwCi77CemTqsZ6bm5e7QqvxCtU9LUpuUvWcojoszDe3eTkO7t9OPju2nkvKAZq0u8FsW5mnhhp1y/s3SgqJyvTNvo96Z53V37N0xraoL8ri+WWqXxgzI2DdbdpXoqv99pYUbdlaVbdpZou89NF2PXDGqVcyqvbu0Qtc/803VOJ+StHFHiS747zSN6p2pp6ev1e7Siqp1Xdul6KYTBuj8kTlKiGfsbjSM/1IBoAnNWLlVP3l+tjbv3NON+E9nDtH3Dqcb8b5qzZOuoGF8/gDQcqUkxuvIAVk6ckCWpIO0rbBM01Zs1RfL8zRlWb5yC4qrtl29tUirt67VMzPWKs6kQ7q305EDvFaFI3tlKjkhMuMVTnhshnILipWTmdpq6yDnah9DsiVzzumGZ2ZXSwxWmrl6m3718lw9NGFUFCJrWre8Mq9aYrDSrDUFmrWmoOp1Zlqirj+uvy4b24uxPrFPSA4CQBMIBp3+8/kK3fXhkj3diLPS9cClhzELGAAAiGkd0pN0+rCuOn1YV0nSmq2FXqvCZfmaumKrdhR74xUGnTQ3d4fm5u7QA5+tUEpinEb36agj+3fUkf2zdVCXNmHrGplbUKxV+YVhOXa0TVqyRQ99vlKrtxZJkrbsLNHiTTtbzFA3ZRVBFZVVqLAsoOKyChWWBlRUFlBRWYW+3bhTM1dvq3PfDxZu1m9ena+2qdVTH6bqP0c17+HX/Cnbe/2+7V9zg305/o7isqqWtnVJTYzXdUf31feP6lNr616gISQHAeAAbd1dqp++OFeTQ+7mnTm8m/527iHKoBsxAABANb06pqtXx3RdOqaXAkGnBet36Ivl3niFs1YXqCzgjZlWUh7U5KV5/nesxeqYnlQ1scmRA7LUrT1DTzTk2Rlr9dvX5lcrKywL6JwHpmriNaM1qnfTzFrrnFNpRVDFZQEVllX4ybuAikq95cKyCn+dX1a+Z11loq/Qf/b227NcUces2Y313My1TXKNzdlPTxqg647uF+0w0ILxXysAHICZq7bpxue+qepGnJQQp1vPGKKLR9ONGED9GFcRAKT4ONPwHu01vEd7XX9cfxWXBfTV6m1VLQsXbdzTXXRrYZnenLtBb87dIMnrpVHZBXls346tYmy5plRQWKY/vbWw1nXF5QH96uV5evDSw1RUHvASd6UVKi4P+C3zaiT1SgMqLvda7VVPAFZ4ibzyQLXZcRFZHdKTox0CWjiSgwCwH4JBp/9OXqG7Plxa9UWoT1a6HrjkMA3u1jK6aACIrtY6phUAHIjUpHgdPTBbRw/MluRN9DZ1xVZ9sSxPXyzL14YdJVXbrswv1Mr8Qj01bY3iTBreo73XqrB/lg7tmamkhNY9EUNpRUDbi8q1rbBMBUVlVcvbi8q0rbBcs9cWqDRk5tqaVuYX6tR7p0Qw4rqZSWmJ8UpNSlB6crzSkhKUlhRf9UhPSlBqUrzSk0PLExQMOv3p7YUK1HGZ6Unxev66cUpJ3POzUDOFWXMoRldji73W7+P2Ne3r/rtLK3T1E1/V+VkmxpuOHZRd/0mBBpAcBIB9tK2wTD97cY4mLdnTjfi7w7rq7+cewhgfAAAATSgrI1lnDu+mM4d3k3NOq7cWeYnC5d54hbtKvBlag06avXa7Zq/drvs/Xa7UxHiN6duhqgvyoM5tau3VsaOoXC9/k6stO72kY1FphQJBp/gwjW1Yl+KygLYVlanAT/QVFJX7Sb6yagnAgqIyFRR66wrLAhGNUZLiTDUSd/5ycoLSk+K9BF7V+sp1Icm9qiRf9bKUxLj97nWztbBM936yrNZ1vzxlkA7JaXcgl9ws/OSEAbrjgyW1rrvmyL7KyqDlIA4MyUEA2AezVm/TDc/O1ib/C2RSQpz+8N3BunRMT7oRAwCAVqm5DINgZuqTla4+WemaMK63KgJBzV+/Q18sy9cXy/P1zdoClQe8ZlfF5QFNWpJXdTM3KyPZm9hkQLaO7J+lLu1SNGfddl31v5kqKCqvOsfmXaW69NHpeuyKw5W+H2NHO+e0u7SizoTeNj/x5yUBy6uSgfW18AunS0b3VJd2KVXJvL1b7e1ZTk9OUHLC/ifxwuXmEweobWqi/vv5CuXt8ob6iY8z/f2cQ3Th4T2iHF3T+PGx/ZScEKcHJ63QtsIySV6i9mcnDdSPj+0f5ejQGlhrnM5ckkaNGuVmzZoV7TAAtBLBoNPDU1bqjg+WVHUj7t0xTf++5DAN7d7y70aiyn5926XOAQDsJ+qdJlRYWqGZq7fpi2Xe5CaLN+2qc9t+2elav71YJeW1J+UuHt1Tt509VLtKKvyEXtleCb3qy3ta+1UmKJtafJwpMy1R7dOS1CEtSe3TEpWZlqTM9CRlpiX6z3uW0xLj9d37v9BWP5lU01EDslrVEBflgaCOvWOS1m8vVu+OaZr0y+OiHVKTKykP6Lg7J2njjhL16pimz1vhNSLsaq13aDkIAA0o8LsRfxbSjfj0YV31D7oRAwAANBvpyQk6blAnHTeokyRpy64STV2+tWpyk8qeH5K0Iq+w3mM9N3OtXvhqrcI1x0ZSfJzapyWqQ3otSb40L8lXc12b5ATF7WN35/9cNlJXPTFThaXVuyD37JCmf543rCkvKeoS4+Oqxplsbq0bm0pKYrxSEuMlSXGt9BoRHSQHAaAeX6/xuhFv9Ae/ToqP0+/PGKzL6EYMAADQrHVqk6KzD+2usw/tLuecVuQV6svl+ZqyLF+fL93SYAu/xiYGUxLj/JZ8ScpM35Pcq0z2eUm+kMRfepLSk+Ij8l1ydJ8O+uinx+jp6Wv02BerVFoRVIf0JL3zkyO5yQ2gCslBAKhFMOj0yJSVuj2kG3Gvjml6gG7EAAAALY6ZqX+nDPXvlKErxvfW3R8u1b2f1j6JRaXDe2eqW/vUkJZ8fpfe0BZ9aUlKTYqP0FXsn27tU/WrUw/Sews2aVV+odqlJpIYBFANyUEAqKGgsEy/eGmuPlm8pars9EO66u/nHaK2fJECAABo8c48tFu9ycEh3drqxR+Mo6cIgJhAchAAQny9pkA3PvuNNoR0I/6/7x6sCWN78eUQAACgleiXnaGrj+ijx79ctde6xHjT7787mO9+LVBzmVkbaGlIDgKAJOecHp2ySv98f7Eq/G7EPTt43YgPyaEbMQAAQGvz++8erB4dUvXolFVav71Ykjd+4DPfH6uRvTKjHB32R2uafRmIJJKDAGLe9iKvG/HH3+7pRvydoV30z/OH0Y0YAACglTIzXXVEH10xrreOufMzrdtWrK7tUkkMolmjdSTCgeQggJj2zdoC3fjs7Kq7xYnxpv87fbAuH0c3YgAAgFgQF2dKiIuLdhhAo9A6EuFAchBATHLO6bEvVukf7+3pRtyjQ6oeuOQwDctpH93gAAAAAACIEJKDTWDCYzOUW1CsnMxUsvhAC7CjqFw/f2muPv52c1XZKUM66/bzh6tdKt2IAQAAAACxg+RgE8gtKNaq/MJohwGgEeas267rn/mmWjfi3552sK4c35tuxAAAADEqFsZxi4VrBLB/SA4eAOecZq7apq27SyVJO4rLtaO4nJZHQDPknNPjX67WP977VuUBrxtxTmaq/n3JYRrRo310gwMAAEBUxUIPsFi4RgD7h+TgfiqrCOrmF2br3fmbqsq2FZbpmNs/06NXjNKo3h2iGB0Qu2rr5r+jqFy/fHmuPly0pxvxyYM7647zh6tdGsl8AAAAAEDsIjm4n+79ZGm1xGCl7cXl+v6TszT5luPUNoWkAxBJa7cWaX7uDm0vLldBYZnWbSvStsIyXf/sN8ot2NON+DffOVhXHUE3YgAAAAAASA7uh9KKgJ6evrbO9duLy/XqN7m6cnyfCEYFxLb/fblKf3l7kfyJh7W9uFxH3/6ZzFRV1r19qh64lG7EAAAAAABUIjm4HzZsL9GO4vJ6t/nL29/qha9y1Tc7Xf2y0tUnO119szLUNztdbWhRCDSpaSu26k9vLdqr3ElyfmLwxIM7684Lhql9WlJkgwMAAAAAoBkjObgf0pPiG9wmEHT6duNOfbtx517rstskq29WuvpmZ6hfdrr6ZqerT1aGemSmKiE+LhwhI8aVlAf01LTVuufjZSopDygtKUG//+7BOn9kD8XHNf+uteWBoIpKAyosq1BhaYUKywIqqnwuq9B/P19R7/6DOmfokctH0o0YAAAAAIAaSA7uh05tUzS6dwfNXL2tzm0GdWmjvF2l2lZYtte6vF2lyttVqhmrqu+fGG/q2SFNfbMz/BaHGX7iMF0d0pNIbIRZbRNZtAYl5QFd/tjMaj+vu0srdMsr8/Xl8q2653sjFNdECULnnMqqJfK858rXRX5ZteeygJfwC3ldVFqhorI9+5YFggcU1+7SAL8/AAAAAADUguTgfvr1aQfp4oenq7Ri76TFmcO76d6LRsjMtL2oTCvyCrUyb7dW5vvPeYVas7Vor4RHecBpRV6hVuQV7nXMdqmJ6hvSNdlrcZihnh3SlJLYcEtG1K20IqAXZ+Vq5qptKq0IatOOEn22ZIuOG9Qp2qE1if99ubrORPabczdofL+OGt8vq97kXWgrvd2lla/9BF5p9eeKygH+mpGMZP7UAQAAAABQG/5j3k+H9czUc9eN1T/fW1zVAjDOpBuPH6Abj+9f1UqpfVqSRvZK0shemdX2DwSd1hcUa0W+lyysTBquzN+tzTtL9zrfjuJyzV67XbPXbq9WHmdS98zUqqRh3+wM9fO7LHdum9xkraVaa6u64rKArni8equ64vKArvrfV/rxsf30q1MPimg8lS3vSsqDKi0PqKQ8qOLygEoqHxVBFZcFVFpRWRa6Prhnu5B1M1Zurfecv351foSurmHJCXFKT05QWlK80pMSlJbsPyfFKyM59HWC0pPjq54//naz3pq7sc7jnnZI1wheBQAAAAAALQfJwQNwWM9MvfCDcTr69s+0dluRenZI009PGtiofePjTD07pqlnxzQdN6j6ut2lFVqdX6gVVQlDL3m4Kr9QRWWBatsGnbRuW7HWbSvW50vzqq1LS4pXHz9R6I1xmK5+2Rnqk5Wu9Ea2pMrfXar/Tlqhqcu3KuCc1m8v1muzc3X2iO6topvmfz9fUWerugcnrdDxB3XSoT0zqxJuxXsl4fzlioCKy7zkXWkt2xaXB1Rac9vyoEoqvPLQBGAzbHhXq9TE+KoEXVpSfO1JveR4ZSQlKC05QelJ8XueayT30pMTlJYYv99jbh4zMFvzcndozdaivdb17pimK8b3OtDLBQAAAACgVSI52AQqJ3RoqmRZRnKChnZvp6Hd21Urd85p086SagnDytaGuQXFVbOyVioqC2jhhp1auGHvSVG6tE3xE4fp1cY47J6ZWnU9W3aW6Lz/TtW6bcVV+5VVBPXTF+ZqXu4O/fGMIU1yvY3lnFNpRdBLsvlJtb2eywMqrdjz7LWwq/25tDyoj77dXO85L3ho2l7va0tiJsl5s/bWpVu7FJ00uHO1xF31VnohiT//OS0poVlNZNI+LUkv/WCcbnv3W70xZ0NV+Vkjuul3px3MDMUAAAAAANSB5GATyMlMrfYcLmamru1S1bVdqo7on1VtXUl5QGu2FoWMbeglDVfmFWpHcflex9q0s0SbdpZoWo0up0kJcerdMU19szK0emthtcRgqP99uVrHDsrWoM5t90rI1fVcMzFXX+Ku2rPfnbaslvEdwy0cicGkhDilJMQpJTFeKYnxSk2MV0pinJJDllMS45WSEK/UpHglJ8YpJaFy2z37pVRb3nvflKQ4JcXH6Ympq/WntxbVGc9fzh6qEw7u3PQXGmGd2qbo3osO1Zy127VmW5F6dUjTvRcdGu2wAAAAAABo1kgONoHmMAZfSmK8BnVpo0Fd2lQrd85pW2FZtZaGK/IKtSp/t9ZsLdpr8oiyiqCWbt6tpZt3N3jOKx7/qkmvoTnKzkjS8B7tqyfk/KRdSmK8kv0kX2rI+tTEeCWHJOpC1yUnxEe8xd1lY3vpi2X5+mTxlr3WTRjbS8cf1DomXqnUs2Oa4uIs7Ml6AAAAAABaA5KDrZyZqWNGsjpmJOvw3h2qrasIBLWuoLha92QvcViovF17T4oSbpUt6pJDEmk1nyuTcaHPyYm1lzfm+Z5Pluqhz1fWGdM9Fx26VyvNliYxPk4PTRip12av1+9fX6CSiqBSE+N170UjdNLgzq1i7MhQzSFZDwAAAABAS0FyMIYlxMepT1a6+mSl64SDq68rKCzVkf/8TIU1JkAJNaBThkb17tD4hJzf6q6256T4OMVFYQy7G47rr6nLt2r++h17rZswtpfG9+sY8ZjCISE+TheM6qE3526omnX65CFdoh0WAAAAAACIMpKDqFVmerIuHdtLD0+uvVVdnEn/nTBS/bIzIhxZ02qTkqjnrxurJ6au1j0fL1V5wCk5IU53XDBcZwzrSqs6AAAAAADQqsVFOwA0XzedMECjemXWuu5PZw5p8YnBSunJCbr+uP7KyUyTJHVrn6ozh3drdYlBAAAAAACAmqKSHDSz9mb2spktNrNvzWycmXUws4/MbJn/nBmy/W/MbLmZLTGzU6IRcyxKT07QM9eO0T/OPUQpid6PSkZygt64/ghNGNc7usGFQU5mqvpkpTORBQAAAAAAiBnR6lZ8r6T3nXPnm1mSpDRJv5X0iXPuH2b2a0m/lnSLmQ2WdJGkIZK6SfrYzAY65+oeDA9NJjkhXheN7ql35m+sGqtueI/20Q4rLOhyCwAAAAAAYk3Ek4Nm1lbS0ZKulCTnXJmkMjM7S9Kx/mZPSpok6RZJZ0l63jlXKmmVmS2XNFrStIgGHuNInAEAAAAAALQ+0ehW3FdSnqT/mdlsM3vUzNIldXbObZQk/7mTv313SetC9s/1y/ZiZteZ2Swzm5WXlxe+KwAAxDzqHABAJFHvAADCJRrJwQRJh0n6j3PuUEmF8roQ16W2WSFcbRs65x52zo1yzo3Kzs4+8EgBAKgDdQ4AIJKodwAA4RKN5GCupFzn3Az/9cvykoWbzayrJPnPW0K27xGyf46kDRGKFQAAAAAAAGi1Ip4cdM5tkrTOzAb5RSdIWiTpTUlX+GVXSHrDX35T0kVmlmxmfSQNkDQzgiEDAAAAAAAArVK0Ziu+UdIz/kzFKyVdJS9R+aKZXSNpraQLJMk5t9DMXpSXQKyQdD0zFQMAAAAAAAAHLirJQefcHEmjall1Qh3b3ybptnDGBAAAAAAAAMSaaIw5CAAAAAAAAKAZIDkIAAAAAAAAxCiSgwAAAAAAAECMIjkIAAAAAAAAxCiSgwAAAAAAAECMIjkIAAAAAAAAxCiSgwAAAAAAAECMIjkIAAAAAAAAxChzzkU7hrAwszxJayJ4yixJ+RE8XzRwja0D19h6xMJ1Rvoa851zp+7rTlGocyQ+/9aCa2wduMbWIRrXSL3TvHCNrQPX2DpwjeFRa73TapODkWZms5xzo6IdRzhxja0D19h6xMJ1xsI17q9YeG+4xtaBa2wduEbEwvvDNbYOXGPrwDVGFt2KAQAAAAAAgBhFchAAAAAAAACIUSQHm87D0Q4gArjG1oFrbD1i4Tpj4Rr3Vyy8N1xj68A1tg5cI2Lh/eEaWweusXXgGiOIMQcBAAAAAACAGEXLQQAAAAAAACBGkRwEAAAAAAAAYlTMJwfNzJnZxJDXCWaWZ2ZvN9HxbzWzXzTFsfbj3B3NbI7/2GRm60NeJzXheY5tqverlmPfbWY3h7z+wMweDXl9l5n9rBHH6W1mC8IRY8g5djfhser67Lab2aKmOk8957/SzP4d7vM0Io5AyPswx8x617LNu2bWvpbyqP3u1cbMfmdmC81snn8tY+rZ9koz69YE51xtZlkHepwDOH+jr3kfjtmsPtd91ZrrHP/81Dt7tqPe2bfzU+80Ieoc6pxKrbneoc6pdpwWVef4x6PeEfXOAZ6TeqcJJUTjpM1MoaShZpbqnCuWdJKk9VGOqUk457ZKGiF5P2SSdjvn7oxmTPthqqQLJN1jZnGSsiS1DVk/XtLNUYgrrOr67PzKYr+/nJhZgnOuoilijJBi59yI2laYmckbN/W0yIa078xsnKTvSjrMOVfqV2L1fWm9UtICSRv24RzN6rPdj2uOFa22zpGod1oy6p0qLb7eoc6hzqmh1dY71DktG/VOFeqdxp2jWX2urbHeifmWg773JJ3uL18s6bnKFWbWwcxe97PB081smF9+q5k9bmaTzGylmf0kZJ/fmdkSM/tY0qCQ8mvN7Cszm2tmr5hZmpm1MbNVZpbob9PWz4AnhuNCzewJMzs/5PXukOVf+vHNM7M/+WXpZvaOH/MCM/ueX36qmS02sy8knRtyjNFmNtXMZvvPg/zyKWY2ImS7LyvfywZ8Ka9SlKQh8v6I7DKzTDNLlnSwf7zPzexr8+62dfXLRvpxT5N0fci5rzSzV83sfTNbZma3h6w72cymmdk3ZvaSmWX45f8ws0X+e3OnX9bH3/YrM/tLyDEyzOwT/xjzzewsv/wvZnZTyHa3hf7c7IN4M3vEvLsUH5pZqn+8SWY2yl/OMrPVIdf7kpm9JelDM+tqZpPNu7uxwMyO8re7ysyWmtnnko4IifMMM5vhf6Yfm1lnM4vz37tsf5s4M1tuYb5zY95d0W/N7EFJ30jqYSF3jJrx715XSfnOuVJJcs7lO+c2mNkf/LgWmNnD5jlf0ihJz/ifUWqNaxxlZpP85Vv9/T6U9JR5d2A/9D+rhyRZyHvwuv87stDMrvPLrjGzu2u8T/8K8zXXdy0t7m/qfoqZOsc/B/UO9Q71TmR//6hzqHNqipl6x6hzWkOdI1HvUO+Ieifin6lzLqYfknZLGibpZUkpkuZIOlbS2/76+yX90V8+XtIcf/lWeXd6kuXd4dkqKVHSSEnzJaXJu+uzXNIv/H06hpz3r5Ju9Jf/J+lsf/k6SXeF4TpvlfQLSU9IOj/0+v3nk+VNo23yksZvSzpa0nmSHgnZvp3/Pq2TNMDf/sWQ96utpAR/+URJr/jLV0i6x18eKGnWPsS+WlJPST+Q9ENJf5F0mrw/6NP8zyHb3/Z7kh73l+dJOsZfvkPSAn/5SkkrQ65ljaQe/uc4WVK6v90tkv4gqYOkJVLV7N7t/ec3JV3uL18f8l4mSGrrL2f5PwMmqbekb/zyOEkrQn8mGvrs/OXekiokjfBfvyjpMn95kqRRIeddHXK9uZI6+K9/Lul3/nK8pDby/ritlZQt747Hl5L+7W+TGXLt35f/8ynpj5JuDvn5eSUMP7cBeb+TcyS95l9/UNLYGj8fWWqmv3v+sTP8a1gq6UHt+bnsELLNREln1PwsQ6/RXx4laVLIz8bXklL91/dJ+oO/fLokF7Jf5eefKu+LZ0dJ6f7PYaK/bqqkQ8J8zfVdS4v5m3oA70tM1DkhMVPvOOodUe9E+jsfdQ51Tuh7ExP1jqhzWmydE/r5+cu9Rb1DvbPn54J6J0KfKS0HJTnn5sn7JbxY0rs1Vh8p7wdZzrlPJXU0s3b+unecc6XOuXxJWyR1lnSUpNecc0XOuZ3y/qhWGmreXaX5ki6Vd3dIkh6VdJW/fJW8DzvSTvYfs+XdoThIXoU4X9KJZvZPMzvKObfDX7fKObfMeT+RT4ccp52kl8wb8+Ju7bnGlyR9189oXy2v4m6syjtq4+VVkNNCXq+XNFTSR2Y2R9L/ScrxP6P2zrnP/WNMrHHMT5xzO5xzJZIWSeolaaykwZK+9I91hV++U1KJpEfN7FxJRf4xjtCeO6+hxzdJfzOzeZI+ltRdUmfn3GpJW83sUPnvtfOa0++rVc65Of7y1/J+dhvykXNum7/8laSrzGu+f4hzbpekMfL+cOU558okvRCyb46kD/yf219qz2f6uKTL/eWrFZ6f22Ln3Aj/cY5ftsY5N72WbZvt755zbre8P/zXScqT9IKZXSnpOP8u5Xx5X8iH1H2UOr3pvG5Ckvcl92n/nO9IKgjZ7idmNlfSdHlfEAc45wolfSrvd/MgeRXn/P2IYS/1XHN9WtPf1DpR51Sh3qHeod4JwzVQ51Dn1ES9I4k6pyXVORL1DvXOHtQ7EfpMGXNwjzcl3SnvTlrHkHKrZVvnP5eGlAW05/10qt0T8jK8c/0fnGMlyTn3pXnNh4+RFO+cW7Af8TdWhfzu5GZm2tMv3iT93Tn3UM0dzGykvLtXf/eb9L6puq/xL5I+c86dY954EZMkyTlXZGYfSTpL0oXysuiNNVVe5XiIvLsA6+TdDdop75e9u3NuXI2Y29cTo1T7Z2fyKpWLa25sZqMlnSDpIkk3yPvjpjrOcam8O1IjnXPl5jV3T/HXPSrvzlYXeZXN/qgZe6q/XPXZhpyvUmHlgnNuspkdLe9uy0Qzu0Pee1nX+3W/pH855940s2Pl3fWQc26dmW02s+PlVbaX7uf17KvCetY1298951xA3u/DJP8P/A/k3ckf5b+Xt2rvz61Soz7bylPV3Nn/3E6UNM7/XZyk6j+Tv5W0WE38ZaGWa75C9V9LS/ybur9ipc6RqHcqUe9Q70Ts9486hzqnFrFS71DneFpynVNb/NQ7ezTL3z/qnZZf79BycI/HJf25lkzyZPl/BPwfunw/o1uXyZLOMa/vfBtJZ4SsayNpo39HqeYflqfk3ZkJ95201fIy3JJXeVX2Tf9A0tW2Z9yJ7mbWybxZhIqcc0/L+0JxmLxfrD5m1s/fN7SCaac9gxxfWePcj8prCvxVyF2dxvhS3mCf25xzAX/f9pLGybvjk23egKAys0QzG+Kc2y5ph5kd6R+jMX/Ip0s6wsz6+8dKM7OB/nvSzjn3rrwBgUeExHVRLcdvJ2mLX1keJ++OXKXXJJ0q6XB573lTWq09n+35dW1kZr38+B6R9Ji8z3SGpGPNG8chUd7AyJVCP9MrahzuUXl3b170/zhGU7P93TOzQWY2IKRohLzuG5KU7/+MhX5mu+TFXGm19ny259VzqtC/V9+R10VC8j7DAr+yPEjenWNJknNuhry7a5coZAyiA1XHNa9R46+lUrP9XA9QrNQ5EvVOfah3qHeaHHWOJOqc2sRKvbNa1Dl1ael1jkS90yx//6h3JLWCeoeWgz7nXK6ke2tZdauk/5nXbLpIe/+xqHmcb8zsBXn9z9dImhKy+vfy/iitkdeEPfQX4hl5/cib7Ae2Do9IesPMZkr6RH4m3jn3oZkdLGmamUne+CSXSeov6Q4zC0oql/Qj51yJeYN8vmNm+ZK+kNfcXZJul/Skmf1M3p2uKs65r81sp/b9B3i+vH75z9Yoy3DObTFvUNP7zGtenyDpHkkL5TWxfdzMitSIysk5l2deRv458wYAlrym+7vkvWcp8u64/dRfd5OkZ80bePeVkEM9I+ktM5sl7+dgccg5yszsM0nbw1C53CnpRTOboBrvfQ3HSvqlmZXL+5wvd85tNO9uzjRJG+V1t4j3t79VXveJ9fK+VPQJOdab8j7PqH8Zbua/exmS7jfvLm+FvLEkrpO03Y9ntbzuD5WekPRfMyuW98XwT5IeM7PfyruOuvxJ3s/vN5I+lzeuiiS9L+mH/t+xJfI+x1AvyhvXpUBNp65rPliNuxZJzf5z3W8xVOdI1Dt1ot6h3glTaNQ51Dl7iaF6hzqnDq2gzpGod5rr7x/1TiuodyoH3USU+X/0z3LOTYh2LOFi3p25SZIOcs4FoxxOVJhZnLyK6ALn3LJox3OgzJst7G7n3FHRjmV/xcLvXkPM7G15n+Mn0Y6lqfC51i9W3h/qHeqd5ihWfv/qQp0Tm2LhPaLOaX11jkS90xpQ7zQOLQebATO7X9J35I110SqZ2eWSbpP0sxiuLAfLmxnttdZQWZrZryX9SJEbe6PJxcLvXn38O10zJc1tZZVlTH+uDYmV94d6h3qnOYqV37/aUOfErlh4j6hzWl+dI1HvtHTUO/t4XFoOAgAAAAAAALGJCUkAAAAAAACAGEVyEAAAAAAAAIhRJAcBAAAAAACAGEVyEAAAAAAAAIhRJAeBKDCzgJnNMbOFZjbXzH5mZhH5fTSz58xsnpn9NELnu9LM/h2JcwEA9kadAwCIJOodoOVJiHYAQIwqds6NkCQz6yTpWUntJP0xnCc1sy6SxjvneoXzPACAZoU6BwAQSdQ7QAtDy0EgypxzWyRdJ+kG8/Q2sylm9o3/GC9JZjbRzM6q3M/MnjGzM2s7ppmlmNn/zGy+mc02s+P8VR9K6uTfyTuqlv06mdnX/vJwM3Nm1tN/vcLM0sws28xeMbOv/McR/vp0M3vcL5sdGmvI8U83s2lmlnVg7xoAYH9Q5wAAIol6B2gZaDkINAPOuZXmNbXvJGmLpJOccyVmNkDSc5JGSXpU0k8lvWFm7SSNl3RFHYe83j/uIWZ2kKQPzWygpDMlvV15J6+WOLb4lW1bSUdJmiXpKDP7QtIW51yRmT0q6W7n3Bd+ZfqBpIMl/U7Sp865q82svaSZZvZx5bHN7BxJP5N0mnOuYH/fKwDAgaHOAQBEEvUO0PyRHASaD/OfEyX928xGSApIGihJzrnPzewB85rmnyvpFedcRR3HOlLS/f5+i81sjX+cnY2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      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=gobike_clean, col='member_gender', height=6)\n",
    "g.map(sns.pointplot, 'day_of_week','duration_sec')\n",
    "g.set_xlabels('Day_of_week')\n",
    "g.set_ylabels('Duration [sec]');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1652432a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "member_gender  day_of_week\n",
       "Female         Monday          785.126680\n",
       "               Tuesday         721.625844\n",
       "               Wednesday       739.645865\n",
       "               Thursday        750.383132\n",
       "               Friday          750.159980\n",
       "               Saturday        875.507973\n",
       "               Sunday          977.237815\n",
       "Male           Monday          663.207315\n",
       "               Tuesday         627.142029\n",
       "               Wednesday       635.010014\n",
       "               Thursday        643.620405\n",
       "               Friday          654.846165\n",
       "               Saturday        833.361307\n",
       "               Sunday          801.795063\n",
       "Other          Monday         1032.991770\n",
       "               Tuesday         889.807277\n",
       "               Wednesday       893.418722\n",
       "               Thursday       1139.608577\n",
       "               Friday          971.683874\n",
       "               Saturday       1547.112941\n",
       "               Sunday         1707.706818\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean.groupby(['member_gender','day_of_week'])['duration_sec'].mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f02640",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "We can see that the least represented population `member_gender` **Other** and `day_of_week` **Saturday** and **Sunday** have the highest average duration with **1547** and **1707** seconds. We can also see that these also applies to other member gender Male and Female on week day Saturday and Sunday."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a9083f",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Relationship between `user_type`, `bike_share_for_all_trip` and `duration_sec`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "63c9b386",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:670: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x273a8a899d0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=gobike_clean, col='user_type', height=4)\n",
    "g.map(sns.barplot, 'bike_share_for_all_trip','duration_sec')\n",
    "g.set_xlabels('Bike Share for all Trip')\n",
    "g.set_ylabels('Duration [sec]');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "59dde796",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_type   bike_share_for_all_trip\n",
       "Customer    No                         1433.475734\n",
       "Subscriber  No                          634.959498\n",
       "            Yes                         683.997521\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gobike_clean.groupby(['user_type', 'bike_share_for_all_trip'])['duration_sec'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f5917b",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "I posed a question when reporting my findings on percentage of riders who share bike?. Are customers allowed to share bikes, or is the right to share bike only given to subcribers?. This visualization answers the question and more. First from the visualization Customers do not share bike, Secondly Customer who do not share bike have an average of about 1400 seconds greater than Suscribers who fall at an average of about 650 seconds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75ab5e0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### How long does the average trip take for different age class?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4d07e6d0",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age_class', ylabel='duration_sec'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[6,6])\n",
    "sns.barplot(data=gobike_clean, x='Age_class', y='duration_sec', palette=['#157fa1','#a4daeb'])\n",
    "plt.xlabel('Age Group')\n",
    "plt.title('Bar Plot of Age Group');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435c777c",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report \n",
    "From the visualization above we can see that there is little difference in average duration between each age group, with an average corresponding to average total time of all riders 700 seconds. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a11d8f2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 8.  Which stations are most popular?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27f6dae3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "station_start = gobike_clean['start_station_name'].value_counts()\n",
    "station_start[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9a547d6",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "station_start.iloc[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e43b4d",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "station_end = gobike_clean['end_station_name'].value_counts()\n",
    "station_end[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f6081ca",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "station_end[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "167bfca3",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "print('Start station: ', len(station_start))\n",
    "print('End station: ', len(station_end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed7059b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "station = {}\n",
    "for i, row in gobike_clean.iterrows():\n",
    "    col_name = ['start_station_name', 'end_station_name']\n",
    "    for col in col_name:\n",
    "        station_name = row[col]\n",
    "        if station_name not in station:\n",
    "            station[station_name] = 1\n",
    "        else:\n",
    "            station[station_name] += 1\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b982a24",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "station = pd.Series(station)\n",
    "station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dca53fc8",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "top_stations = station.sort_values()[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "233edcbc",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams['axes.spines.bottom'] = True\n",
    "plt.barh(top_stations.index, top_stations.values)\n",
    "for index, values in enumerate(top_stations):\n",
    "    text = \"{}\".format(values)\n",
    "    plt.text(values-400, index, text, va='center',color='white', fontweight='bold')\n",
    "    \n",
    "plt.xlabel('Count')\n",
    "plt.title('Top 10 Stations in the Dataset'); "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3118271",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 9. At what point do we need to check or service a bike in the sharing system?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de4c306",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "total_bike_sec = gobike_clean.groupby('bike_id')['duration_sec'].sum().sort_values()\n",
    "total_bike_sec\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2dcd2cb",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "total_bike_sec.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08012e7a",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "mean_bike = total_bike_sec.describe()[1]\n",
    "upper_bond_bike = (total_bike_sec.describe()[-2] - total_bike_sec.describe()[-4]) * 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32f98638",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "plt.plot(total_bike_sec.values)\n",
    "plt.axhline(mean_bike, color='black', linestyle='-.', linewidth=1, label='Mean')\n",
    "plt.axhline(upper_bond_bike, color='orange', linestyle='-', linewidth=1, label='Upper bond')\n",
    "plt.axhspan(0, mean_bike, facecolor='green', alpha=0.3, label='Good Usage')\n",
    "plt.axhspan(mean_bike, upper_bond_bike, facecolor='yellow', alpha=0.2, label='Moderate Usage')\n",
    "plt.axhspan(upper_bond_bike,total_bike_sec.max(), facecolor='red', alpha=0.1, label='Heavy Usage')\n",
    "plt.legend()\n",
    "plt.ylabel('Duration [sec]')\n",
    "plt.xlabel('Count')\n",
    "plt.title('Visual Representation of Bike Usage');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c066db12",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "This visualization was done to show the usage of bike id's and there total time duration in seconds within the months of Feburary and March 2019. With this graph, stations can determine when a bike should be checked or repaired. The area coloured green represence Good usage, the area coloured yellow represence Moderate bike usage, and the area coloured red represence bikes with heavy usage."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e891e44",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 10. Heatmap visualization of each cooridinate of bike stations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba59d2e2",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "m = folium.Map([37.8272, -122.2913], zoom_start=9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7913dee",
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "coordinate = [tuple(x) for x in gobike_clean[['start_station_latitude', 'start_station_longitude']].to_numpy()]\n",
    "m.add_child(plugins.HeatMap(coordinate, radius=15))\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8aced9",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Report\n",
    "From the visualization of the stations geographic location, we can see that stations are majorly seperated into 3 geographical concentration. Feel free to locate the most popular stations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13ba701a",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id=\"conclusions\"></a>\n",
    "## Conclusions\n",
    "\n",
    "These project has successfully analized different features from the data set. I analized the popularity of different categorical variable with the result been that the most popular:\n",
    "- Member gender are Males with a popularity of over 70 percent.\n",
    "- Day of the week is Thursday.\n",
    "- Hour of the day to start a ride is at 5 pm.\n",
    "- Period of the day are during evenings.\n",
    "- Age group are adults.\n",
    "\n",
    "One relationship i found while investigating this data set is that categories that were least represented had higher average time duration than the most popular categories.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ae303e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a id=\"limitation\"></a>\n",
    "## Project Limitations\n",
    "\n",
    "These are some limitations that be further reasearched on.\n",
    "\n",
    "1. No predictive algorithms was used in these project.\n",
    "2. While clean the missing station names, a better approach could have been implimented by finding the nearest know station cooridinate to that location and then matching the station name to the missing stations, instead i droped all missing values.\n",
    "3. The project could have explored more goegraphical maps e.g.(visualization of the top 10 stations with custom markers, and the most popular routes to take while taken in ride)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46137abd",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# The command below will generate the HTML slideshow.\n",
    "!jupyter nbconvert Main_Project.ipynb --to slides --post serve --no-input --no-prompt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9276bc5f",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "<a id=\"ref\"></a>\n",
    "## Refrences\n",
    "\n",
    "- https://stackoverflow.com/questions/16180946/drawing-average-line-in-histogram-matplotlib\n",
    "\n",
    "- https://stackoverflow.com/questions/9957637/how-can-i-set-the-background-color-on-specific-areas-of-a-pyplot-figure"
   ]
  }
 ],
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