Data-Visualization-project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Ford GoBike System Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data wrangling and cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "This data set includes information about individual rides made\n",
    "in a bike-sharing system covering the greater San Francisco\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# import all  needed packages \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "# loading the dataset\n",
    "df = pd.read_csv('201902-fordgobike-tripdata.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183412, 16)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get number of rows and columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "#get column names and types\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# as we see  start_time and end_time columns classified as object so we have to convert them to datetime\n",
    "df.start_time = pd.to_datetime(df.start_time)\n",
    "df.end_time = pd.to_datetime(df.end_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking  duplicated values \n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                  0\n",
       "start_time                    0\n",
       "end_time                      0\n",
       "start_station_id            197\n",
       "start_station_name          197\n",
       "start_station_latitude        0\n",
       "start_station_longitude       0\n",
       "end_station_id              197\n",
       "end_station_name            197\n",
       "end_station_latitude          0\n",
       "end_station_longitude         0\n",
       "bike_id                       0\n",
       "user_type                     0\n",
       "member_birth_year          8265\n",
       "member_gender              8265\n",
       "bike_share_for_all_trip       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "330 330 4646\n"
     ]
    }
   ],
   "source": [
    "#check uniqueness of the ids\n",
    "print(len(df.start_station_id.unique()),len(df.end_station_id.unique()),len(df.bike_id.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "* the ids appear unique so we will drop it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#drop ids columns\n",
    "df = df.drop(['start_station_id', 'end_station_id', 'bike_id'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</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 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</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>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</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 18:53:21.789</td>\n",
       "      <td>2019-03-01 06:42:03.056</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</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 12:13:13.218</td>\n",
       "      <td>2019-03-01 05:24:08.146</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</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>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 17:54:26.010</td>\n",
       "      <td>2019-03-01 04:02:36.842</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</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 23:54:18.549</td>\n",
       "      <td>2019-03-01 00:20:44.074</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.792714</td>\n",
       "      <td>-122.248780</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 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "1         42521 2019-02-28 18:53:21.789 2019-03-01 06:42:03.056   \n",
       "2         61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "3         36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842   \n",
       "4          1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074   \n",
       "\n",
       "                                 start_station_name  start_station_latitude  \\\n",
       "0  Montgomery St BART Station (Market St at 2nd St)               37.789625   \n",
       "1                     The Embarcadero at Steuart St               37.791464   \n",
       "2                           Market St at Dolores St               37.769305   \n",
       "3                           Grove St at Masonic Ave               37.774836   \n",
       "4                               Frank H Ogawa Plaza               37.804562   \n",
       "\n",
       "   start_station_longitude                              end_station_name  \\\n",
       "0              -122.400811                Commercial St at Montgomery St   \n",
       "1              -122.391034                            Berry St at 4th St   \n",
       "2              -122.426826  Powell St BART Station (Market St at 4th St)   \n",
       "3              -122.446546                        Central Ave at Fell St   \n",
       "4              -122.271738                         10th Ave at E 15th St   \n",
       "\n",
       "   end_station_latitude  end_station_longitude   user_type  member_birth_year  \\\n",
       "0             37.794231            -122.402923    Customer             1984.0   \n",
       "1             37.775880            -122.393170    Customer                NaN   \n",
       "2             37.786375            -122.404904    Customer             1972.0   \n",
       "3             37.773311            -122.444293  Subscriber             1989.0   \n",
       "4             37.792714            -122.248780  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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# instead of duration column with sec unit we will make it with minutes\n",
    "#first rename it \n",
    "df = df.rename(columns = {'duration_sec' : 'duration'})\n",
    "#second convert to minutes\n",
    "df['duration'] = df['duration']/60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "* we can also get day of the week of the ride it will help us to find what days the most ride happens\n",
    "* we can also get hour of the day of the ride it will help us to find what is the peak hours the most ride happens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df['day_of_week'] = df['start_time'].dt.day_name()\n",
    "df['hour']= df['start_time'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#removing null values\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#instead of coulmn member birth year we can get member age it will help us in our anlaysis\n",
    "from datetime import datetime\n",
    "from datetime import date\n",
    "def calculate_age(born_year):\n",
    "    today = date.today()\n",
    "    return today.year - born_year\n",
    "df['age'] = df['member_birth_year'].apply(calculate_age)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age [bike riders]')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's see age dist\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "sb.boxplot(x='age', data=df, color='Red', orient='h')\n",
    "plt.title(\"The age distribution of Ford GoBike users\", fontsize=20, y=1.03)\n",
    "plt.xlabel(\"Age [bike riders]\", fontsize=18, labelpad=10)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* as we see here we can remove user with more than 60 as the distrubition of users between  20 and 60 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df = df[df['age']<=60]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "#we can drop member_birth_year col\n",
    "df = df.drop(['member_birth_year'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>169992.000000</td>\n",
       "      <td>169992.000000</td>\n",
       "      <td>169992.000000</td>\n",
       "      <td>169992.000000</td>\n",
       "      <td>169992.000000</td>\n",
       "      <td>169992.000000</td>\n",
       "      <td>169992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.717717</td>\n",
       "      <td>37.771228</td>\n",
       "      <td>-122.351767</td>\n",
       "      <td>37.771418</td>\n",
       "      <td>-122.351325</td>\n",
       "      <td>13.472016</td>\n",
       "      <td>35.305403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.676933</td>\n",
       "      <td>0.100454</td>\n",
       "      <td>0.117746</td>\n",
       "      <td>0.100358</td>\n",
       "      <td>0.117304</td>\n",
       "      <td>4.744409</td>\n",
       "      <td>8.669889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.016667</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.383333</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.411901</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.411647</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.483333</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.398279</td>\n",
       "      <td>37.781010</td>\n",
       "      <td>-122.397437</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.100000</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.283093</td>\n",
       "      <td>37.797673</td>\n",
       "      <td>-122.285633</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1409.133333</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            duration  start_station_latitude  start_station_longitude  \\\n",
       "count  169992.000000           169992.000000            169992.000000   \n",
       "mean       11.717717               37.771228              -122.351767   \n",
       "std        27.676933                0.100454                 0.117746   \n",
       "min         1.016667               37.317298              -122.453704   \n",
       "25%         5.383333               37.770407              -122.411901   \n",
       "50%         8.483333               37.780760              -122.398279   \n",
       "75%        13.100000               37.797320              -122.283093   \n",
       "max      1409.133333               37.880222              -121.874119   \n",
       "\n",
       "       end_station_latitude  end_station_longitude           hour  \\\n",
       "count         169992.000000          169992.000000  169992.000000   \n",
       "mean              37.771418            -122.351325      13.472016   \n",
       "std                0.100358               0.117304       4.744409   \n",
       "min               37.317298            -122.453704       0.000000   \n",
       "25%               37.770407            -122.411647       9.000000   \n",
       "50%               37.781010            -122.397437      14.000000   \n",
       "75%               37.797673            -122.285633      17.000000   \n",
       "max               37.880222            -121.874119      23.000000   \n",
       "\n",
       "                 age  \n",
       "count  169992.000000  \n",
       "mean       35.305403  \n",
       "std         8.669889  \n",
       "min        20.000000  \n",
       "25%        29.000000  \n",
       "50%        34.000000  \n",
       "75%        40.000000  \n",
       "max        60.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get some statistics about our data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(169992, 15)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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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</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>hour</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>869.750000</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</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>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>Customer</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1030.900000</td>\n",
       "      <td>2019-02-28 12:13:13.218</td>\n",
       "      <td>2019-03-01 05:24:08.146</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</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>Customer</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>12</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>608.166667</td>\n",
       "      <td>2019-02-28 17:54:26.010</td>\n",
       "      <td>2019-03-01 04:02:36.842</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.773311</td>\n",
       "      <td>-122.444293</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>17</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.416667</td>\n",
       "      <td>2019-02-28 23:54:18.549</td>\n",
       "      <td>2019-03-01 00:20:44.074</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.804562</td>\n",
       "      <td>-122.271738</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.792714</td>\n",
       "      <td>-122.248780</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>19.116667</td>\n",
       "      <td>2019-02-28 23:55:35.104</td>\n",
       "      <td>2019-03-01 00:14:42.588</td>\n",
       "      <td>Palm St at Willow St</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-121.884995</td>\n",
       "      <td>San Jose Diridon Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>23</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      duration              start_time                end_time  \\\n",
       "0   869.750000 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "2  1030.900000 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "3   608.166667 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842   \n",
       "4    26.416667 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074   \n",
       "6    19.116667 2019-02-28 23:55:35.104 2019-03-01 00:14:42.588   \n",
       "\n",
       "                                 start_station_name  start_station_latitude  \\\n",
       "0  Montgomery St BART Station (Market St at 2nd St)               37.789625   \n",
       "2                           Market St at Dolores St               37.769305   \n",
       "3                           Grove St at Masonic Ave               37.774836   \n",
       "4                               Frank H Ogawa Plaza               37.804562   \n",
       "6                              Palm St at Willow St               37.317298   \n",
       "\n",
       "   start_station_longitude                              end_station_name  \\\n",
       "0              -122.400811                Commercial St at Montgomery St   \n",
       "2              -122.426826  Powell St BART Station (Market St at 4th St)   \n",
       "3              -122.446546                        Central Ave at Fell St   \n",
       "4              -122.271738                         10th Ave at E 15th St   \n",
       "6              -121.884995                      San Jose Diridon Station   \n",
       "\n",
       "   end_station_latitude  end_station_longitude   user_type member_gender  \\\n",
       "0             37.794231            -122.402923    Customer          Male   \n",
       "2             37.786375            -122.404904    Customer          Male   \n",
       "3             37.773311            -122.444293  Subscriber         Other   \n",
       "4             37.792714            -122.248780  Subscriber          Male   \n",
       "6             37.329732            -121.901782  Subscriber        Female   \n",
       "\n",
       "  bike_share_for_all_trip day_of_week  hour   age  \n",
       "0                      No    Thursday    17  37.0  \n",
       "2                      No    Thursday    12  49.0  \n",
       "3                      No    Thursday    17  32.0  \n",
       "4                     Yes    Thursday    23  47.0  \n",
       "6                      No    Thursday    23  38.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# structure of our data \n",
    "\n",
    "### finaly our data have  a structure as  169992 rows and 15 columns with some main features of interest like\n",
    "* user_type\n",
    "* member_gender\n",
    "* age\n",
    "* Average riding duration\n",
    "* Weekly day distrubition "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# what type of users use our system most?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2461017a9a0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.user_type.value_counts().plot( kind='pie' ,autopct='%1.1f%%' , title = \"rides per user type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* as we can see here the majorty of users are subscriber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x24613b3a730>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.member_gender.value_counts().plot( kind='pie' ,autopct='%1.1f%%' , title = \"rides per user gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* as we can see here the male users tend to use bikes more "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'count [rides]')"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('rides per user age', fontsize=20, y=1.015)\n",
    "sb.countplot( y= pd.cut(df['age'], bins=6 ,labels = ['20-26','26-33', '33-40', '40-46','46-53' ,'53-60'] ), data = df )\n",
    "plt.ylabel('Age', labelpad=16)\n",
    "plt.xlabel('count [rides]', labelpad=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* as we see here that youth tend to ride bikes more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# how rides distrbute over day's hours?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hour')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Rides Distrbution over Hours', fontsize=20, y=1.015)\n",
    "order = df['hour'].value_counts().index\n",
    "sb.countplot(data = df, x = 'hour',color = 'blue', order = order)\n",
    "plt.ylabel('Count [rides]', labelpad=16)\n",
    "plt.xlabel('Hour', labelpad=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* we  see that the peaks hours from 7 to 9 am and from 16 to 17 which we can conclude that most ride for Commuting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# how ride distrbute over days?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Day Of Week')"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Rides Distrbution over Days', fontsize=20, y=1.015)\n",
    "base_color = sb.color_palette()[0]\n",
    "cat_order = df['day_of_week'].value_counts().index\n",
    "sb.countplot(data = df, x = 'day_of_week', color = base_color , order = cat_order);\n",
    "plt.ylabel('Count [rides]', labelpad=16)\n",
    "plt.xlabel('Day Of Week', labelpad=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* here we can conclude that our users using bikes for Commuting not for leisure as we can see the least rides in vacation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### what Average trip duration per user type?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'trip duration(minutes) ')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.groupby('user_type')['duration'].mean().plot(kind='barh', color=['brown', 'blue'])\n",
    "ax.set_title('Average trip duration per user type', fontsize=20, y=1.015)\n",
    "ax.set_ylabel('user type', labelpad=16)\n",
    "ax.set_xlabel('trip duration(minutes) ', labelpad=16)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* Subscriber average trip duration is around 11 minutes where Customer average trip duration is around 24 minutes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "user_type_count_per_day_of_Week_df = df.groupby([\"day_of_week\", \"user_type\"]).size().reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1g3MQ93mcH8Of4fzIW2CM6enennml5WT/cyrDczwLZf7sdf/WzWCd/Dj3IGv7sT8b28vqeXElj1lW7MNp1QnlUmuwr9B06u3B+Xx63lqbrR/m1tqfcEb+fA3Anf7hE5z+p/cC72Rjc+nF51vmexxzHHcunXf/lk2nvE56Fd3uHGPdB8aYFjgtvOE4I0b/zV21oJ1baU3EGRfgD8aYJ7h0AXR0JvVCcS7W+FsOlx9fgIXW2sdyFKVIHtMtryJXIffqt6dl6tY82qzn9rbfGmP83Z56dzr1PHPs7UlbYIwJAQZm8rxjcfoiDgcecpe97fYTyRL39Vjq/veedFYb7P6Nzep2s2AFTl+lSMAz76fvLa3zcW71egIn2fzeWnshzTa+cf/elo3nXYlzdb6lMSbdhD0j1trz7nN+gjNKZqwxplo2N+P5gZmbi5c52f+cisdJ6msZYzqnLTTGNMUZmCe72zwF1DTGRKctdC8m/Nb9b2w2t52ZFm7MaZ+zC87ttafc+LIsi+fFlTxmWeH57LornfL0lufZflhrl3Cp/3aLbFavYHzmLvQwxtTj0nQUi32KAvX6ey6I3JC2wO0b2SqrG7LW/ojT0gepX69A7VuWPsvcltRJOF04ngZ64XwWf5bJ9jM7N2N9lnleg/4mg/mfRa4kJZQiV69/4iRir7uD4aS6rc4YU8wYE22MuSmL2/seZy7Ga9xteiePNsY0Bv6RTj3PLZUDfQdcMMaUwBnEoJ7fWi73NtepOD+A78K5heu9LMbs63/u30eMMb/xLTDG/Aln8ILjOHNm5gk3kV0MFMdJiH/BGYTCw5P0P5Dm/77ewUnGBxljnjP+J6Rvaoz5o8/zXsC5jTYImG6MaeenThljzO/cY5dR/HfjvN43AotMBhN3++H5gVk/Fz98sr3/OWWtPcOl4/+68Zkc3G2BH0M2b091B83wDB71ujGmus82SwJv47ToxFlrl/rZRG4YYIwbu+c5q3Dph/o7/gb1yEwWzosrdsyyaDTOrc9/SJuYGWMexbnrwZ9XcPra/s0Yc7+/c9gYE2mMuc3n/zHGmM5u3znf9Upxqb90Tvrx/TfNuVMW59wJAqZZa337Cmc77jziuVh2f5pYr8VJsi5ruTTGdDPG9E4bo9t30nOsfF+vQJ1bns+ydD8vfXhG0H4M53f2e9bazG49HmiMSXVx1e0OEoVz4WeCZ7m1dg3OFFP1gc/9fSYbY6obYx5RwilXTKCHmdVDj6L6IPvThkzMZDuxfsruxOmn5xlifDbOldKlXJoY+z/ZiLkplyaP3oUzF9a3OLeTfsmlIdBDfeoE4yRRnmkKZuIMCLMP54vy9Yz2z91Gey4Nn/9+Ll7z/3Bpzq9YLk1ubnFuEfY3NP+z5GDaEJ/6j/rEPjVNmfE5DhYIT2cbzdzX27qv/0KcFqIFXJrIOs5Pvf/5bPtH9xhN51JLnMUZXMWzfqi/c9KN8023LBGom4399xz7TTi3sI0HHvMpjyWTqTFysv/4maIgK8cV50evZ77V4zgTwk9xn3cHMCOj7abzXCXdmC2XJqf/jEtTu+wC6vmpl+lrk87zDXbrzXBjPuLuw3R3nyxOK3baef+iyMK0IVk5L3J6zmawTxkezzT7fdlnCc6chZ4BaZYAH+GM2JvCpc+gWD/1unLpM28/zjyHn+JcKNrnLv/UZ33PBPeHgO9w+rB/5bONBPxMu5TO/niOxzKcux1Ousf0Cy7N+7sd/9NJZSvu3JxvPvVLcOn9fszd7+/cf2/AeS+lOobAI+6yX91z4yN3Pc9742egTn5/HmRh30LcWCzONDOTcD7L/pjO+t9x6bvmsve2z3o73fVedf8ux/leWuNT39/UPOV9jtdZnK4Sn+G0Xm5w61myOE+zHnrk9qEWSpGrmHWG9G+GM1rqGZzh8/sA1XC+sB7m0kiqWdneBpyr+R/i9Ivsj/Nj8zng9nTqJLvP+zLOF3JPnIFqFuNMWfBDFp56JZdG5czOYDxpY3kC5/bCuTivy61ARZxEp7X1MzR/HvC9xTVVC6S11uL8GALnR7+/PjRYZ+qH5jj9iLbh3Do2AGeqlQM4rZHD/dT7E85r/ynOft6C8yO1NM6PvbtwWp4zZB0P4hzDusBiY0zDTKp5DMC5gHAt8DucIfRvyWJdz/PnaP9zwjr9jbvgXHw4htNKEomTkEXgtDJnd5vncM77h3AS6644fRFP4LymrWzWRyzOjl9wYp+Gc1HmZpwf4C8AXa0zEnGOZXReXMljlsVYX8S5vT4O51bdPjgXc3rgvD7p1VuI0wr7Ak4SF4nzuVcbZ7+eBJ7yqTIRZx7IrTgX4G7DmRpoO87FpXbW/7RLGTkPdMMZvKw5zu3z53FaXiOtn+mkchB3rlnndujuOC2nZ3Fu97wBJ/nqgP9RwL/C+f5Yg9PiNhDn++EA8AzQ3FqbqkU3EOeWtTYJZzCqr3HO9btxPsu6pFPFM0VQVkcjfx3nArDBOb71cb4vulo/U/NY59baaJwuHIuBMJzXoDXO3KRjgV4285ZRkTzhmQRbRKTAMs60I9OBldbaiMzWFxEp7Iwz/+5CYJG1Niqw0Uh2GGN+wBlQqLe19psM1tuJM1hRXWvtzisTnUjeUwuliBRobh8QT//M/2W0roiISCAZY2JwkskEsjcauUihpc66IlIguYMqdMa5XawJTh+izwMalIiISBruaM0v4dza7xlM6DGr2wCliFBCKSIFVRdgEE4/sM+BR/TlLCIiBVA5nD6VyTj9ZV+0BXOeTJF8oT6UIiIiIiIikiPqQykiIiIiIiI5ooRSREREREREckQJpYiIiIiIiOSIEkoRERERERHJESWUIiIiIiIikiNKKEVERERERCRHlFCKiIiIiIhIjiihFBERERERkRwJDnQABV3lypVtaGhooMMQEREREREJiPj4+CPW2ir+ypRQZiI0NJTVq1cHOgwREREREZGAMMbsSq9Mt7yKiIiIiIhIjiihFBERERERkRxRQikiIiIiIiI5oj6UIiIiIiJSKFy4cIG9e/dy7ty5QIdyVSpZsiS1atWiePHiWa6jhFJERERERAqFvXv3Uq5cOUJDQzHGBDqcq4q1lqNHj7J3717q1q2b5Xq65VVERERERAqFc+fOUalSJSWT+cAYQ6VKlbLd+quEUkRERERECg0lk/knJ6+tEkoRERERERHJESWUIiIiIiJSqA0ePJhatWr5LYuNjcUYw7x5865wVJdMnDgRY0ymj2effTZgMeaUBuURERERERHJR7fccgvLly/3/n/NmjXcf//9vPHGG7Rt29a7PL2kuCBTQikiIiIiIpJHkpKSCAkJSbWsSpUqVKlSxft/z8A3jRs3JjIy8orGl9d0y6uIiIiIiBQpH3/8MS1btqRs2bJcc801NGvWjHHjxqVaZ9GiRURHR1OuXDnKlClDr1692LBhQ6p1oqKi6NixI1999RUtW7YkJCSEMWPGZDueKVOmYIzhxx9/vKwsKiqK9u3be/9vjOGpp57i3//+N7Vq1aJUqVJ07tyZtWvXXlb3yy+/JDIyktKlS1OhQgVuu+02du/ene34MqKEUkREREREiowlS5Zw991306VLF6ZPn84XX3zBsGHD+PXXX73rfP3110RHR1O2bFk+/PBDPv74Y06ePEmnTp3Ys2dPqu1t3bqVhx56iAcffJDvvvuO6OjobMfUv39/atSocVlSu2XLFhYtWsSIESNSLZ88eTKzZ8/mrbfeYuLEiRw8eJDo6GiOHTvmXWfs2LEMHDiQJk2aMGXKFMaNG8eGDRvo0qULJ0+ezHaM6dEtryIiIiIiUmTExcVRoUIFXnvtNe+ynj17plrn4YcfpkuXLsyYMcO7rGvXrtSrV4///ve/qeoeOXKEOXPmEB4enuOYgoODGTZsGK+++iqvvPIKZcqUAWDcuHFUqFCBO+64I9X6Z8+eZc6cOd71IiIiaNCgAa+++irPP/88p06d4vHHH+ePf/wj7733nrdeREQEDRs2ZMKECTzyyCM5jteXWihFRERERKTIaNu2Lb/88gt33303s2bNStUyCbBt2zZ27NjBXXfdRXJysvdRunRp2rdvz+LFi1OtHxoamqtk0mP48OGcOXOGTz75BHD6WU6aNIl77rmHUqVKpVq3d+/e3mTSE0NkZKR34J/ly5dz4sSJy/ahVq1a3HDDDZftQ24ooRQRERERkUItODiYlJQUv2We5cHBzs2ZXbp04YsvvmDPnj3ExMRQpUoVunfvzrp16wA4dOgQAEOGDKF48eKpHrNmzeLo0aOptl+9evU82YcaNWrQr18/xo4dC8AXX3zBsWPHLrvdFeC6667zu2zfvn2p9qF79+6X7cP69esv24fcKDC3vBpjSgKLgRCcuKZYa58xxlwLfAaEAjuB2621v7h1ngSGACnAQ9ba79zlrYGJQClgNvCwtdYaY0KAyUBr4Chwh7V25xXaRRERERERyQdVq1blyJEjnD9/nhIlSqQq279/P5A6Cbv11lu59dZbOXXqFLGxsTz++OPcdNNN7N27l0qVKgHw4osv0r1798ueK+32jTF5th+jRo0iOjqa+Ph4xo0bR6dOnWjSpMll6x08eNDvspo1awJ492HixInceOONl61brly5PIu5wCSUQBLQzVp7yhhTHFhijPkGGADMt9b+xxjzBPAE8LgxpglwJ3AjUAOYZ4xpaK1NAd4GhgNxOAnlTcA3OMnnL9ba+saYO4GXgNQ3JIuIiIjks6SkJMaPH09iYiL16tVj6NChl00zICJZ17VrV1588UVmzpzJrbfemqps6tSpVK9enUaNGl1Wr2zZsvTp04fExEQefvhhjh49SqNGjQgNDWXjxo088cQTV2oXAOjWrRuNGzfmT3/6E0uXLuWjjz7yu97s2bM5ffq097bXnTt3EhcX5423Q4cOlCtXju3btzNo0KB8jbnAJJTWWguccv9b3H1YoB8Q5S6fBMQCj7vLP7XWJgE/GWO2A+2MMTuB8tba5QDGmMlAf5yEsh/wrLutKcBbxhjjPreIiIhIvktKSiIqKoq4uDjvbXoffvghsbGxSipFcqh79+706NGDwYMHs3nzZiIiIjh58iSffvopM2bM4P3336dYMae339NPP83Bgwfp2rUrNWrUYO/evbzxxhuEh4d754ocPXo0/fr14/z589x+++1UrlyZgwcPsmzZMmrXrs2f/vSnfNuXkSNH8vDDD1O5cmUGDhzod51SpUrRs2dPHnvsMZKSknjmmWcoX748jz76KADly5fnlVde4f777+fw4cPcfPPNXHPNNezbt49FixYRFRXF73//+zyJt0D1oTTGBBlj1gKHgLnW2hXAddbanwHcv1Xd1WsCvmP27nWX1XT/nXZ5qjrW2mTgOFDJTxzDjTGrjTGrDx8+nFe7JyIiIsL48eNZsWIFAMnJyVhriYuL46WXXgpwZCKFlzGGmTNn8sgjjzB58mT69OnDoEGD+Pnnn5k+fTqDBw/2rhsREcHOnTt59NFH6dGjB48//jhdunTh66+/9q7Tu3dvFi9ezOnTpxk6dCi9evXir3/9KwcOHEg1J2R+uO222wAYPHhwuheZ7rnnHm655RYeeOABBg0aRJUqVZg/fz7XXnutd50RI0Ywc+ZMtmzZwh/+8AduvvlmnnnmGZKTk/NkECEPUxAb54wxFYBpwIPAEmttBZ+yX6y1FY0xo4Hl1toP3eUTcG5v3Q28aK3t7i7vBPzVWvtbY8xGoJe1dq9btgNoZ61Nt1dqmzZt7OrVq/NnR0VERKTI+fOf/8wbb7xBcnLyZWXNmzcnJiaGmJgYmjdvnqd9s0SuBgkJCTRu3DjQYeSrd999lxEjRrB161bq169/Wbkxhqeeeop//etf+fL8/l5jY0y8tbaNv/ULVAulh7X2V5xbW28CDhpjqgO4fw+5q+0FrvepVgvY7y6v5Wd5qjrGmGDgGuAYIiIiIldIvXr1/CaTVatWZd26dTz33HOEh4dTv359/vznP7NkyZJ0R68UkavHpk2b+Oqrr3jmmWfo37+/32SyICowCaUxporbMokxphTQHdgMzAQ8PUkHAZ7ZRWcCdxpjQowxdYEGwEr3ttiTxphI41zWuydNHc+2bngZJnYAACAASURBVAUWqP+kiIiIXEl33HGHty9XUFAQxhgiIyPZtWsXGzZs4Pnnn6dly5YkJibyv//9j06dOlGzZk1GjBjBt99+y/nz5wO8ByKSH0aNGsXAgQNp2LAhb731VqDDybICc8urMaY5zqA7QTiJ7ufW2n8aYyoBnwO1cW5nvc1ae8yt8xRwL5AMPGKt/cZd3oZL04Z8AzzoThtSEvgAaInTMnmntTYxo7h0y6uIiIjkpbFjx3LffffRtm1bOnXqRFhYGEOGDLmsr9TOnTuZPn0606ZNY8mSJVy8eBFwBtu45ZZbiImJ4eabb6Zs2bKB2A2RgCgKt7wGWnZveS0wCWVBpYRSRERE8oq1lhYtWrB+/XqWLVuW5cE9Dh8+zMyZM5k2bRpz5871tlKGhITQo0cPYmJi6Nu3L5UrV87P8EUCTgll/rsq+lCKiIiIXI2WLl3K+vXradGiBZGRkVmuV6VKFYYMGcKsWbM4cuQIn332GXfccQclSpRg1qxZDBkyhOuuu46uXbvyxhtvsHv37nzcCxGRS5RQioiIiFwhY8aMAZy+UjkdwbVcuXLcfvvtfPrppxw+fJivv/6aoUOHUqlSJWJjY3n44YepU6cObdq04d///jebNm1Cd6SJSH7RLa+Z0C2vIiIikhcOHTpErVq1KFWqFPv27cvzvo8pKSksW7aMadOmMW3aNHbu3Okta9iwoXc6krZt23oHBRIpbHTLa/7TLa8iIiIiBdCECRO4cOECgwYNypeBdIKCgujUqRP/+9//SExM5IcffuDpp5+mWbNmbN26lZdeeonIyEhq167N/fffz/z587lw4UKexyFytTtw4AB33nknYWFhNGnShN69e7N169ZsbWP69Ols2rQpnyK8spRQioiIiOSzlJQUxo4dC8B9992X789njCE8PJznnnuOdevWsW3bNl5++WXat2/Pvn37GDNmDN27d+e6665j0KBBTJ8+nTNnzuR7XCJXWlISjB4Nf/6z8zcpKXfbs9YSExNDVFQUO3bsYNOmTbzwwgscPHgwW9sJREKZX/PZKqEUERERyWezZ89m9+7dREVFBeR2vfr16/PYY4+xbNky9u/fz9tvv03Pnj05efIkkydPJiYmhsqVKzNgwAA++OADfvnllyseo0heS0qCqCh48EF4803nb1RU7pLKhQsXUrx4cUaOHOldFh4eTkpKCn369PEue+CBB5g4cSIATzzxBE2aNKF58+b85S9/YdmyZcycOZPHHnuM8PBwduzYwdq1a4mMjKR58+bExMR434NRUVE8+uijdO7cmcaNG7Nq1SoGDBhAgwYN+Pvf/+59vg8//JB27doRHh7OiBEjvMlj2bJlefrpp4mIiGD58uU53/EMKKEUERERyWdvv/024AzGE2jVq1dn5MiRfPfddxw+fJgPP/yQgQMHYoxh2rRp3HPPPVStWpUePXrw9ttvs3///kCHLJIj48fDihVgLVy44PxdsQImTMj5Njds2EDr1q2zvP6xY8eYNm0aGzduZN26dfz973+nQ4cO9O3bl1deeYW1a9cSFhbGPffcw0svvcS6deto1qwZzz33nHcbJUqUYPHixYwcOZJ+/foxevRoNmzYwMSJEzl69CgJCQl89tlnLF26lLVr1xIUFMRHH30EwOnTp2natCkrVqygY8eOOd/xDCihFBEREclHO3bs4Ntvv6VatWr0798/0OGkUqFCBe666y6mTJnCkSNHmD59OoMGDaJ8+fLMmzePUaNGUbNmTdq3b8/LL7/Mtm3bAh2ySJYlJkJwcOplQUGwY8eVi6F8+fKULFmSoUOH8uWXX1K6dOnL1jl+/Di//vorXbp0AWDQoEEsXrzYW963b18AmjVrxo033kj16tUJCQmhXr167Nmzh/nz5xMfH0/btm0JDw9n/vz5JCYmAk7f6oEDB+brPiqhFBEREclH48aNw1rL8OHDKV68eKDDSVepUqXo168fEydO5ODBg8yfP58HHniAWrVqERcXx+OPP07Dhg1p2rQp//jHP1izZo2mI5ECrV49SE5OvSwlBcLCcr7NG2+8kfj4+MuWBwcHc/HiRe//z507512+cuVKBg4cyPTp07npppuy/ZwhISEAFCtWzPtvz/+Tk5Ox1jJo0CDWrl3L2rVr2bJlC88++ywAJUuWJCgoKNvPmR1KKEVERETyydmzZ5kwYQJBQUEMGzYs0OFkWXBwMN26dePNN99k9+7drFy5kieffJIbbriBjRs38q9//YvWrVtTt25dHnnkERYtWpRvA36I5NTQoRARAcY4LZXGOP8fMiTn2+zWrRtJSUm8++673mWrVq0iJSWFTZs2kZSUxPHjx5k/fz4Ap06d4vjx4/Tu3ZvXXnuNtWvXAs58sidPngTgmmuuoWLFinz//fcAfPDBB97WyqyIjo5mypQpHDp0CHBus921a1fOdzKbgjNfRURERERy4osvvuDYsWPExMRQq1atQIeTI8YY2rZtS9u2bXnhhRdISEjwznW5evVqXn/9dV5//XUqV65M3759GTBgANHR0ZQsWTLQoUsRFxICsbFOn8kdO5yWySFDnOU55elr/Mgjj/Cf//yHkiVLEhoaymuvvcbtt99O8+bNadCgAS1btgTg5MmT9OvXj3PnzmGt5dVXXwXgzjvvZNiwYbzxxhtMmTKFSZMmMXLkSM6cOUO9evV4//33sxxTkyZN+Ne//kXPnj25ePEixYsXZ/To0dSpUyfnO5oNRrcqZKxNmzZ29erVgQ5DRERECqH27dsTFxfH3Llz6d69e6DDyXN79uxh+vTpTJs2jcWLF6caWbJ3797ExMTQu3dvypcvH+BI5WqRkJAQkJGSixJ/r7ExJt5a28bf+kooM6GEUkRERHJizZo1tG7dmoYNG5KQkECxYld3T6OjR4/y1VdfMW3aNObMmePtQ1aiRAmio6OJiYmhX79+VK1aNcCRSmGmhDL/ZTehvLo/2UREREQCxDNVyMiRI6/6ZBKgUqVKDB48mBkzZnD48GG++OILfv/731OqVCm++eYbhg8fTrVq1ejUqROvvvoqO3fuDHTIIpIH1EKZCbVQioiISHb9+uuv1KhRA4B9+/ZRsWLFAEcUOOfPn2fhwoVMmzaNGTNmcODAAW9ZeHg4MTExxMTE0LRpU4wxAYxUCgO1UOY/tVCKiIiIBNjkyZM5e/Ysv/vd74p0MgnOLa+9evVi7Nix7Nu3j6VLl/KXv/yFsLAw1q5dyzPPPOMdyOSxxx5j2bJlqaZfEJGCTS2UmVALpYiIiGSHtZbGjRuzZcsWVq9eTevWrQMdUoFkrWXDhg3eEWM90ykAVKtWjX79+hETE0PXrl0pUaJEACOVgkQtlPlPLZQiIiIiAbRw4UK2bNlCu3btlExmwBhDs2bNePrpp/nhhx9ITEzkv//9Lx07duTgwYOMGzeOm266iapVq3L33XczdepUTp8+HeiwRSQNJZQiIiIieWjMmDEA3HfffQGOpHCpW7cuf/rTn/j+++/5+eefeeedd7j55ps5c+YMH330EbfeeiuVK1emX79+TJw4kaNHjwY6ZCnC/v3vf3PjjTfSvHlzwsPDWbFiRbrrPvvss/zf//1fnj7/6tWreeihh/Jt+9kRHLBnFhEREbnK7Nu3j+nTp1OxYkXuuOOOQIdTaF133XUMGzaMYcOGceLECWbPns20adOYPXs2M2fOZObMmQQFBdG5c2diYmLo378/119/faDDlgIoKSmJ8ePHk5iYSL169Rg6dCghISG52uby5cuZNWsWa9asISQkhCNHjnD+/Pk8ijhzycnJtGnThjZt/N6Bmi3WWqy1uRqJWi2UIiIiInlk/PjxpKSkcO+991KqVKlAh3NVKF++PHfeeSefffYZhw8f5quvvuLee++lYsWKLFy4kIceeojatWvTrl07XnzxRTZv3hzokKWASEpKIioqigcffJA333yTBx98kKioKJKSknK13Z9//pnKlSt7E9PKlStTo0YNQkNDOXLkCOC0IEZFRXnr/Pjjj3Tr1o0GDRrw7rvverfTuXNnwsPDadq0Kd9//z0A3377La1ataJFixZER0cDTivk8OHD6dmzJ/fccw+xsbH06dMnw+0DvPLKK7Rt25bmzZvzzDPPALBz504aN27MqFGjaNWqFXv27MnV66EWShEREZE8cOHCBd555x3AmXtS8l7JkiXp06cPffr0ITk5maVLl3oH9Vm1ahWrVq3ib3/7GzfccIN3OpI2bdpoOpIiavz48axYsQJrLRcuXABgxYoVTJgwgVGjRuV4uz179uSf//wnDRs2pHv37txxxx106dIlwzrr1q0jLi6O06dP07JlS2655RY++eQTevXqxVNPPUVKSgpnzpzh8OHDDBs2jMWLF1O3bl2OHTvm3UZ8fDxLliyhVKlSxMbGZrr9DRs2sG3bNlauXIm1lr59+7J48WJq167Nli1beP/997236OeGWihFRERE8sDMmTPZv38/PXv2pH79+oEO56oXHBxMly5deO2119i5cyfx8fH8/e9/p0mTJmzevJkXX3yRdu3aUbt2bR588EEWLFhAcnJyoMOWKygxMZHg4NTtZ0FBQezYsSNX2y1btizx8fG88847VKlShTvuuIOJEydmWKdfv36UKlWKypUr07VrV1auXEnbtm15//33efbZZ1m/fj3lypUjLi6Ozp07U7duXQCuvfZa7zb69u2b7p0P/rY/Z84c5syZQ8uWLWnVqhWbN29m27ZtANSpU4fIyMhcvQ4eaqEUERERyQOeK/25afmQnDHG0KpVK1q1asXzzz/P1q1bvS2XK1as4K233uKtt97i2muv5be//S0xMTH07NlTtyVf5erVq3fZRYSUlBTCwsJyve2goCCioqKIioqiWbNmTJo0ieDgYO8cqufOnUu1ftpWcmMMnTt3ZvHixXz99df84Q9/4LHHHqNChQrptqiXKVMm3Xj8bd9ay5NPPsmIESNSle3cuTPDbWWXWihFREREcmnz5s0sWLCA66+/nltuuSXQ4RR5DRs25PHHHycuLo69e/cyevRounfvzokTJ5g0aRL9+/encuXK3HrrrXz00Uf8+uuvgQ5Z8sHQoUOJiIjAGENwcDDGGCIiIhgyZEiutrtlyxZvSx/A2rVrqVOnDqGhocTHxwMwderUVHVmzJjBuXPnOHr0KLGxsbRt25Zdu3ZRtWpVhg0bxpAhQ1izZg3t27dn0aJF/PTTTwCpbnnNiL/t9+rVi/fee49Tp04BzqBhhw4dytW++6MWShEREZFcGjt2LAAjRoy47BY7CayaNWsyatQoRo0axS+//MKsWbP48ssv+e6775g6dSpTp04lODiYbt26ERMTQ79+/ahevXqgw5Y8EBISQmxsLBMmTGDHjh2EhYUxZMiQXI/yeurUKR588EF+/fVXgoODqV+/Pu+88w4JCQkMGTKEF154gYiIiFR12rVrxy233MLu3bv5xz/+QY0aNZg0aRKvvPIKxYsXp2zZskyePJkqVarwzjvvMGDAAC5evEjVqlWZO3dupjH5236NGjVISEigffv2gHOr7ocffkhQUFCu9j8tY63N0w1ebdq0aWNXr14d6DBERESkgDp9+jQ1a9bkzJkz7N69m2rVqgU6JMmCM2fO8N133zFt2jS++uorbyulMYbIyEjvoD7qD1uwJCQk0Lhx40CHcVXz9xobY+KttX7nKdEtryIiIiK58Mknn3D8+HEGDBigZLIQKV26NDExMUyePJlDhw4xd+5cRo0aRfXq1Vm+fDl//etfadCggXe6hbVr16KGGJHLqYUyE2qhFBERkfRYa2ndujU//PADixYtonPnzoEOSXLp4sWLrFq1yjuoz9atW71loaGh3pbLDh065Pmtg5I5tVDmv+y2UCqhzIQSShEREUnPihUriIyM5MYbb2T9+vWa7/AqY60lISGBL7/8kmnTprFmzRpvWdWqVenbty8DBgygW7duue6XJ1mTkJDADTfcoPdaPrHWsnnzZt3yKiIiInIl+E4Voh+4Vx9jDE2aNOHvf/878fHx7Ny5k9dee40uXbpw5MgRxo8fT+/evalSpQq/+93v+Pzzzzl58iRJSUmMHj2aP//5z4wePZqkpKRA78pVo2TJkhw9elS3H+cDay1Hjx6lZMmS2aqnFspMqIVSRERE/Dly5Ai1atUiODiY/fv3U758+UCHJFfQ4cOH+eqrr5g2bRpz5871Jo0hISGUKlXKOwJoSkoKERERxMbGqhUzD1y4cIG9e/deNs+j5I2SJUtSq1Ytihcvnmp5Ri2UGtdaREREJAfef/99kpKS+OMf/6hksgiqUqUK9957L/feey8nT57k22+/5csvv2T69OneEWOTk5MBiIuL49FHH+Xll1+mbNmygQy70CtevDh169YNdBjiQy2UmVALpYiIiKR18eJFGjRoQGJiIj/++CPNmzcPdEhSQDz88MOMHj2alJSUy8qCg4Np3749PXr0oHv37rRt21bzlkqhoD6UIiIiInlozpw5JCYm0rFjRyWTkkrDhg25ePHiZctbtmxJuXLl+P7773n66afp0KEDlSpVon///owePZotW7aoX6AUSkooRURERLLJMxjPfffdF+BIpKAZOnQoERERGGMIDg7GGENkZCTLly/n8OHDrFq1ihdeeIFu3bpx7tw5ZsyYwQMPPMANN9xAnTp1uPfee/nkk084dOhQoHdFJEt0y2smdMuriIiI+Nq1axd169alcuXK7NmzRwOtyGWSkpKYMGECO3bsICwsjCFDhvg9T86cOcOSJUuYO3cu8+bNY+3atanKW7RoQffu3enRowedOnWidOnSV2oXRFLRPJS5oIRSREREfD311FO88MILPPnkk7zwwguBDkeuIocOHWLBggXMmzePuXPnsnv3bm9ZiRIl+M1vfuNNMFu1akVQUFAAo5WiRAllLiihFBEREY+kpCRq167N4cOH+emnn6hTp06gQ5KrlLWW7du3e1svFyxYwPHjx73lFStWpFu3bnTv3p3u3bsTFhamuVAl3yihzAUllCIiIuLxySef8Pvf/57f/va3zJw5M9DhSBGSnJxMfHw8c+fOZe7cuSxfvpwLFy54y0NDQ72jx0ZHR1OpUqUARitXGyWUuaCEUkRERDw6derEkiVLmD17NjfffHOgw5Ei7NSpUyxevNh7e+yGDRu8ZcYYWrZs6U0wO3bsSMmSJQMYrRR2SihzQQmliIiIAKxfv57mzZtTt25dtm/fTrFiGixfCo4DBw4wf/58bwvm/v37vWUlS5akY8eO3gQzPDxc569kS0YJpWZSFREREcmCt99+G3CmCtGPcSloqlWrxl133cVdd92FtZbNmzd7Wy9jY2OZN28e8+bNA6BSpUpER0d7B/gJDQ0NbPBSqKmFMhNqoRQREZETJ05Qs2ZNLly4wN69e6lcuXKgQxLJsgsXLrBy5UpvghkXF0dKSoq3PCwszNt62a1bNypWrBjAaKUg0i2vuaCEUkRERMaMGcP999/PH/7wByZPnhzocERy5cSJEyxatMibYCYkJHjLihUrRuvWrb0JZocOHTTXqiihzA0llCIiIkWbtZZmzZqxceNGli9fTmRkZKBDEslT+/bt894SO2/ePA4cOOAtK126NJ07d/beHtu0aVPd8l0EKaHMBSWUIiIiRdv3339P586dadmyJfHx8ZrrT65q1lo2btzonf9y0aJFnD592ltetWpVoqOjvS2Y119/fQCjlStFCWUuKKEUEREp2n73u9/x6aef8u677zJ06NBAhyNyRZ0/f564uDhvgrly5UouXrzoLW/UqJG39TIqKoprrrkmgNFKflFCmQtKKEVERIquAwcOULt2bUqXLs2+ffsoU6ZMoEMSCahff/2V2NhYb4K5detWb1lQUBDt2rXzJpgRERGUKFEigNFKXtG0ISIiIiI5MGHCBC5cuMCgQYOUTIoAFSpUoH///vTv3x+A3bt3ewf3mTdvHsuXL2f58uU8//zzlClThqioKG+C2aRJE90yfhVSC2Um1EIpIiJSNKWkpFC3bl327NlDQkICN9xwQ6BDEinQLl68yLp167wJ5uLFizl37py3vHr16nTv3t37qFGjRgCjlezQLa+5oIRSRESkaJo5cyb9+vWjW7duzJ8/P9DhiBQ6586dY9myZd4EMz4+Ht/co0mTJt7Bfbp06UK5cuUCGK1kRAllLiihFBERKZpuuukmvvvuO6ZMmcLAgQMDHY5IoXfs2DEWLFjgTTATExO9ZcHBwURGRnoTzHbt2hEcrN55BYUSylxQQikiIlL0bN++nQYNGlC9enV27dpF8eLFAx2SyFUnMTHRO/fl/PnzOXbsmLesfPnyREVFeRPMRo0aqf9lAGlQHhEREZFsGDt2LADDhw9XMimST+rVq8fw4cMZPnw4KSkprF271ju4z5IlS5g5cyYzZ84EoFatWt7BfaKjo7nuuusCHL14qIUyE2qhFBERKVrOnj1LrVq1OH78OLt27aJmzZqBDkmkyDlz5gxLly71Jpg//PBDqvLmzZt7E8xOnTppFOZ8pltec0EJpYiISNEyadIkBg8ezMCBA5kyZUqgwxER4PDhwyxYsIC5c+cyd+5cdu/e7S0rUaIEHTp08CaYrVu3JigoKIDRXn2UUOaCEkoREZGiJSIigpUrVzJv3jyio6MDHY6IpGGtZfv27d7BfRYsWMDx48e95RUqVKBbt27eBDMsLEz9L3NJCWUuKKEUEREpOuLj42nTpg2NGjUiISFBP0JFCoHk5GTi4+O9CeayZcu4cOGCt7xOnTrewX2io6OpXLlyAKMtnJRQ5oISShERkaJj6NChTJgwgddee42HH3440OGISA6cPn2axYsXexPM9evXpypv2bKlN8Hs2LEjpUqVClCkhYcSylxQQikiIlI0/PLLL94BePbv30+FChUCHJGI5IUDBw4wf/58b4K5b98+b1lISAgdO3b0JpgtW7akWLFiAYy2YNK0ISIiIiKZmDRpEmfPnmXIkCFKJkWuItWqVeOuu+7irrvuwlrLli1bvKPHLly4kPnz5zN//nwAKlWqRLdu3bwJZt26dQMcfcGnFspMqIVSRETk6nfx4kVuuOEGtm3bRnx8PK1atQp0SCJyBVy4cIFVq1Z5E8y4uDiSk5O95WFhYd7Bfbp27UqZMmUYP348iYmJ1KtXj6FDhxISEhLAPbgydMtrLiihFBERufrNmzePHj16EBERQVxcXKDDEZEAOXnyJIsWLfImmJs2bfKWGWMoXbo0p0+fJigoiJSUFCIjI4mNjb3qk8qMEkrdICwiIiJF3ttvvw3AqFGjAhyJiARSuXLl6NOnD6+//jobN25k7969TJo0ibvvvpty5cpx+vRpAFJSUgBYsWIFEyZMCGTIAaeEUkRERIq0vXv3MmPGDK699lpuv/32QIcjIgVIzZo1ueeee/jggw8YMmQIwcGph6AJCgpix44dAYquYFBCKSIiIkXau+++S0pKCvfeey8lS5YMdDgiUkCFhYV5WyY9UlJSCAsLC1BEBYMSShERESmyLly4wLvvvgvAyJEjAxyNiBRkQ4cOJSIiAmMMwcHBGGOIiIhgyJAhgQ4toDRtiIiIiBRZM2bM4Oeff+amm24q8q0MIpKxkJAQYmNjmTBhAjt27CAsLIwhQ4Zc9QPyZEYJpYiIiBRZY8aMATQYj4hkTUhIiD4v0tAtryIiIlIkJSQksHDhQmrXrk3v3r0DHY6ISKFUYBJKY8z1xpiFxpgEY8xGY8zD7vJnjTH7jDFr3UdvnzpPGmO2G2O2GGN6+SxvbYxZ75a9YYwx7vIQY8xn7vIVxpjQK72fIiIiUjB4pgoZMWIEQUFBAY5GRKRwKjAJJZAM/Nla2xiIBO43xjRxy1611oa7j9kAbtmdwI3ATcAYY4zn2+BtYDjQwH3c5C4fAvxira0PvAq8dAX2S0RERAqY06dPM2nSJIoXL17kB9QQEcmNApNQWmt/ttaucf99EkgAamZQpR/wqbU2yVr7E7AdaGeMqQ6Ut9Yut9ZaYDLQ36fOJPffU4BoT+uliIiIFB0ff/wxJ06c4NZbb+W6664LdDgiIoVWgUkofbm3orYEVriLHjDGrDPGvGeMqeguqwns8am2111W0/132uWp6lhrk4HjQCU/zz/cGLPaGLP68OHDebJPIiIiUjBYa72D8dx3330BjkZEpHArcAmlMaYsMBV4xFp7Auf21TAgHPgZ+K9nVT/VbQbLM6qTeoG171hr21hr21SpUiWbeyAiIiIFWVxcHGvXrqVp06Z07Ngx0OGIiBRqBSqhNMYUx0kmP7LWfglgrT1orU2x1l4E3gXauavvBa73qV4L2O8ur+Vneao6xphg4BrgWP7sjYiIiBREvlOFqOeLiEjuFJiE0u3LOAFIsNb+z2d5dZ/VYoAN7r9nAne6I7fWxRl8Z6W19mfgpDEm0t3mPcAMnzqD3H/fCixw+1mKiIhIEXDkyBE+//xzypYty9133x3ocERECr3gQAfg4zfAH4D1xpi17rK/Ab8zxoTj3Jq6ExgBYK3daIz5HNiEM0Ls/dbaFLfefcBEoBTwjfsAJ2H9wBizHadl8s583icREREpQN577z3Onz/P0KFDKVeuXKDDEREp9Iwa6DLWpk0bu3r16kCHISIiIrmUkpJCgwYN+Omnn1i/fj1NmzYNdEgiIoWCMSbeWtvGX1mBueVVREREJD999913/PTTT3Tq1EnJpIhIHilIt7xKACUlJTF+/HgSExOpV68eQ4cOJSQkJNBhiYiI5Jm3334bcAbjERGRvKGEUkhKSiIqKoq4uDiCg4NJSUnhww8/JDY2VkmliIhcFXbu3MnXX39N1apVGTBgQKDDERG5auiWV2H8+PHExcUBkJycjLWWuLg4Bg8ezLp160hJSclkCyIiIgXbuHHjsNYyMTeWHwAAIABJREFUdOhQSpQoEehwRESuGkoohcTERIKDL2+s/vTTT2nRogXXXnstvXr14rnnnmPu3LmcOHEiAFGKiIjkjKdbR7FixRg+fHigwxERuarollehXr16flshe/TowdmzZ1m1ahVz5sxhzpw5ABQrVoxmzZrRoUMHOnTowG9+8xtCQ0M1ObSIiBRIU6ZM4ciRI/Tt25c6deoEOhwRkauKpg3JRFGYNsTTh3LFihUEBQWRkpJCRESEtw/l+fPn+eGHH1i6dCnLli1j6dKlHDhwINU2qlWrlirBbNmypfpfiohIgdCxY0eWLl3Kt99+S69evQIdjohIoZPRtCFKKDNRFBJKcJLKCRMmsGPHDsLCwhgyZEi6CaG1ll27dnkTzGXLlrFu3TouXrzoXSckJIQ2bdp4E8z27dtTtWrVK7U7IiIiAPz444+Eh4cTFhbG1q1bKVZMvX1ERLJLCWUuFJWEMrdOnjzJihUrvAnm8uXLL+trWb9+/VStmE2aNNEXu4iI5KuRI0cybtw4XnnlFf7yl78EOhwRkUJJCWUuKKHMmZSUFDZt2uRNMJctW8b27dtTrXPNNdcQGRnpTTDbtWtHuXLlAhSxiIhcbU6cOEGNGjVITk5m3759VKpUKdAhiYgUSkooc0EJZd45ePAgy5cv9yaYq1at4vz5897yYsWK0bx5c2+C2aFDB+rUqaPBfkREJEdGjx7NAw88wKBBg5g4cWKgwxERKbSUUOaCEsr8k5SUxJo1a7wJ5tKlSzl48GCqdapXr54qwWzZsqXmDxMRkUxZa2natCmbNm1ixYoVtGvXLtAhiYgUWkooc0EJ5ZVjreWnn35KlWCuX78e33O0ZMmStGnTxptgtm/fnipVqgQwahERKYgWLVpEVFQUrVq1YvXq1brbRUQkFzJKKDUPpRQYxhjq1atHvXr1uPvuuwGn/4tnsJ+lS5cSFxfHkiVLWLJkibdew4YNvYP9dOjQgcaNG2uwHxGRIm7MmDEAjBo1SsmkiEg+UgtlJtRCWbCkpKSwceNGb4K5bNkyEhMTU61ToUIF2rdv700w27VrR9myZQMUsYiIXGkHDhzg+uuvp0yZMuzfv5/SpUsHOiQRkUJNLZRy1QgKCqJ58+Y0b96ckSNHAs4Ph+XLl3sTzPj4eL755hu++eYbb50WLVqkmrLk+uuv1xVrEZGr1Pjx40lOTuaPf/yjkkkRkXymFspMqIWy8Dl37hxr1qzxJpjLli3j0KFDqdapWbNmqgQzPDyc4sWLByhiERHJK8nJydStW5e9e/eyefNmGjVqFOiQREQKPQ3KkwtKKAs/ay2JiYmpEswNGzakGuynVKlStG3b1ptgtm/fXvOViYgUQtOnTycmJobo6GjmzZsX6HBERK4KSihzQQnl1en48ePExcV5E8y4uDhOnTqVap1GjRqlmrKkUaNGGuxHRKSA69mzJ3PnzmXq1KkMGDAg0OGIiFwVlFDmghLKoiElJYX169d7E8xly5bx008/pVqnYsWKtG/f3ptgtm3bljJlygQoYhERSWvbtm00bNiQGjVqsGvXLoKDNVSEiEhe0KA8IpkICgoiPDyc8PBwRo0aBcD+/ftZvny5N8GMj49n9uzZzJ49O1UdT4LZoUMHrr/++kDuhohIkTZ27FgAhg8frmRSROQKUQtlJtRCKR5nz54lPj7em2AuXbqUI0eOpFqnVq1aqRLMFi1aaLAfEZEr4MyZM9SqVYsTJ06we/duatSoEeiQRP6fvfuOj6rM2z9+fZNADCBSRWkLKCpSRA1FWRV114K9oxJ6C2JdXX3cx9XHtaz+bMuqdClBV9fe1sLqoq4CErFjoYgivUknkOT+/TEnMGBIJiflnsx83q/XeWXmnnMm1zAkk2vOmfsACYNDXsuBQol9cc5pwYIFexTMr7/+eo91atWqpa5du+4qmMcdd5waNGjgKTEAJK5JkyZp4MCBuvjii/Xss8/6jgMACYVCWQ4USpTF+vXrNXv27F0Fc/bs2dqyZcse67Rr125XwSya7IdzYgJA+XTt2lVz5szRu+++q5NPPtl3HABIKBTKcqBQojzy8/N3TfZTdNqSH3/8cY91GjRosEfB7NKlCyfiBoAymDNnjrp27aojjjhC8+bN4006AKhgFMpyoFCioi1dunSP2WTnzp2r/Pz8XbenpaXp6KOP3lUwe/TooWbNmnlMDADxbeDAgZo0aZL+9re/6ZprrvEdBwASDoWyHCiUqGzbtm3TnDlz9iiZa9eu3WOdli1b7nFOzE6dOjGDIQBIWrdunZo1a6aUlBQtXbpU9erV8x0JABIOpw0B4lhGRoZOPPFEnXjiiZIik/18//33exTMefPm6aefftLTTz8tKTLZT7du3XYVzO7du6t+/fo+HwYAeDF58mRt375dQ4YMoUwCgAfsoSwFeygRD9atW6dZs2btKpizZ8/W1q1b91jnyCOP3OOUJW3btpWZKS8vTxMmTNCiRYvUpk0bDR48WOnp6Z4eCQBUnMLCQh1xxBGaP3++Pv30U3Xu3Nl3JABISBzyWg4USsSjnTt36osvvtjjlCVLlizZY51GjRqpe/fu+vzzz7VkyRKlpaWpoKBA3bp104wZMyiVAKq96dOn67TTTlP37t01c+ZM33EAIGFRKMuBQonqYsmSJZo5c+augvnpp5+qoKDgV+uZmR599FGNGDHCQ0oAqDgXXHCBXnrpJU2dOlVZWVm+4wBAwuIzlEASaNGihVq0aKFLL71UkrRlyxYNGDBAzz//vAoLC3etl5KSooULF/qKCQAVYsmSJXrllVfUsGFDXXLJJb7jAEDSSvEdAEDlqF27tk466STtfRRCQUGB2rRp4ykVAFSM8ePHq7CwUIMGDdJ+++3nOw4AJC0KJZDABg8erG7dusnMlJqaumu8Tp06HlMBQPns2LFD48ePl5lp2LBhvuMAQFLjkFcggaWnp2vGjBmaOHGiFi5cqJ07d+rvf/+7rrvuOp1yyilq0aKF74gAUGYvvfSSVqxYoTPPPJMjLgDAMwolkODS09P3mIAnJSVFf/vb3zRgwAC9/fbbSknhQAUA1cvjjz8uSUwuBgBxgL8kgSRz7733ql27dnrnnXf06KOP+o4DAGUyb948vffee/rNb36jM88803ccAEh6FEogyWRkZCgnJ0dpaWm6+eab9c033/iOBAAxGz16tCRp+PDhe3w2HADgB4USSELHHnusbr/9dm3fvl19+vTRjh07fEcCgFJt3rxZU6ZMUY0aNTRw4EDfcQAAolACSeuWW25R9+7dNXfuXP3lL3/xHQcASvXkk09q06ZNuuSSS3TggQf6jgMAEIUSSFppaWnKyclRrVq1dM8992jWrFm+IwHAPjnnmIwHAOIQhRJIYoceeqgeeughFRYWKisrS1u2bPEdCQCKNXPmTH3xxRfq1KmTjj/+eN9xAAABCiWQ5IYOHapevXppwYIFuvHGG33HAYBiFe2dzM7Olpl5TgMAKGLOOd8Z4lpmZqbLzc31HQOoVMuXL1fHjh21du1avf766+rVq5fvSACwy6pVq9SiRQulp6dr6dKl2n///X1HAoCkYmafOOcyi7uNPZQAdPDBB2vs2LGSpEGDBmnNmjWeEwHAbk888YR27Nihvn37UiYBIM5QKAFIki666CL17dtXK1as0PDhw8XRCwDiQUFBwa43vLKzsz2nAQDsjUIJYJdRo0apZcuWev755zVt2jTfcQBAb775phYvXqyTTjpJ7du39x0HALAXCiWAXQ444ABNmTJFZqaRI0fqp59+8h0JQJKLnowHABB/KJQA9tCzZ09df/312rhxo/r166fCwkLfkQAkqUWLFumNN95QkyZNdMEFF/iOAwAoBoUSwK/cfffdat++vWbMmKFHHnnEdxwASWrs2LFyzmnIkCGqWbOm7zgAgGJw2pBScNoQJKvPPvtMXbt2VUpKinJzc9WhQwffkQAkke3bt6tFixZat26dFi9erBYtWviOBABJi9OGACizzp07684771ReXp769OmjHTt2+I4EIIk899xzWrNmjc455xzKJADEMQolgH266aab1KNHD33++ee64447fMcBkESKJuMZMWKE5yQAgJJQKAHsU2pqqqZOnao6derovvvu04cffug7EoAk8Nlnn2nmzJk69NBD9bvf/c53HABACSiUAErUpk0bPfzwwyosLFTfvn21adMm35EAJLjRo0dLipwqJCWFP1UAIJ7xWxpAqQYNGqRzzjlHixYt0g033OA7DoAEtmHDBk2bNk377bef+vfv7zsOAKAUFEoApTIzjR8/Xo0bN9aECRP06quv+o4EIEFNnTpVW7duVe/evdWgQQPfcQAApaBQAohJkyZNNG7cOEnS4MGDtXr1as+JACQa59yuw12ZjAcAqgcKJYCYnX/++RowYIBWrVqloUOHivPYAqhI7733nr755htlZmaqS5cuvuMAAGJAoQRQJo888ohatWqll156SZMnT/YdB0AC4VQhAFD9UCgBlEndunU1depUmZmuvfZa/fDDD74jAUgAy5Yt04svvqh69erpsssu8x0HABAjCiWAMjvhhBN00003adOmTerXr58KCgp8RwJQzU2YMEH5+fkaMGCAatWq5TsOACBGFEoAodx5553q1KmTPvjgAz300EO+4wCoxvLz83dN+jV8+HDPaQAAZUGhBBBKenq6cnJyVLNmTf3v//6vvvjiC9+RAFRTr776qpYuXarf//73Ouyww3zHAQCUAYUSQGidOnXSXXfdpR07dqhPnz7Ky8vzHQlANVQ0GU92drbnJACAsqJQAiiXG264QSeeeKK+/PJL3Xbbbb7jAKhmvvvuO/373/9Ws2bNdM455/iOAwAoIwolgHJJTU3VlClTtP/+++uBBx7Q+++/7zsSgGpkzJgxkqRhw4YpLS3NcxoAQFlRKAGUW6tWrTRq1Cg559S3b19t3LjRdyQA1cDWrVs1efJkpaWlafDgwb7jAABCoFACqBD9+vXT+eefrx9//FHXXXed7zgAqoGnn35av/zyiy644AIdfPDBvuMAAEKgUAKoEGamcePG6cADD9SkSZP00ksv+Y4EII455/TYY49JkkaMGOE5DQAgrLgplGbWwsz+Y2bfmNnXZnZtMN7AzKab2fzga/2obf7HzBaY2XdmdnrU+LFm9mVw2ygzs2A83cyeCcZnm1mrqn6cQCJr3LixJk6cKEkaMmSIVq5c6TkRgHg1Z84czZ07V+3atdNJJ53kOw4AIKS4KZSS8iX9wTnXTlJ3SVeZ2ZGSbpH0jnOuraR3gusKbustqb2kMyQ9bmapwX2NljRUUttgOSMYHyRpvXPuUEkPS7qvKh4YkEzOPvtsDRkyRGvWrNHgwYPlnPMdCUAcKjpVyIgRIxS87wsAqIbiplA655Y75+YGlzdJ+kZSM0nnSZoSrDZF0vnB5fMkPe2cy3PO/SBpgaSuZnawpLrOuZku8pfs1L22Kbqv5ySdaryKARXuoYceUps2bfTaa6/t2mMJAEXWrl2rZ555RrVr11ZWVpbvOACAcoibQhktOBT1aEmzJTVxzi2XIqVT0oHBas0kLYna7OdgrFlwee/xPbZxzuVL2iCpYTHff6iZ5ZpZ7urVqyvmQQFJpE6dOpo6dapSUlJ03XXXaeHChb4jAYgjkydP1vbt23XllVfqgAMO8B0HAFAOcVcozayOpOclXeecK+ncA8XtWXQljJe0zZ4Dzo1zzmU65zIbN25cWmQAxejRo4duvvlmbdmyRX379lVBQYHvSADiQGFhoUaPHi1Jys7O9pwGAFBecVUozayGImXySefcC8HwyuAwVgVfVwXjP0tqEbV5c0nLgvHmxYzvsY2ZpUk6QNK6in8kACTpjjvuUOfOnfXRRx/p/vvv9x0HQByYPn26Fi5cqOOPP16dO3f2HQcAUE5xUyiDzzJOlPSNc+6hqJtekdQvuNxP0stR472DmVtbKzL5zsfBYbGbzKx7cJ9999qm6L4ulvSuY8YQoNLUrFlT06ZNU3p6um6//XZ9+umnviMB8Kxo7ySnCgGAxBA3hVJSD0lZkk4xs8+CpZekv0r6vZnNl/T74Lqcc19L+qekeZLelHSVc67omLpsSRMUmahnoaQ3gvGJkhqa2QJJNyiYMRZA5Wnfvr3uvfde7dy5U3369NH27dt9RwLgyU8//aRXX31VjRo10sUXX+w7DgCgAqT5DlDEOfdfFf8ZR0k6dR/b3C3p7mLGcyV1KGZ8u6RLyhETQAjXXnutXn31Vf3nP//Rn/70Jz344IO+IwHwYNy4cSosLNSgQYOUnp7uOw4AoAIYR3yWLDMz0+Xm5vqOAVR7P/30kzp27KiNGzfq3Xff1cknn+w7EoAqtGPHDrVo0UKrV6/WwoUL1bp1a9+RAAAxMrNPnHOZxd0WT4e8AkhgLVu21KOPPipJ6tevnzZs2OA5EYCq9OKLL2rVqlXq1asXZRIAEgiFEkCV6dOnjy6++GItWbJEV199te84AKrQ448/LolThQBAoqFQAqgyZqYxY8booIMOUk5Ojp577jnfkQBUga+++krvv/++WrVqpTPOOMN3HABABaJQAqhSDRs21BNPPCFJGjZsmJYvX+45EYDKVnSqkOHDhys1NdVzGgBARaJQAqhyZ555poYPH65169Zp0KBBYnIwIHFt2rRJU6dOVc2aNTVw4EDfcQAAFYxCCcCLBx54QIceeqjeeOMNjR071nccAJXkySef1ObNm3XppZeqcePGvuMAACoYhRKAF7Vr11ZOTo5SUlL0hz/8QfPnz/cdCUAFc84xGQ8AJDgKJQBvunfvrj/96U/aunWrsrKylJ+f7zsSgAr04Ycf6ssvv9RRRx2l4447znccAEAloFAC8Oq2227Tscceq9mzZ+uvf/2r7zgAKlDR3skRI0bIzDynAQBUBmMyjJJlZma63Nxc3zGAhPbNN9/omGOOUX5+vmbNmqVjjz3WdyQA5bRq1So1b95cGRkZWrp0qerUqeM7EgAgJDP7xDmXWdxt7KEE4F27du103333KT8/X3369NG2bdt8RwJQThMnTtTOnTvVt29fyiQAJDAKJYC4MHLkSP3ud7/Tt99+q1tuucV3HADlUFBQoDFjxkhiMh4ASHQUSgBxISUlRZMmTVK9evU0atQo/fvf//YdCUBI//rXv/TTTz+pZ8+eOvLII33HAQBUIgolgLjRvHnzXZN49O/fX+vXr/ecCEAYo0ePlhSZjAcAkNgolADiyuWXX67LLrtMS5cu1ciRI33HAVBGCxcu1JtvvqmDDjpI559/vu84AIBKRqEEEHcef/xxNW3aVE899ZSeeeYZ33EAlMHYsWPlnNOQIUNUo0YN33EAAJWMQgkg7jRo0ECTJk2SFJnQY+nSpZ4TAYjFtm3bNHHiRKWmpmro0KG+4wAAqgCFEkBcOu200zRy5EitX79eAwcOFOfMBeLfs88+q3Xr1uncc89V8+bNfccBAFQBCiWAuHXffffp8MMP19tvv71rsh4A8YvJeAAg+VAoAcStWrVqKScnR6mpqbrpppv03Xff+Y4EYB/mzp2rWbNmqW3btjrllFN8xwEAVBEKJYC41qVLF912223atm2bsrKytHPnTt+RABSjaO9kdna2UlL48wIAkgW/8QHEvVtvvVVdu3bVnDlzdPfdd/uOA2Avv/zyi5588kllZGSof//+vuMAAKoQhRJA3KtRo4ZycnKUkZGhu+66Sx9//LHvSACiTJ06Vdu2bdPll1+u+vXr+44DAKhCFEoA1cJhhx2mBx54QAUFBcrKytLWrVt9RwIgyTm3a9Ks7Oxsz2kAAFWNQgmg2sjOztbpp5+u77//Xn/84x99xwEg6T//+Y++++47denSRZmZmb7jAACqGIUSQLVhZnriiSdUv359PfbYY3rrrbd8RwKSXtHeSU4VAgDJiUIJoFpp2rSpxowZI0kaMGCA1q5d6zkRkLyWLl2ql156SfXr19dll13mOw4AwIMSC6WZNYhhqVdVYQFAki699FJdeeWVWr58uUaMGCHnnO9IQFKaMGGCCgoKNGDAAGVkZPiOAwDwwEr6Q8zMtktaJslKuI9U51zLig4WLzIzM11ubq7vGAD28ssvv6hjx476+eef9eSTT+qKK67wHQlIKjt37lSrVq20bNkyff/992rbtq3vSACASmJmnzjniv2gfGmHvH7jnGvjnGu9r0USx5sBqHL16tXT5MmTJUU+u7VkyRK/gYAk88orr2jZsmU67bTTKJMAkMRKK5THxXAfsawDABXu1FNP1bXXXqsNGzZowIABKiws9B0JSBpMxgMAkEoplM657ZJkZoeYWXpwuaeZXVP02cmidQDAh3vvvVft2rXTO++8o7///e++4wBJ4dtvv9W7776rFi1a6KyzzvIdBwDgUayzvD4vqcDMDpU0UVJrSU9VWioAiFFGRoamTZumtLQ03XLLLZo3b57vSEDCK5ppeejQoUpLS/OcBgDgU6yFstA5ly/pAkmPOOeul3Rw5cUCgNgdc8wxuuOOO7R9+3ZlZWVpx44dviMBCWvLli2aPHmy0tLSNHjwYN9xAACexVood5rZ5ZL6SXotGKtROZEAoOxuvvlmde/eXXPnztVf/vIX33GAhPWPf/xDGzZs0EUXXaSDDjrIdxwAgGexFsoBiky+c7dz7gczay1pWuXFAoCySUtLU05OjmrVqqV77rlHM2fO9B0JSDjOOSbjAQDsIaZC6ZybJ+lmSXOD6z845/5amcEAoKwOPfRQPfTQQyosLFTfvn21efNm35GAhPLxxx/r008/Vfv27XXCCSf4jgMAiAMxFUozO0fSZ5LeDK53NrNXKjMYAIQxdOhQ9erVSwsWLNCNN97oOw6QUIr2TmZnZ8vMPKcBAMQDc86VvpLZJ5JOkTTDOXd0MPalc65jJefzLjMz0+Xm5vqOAaAMVqxYoQ4dOmjt2rV6/fXX1atXL9+RgGpvzZo1at68udLS0rRs2TLVrVvXdyQAQBUxs0+cc5nF3RbrZyjznXMb9horvYkCgAcHHXSQxo0bJ0kaOHCg1qxZ4zkRUP1NmjRJeXl5ysrKokwCAHaJtVB+ZWZXSEo1s7Zm9ndJH1ViLgAolwsvvFB9+/bVypUrNWzYMMVyNAaA4hUWFu4692R2drbnNACAeBJrobxaUntJeZL+IWmjpOsqKxQAVIRRo0apZcuWeuGFF5STk+M7DlBtvf3221q0aJF69OihTp06+Y4DAIgjsc7yutU59yfnXBfnXGZweXtlhwOA8jjggAM0ZcoUmZmuvvpq/fjjj74jAdUSpwoBAOxLiZPymNmrKuGzks65cysjVDxhUh6g+rvxxhv14IMPqmfPnnrnnXeUkhLrwRkAfvzxR7Vu3VqNGjXSkiVLlJ6e7jsSAKCKlWdSngckPSjpB0nbJI0Pls2SvqrIkABQWe666y516NBBM2bM0COPPOI7DlCtjBs3Ts45DR48mDIJAPiVWE8b8r5z7sTSxhIReyiBxPDZZ5+pa9euMjN98skn6tChg+9IQNzLy8tTy5YttXr1ai1atEitWrXyHQkA4EFFnDaksZm1ibrD1pIaV0Q4AKgKnTt31p133qkdO3aoT58+ysvL8x0JiHsvvPCCVq1apbPOOosyCQAoVqyF8npJM8xshpnNkPQfMcsrgGrmpptuUo8ePfT555/rjjvu8B0HiHtMxgMAKE1Mh7xKkpmlSzoiuPqtcy4p3t7nkFcgsSxatEhHHXWUtm7dqvfee0+//e1vfUcC4tKXX36pTp06qXXr1lqwYAGTWQFAEgt9yKuZnRJ8vVDSWZIOCZazgjEAqFbatGmjRx55RIWFherbt682bdrkOxIQl0aPHi1JGj58OGUSALBPpb1CnBR8PaeY5exKzAUAlWbgwIE655xz9MMPP+iGG27wHQeIOxs3blROTo7S09M1cOBA33EAAHEsraQbnXO3m1mKpDecc/+sokwAUKnMTOPHj1fHjh01YcIEnXPOOTr33IQ/rS4Qs2nTpmnz5s3KyspSo0aNfMcBAMSxUo9hcc4VShpZBVkAoMo0adJE48ePlyQNGTJEq1at8pwIiA/OOSbjAQDELNYPRUw3sxvNrIWZNShaKjUZAFSy8847TwMHDtSqVas0dOhQxTpJGZDI/vvf/+rrr7/W0UcfrW7duvmOAwCIc7EWyoGSrpL0vqRPgoWpTwFUew8//LBatWqll19+WZMnT/YdB/CuaO9kdna2zMxzGgBAvIv5tCHJitOGAInvgw8+0EknnaTatWvriy++UOvWrX1HArxYsWKFWrZsqYyMDC1btky1a9f2HQkAEAfKc9qQY2K481LXAYB4dsIJJ+imm27S5s2b1a9fPxUUFPiOBHgxceJE7dy5U/3796dMAgBiUtohr5PMrH705yb3XiRNrIqgAFCZ7rzzTnXq1EkffPCBHnzwQd9xgCpXUFCgsWPHSooc7goAQCxKK5QHaPdnJve17KzMgABQFdLT0zVt2jTVrFlT//u//6vPP//cdySgSr3++utasmSJTj75ZB1xxBG+4wAAqokSC6VzrpVzro1zrnUJS9eqCgsAlaljx466++67tXPnTmVlZSkvL893JKDKcKoQAEAYsc7yCgBJ4frrr9eJJ56oL7/8UrfddpvvOECVWLBggd566y0dfPDBOu+883zHAQBUIxRKAIiSmpqqKVOmaP/999cDDzyg9957z3ckoNKNGTNGkjR06FDVqFHDcxoAQHVCoQSAvbRq1UqjRo2Sc079+vXTxo0bfUcCKs22bds0adIkpaamasiQIb7jAACqmZgKpZm9E8sYACSKfv366YILLtCPP/6oa6+91nccoNL885//1Lp163TeeeepWbNmvuMAAKqZ0s5DuV9wapBGe50+pJWkplUREAB8MDONHTtWTZo00eTJk/UrrWDxAAAgAElEQVTiiy/6jgRUCibjAQCUR2l7KIcpcmqQI7TnqUJelvRY5UYDAL8aN26sCRMmSIp8tmzFihWeEwEV65NPPtHHH3+sww8/XKeccorvOACAaqi004b8zTnXWtKNe50+5Cjn3KNVlBEAvDn77LM1ZMgQrVmzRkOGDJFzznckoMKMHj1akpSdnS0z85wGAFAdWax/HJnZ8ZJaSUorGnPOTa2cWPEjMzPT5ebm+o4BwKPNmzfrqKOO0qJFizRu3DgmLkFCWL9+/a7PTC5btkz16tXznAgAEK/M7BPnXGZxt8U6KU+OpAck/VZSl2Ap9g4BINHUqVNHOTk5SklJ0fXXX6+FCxf6jgSU25QpU7Rt2zZdccUVlEkAQGixnjYkU1IP59wI59zVwXJNZQYDgHhy/PHH65ZbbtGWLVvUt29fFRQU+I4EhFZYWMhkPACAChFrofxK0kGVGcTMnjCzVWb2VdTYHWa21Mw+C5ZeUbf9j5ktMLPvzOz0qPFjzezL4LZRFnwoxMzSzeyZYHx2MFMtAMTs9ttv19FHH62PPvpI999/v+84QGjvvvuu5s+fr27duumYY47xHQcAUI3FWigbSZpnZm+Z2StFSwVnmSzpjGLGH3bOdQ6Wf0mSmR0pqbek9sE2j5tZarD+aElDJbUNlqL7HCRpvXPuUEkPS7qvgvMDSHA1a9ZUTk6O0tPT9ec//1mffvqp70hAKNGT8QAAUB6xFso7JJ0v6R5JD0YtFcY5976kdTGufp6kp51zec65HyQtkNTVzA6WVNc5N9NFZhuaGuQu2mZKcPk5SacW7b0EgFi1b99e9957r/Lz89WnTx9t377ddySgTH7++We9/PLLatCggS699FLfcQAA1VxMhdI5915xS2WHC4w0sy+CQ2LrB2PNJC2JWufnYKxZcHnv8T22cc7lS9ogqWFx39DMhppZrpnlrl69uuIeCYCEcO211+rkk0/WvHnzdOutt/qOA5TJ+PHjVVBQoIEDByojI8N3HABANRfrLK+bzGxjsGw3swIz21jZ4RQ5fPUQSZ0lLdfuvaLF7Vl0JYyXtM2vB50b55zLdM5lNm7cuGyJASS8lJQUTZ48WXXr1tXDDz+sd99913ckICY7d+7U+PHjJUnDhw/3nAYAkAhi3UO5v3OubrDsJ+kiSY9WbjTJObfSOVfgnCuUNF5S1+CmnyW1iFq1uaRlwXjzYsb32MbM0iQdoNgPsQWAPbRs2VKPPfaYJKl///765ZdfPCcCSvfyyy9r+fLlOv3003XIIYf4jgMASACxfoZyD865lySdUsFZfiX4TGSRCxSZbVaSXpHUO5i5tbUik+987JxbLmmTmXUPPh/ZV9LLUdv0Cy5fLOnd4HOWABDKlVdeqYsvvlhLlizRNddwJiXEP04VAgCoaBZLpzKzC6OupihyXsqTnHPHVVgQs39I6qnIjLIrJd0eXO+syKGpiyUNC0qjzOxPkgZKypd0nXPujWA8U5EZYzMkvSHpauecM7P9JOVIOlqRPZO9nXOLSsuVmZnpcnNzK+phAkgwa9euVceOHbV8+XI9++yzuvjii31HAor1zTff6Mgjj1TLli21aNEipaamlr4RAACSzOwT51xmcbelxXgf50Rdzlek3J1Xzlx7cM5dXszwxBLWv1vS3cWM50rqUMz4dkmXlCcjAOytYcOGmjhxonr16qVhw4apR48eOvjgg0vfEKhiRacKGTZsGGUSAFBhYtpDmczYQwkgFiNGjNDo0aN15pln6vXXXxdnJUI82bJli5o2bapt27ZpyZIlatKkie9IAIBqpKQ9lLHO8trczF40s1VmttLMnjez5qVvCQDJ4f/9v/+ntm3b6o033tDYsWN9xwH28NRTT2njxo266KKLKJMAgAoV66Q8kxSZ1KapIudzfDUYAwBIql27tnJycpSamqo//OEPmj9/vu9IgCTJOcdkPACAShNroWzsnJvknMsPlsmSOEEjAETp1q2bbr31Vm3dulVZWVnKz8/3HQnQrFmz9Nlnn6lDhw767W9/6zsOACDBxFoo15hZHzNLDZY+ktZWZjAAqI5uu+02HXvssZo9e7buvfde33GAPfZO8tleAEBFi/W0IS0lPSrpOEVO4fGRpGudcz9Wbjz/mJQHQFl9++23Ovroo5Wfn6+ZM2cqM7PYz7ADlW7NmjVq1qyZatasqWXLlmn//ff3HQkAUA2Ve1Ie59xPzrlznXONnXMHOufOT4YyCQBhHHHEEbr//vuVn5+vrKwsbdu2zXckJKknnnhCO3bsUFZWFmUSAFApYp3ldYqZ1Yu6Xt/Mnqi8WABQvV111VX63e9+p2+//Va33HKL7zhIQgUFBRozZowkKTs723MaAECiivUzlJ2cc78UXXHOrZd0dOVEAoDqLyUlRZMmTVK9evU0atQoTZ8+3XckJJm33npLP/zwg0444QR17NjRdxwAQIKKtVCmmFn9oitm1kBSWuVEAoDE0Lx5810TogwYMEDr16/3nAjJZPTo0ZLYOwkAqFyxFsoHJX1kZn8xszsVmZTn/sqLBQCJ4fLLL1fv3r21dOlSXXXVVb7jIEksXrxYr7/+ug488EBdeOGFvuMAABJYrJPyTJV0kaSVklZLutA5l1OZwQAgUTz22GNq2rSp/vGPf+jpp5/2HQdJYOzYsXLOafDgwUpPT/cdBwCQwGI6bUgy47QhACrC9OnTddppp6levXr66quv1KxZM9+RkKDy8vLUvHlzrVu3TosWLdJvfvMb35EAANVcuU8bAgAon9///vcaOXKkfvnlFw0YMECFhYW+IyFBPffcc1qzZo3OPvtsyiQAoNJRKAGgitx33306/PDDNX369F2T9QAVjcl4AABViUIJAFWkVq1aysnJUWpqqv74xz/q22+/9R0JCebzzz/Xhx9+qDZt2ui0007zHQcAkAQolABQhbp06aI///nP2rZtm7KysrRz507fkZBAovdOpqTwEg8AqHy82gBAFbv11lvVtWtX5ebm6u677/YdBwli48aNmjZtmtLT0zVgwADfcQAASYJCCQBVLC0tTTk5OcrIyNBdd92ljz/+2HckJICcnBxt2bJFl112mRo2bOg7DgAgSVAoAcCDww47TA888IAKCgrUp08fbdmyxXckVGPOuV0TPY0YMcJzGgBAMqFQAoAn2dnZOv300zV//nz98Y9/9B0H1dj777+vefPm6ZhjjlHXrl19xwEAJBEKJQB4YmZ64okn1KBBAz3++ON68803fUdCNRW9d9LMPKcBACQTCiUAeNS0aVONGTNGkjRw4ECtXbvWcyJUNytWrNALL7ygAw44QL179/YdBwCQZCiUAODZJZdcoiuvvFLLly9Xdna2nHO+I6EamTBhgvLz89W/f3/Vrl3bdxwAQJKhUAJAHHj00UfVvHlzPfvss3rqqad8x0E1kZ+fr7Fjx0qKfCYXAICqRqEEgDhQr149TZ48WZJ01VVXacmSJX4DoVp47bXX9PPPP+vUU0/V4Ycf7jsOACAJUSgBIE6ceuqpuu6667Rhwwb1799fhYWFviMhznGqEACAbxRKAIgj99xzj9q1a6d3331Xf//7333HQRybP3++pk+frqZNm+rcc8/1HQcAkKQolAAQRzIyMjRt2jSlpaXp5ptv1rx583xHQpwqmh146NChSktL85wGAJCsKJQAEGeOOeYY3XHHHcrLy1NWVpZ27NjhOxLizNatWzVp0iSlpqZqyJAhvuMAAJIYhRIA4tDNN9+s4447TnPnztWdd97pOw7izDPPPKP169frggsuUNOmTX3HAQAkMQolAMShtLQ0TZ06VbVr19a9996rmTNn+o6EODJ69GhJnCoEAOAfhRIA4tShhx6qhx56SIWFhcrKytLmzZt9R0IcmDNnjubMmaPDDz9cJ598su84AIAkR6EEgDg2ZMgQnXXWWVq4cKFuvPFG33EQB4r2To4YMUJm5jkNACDZmXPOd4a4lpmZ6XJzc33HAJDEVqxYoQ4dOmjt2rV67bXXdNZZZ/mOBE/WrVunZs2aKSUlRUuXLlW9evV8RwIAJAEz+8Q5l1ncbeyhBIA4d9BBB2ncuHGSpEGDBmnNmjWeE8GXyZMna/v27briiisokwCAuEChBIBq4MILL1S/fv20cuVKDRs2TBxdknwKCwt3nXuSyXgAAPGCQgkA1cTf/vY3tWzZUi+88IJycnJ8x0EVe+eddzR//nx1795dxxxzjO84AABIolACQLVxwAEHaOrUqTIzjRw5Uj/++KPvSKhCjz/+uKTIZDwAAMQLCiUAVCMnnXSSbrjhBm3atEn9+vVTYWGh70ioAkuWLNErr7yihg0b6pJLLvEdBwCAXSiUAFDN3HXXXerQoYPee+89Pfzww77joAqMHz9ehYWFGjhwoPbbbz/fcQAA2IXThpSC04YAiEeff/65unTpIjPTjTfeqO3bt6tNmzYaPHiw0tPTfcdDBdqxY4d+85vfaOXKlZo/f74OOeQQ35EAAEmG04YAQII56qijdPvtt2vHjh265557NGrUKF199dXq2bOn8vLyfMdDBcnLy1N2drZWrFihdu3aqXnz5r4jAQCwhzTfAQAA4dStW3fX5fz8fEnSrFmzdO655yozM1MpKSlKSUmRmRV7uaTbYl0vke7DzHYt8SAvL089e/bUrFmzJEnz5s1Tz549NWPGDPZCAwDiBoUSAKqpxYsXKy0tbVeZLPL222/r7bff9pSq+isqmb4L8MqVK7Vo0aI9ss2ePVsTJ05kplcAQNygUAJANdWmTRsVFBT8arxv37465ZRTVFhYqMLCQjnndl3e+3qst3Efuy/n5+fvc73KnpcgNTVVCxcurNTvAQBAWVAoAaCaGjx4sKZNm6bZs2crNTVVBQUF6tatm8aNG8chkR5VVLGdPHmybr/99j3uu6CggEl5AABxhVleS8EsrwDiWV5eniZOnKiFCxfqkEMO0aBBgyiTCaLoM5R7v2HAZygBAFWtpFleKZSloFACAHzhDQMAQDwoqVByyCsAAHEqPT2dCXgAAHGN81ACAAAAAEKhUAIAAAAAQqFQAgAAAABCoVACAAAAAEKhUAIAAAAAQqFQAgAAAABCoVACAAAAAEKhUAIAAAAAQqFQAgAAAABCoVACAAAAAEKhUAIAAAAAQqFQAgAAAABCoVACAAAAAEKhUAIAAAAAQqFQAgAAAABCoVACAAAAAEKhUAIAAAAAQqFQAgAAAABCoVACAAAAAEKhUAIAAAAAQqFQAgAAAABCiZtCaWZPmNkqM/sqaqyBmU03s/nB1/pRt/2PmS0ws+/M7PSo8WPN7MvgtlFmZsF4upk9E4zPNrNWVfn4AAAAACDRxE2hlDRZ0hl7jd0i6R3nXFtJ7wTXZWZHSuotqX2wzeNmlhpsM1rSUEltg6XoPgdJWu+cO1TSw5Luq7RHAgAAAABJIG4KpXPufUnr9ho+T9KU4PIUSedHjT/tnMtzzv0gaYGkrmZ2sKS6zrmZzjknaepe2xTd13OSTi3aewkAAAAAKLu4KZT70MQ5t1ySgq8HBuPNJC2JWu/nYKxZcHnv8T22cc7lS9ogqWFx39TMhppZrpnlrl69uoIeCgAAAAAklngvlPtS3J5FV8J4Sdv8etC5cc65TOdcZuPGjUNGBAAAAIDEFu+FcmVwGKuCr6uC8Z8ltYhar7mkZcF482LG99jGzNIkHaBfH2ILAAAAAIhRvBfKVyT1Cy73k/Ry1HjvYObW1opMvvNxcFjsJjPrHnw+su9e2xTd18WS3g0+ZwkAAAAACCHNd4AiZvYPST0lNTKznyXdLumvkv5pZoMk/STpEklyzn1tZv+UNE9SvqSrnHMFwV1lKzJjbIakN4JFkiZKyjGzBYrsmexdBQ8LAAAAABKWsZOuZJmZmS43N9d3DAAAAADwwsw+cc5lFndbvB/yCgAAAACIUxRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAoFEoAAAAAQCgUSgAAAABAKBRKAAAAAEAo1aJQmtliM/vSzD4zs9xgrIGZTTez+cHX+lHr/4+ZLTCz78zs9KjxY4P7WWBmo8zMfDweAAAAAEgE1aJQBk52znV2zmUG12+R9I5zrq2kd4LrMrMjJfWW1F7SGZIeN7PUYJvRkoZKahssZ1RhfgAAAABIKNWpUO7tPElTgstTJJ0fNf60cy7POfeDpAWSuprZwZLqOudmOuecpKlR2wAAAAAAyqi6FEon6W0z+8TMhgZjTZxzyyUp+HpgMN5M0pKobX8OxpoFl/ce/xUzG2pmuWaWu3r16gp8GAAAAACQONJ8B4hRD+fcMjM7UNJ0M/u2hHWL+1ykK2H814POjZM0TpIyMzOLXQcAAAAAkl212EPpnFsWfF0l6UVJXSWtDA5jVfB1VbD6z5JaRG3eXNKyYLx5MeMAAAAAgBDivlCaWW0z27/osqTTJH0l6RVJ/YLV+kl6Obj8iqTeZpZuZq0VmXzn4+Cw2E1m1j2Y3bVv1DYAAAAAgDKqDoe8NpH0YnCGjzRJTznn3jSzOZL+aWaDJP0k6RJJcs59bWb/lDRPUr6kq5xzBcF9ZUuaLClD0hvBAgAAAAAIwSITnmJfMjMzXW5uru8YAAAAAOCFmX0SdfrGPcT9Ia8AAAAAgPhEoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhEKhBAAAAACEQqEEAAAAAIRCoQQAAAAAhJLmOwAAAECyycuTJkyQFi2S2rSRBg+W0tN9pwKAsqNQAgAAVDLnpK1bpbVrpeXLpQEDpG++kVJTpYICacwY6YUXpObNpYwM32kBIHYUSgAAgDLIz5fWr4+Uw30t69b9eiwv79f3VVAQ+frVV9Jhh0Uu164tNW4sNWoU+Vq07Ov6AQdIZlX3+AEgGoUSAAAkpei9hmVZfvmlbN/HTKpXT2rRQmrYUFq1Slq8OPL9o9dp0kSqVUtavTpy++LFsd1/jRq7y2VpJbRxY6lBAymNvwABVBB+nQAAgGovlr2Gxe1FLG6vYUn2209q1ixSDBs0iHwtbalfP3Joa5HHHpOuvvrX933bbdKIEZHL27dLa9ZEltWrdy97Xy8aW7EicihtLMwimUrb8xl9ncNwAeyLuei3x/ArmZmZLjc313eMSsfkAACAeOCctGVL6YePVtRew1gKYfRSq1b5H2NentSzpzR79u7PUHbrJs2YEf61t6Ag8u9UXOnc1/UdO2K//6LDcGMtoXXrchgukEjM7BPnXGaxt1EoS5YMhTIvTzrxROnjj3e/sLVrF3kHtW7dyLux6emRr9ELh8sAAEoSZq/h2rVlKzpS5DVpXwVwX3sR995rWNXy8qSJE6WFC6VDDpEGDaraN3KdkzZtKr10Rl/ftCn2+48+DDeWEtqwod/nA0DJKJTlkAyF8rHHpJEjy75damrxRbO4sX2Nl2fd9HRefACgKhS31zCWiWiq015DlK7oMNySDr2Nvr5u3Z6fEy2JWaT8x/IZ0KKx/far3McLYLeSCiX7mKBFiyJ7G/Pzd4+ZSW3bSkccEXkXdfv2PZfosa1bIy8aPtSoUTmlNtbt09Pj/5AeDmcGqq/K+Pmtyr2GRZ81LGmJ3oPoe68hSrbffpHTmjRvHtv60YfhxrIXdPXqyP+1776L7f7r1Il9IqJGjTgMF6gsFEqoTZvd05ZHu/ba3ZMDlMa5yB8bxZXPvQtoSWNlHS8a27gx8sLkQ82aVbNHdl/jNWrs+wUy+nM6RW8aTJtWvs/pAKgapf38xrrXcO9lw4ay5Sjaa9iyJXsNUTapqbsLXSz2Pgw3lhL6ww+RJRY1a5Z9Ntywb3DwZi6SCYe8liIZDnmtjMkBfCgs3F0wK7KslmU8ei9vVTHbdyHdsKH4aee7dJE6dIj8kVqjRmQp7nKY28NswzvG4fFHS+VzLvL7JT9f2rkz8rVoKel6edf96CPpX//6dZ6mTSOZKvqzhvta6tVjryHiV/RhuLGU0DCH4cY6EVHjxpHfv8W9GVQd/64CovEZynJIhkIp+Z8cIFHk5+9ZOH0U2+r4I52SEh/FtjIKdkpK5RVmn3+0FBZWbHmq6Pup6O8Zb4pO+1DaxDPsNQT2tPdhuLGU0LL8DqhTJ/L7d+3aX9923HFSp067XydKW6JfUypim9RU3sBFeBTKckiWQonE4FzkD+Cicjl2bOS8Znu75hqpV6/dfywX/QEd/bW0sarYJlFUVrH99lvp/fd//f1++1vp8MMrt4Ql6ktHWtrupejfurjrJd1WUet+8IH09NO/znjffdIf/sBeQ6AqOLf7YzWxnA909Wpp82bfqfetKoprVX0PXwU5WY8MolCWA4US1Vl1PpzZuUjeeC275d2msND3v3BsUlOrvkj5Wjfe3r2vzj+/QDJ75BHp+ut/PT5ihHTGGcW/LsS6lHWbWNavzlWgqsuxWeTsCIsX7/693L17cvxeplCWA4US1R2HM8en6M/khS2ur7wijR//6/sePlw677zyl654K1jJiJ9foPqpbm8GFb15W5mltTzrl3Wbqq42ZtKjj8Y+kWV1RaEsBwolgHhV3f5oAYBkwZtB/hQUVF7JfeEF6b//3fMoo7S0yEeJHnzQ32OuCpyHEgASUHp6pDzyRwsAxJf09MTfYxWvUlMjS2W8FhZ9vj1aQUHk9TeZsYeyFOyhBAAAAJDMRwaxhxIAAAAAyoEjg4pHoQQAAACAGHA486+l+A4AAAAAAKieKJQAAAAAgFAolAAAAACAUCiUAAAAAIBQKJQAAAAAgFAolAAAAACAUCiUAAAAAIBQKJQAAAAAgFAolAAAAACAUCiUAAAAAIBQKJQAAAAAgFAolAAAAACAUCiUAAAAAIBQKJQAAAAAgFAolAAAAACAUCiUAAAAAIBQKJQAAAAAgFDMOec7Q1wzs9WSfvSdowo1krTGdwhUCp7bxMbzm9h4fhMXz21i4/lNXMn23P7GOde4uBsolNiDmeU65zJ950DF47lNbDy/iY3nN3Hx3CY2nt/ExXO7G4e8AgAAAABCoVACAAAAAEKhUGJv43wHQKXhuU1sPL+Jjec3cfHcJjae38TFcxvgM5QAAAAAgFDYQwkAAAAACIVCCQAAAAAIhUKZoMyswMw+i1paFbPOv8ysXjHjd5jZjVWRE6UzM2dmOVHX08xstZm9VkH3z/NdxczsT2b2tZl9Efx8dith3f5m1rQCvudiM2tU3vtB2ZXl+S7DffJzW4XMrGHU6+kKM1saXP7FzOZVwffvb2aPVvb3SXYlPM+fmVnNCvw+PSvqNRy7mdnDZnZd1PW3zGxC1PUHzeyGGO6nlZl9VVk5g++xuTLvv6ql+Q6ASrPNOde5uBvMzBT5/GyvKs6EcLZI6mBmGc65bZJ+L2mp50wIycyOk3S2pGOcc3lBySvpD5X+kr6StKwM3yPNOZdfrqCoECGeb8Qh59xaSZ2lSJmXtNk590DwZm3oYsDPanzZ1/PsNRTK4iNJl0h6xMxSJDWSVDfq9uMlXVfchigf9lAmieDdlm/M7HFJcyW1iN5jEbyD/p2Z/VvS4VHbDTGzOWb2uZk9b2a1zGx/M/vBzGoE69QN7quGlweXHN6QdFZw+XJJ/yi6wcwamNlLwd6PWWbWKRi/w8yeMLMZZrbIzK6J2obn25+DJa1xzuVJknNujXNumZn9Ofi3/8rMxlnExZIyJT0ZvEOesdfPbaaZzQgu3xFs97akqcE77W+b2admNlaSFQUI/r98Euw1GxqMDTKzh6PWGWJmD1XVP0oC29fzXdLzyM9t9ZJqZuODn6e3zSxDkoLnMDO43MjMFgeX+5vZs2b2qqS3zexgM3s/+Bn/ysxOCNYbYGbfm9l7knoUfTMzO8fMZgc/2/82syZmlmJm882scbBOipktMI5KKDczmxz8Li66vjnq8k3Bz94XZvZ/wVhtM3s9+Hn8yswuC8bPMLNvzey/ki6Muo+uZvZR8Hx+ZGaHB+MfmFnnqPU+LHp9xz59qEhplKT2irwZu8nM6ptZuqR2kmRm7wWvgW+Z2cHB2LHBczZT0lVFdxj8vL5gZm8GP2P3R912mpnNNDU/MMsAAAugSURBVLO5wc90nWD8r2Y2L/h/8UAw1jpYd46Z/SXqPuqY2TvBfXxpZucF438xs2uj1rs7+vUg3lAoE1eG7T5M48Vg7HBJU51zRzvnfixa0cyOldRb0tGK/JLrEnU/LzjnujjnjpL0jaRBzrlNkmZod8HpLel559zOyn1ISe1pSb3NbD9JnSTNjrrt/yR96pzrJOlWSVOjbjtC0umSukq63cxq8Hx797Yib+h8b2aPm9lJwfijwb99B0kZks52zj0nKVfSlc65zsEe6pIcK+k859wVkm6X9F/n3NGSXpHUMmq9gc65YxUpq9eYWUNF/o+dG1U4BkiaVAGPN9nt6/kuCT+31UtbSY8559pL+kXSRTFsc5ykfs65UyRdIemt4KiioyR9FvyR+3+KFMnfSzoyatv/Suoe/Gw/LemPzrlCSdMkXRms8ztJnzvn1pT70aFYZnaaIs99V0X2ah5rZidKOkPSMufcUcHv8zeD1+7xks6RdIKkg6Lu6ltJJwbP558l3ROMT1DkCBWZ2WGS0v9/e3cfbFVVxnH8+wudMcPBcpQKTS0tfBsRXxlfAjXLasZwMCpfIKZMZxo0G5umqRlfUqewKPmjTERK0UFzKEoTDUGMQBQErpaONmKo+A4CIoL49MezTnd7vPdy74HL4XJ/n5k7nLv3Putszjr77PWsZ611I2Jpt//HerCIeAF4R9InyMByHtleGkLe7/4NjAdGlHvgJOCq8vSbgLERMaSNogcBI4HDgJGS9imdNT8GTo2IweS9+hJJHwGGA4eUdtlPSxm/Bn4TEUcDL1bKXg8ML2UMA34hScCNwCjIDiLyO3zKFr1B3cgB5Y7rrdIAHRQRw8u2ZyNifhvHnghMi4h1EbGabHzWHFp6yVrIG9UhZftEssEJbnh2u3IT2Y/MTt5dt/sE4OZy3P3AHpL6lX13RcTbpVHxMtAf13dTRcRaMvA7H3gFmCppNDCsZB1agJNpfe+7Ynol6DyJbGASEXcBKyvHjZW0BJgP7AMcGBFvAvcDX5Y0ENg5IloaOAer6KC+O+Lrtmd5JiIWl8cLye/qzbkvIl4vjx8GvqkcYnlY6Qw4FpgdEa9ExAZgauW5ewMzSn1fSmt9TwLOK4/H4PrubqeVn0fJkV8DyQCzBThV0s8knRgRb5R9z0TEU5F/r++WSjn9gDuUc/bG01qfd5DfxzuT9Tl5G/yfdgS1LGUtoJxX+f154FDgPkmLyYBw79Jm2j0iHihl3FxX5syIeCMi1gP/AvYFjiM7euaWskaV7avJIHGipDOBdaWM42kdXVYtX8DVkpYCfwcGAP0jYhnwmqQjKJ+zMiR7u+Q5lL3Lmx3sa+8Pkk4GvhIRS0ojaChARMxVDqP9LNAnIrp18rIB2YC8lqyDPSrb1caxtfp8u7JtE63XvOu7iSJiE5k9ml0ahd8hM89HRcTy0rDcpZ2nv0NrZ2D9MfXX+PvqWdJQMnsxJCLWKYda1sqZSGa5n8CN0a2mjfoeRcf16Ou2Z6mvrw+Wx526ViNiTslsfQm4WdI4slHaXn1PAH4ZEdPL9XxZKWe5pJcknUwGpGe383zrmv/XY8kc1eZAC7gmIq6vf0IZUfBF4BrlNITptF+fVwKzImK4ck7ubIDy/XwfcAbwVTLDZpv3TzJ4PIwc8roc+D55Td0PDKjPQioXqGyvfqDt72SRHUNfrz9Y0jHAKWRW8btkJzHtvMbZwJ7AkRGxUTk0vnpPHk1mtCd1cH5N5wylAcwBhivnZ+1GDsmo2Q1YUXrI6m9OfyB7W9zw3DYmAVe0kTWaQ6mb0rh4tWQw2uP6biJJn5F0YGXTIODJ8vjVMgdjRGX/GrJeapaRGS/oeGhd9XNxOvDhsr0fsLI0VgaSvawARMRDZMbyG1Tm6Vrj2qnvZ+l8Pdb4uu15ltFaxyPaO0jSvsDLEXEDOcxtMDlMb6hyLvTO5EIjNf1oXZhtVF1xE8ns1+2lI8O23DJa6/EMoDYtYAYwpjJvboCkvZSrcq+LiFvITuDBZCfd/pI+VZ5bDUKq9Tm67rUnAtcBD1cy2taxueRCaK9HxKbyvu1ODnudCuypXCyNMp3gkIhYBbwh6YRSRmc6Y+YDx0s6oJS1q6RPl89Dv4i4m1wAqDYPdi4ZYNaX34+8/jdKGkZmOWumkUOojyY/b9stZyiNiFgkaSqwmGzoPFjZ/RPyxvYsOYyj2rCdQo4Nd8NzG4iI58gx+PUuA24qwyXW8f4GRn05ru/m6gtMKD2i7wBPk8MhV5Hv+TJyCFzNZOC3kt4ib4iXAzdK+hHvnUtb73LgNkmLgAeA/5bt9wAXlM/Lk+RNsep2YFBErMS2hvbq+yA6V4+Ar9se6lrgdknnkpmR9gwFLpW0EVgLnBcRK8pIhXnACnJIZZ9y/GXkEMnnyet3/0pZ08nOA3cgbD03AH+WtACYSckuR8S9kg4C5mXikrXAOcABwDhJ7wIbgQsjYr1yAbS7JL1KzoM9tJT/c+D3yj9n8Z7PSUQslLQa12dXtJCru95at61vRLysXGDpujLMdSfgV8Dj5LSASZLW0YngLSJeKSNCblMu+AM5hHYN+XnZhcxifq/suwi4VbnQzp2VoqYAf5H0CPn9/kTlNTZImgWs2t47iJRDuc26rlyUZ0TEuc0+F+t+ru/eQfm30cZHxMxmn4ttOV+3vYtyVdnxEXFis8/FtlzJds4GBpaFl6wXUS7Gswg4KyKeavb5dMQZSmuIpAnA6eQcAdvBub53fCWDtoBcGdLB5A7A123vIumHwIV47uQOQdJ55AqklziY7H0kHUz+jdtp23swCc5QmpmZmZmZWYO8KI+ZmZmZmZk1xAGlmZmZmZmZNcQBpZmZmZmZmTXEAaWZmZmZmZk1xAGlmZmZmZmZNcQBpZmZmZmZmTXEAaWZmZmZmZk1xAGlmZmZmZmZNcQBpZmZmZmZmTXEAaWZmZmZmZk1xAGlmZmZmZmZNcQBpZmZmZmZmTXEAaWZmZmZmZk1xAGlmZmZmZmZNcQBpZmZmZmZmTXEAaWZmVkvIeliSbtWfl/bzPMxM7OezwGlmZlZk0nqs41e6mJg180eZWZm1kkOKM3MzLpA0pWSLqr8fpWksZIulfSwpKWSLq/s/5OkhZIel3R+ZftaSVdIeggYUvcayyRdLWmepEckDZY0Q9J/JF1QjpGkcZIek9QiaWTZPlTSbEl/lPSEpCnl2LHAx4FZkmbVnf8SSfMl9e+2N87MzHZIDijNzMy65kZgFICkDwBfA14CDgSOAQYBR0o6qRw/JiKOBI4Cxkrao2z/EPBYRBwbEf9o43WWR8QQ4EFgMjACOA64ouw/s7zW4cCpwDhJHyv7jiCzkQcDnwSOj4jrgBeAYRExrHIO8yPicGAO8O2G3xUzM+uVdmr2CZiZmfUkEbFM0muSjgD6A48CRwOnlccAfckAcw4ZRA4v2/cp218DNgF3dvBS08u/LUDfiFgDrJG0XtLuwAnAbRGxCXhJ0gPlPFYDCyLiOQBJi4H9gLaC1g3AX8vjhcDnOv1GmJmZ4YDSzMysEROB0cBHgUnAKcA1EXF99SBJQ8ns4ZCIWCdpNrBL2b2+BINImkEGp49ExLfK/rfLv+9WHtd+3wlQB+dXPX4T7d/vN0ZEdOI4MzOzNnnIq5mZWddNA75AZgRnlJ8xkvoCSBogaS+gH7CyBJMDySGr7xMRn4+IQZVgsjPmACMl9ZG0J3ASsGAzz1kD7NaF1zAzM+uQeyLNzMy6KCI2lIVtVpUs472SDgLmSQJYC5wD3ANcIGkp8CQwfyuexjRyMZ8lQAA/iIgXS+Dant8Bf5O0ojKP0szMrGFqHeliZmZmnVEW41kEnBURTzX7fMzMzJrFQ17NzMy6QNLBwNPATAeTZmbW2zlDaWZmZmZmZg1xhtLMzMzMzMwa4oDSzMzMzMzMGuKA0szMzMzMzBrigNLMzMzMzMwa4oDSzMzMzMzMGvI/bp1szAX6zyAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,9))\n",
    "my_palette = {'Subscriber':'black', 'Customer':'blue'}\n",
    "ax = sb.pointplot(x='day_of_week', y=0, hue='user_type', palette=my_palette, scale=.7, data=user_type_count_per_day_of_Week_df)\n",
    "plt.title('The day of week trend of bike rides per user type', fontsize=22, y=1.015)\n",
    "plt.xlabel('year-month', labelpad=16)\n",
    "plt.ylabel('count [rides]', labelpad=16)\n",
    "leg = ax.legend()\n",
    "leg.set_title('User Type',prop={'size':16})\n",
    "ax = plt.gca()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* we can conclude that subscriper ride decrease in weekend days and great increase in work days while customers has slightly increase in work days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    " # Main purpose bike rides for subscribers and customers (20~40 years age group)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "counts = df[(df['age']>=20) & (df['age']<=40) & (df['user_type']=='Customer')].groupby(['day_of_week', 'hour']).size()\n",
    "counts = counts.reset_index(name = 'count')\n",
    "counts = counts.pivot(index='hour', columns = 'day_of_week', values = 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.5,  2.5,  4.5,  6.5,  8.5, 10.5, 12.5, 14.5, 16.5, 18.5, 20.5,\n",
       "        22.5]),\n",
       " <a list of 12 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(counts,  cmap='YlGnBu_r', cbar_kws={'label': 'Rank of frequently used timing'})\n",
    "plt.title(\"Rank of Customer' most frequently used timing\", y=1.015)\n",
    "plt.xlabel('Weekday', labelpad=16)\n",
    "plt.ylabel('Hour [day]', labelpad=16)\n",
    "plt.yticks(rotation=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* here we conclude that in weekend days customers most frequently used this service around 10am ~ 4 pm where in work days from 7am ~ 9am and 4pm ~ 6 pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "counts2 = df[(df['age']>=20) & (df['age']<=40) & (df['user_type']=='Subscriber')].groupby(['day_of_week', 'hour']).size()\n",
    "counts2 = counts2.reset_index(name = 'count')\n",
    "counts2 = counts2.pivot(index='hour', columns = 'day_of_week', values = 'count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.5,  2.5,  4.5,  6.5,  8.5, 10.5, 12.5, 14.5, 16.5, 18.5, 20.5,\n",
       "        22.5]),\n",
       " <a list of 12 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SHXnfN/JiV03N7arnin911TW3nis3SvWteR3K9d2h76JEUnO2rwdUdRwhhNBaJJIQQghtGOYD2yORhBBCu4Z7RWckkhBCaNMwL5BEIgkhhHZ1DfNMEokkhBDaZA/v/kD17V8ZQggdYqDGtbea7VzSsZKelHRH3nYpnHO0pIckPSDpQ4X9m0m6O992Uh6T1+pxL5M0qdt2rqTDJS3WW9yRSEIIoU1dLreV0Jjt/O3AlsChkjbIt/3U9iZ5+zNAvm0fYENgZ+CXkhbJx58CTADWzdvOPTzuw8CrwKl5ewV4BlgvX+9RVG2F2opBf6FTDFQTie1pwLR8ebqk3mY73x240PYbwCOSHgK2kPQoMNb2jQCSzgH2AC5vcT+b2t6ucP0ySdfZ3k7Svb3FHSWSEEJok11uK85SnrcJre6zyWznX5B0l6QzJC2b960KPFE4bWret2q+3H1/K+MkrV547NWBFfLVXqcK6ZgSSS6uTQGetP2RquMJIYSGsr22bE8EJvZ2XPfZziWdAnyXVPj5LvBj4CCaz/rhHva38hXgekn/zOeuBXxe0hLA2b3F2zGJBDictKjV2KoDCSGEooGshG0227ntZwq3nwr8MV+dCqxWOH088FTeP77J/qZs/1nSusDbSInkftuv55v/q7eYO6JqS9J4YFfgtKpjCSGE7spWbfWm1WznklYuHPYx4J58eRKwj6TRktYiNarfkttapkvaMt/n/sAfenn4zUiN9hsBe0vav9STp3NKJP8FfB1YqtUBua5xAsDIZTdn5JJvHaTQQgjD3QCOR2w62zmwr6RN8kM9CnwWwPa9ki4G/k7q8XWo589p/zngLGAMqZG9VUM7ks4F1gHuABrnGzinTNByzWcbk/QRYBfbn5f0PuCrvbWRjFl933o/qRBCbcx8/IK2RxM++PIfS33nrLv0R2o5cjH3DtvA/UwInVAi2Qb4aB6AsxgwVtJ5tverOK4QQgBgbuf/dL0HeAu563Ff1T6R2D4aOBqgUCKJJBJCqI3OzyOsAPxd0i3AG42dtj9a5uTaJ5L+WHTkElWH0FFmz5lRdQgdZ9SiLZvrwjA0BBLJse2c3FGJxPY1wDUVhxFCCAuoeVNzr2xf2875HZVIQgihjjo1j0i63va2kqaz4NMQYNulxu1FIgkhhDZ16noktrfNf9uqq41EEkIIberURFKU5+9ajUJesH1bmXMjkYQQQps6PY9I+i7wadJ08o0ZXwy8v8z5kUhCCKFNQ2CFxL2BdWz3OtNvMx0x11YIIdRZV8mtxu4BlunvyVEiCSGENnV691/g+8Dtku4hBiQmKy73zqpDaEo1LQBOe25K1SE01eU5VYfQ0korvavqEJqr50eMOa+9VnUIC9UQaGw/G/gBcDf9KDwNyUQSQgiDqebVVmU8Z/uk/p7cEYlE0jKktUjeQepJcFBjLeIQQqjaECiR3Crp+6T1TYpVW0Oq++/PgCts7ylpFLB41QGFEELDEEgkm+a/Wxb2DZ3uv5LGAtuR+jiTu6f1q4taCCEsDF0d3v3X9g7tnF/7RAKsDfwLOFPSxsCtwOG2F5iytrhC4rjV92bsuK0HPdAQwvDUqeuRSNrP9nmSvtzs9uJyvz2paR+PBYwE3gWcYntTYAZwVPeDbE+0vbntzSOJhBAG0xyX22qosebGUk22JcveSSeUSKYCU23fnK//hiaJJIQQqtKpVVu2/ztf/B/bNxRvk7RN2fupfYnE9tPAE5LWz7t2JC10H0IItTDX5bYaO7nkvqY6oUQC8EXg/Nxj62HgwJ4OHn3opj3dXJlXn6/nALvREx+sOoSm5sx9veoQWlr1P+o56LWrpgMapk2bW3UIC1Wn9tqStBWwNTCuWzvJWGCRsvfTEYnE9h3A5lXHEUIIzXRqIgFGkdpCRpLaRRpeAfYseycdkUhCCKHOOjWR5CV2r5V0lu3H+ns/kUhCCKFNs7s6s7G9oZ0kApFIQgihbZ1aIhkokUhCCKFNNe3jMGgikYQQQpvmdug4Ekkn08NKwbYPK3M/tR9HEkIIddflcltvJK0m6WpJ90m6V9Lhef9ykiZLejD/XbZwztGSHpL0gKQPFfZvJunufNtJkppluymkaacWI80g8mDeNgFK99mORBJCCG2a01VuK3NXwFdsv500E++hkjYgzeZxle11gavydfJt+wAbAjsDv5TUGP9xCmn+wXXztnP3B7N9tu2z8+072D7Z9smkgd+blH3+Q7Jq6+p9X6o6hKbueL6eL/dnzx9fdQhNzXzjxapDaOnqXcZVHUJTr899vuoQmnp0en3fy4EwUKPWbU8DpuXL0yXdB6wK7A68Lx92NnANcGTef6HtN4BHJD0EbCHpUWBsY90mSecAewCXt3joVUjjSF7I15fM+0qp5zdbCCF0kIUx15akNUnrhNwMrJSTDLanSVoxH7YqcFPhtKl53+x8ufv+Vk4grdl+db6+PXBs2Vg7IpFI+hLwGVKj0N3AgbbrO39GCGFYKdv9t7jcRTbR9sQmxy0J/BY4wvYrzZs30qFN9rmH/U3ZPlPS5cB78q6j8jyHpdS+jUTSqsBhwOa230Ga/2WfaqMKIYT55pbcistd5K1ZElmUlETOt/27vPsZSSvn21cGns37pwKrFU4fDzyV949vsr+p3BD/AWBj238ARknaouzzr30iyUYCYySNJC2z2/IFCSGEwTaAvbYEnA7c121RqUnAAfnyAcAfCvv3kTRa0lqkRvNbcjXYdElb5vvcv3BOM78EtgL2zdenA78o89yhAxKJ7SeBE4HHSY1QL9u+svtxkiZImiJpyjmn/2WwwwwhDGNzulRqK2Eb4FPA+yXdkbddSG0YO0l6ENgpX8f2vcDFpKU1rgAOtd3otvs54DTgIeCftG5oB3iP7UOB1/P9vkia0LGU2reR5P7SuwNrAS8BlzSWhywel4uIEwGefX3SMJ+wIIQwmAaw19b1NG/fgNQlt9k5xwPHN9k/BXhHyYeenbsNG0DSOPowYL/2JRJSvd0jtv9lezbwO9L8+SGEUAsDVbVVoZOAS4EVJR0PXA98r+zJtS+RkKq0tpS0ODCTlJWnVBtSCCHMV/Mk0Svb50u6lfT9KmAP2/eVPb/2JZK8VvtvgNtIXX9HkKuwQgihDrqsUltdSVqHVPPzC+AeUnvMMmXP74QSCbaPAY4pe/yfHh+9EKPpv4sfXaLqEJoaufxyVYfQ1FIvjak6hJZOuLOt5RsWmh1WnlV1CE2d/896fvYBTtqq/fsYAgsJ/xbYXNJbSQ30lwG/BnYpc3JHJJIQQqizkvNo1VmX7TmSPg78zPbJkm4ve3IkkhBCaFOdq61Kmi1pX9J4k93yvkXLntwykUg6qcT5r9j+j7IPFkIIQ1HnF0g4EDgEON72I3lw43m9nDNPTyWS3YFv93L+UUAkkhDCsDYEem39nTQVVeP6I+RBj2X0lEh+muepb6m4uEoIIQxX7vCqLUmP0GRSR9trlzm/ZSKx/V/5AZaz/UJPx4QQwnA2p8NLJMDmhcuLAXsBpbtzlhlHcrOkSyTt0mKpxhBCGNY6fWS77ecL25O5kPD+sueX6bW1HmmakoOAkyVdBJxl+x/9CzmEEIaWTu+1JeldhasjSCWUpUqfb5dPk5J2ILXkLwHcSVr85MbSdzBI1jryj7XM/Ys8+nLVITRX13L5rDlVR9BS11qlB/0OKs2YXXUIzc2s73v5z1/v23YW+MKNV5f6T/TzrXaoZcYprIwIad34R4Af236gzPm9lkgkLQ/sR5ra+Bngi6Q58DcBLiHNyts2SWcAHwGezQtYIWk54CJgTeBRYO88vXEIIdRGnautyrC9Qzvnl2kjuREYS5rEa1fbv7M9J09R/Kt2Hrybs4Cdu+07CrjK9rrAVfl6CCHUSqe3kbSrTBvJ+m5R/2X7BwMViO3r8mL3RbsD78uXzwauAY4cqMcMIYSBMKfD20jaVSaRrCDp68CGpG5hANgu3aLfhpXykpHYniZpxUF4zBBC6JOhXNooo0zV1vnA/aS2kONIbRV/W4gx9Utxqd3pd1xRdTghhGFkCEwjP0XSof0dZF4mkSxv+3Rgtu1rbR8EbNmfB+uHZyStDJD/PtvqQNsTbW9ue/OlNune1BJCCAvPEGgj2QdYBfibpAslfagv4wbLJJJGf8JpknaVtCkwvh+B9sck4IB8+QDgD4P0uCGEUFpXya2ubD9k+5ukcYO/Bs4AHpd0XO4926MybST/KWlp4CvAyaQeXF9qI+amJF1AalhfQdJU0kJWJwAXSzqYtOTuXgP9uCGE0K6alzZKkbQRaRbgXUgLXZ0PbAv8L2m4R0u9JhLbf8wXXwba6mvcy+Ps2+KmHft6X3p+ZpvRLCSz6/mbxKMXqTqEprRI6eUQBt2IJ16pOoTm6vqFNmsIrCHYg07vtZXXa38JOJ000PyNfNPNkrbp7fye1iM5mR4+lrYPa3VbCCEMJ0OgRLKX7Yeb3WD7472d3FOJZEr+uw2wAWmEOaTqpVv7EmEIIQxlnTqNvKQvFy6/6XbbPylzPz1NI392vvNPAzvYnp2v/wq4sm/hhhDC0NXBJZKeJmYs/azKNLavkh+ssSbJknlfCCEE6t0jqye2jwOQtI3tG4q3lWkbaSiTSE4Abi/MDrk9cGzZBwghhKFuTldnVm0VnAy8q8S+psr02jpT0uXAe/Kuo2w/3acQQwhhCOvUEomkrYCtgXHF9hLSMI/S3TlbDkiU9JbGZdtP2/5D3p5udkwIIQxXAzmyXdIZkp6VdE9h37GSnpR0R952Kdx2tKSHJD0g6UOF/ZtJujvfdlKLkeqjSM0VI0lNGI3tFWDPss+/pxLJn+m9WFPmmBBCGNIGeB6ts4CfA+d02/9T2ycWd0jagDS9yYaktuv/kbSe7bnAKcAE4CbSd/XOwOXF821fC1wr6Szbj/U34J4SycaSehp1JVLWqh3VtAeFlxxVdQjN1bR613XuCrNEPQdL6tVZVYfQ3Kh6DnodKAP5UW2xpEYruwMX5gGEj0h6CNhC0qPA2MYKtpLOAfagWyIpGC1pImkRwXl5oews7z11/x3Ud77FCok/AnYDZgH/BA60/dJgxhVCCL3pQ7XVBFIpoWGi7YklH+YLkvYnjfH7Sl4tdlVSiaNhat43O1/uvr+VS0gLFZ4G9HkagjKTNg6Ws3jzComTgXfY3gj4B3D0YAcVQgi96eoqtxVnKc9b2SRyCrAOac6racCP8/5m9QnuYX8rc2yfYvsW27c2tpKx1SeR2L6O+WNVGvuutD0nX72JwZt1OIQQSrPLbf2/fz9je67tLuBUYIt801RgtcKh44Gn8v7xTfa3cpmkz0taWdJyja1sfGXGkdTFQcyfpiWEEGqjxWrkA0bSyo3VYoGPAY0eXZOAX0v6CamxfV3gFttzJU2XtCVwM7A/aVxIK43lOr5W2Gdg7TLx9ZhIJI0A7mq0WVRF0jeBOaRpjVsdM6/ucYWtJzD2bR8YpOhCCMPdQOaRFktqvE/SJqQv90eBz6bH9b2SLgb+TvqOPDT32AL4HKnJYAypkb1VQzu212on5h4Tie0uSXdKWt324+08UH9JOoDUCL+je0j7ua5xIsA6B19S4+4+IYShxgM4IrHFkhqn93D88cDxTfZPAUoVAiQtDnwZWN32BEnrAusXlhHpUZmqrZWBeyXdAswoBPnRMg/QDkk7A0cC29t+bWE/Xggh9MfcTh3aPt+ZpFndt87Xp5J6cg1YIjmuf3H1TYvi3NHAaGByHpR5k+1DBiOeEEIoayE3kQyGdWx/QtK+ALZn9mXN9jJzbV3bTnRl9bU41xNNr+mgrJquEte18pJVh9DcIjUdKQnohderDqG5uv4yHsi6nxoaAolklqQx5C7CktYB3uj5lPl6TSSSpjfunDQvy6LADNtj+x5rCCEMPUMgkRwDXAGsJul80oKGny57cpkSyQILn0jag/l9mEMIYdir9XQ+JdieLOk2YEvSYMbDbT9X9vw+jyOx/XtJR/X1vBBCGKo6vUQiabt8cXr+u4GkxkDxXpWp2iou/D4C2Jw+LMEYQghD3RDotVUciLgYqdbpVqC9SRsLditcnkMaDLN7yeBCCGHI6/QSie3i9zySVgN+WPb8MthY0ooAAB/+SURBVG0kB/YjrhBCGDY6PZE0MZWSgxmhXNXWeNIcLduQqrSuJzXETO3xxBBCGCY6PZFIOpn5TRYjSLMM31n2/DJVW2cCvwb2ytf3y/t2Kh9mCCEMXUNgmMyUwuU5wAW2byh7cplEMs72mYXrZ0k6ouwDVEEv1XSwWI0H2IW+0ex6Di6tz8IQ3cyY0/sxHWxhz/67sNk+u53zyySS5yTtB1yQr+8LPN/Og4YQwlDS1eElEkl307w3rgDnxQVbKpNIDiItRP/T/EB/zfsGVLOldgu3fRX4Eal0VHqQTAghDIYOH48I86eYPzf//STwGlCqpFKm19bjwEKf6Zc0b/7PgXOKO3M3tJ2ASqaxDyGE3nR4zRbANra3KVw/StINtr9T5uSWiaRbK/6b2D6sfIy9s32dpDWb3PRT4OvAHwby8UIIYaAMgcb2JSRta/t6AElbA0uUPbmnEkmxFf840qReg0rSR4Enbd/Z24zGxRUSx739Uyw9fvtBiDCEEIZEieRg4AxJS5MKEC/ThyaMlomk2Iov6Yh2W/X7Kq/Y9U3gg2WOL66QuO4HT+/8tzWE0DG6OryRxPatwMaSxgKy/XJfzi/bWbCKV2kdYC3gTkmPAuOB2yS9pYJYQgihJbvcVleSVpJ0OnCR7ZclbSDp4LLn17XXObbvtr2i7TVtr0kasv8u209XHFoIISyg0xMJqbPTX4BV8vV/AKXHC/bU2F5c0GpxSa80biL1Kx7Qha2aLbVru18rJHaNW3wgQxs4NS3+asbsqkNoyostUnUILXUtPbrqEJrSa/Uc+KdRnd8a3ZNOH0cCrGD7YklHA9ieI6n0qNue2kiWanXbwtBiqd3i7WsOUighhNA39fyN2BczJC3P/KV2tyQ1uJfS54WtQgghdFPT2oY++DIwCVhH0g3AOGDPsidHIgkhhDapg1e2krQIsH3e1ic1Xzxgu3Sdd20b20MIoWO45FZDtucCu9ueY/te2/f0JYlAlEhCCKF9nV+1dYOknwMXATMaO23fVubkSCQhhNCuzk8kW+e/xbm1TMk126NqK4QQ2jWAA0kknSHpWUn3FPYtJ2mypAfz32ULtx0t6SFJD0j6UGH/ZpLuzredpCbzTEk6PF/8lu0dum2lkghEIgkhhPZ1ldzKOQvYudu+o4CrbK8LXJWvI2kDYB9gw3zOL3PjOcAppPkH181b9/sEODD/Pal0dE0Myaqt0TuuWnUITc2p6aJ6c69+suoQmvKYxaoOoaUNdl2+6hCamvpsPatYhsCAvR4NZK+tFjOh704asA1pjZBrgCPz/gttvwE8IukhYIs8rdRY2zcCSDoH2IP564403JePHSfprsL+UgtaNQzJRBJCCIOqZBtJcZbybGKecLY3K9meBmB7mqQV8/5VgZsKx03N+2bny933L8D2vnn+wr/QxrpTtUkkrVZIlPRF4AukBen/ZPvrFYUYQgjNlWz/KM5SPkCara/hHvY3i+lpYON2gqhTG8lZdKvDk7QDqei2ke0NgRMriCuEEHo2sG0kzTwjaWWA/PfZvH8qsFrhuPHAU3n/+Cb7F4raJBLb1wEvdNv9OeCEXP+H7WffdGIIIVRt4U//Owk4IF8+gPkrxk4C9pE0WtJapEb1W3I12HRJW+beWvuzEFeZrU0iaWE94L2SbpZ0raR3tzpQ0gRJUyRNefHaSYMYYghhuNOcrlJbqftKM6HfCKwvaWpeF+QEYCdJDwI75evYvhe4GPg7cAVwaB6pDumH+GnAQ8A/eXNDO5LOzX8P735bX9SmjaSFkcCywJbAu4GLJa1tvzm1F+seNzjjunp2XQkhDE0D+I3Tw0zoO7Y4/njg+Cb7pwDvePMZC9hM0hrAQbln1wJtK7a71xI1VfdEMhX4XU4ct0jqAlYA/lVtWCGEUNC5I9t/RSrJrA3cyoKJxHl/r+petfV78hB9SesBo4DnKo0ohBC669AlEm2fZPvtwBm217a9VmErlUSgRiWSZiskAmcAZ+SpAmYBBzSr1uruyPfOXJih9ttKY+o5KuvQq6uOoLkRL71edQgtTdppQBcIHTDPvf5o1SE0NXNOs96oQ0jnlkgAsP05SRsD7827rrN9V0/nFNUmkfRQL7jfoAYSQgh9Vc/fiKVJOow0UPJ3edf5kibaPrnM+bVJJCGE0Kk6eWGr7DPAe2zPAJD0A1LPsUgkIYQwKGrY/tFHAoqzAc6l+ej4piKRhBBCuzq+QMKZwM2SLs3X9wBOL3tyJJIQQmhX5ze2/0TSNcC2pJLIgbZvL3t+JJIQQmhX51dtNZbVLbW0bneRSEIIoV0dXiJpVySSEEJok+ZGIhlyLntiTNUhNLXcqHq2yI14bqHNLt0WLz266hBamvbaY1WH0NQj0xfp/aAKvDK7vpNorLnUANxJh5dIJH3Y9uXd9h1i+1dlzq/vuxtCCJ3CJbf6+pak9zeuSGos41tKbRKJpDMkPZunQ2ns20TSTZLuyFPEb1FljCGE0FSHzrVV8FHge5LeK+l4YAv6sPRubRIJTVZIBH4IHGd7E+Db+XoIIdRLl8ttNWX7OVLi+AWwCrCn7dllz69NG4nt6ySt2X030Jgdb2kW4lKRIYTQbzVOEj2RNJ0FK91GkaaO31OSbZeanbQ2iaSFI4C/SDqRVHrautWBkiaQJh1j0y99hbU/UrpUFkIIbVGHJhLbA9HVoPaJ5HPAl2z/VtLepCH7H2h2YHGFxD3/N1ZIDCEMoiHwjSNpVWANCnnB9nVlzq17IjkAaKwlfAlp/eEQQqiXejek9yrP9vsJ0trvjckbDQyJRPIUsD1wDWmlxAcrjSaEEJqp5xCxvtgDWN/2G/05uTaJpMUKif8O/EzSSOB1chtIb2697MWFFWZbulYZkOrIATdq1tzeD6rCjNKdRgbdJ65ctuoQmnrxxXp+oy06qr4rJO46EEvndXiJBHgYWBTo7ETSwwqJmw1qICGE0Fcd2the8Bpwh6SrKCQT24eVObk2iSSEEDpW5yeSSXnrl0gkIYTQrg6v2rJ9djvnRyIJIYR2dXYeQdK6wPeBDYDFGvttr13m/DpNkRJCCJ2pw6dIIS21ewowB9gBOAc4t+zJkUhCCKFNskttNTbG9lWAbD9m+1jSkItSIpGEEEK75rrcVoKkRyXd3Zj1PO9bTtJkSQ/mv8sWjj9a0kOSHpD0oX4+g9cljQAelPQFSR8DVix7ciSSEEJo18CvR7KD7U1sb56vHwVcZXtd4Kp8HUkbAPsAG5JmT/+lpP6sbnYEsDhwGGnIxaeA/cuePCQb20fe+WzVITTV9cxrVYfQ1NyXX6k6hKZGvr541SG09OQ99XwvRzwzo+oQmpq5SI1/s3bGgMTdSQO2Ac4mzfbRWHzqwjwi/RFJD5HWErmxL3du+2/54qvAgXkQ+CeAm8ucX+N3N4QQOkTJxnZJE/IifY2t2WwdBq6UdGvh9pVsTwPIfxvVTqsCTxTOnZr3lSJpbK4a+7mkDyr5AvAQsHfZ+6lNiUTSaqSeAm8hzVwz0fbPJC0HXASsCTwK7G27nnOghBCGp5IFkuIs5T3YxvZTklYEJku6v4djm80905fi0bnAi6QSzGeAr5HWJNnD9h1l76Q2iYTU7ewrtm+TtBRwq6TJwKdJdYMnSDqKVDd4ZIVxhhDCguYO3Bxntp/Kf5+VdCmpquoZSSvbniZpZaBRfz8VWK1w+nj6tgDg2rbfCSDpNOA5YHXb0/sSc22qtmxPs31bvjwduI9URNudVCdI/rtHNRGGEEILA9TYLmmJ/EMaSUsAHwTuIU1fckA+7ADgD/nyJGAfSaMlrQWsC9zSh8jnzYxqey7wSF+TCNSrRDJPXnJ3U1JDzwJ1g7m41+yceSskjlt/P5ZeZbvBCTaEMOwN4BiRlYBLJUH6fv617Ssk/Q24WNLBwOPAXgC275V0MWkdkTnAoTkhlLWxpEZvGwFj8nWlu+/QpXYlLQn8FjjC9iv5Be1Vse5x3fefWuuRPyGEIWaAarZsPwxs3GT/88COLc45Hji+n4/Xn67Cb1Kbqi0ASYuSksj5tn+Xdz+T6wTpVjcYQgj1YJfbhqjaJBKlosfpwH22f1K4qVXdYAgh1MMwTyR1qtrahjSa8m5JjW5n3wBOoEndYE+6Xq7noKwRc+q5et2srllVh9DcrNr8znmTkXfUs2CsF2dWHUJTXnp01SEsXCWnPxmqapNIbF9P8z7R0KJuMIQQ6sCu54/EwVKbRBJCCJ3KA9Xa3qEikYQQQps8hNs/yohEEkIIbYqqrRBCCG2x51QdQqUikYQQQpuiRBJCCKEtkUhCCCG0JXpthUEz6+Xnqw6hqTdm93myz8Ex++WqI2hp7JOLVR1CU6/PfKHqEJpa7NXlqg5hoYoSSQghhLYM9+6/tZmDQtJqkq6WdJ+keyUdnvf/SNL9ku6SdKmkZaqONYQQiro8p9Q2VNUmkTB/hcS3A1sCh0raAJgMvMP2RsA/gKMrjDGEEN7E7iq1DVW1qdrKi1c1FrCaLuk+YFXbVxYOuwnYs4r4QgihlaGcJMqoU4lknm4rJBYdBFze4pwJkqZImvLKc39duAGGEMICukpuQ1PtEkn3FRIL+79Jqv46v9l5tifa3tz25mNX2Hpwgg0hBKJqqzZVW9ByhUQkHQB8BNjRw717RAihdoZykiijNomk1QqJknYGjgS2t/1aVfGFEEIrXZ5bdQiVqk0iofUKiScBo4HJKddwk+1DerqjGTOnLcw4+23W7Hqu3LjIiEWrDqGp2XPrudofAK88UXUETc2q6eDSma/XczDuQIkSSU30sELinwc7lhBC6ItIJCGEENoSiSSEEEJbYtLGEEIIbenqGrrTn5QRiSSEENoWJZIQQghtGO7D2yKRhBBCm6KxPYQQQlsikYQQQmjLcO+1peFet9cbSRNsT6w6ju7qGhfUN7aIq2/qGhfUO7bhqHaz/9bQhKoDaKGucUF9Y4u4+qaucUG9Yxt2IpGEEEJoSySSEEIIbYlE0ru61sPWNS6ob2wRV9/UNS6od2zDTjS2hxBCaEuUSEIIIbQlEkkIIYS2RCIJYZBJWq7qGJqR9I6qYwidKRJJE5IWqTqGZuoaV13V+PW6WdIlknZRXj+6Jn4l6RZJn5e0TNXBhM4RiaS5hyT9SNIGVQfSTS3jknSipA2rjqOJWr5ewHqkXkefIsX4PUnrVRwTtrcFPgmsBkyR9GtJO1UcFgCSviBp2arjCM1FImluI+AfwGmSbpI0QdLYqoOivnHdD0yUdLOkQyQtXXVAWS1fLyeTbe8LfAY4ALhF0rWStqo4tgeB/wCOBLYHTpJ0v6SPVxkX8Bbgb5IulrRzzUpyw150/+2FpO2AC4BlgN8A37X9ULVR1TMuSesDBwL7AjcAp9q+usqYGur0eklaHtiPVCJ5BjgdmARsAlxie62K4tqI9P7tCkwGTrd9m6RVgBttr1FFXIX4BHwwx7g5cHGO8Z9VxhWiRNKUpEUkfVTSpcDPgB8DawOXAX+OuJrHBrwtb88BdwJflnRhlTHV9PW6ERgL7GF7V9u/sz3H9hTgVxXG9XPgNmBj24favg3A9lOkUkqlnH71Pp23OcCywG8k/bDSwEKUSJqR9DBwNenXzl+73XaS7cMirgUe+yfAR4Grcmy3FG57wPb6FcVV19dLjv94fSLpMFIV4HPAacDvbc+WNAJ40PY6lQY4zEUiaULSkrZfrTqO7moc10HAhbZfa3Lb0rZfriCsOr9e44CvAxsCizX2235/ZUEBktYFvg9swIJxrV1ZUJmk75B+EDzW5La3276vgrBCFomkCUmLAQfz5v/oB1UWFPWNCyD3qFmXBeO6rrqI6vt6SboSuAj4KnAI6Zf2v2wfWXFc1wPHAD8FdiO1Rcj2MVXGVSRpRRZ8Lx+vMJyQRRtJc+eSeol8CLgWGA9MrzSipJZxSfoMcB3wF+C4/PfYKmPKavl6AcvbPh2YbfvanNi2rDooYIztq0jJ4zHbxwKVlpIaJO0m6UHgEdJ7+ShweaVBhXkikTT3VtvfAmbYPpvUi+WdFccE9Y3rcODdwGO2dwA2Bf5VbUhAfV+v2fnvNEm7StqUlOSq9nqjzSGP2/gYsGLVQWX/SUq2/8i92nYk9QwMNRCJpLnGf/SX8rQRSwNrVhfOPHWN63XbrwNIGm37fqCSBvZu6vp6/Wcea/MVUvXWacCXqg0JgCOAxYHDgM1I3ZMPqDSi+Wbbfh4YIWlE7la+SdVBhWRk1QHU1MRc5/8tUv/+JYFvVxsSUN+4puYpNX4PTJb0IvBUxTFBTV8v23/MF18GdqgyliLbf8sXXyW1j9TJS5KWJFWhni/pWVIX4FAD0dgeBpSk7Um//K+wPavqeOpE0slAy/9wFXZHvoye4/roIIbTlKQlgNcBkaZxWRo4P5dSQsWiRFIg6cs93W77J4MVS1GN42o2i+3d+e+SwAuDGM48dX29gCn57zakLrYX5et7AbdWElFyYv77cVLnhPPy9X1JjdqVsz2jcPXsygIJTUUiWdBS+e/6pMbjSfn6bqQidVXqGtetpF+yAlYHXsyXlwEeByqZ6oOavl65wR9JnwZ2sD07X/8VcGWFcV2b4/iu7e0KN10mqeou3NPpubRU+dxpIRLJAmwfB/P6+b/L9vR8/VjgkojrTXGtleP4FTDJ9p/z9Q8DH6gwrlq+XgWrkJJdo8S2ZN5XtXGS1rb9MICktYBxVQZke6kcy3dIU6Ocy/zqraV6ODUMokgkza0OFOv3Z1GP3j51jevdtg9pXLF9uaTvVhlQVtfX6wTgdkmNCS23px7jbr4EXJOnloH0Wn22unAW8CHb7ylcP0XSzUDMs1UDkUiaO5c0rfelpGL1x4Bzqg0JqG9cz0n6D1Ldukkz29ahEbSWr5ftMyVdDjS+GI+y/XSVMQHYviJPk/K2vOt+229UGVPBXEmfBC4kvZf7AnOrDSk0RK+tFiS9C3hvvnqd7durjKdB0mbAtvlqLeLKje7HANuR/pNfB3zHdiWN7UU1fb22Ae6wPUPSfsC7gJ81m0dqkOPai9Tbbnr+YfAu4D8bswBXSdKapBmctyF9xm4AjrD9aHVRhYZIJAWSxtp+pUVvJGryxbgIsBKF0mSV8w3leM62vV9VMfSkbq8XgKS7gI1JC2+dA5wBfNz29lXHZXsjSduSJm88EfhGtyqlEN4kRrYv6Nf5762krpqNrXG9UpK+SFoIaTLwR+BP+W9lbM8lNdKOqjKOZur4emVz8jTyuwMn2f4Z9Wg4blQV7QqcYvsPQC3eV0k/lDRW0qKSrpL0XC7NhRqIEkk3eRW21ar+1dqMpIeA99RtEJak/yZVg0wC5vX3r3C8BlDr1+ta4ArS6PHtSPOS3WG70nnAJP0ReJLU424zYCZwi+2Nq4wLQNIdtjfJ83/tQeoYcHUdYgtRInmT/Evx0qrjaOEJ0rQadfMU6Zf+CNIv68ZWtbq+Xp8A3gAOzo3sqwI/qjYkAPYmzdy8s+2XgOWAr1Ub0jyL5r+7ABfUoZo5zBe9tpq7SdK7C3MP1cXDpO6ZfyJ9EQHV//JvjNuoodq9XrnN5jzb88bZ5NJvHXqTvZbnsNoWeJA0l9WD1UY1z2WS7ieVkj6fFwd7veKYQhaJpLkdgEMkPUqqqhGpsLJRpVGl0eKPk+qta1F3DZDHQ7ypjrTqFf+o4etle66k16pcObIVSccAm5NmBDiTVAo4j9RTqlK2j5L0A+CVxmtIamMKNRBtJAWSVrf9uKQ1mt1edffMBklLkRJbLZaRzV1sGxYD/o3UoPz1ikKqNUkXk9bWmMyCbUqVTNrYIOkO0loyt9neNO+7qwY/oJC0OPBlYHXbE/J4l/ULMymHCkWJZEG/J02p8Zik39r+t6oDKsprapxLqrtG0nPA/rbvrTIu290nHLwhNyhXqsYlpT/lrW5m2bYkw7wZd+viTFLvya3z9amk6W4ikdRAJJIFqXB57cqiaG0i8OW8qA+S3gecyvz/XJXoNu5mBKnHz1sqCqfoq4XL80pKFcUyT2Pyxhq6OPfAW0bSvwMHkT5fdbCO7U9I2hfA9szcwzLUQCSSBbnF5bpYopFEAGxfU5NfjcVZgOeQ1tU+uNKIqHVJ6RGal5Qq/fFi+0RJOwGvkNpJvm17cpUxFcySNIb8uklah0IHilCtSCQL2ljSK6QvxDH5MsxvbK96yuqHJX2LVL0FaU6rRyqMp+HtjaV2GySNriqYQgzdS0qbU4+S0uaFy4uR1iNpOpvCYMuJoy7Jo+gY0tib1SSdT+oA8OlKIwrzRGN7B8nLxh5H6p4p0pxWx9p+seK4brP9rt72DbZuv/znkBZp+o7t6ysLqgVJ19vetvcjF2oMxbU/RpF6bc2owQ8oACQtT+qkIOAm289VHFLIokTSQXLCqLRnT5Gkt5AG042RtCnz25jGAotXGNe7gScK66UcQGofeRT4e1VxNeQJQRsaJaXKB3A21v5okLQHsEVF4TSzGGnxtJHABpKwXenCWyGJEkkHkDSpp9urWlM7f0F/mvRFWJyLbDpwlu3fVRTXbcAHbL8gaTvS1ONfBDYhVcPtWUVchfiuLlxtlJROtP1ARfGMtN20E4Kkm2xvOdgxNYnjB6QZAe4FuvJuV/XZDwuKRNIBJP2LNN3HBcDNLNi7bN5SqVWR9G+2f1tlDEWS7mzMwSTpF8C/bB+br99he5Mq46ubRjWkpI8XdjdKStvb3qqi0OaR9ACwUY3WRwkFUbXVGd4C7ERazOf/kcYgXFD1+JEG27+VtCuwIan6obH/OxWFtEjhV/aOwITCbZV/5nNHhH8jrUBYnN6+qterYTfe3KZUl1/8D5PabCKR1FDl/6lC7/JU7VcAV+QvoX1Jc0h9x/bJ1UY3b832xUlTy5wG7AncUmFIFwDX5gGbM4H/A5D0VuoxieMfSHHcSj2+GFeU9GXgnm77DXwKqHQut+w14A5JV7HgvGm1aTMcziKRdIicQHYlJZE1gZOAStogmtg6L4h0l+3jJP2YCmOzfXz+wlkZuNLz629HkNpKqjbe9s5VB1GwCLAk3apMa2ZS3kINRSLpAJLOBt4BXA4cZ7v7L8eqzcx/X5O0CvACsFaF8WD7pib7/lFFLE38VdI7bd9ddSDZtBpUq/WoxrMBBCKRdIpPkSb3Ww84rDAzRF0GSv5R0jLAD0nVNZCquEKBpHtIPY5GAgdKephUTVP17NK1LYlIupseZpmow4SSIRJJR7BdywXICuM1vpuvLwncDdwP/LTK2GpqVVIX5LrZseoAevCR/PfQ/Lcxq8MnSe0moQai+2/ot7qP16ibOoz271SSbrC9TW/7QjWiRBLasUhhydNPABPzeJLf5rUtwoIavaOaqnqly5pbQtK2jeltJG0N1GHC0kAkktCeWo/XqKFO6B1VVwcDZ0hamtRm8jJpmvtQA/GfPbSj7uM16qb2vaPqKi8JsLGksaQq+fh81Ui0kYS2SNqS+eM1ZuR96wFL2r6t0uBqRtLtjSVsQ99IWgn4HrCK7Q9L2gDYyvbpFYcWiEQSwqCRtFyhTSn0gaTLScvtftP2xpJGArfbfmfFoQXSSN8QwiCIJNKWFWxfTJ75N7fLza02pNAQiSSE0Alm5IWtGkvtbkm0w9VGVG2FEGpL0hHADaSebj8hTRV0LzAO2Mv2nRWGF7JIJCGE2pJ0IrA18DbSjAlPAtcAF8VSu/URiSSEUHuSRpEW2toa2CpvL9neoNLAAhDjSEIInWEMMBZYOm9PkeZ1CzUQJZIQQm1JmkhaeXM6aZnpm4CbbL9YaWBhAdFrK4RQZ6sDo4GnSe0jU4GXKo0ovEmUSEIItaa0AM+GpPaRrUk9t14AbrR9TJWxhSQSSQihI0gaD2xDSiYfAZa3vUy1UQWIRBJCqDFJh5ESxzbAbNKYkhvz37ttd1UYXsii11YIoc7WBH4DfMn2tIpjCS1EiSSEEEJbotdWCCGEtkQiCSGE0JZIJCGEENoSiSSEEEJbIpGEEEJoSySSEEIIbYlEEkIIoS2RSEIIIbQlEkkIIYS2RCIJIYTQlkgkIYQQ2hKJJIQQQlsikYQQQmhLJJIQQghtiUQSakXSTyUdUbj+F0mnFa7/WNKX+3ifZ0nas5dj3ifpj32POIQQiSTUzV9JK+IhaQSwAmm97oatSavjhRBqIhJJqJsbyImElEDuAaZLWlbSaODtAJKulXRrLrGsnPetI+mKvP//JL2t+51L+m4uoYyQtLOk+yVdD3y8cMwWkv4q6fb8d/28//8kbVI47gZJGy2sFyKEThGJJNSK7aeAOZJWJyWUG4Gbga2AzYH7gJ8Ce9reDDgDOD6fPhH4Yt7/VeCXxfuW9ENgReBAYBRwKrAb8F7gLYVD7we2s70p8G3ge3n/acCn832tB4y2fddAPfcQOlWs2R7qqFEq2Rr4CbBqvvwy8CTwQWCyJIBFgGmSlszHXJL3A4wu3Oe3gJttTwDIpZVHbD+Yr58HTMjHLg2cLWldwMCief8lwLckfQ04CDhrQJ91CB0qEkmoo0Y7yTtJVVtPAF8BXgH+F1jV9lbFEySNBV6yvQnN/Q3YTNJytl/I+9zi2O8CV9v+mKQ1gWsAbL8maTKwO7A3qYQUwrAXVVuhjm4APgK8YHtu/uJfhlS9dREwTtJWAJIWlbSh7VeARyTtlfdL0saF+7wCOAH4k6SlSNVXa0laJ9++b+HYpUklH8hVWQWnAScBfyskpBCGtUgkoY7uJvXWuqnbvpdtPwvsCfxA0p3AHcxvnP8kcHDefy+p5DCP7UtI7SKTAJGqsv6UG9sfKxz6Q+D7km4gVZ0V7+NWUsnozAF4niEMCbJble5DCN1JWoVU1fU2210VhxNCLUSJJISSJO1P6kH2zUgiIcwXJZIQQghtiRJJCCGEtkQiCSGE0JZIJCGEENoSiSSEEEJbIpGEEEJoy/8HYZLKVfcT/h0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.heatmap(counts2,  cmap='YlGnBu_r', cbar_kws={'label': 'Rank of frequently used timing'})\n",
    "plt.title(\"Rank of Subscriber' most frequently used timing\", y=1.015)\n",
    "plt.xlabel('Weekday', labelpad=16)\n",
    "plt.ylabel('Hour [day]', labelpad=16)\n",
    "plt.yticks(rotation=360)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "* here we conclude that in weekend days Subscriber in work days from 8am ~ 9am and 4pm ~ 7 pm\n",
    "* relative to customer heatmap subscriber rearly use the bike system in weekends  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "\n",
    "    over all rides  20-40 years old users are rapidly growing compared to other user groups.20 to 40 years old people took the more than %70 of bike rides. Among those, 30 to 40 years old people's rides account almost %40 of all bike rides. Male took around %74 of all bike rides, and female took around %24 of them. People use this service on weekdays more than weekends. 8am and 5pm are the peak hours for this service. Also, people use this service when they are in lunch time as well. Percentage of subscribers is almost %90. Percentage of customers is almost %10. Subscribers' average trip duration is around 11 minutes. Customers' average trip duration is around 24 minutes.90% of bike rides take place on weekday. The peak bike rides time for all members is around commute time.\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}