communicate_data_findings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Citi Bike Data\n",
    "## by Shakhawat Hassan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [

   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "Citi bike is a public bike-sharing system and it is now owned by Lyft. Lyft operates Citi bike around New York City, and Jersey City, NJ. Citi bike bicycle sharing system was opened in May 2013 with 332 stations and 6,000 bikes. Due to the high volume of growth rate, Citi bike is expanding around the five boroughs of NYC. As of July 2017, there are 130,000 annual subscribers. The average is 48,315 rides per day in 2018. Rides per day/week/month/year are still growing rapidly.\n",
    "* Due to staying home order for Covid-19 there might be a decrease in rides per day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from timeit import default_timer as timer\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# suppress warnings from final output\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminary Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('2020feb_citibike.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22962, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#22,962 rows and 15 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tripduration               0\n",
       "starttime                  0\n",
       "stoptime                   0\n",
       "start station id           0\n",
       "start station name         0\n",
       "start station latitude     0\n",
       "start station longitude    0\n",
       "end station id             0\n",
       "end station name           0\n",
       "end station latitude       0\n",
       "end station longitude      0\n",
       "bikeid                     0\n",
       "usertype                   0\n",
       "birth year                 0\n",
       "gender                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()       #nulls checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()          #There is no duplicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</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>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22957</th>\n",
       "      <td>262</td>\n",
       "      <td>2020-02-29 23:40:53.1620</td>\n",
       "      <td>2020-02-29 23:45:15.3180</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>40.719252</td>\n",
       "      <td>-74.034234</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>-74.036486</td>\n",
       "      <td>42467</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22958</th>\n",
       "      <td>216</td>\n",
       "      <td>2020-02-29 23:41:01.3350</td>\n",
       "      <td>2020-02-29 23:44:38.0350</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3483</td>\n",
       "      <td>Montgomery St</td>\n",
       "      <td>40.719420</td>\n",
       "      <td>-74.050990</td>\n",
       "      <td>42377</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1958</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22959</th>\n",
       "      <td>101</td>\n",
       "      <td>2020-02-29 23:45:22.1700</td>\n",
       "      <td>2020-02-29 23:47:03.2970</td>\n",
       "      <td>3214</td>\n",
       "      <td>Essex Light Rail</td>\n",
       "      <td>40.712774</td>\n",
       "      <td>-74.036486</td>\n",
       "      <td>3681</td>\n",
       "      <td>Grand St</td>\n",
       "      <td>40.715178</td>\n",
       "      <td>-74.037683</td>\n",
       "      <td>42485</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22960</th>\n",
       "      <td>279</td>\n",
       "      <td>2020-02-29 23:47:56.8380</td>\n",
       "      <td>2020-02-29 23:52:36.6400</td>\n",
       "      <td>3186</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>40.719586</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>3483</td>\n",
       "      <td>Montgomery St</td>\n",
       "      <td>40.719420</td>\n",
       "      <td>-74.050990</td>\n",
       "      <td>42531</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1988</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22961</th>\n",
       "      <td>212</td>\n",
       "      <td>2020-02-29 23:49:18.4510</td>\n",
       "      <td>2020-02-29 23:52:50.7490</td>\n",
       "      <td>3184</td>\n",
       "      <td>Paulus Hook</td>\n",
       "      <td>40.714145</td>\n",
       "      <td>-74.033552</td>\n",
       "      <td>3276</td>\n",
       "      <td>Marin Light Rail</td>\n",
       "      <td>40.714584</td>\n",
       "      <td>-74.042817</td>\n",
       "      <td>42385</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1967</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration                 starttime                  stoptime  \\\n",
       "22957           262  2020-02-29 23:40:53.1620  2020-02-29 23:45:15.3180   \n",
       "22958           216  2020-02-29 23:41:01.3350  2020-02-29 23:44:38.0350   \n",
       "22959           101  2020-02-29 23:45:22.1700  2020-02-29 23:47:03.2970   \n",
       "22960           279  2020-02-29 23:47:56.8380  2020-02-29 23:52:36.6400   \n",
       "22961           212  2020-02-29 23:49:18.4510  2020-02-29 23:52:50.7490   \n",
       "\n",
       "       start station id start station name  start station latitude  \\\n",
       "22957              3639         Harborside               40.719252   \n",
       "22958              3186      Grove St PATH               40.719586   \n",
       "22959              3214   Essex Light Rail               40.712774   \n",
       "22960              3186      Grove St PATH               40.719586   \n",
       "22961              3184        Paulus Hook               40.714145   \n",
       "\n",
       "       start station longitude  end station id  end station name  \\\n",
       "22957               -74.034234            3214  Essex Light Rail   \n",
       "22958               -74.043117            3483     Montgomery St   \n",
       "22959               -74.036486            3681          Grand St   \n",
       "22960               -74.043117            3483     Montgomery St   \n",
       "22961               -74.033552            3276  Marin Light Rail   \n",
       "\n",
       "       end station latitude  end station longitude  bikeid    usertype  \\\n",
       "22957             40.712774             -74.036486   42467  Subscriber   \n",
       "22958             40.719420             -74.050990   42377  Subscriber   \n",
       "22959             40.715178             -74.037683   42485  Subscriber   \n",
       "22960             40.719420             -74.050990   42531  Subscriber   \n",
       "22961             40.714584             -74.042817   42385  Subscriber   \n",
       "\n",
       "       birth year  gender  \n",
       "22957        1967       1  \n",
       "22958        1958       1  \n",
       "22959        1967       1  \n",
       "22960        1988       1  \n",
       "22961        1967       1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22962"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bikeid'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 22962 users who rented citi bikes in February 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4994     1495458\n",
       "4860      345608\n",
       "19204     290697\n",
       "6293      245410\n",
       "12027     221936\n",
       "Name: tripduration, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tripduration'].nlargest() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22962 entries, 0 to 22961\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   tripduration             22962 non-null  int64  \n",
      " 1   starttime                22962 non-null  object \n",
      " 2   stoptime                 22962 non-null  object \n",
      " 3   start station id         22962 non-null  int64  \n",
      " 4   start station name       22962 non-null  object \n",
      " 5   start station latitude   22962 non-null  float64\n",
      " 6   start station longitude  22962 non-null  float64\n",
      " 7   end station id           22962 non-null  int64  \n",
      " 8   end station name         22962 non-null  object \n",
      " 9   end station latitude     22962 non-null  float64\n",
      " 10  end station longitude    22962 non-null  float64\n",
      " 11  bikeid                   22962 non-null  int64  \n",
      " 12  usertype                 22962 non-null  object \n",
      " 13  birth year               22962 non-null  int64  \n",
      " 14  gender                   22962 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() #data type checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tripduration                 665.692927\n",
       "start station id            3307.954446\n",
       "start station latitude        40.722765\n",
       "start station longitude      -74.046948\n",
       "end station id              3308.074819\n",
       "end station latitude          40.722323\n",
       "end station longitude        -74.046393\n",
       "bikeid                     41376.724153\n",
       "birth year                  1981.129997\n",
       "gender                         1.195976\n",
       "dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean() #mean checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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>tripduration</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>end station longitude</th>\n",
       "      <th>bikeid</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.296200e+04</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "      <td>22962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.656929e+02</td>\n",
       "      <td>3307.954446</td>\n",
       "      <td>40.722765</td>\n",
       "      <td>-74.046948</td>\n",
       "      <td>3308.074819</td>\n",
       "      <td>40.722323</td>\n",
       "      <td>-74.046393</td>\n",
       "      <td>41376.724153</td>\n",
       "      <td>1981.129997</td>\n",
       "      <td>1.195976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.092407e+04</td>\n",
       "      <td>184.877308</td>\n",
       "      <td>0.006696</td>\n",
       "      <td>0.011348</td>\n",
       "      <td>189.016475</td>\n",
       "      <td>0.006509</td>\n",
       "      <td>0.011213</td>\n",
       "      <td>3551.166582</td>\n",
       "      <td>10.300961</td>\n",
       "      <td>0.494564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.100000e+01</td>\n",
       "      <td>3184.000000</td>\n",
       "      <td>40.711130</td>\n",
       "      <td>-74.083639</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>40.683826</td>\n",
       "      <td>-74.083639</td>\n",
       "      <td>17273.000000</td>\n",
       "      <td>1888.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.260000e+02</td>\n",
       "      <td>3195.000000</td>\n",
       "      <td>40.718355</td>\n",
       "      <td>-74.050656</td>\n",
       "      <td>3194.000000</td>\n",
       "      <td>40.718355</td>\n",
       "      <td>-74.050444</td>\n",
       "      <td>42192.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.295000e+02</td>\n",
       "      <td>3211.000000</td>\n",
       "      <td>40.721630</td>\n",
       "      <td>-74.044247</td>\n",
       "      <td>3211.000000</td>\n",
       "      <td>40.721124</td>\n",
       "      <td>-74.043117</td>\n",
       "      <td>42325.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.170000e+02</td>\n",
       "      <td>3278.000000</td>\n",
       "      <td>40.727224</td>\n",
       "      <td>-74.038526</td>\n",
       "      <td>3278.000000</td>\n",
       "      <td>40.726012</td>\n",
       "      <td>-74.038051</td>\n",
       "      <td>42458.000000</td>\n",
       "      <td>1989.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.495458e+06</td>\n",
       "      <td>3792.000000</td>\n",
       "      <td>40.748716</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3792.000000</td>\n",
       "      <td>40.748716</td>\n",
       "      <td>-73.976323</td>\n",
       "      <td>42636.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       tripduration  start station id  start station latitude  \\\n",
       "count  2.296200e+04      22962.000000            22962.000000   \n",
       "mean   6.656929e+02       3307.954446               40.722765   \n",
       "std    1.092407e+04        184.877308                0.006696   \n",
       "min    6.100000e+01       3184.000000               40.711130   \n",
       "25%    2.260000e+02       3195.000000               40.718355   \n",
       "50%    3.295000e+02       3211.000000               40.721630   \n",
       "75%    5.170000e+02       3278.000000               40.727224   \n",
       "max    1.495458e+06       3792.000000               40.748716   \n",
       "\n",
       "       start station longitude  end station id  end station latitude  \\\n",
       "count             22962.000000    22962.000000          22962.000000   \n",
       "mean                -74.046948     3308.074819             40.722323   \n",
       "std                   0.011348      189.016475              0.006509   \n",
       "min                 -74.083639       83.000000             40.683826   \n",
       "25%                 -74.050656     3194.000000             40.718355   \n",
       "50%                 -74.044247     3211.000000             40.721124   \n",
       "75%                 -74.038526     3278.000000             40.726012   \n",
       "max                 -74.032108     3792.000000             40.748716   \n",
       "\n",
       "       end station longitude        bikeid    birth year        gender  \n",
       "count           22962.000000  22962.000000  22962.000000  22962.000000  \n",
       "mean              -74.046393  41376.724153   1981.129997      1.195976  \n",
       "std                 0.011213   3551.166582     10.300961      0.494564  \n",
       "min               -74.083639  17273.000000   1888.000000      0.000000  \n",
       "25%               -74.050444  42192.000000   1976.000000      1.000000  \n",
       "50%               -74.043117  42325.000000   1983.000000      1.000000  \n",
       "75%               -74.038051  42458.000000   1989.000000      1.000000  \n",
       "max               -73.976323  42636.000000   2003.000000      2.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() #stats checking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'start station id' , 'end station id', bikeid, should to be string, \"birth year' is timedate, 'gender' is categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grove St PATH                 2328\n",
       "Sip Ave                       1402\n",
       "Hamilton Park                 1202\n",
       "Harborside                    1058\n",
       "Newport PATH                   978\n",
       "Columbus Dr at Exchange Pl     922\n",
       "Marin Light Rail               746\n",
       "City Hall                      683\n",
       "Newport Pkwy                   657\n",
       "Brunswick & 6th                654\n",
       "Newark Ave                     631\n",
       "McGinley Square                531\n",
       "Jersey & 3rd                   526\n",
       "Monmouth and 6th               521\n",
       "Manila & 1st                   517\n",
       "Name: start station name, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_start_station = df['start station name'].value_counts().head(15)\n",
    "top_start_station"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3 start stations are Grove St PATH, Sip Ave, and Hamilton Park."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Grove St PATH                 3109\n",
       "Sip Ave                       1342\n",
       "Harborside                    1087\n",
       "Hamilton Park                 1051\n",
       "Columbus Dr at Exchange Pl    1003\n",
       "Newport PATH                   964\n",
       "Marin Light Rail               716\n",
       "City Hall                      708\n",
       "Newport Pkwy                   659\n",
       "Newark Ave                     648\n",
       "Manila & 1st                   565\n",
       "Warren St                      556\n",
       "Jersey & 3rd                   528\n",
       "Columbus Drive                 522\n",
       "Washington St                  499\n",
       "Name: end station name, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_end_station = df['end station name'].value_counts().head(15)\n",
    "top_end_station"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 3 end stations are Grove St PATH, Sip Ave, and Harborside."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tripduration               10924.072660\n",
       "start station id             184.877308\n",
       "start station latitude         0.006696\n",
       "start station longitude        0.011348\n",
       "end station id               189.016475\n",
       "end station latitude           0.006509\n",
       "end station longitude          0.011213\n",
       "bikeid                      3551.166582\n",
       "birth year                    10.300961\n",
       "gender                         0.494564\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std() #standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</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>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>841</td>\n",
       "      <td>2020-02-01 00:02:13.7650</td>\n",
       "      <td>2020-02-01 00:16:15.4720</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3213</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>40.718489</td>\n",
       "      <td>-74.047727</td>\n",
       "      <td>42217</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206</td>\n",
       "      <td>2020-02-01 00:16:11.3850</td>\n",
       "      <td>2020-02-01 00:19:37.4460</td>\n",
       "      <td>3273</td>\n",
       "      <td>Manila &amp; 1st</td>\n",
       "      <td>40.721651</td>\n",
       "      <td>-74.042884</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>40.719252</td>\n",
       "      <td>-74.034234</td>\n",
       "      <td>42160</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>859</td>\n",
       "      <td>2020-02-01 00:23:22.1090</td>\n",
       "      <td>2020-02-01 00:37:41.5060</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717733</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>-74.062859</td>\n",
       "      <td>42204</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>693</td>\n",
       "      <td>2020-02-01 00:25:14.5170</td>\n",
       "      <td>2020-02-01 00:36:48.0280</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717733</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3277</td>\n",
       "      <td>Communipaw &amp; Berry Lane</td>\n",
       "      <td>40.714358</td>\n",
       "      <td>-74.066611</td>\n",
       "      <td>42165</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1473</td>\n",
       "      <td>2020-02-01 00:32:30.5050</td>\n",
       "      <td>2020-02-01 00:57:04.3740</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>40.719252</td>\n",
       "      <td>-74.034234</td>\n",
       "      <td>26156</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0           841  2020-02-01 00:02:13.7650  2020-02-01 00:16:15.4720   \n",
       "1           206  2020-02-01 00:16:11.3850  2020-02-01 00:19:37.4460   \n",
       "2           859  2020-02-01 00:23:22.1090  2020-02-01 00:37:41.5060   \n",
       "3           693  2020-02-01 00:25:14.5170  2020-02-01 00:36:48.0280   \n",
       "4          1473  2020-02-01 00:32:30.5050  2020-02-01 00:57:04.3740   \n",
       "\n",
       "   start station id  start station name  start station latitude  \\\n",
       "0              3199        Newport Pkwy               40.728745   \n",
       "1              3273        Manila & 1st               40.721651   \n",
       "2              3185           City Hall               40.717733   \n",
       "3              3185           City Hall               40.717733   \n",
       "4              3192  Liberty Light Rail               40.711242   \n",
       "\n",
       "   start station longitude  end station id         end station name  \\\n",
       "0               -74.032108            3213           Van Vorst Park   \n",
       "1               -74.042884            3639               Harborside   \n",
       "2               -74.043845            3268           Lafayette Park   \n",
       "3               -74.043845            3277  Communipaw & Berry Lane   \n",
       "4               -74.055701            3639               Harborside   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0             40.718489             -74.047727   42217  Subscriber   \n",
       "1             40.719252             -74.034234   42160  Subscriber   \n",
       "2             40.713464             -74.062859   42204    Customer   \n",
       "3             40.714358             -74.066611   42165  Subscriber   \n",
       "4             40.719252             -74.034234   26156    Customer   \n",
       "\n",
       "   birth year  gender  \n",
       "0        1980       1  \n",
       "1        1990       1  \n",
       "2        1991       1  \n",
       "3        1987       1  \n",
       "4        1969       0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 22,962 users who rented Citi bikes in February 2020. The highest trip was 1,495,458 secs and it was taken by the bike id of 42228. The average trip duration is 665 seconds. Top 3 start stations are Grove St PATH, Sip Ave, and Hamilton Park. Top 3 end stations are Grove St PATH, Sip Ave, and Harborside. Some data types should be cleaned. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22962 entries, 0 to 22961\n",
      "Data columns (total 15 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   tripduration             22962 non-null  int64  \n",
      " 1   starttime                22962 non-null  object \n",
      " 2   stoptime                 22962 non-null  object \n",
      " 3   start station id         22962 non-null  int64  \n",
      " 4   start station name       22962 non-null  object \n",
      " 5   start station latitude   22962 non-null  float64\n",
      " 6   start station longitude  22962 non-null  float64\n",
      " 7   end station id           22962 non-null  int64  \n",
      " 8   end station name         22962 non-null  object \n",
      " 9   end station latitude     22962 non-null  float64\n",
      " 10  end station longitude    22962 non-null  float64\n",
      " 11  bikeid                   22962 non-null  int64  \n",
      " 12  usertype                 22962 non-null  object \n",
      " 13  birth year               22962 non-null  int64  \n",
      " 14  gender                   22962 non-null  int64  \n",
      "dtypes: float64(4), int64(6), object(5)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</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>bikeid</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>841</td>\n",
       "      <td>2020-02-01 00:02:13.7650</td>\n",
       "      <td>2020-02-01 00:16:15.4720</td>\n",
       "      <td>3199</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>40.728745</td>\n",
       "      <td>-74.032108</td>\n",
       "      <td>3213</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>40.718489</td>\n",
       "      <td>-74.047727</td>\n",
       "      <td>42217</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206</td>\n",
       "      <td>2020-02-01 00:16:11.3850</td>\n",
       "      <td>2020-02-01 00:19:37.4460</td>\n",
       "      <td>3273</td>\n",
       "      <td>Manila &amp; 1st</td>\n",
       "      <td>40.721651</td>\n",
       "      <td>-74.042884</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>40.719252</td>\n",
       "      <td>-74.034234</td>\n",
       "      <td>42160</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>859</td>\n",
       "      <td>2020-02-01 00:23:22.1090</td>\n",
       "      <td>2020-02-01 00:37:41.5060</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717733</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3268</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>40.713464</td>\n",
       "      <td>-74.062859</td>\n",
       "      <td>42204</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>693</td>\n",
       "      <td>2020-02-01 00:25:14.5170</td>\n",
       "      <td>2020-02-01 00:36:48.0280</td>\n",
       "      <td>3185</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>40.717733</td>\n",
       "      <td>-74.043845</td>\n",
       "      <td>3277</td>\n",
       "      <td>Communipaw &amp; Berry Lane</td>\n",
       "      <td>40.714358</td>\n",
       "      <td>-74.066611</td>\n",
       "      <td>42165</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1473</td>\n",
       "      <td>2020-02-01 00:32:30.5050</td>\n",
       "      <td>2020-02-01 00:57:04.3740</td>\n",
       "      <td>3192</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>40.711242</td>\n",
       "      <td>-74.055701</td>\n",
       "      <td>3639</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>40.719252</td>\n",
       "      <td>-74.034234</td>\n",
       "      <td>26156</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration                 starttime                  stoptime  \\\n",
       "0           841  2020-02-01 00:02:13.7650  2020-02-01 00:16:15.4720   \n",
       "1           206  2020-02-01 00:16:11.3850  2020-02-01 00:19:37.4460   \n",
       "2           859  2020-02-01 00:23:22.1090  2020-02-01 00:37:41.5060   \n",
       "3           693  2020-02-01 00:25:14.5170  2020-02-01 00:36:48.0280   \n",
       "4          1473  2020-02-01 00:32:30.5050  2020-02-01 00:57:04.3740   \n",
       "\n",
       "   start station id  start station name  start station latitude  \\\n",
       "0              3199        Newport Pkwy               40.728745   \n",
       "1              3273        Manila & 1st               40.721651   \n",
       "2              3185           City Hall               40.717733   \n",
       "3              3185           City Hall               40.717733   \n",
       "4              3192  Liberty Light Rail               40.711242   \n",
       "\n",
       "   start station longitude  end station id         end station name  \\\n",
       "0               -74.032108            3213           Van Vorst Park   \n",
       "1               -74.042884            3639               Harborside   \n",
       "2               -74.043845            3268           Lafayette Park   \n",
       "3               -74.043845            3277  Communipaw & Berry Lane   \n",
       "4               -74.055701            3639               Harborside   \n",
       "\n",
       "   end station latitude  end station longitude  bikeid    usertype  \\\n",
       "0             40.718489             -74.047727   42217  Subscriber   \n",
       "1             40.719252             -74.034234   42160  Subscriber   \n",
       "2             40.713464             -74.062859   42204    Customer   \n",
       "3             40.714358             -74.066611   42165  Subscriber   \n",
       "4             40.719252             -74.034234   26156    Customer   \n",
       "\n",
       "   birth year  gender  \n",
       "0        1980       1  \n",
       "1        1990       1  \n",
       "2        1991       1  \n",
       "3        1987       1  \n",
       "4        1969       0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unnecessary columns\n",
    "df.drop(['start station id', 'start station latitude',\n",
    "         'start station longitude', 'end station id', 'end station latitude', 'end station longitude'], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Change columns' names\n",
    "df.rename(columns={'tripduration': 'duration_sec' , 'starttime': 'start_time', \n",
    "                   'start station name':'start_station_name', 'end station name':'end_station_name',\n",
    "                   'stoptime': 'stop_time', 'bikeid':'bike_id', 'usertype':'user_type', 'birth year': 'birth_year'}, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration_sec', 'start_time', 'stop_time', 'start_station_name',\n",
       "       'end_station_name', 'bike_id', 'user_type', 'birth_year', 'gender'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fixing data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# string to datetime\n",
    "df['start_time'] = pd.to_datetime(df['start_time'].str.strip())\n",
    "df['stop_time'] = pd.to_datetime(df['stop_time'].str.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# integer to object\n",
    "df['bike_id'] = df['bike_id'].apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# integer to category\n",
    "df['gender'] = df['gender'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract weekdays, day, and hour from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract start month name\n",
    "df['start_time_month_name'] = df ['start_time'].dt.strftime('%B')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract start time weekdays\n",
    "df['start_time_weekday'] = df ['start_time'].dt.strftime('%a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract start time day\n",
    "df['start_time_day'] = df['start_time'].dt.day.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract start time hour\n",
    "df['start_time_hour'] = df['start_time'].dt.hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "df.drop(['start_time'], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate the rider's age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calucaling the rider's age\\\n",
    "df['rider_age'] = 2020 - df['birth_year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22962 entries, 0 to 22961\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   duration_sec           22962 non-null  int64         \n",
      " 1   start_time             22962 non-null  datetime64[ns]\n",
      " 2   stop_time              22962 non-null  datetime64[ns]\n",
      " 3   start_station_name     22962 non-null  object        \n",
      " 4   end_station_name       22962 non-null  object        \n",
      " 5   bike_id                22962 non-null  object        \n",
      " 6   user_type              22962 non-null  object        \n",
      " 7   birth_year             22962 non-null  int64         \n",
      " 8   gender                 22962 non-null  category      \n",
      " 9   start_time_month_name  22962 non-null  object        \n",
      " 10  start_time_weekday     22962 non-null  object        \n",
      " 11  start_time_day         22962 non-null  int32         \n",
      " 12  start_time_hour        22962 non-null  int64         \n",
      " 13  rider_age              22962 non-null  int64         \n",
      "dtypes: category(1), datetime64[ns](2), int32(1), int64(4), object(6)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_time_month_name</th>\n",
       "      <th>start_time_weekday</th>\n",
       "      <th>start_time_day</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>rider_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>841</td>\n",
       "      <td>2020-02-01 00:02:13.765</td>\n",
       "      <td>2020-02-01 00:16:15.472</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>42217</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206</td>\n",
       "      <td>2020-02-01 00:16:11.385</td>\n",
       "      <td>2020-02-01 00:19:37.446</td>\n",
       "      <td>Manila &amp; 1st</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>42160</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>859</td>\n",
       "      <td>2020-02-01 00:23:22.109</td>\n",
       "      <td>2020-02-01 00:37:41.506</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>42204</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>693</td>\n",
       "      <td>2020-02-01 00:25:14.517</td>\n",
       "      <td>2020-02-01 00:36:48.028</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>Communipaw &amp; Berry Lane</td>\n",
       "      <td>42165</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1473</td>\n",
       "      <td>2020-02-01 00:32:30.505</td>\n",
       "      <td>2020-02-01 00:57:04.374</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>26156</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>129</td>\n",
       "      <td>2020-02-01 00:34:14.438</td>\n",
       "      <td>2020-02-01 00:36:23.554</td>\n",
       "      <td>Newport PATH</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>29302</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1992</td>\n",
       "      <td>2</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>394</td>\n",
       "      <td>2020-02-01 00:36:33.006</td>\n",
       "      <td>2020-02-01 00:43:07.413</td>\n",
       "      <td>Dey St</td>\n",
       "      <td>Leonard Gordon Park</td>\n",
       "      <td>42241</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1969</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>279</td>\n",
       "      <td>2020-02-01 00:52:29.410</td>\n",
       "      <td>2020-02-01 00:57:08.521</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>Newark Ave</td>\n",
       "      <td>42184</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>242</td>\n",
       "      <td>2020-02-01 01:11:13.010</td>\n",
       "      <td>2020-02-01 01:15:15.382</td>\n",
       "      <td>Hamilton Park</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>42254</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>451</td>\n",
       "      <td>2020-02-01 01:16:04.946</td>\n",
       "      <td>2020-02-01 01:23:36.458</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>42217</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time               stop_time  \\\n",
       "0           841 2020-02-01 00:02:13.765 2020-02-01 00:16:15.472   \n",
       "1           206 2020-02-01 00:16:11.385 2020-02-01 00:19:37.446   \n",
       "2           859 2020-02-01 00:23:22.109 2020-02-01 00:37:41.506   \n",
       "3           693 2020-02-01 00:25:14.517 2020-02-01 00:36:48.028   \n",
       "4          1473 2020-02-01 00:32:30.505 2020-02-01 00:57:04.374   \n",
       "5           129 2020-02-01 00:34:14.438 2020-02-01 00:36:23.554   \n",
       "6           394 2020-02-01 00:36:33.006 2020-02-01 00:43:07.413   \n",
       "7           279 2020-02-01 00:52:29.410 2020-02-01 00:57:08.521   \n",
       "8           242 2020-02-01 01:11:13.010 2020-02-01 01:15:15.382   \n",
       "9           451 2020-02-01 01:16:04.946 2020-02-01 01:23:36.458   \n",
       "\n",
       "   start_station_name         end_station_name bike_id   user_type  \\\n",
       "0        Newport Pkwy           Van Vorst Park   42217  Subscriber   \n",
       "1        Manila & 1st               Harborside   42160  Subscriber   \n",
       "2           City Hall           Lafayette Park   42204    Customer   \n",
       "3           City Hall  Communipaw & Berry Lane   42165  Subscriber   \n",
       "4  Liberty Light Rail               Harborside   26156    Customer   \n",
       "5        Newport PATH             Newport Pkwy   29302  Subscriber   \n",
       "6              Dey St      Leonard Gordon Park   42241  Subscriber   \n",
       "7           Warren St               Newark Ave   42184  Subscriber   \n",
       "8       Hamilton Park           Van Vorst Park   42254  Subscriber   \n",
       "9      Van Vorst Park           Lafayette Park   42217  Subscriber   \n",
       "\n",
       "   birth_year gender start_time_month_name start_time_weekday  start_time_day  \\\n",
       "0        1980      1              February                Sat               1   \n",
       "1        1990      1              February                Sat               1   \n",
       "2        1991      1              February                Sat               1   \n",
       "3        1987      1              February                Sat               1   \n",
       "4        1969      0              February                Sat               1   \n",
       "5        1992      2              February                Sat               1   \n",
       "6        1969      1              February                Sat               1   \n",
       "7        1987      1              February                Sat               1   \n",
       "8        1989      1              February                Sat               1   \n",
       "9        1999      1              February                Sat               1   \n",
       "\n",
       "   start_time_hour  rider_age  \n",
       "0                0         40  \n",
       "1                0         30  \n",
       "2                0         29  \n",
       "3                0         33  \n",
       "4                0         51  \n",
       "5                0         28  \n",
       "6                0         51  \n",
       "7                0         33  \n",
       "8                1         31  \n",
       "9                1         21  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Wrangling Summary\n",
    "- drop unnecessary columns\n",
    "#### Fixing data types\n",
    "- string to datetime\n",
    "- integer to object\n",
    "- integer to category\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "The dataset has 22,962 rows and columns. This means there were 22,962 rides that have been taken in the month of February 2020.\n",
    "\n",
    "\n",
    "- trip_duration: total time in a single trip (seconds)\n",
    "- start_time/stop_time: start or end time for a trip\n",
    "- start_station_name/end_station_name: name of the location where the trip was taken or ended.\n",
    "- bike_id : id number for a bike\n",
    "- user_type: (Customer = 24-hour pass or 3-day pass user; Subscriber = Annual Member)\n",
    "- birth_year : birth year for a customer\n",
    "- Gender (Zero=unknown; 1=male; 2=female)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "I am interested in the following\n",
    "- highest trip duration\n",
    "- what kind of a user took the most ride\n",
    "- which gender took the most rides\n",
    "- top start/end location's names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "- trip_duration: total time in a single trip (seconds)\n",
    "- user_type: (Customer = 24-hour pass or 3-day pass user; Subscriber = Annual Member)\n",
    "- birth_year : birth year for a customer\n",
    "- Gender (Zero=unknown; 1=male; 2=female)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm going to analyze the riders' total trips by their gender, age, and user type. Next, I look into which stations are most popular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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>stop_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_time_month_name</th>\n",
       "      <th>start_time_weekday</th>\n",
       "      <th>start_time_day</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>rider_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>841</td>\n",
       "      <td>2020-02-01 00:02:13.765</td>\n",
       "      <td>2020-02-01 00:16:15.472</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>Van Vorst Park</td>\n",
       "      <td>42217</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>206</td>\n",
       "      <td>2020-02-01 00:16:11.385</td>\n",
       "      <td>2020-02-01 00:19:37.446</td>\n",
       "      <td>Manila &amp; 1st</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>42160</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>859</td>\n",
       "      <td>2020-02-01 00:23:22.109</td>\n",
       "      <td>2020-02-01 00:37:41.506</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>Lafayette Park</td>\n",
       "      <td>42204</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1991</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>693</td>\n",
       "      <td>2020-02-01 00:25:14.517</td>\n",
       "      <td>2020-02-01 00:36:48.028</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>Communipaw &amp; Berry Lane</td>\n",
       "      <td>42165</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1473</td>\n",
       "      <td>2020-02-01 00:32:30.505</td>\n",
       "      <td>2020-02-01 00:57:04.374</td>\n",
       "      <td>Liberty Light Rail</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>26156</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time               stop_time  \\\n",
       "0           841 2020-02-01 00:02:13.765 2020-02-01 00:16:15.472   \n",
       "1           206 2020-02-01 00:16:11.385 2020-02-01 00:19:37.446   \n",
       "2           859 2020-02-01 00:23:22.109 2020-02-01 00:37:41.506   \n",
       "3           693 2020-02-01 00:25:14.517 2020-02-01 00:36:48.028   \n",
       "4          1473 2020-02-01 00:32:30.505 2020-02-01 00:57:04.374   \n",
       "\n",
       "   start_station_name         end_station_name bike_id   user_type  \\\n",
       "0        Newport Pkwy           Van Vorst Park   42217  Subscriber   \n",
       "1        Manila & 1st               Harborside   42160  Subscriber   \n",
       "2           City Hall           Lafayette Park   42204    Customer   \n",
       "3           City Hall  Communipaw & Berry Lane   42165  Subscriber   \n",
       "4  Liberty Light Rail               Harborside   26156    Customer   \n",
       "\n",
       "   birth_year gender start_time_month_name start_time_weekday  start_time_day  \\\n",
       "0        1980      1              February                Sat               1   \n",
       "1        1990      1              February                Sat               1   \n",
       "2        1991      1              February                Sat               1   \n",
       "3        1987      1              February                Sat               1   \n",
       "4        1969      0              February                Sat               1   \n",
       "\n",
       "   start_time_hour  rider_age  \n",
       "0                0         40  \n",
       "1                0         30  \n",
       "2                0         29  \n",
       "3                0         33  \n",
       "4                0         51  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trip Duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.296200e+04\n",
       "mean     6.656929e+02\n",
       "std      1.092407e+04\n",
       "min      6.100000e+01\n",
       "25%      2.260000e+02\n",
       "50%      3.295000e+02\n",
       "75%      5.170000e+02\n",
       "max      1.495458e+06\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duration_sec'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The max trip duration is 1,495, 458 seconds which is unusal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Trip duration (seconds) is not normally distributed!\n",
    "- IQR : Q3 - Q1 = 517 - 226 = 291 seconds\n",
    "- Upper Whisker Bound: (1.5*IQR)+ Q3 = 436.5 + 517 = 953.5 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistic analysis\n",
    "- About 75% of the time, riders spend on bicycles about 517 seconds or 517/60 = 8.67 minutes\n",
    "- Riders take bikes at least for 61 seconds\n",
    "- On average, 665.69 seconds which means 11 minutes and 9 seconds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_time_month_name</th>\n",
       "      <th>start_time_weekday</th>\n",
       "      <th>start_time_day</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>rider_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4860</th>\n",
       "      <td>345608</td>\n",
       "      <td>2020-02-06 18:52:31.856</td>\n",
       "      <td>2020-02-10 18:52:40.774</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>Atlantic Ave &amp; Fort Greene Pl</td>\n",
       "      <td>42217</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1994</td>\n",
       "      <td>2</td>\n",
       "      <td>February</td>\n",
       "      <td>Thu</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4994</th>\n",
       "      <td>1495458</td>\n",
       "      <td>2020-02-06 23:36:42.394</td>\n",
       "      <td>2020-02-24 07:01:00.938</td>\n",
       "      <td>JC Medical Center</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>42228</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Thu</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      duration_sec              start_time               stop_time  \\\n",
       "4860        345608 2020-02-06 18:52:31.856 2020-02-10 18:52:40.774   \n",
       "4994       1495458 2020-02-06 23:36:42.394 2020-02-24 07:01:00.938   \n",
       "\n",
       "     start_station_name               end_station_name bike_id user_type  \\\n",
       "4860            Sip Ave  Atlantic Ave & Fort Greene Pl   42217  Customer   \n",
       "4994  JC Medical Center                  Grove St PATH   42228  Customer   \n",
       "\n",
       "      birth_year gender start_time_month_name start_time_weekday  \\\n",
       "4860        1994      2              February                Thu   \n",
       "4994        1969      0              February                Thu   \n",
       "\n",
       "      start_time_day  start_time_hour  rider_age  \n",
       "4860               6               18         26  \n",
       "4994               6               23         51  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['duration_sec'] >=300000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The max trip duration is 1,495, 458 seconds which is unusal. Let's remove this outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#only keeping trip duration below 500,000 seconds\n",
    "df = df[df['duration_sec'] <=300000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_time_month_name</th>\n",
       "      <th>start_time_weekday</th>\n",
       "      <th>start_time_day</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>rider_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [duration_sec, start_time, stop_time, start_station_name, end_station_name, bike_id, user_type, birth_year, gender, start_time_month_name, start_time_weekday, start_time_day, start_time_hour, rider_age]\n",
       "Index: []"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['duration_sec'] >=300000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outlier has been removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the clean data\n",
    "df.to_csv('clean_master_file.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performing Normal Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Normal Distribution of Trip Duration')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_binsize = 0.025\n",
    "bins = 10 ** np.arange(0, np.log10(df['duration_sec'].max())+log_binsize, log_binsize)\n",
    "\n",
    "\n",
    "plt.figure(figsize = [15,10])\n",
    "plt.hist(data = df, x = 'duration_sec', bins =bins)\n",
    "plt.xscale('log')\n",
    "plt.xticks([50, 100, 500, 1000, 10000, 5000, 10000, 100000, 300000], [50, 100, 500, 1000, 10000, 5000, 10000, 100000, 300000])\n",
    "plt.xlabel('Duration (secs)')\n",
    "plt.ylabel('total trips')\n",
    "plt.title('Normal Distribution of Trip Duration')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trip duration is now normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rider's age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22960.000000\n",
       "mean        38.870035\n",
       "std         10.300748\n",
       "min         17.000000\n",
       "25%         31.000000\n",
       "50%         37.000000\n",
       "75%         44.000000\n",
       "max        132.000000\n",
       "Name: rider_age, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rider_age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### a rider's age 132! this is unsual. Let's remove this outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_time_month_name</th>\n",
       "      <th>start_time_weekday</th>\n",
       "      <th>start_time_day</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>rider_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3038</th>\n",
       "      <td>178</td>\n",
       "      <td>2020-02-04 18:54:41.561</td>\n",
       "      <td>2020-02-04 18:57:39.707</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>29496</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1888</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Tue</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3694</th>\n",
       "      <td>976</td>\n",
       "      <td>2020-02-05 12:48:53.916</td>\n",
       "      <td>2020-02-05 13:05:09.981</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>42220</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Wed</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8507</th>\n",
       "      <td>226</td>\n",
       "      <td>2020-02-12 07:47:17.171</td>\n",
       "      <td>2020-02-12 07:51:03.941</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>42185</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1888</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Wed</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16074</th>\n",
       "      <td>378</td>\n",
       "      <td>2020-02-21 13:39:46.219</td>\n",
       "      <td>2020-02-21 13:46:04.617</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>Washington St</td>\n",
       "      <td>42593</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1900</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Fri</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_sec              start_time               stop_time  \\\n",
       "3038            178 2020-02-04 18:54:41.561 2020-02-04 18:57:39.707   \n",
       "3694            976 2020-02-05 12:48:53.916 2020-02-05 13:05:09.981   \n",
       "8507            226 2020-02-12 07:47:17.171 2020-02-12 07:51:03.941   \n",
       "16074           378 2020-02-21 13:39:46.219 2020-02-21 13:46:04.617   \n",
       "\n",
       "      start_station_name end_station_name bike_id   user_type  birth_year  \\\n",
       "3038           Warren St    Grove St PATH   29496  Subscriber        1888   \n",
       "3694       Washington St    Washington St   42220  Subscriber        1900   \n",
       "8507       Grove St PATH        Warren St   42185  Subscriber        1888   \n",
       "16074      Grove St PATH    Washington St   42593  Subscriber        1900   \n",
       "\n",
       "      gender start_time_month_name start_time_weekday  start_time_day  \\\n",
       "3038       0              February                Tue               4   \n",
       "3694       1              February                Wed               5   \n",
       "8507       0              February                Wed              12   \n",
       "16074      1              February                Fri              21   \n",
       "\n",
       "       start_time_hour  rider_age  \n",
       "3038                18        132  \n",
       "3694                12        120  \n",
       "8507                 7        132  \n",
       "16074               13        120  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['rider_age'] >=75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop ages more than 75\n",
    "df = df[df['rider_age'] <=75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22956.000000\n",
       "mean        38.854853\n",
       "std         10.236920\n",
       "min         17.000000\n",
       "25%         31.000000\n",
       "50%         37.000000\n",
       "75%         44.000000\n",
       "max         73.000000\n",
       "Name: rider_age, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rider_age.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rider Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22956.000000\n",
       "mean        38.854853\n",
       "std         10.236920\n",
       "min         17.000000\n",
       "25%         31.000000\n",
       "50%         37.000000\n",
       "75%         44.000000\n",
       "max         73.000000\n",
       "Name: rider_age, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rider_age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total trips vs Age')"
      ]
     },
     "execution_count": 47,
     "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.hist(data = df, x = 'rider_age')\n",
    "plt.xlabel('rider_age')\n",
    "plt.ylabel('total trips')\n",
    "plt.title('Total trips vs Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most riders born between 1975 and 1990 and rheir ages between 30 and 40"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q. Does the above depend on if a user is a subscriber or customer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total trips vs User Type')"
      ]
     },
     "execution_count": 48,
     "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": [
    "base_color = sb.color_palette()[0]\n",
    "user_order = df['user_type'].value_counts().index\n",
    "sb.countplot(data= df, x = 'user_type', color = base_color, order = user_order)\n",
    "plt.xlabel('user type')\n",
    "plt.ylabel('Total trips')\n",
    "plt.title('Total trips vs User Type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subscribers are renting more bikes than regular customers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Total trips vs gender')"
      ]
     },
     "execution_count": 49,
     "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": [
    "base_color = sb.color_palette()[0]\n",
    "gender_order = df['gender'].value_counts().index\n",
    "sb.countplot(data= df, x = 'gender', color = base_color, order = gender_order)\n",
    "plt.xticks(rotation =90)\n",
    "plt.xlabel('gender')\n",
    "plt.ylabel('Total trips')\n",
    "plt.title('Total trips vs gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most riders are males"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 Start Stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top Ten Start Station')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMJDZIrCqLqyqm6rqqqW1f1FVH6+q90w/j1t67eer6tqq+vOqeszS+hnT2rVVtWuueQEAAEYw55HA30xyxn7Wf627Hzb9vDlJquohSZ6U5K9P73l5Vd2jqu6R5GVJHpvkIUmePG0LAADAATh6rg/u7j+qqp0rbv6EJL/d3V9I8hdVdW2S06bXru3ujyRJVf32tO3V2zwuAADAEA7GdwKfVVXvm04Xvf+0dnyS65a2uX5a22gdAACAA7DuCHxFkm9N8rAkn0jyq9N67Wfb3mT9q1TVOVV1ZVVduWfPnu2YFQAA4Iiz1gjs7hu7+87u/lKSX89XTvm8PsmJS5uekOSGTdb399mv7O5Tu/vUHTt2bP/wAAAAR4C1RmBVPWjp6Q8k2Xvl0EuSPKmqvqaqHpzk5CR/luQdSU6uqgdX1b2yuHjMJeucGQAA4Egy24VhquriJN+b5Liquj7J+Um+t6oelsUpnbuTPCNJuvsDVfXaLC748sUk53b3ndPnPCvJW5PcI8mF3f2BuWYGAAA40s15ddAn72f5VZts/8IkL9zP+puTvHkbRwMAABjWwbg6KAAAAAfJhkcCq+qYzd7Y3bdu/zgAAADMabPTQT+Qr9ym4ZuSfGb6/b5JPp7kpNmnAwAAYFtteDpod5/Y3ScleWOSH+juY7v7fknOSvL/rWtAAAAAts8q3wk8rbu/fFuG7n5jku+bbyQAAADmskoE3lxVu6rqhKo6vqp+Lsmn5h4MAACA7bdKBD4lyYlJ/sv0c2KS/d3+AQAAgEPclvcJ7O5PJjm3qu7d3betYSYAAABmsuWRwKp6eFW9P8mHpuffVVUvnX0yAAAAtt0qp4P+P0ken+Qvk6S73xsXhgEAADgsrRKBR3X3R/dZu3OOYQAAAJjXlt8JTHJdVZ2WpKvqHkl+MtOpoQAAABxeVjkS+BNJzktyUpIbk5ye5JlzDgUAAMA8VjkS+C3d/aTlhao6PdN3BAEAADh8rHIk8OX7WXvZdg8CAADA/DY8Ejh9D/BvJdlRVT+19NIxSe4592AAAABsv81OB/26JMdN2+xYWv9MkifOORQAAADz2DACu/vyJJdX1W9090fWOBMAAAAzWeXCMEdV1cuT7FzevrsfPddQAAAAzGOVCPzPSV6V5D/ETeIBAAAOa6tE4Je6+6WzTwIAAMDsVrlFxBuq6pyq2lFVx+z9mX0yAAAAtt0qRwL/8fT4C0trneSk7R8HAACAOW0Zgd194joGAQAAYH5bng5aVfepql1V9Yrp+bdV1WPnHw0AAIDttsp3Ai+ctnvk9PyGJC+abSIAAABms0oEntzdL0pyR5J09+eT1KxTAQAAMItVLgxze1XdO4uLwaSqHpzk9lmnYhY7d1261v3tvuDMte4PAADY2ioR+Pwkb0lyQlVdlORRSZ4+61QAAADMYtMIrKpK8t4kT0zyt7M4DfQ53X3TGmYDAABgm20agd3dVfWm7v7uJG9Y00wAAADMZJULw/xZVZ0y+yQAAADMbpXvBD4iyY9X1YeTfC6LU0K7u4UhAADAYWaVCDxr9ikAAABYiy0jsLs/vI5BAAAAmN8q3wkEAADgCCECAQAABrJlBFbVfab7BaaqvrWqHldVq3yXEAAAgEPMKkcC/2uS+1TVg5L8/0l+IsmFs04FAADALFaJwKO6+/NJfijJv+3uv5fkofOOBQAAwBxWisCq+ptJnpLkTdPaPeYbCQAAgLmsEoHnJXlekku7+6qq+pYsThEFAADgMLPKfQL/IMkfVNXXTM8/kuSfzD0YAAAA22+Vq4OeVlXvT3LN9Py7quqls08GAADAtlvldNCXJHl8kr9Mku5+b5Lvm3MoAAAA5rHq1UE/us/anXMMAwAAwLxWuen7dVV1WpKuqnsk+ckkH5p3LAAAAOawypHAn8jiCqEnJbkxyenTGgAAAIeZVY4E3t7dT1peqKpvTvLJeUYCAABgLqscCXxjVd1375Oq+o4kl843EgAAAHNZJQJ/Jcmbquprq+phSX4nyY/OOxYAAABzWOVm8ZdU1T2T/F6S+yX5oe7+77NPBgAAwLbbMAKr6teS9N6nSY5Ncm2Sc6oq3X3eGuYDAABgG212JPCqLZ4DAABwmNkwArv7VescBAAAgPltdjroxd395Kp6d75yWuiXdfcps04GAADAttvsdNDnTI9/fx2DAAAAML8NbxHR3ddPjx9OcmuS70jy7UlundYAAAA4zGx5n8CqelqSdyV5SpIfSXJlVZ0992AAAABsvy3vE5hkV5JTuntPklTVcUn+W5KL5hwMAACA7bflkcAkH09yy9LzTye5fp5xAAAAmNNmVwf9qenXjyX506r63SyuEnpWknesYTYAAAC22Wang+6YHq+bfr5mev6WWScCAABgNpvdLP4X1jkIAAAA81vlO4EAAAAcIUQgAADAQEQgAADAQFa5Wfy/rKpjquroqnprVd1YVU9Zx3AAAABsr1WOBD62u29N8vgkNyX560l+btapAAAAmMUqEbj3CqKPS3Jxd38yi/sFAgAAcJjZ7D6Be/2XqroqyZ1Jzq2q45J8Yd6xAAAAmMOWRwK7+zlJ/m6S7+7uO5LcluQH5x4MAACA7bfKhWHuk+THkrx0WvrGJA+dcygAAADmscp3Ai+ctnvk9PyGJC+abSIAAABms0oEntzdL0pyR5J09+eT1KxTAQAAMItVIvD2qrp3piuCVtWDk9w+61QAAADMYpWrgz4/yVuSnFBVFyV5VJKnzzoVAAAAs9gyArv7LVX1ziR/O4vTQJ/T3TfNPhkAAADbbpXTQZNkRxbfCfxSktOr6v+abyQAAADmsuWRwKr69SSnJrk6iwhMFt8PvGTGuQAAAJjBKt8JfESSh3R3zz0MAAAA81rldNC3J/n2uQcBAABgfqscCXxVkrdX1ceTfCGLi8N0d58y62QAAABsu1Ui8MIkP5bk/fnKdwIBAAA4DK0Sgdd19+/MPgkAAACzWyUCr66qVyd5YxangyZJutvVQQEAAA4zq0Tg/abH5XsDukUEAADAYWjLCOzup65jEAAAAOa3YQRW1c90969W1a9lceTvLrr7vFknAwAAYNttdiTww9PjVesYBAAAgPltGIHd/bvTr5/a9+qgVfWDs04FAADALI5aYZt/vp+15273IAAAAMxvs+8EPibJGUmOr6oXL710TNw0HgAA4LC02XcCb8ri+4C3JfnA0vpnkuyacygAAADmsdl3At+d5N1V9VvdfdsaZ4IDsnPXpWvd3+4Lzlzr/gAAYDts+Z1AAQgAAHDkWOXCMAAAABwhNozAqvrN6fFZa5sGAACAWW12JPC0qjo+yY9X1ddX1THLP+saEAAAgO2z2dVB/98kf5jkpCyuDlpLr/W0DgAAwGFkwyOB3f3i7j45yau7+6TuPnHpRwACAAAchjY7Epgk6e4fr6rvTPKIaemPuvvqeccCAABgDlteHbSqzk3y2ixO/zwpyX+qqn8y92AAAABsvy2PBCZ5RpLTuvuzSVJVL0ryJ0lePudgAAAAbL9V7hNYSe5Yen5H7nqRGAAAAA4TqxwJfE2SK6rqddPzH0hy0XwjAQAAMJdVLgzzy1V1eZJHZnEE8Jnd/Y7ZJwMAAGDbrXIkMFP0CT8AAIDD3CrfCQQAAOAIIQIBAAAGsmkEVtU9quqt6xoGAACAeW0agd19Z5Lbq+qYNc0DAADAjFa5MMxnk7y3qn4vyef2Lnb3ebNNBQAAwCxWicDfn34AAAA4zK1yn8BXVdW9kpzU3deuYSYAAABmsuXVQavqzCTvT3LZ9PxhVfX6uQcDAABg+61yi4jnJ3l4kluSpLvfk+Tb5hwKAACAeawSgXd09y37rPUcwwAAADCvVS4M88Gq+gdJjqqqByd5dpIr5h0LAACAOaxyJPBZSb47yZeSvD7JF5L89JxDAQAAMI9Vrg76uSQ/V1XPWzztv5p/LAAAAOawytVBT6mqdyf5UJJrquqdVXXK/KMBAACw3VY5HfQ3kpzX3Sd09wlJfmZaAwAA4DCzSgR+rrsv3/uku/8wyWdnmwgAAIDZbPidwKp66PTr26vqZUkuzuLWEP8wyeUbvQ8AAIBD12YXhnnZPs8fuvS7+wQCAAAchjaMwO5+5DoHAQAAYH5b3iKiqo5J8iNJdi5v393nzTcWAAAAc9gyApO8Ocm7krw/ixvGAwAAcJhaJQK/trt/avZJAAAAmN0qt4j4j1X1tKraUVXH7P2ZfTIAAAC23SpHAj+b5N8keUG+clXQTnLSXEMBAAAwj1Ui8DlJTu7um+YeBgAAgHmtcjro1UlunXsQAAAA5rfKkcDbk7y7qv4gyRf2LrpFBAAAwOFn1VtEvHnuQQAAAJjflhHY3a9axyAAAADMb8sIrKpr8pWrgn5Zd3/7LBMBAAAwm1VOB33E0u/3TvLEJPebZxwAAADmtMrpoDfus/Svq+qPZ5oHAACAGa1yOuhDl54eleTUOBIIAABwWFrldNCXLf3+xSS7k/zDWaYBAABgVqucDvrIdQwCbG7nrkvXur/dF5y51v0BALAeq5wOeq8kZyXZubx9d79ovrEAAACYwyqng74+yW1J3pnkznnHAQAAYE6rROA3d/d3zj4JAAAAsztqhW2uqKqHzD4JAAAAs1vlSODDk7y7qq5N8oUklaS7+5RZJwMAAGDbrRKBZ80+BQAAAGuxyi0iPryOQQAAAJjfKt8JBAAA4AghAgEAAAYiAgEAAAay4XcCq+pTSXp/L2VxddAHzDYVAAAAs9jswjDHrW0KAAAA1mLDCOzuO5efV9UDktx7aemGuYYCAABgHlt+J7CqzqyqDyW5Psnbp8c/mHswAAAAtt8qF4Z5YZLvSfLn3X1iksck+cM5hwIAAGAeq0TgF7t7T5Kjqqq6+7Ikp2z1pqq6sKpuqqqrltYeUFWXVdU10+P9p/WqqpdU1bVV9b6qOmXpPWdP219TVWcfwN8IAADAZJUI/HRVfV2SP07y6qr61SRfWuF9v5nkjH3WdiV5W3efnORt0/MkeWySk6efc5K8Ivny9xDPT/LwJKclOX9vOAIAAHD3rRKBZyW5LclPZ3Ea6MeTPH6rN3X3HyW5eZ/lJyS5aPr9oumz966/uheuSHJsVT0oi1NPL+vum7v7U0kuy1eHJQAAACtaJQJ/vrvv7O47uvtV3f3iJOcd4P6+obs/kSTT4wOn9eOTXLe03fXT2kbrX6WqzqmqK6vqyj179hzgeAAAAEe2VSJwf0feztzmOWo/a73J+lcvdr+yu0/t7lN37NixrcMBAAAcKTaMwKp6RlW9O8l3VNW7ln6uSXL1Ae7vxuk0z0yPN03r1yc5cWm7E7K4D+FG6wAAAByAzY4EvjbJE5O8eXrc+/M93f3kA9zfJUn2XuHz7CRvWFr/0ekqoacn+fR0uuhbkzy6qu4/XRDm0dMaAAAAB+DojV6YLsTyqSRPrKrvTPKI6aX/mq8cwdtQVV2c5HuTHFdV12dxlc8Lkry2qp6e5GNZRGWyCM3HJbk2yeeTPG2a4eaqekGSd0zbPb+7973YDAAAACvaMAL3qqpzk5yb5HenpddW1cu6++WbvW+To4Xfv59te9rH/j7nwiaq73UAACAASURBVCQXbjUnAAAAW9syApM8I8lp3f3ZJKmqFyX5kySbRiAAAACHnlWuDlpJ7lh6fkf2f9VOAAAADnEbHgmsqqO7+4tJXpPkiqp63fTSD+QrN3wH2BY7d1261v3tvmC773QDAHB42Ox00D9Lckp3/3JVXZ7kkVkcAXxmd79jk/cBAABwiNosAr98yucUfcIPAADgMLdZBO6oqvM2erG7XzzDPAAAAMxoswi8R5L7xkVgAAAAjhibReAnuvv5a5sEAACA2W12iwhHAAEAAI4wm0Xg969tCgAAANZiwwjs7pvXOQgAAADz2+xIIAAAAEcYEQgAADAQEQgAADAQEQgAADAQEQgAADAQEQgAADAQEQgAADAQEQgAADAQEQgAADCQow/2AABHup27Ll3r/nZfcOZa9wcAHF4cCQQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABjI0Qd7AAAObzt3XbrW/e2+4My17g8AjjSOBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAxEBAIAAAzk6IM9AAAcynbuunSt+9t9wZlr3R8A43EkEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCBuFg8AA9u569K17m/3BWeudX8AfDURCAAcsUQuwFdzOigAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBA3CICAOAw5PYXwIFyJBAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgIhAAAGAgRx/sAQAAYF87d1261v3tvuDMte4PDiYRCAAAayZyOZicDgoAADAQRwIBAIBt5UjnoU0EAgAA3A2He+Q6HRQAAGAgIhAAAGAgByUCq2p3Vb2/qt5TVVdOaw+oqsuq6prp8f7TelXVS6rq2qp6X1WdcjBmBgAAOBIczCOB39fdD+vuU6fnu5K8rbtPTvK26XmSPDbJydPPOUlesfZJAQAAjhCH0umgT0hy0fT7RUnOWlp/dS9ckeTYqnrQwRgQAADgcHewIrCT/F5VvbOqzpnWvqG7P5Ek0+MDp/Xjk1y39N7rp7W7qKpzqurKqrpyz549M44OAABw+DpYt4j4nu6+oaoemOSyqvrvm2xb+1nrr1rofmWSVybJqaee+lWvAwAAcJCOBHb3DdPjTUlen+S0JDfuPc1zerxp2vz6JCcuvf2EJDesb1oAAIAjx9ojsKq+rqq+fu/vSR6d5KoklyQ5e9rs7CRvmH6/JMmPTlcJPT3Jp/eeNgoAAMDdczBOB/2GJK+vqr37/4/d/ZaqekeS11bV05N8LMkTp+3fnORxSa5N8vkkT1v/yAAAAEeGtUdgd38kyXftZ/0vk3z/ftY7yblrGA0AAOCIdyjdIgIAAICZiUAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAID/2d6dx1tXz/0ff72b06SUSDRJSvNAQhQZ7kgIJZS521BC5pRw36hMyZ1KCamE/MKdQpOieR4MqUhSyt2glIb374/v2l37Op3rXNe5zj7nu9fa7+fjcR7nrLX2OddnXefsvddnfb/fz2eEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIUkCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIiIiImKEJAmMiIiIiIgYIa1JAiW9WNLvJF0j6cO144mIiIiIiGijViSBkhYEDgZeAqwD7CRpnbpRRUREREREtE8rkkDg6cA1tq+1/W/gWODllWOKiIiIiIhoHdmuHcNcSdoBeLHttzbbbwCeYfvdfY95O/D2ZnMt4HczGOLywK0z+O/NtJxfu+X82qvL5wY5v7bL+bVXl88Ncn5tl/MbnFVsrzDegYVmKICp0jj7ZstebR8KHDoz4cxO0gW2N63xb8+EnF+75fzaq8vnBjm/tsv5tVeXzw1yfm2X85sZbZkO+hfgiX3bKwN/rRRLREREREREa7UlCTwfWFPSapIWAXYETqwcU0REREREROu0Yjqo7QckvRs4GVgQOML2lZXD6ldlGuoMyvm1W86vvbp8bpDza7ucX3t1+dwg59d2Ob8Z0IrCMBERERERETEYbZkOGhEREREREQOQJDAiIiIiImKEJAmMkSNp0XH2LVcjloiItpD0bknL1o4j5o+kxSWtVTuOiFEj6dXzsm+mZU3gJEj6MWP6E/azvd0MhjNtJK0I/Bewku2XSFoHeKbtb1QObSAk/RTY3vb9zfbjgZ/Y3qRuZIMlaXHgSbZ/VzuWQZB0EBM//3afwXAGStJdlHPr9UTtnacA2166SmADImnjiY7bvmimYpkOc7uJZPsfMxXLdJL0aUp17ouAI4CT3bGLCEkLAivSVzjP9p/rRTQYkl4GHAAsYns1SRsC+3XouuUpwP8AK9peV9L6wHa2P105tPkm6ZUTHbf9w5mKZSZ09bkHIOki2xvPbd9MSxI4CZKe2/sSOAx4a/9x22fMeFDTQNJJwJHAx2xvIGkh4GLb61UObSAkvQ3YFngVpf/kicAHbJ9SNbAB6uIbvqRd+jY/CezTf9z2UTMbUcwrSadNcNi2t56xYKaBpOuYPYnvZ9urz3BI00aSgBcCbwI2Bb4HfMP2H6sGNgCS3kN5XbkZeKjZbdvr14tqMCRdCGwNnG57o2bfZV04NwBJZwB7AV/vO78rbK9bN7L5J+nI5svHAlsApzbbW1F+jxMmiW3S1eeepJcA/wG8Bjiu79DSwDq2n14lsEYrWkQMi/4kT9I/u5L0jWN529+T9BF4uEXHg7WDGhTbhzX9Jn8ErAq8w/av60Y1cPsCTwdOB7B9iaRV64Uzdf1JnqT3dinpk7QYsBvwZOAyShucB+pGNTi2t6odw3SyvVrtGGaKbUv6G/A34AFgWeD7kn5u+4N1o5uyPYC1bN9WO5Bp8IDtO0oO30mPsn3emPNr9Wuo7TcBSPoJJWG4qdl+PHBwzdimQVefe38FLgC2Ay7s238XsGeViPokCZx/XR5CvVvSY2jOUdLmwB11Q5o6Se/r36SMAl4CbC5pc9tfqBPZtOj6G37Xnn9HAfcDv6LcNXwa5U2xEyRtbfvUOU1vavu0pq5Pd+2RtDuwC3ArcDiwl+37JS0A/AFoexJ4Ax14r5uDKyS9DlhQ0prA7kCXbn7eKmkNZl237ADcVDekgVm1lwA2bgaeUiuYadLJ557tS4FLJX23bwnSssATbf9f3eiSBE7KmHUfCza/yIevsruy7gN4P2WK5BqSzgZWAHaoG9JALDVm+4Q57O+Crr/hd806venWkr4BnFc5nkF7LmUq08vGOWag1UkgcOAEx0yZhtcFywOvtP2n/p22H5L00koxTVnfDcJrgdObdeP39Y535Abhe4CPUc7ru8DJQGvXy43jXZQG3E+VdCNwHbBz3ZAG5nRJJwPHUF5PdgQmmmLfGiPy3AP4uaTtKHnXJcDfJZ1h+31z+b5plTWBkzBi6z4WAtainOvvencw2q5ZePxZ23vVjmU6SXoU5Q3/hZTf4cnAp2zfWzWwKegrngLwKOCe3iFaXjxl7ALxYVgwHjGWpE8BZwK/tn137XgGRdI+Exy27f1mLJhpImkj2xfXjmM6NCPROzTLWJYAFrB9V+24BknSK4Atm80zbZ8w0ePbYhSeewCSLra9kaS3UkYB9xmGNblJAidB0ipj74B2kaRLKQtYj+vCYv+xJP3S9vNrxxGTI2nhrtyMGKtZc9u7qBawOCXJbX2CO5akbSnTXRfr7evKGz2ApHWBdZj9/L5VL6LBkfRm4NnAMylrWn5FuSD9f1UDGxBJr7Z9/Nz2tVFTnOnxwPHAsbavrBzSQEk60/aWc39kuzQ3rk+2/YLasUynLj/3ACRdTrkpfxSl6OL5SQJbZlTuzktaBXht8/EQJSH8XodK9R4IrEl5M3z4bnbb1yVBt9uYjMrzr8skHUIZxd2KsqZsB+A822+pGtiANHe1n0dJAv8XeAlwlu0uTKd/mKTHUardfQBY1nYnptQPaxn3Qen7vb2WUp3wuDa3UOgnaW/gX5Trlf739dYv05F0IvAG251bM9czAs+9VwN7U94P3ilpdWB/26+qGleSwHnXG86tHcdMataT7Q3sbHvB2vEMQl/Z5X62/eYZD2bA+tqYvBJ4HPCdZnsn4HrbH60S2ACM4vOva3p3Pvs+Lwn80PYLa8c2CM3d3g0oLXU2UOm5erjt8dZCto6kwykJ7s2UUcCzgIvaXsl22Mu4D5qk9ShFfF5re5Ha8QxCs1xnrE4s05H0PWBz4OfMnuC2tjduz6g994ZNCsNMzhMkfWVOB7vwhOxp2gn07hg+SPurvj2sV3a5i3ptSyR9aszUmB9LOrNSWIOywpgKr7Pp0ALyLvtX8/keSSsBtwFdaq/wr6ZIygOSlgZuAVp/EdrnMcCCwO3AP4Bb254ANoa6jPsgSFqb8n6+A+V5dyylCFwndLxNy0+bjy7q/HNvTiR9ovZSiCSBk/MvZv8j7SRJ5wILU5oAv9r2tZVDGqimJ9tbeOS6pNaPBPZZQdLqvd+dpNUoVV7bbEFgScYvzBTt8BNJjwb2By6iTF0+vG5IA3VBc36HUd4r/kmHKr3afgU8nFC8CDhN0oK2V64b2dTMoYz7YzrWs+xISnXJF9r+a+1gpkNX1+N2qSfuWCPy3JuTtwJVk8BMB52ELs1Pnoikp9r+7Zh9K9q+uVZMgyTpeOC3wOsoT8Cdgattd6kv24sp5bJ7CfyqwDtsn1wtqCkaoeffKsCatn8haXFgoa5VugOQtCiwWFfXuTSzKZa2fVnlUAamaQPxHEqVwmWB3wC/sn1E1cCmSNJngQNs3yppU8oN0IcoN0Pf2Jth0SWSngjsaHv/2rEMQhfX40p6KvBFyt/i7pSlOdsDvwd2sX11xfAGouvPPUl3zukQsLjtqoNxSQInQdI5tjevHcdMkbQM8CpKsrS27SdUDmkg+kr19tYlLUypvtWVXl7AwxfZT202f2v7vokeP+xGYU2gpLcBbweWs71Gsyb3kK5Ws5W0DfBB29vUjmUqNDrN4g+mtIj4VZdGkyRd7ll9Ok+j/E2eL+kpwHdtb1o3wsGQtDzwasoa8ScAJ9j+QN2oBqOL63GbJRz7U2bAfBb4EGXd3EuB93bhfaHrzz1JfwY2G28QRdINtp9YIayHZTroJIyXAEpag/KCuqPtdWc+qsFqRh62oyR+G1MaqW9PeePvil6bgdub6SN/o4yUtZ6krW2fKumVYw6tIantFVAf8YbX9IR6BbCT7W1nPqSBexfwdOBcANt/kPTYuiFNnaStgUOAlYAfAf8FfItyN/QzFUMblP5m8Zsw+7KBLjWL/4ntk/p3SNrN9iG1AhqQhSUt1KxvXNz2+QC2f9/cTGstSUtRXiNfBzwFOAFYve1TeMfRxfW4S9n+MTy8zv/YZv+PJX2yYlyD1NnnXuNbwCqUYlpjfXeGY3mEJIHzQdLjKQusXwesD/w3JRFsNUlHU6b5nAJ8FTgVuMb26TXjmgaHSlqWMrXiRMpdtr3rhjQwz6X83sa7+2mgtUlgr9S3pEUo1cReB7wY+AElweiC+2z/WyrLHiUtxAQtP1rkQMoI528o07TOAfa2/eWqUQ2I7a16Xzcj1ltN9PgW21vSfbZPBZD0IcoUvLY//w4G/reZmvYzSV+ivFY+H7ikamRTdwtlXerHKdMjrdJ4vGu6uB63vyL72MJnnajqSrefe9j++ATHPjSTsYwn00EnoZmqtROwMmXe8veA/9eVqlQqTeJFuXNxnO0bJF3bhRLL0X7N1MGdaApSUKbFHGR71ZpxDZKkz1MqL74ReA/wTuAq2x+rGtgUjV3PKemPtteoGdN06fLa1WY64U+AvSg3YJ5KmQVz/4Tf2AKSngf8J2W0bCHgBsqo9ZFtPj9JewI7AktQRh6OA37e5ff1rqzHlfQO4Gjb/xyz/8nAu22/t05kg9XV514bJAmcBEn/ptzJfr/tC5p9nUqSmoXIr6OMdN5CeZNfz/bfqgY2AJKeQSmWsgZwOfAW21fVjWp6SNqDUg3uLsqd0Y2BD9s+pWpgUyDpIUpvsl1tX9fs69rzbwFK5doXUm7InExZ19LqF2pJ11Iai/cc0L/d8mnKs+lyEgjQTE/+BWW05c1t/9scFSrNqXeiJIRrAvtQ1gT+vmpgAyJpy/H22+7SUpaIgUoSOAljFlWvSBkJ3LX2ws7p0lRq2olyzn+xvUXlkKZE0gXARyjrG7cD3mr7RXWjmh6SLm0Wx7+Iss5sb8pdtdZenEraiHIBswOl6umxwCdsr1I1sJgrSUdOcNhueXsWSQcxa9rujpS/zYe55T1kJd1FOT81nxcBHmi+tu2lK4YXk6TSLH4nSrP4TozIS/px3+ZilLXVF3at4FvEICUJnE9NeeXXUl5IH0W5o/bRulFND5UFSlt2oFTv2Clpnb1j31f59MvA6bZP6FJ1TUnPojz3XkVZN3CC7UPrRjV1TYW7sS/Kd1Ca6X7ao9E7qXUk7TLRcXe4z1fEMGqu0T5vu/X1GqI7JK3da+0haXPb51SNJ0ng1Elai7IuoivVmjppxKakHUkpAb4apWz2gpRkcJOqgQ1YM31yG8rz702145mqZk3gg8yqGrZj8/lO4NltLnce7dVMAf0o8GTgMuCztufU/yqiuubm9WW99gMRw0DST4FlKEUJ32r7KVXjSRI475qeXQcwa03ZB2zfWDeqmFddn5LW07z5rQysAFxr+3ZJjwGe0OaF8mMuRC8H/rtrF6KSzrb9rPH29fdTiphJkn5GWQN4JqVH2VK2d60a1DSS9J/AibZvlPRe21+qHVNMbMyU7AWADYHrbb++XlSDJelzwPdsXyjpi7b3rB3TVI3Tzmo2bb853xQp+kf/tYqk3Sn9H19n+weVQgPSImKyjqBUzuytKTsImPAPOIZHF0aK5kVTAvxH/aN+zTTCtk8l/BblQvQgyoXoV4BdawY0DZaU9Azb5wJIejqlhQmUNVitJmlR2/fNbV8Mncf1Vag9WdJFVaOZfksC35J0H2UWReuTQEmb2L5wzL6X9frQdcAFfV8/ABxj++xawUyTC4C9JD0NOL92MAPSm93yWGALSosrgK2A02lxW6vGD+jrE9skgK+l3KQ4uDleTZLAyVnK9mHN1/t39Y2wGTXaF3gW5c7aWcB+WY/UKudI2qzXeLUjRuFC9K3AEZKWpBThuBN4q6QlKP1I2+43lEq1c9vXSpKW6/Wz7Bg1vVXVbC/Yv932c5b0cuB8239tdh1AaTT+Asod+y44TNIuti8HkLQT8F6gE0lgF9fdStoN+F/bf252/QTYBfgH8IdqgQ1Q7+a8pJ8A69i+qdl+PCVJaruFbd8BIOm/gI2AbWzfI2mZuqElCZysxZoKhb03wsX7t2135aL0WMpo56ua7Z0pvYVeUC2imKytgN0kXQ/cTVPVz/b6VaOamk5fiAI0Sft6zZuDbN/ed/h7lcKaMkmPo6xRXXzMa+jSlMJaXXGupEso7VlO6lD7hGUoo/Dq29d7vzMlYWqzT1HuzCNpYeAY4EZgPcpoROuLTlGqKn9f0s7Asym9SF9YN6TBmUNRLWj3e9+7bB8C0LzX/ZgyMvZF4Fy6cWOwZ9VeAti4mdI3sO3+2CxFWplys/NpTQK4duW4gKwJnBRJp01w2F0pRSzpwrEFRCRdYHvTWjHF5Egat22C7T/NdCyD0iS0DzH7hWiPu9AvUNKilJsvq9J3k872frViGoSmeuauwKbMPm3rLuCbbV/30dOsx30B8GZKifrjKOfXiV5sXSXpauBpwFKUi+xTbX+mOXap7Q1qxjcokp5CacJ9A7C97X9VDmlgmqJaAN9uPu8M3AMcBe1875P0W2ATYHnK7+1A299pjl1he92a8Q2SpK9S+lceQ0nmdwSusf2eqoFNkaRFgNcA/6a0tvoms3pw72L75/WiSxIY45B0AOVCrTfysAPl7sU+9aIaLElb8MgL7W9VC2gaSNoAeE6z+Svbl9aMJ+auKcBxB2XU5cHeftsHVgtqgCS9qvZC+JkiaSvgO8ASwKXAh23/pm5UMR5J+wKvoySBt1CmSZ4OvAF4g+1tqgU3ReOMkD2W8hpzH0BLR8geYaKiWrVimipJbwM+TVmXejllXfwvKVNCN7T9qgm+vXWaIjG9a5YzbZ9QM57pIGkxygyDP4yZ6VMnniSBMVbTGHgJykWoKJW27m4Ot74xsKRvUyq8XsKsC223vaFzP0l7AG9j1qLqVwCH2j6oXlQxN127uztWV0c6e5r11K+nJA83A9+glALfEDje9moVw4sJSFqZUlDkPsrv7QWU5P2Ntq+rGdtUzGlWSE8bR8jG00zDfrfts5rtLYCv2d6wbmRTI2nB5suFKNM/X0SZir2n7VurBRadkCQwRk4z9WedDq3XeQRJlwHPtH13s70E8Juu3PXtKkmHAgf1ijd0zQiMdP6eMh3tSNt/GXPsQ7Y/VyeyGGVNP9XLOn6DaRNKBfdesY3bgTd3qFZDpzWjgJ+jjFSLWWs5Wz3oMOxSGCYeJumptn8radxKfR16Mb0CeBxw09we2GKi7yKbWaO6MdyeDewq6TrKiESbixqMZ2XbL64dxDT6uO3ZCvhIerXt47uQAEr6tu03zG1fDBfbD0m6VNKT+ipNdkrT/mIDSUtTBjjuqB1TTMrngZfZvrp2IKMkSeB8kPRL28+f274Weh/wdmC8u/Kmr9dJyy0P1S1pMgAAIABJREFUXCXpPJp1EQC2t6sX0sAdSalUeAIlkXg5ZYpT63X8QvQltQOYZr+WtF5XRzqBD/PIKq4fAY6vEMt0eFr/RjNVbZM5PDaGy+OBK5v3vd7yjta/70l6GWWUszet9b3AqyT9CdijzVN5R8zNo5AASnossFhvu/ZNmSSBk9As6HwUsPyYUvVLAytVC2xAbL+9+bxV7Vim2b61A5hutr8g6XTKyBLAm2xfXDGkQershWjvQmbsG0Xb9RWnWAh4k6Rr6dBIp6SXAP8BPEHSV/oOLU1ZZ9Zqkj4CfJTS4uPO3m5KxbsutE+YI0mL2r5v7o8cep+sHcA0+QywOYCkl1LW5O5E6cd2CGUNXatJWtD2g3N/ZKtdIOk4ShXU/pvzXakcvR1lgGUlSvGpVYBeVeJqkgROzjsod5lWYvaeSXfSgaaWkjYDbrD9t2b7jZQiDn8C9u1CHzYA22dIWhHYrNl1nu1basY0jcSc2yq0yihciA7rG8UAvLR2ANPsr5SKyttR3ht67gL2rBLRANn+b0mfAw63/eba8UwXSUf0n5+kJYH/B7R9lg+2z6gdwzSx7Xuar18JfKOZGnqhpHdWjGuQrpH0fcpa46tqBzNNlqa09OjvXWlmFbdru09Rblb8wvZGTfXonSrHlMIw80PSe7pYZVHSRcALbP9D0paUpvHvoVS2W9v2DlUDHBBJrwH2p5QAF6Uk8V62v18zrkGS9Ang1cAPKOe4PaU64aerBjZFTYGDzl6ISrqUMu16tjeK3ih920labpzdd9m+f8aDmQaSFrLd+pG/ORmvh2yXSPoUsLzt/2xm+/wUOMz2kZVDmzJJmwMHAWsDi1DaDtzd9sIbTRG0LSgJxHXAq2xf0By7yvY6NeMbBElLUfrmvYlSrf0I4Fjbd074jTE0er22m/f4jZp1uufZfnrVuJIEzruuj5T1N8WVdDDwd9v7NtuXtL3Uck/zJNymN/onaQXKRXcnGgLDwxVQN7J9b7O9OHCR7bXrRjZ1Xb4QHdY3ikGRdD3wROD/KDcnHk0p0HQL8LbmDn7rSPqe7deM05OtE9Nde5r3hW/aPr92LNOlGfFchjLF/LNd6Wsp6QJKInE8sCnwRmBN2x+tGtgUSXozZYbIncAtvcJTkjYCDuhArYbZNDfoj6G8dn4f+JTta+pGNXXNcqu3UGa99K+Z68QNX0m/oNyM/29KXYpbgM1sb1EzrkwHnZyvU3oH9Z6In2XWSNmhlKbqbbZg353s51OKxPR06W9lgTHTP2+j3F3rkuspL6T3NtuLAn+sFs1gnSNps45eiN7eTEE7Ezha0i10YE1Zn58BJ9g+GUDSC4EXU4qpfA14RsXYpmKP5nPXp71uBbyjKbpxNx1Jcpvy9D3nAXs3ny3plV1Zl2T7mr71ZUdK+nXtmKbK9hGSTqa0Fri079DfKCNnrdese9+Wcj6rUpYMHE2ZxfS/wFOqBTc43wZ+S1nDuR+wM2UpRFe8nHI9tifl3JahnGdVGQmchK6PlEn6GKW4wa3Ak4CNbVvSk4GjbD+raoADIml/YH3K3TSA1wKX2/5gvagGQ9JBlJGIJ1HWPP682d4GOMv2jhXDGwhJV1He9Dp1IQoP93O8l3JOvTeKo23fVjWwAemNdI63r82voc1r5Iq2zx6z/znAX2134gaM5tB43C1vOC5poume7sJohKQzKTexD6ckSDcBu3ZpBkxXNYW0TqOsd/z1mGNfsb17ncgGR9LFzRKIy2yvL2lh4GTbXalKP5SSBE6CpCuADW0/IOm3wNttn9k75g40Ym3WDTweOMWzGo0/BVjS3ekT2Lvz+2zKxfaZtk+oHNJASNplouO2j5qpWKZLVy9ER4GkU4BfUtYbQ7kBsw1lNPB82+P2KB12kn4CfNT2ZWP2bwrsY/tldSIbPEkbUEYgAH5l+9KJHh/DoXndvJmyHnBPyg2mr3VhKmHXSVrS9j9rxzGdessempsV76TcqDjP9uqVQ5sSSXcx+xKBhw9RbjBVXZObJHASRmWkrOskfc72h+a2r62aqSNH2X597VimS1cvRJubE5+jTG0SQ/JGMSiSlgf2YdYNmLMopevvAJ7U1gvSiW4CSrrc9nozHdN0kLQH8DZmVex7BXBoVwqlSTqK0lvu9mZ7WeDAjowEbg2c01dJM1qiuRH/P5TZButKWh/Yru2F3vpJeiulkN16wDeBJYG9bX+9ZlxdlyRwkkZlpKzLJF00dsShNwWhVkyD1qyReJntf9eOZdC6fCEq6RrK761LayE6T9I1tp882WNt01RifGbfe98SwG+68trZm5I2t31tJOlblBL1twG/aj7Osv1/VQMbEEkHUFooXFk7lkGTdAawF/D13t9iV2afdZ2kpW3fOYfK2NQuKNmlYh8zwvY54+z7fY1YYnIk/SdlmsHqzcVMz1LA2eN/V2tdD5wt6UTKujmgNJGvFtHgvAV4Rt+F6OeA31DKn7fdzV1MACV9yfZ7Jf2YcabG2N6uQliDdL6kt9k+rH+npLcwe9/AthPQ37T6wWZfVywgadleYtRcuHXiOsn2GwEkrUQpYncwpR9pJ86PUlTkUEkLAUcCx9i+o3JMg/Io2+dJsz3VulQwrMu+SykYdiHlva//l2ig6nTXrjz5I+bFd4GTKCV6P9y3/67ad2OmwV+bjwUoSW6XdO5CtK864QWSjgN+BNzXO96B6oTfbj4fUDWK6fNe4ARJOzMr6duUsv7qFdWiGrwjgXMlnUB5zr0c+EbdkAbqQODXKo25ofRa/UzFeAZG0uspU+jXoyxp+SplNLATbB8OHC5pLUoVzcsknU3p83ha3eim7FZJa9DcQJO0A6WwTww52y9tPq9WO5bxZDpojIw5Dcf3dDAR7CRJ7wN2AfovRL9p+0tVA5uCUahOOCeSjrP92tpxDIKkrYDeFK0rbZ9aM57pIGljyppOKOtxL64Zz6BJWgfYmvLa8kvbV1UOaSAk3UppE3QIcJrt6+tGNHjNeviXUpLAJ1JazzwbuLvNlbElrU5pQ7YFpcfqdcDru/g77DJJTwBWoW8ArldcspYkgfOpqbS1pu1fqDTiXsj2XbXjijmTdB2zpqKNHTly26tQ9ZO0AvBBHtl4tRPllrt+ITpKJP3Z9pNqxxHzpnnuPQd4CDi7C2vhh33dzqBIehqwJeW1c03gd7bfUDeqwZD0BWA7SvXhb9g+r+/Y72yvVS24AWnW4C7Q1WtNSVtQ+iD2J0nfqhbQADXLVl4LXMWsmUyuvRQi00Hng6S3URqpLwesAaxMubv2/JpxxcSGdTh+mhwNHEe5K7obZeTs71UjGjxRLkRbPRW0X5erE0b7SfoEZYrkDyjPuyMlHd+BKoVj1+30iCFYtzMIkpamVDVfhXKhvQzl9bMrrgA+Pofqp0+f6WAGqSmGdiRwF3BYcyPmw7ZPqRvZ4Ej6NuV6+hL6kiSgE0kgsD2wlu375vrIGZSRwPkg6RLKi8q5fZWaOlMGvKskPdX2b5sX0Efowh3tHkkX2t6kv+qppDNsP7d2bFM1zoXo9kAXLkQ7W51wTs85yu/vJ7YfP5PxxPyRdDWwke17m+3FgYtsr103spibphjaWc3Hmbb/UjmkgZK0APA6YHXb+0l6EvC4/hHBtpJ0qe0NJL0IeBewN6USaiv7qo6neW1Zxx1NSiSdBLx62Po9ZiRw/txn+9+9Sk1NNapO/uF2zPsoI7gHjnPMlHUgXXF/8/kmSdtSisSsXDGeQdqJ2S9EPwtcBLQ+CaS71QnHe871/HbGooipup4yvfzeZntRyjqzTmjaKPyKMsW8U3+XfTcDl+hVVu6Ygykjm1sD+1FGzX4AbFYzqAHpzXb5D0ryd6nGlArtgCuAx9Hdgjf3AJdI+iWzF33bvV5I3bi4qOEMSR8FFpe0DaXtwI8rxxRzYfvtzeetascyAz4taRng/ZTWCUsDe9YNaWCup7sXov3VCQ28hg5UJxyR59wouA+4UtLPKX+f2wBnSfoK1L+gGYBvUtbLHdQU47iEMmr25apRDYCkZ1IquS4JPEnSBsA7bL+zbmQD8wzbG0u6GMD2/0lapHZQA3KhpFOA1YCPSFqKjkzl7WsbtBRwlaTzmD1Janv7oJ4Tm4+hkumg86GZdvAW4IWUOzQnA4d3dRi7a5oKYtvyyAXIXeih13mSfkS5uzvbhShwC7T/QrQp3rAVHatOGO0naZeJjts+aqZimS7N+8NmlOfgbsC/bD+1blRTJ+lcSn/AE7vYcLw5vy2A85tkcAXglA5MpRdlFs8KwLW2b5f0GOAJti+b+LuHn6QJl6jYPmOmYhlFGQmcPy8HvjW2MXC0xo8po0iX05G7aT2SDmKCqcltT5AaJzQfPadXimPgJL3A9i+AK/v27dKFi+vohBuAc+ZQfKP1mqlaSwC/oUwL3cz2LXWjGhzbN4yZRfjgnB7bQl+hvC88VtJnKAnvx+uGNHW2LelHtjfp23cbcFvFsAaml+RJ+pztD/UfaypqdiIJlLQmpUf1OsxesT3N4ltoO+BLks4EjgVOtv1A5Zhi3q3cWx/RQRf0ff1JYJ9agUyjLl+IfkLSqyjTeJcCDqdMjUkSGMNgV+AQSbfRrJ0DzuqtYe2Ay4BNKL0e7wBul/Qb2/+qG9ZA3NCU4HczTXJ34OrKMQ2M7aMlXUip0i5ge9tdOb9zJG1m+/zagUyjbYAPjdn3knH2tdWRlOuxL1JmGbyJIahsnumg80nSwpQ/0NdS1hD83PZb60YV86K5u/TLLpVXHk8XqkqOpynesDnlTminLkSbqT/vB97R7PqE7WMqhjRww9gwNyZH0kqUkZYPACvZ7tQNZUlLUi7SPkCpMLlo5ZCmTNLywJeBF1AuPk+htKNp9YjSnHo79nShx6Okq4C1KOvh76ZpXdKFm9mS/pNSV2MN4Jq+Q0sBv7a9c5XABqyvYvvDnQQk/cr2c2rG1akX7plk+/6m5KuBxSlTRJMEtsM5wAnN2s77mfWCunTdsAauk3d4bL8RZrsQPRhYiW68ni0LPINS6GZlYBVJ6sp64zk1zAWSBLaApNdTGsWvB9wKfJVyE6YTJL2bcn6bAH8CjqAj52f7VqATF9Rj9Ho79o+q9LY70eORMuDQVd8FTqJMlfxw3/67upDA97m3ueb8Q/M6cyPw2MoxZSRwfkh6MbAjZUj3dEpT7lMyJbQdJF1L6S13eVcurscj6aIu9RHqGedC9CxKSfffVA1sACT9Hvis7SOaHmyfAza1vUXl0AZC0u+A9YetYW7MG0m3Um5QHAKcZvv6uhENlqS9KDckLuzK+3nTV3VObPtTMxZMzDdJzwbWtH1kU/RmSdvX1Y5rUJq+jo9g+88zHct0kLQZZfr1o4FPAcsAn7d9TtW4OnwNPG0kHUtZC3hSLmbaR9LJwEtsd6ooDICku5g1AvgoSm8a6NBoZ5cvRCU9aeybnqQtuzJdclgb5sa8a6rXbklZBrEm8Dvbb6gb1WBIWgP4i+37JD0PWJ9SBO72upHNP0nvH2f3EpQK54+xveQMhzRtujrVXNI+wKbAWraf0syCOd72syqHNjCSLmfWCO5ilHYYv7P9tKqBdVwXpk/NONs7SloR2KaptHVelyqIjYCbgNObC9L+fjStbxFhe6naMUw328v3XYh+pqm61eoLUUmvt/0d23+W9CzbZ/cdXp/uTJccyoa5MW8kLQ08iXKhvSrlbnaXbqb9ANhU0pMpPfVOpExX+4+qUU2B7QN7Xzf95fagrHc8ltKXtBM6PtX8FcBGwEUAtv/a/C47o7dOrkfSxsxaG99akibsDVi7D2KSwPkg6dXAAZSpoKI0lt3L9verBhbz6rrmY5HmI1qkoxei7wO+03x9ENA/jffNlLVXXTCUDXNjnp3V9/FV23+pHM+gPWT7AUmvAL5k+6Be8/E2a4qnvI+yJvAoYOMuFNIaY3vKSFkXZ2f9u2kVYQBJS9QOaLrZvqiZQtl2z6RUND8GOJchqAjaL0ng/Pk4ff2DmvnZvwCSBLaA7U/WjiGmpIsXoprD1+Ntt1b6HbZbrxqhpCVs3107nmlwv6SdgF2AlzX7Fq4Yz5RJ2h94JXAosF6Hp2JfS/lddTEJ/J6krwOPlvQ2ylTewyvHNFCS3te3uQDlRujfK4UzSI+jtL/YCXgd8FPgGNtXTvhdMyRJ4PxZYMz0z9sof7TRAk3S/kHgaczetHPrakHFPOvohajn8PV4260j6Xu2X9O37mM2XSh1PgokPZMyTXJJ4EmSNgDeYfuddSMbmDcBuwGfsX2dpNWYNULfVu+nJEYfBz7W1yy+M+vEG52dam77AEnbAHcCTwE+bvsXlcMatP7prQ9QkqUfVIplYGw/CPwM+JmkRSnJ4OmS9rN9UN3oUhhmvjR31tanDO9CmYd+me2uNLXsNEmnUCq6foDyhr8L8Pf8/tqh/0LUdicuRCXdQ+mRJGbvlyRgddutnv4j6fG2b5K0ynjHbf9ppmOKyZN0LqUty4m9HqSSrrC9bt3IYtRJ2mW8/W2efTCm0NvYGSH3Ugqkfcz2L2c0sGnUrHV0l0asm+RvW0oCuCplScQRtm+sGRckCZxvkl5JqY4m4EzbJ1QOKeZRX9POy/pGlc6w/dzascXcdfFCdE7JUU8XkiRJCwIn235B7Vhi/kg61/YzJF3c99y71PYGtWMbBEnPAvZlVoXJ3mhZF3rNRYc0r6frAke3+b2vR9K6wLeB5ZpdtwK72L6iXlRTJ+koyu/pJODYYTufTAedpDEXMj+sHU/Ml/ubzzdJ2hb4K6Uxd7SE7Rv6pjXBrGpwrdSFJG9ubD8o6R5Jy9i+o3Y8MV9ukLQFYEmLALtTel91xTeAPSkNyFv9mjIqRnWqeTPN8FJJ1acUDsihwPtsnwbQtGg5FGh7j9w3AHdTpvHuPmzTsZMETlIuZDrh05KWoayVOAhYmvLGH+3Q9QvRLrsXuFzSzylvjEA31u2MiN2ALwNPAP4CnAK8q2pEg3WH7ZNqBxGTskfz+aVVo6jE9tdrxzAgS/QSQADbp3ehCqrtoa4Xkumg80HS94DNgVzIRMwwSctTLkRfQLmbdgqwh+3bqgYWc9XFdTvRHZI+CyxImeXTX1zkompBRYwASSdQ+iB+u9n1emBT29vXi6r7kgTOh1zItFMzbWKOf/BJ4qM2SXvY/vLc9kXMJEmfmOCwbX9qxoKZRpJOG2e3Uzl6eEl6C7Cc7f2b7RsplSYFfND2/9SML+aNpGWBT9JXawPYt4P9LIdKksAYGWOS908C+/QfTxI/3EbhQlTSRbY3HrPv4SIcbSdpTeC/gXWYvT1LCm8MMUnvH2f3EpR+ZY+xveQMhxQBgKTzgRf3ZoL0Xi8lLQacYnvLuhFGDK+sCZwESS8HVrZ9cLN9LrBCc/iDttMsfoj1J3mS3pukr3XG6wn48IUo0NoksGlQ/TpgNUkn9h1aitKHtCuOpNx8+SKwFaUv29jS5zFkbB/Y+7op4b4H5Xd3LHDgnL6vbeZ0o8n2fjMdS8yzBcYsBTgewPa9khavFFNMkqRNgY9SWig8nJt0tbDPsEgSODkfBHbs214U2IxyIXokkCSwPTIE3jIdvxD9NXATsDyzn8tdwGVVIpoei9v+pSQ1FVH3lfQrxozKx/CRtBzwPmBn4Chg4w5O1eq/0bQYpdhIik4Nt2X6N2z/F4CkBSg3B6Mdjgb2Ai4HHqocy8hIEjg5i9i+oW/7rOYO1G1dqGIUMey6eiHaJER/Ap5ZO5Zpdm9zcfYHSe8GbgQeWzmmmAtJ+wOvpJRsX69LjZz79d9oApB0AKWxcwyvUyR92vbHx+zfj1I0LNrh77bzXJthWRM4CZKusf3kORz7o+01ZjqmmHeS7mLWCOCjgHt6hxiCfi0xsTEXogd38UJU0uaUtiVrA4tQKhXe3ZW/TUmbUUZWHk2ZvrsM8Hnb51QNLCYk6SFKtcwHmH0WRadfO5tiFefZXrN2LDG+5gb84ZRZWZc2uzcALgDe2sX3iS6S9HxgJ+CXzF6ZN/24p1GSwEmQdDRwuu3Dxux/B/A82zvViSyi+0bhQlTSBZQp58cDmwJvBJ5s+2NVA4sYAWMaji9IWfO/n+2v1osq5oWk1YGnNZtX2f5jzXhiciR9B3gqcCWzpoPa9pvrRdV9SQInQdJjgR9RLkR7fYM2oawN3N72zbVii4j2k3SB7U0lXdZbEC/p17a3qB3bVIwpdvMItrebqVgi5kTSKn2bDwA3236gVjwx7yS9AjjV9h3N9qMpN+d/VDeymBeSLre9Xu04Rk2SwPkgaWtm3XG60vapNeOJiG6QdCbwAsr0pr9RisXsanuDqoFNkaS/AzcAxwDnMqYiqO0zasQV0dOsVb3M9rq1Y4nJk3SJ7Q3H7OtMe52uk3QY8EXbV9WOZZQkCYyIGBLNSMTNlPWAe1LWzH3N9jVVA5siSQsC21DWfKwP/BQ4xvaVVQOL6NMs+fiI7T/XjiUmp3/2RN++jC61hKSrgTWA6yiz7XrLPNIiYholCYyIiBkjaVFKMrg/Zb3VQZVDigBA0qmUAiPn0dcuItOVh5+kI4DbgYMp6zrfAyxre9eaccW8GTMV+2FN5eyYJkkCIyJi2jXJ37aUBHBVSun9I2zfWDOuiB5Jzx1vf6YrD7+mSujelOn0orSH+LTtuyf8xhgazYyRFZm9WXxG5adRksCIiJhWko4C1gVOAo61fUXlkCImJGl54DbnIili2kl6D7APZTlEf3XQTAedRkkCIyKGQHMX9LO296ody6A17T16d+Q72d4j2qvpz/lZ4B+U/pXfBpYHFgDeaPtnFcOLCaTycDdIugZ4hu3bascyShaa+0MiImK62X5Q0iaS1LXRB9sL1I4hYgJfBT5KKcR0KvAS2+dIeiqlom2SwOH1TCaoPBytcQNwR+0gRk1GAiMihoSkA4E1Kc3i+wtT/LBaUBEd199eQNLVttfuO5Y2A0MslYe7QdI3gLUov7/7evttf6FaUCMgI4EREcNjOeA2YOu+fQaSBEZMn4f6vv7XmGO5Uz7EbD9IGan9WV/l4dMlpfJwu/y5+Vik+YgZkJHAiIiIGFmSHqSMvAtYHLindwhYzPbCtWKLuUvl4e6QtBRlnfg/a8cyCpIERkQMAUlbAe8Gntrsuhr4qu3TqwUVETHEUnm4GyStSynItFyz61ZKUaZM651GSQIjIiqTtC2lOMV+wEWUEYiNgY8D77b9vxXDi4gYSqk83A2Sfg18zPZpzfbzgP+yvUXVwDouSWBERGWSTgf2sH3pmP3rAwfZHreJdURERNtJutT2BnPbF4OVwjAREfU9bmwCCGD7Mkkr1ggoIiJihlwraW/KlFCA1wPXVYxnJKR3U0REfXfP57GIiIi2ezOwAqUS9gnN12+qGtEIyHTQiIjKJN0OnDneIeDZtped4ZAiIiKiw5IERkRUJmnCNX+2z5ipWCIiImaCpBMnOm57u5mKZRQlCYyIiIiIiBkl6e/ADcAxwLmU2S8Pyw3Q6ZUkMCIiIiIiZpSkBYFtgJ2A9YGfAsekP+DMSGGYiIiIiIiYUbYftP0z27sAmwPXAKdLek/l0EZCWkRERAwhSQsAS9q+s3YsERER00HSosC2lNHAVYGvUKqExjTLdNCIiCEh6bvAbsCDwIXAMsAXbO9fNbCIiIgBk3QUsC5wEnCs7SsqhzRSkgRGRAwJSZfY3lDSzsAmwIeAC22vXzm0iIiIgZL0ELN64fYnJAJse+mZj2p0ZDpoRMTwWFjSwsD2wFdt3y8pd+oiIqJzbKc2SUX5z4+IGB5fB64HlgDOlLQKkDWBERERMVCZDhoRMcQkLWT7gdpxRERERHdkOmhExJCQ9Ik5HNpvRgOJiIiITksSGBExPO7u+3ox4KXA1ZViiYiIiI7KdNCIiCHV9E860faLascSERER3ZHCMBERw+tRwOq1g4iIiIhuyXTQiIghIelyZvVKWhBYgawHjIiIiAHLdNCIiCHRtIToeQC4OZVBIyIiYtCSBEZEDBFJGwPPpowInmX74sohRURERMdkTWBExJBoWkQcBTwGWB74pqSP140qIiIiuiYjgRERQ0LS1cBGtu9tthcHLrK9dt3IIiIioksyEhgRMTyup/QH7FkU+GOdUCIiIqKrUh00IqIySQdR1gDeB1wp6efN9jbAWTVji4iIiO7JdNCIiMok7TLRcdtHzVQsERER0X1JAiMiIiIiIkZI1gRGRAwJSS+VdLGkf0i6U9Jdku6sHVdERER0S0YCIyKGhKRrgFcClzsvzhERETFNMhIYETE8bgCuSAIYERER0ykjgRERQ0LSZsCngDMolUIBsP2FakFFRERE56RFRETE8PgM8E9Kr8BFKscSERERHZUkMCJieCxn+4W1g4iIiIhuy5rAiIjh8QtJSQIjIiJiWmVNYETEkJB0F7AEZT3g/YAA2166amARERHRKUkCIyIiIiIiRkjWBEZEDAlJW4633/aZMx1LREREdFdGAiMihoSkH/dtLgY8HbjQ9taVQoqIiIgOykhgRMSQsP2y/m1JTwQ+XymciIiI6KhUB42IGF5/AdatHURERER0S0YCIyKGhKSDgN4c/QWADYFL60UUERERXZQ1gRERQ0LSLn2bDwDX2z67VjwRERHRTUkCIyIiIiIiRkimg0ZEVCbpcmZNA53tEKVZ/PozHFJERER0WEYCIyIqk7TKRMdt/2mmYomIiIjuSxIYETFEJK0IbNZsnmf7lprxRERERPekRURExJCQ9BrgPODVwGuAcyXtUDeqiIiI6JqMBEZEDAlJlwLb9Eb/JK0A/ML2BnUji4iIiC7JSGBExPBYYMz0z9vI63REREQMWKqDRkQMj59JOhk4ptl+LXBSxXgiIiKigzIdNCJiiEh6JfBsSnuIM22fUDmkiIiI6JgkgRERlUl6MrCi7bPH7N8SuNH2H+tEFhEREV2UtSYREfV8A0d8AAAFCElEQVR9CbhrnP33NMciIiIiBiZJYEREfavavmzsTtsXAKvOfDgRERHRZUkCIyLqW2yCY4vPWBQRERExEpIERkTUd76kt43dKektwIUV4omIiIgOS2GYiIjKJK0InAD8m1lJ36bAIsArbP+tVmwRERHRPUkCIyKGhKStgHWbzSttn1oznoiIiOimJIEREREREREjJGsCIyIiIiIiRkiSwIiIiIiIiBGSJDAiIoaSpI9JulLSZZIukfSMZv97JT1qPn7erpJWmsTjnydpi8k+TtJukt442fgiIiJmykK1A4iIiBhL0jOBlwIb275P0vKUaqkA7wW+A9wziZ+3ILArcAXw13n8tucB/wR+PZnH2T5kXuOKiIioISOBERExjB4P3Gr7PgDbt9r+q6TdgZWA0ySdBiDpfyRd0IwafrL3AyRdL+kTks4CdqK03Ti6GVVcvP8fk7S7pKuaUcdjJa0K7Abs2Tz+OZJeJulcSRdL+oWkFefwuH0lfaD5uRtKOqf5uSdIWrbZf7qkz0k6T9LvJT1n7H9AM8J4uqTvS/qtpKMlqTn2CUnnS7pC0qF9+0+X9EVJZ0q6WtJmkn4o6Q+SPt33s1/f/NuXSPq6pAWbj282P/NySXsO4hcZERHDJ0lgREQMo1OAJzYJ0tckPRfA9lcoI3lb2d6qeezHbG8KrA88V9L6fT/nXtvPtv0d4AJgZ9sb2v7XmH/vw8BGttcHdrN9PXAI8MXm8b8CzgI2t70RcCzwwTk8rt+3gA81P/dyYJ++YwvZfjplZHMfxrdRc3wdYHXgWc3+r9rezPa6wOKUUdOef9vesonr/wHvorQe2VXSYyStDbwWeJbtDYEHgZ2BDYEn2F7X9nrAkXOIKSIiWi5JYEREDB3b/wQ2Ad4O/B04TtKuc3j4ayRdBFwMPI2SMPUcN4//5GWUUcLXAw/M4TErAydLuhzYq/m35kjSMsCjbZ/R7DoK2LLvIT9sPl8IrDqHH3Oe7b/Yfgi4pO9xWzWjkpcDW4+J5cTm8+WUfpM3NSOq1wJPBJ5P+b89X9IlzfbqzfHVJR0k6cXAnROdX0REtFeSwIiIGEq2H7R9uu19gHcDrxr7GEmrAR8Ant+Mtv0UWKzvIXfP4z+3LXAwJTm6UNJ4a+YPoozArQe8Y8y/Mz/uaz4/yJzX6N/X9/WDwEKSFgO+BuzQxHLYmFh63/PQmO9/qPl3BBzVjFxuaHst2/va/j9gA+B0yujh4fN9ZhERMdSSBEZExNCRtJakNft2bQj8qfn6LmCp5uulKYneHZJWBF4ywY/t/77+f2sB4Im2TwM+CDwaWHKcxy8D3Nh8vcvcfq7tO4D/61vv9wbgjLGPmw+9hO9WSUsCO0zy+38J7CDpsQCSlpO0SlN8ZwHbPwD2BjYeQKwRETGEUh00IiKG0ZLAQZIeTZmeeQ1laijAocBJkm6yvZWki4ErKdMZz57gZ34TOETSv4Bn9q0LXBD4TjN9U5T1fbdL+jHwfUkvB94D7AscL+lG4Bxgteb7xz6u3y7Nv/moJr43zcf/xWya2A6jTPe8Hjh/kt9/laSPA6c0CfD9lJG/fwFHNvsAPjLVWCMiYjjJdu0YIiIiIiIiYoZkOmhERERERMQISRIYERERERExQpIERkREREREjJAkgRERERERESMkSWBERERERMQISRIYERERERExQpIERkREREREjJAkgRERERERESPk/wPjfjn/jSmBWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_start_station.plot.bar(figsize = (15,15))\n",
    "plt.xticks(rotation =90)\n",
    "plt.xlabel('Start station names')\n",
    "plt.ylabel('Total number of times bikes rented')\n",
    "plt.title('Top Ten Start Station')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of top 10 start stations.\n",
    "Grove St PATH, Sip ave, and Hamilton Park places are the most popular stations where people get their bikes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 End Stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top Ten End Station')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCAiEAAAYCCzRWBVHV1V76yqy6vqsqr6t9P6farq4qq6Yvp972m9quplVbW9qj5aVccvvNfp0/5XVNXpc80MAACw2c15JvDWJL/S3Q9KclKSM6vqwUm2JXl7dx+b5O3T8yR5bJJjp58zkrwyWYnGJM9P8tAkJyZ5/q5wBAAA4I6ZLQK7+5ru/tD0+MtJLk9yZJLHJzlv2u28JE+YHj8+yat7xXuT3Kuq7pfkMUku7u7ru/sLSS5OcvJccwMAAGxmG/KZwKramuQHk7wvyXd09zXJSigmue+025FJrlp42Y5pbbX13Y9xRlVdUlWX7Ny5c73/BAAAgE1h9gisqnskeV2S53T3l9badQ9rvcb6ty50v6q7T+juE7Zs2bJvwwIAAGxys0ZgVd0pKwH4Z939+mn52ukyz0y/r5vWdyQ5euHlRyW5eo11AAAA7qA57w5aSc5Jcnl3v2Rh0wVJdt3h8/Qkb1pYf8p0l9CTktwwXS76tiSPrqp7TzeEefS0BgAAwB102Izv/bAkP5/kY1V16bT275OcneT8qnpakr9Pcuq07aIkj0uyPclXkzw1Sbr7+qr6zSQfmPZ7YXdfP+PcAAAAm9ZsEdjd786eP8+XJI/aw/6d5MxV3uvcJOeu33QAAABjWjUCq+rwtV64l5u8AAAAcABa60zgZfnm3Tnvn+TL0+N7JPlckmNmnw4AAIB1teqNYbr76O4+Jsmbkzyxu+/V3ffMype7//lGDQgAAMD6WebuoCd29wW7nnT3m5P82HwjAQAAMJdlIvD6qtpWVUdV1ZFV9bwkX5h7MAAAANbfMhH45Kx8Wft/nX6OTvKkOYcCAABgHnv9ioju/nySM6vqrt39tQ2YCQAAgJns9UxgVT20qj6W5FPT8x+oqj+YfTIAAADW3TKXg/5fSX4yyT8mSXd/JG4MAwAAcFBaJgIP6e7P7rZ22xzDAAAAMK+9fiYwyVVVdWKSrqpDk/xSpktDAQAAOLgscybwF5OcleSYJNcmOSnJM+ccCgAAgHkscybwu7v7tMWFqjop02cEAQAAOHgscybwFXtYe/l6DwIAAMD8Vj0TOH0O8IeTbKmqX17YdHiSO809GAAAAOtvrctB757kiGmfLQvrX05y6pxDAQAAMI9VI7C735nknVX1x939mQ2cCQAAgJksc2OYQ6rqFUm2Lu7f3Y+eaygAAADmsUwE/kWSc5L85/iSeAAAgIPaMhH49e7+g9knAQAAYHbLfEXEm6rqjKraUlWH7/qZfTIAAADW3TJnAv/N9PvXF9Y6yTHrPw4AAABz2msEdvfRGzEIAAAA89vr5aBVdbeq2lZVr5yef29VPXb+0QAAAFhvy3wm8Nxpv0dMz69O8uLZJgIAAGA2y0Tgsd394iS3JEl3fzVJzToVAAAAs1gmAm+uqrtm5WYwqaoHJLl51qkAAACYxTJ3B31hkrcmOaqqzkvyo0meNutUAAAAzGLNCKyqSvKRJKcm+ZGsXAb63O6+bgNmAwAAYJ2tGYHd3VX1lu7+oSRv2qCZAAAAmMkynwl8f1UdP/skAAAAzG6ZzwQ+PMnTq+rTSb6SlUtCu7uFIQAAwEFmmQh8wuxTAAAAsCH2GoHd/emNGAQAAID5LfOZQAAAADYJEQgAADCQvUZgVd1t+r7AVNX3VNXjqmqZzxICAABwgFnmTOB/S3K3qrpfkr9O8otJzp11KgAAAGaxTAQe0t1fTfKvkvyn7v6XSR4y71gAAADMYakIrKr/OcmTk7xlWjt0vpEAAACYyzIReFaSFyS5sLs/XlXfnZVLRAEAADjILPM9ge9I8o6qusv0/DNJnjX3YAAAAKy/Ze4OemJVfSzJFdPzH6iqP5h9MgAAANbdMpeDvizJTyb5xyTp7o8k+bE5hwIAAGAey94d9LO7rd02xzAAAADMa5kvfb+qqk5M0lV1aJJfSvKpeccCAABgDsucCfzFrNwh9Jgk1yY5aVoDAADgILPMmcCbu/u0xYWq+q4kn59nJAAAAOayzJnAN1fVPXY9qap/nuTC+UYCAABgLstE4H9M8paq+raqOi7J65M8Zd6xAAAAmMMyXxZ/QVXdKclfJrlnkn/V3X87+2QAAACsu1UjsKpemqR3PU1yryTbk5xRVenuszZgPgAAANbRWmcCP76X5wAAABxkVo3A7j5nIwcBAABgfmtdDvqa7n5SVX0437ws9Bu6+/hZJwMAAGDdrXU56HOn3z+9EYMAAAAwv1W/IqK7d0y/P53kS0n+eZIHJvnStAYAAMBBZq/fE1hVT03yoSRPTvJzSS6pqtPnHgwAAID1t9fvCUyyLcnx3b0zSarqiCT/Pcl5cw4GAADA+tvrmcAkn0vyxYXnNyTZMc84AAAAzGmtu4P+8vTw75P8j6p6Y1buEvqEJB/YgNkAAABYZ2tdDrpl+n3V9HOX6flbZ50IAACA2az1ZfG/vpGDAAAAML9lPhMIAADAJiECAQAABiICAQAABrLMl8X/h6o6vKoOq6q3VdW1VfXkjRgOAACA9bXMmcDHdveXkvxkkuuSfF+S5806FQAAALNYJgJ33UH0cUle092fz8r3BQIAAHCQWet7Anf5r1X18SS3JTmzqo5IctO8YwEAADCHvZ4J7O7nJvnxJD/U3bck+VqSn5p7MAAAANbfMjeGuVuSX0jyB9PSdyZ5yJxDAQAAMI9lPhN47rTfI6bnVyd58WwTAQAAMJtlIvDY7n5xkluSpLu/mqRmnQoAAIBZLBOBN1fVXTPdEbSqHpDk5lmnAgAAYBbL3B30hUnemuSoqjovyY8medqsUwEAADCLvUZgd7+1qj6Y5Eeychnoc7v7utknAwAAYN0tczlokmzJymcCv57kpKr6X+cbCQAAgLns9UxgVf1RkhOSfCIrEZisfD7wghnnAgAAYAbLfCbw4Uke3N099zAAAADMa5nLQd+X5IFzDwIAAMD8ljkTeE6S91XV55LclJWbw3R3Hz/rZAAAAKy7ZSLw3CS/kORj+eZnAgEAADgILROBV3X362efBAAAgNktE4GfqKpXJ3lzVi4HTZJ0t7uDAgAAHGSWicB7Tr8XvxvQV0QAAAAchPYagd398xsxCAAAAPNbNQKr6le6+/eq6qVZOfP3Lbr7rFknAwAAYN2tdSbw09Pvj2/EIAAAAMxv1Qjs7jdOD7+w+91Bq+qnZp0KAACAWRyyxD7/xx7Wfm29BwEAAGB+a30m8DFJTk5yZFW9ZGHT4fGl8QAAAAeltT4TeF1WPg/4tSSXLax/Ocm2OYcCAABgHmt9JvDDST5cVX/W3V/bwJkAAACYyV4/EygAAQAANo9lbgwDAADAJrFqBFbVn0y/n71h0wAAADCrtc4EnlhVRyZ5elV9e1UdvvizUQMCAACwfta6O+j/k+RdSY7Jyt1Ba2FbT+sAAAAcRFY9E9jdL+nuY5O8uruP6e6jF34EIAAAwEForTOBSZLufnpVfX+Sh09Lf9Pdn5h3LAAAAOaw17uDVtWZSc7PyuWfxyT5L1X1rLkHAwAAYP3t9UxgkmckObG7b0ySqnpxkvckecWcgwEAALD+lvmewEpyy8LzW/KtN4kBAADgILHMmcA/TfLeqnrd9PyJSc6bbyQAAADmssyNYX6nqt6Z5BFZOQP4zO7+wOyTAQAAsO6WOROYKfqEHwAAwEFumc8EAgAAsEmIQAAAgIGsGYFVdWhVvW2jhgEAAGBea0Zgd9+W5OaqOnyD5gEAAGBGy9wY5sYkH6mqv0zylV2L3X3WbFMBAAAwi2Ui8K+mHwAAAA5yy3xP4DlVdeckx3T39g2YCQAAgJns9e6gVXVKko8luXh6flxVvWHuwQAAAFh/y3xFxAuTPDTJF5Okuy9N8r1zDgUAAMA8lonAW7r7i7ut9d5eVFXnVtV1VfXxhbXfqKrPVdWl08/jFrb9alVtr6pPVtVjFtZPnta2V9W2Zf4oAAAA9myZCLy8qn4mySFV9YCq+v0k713idX+S5OQ9rL+0u4+bfi5Kkqp6cJLTknzf9JpXTN9ReGiSlyd5bJIHJ3nStC8AAAD7YJkIfHaSH0ry9SRvSHJTkufs7UXd/TdJrl9yjscneW1339Tdf5dke5ITp5/t3f2Z7r45yWunfQEAANgHe43A7v5Kdz8vycOS/HB3P6+7v/pPOOazq+qj0+Wi957Wjkxy1cI+O6a11dZvp6rOqKpLquqSnTt3/hPGAwAA2LyWuTvo8VX14SSfSnJFVX2wqo7fx+O9Msn3JDkuyTVJfm/XYfawb6+xfvvF7ld19wndfcKWLVv2cTwAAIDNbZkvi//jJM/p7ncmSVU9clr7gTt6sO6+dtfjqvqjJG+Znu5IcvTCrkcluXp6vNo6AAAAd9Aynwn8yq4ATJLufleSG/flYFV1v4WnT0yy686hFyQ5raruUlUPSHJskvcn+UCSY6cb0tw5KzePuWBfjg0AAMAaZwKr6iHTw/dV1cuTvCYrl2L+6yTvXO11C69/TZJHJjmiqnYkeX6SR1bVcdP7XJnkGUnS3ZdV1flJPpHk1iRndvdt0/s8O8nbkhya5NzuvuyO/5kAAAAka18O+vLdnj9k4fFevyewu5+0h+Vz1tj/RUletIf1i5JctLfjAQAAsHerRmB3P2IjBwEAAGB+e70xTFUdnuTnkmxd3L+7z5pvLAAAAOawzN1BL0ryoSQfy8oXxgMAAHCQWiYCv627f3n2SQAAAJjdMl8R8f9W1VOraktVHb7rZ/bJAAAAWHfLnAm8McnvJ/nNfPOuoJ3kmLmGAgAAYB7LROBzkxzb3dfNPQwAAADzWuZy0E8k+dLcgwAAADC/Zc4E3pzkw1X1jiQ37Vr0FREAAAAHn2W/IuKiuQcBAABgfnuNwO4+ZyMGAQAAYH57jcCquiLfvCvoN3T3A2eZCAAAgNkscznowxce3zXJqUnuOc84AAAAzGmZy0Gv3W3pd6vq3TPNAwAAwIyWuRz0IQtPD0lyQpwJBAAAOCgtcznoyxce35rkyiT/epZpAAAAmNUyl4M+YiMGAQAAYH7LXA565yRPSLJ1cf/ufvF8YwEAADCHZS4HfUOSryX5YJLb5h0HAACAOS0Tgd/V3d8/+yQAAADM7pAl9nlvVT149kkAAACY3TJnAh+a5MNVtT3JTUkqSXf38bNOBgAAwLpbJgKfMPsUAAAAbIhlviLi0xsxCAAAAPNb5jOBAAAAbBIiEAAAYCAiEAAAYCCrfom+nEcAACAASURBVCawqr6QpPe0KSt3B73PbFMBAAAwi7VuDHPEhk0BAADAhlg1Arv7tsXnVXWfJHddWLp6rqEAAACYx14/E1hVp1TVp5LsSPK+6fc75h4MAACA9bfMjWFelORhST7Z3UcneUySd805FAAAAPNYJgJv7e6dSQ6pqurui5McP/NcAAAAzGCtG8PsckNV3T3Ju5O8uqquS/L1eccCAABgDstE4BOSfC3Jc5I8Jck9k/zknEMxj63bLtzQ41159ikbejwAAGDvlrkc9Fe7+7buvqW7z+nulyQ5a+7BAAAAWH/LRODJe1hzigcAAOAgtOrloFX1jCTPTPLAqvrQwqZvT3LJ3IMBAACw/tb6TOD5Sd6e5D8k2baw/uXuvm7WqQAAAJjFqhHY3V9I8oUkp1bV9yd5+LTpvyURgQAAAAehvX4msKrOzMpZwWOmn/Or6llzDwYAAMD6W+YrIp6R5MTuvjFJqurFSd6T5BVzDgYAAMD6W+buoJXkloXnt0xrAAAAHGTWujvoYd19a5I/TfLeqnrdtOmJSc7biOEAAABYX2tdDvr+JMd39+9U1TuTPCIrZwCf2d0f2JDpAAAAWFdrReA3Lvmcok/4AQAAHOTWisAtVXXWahu7+yUzzAMAAMCM1orAQ5PcI24CAwAAsGmsFYHXdPcLN2wSAAAAZrfWV0Q4AwgAALDJrBWBj9qwKQAAANgQq0Zgd1+/kYMAAAAwv7XOBAIAALDJiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBHLa/B4D1snXbhRt6vCvPPmVDjwcAAOvBmUAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICBiEAAAICB+LJ4OEhs3Xbhhh7vyrNP2dDjAQCwMZwJBAAAGIgIBAAAGIgIBAAAGIgIBAAAGIgIBAAAGIgIBAAAGIgIBAAAGIgIBAAAGIgIBAAAGMhsEVhV51bVdVX18YW1+1TVxVV1xfT73tN6VdXLqmp7VX20qo5feM3p0/5XVNXpc80LAAAwgjnPBP5JkpN3W9uW5O3dfWySt0/Pk+SxSY6dfs5I8spkJRqTPD/JQ5OcmOT5u8IRAACAO+6wud64u/+mqrbutvz4JI+cHp+X5F1Jnjetv7q7O8l7q+peVXW/ad+Lu/v6JKmqi7MSlq+Za25g/9i67cINPd6VZ5+yoccDADhQbPRnAr+ju69Jkun3faf1I5NctbDfjmlttfXbqaozquqSqrpk586d6z44AADAZnCg3Bim9rDWa6zffrH7Vd19QnefsGXLlnUdDgAAYLPY6Ai8drrMM9Pv66b1HUmOXtjvqCRXr7EOAADAPtjoCLwgya47fJ6e5E0L60+Z7hJ6UpIbpstF35bk0VV17+mGMI+e1gAAANgHs90Ypqpek5UbuxxRVTuycpfPs5OcX1VPS/L3SU6ddr8oyeOSbE/y1SRPTZLuvr6qfjPJB6b9XrjrJjEAAADccXPeHfRJq2x61B727SRnrvI+5yY5dx1HAwAAGNaBcmMYAAAANoAIBAAAGIgIBAAAGMhsnwkEYMXWbRdu6PGuPPuUDT0eAHBwcSYQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgICIQAABgIIft7wEAOLht3Xbhhh7vyrNP2dDjAcBm40wgAADAQJwJBIA1ONMJwGbjTCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBARCAAAMBADtvfAwAA+8/WbRdu6PGuPPuUDT0eALfnTCAAAMBARCAAAMBAXA4KAGxaLncFuD1nAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAYiAgEAAAZy2P4eAACAO27rtgs39HhXnn3Khh4PmI8zgQAAAANxJhAAgAOOM50wH2cCAQAABiICAQAABiICAQAABiICAQAABiICAQAABiICAQAABuIrIgAAYIP5Cgz2J2cCAQAABuJMIAAAsK6c6TywORMIAAAwkP0SgVV1ZVV9rKourapLprX7VNXFVXXF9Pve03pV1cuqantVfbSqjt8fMwMAAGwG+/NM4I9193HdfcL0fFuSt3f3sUnePj1PkscmOXb6OSPJKzd8UgAAgE3iQLoc9PFJzpsen5fkCQvrr+4V701yr6q63/4YEAAA4GC3v24M00n+sqo6yf/d3a9K8h3dfU2SdPc1VXXfad8jk1y18Nod09o1i29YVWdk5UxhjjnmmJnHBwAARnWw3/hmf0Xgw7r76in0Lq6qv11j39rDWt9uYSUkX5UkJ5xwwu22AwAAsJ8uB+3uq6ff1yV5Q5ITk1y76zLP6fd10+47khy98PKjkly9cdMCAABsHhsegVV196r69l2Pkzw6yceTXJDk9Gm305O8aXp8QZKnTHcJPSnJDbsuGwXg/2/vzuNtref+j7/e5ySlSSpRadAgaSAVDaIoEhFNxxQq/G6UkPGm23RnyhT3TaWESCIqGoTmeT6FbqkIkUocpfn9++N7rXPW2e2zz9nnrL2/+7rW+/l47MfZ61p7n/NZtfe6rs/1/Xw/n4iIiIjxqVEOujJwoqTev/8d26dJuhQ4XtI+wB+A3Zuv/ynwYuAG4B7gDZMfckRERERERDdMehJo+0Zgk1GO3wE8f5TjBt46CaFFRERERER03lQaERERERERERETLElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyRJIERERERERFDJElgRERERETEEEkSGBERERERMUSSBEZERERERAyR1iSBkl4k6XpJN0h6X+14IiIiIiIi2qgVSaCk6cBXgJ2ADYAZkjaoG1VERERERET7tCIJBLYAbrB9o+37geOAl1WOKSIiIiIionVku3YM8yVpN+BFtvdtHr8WeJbtt/V9zZuANzUPnwJcP4khrgjcPon/3mTL62u3vL726vJrg7y+tsvra68uvzbI62u7vL7BWcP2SqM9sdgkBbCoNMqxubJX24cDh09OOHOTdJntzWr825Mhr6/d8vraq8uvDfL62i6vr726/Nogr6/t8vomR1vKQf8IPKnv8WrAnyvFEhERERER0VptSQIvBdaVtJakxYG9gJMqxxQREREREdE6rSgHtf2gpLcBpwPTgaNsX1c5rH5VylAnUV5fu+X1tVeXXxvk9bVdXl97dfm1QV5f2+X1TYJWNIaJiIiIiIiIwWhLOWhEREREREQMQJLAiIiIiIiIIZIkMKJjJD16lGOPqxFLRHSHpLdJWr52HBNJ0pKSnlI7jkGTtPuCHIuIwZN0wIIcm2zZEzgOkk5mxHzCfrZ3mcRwJoyklYH/BlaxvZOkDYAtbX+9cmgDJWlJYHXb19eOZZAk/QR4ue0HmsdPBE6x/cy6kS0aSbMov3+9uaG930UBtr1slcAGQNJhjP3esv8khjNwkjYd63nbV0xWLBNhfjdZbN85WbFMJEkfp3TnvgI4CjjdHbqIkPRS4LPA4rbXkvR04KNdOLdLusL2pvM71laSvmX7tfM71iaSXjHW87Z/OFmxTDRJ6wH/C6xse0NJGwO72P545dAGYh6/f1fafkatmCBJ4LhIem7vU+AIYN/+522fPelBTQBJpwJHAx+0vYmkxYArbW9UObSB6fjJfj9gZ+CVlPmaJwHvtn1G1cBiniTt3ffwI8DB/c/bPmZyIxosSb8c42nb3n7SgpkAkm5i7hsU/Wz7yZMc0oSRJGBH4A3AZsDxwNdt/65qYAMg6XJge+Cs3sWZpGtsb1w3soUnaSfgxcAewPf6nloW2MD2FlUCG7CRF9mSpgMzbW9QMaxFIuno5tPHA1sBv2geb0f5GR0zSWwTSWcDBwFf6/vdu9b2hnUjWzSSZgCvArYBzu17alngQdsvqBJYoxUjIqaK/iRP0r+6kvSNYkXbx0t6P8we0fFQ7aAG7L+ALYCzAGxfJWnNeuEMju0jmnmaPwLWBN5s+4K6US06SUsAbwHWAa6hjIp5sG5Ug9Gf5El6R9uTvpFsb1c7holke63aMUwW25b0F+AvwIPA8sAJkn5m+z11o1tkD9r+R8lzO+PPwGXALsDlfcdnAQdWiWiAmuuUDwBLSvpn7zBwP1OkDf/Csv0GAEmnUBL2W5vHTwS+UjO2CfAY25eM+N3rwvn9AuBWYEXg0L7jsyjXMVUlCVx4XV5CvVvSCjSvUdKzgX/UDWngOneyl/TO/oeUVcCrgGdLerbtz9WJbGCOAR6g3E17MfA0oHpN/QTo3HuLpO1t/2Je5U1tL2vqerlrj6T9gb2B24EjgYNsPyBpGvBboO1J4LWSXgVMl7QusD/lIq61bF8NXC3pO31bBJYHnmT773WjW3S2DwEOkXSI7ffXjmeCrNlLABt/BdarFcwEuV3S2sy57tyNkjy1mu3fA78HtgRorq23Bf41FW5iJwkchxH7PqY3b6Szs4iu7PsA3kUpIVxb0vnASsBudUMauM6d7IFlRjw+cR7H22qDXkmypK8Dl1SOJxbccymlTC8d5TkDrU4CmfsO70imlBh2wYrAK5oLm9lsPyzpJZViGqS3Ax8E7gO+A5wOdGJPEvAzSbtQrvuuAv4m6Wzb75zP901pktYA7uolgJK2A14O3Ax8xfb9FcMblLMknQ58l/J+shcwVol9G72VsnK7vqQ/ATcBr64b0qJrVnHfZ/vaZgX3CsrK/NqSDrf9harxZU/gghuyfR+LAU+hvNbre3cQu0LSYygn+x0pr/F04GO2760a2CJq9kF80vZBtWMZtFH2fHSpqUGv6Q3AY4B7ek/R8qY30R2SPgacA1xg++7a8QyapGfYvrJ2HBOh14RC0r6UVcCD277fEUDSxcCutv/c7O0/EzgE2Bh4wPa+Y/4FLSFpV8oKEsA5tk8c6+vbRtJ02w9JWgqYZntW7ZgGQdJ1tp/WfP4BYH3br5O0DHB+7d+/rASOz/NG3gHtIklXUzaQf68Lm/1HY/seShL4wdqxDFLzJtqJxGgUm4zY89HbA9KFROlxXbvRMi+SdqaU8i7RO2b7o/UiGixJGwIbMPfr+2a9iAbqJmAG8KXmxsW5lAvSH9cNa2A+19yt/z5wnO3ragc0QIs1r20PunXeW9L2n5vPX0PZK35oU6J8VcW4BqK5sXt600CkU4nfCDdJOo1y7fmL+X1xi/Sf159PaSqJ7VmSHq4T0hxJAsfnRKCrF9j9dgH2BI5vfki/Bxxv+w91w1p0QzLm4ypJJ1EuZGbfrW/7vivb02vHMIEuZgjeWyR9lbLSuR1lT9ludKisV9LBwPMoSeBPgZ2A84BOJIG2jwKOkvQESjLxbuBNdKTk3PZ2fa/tcEnLUm6GdqEk9KOUipfzbF8q6cmUfZxt11+ZtT3Qa2j3cBf2/Dc3du+RtJztrvVm6PcUynaBtwJfb8ooj7N9Xt2wFtktkt4O/JFyjj8NZo8oe1TNwCDloOMyFWZ6TLZmv9yHgFd34SK8b8zHK4AnAN9uHs8Abrb9gSqBDVBfW+l+tv3GSQ8mFsiwvLf0ys/6/lwa+KHtHWvHNgiSZgKbUEbqbKIyc/VI26PthWwdSUdSEty/UlYBzwOumAoNDgZN0kaURjd72l68djwxOklfBJ5IaSKyC7Be06zoicDJtjerGuAASDoeeDbwM+a+sdvq+bHz0vTb+CIduO6U9HjKDZgnUvaontEc3w54pu3P1owvK4Hjs6qkL83ryS79QjbjEvagrAg+RPu7vgFzxnxI+pjtbfueOlnSOZXCGqheW+lolZVGdHedSwc6u/b8u/nzHkmrAHcAXRqv8O9mBeLBZhXpNqAze8WBFYDpwF3AncDtXUoAJT2Vcs7bjfKzeRylUVonSfpwB0qx30H5f/ZEYJu+svon0J2y1580H53W3KTfk1JBcSnlGrTVbN9GGW018vgvmQLNfZIEjs+/mXvOTic1G60fRRkCvLvtGyuHNBFWkvTk3muTtBalC2rrNfP09uGR+66yEjh1TQeWZvSmU11yiqTHAp+hdEkzpSy0Ky5rXt8RlHPFv+hQuavtXWF2svRC4JdNQ4fV6kY2MEdTOjDu2LfPrMv2paxStJZLOdtxoxzvTIOfrs2NHU3TePEqynXnQV1sPDUVpRx0HLrUjXAskta3/ZsRx1a2/ddaMQ2apBdR2hH3Etw1KUPVT68W1IBI+j7wG+BVlBP8q4Ff2+7MTL2mLfi6ts9sausXa3M3sWF5b+kn6dHAEl3d59JUUyxru/pA4EFpxkA8h9KlcHngQuDcZq9g50h6ErCX7c/UjmVh9TXTesRTlKYqWQyYoiStD3weeJgyxupDlPEX/wfsbfvXFcMbKEnL2p7Xz2pMkPzyj08X5s3MVy8BlLQc8EpKMvFUYNWacQ2S7dOa/Y7rN4d+Y/u+mjEN0Dq2d5f0MtvHSOrNu+oESftRmlE8DlgbWA34KqXzVlt1fQXwEWzfJ2lbSe+xvUPteBbFWB15JW3qjgyLp5RpnQN8sasrZZJWBHan7BNflfZ3ZLwL2Hy0m7iSbqkQTyy4wylVE0tTOma+F3gD8BLgy7T7nAdA8/7/aeATkh6xKtWlbVZTUZLAcbD97JHHJK1NOVnsZXvDyY9qsJpVlV0oid+mlK5vL6ec+FtP0va2fyHpFSOeWltS6ztoNnp7Iu5q2tX/hbLS2RVvBbagdNTE9m+bzddt9oiTeTMvaVdghu2dJz+kwZG0PSVRXwX4EfDflI6ZAj5RMbRB6R8W/0zm3jbQpWHxp9g+tf+ApLfY/mqtgAahmdm1K+W8tx4l8XtyR8pcvwmsQWnmM9J3JjmWGJ9lbJ8Ms/sY9MpeT5b0kYpxDVJvNfOyqlFMMEkrAftRrsVm5161t+kkCVwITdepPSknjI0pg0lnVA1qACQdSynzOYNyl+kXwA22z6oZ14A9l/K6RuvWZ6ALSeDhTXetDwEnUe4ifqhuSAN1n+37e+2/JS3GGGM/2sD2nQCSFgdeTHlveRHwA0ry1HaHUlZvL6SsJl0EfMj2F6tGNSC2t+t93nR63W6sr2+xD0m6z/YvACS9lzISo+0/o7dR9m7+J2WEgpvh3K1n+z/HeO69kxnLRJK0NfBflIR3MebMj21zY6b+zpgjm4N1omOt7ZObWYgb2j6odjwT6MeUjspnUpotTgnZEzgOTRnaDEr52fHNx49td6K7XTMkXpQ7h9+zfYukG1v+JhodI+nTlBKn1wFvB/4D+JXt1naCk7QD5b3lhZSOYd8DDrO9Zs24BmXknkdJv7O9ds2YJkqX93c2pZKnAAdRblKsT6mCeWDMb5ziJB0I7AUsRVkd+x7ws5z72kPSb4ADKavwsy+ybd9RLahFJOnNwLG2/zXi+DrA22y/o05kgyfpF7a7UjHxCJKusv302nGMlCRwHCTdT7mT/S7blzXHOpUkNRuRX0VZ6byNcpLfyPZfqgY2YJIOoHSCm0Xp5Lcp8L7eDJc2kvQsyh6CtYGZwD62f1U3qsGTNI3S/XRHyk2L0ymz2Fr7ZibpYcpdwtfbvqk51pn3Fkk3UgaL93y2/3FHyrCBbieBMHvu1ZmUi+03tvn3bqRmgPoMSkK4LnAwcKLt/6saWMyXpIttP6t2HLFwJB1K+Z37PnPPQuzEuUHSx4ELbP+0diz9kgSOw4gN4ytTVgJfb/tJVQObIJI2o7zW3YE/2t6qckgDI+nqZpjzCyl7zD4EHN3mizdJlwHvp+zf3AXY1/YL60YVC0LSMygXnrtROtYeB3zY9hpVAxsQSUeP8bRr74tYVJIOY05J8l6MaFnf9uYGkmZRXp+aPxcHHmw+t+1lK4Y3IZph8TMow+I7uWrdJZI+SSmf/CEwu8lbh5oyddo8zhGtPzf0NO+hS1EaTPYqJ6q/dyYJXEhN6+g9KSeJx1DuFn6gblQTQ2Xz1bZuBq13gaRrbG8s6YvAWbZPbPbyPKN2bAtrlJK7Tq5ISJrJI/cA/oOysfzjbS7/gdl7W2ZQOvNeRXlvObxuVDEWSXuP9fwwzPmKdpD01N5oAUnPtn1R7ZgGQdJog7fd5RLDiEWVJHAAJD2Fsi+iK92aOq+567QqsBawCeUO4lm2n1k1sEUwLCV3zZ7Ah5jT2W6v5s9/AtvYHq3pT+s0Za87UN5b3lA7nhheTQnoB4B1gGuAT2amVztJ+gmwHKVp2L6216scUgw5SdsBb2POyK5fA1/uWFNCJO1Cab4I5XrzlJrxQJLAcWnmyn2WOXuu3m37T3WjivFqVjZXA1YCbrR9l6QVgFXbPNi56yV3PZLOt731aMckzbS9Ua3YFtaIi+yZwCG5yI6pQtJplD2A51BmlC1j+/VVg4oFImlN4M7+9xNJ+1Pmz73K9g8qhTZQklamjJ5ZxfZOkjYAtrT99cqhDYykTwHH275c0udtH1g7pkUlaWdKN/qPAldQSs43pXTqfdtU20O3sJpy5c2BY5tDM4DLbb+vXlRJAsdF0rmUzpm9PVdb2h45by5aQNLlbV71G2ZNF9s32b64ebwFcESzx7OVJb3DcpEt6dG275vfsZhaRna263Cp+TNtXz7i2Et7s9raSNLlwPa2/9E83p+ylWVf4CtdKZeUdCql2dsHm3PBYsCVbbwpOC+SdqdsE3gacGkXbuxKOgs4wPbVI45vTOmQ/dwqgQ2YcJp2lAAAIABJREFUpGuAp9t+uHk8nfLzuXHNuKbV/MdbaBnbR9i+3vZn6NYA7tkkrSDpMElXSLpc0heblbIuuUjS5rWDiIWyL3CkpJsk3QwcCeynMlz9kKqRLbwn2P6g7dNtv50yf7SLLlzAY60k6XG1Y5ggkrS8pMc1r3H6iMddcUTTEAYASTMoKxJt9qi+BPC/KXM6d2j2BS5XNbLBWtH28cDDALYfZArNY1sYkt4iafW+Q6dQ5v7eCfy2TlQD94SRCSBAU5W1coV4JtJj+z6fEr97GRY/Pks0XfzUPF6y/3GHulAdR1mReGXz+NWUuUkvqBbR4G0HvKVJIu5mzmDZrl58d4btS4GNJC1HqWa4q+/p4yuFtagkaXnmvLdM73/sZph8W0l6AmUP7pIj3kOXpTTW6oqLJV1FWZE4tUPjE5ajrFSr71jvfGegE6NMKN15T5D0amAbyizSHeuGtMh+12wVWI1SZvc02/dIemrluAbt7uZmtaE0vaE0DGuzt9r+KkBzPjiZ0v3088DFtPemZ7+7F/K5tjkEuLJpYCTK3sDqzSRTDjoO8+g+1dOZLlSjlUpKusz2ZrViGjRJo7bet/37yY4lxkfSoyk3KNak70aW7Y/WimlRNTcjHmbui+wet31eYNM98/XAZpQurj2zgG90qGmRKDfL3ghsQbl59o3MmWsPSesBPwJuAV5u+9+VQ1okkhYH9qC0pr8R+AZzZgDvbftn9aIbHEmbAocBGwLXUvb879byff6/AZ4JrEj5mTzU9reb5661vWHN+AZB0l2URYdHPEVp9Lb8JIc0YSQ9kbIvUMDFngLzt5MExiNI+izlQq23qrIb5e7hwfWiGjxJmwDPaR6eO1pJQltJ2opHJknfrBbQADX75/5BWZmYXe5j+9BqQcUCkfTKrjSimJ+m4923KbOhrgbeZ7szpa9dMsrYmcdT3mPuA+hShYikJYCNgN+OqKJoraaT8rOBS4CnUC6yr7f9wJjfOMVJ2g/4OKV7+UxKT4qfA3tT9pe9coxvbwVJY+7568poMkk/t/38+R2bbEkC4xE0Z6jlQ5Q302nMWZavPtxyECQdAOxHKa0A2BU43PZh9aIaDEnfonSwvYo5SZLd8oHVPV25AzqMuriK268pR3sN8Frgr8DXKa34nw583/ZaFcOLeZhXZUhPKkSmPkkX2t6ydhyD1jQQgfJ+eQjwQkop9oG2b68WWCyQ5qbLY4BfAs9j7q0Qp9quWpadPYHxCLaXqR3DJNgHeJbtu2F26+ULKeUkbbcZsEGH9iONdIGkjWzPrB1IjNuPmbOK28WOoBcC36KUEf6x7/hlkr5aKaaYD9u/b1aTrskNptY6Q9IrgR926dxnu3cj9yHgnTVjiYXyZuAdwCrM2UcNZa7xV6pE1CcrgTGbpPVt/6aprX+EDjW+6ZX/bG773ubxEpSWy61vJy3p+8D+tm+tHctEkPQryjy9myiJRJr6tETXV3El7dF0KOw/trvt79eKaZAkfcv2a+d3rK0kHQu83/YfascS49NXwfQgcC9zzgutr1yK9pP09qlYaZaVwIUwVWt7B+CdwJuA0fZWGehE45vG0ZROfidSThYvo5RudcGKwK8kXULfaovtXeqFNFA71Q5gonT9Ipvur+K+j0d2qH0/0IkkkDKfbLamVK1L81afCFzXvHfO7kzYofdOJD0eWKL3uAsJb9OQ6WldeC0xe4/n0rb/WTuWAfqTpJFzxf8BzLR9W42AIEnguPTV9q44op37spSl3laz/abmz+1qxzLRbH+uGVK6TXPoDbavrBjSIP1X7QAmUm9/zsiLmY7o5EV2X+ONxYA3SLqRDq3iStoJeDGwqqQv9T21LGVlotUkvZ/SznxJSb0LM1E6Th5eLbDB+0jtACaKpF0oN3hXoXQHXQP4NSPec9rItpsbuq1/rxyNpOl9ZaGdJOk7wFsoZa+XA8tJ+lwzk7sL9gG2pOwNhLI/8CJgPUkftf2tGkElCRyf/tre/plJU6K2d1E1w9Nv6bWtlfQ6ShOH3wP/1fZZZfMg5t2av5Vsny1pZUorYoBLat5pGrQuXswMwUX2S2oHMMH+TOmovAvl3NAzCziwSkQDZPuQZt/0kbbfWDueidKVToTz8DFKB80zbT+j6V47o3JMg3SRpM2bObJdc4OkE4Cjbf+qdjATZAPb/2xmdP4UeC/lvbQrSeDDwFNt/xWguUb7X+BZlBEZVZLA7AlcCFO1tndRSboCeIHtOyVtSxka/3ZKZ7un2t6taoADJOnDwO7ADygX2y+ndO/7eNXABkDSHpQ3zrMor+05wEG2T6gZ16BIuppSmjzXxUxvJbutmhKYTl9kS3rcKIdntb2Ve4+kxWy3fuVvXkabIdslzYDxw4CnAotTWvPf3YV9Zb1Zv8375zNsPyzpEttb1I5tEJq94utRblrfTUeqDAAkLQPsBbyB0q39KOC4LpVLSrqOcq35HeDLzc3sq21vUjm0gZA0s7/nRFPCPNP2hpKutP2MGnFlJXAc+lbKDmsed22lbHrfa9iTMjLhB8APJF1VMa6JMINyIuw1hvkkpXNT65NA4IOUpje3AUhaCTgT6EQSCDxg+w5J0yRNs/3LZpWi1ZqLsk6c8MZwBfAk4O+Ui7THArdKug3Yz/blY33zVCXpeNt7AFdK6r+z2pkL0UaXV1sAvky52P4+pcvy64B1q0Y0OHdJWpqy6nBs8zvXpRsWnd0rbnsWcARwRHOD/rvA55vVwY/ZvqFqgIPxNeBmykzVc5qxLZ1JcoFzJZ3CnP3hr6S8zqWAavM6kwSOz9eAFwA0v4ifZM5K2eGUoeptNr3vTvbzKU1ierr2s3IzZT/Zvc3jRwO/qxbNYE0bUf55B+XuYVd0+WKm6xfZpwEn2j4dQNKOwIsozVT+h1Ia00YHNH92vex1O+DNkjq32tJj+4a+PVhHS7qgdkwD8jLK+e5A4NXAckAn5nNCt/eKN3vDd6asBK5J2Q5xLKXK56eUFdBWs/0loH8/9e+bKp+ueCsl8dua8r75TeAHzTiTaq+zaxf2E63rK2XfBc6WdDvwb+BcAEnrULoYtZ6kwygNKu6jdIH7WfN4B+C8mrEN0GmSTqf8/4Tys3pqxXgGrcsXM12/yN7M9lt6D2yfIem/bb9TZZB8Wy0laWvb5/cflPQcyn7BrujsakvjHkmLA1dJ+jRwK2XsQOv1ZuI2jqkWyATp4l7xPr+lNBT5jO3+mxInNAsSrdds0RlNJ87tTbJ3AlOsIitJ4Ph0eqXM9ick/ZzSJvuMvoGr0ygrnl1wWfPn5cCJfcfPmvxQJobtg5pWxNtQkojDbZ84n29rjY5fzHT9IvtOSe+l7DeGcoPi782d7ofrhbXIvkBp7DPSv5vnXjq54UwMl6Hqm1BWIADOtX11zZgG7LWU893bKDeZnkS5e99azfy80Zo/dG2OXpcb32xs+1+jPWF7/8kOZoL0n9eXoFRV/LpSLAPXXJN9Cng85XdvSvz+pTHMOEj6IKUN+O3A6sCmTWvidYBjbG9dNcBYIM0F5zG2X1M7lokg6VO23zu/Y201Vd9MB6XLF9mSVgQOZs4NivMobfn/Aaze1r0tkq61veE8npurIUCbSToA2A/4YXNoV8pNpk40SpO0PXCR7XtqxxLj0+XGN5LWo3SSXLlpJLIxsEsXGtnNS1MZcpLtF9aOZRAk3QC81PaUSmyTBI5T0z2st1J2d3NsPcpgyyuqBhcLrCmXfKnt+2vHMmiSrrC96Yhj13SlpHCqvpkOQtcvsrtK0g221xnvc20j6Rpgy75z31LAhR16b/kmZTXpDsp2iHOB82z/vWpgi0DSsk3r/dE689KBhnYASDqT0uX7EGBFSkno5ra3qhrYAEg6GzgI+Fqvi+RYN566QGUW9yW2O9GYSdL5U3GhqPUljJPN9kWjHPu/GrHEIrkZOF/SSfSVIdj+XLWIFpGk/wf8B/Dk5mKtZxng/NG/q5X+2sUEsLEP8Ky+i+xPARdS2ta3lqQv2H6HpJMZpTTN9i4VwhqkSyXtZ/uI/oOS9mHuuYFtJ8ow556H6NaM1dcBSFqF0ujtK5Q9Zm2+VvoOpbTucsrvXv//LwNPrhHUBOjyXvHH2L6kTBWYrSvN0IBSMcGcc8N0YCW68/8P4DJJ3wN+ROlJAYDtH877WyZem9/YIhbFn5uPaZQkqQu+Q2kAcwjwvr7js7pwt7cpA4Up+mY6IF29yO4Nwv1s1SgmzjuAE1UGHfeSvs0os+Z2rRbV4B0NXCzpRMrP5cuAr9cNaXAkvYZSir0RZdvHl2kapLWV7Zc0f65VO5aJIOkdlJucVzYdXaF7e8Vvl7Q2TZIkaTdK06Iu6e+s/CDlZm+XEt1lgXuAHfuOmTlVP1WkHDSiI+ZV7tPT9kRQ0tFjPG13YMi6pHcCe1OaFvUusr9h+wtVA5tAkr5ne8/acQxC04yiV6J1ne1f1IxnIkjalLKnE8qe1StrxjNITWfs3wFfBX5p++a6EQ2WpFUpXTNnLwDYPqdeRItO0meBrYD1gWuACyhJ4YVtP+f1SHoyZQzZVpQZqzcBr+ngz2fvvcWUMuzOvLdMVUkCF1IzyHJd22dKWhJYrBnoGS3QDFB/D6V99OyZQra3rxbUIpJ0E3PKKUauHtl2V8p+Oq3LF9mjkfQH26vXjiMWTPPz+RxKN9fzu7YXXtLTgG0pv4PrAtfbfm3dqBZdU1q+J/Ar5lQbuAOl2AA0oz02oyRKWzYfd9neoGpgA9TswZ3WxWvNZkTE7sxZGXs58P22N7+R9B7bn+4bTzaX2t1dUw66ECTtRxkP8ThgbWA1yp3D59eMK8blWOB7lBKEt1BWX/5WNaJF1NVyn5EkHQMcYPuu5vHywKFdWAnsI8pFdhdKQaMj+i7UfkD52TxaUusv1HokLUvp/L0GZSj3crR7dEm/lwNPsX3ffL+ynZaklNwt13z8GZhZNaIBaRqGHQ3MAo5obsS8z/YZdSMbqBmUrq73Akj6JHAF0Pb3ll7/gsvG/KpKshK4EJrB8FsAF/d1aupMG/BhIOly28/s75op6Wzbz60d28KStL7t3zQniEfoyh17SVf2fu/GOtZGo1xkd+Vu6Kg/k5TXeIrtJ05mPLFwJP2auS/UlgSusP3UupENRtNQ67zm4xzbf6wc0sBIOhXYfV7z5tpK0uGUip5ZwMXARZQxH63t6DqSpKttbyLphcBbgQ8BR4/sAt5mzc/njL6bu48Fvt3b0xoTIyuBC+c+2/f3OjVJWozRh7HG1PVA8+etknam3DVcrWI8g/BOygr1oaM8Z6C1pa4jTJO0fO8k3+yF7Mp7WVfvho72M9nzm0mLIhbVzZTy+Xubx4+m7KHrhL4bgkv1OvR2yD3AVZJ+ztwNtdo+bHx1ys/hb4E/AX8E7qoa0eD1KkJeTEn+rtaIVqFt1VcmeR9wnaSfNY93oNyM6YRmlNy7KRUG/Xtyq16XdeXCabKdLekDwJKSdqC05T+5ckwxPh+XtBzwLkr7/WUpraVby/abmj+3qx3LBDsUuEDSCZSTxR7AJ+qGNDA308GL7CH4mRwWo16oSfoStD+hkLQlpdvp0sDqkjYB3mz7P+pGNhAnNR+dYvtFTUL0NMp+wHcBG0q6k9Ic5uCqAQ7G5ZLOANYC3i9pGbpTptwrk7yc0hCt56zJD2VCfZ+ybexI5u4AXlXKQReCpGmUeV47Uu7QnA4c6fzHjClA0nRgZx55x6m1MxBHapo3bEf5/fu57V9VDmkgJP0I2BwYeTf0Nmj/RXa0m6S9x3redqtb80u6mDIf8KRhGcrdJZJWA7amJIMvAVaw/di6US2aJsFdjTI370bbd0laAVjV9jVjf3dMFb0tSLXjGCkrgQvnZcA3Rw4GjqlvXh2aejpykX0yZSVpJt25WzibpBfYPhO4ru/Y3m2/AG2cSLfvhka73ULZb3VP7UAmiu1bRlTaTZm79otC0rqUGbIbMHdH7FZ3jZa0PyXp25qyzeN84ELgKDrQGMa2Jf2oP4GwfQdwR8WwBk7SS4CPMWeEiSgvf9mqgS2ivtFdJ0v6D8r5vb8cu+oYkySBC2cX4AuSzgGOA07v2FDLLuvv0PQRoAulIiOt1tvb0lEflvRKStnPMpTyivvoxoDgzl9kR6u9HviqpDsoQ9TPpczz6koTjlskbQW4GTmwP3O6+7Xd0ZTz3ecpVRRvoBvdh9cETgAOtN21Aeo9F0na3PaltQOZQF8AXgHM7FhV3eWUhYfe79pBfc8ZqHoTJuWgC0nSo4CdKHN3tgF+ZnvfulHFeHSlo+RIzTyon3esffRsTXnMu4A3N4c+bPu7FUMaGEnfBJ5NucvbxYvsTg6sHjaSVqGUTb4bWMV2J24oS1oR+CLwAspF2xmUcTStX3Xp64g9u5O5pHNtP6d2bDE2Sb8CnkLZM343c1bJOnOzV9Ivgefb7lz10lTWiTfuGmw/0LS0NWU+zcuAJIHt0tU7IBcBJzZ7Vx+gI2UVfZYHnkVpmLIasIYkdeHuoe3XwVwX2V8BVqEj79XzGlgNJAlsAUmvoQyK3wi4Hfgy5UZFJ9i+HXh17TgmyL3NOeG3kt5G6aT5+MoxxYLZqXYAk+A9wE8lnc3c5ZKd6GUgaXfgNNuzJP0nsCnwMdtXVo2rA9dNk07Si4C9KCUVZ1GGjp+RktB2kXRFl+bs9Ei6kTJfrmtlFQBI+j/gk7aPauaUfQrYzPZWlUNbZKNcZJ8HnGv7wqqBDYik64GNOzywutMk3U65+fJV4Je2b64b0WA08znnxbY/NmnBTBBJm1NKWx9L2Xu1HPBp2xdVDSwWiKRtgHVtHy1pJWBp2zfVjmtQmu6n/2JELwPbH6kW1AD1ZlI3/x8PAT4LfMD2s6rG1cFrxAkn6TjKXsBTczHTLpJmMWcF8DGU2UnQodUySacDO3W1rELS6rb/MOLYtl0oKezqRXZPVwdWD5OmM++2lG0Q6wLX235t3agWjaR3jXJ4KUoX8BVsLz3JIUXMJulgYDPgKbbXaypFvm9768qhDYyky2xvVjuOidLbfiTpEMoN+u9MhS1JnSgxmmy295K0MrBD00XsEtu3VQ4rFoDtZWrHMAluBc5qLrg7U1Yh6TW2v237D5K2tn1+39Mb04GSQtsr9l1kf6Lp6Nf6i+w+XR1YPRQkLUsZzr0GpSHHcnSgA7HtQ3ufNzPYDqA0TjmOMpe0tSSNORvQ9i6TFUsstF2BZwBXANj+c/Nz2iVnStqxq70MgD9J+hplv/GnJD0amFY5piSBC6Op7f0spRRUwGGSDrJ9QtXAIoqbmo/Fm4+ueCfw7ebzwyg19T1vpOxParWuXmT36eTA6iFyXt/Hl23/sXI8A9O0cn8nZU/gMcCmHWnItCWl6/B3gYvpRkfQYXN/MyrCAJKWqh3QBHgr8B5J99HNXgZ7AC8CPtvMenwic3cKrSLloAtB0tXADr3Vv6Y++0zbm9SNLKK7+ksnRpZRTIWyikGQdA1zLrLP6dJFdnSHpKVs3107jkGR9BlKe/rDga90qVxZ0nRgB2AGpWLiJ8B3bV835jfGlCHp3ZTS6x0o+8n2Ab5j+0tVA4txk/R45p7T+YcxvnzCJQlcCP0tlpvH04Cr+49F1NLclHgP8DTmfrPZvlpQA9DfyGdkU5+uNfnp4EX28bb3kDSTUbrydqnVeZdJ2hL4OqUpxeqSNgHebPs/Koe2SCQ9TClPfpC5fz47tRrRlKDNAD4DfNT2YZVDigUkaQdgx+bh6bbPrBnPoEnadrTjXdjrDyBpF0pp+SrAbZSKn9/YflrNuFIOunBOa5pv9GaT7Qn8tGI8Ef2OpXSsfQnwFmBv4G9VIxqM9ZuVMgFrN5/TPK46cHVQ+i+ygc5cZFP2WEH5mYz2+gLwQpqSXttXz+virU1sV9+bM5Ga5G9nSgK4JvAl4Ic1Y4r5G9HIrr+M9y2S7qU0Efug7Z9PenCD118auQSwBWXQeqtvXvf5GGUG8JlNg5jtKL+PVSUJXAi2D5L0Ckp3NAGH2z6xclgRPSvY/rqkA2yfDZzdzN5pu6fWDmASdPUi+9amLO3rtl9QO55YeLZvaRqi9Tw0r6+N+iQdA2wInAp8xPa1lUOKBTRWI7vm/XRDyk3fDSctqAli+6X9jyU9Cfh0pXAmwgO275A0TdI0279s5uZWlSRwnJpfvNObC5ncSYup6IHmz1sl7Qz8mTJUvdVs/752DJOhqxfZth+SdI+k5Wz/o3Y8sVBukbQVYEmLA/tTZs/F1PVa4G5gPWD/vveWTpW6DhvbDwFXS+pqSe8f6UBy2+cuSUtTupgfK+k2Svl5VUkCxykXMtECH5e0HPAuShfNZYED64YUC6jrF9n3AjMl/YxyYQpkRESLvAX4IrAq5SLtDEpXv5iiul7qOuxsf612DIPQJLO90tdpwNOBq+tFNHAvo5z/DqR0IF4O+GjViEhjmIUi6XhKbW8uZCJiYCStSLnIfgHlTv0ZwAG276ga2IBI2nu047aPmexYIiJiahhxbngQuHnELOBWkvQO4HzgStvVV/5GShK4EHIhE1PRiDtpj9CVmxTNXscvzu9YRAyGpA+P8bRtf2zSgomIaAlJnwW2AtYHrgEuoCSFF9q+s2ZskCQwojNG3Jz4CHBw//NduUkx2jiIts8JHJaLbEnrUuZcbcDc40s60d21qyS9a5TDS1Hmla1ge+lJDikiOmBeY4OYs2e1E+ODmu0dm1ESwi2bj7tsb1AzruwJHAdJLwNWs/2V5vHFwErN0++xfUK14GLo9Sd5kt7RlaSvR9IM4FXAWpJO6ntqGaDt5ZKjzQScfZFNaS/dBUdTbk58HtgOeANztz6PKcj2ob3PJS1DGfnxBuA4yuyriIiFMSxjg5ak9GdYrvn4MzCzakRkJXBcJJ0P7GX7lubxVcDzKRdrR9t+fs34Inq6NjwdQNIawFqUlaT39T01C7hmKtbbL4y+i+x9gOOBQ23fVjeqwZB0ue1nSpppe6Pm2Lm2n1M7thibpMcB76Q0NTgG+KLtv9eNKiK6QtLKwObNw0u6cN6TdDjwNMp1ysXARcBFU+W9MyuB47N4LwFsnNc0bLhD0lK1gooYBs2IiN9Tyig6Z5SL7E2nyoligO6VNA34raS3AX8CHl85ppgPSZ8BXgEcDmxk+1+VQ4qIDpG0B/AZ4CxKdchhkg7qQIXd6sCjgd9Sznd/BO6qGlGfrASOg6QbbK8zj+d+Z3vtyY4pokfSLObU1j8GuKf3FB2aByXp2ZTRF08FFgemA3e3+fWNuMj+SlcvsiVtThl58VhKietywKdtX1Q1sBiTpIeB+yhd+/ovGjr13hIRdUi6Gtiht/onaSXgTNub1I1s0akM53waZT/gVpT5h3dSmsMcPNb3TnhsSQIXnKRjgbNsHzHi+JuB59meUSeyiOEh6TJgL+D7lI3WrwPWsf3BqoEtglxkR0TEsOrfItA8ngZc3X+s7SStBmxNSQRfQmmq9diaMaUcdHwOBH4k6VXAFc2xZ1KWel9eLaqIIWP7BknTbT8EHC3pgtoxLYquD3Qe0cjnEWzvMlmxRETElHOapNOB7zaP9wROrRjPQEjan5L0bQ08QDMeAjiKNIZpJ0nbU5Z2Aa6z/Yua8UQME0nnUIapHwn8BbgVeH0Xyka6StLfgFsoJ/iLGdER1PbZNeKKiIipQdIrgG0o54dzbJ9YOaRFJulzNLMBbd9aO56RkgRGRKs0XUL/StkPeCBlX9n/2L6hamAxT5KmAzsAM4CNgZ8A37V9XdXAIiKiGknrACvbPn/E8W2BP9n+XZ3IhkOSwIiImDSSHk1JBj8DfNT2YZVDioiICiSdAnzA9jUjjm8GHGz7pXUiGw7ZExgREROuSf52piSAawJfAn5YM6aIiKhqzZEJIIDtyyStOfnhDJckgRERMaEkHUNpi30q8BHb11YOKSIi6ltijOeWnLQohlSnO9JFRLdImt7M1It2eS2wHnAAcIGkfzYfsyT9s3JsERFRx6WS9ht5UNI+wOUV4hkq2RMYEa0i6RfA8503r4iIiNaStDJwInA/c5K+zSiN33a1/ZdasQ2DJIER0SqSDgXWpQyLv7t33Hb2l0VERLSMpO0oWwYgo9cmTZLAiGgVSUePcti23zjpwURERES0UJLAiIiIiIiIIZLGMBHRGpK2k/QDSdc1HydIel7tuCIiIiLaJElgRLSCpJ2Bo4BTgFcBrwZ+Chwl6cU1Y4uIiIhok5SDRkQrSDoLOMD21SOObwwcZvu5VQKLiIiIaJmsBEZEWzxhZAIIYPsaYOUK8URERES0UpLAiGiLuxfyuYiIiIjos1jtACIiFtDakk4a5biAJ092MBERERFtlT2BEdEKksbc82f77MmKJSIiIqLNkgRGREREREQMkewJjIiIiIiIGCJJAiMiIiIiIoZIksCIaC1J0yQtWzuOiIiIiDZJEhgRrSLpO5KWlbQU8CvgekkH1Y4rIiIioi2SBEZE22xg+5/Ay4GfAqsDr60bUkRERER7JAmMiLZ5lKRHUZLAH9t+AEib44iIiIgFlCQwItrma8DNwFLAOZLWAP5ZNaKIiIiIFsmcwIhoPUmL2X6wdhwRERERbbBY7QAiIsZD0ofn8dRHJzWQiIiIiJZKEhgRbXN33+dLAC8Bfl0ploiIiIjWSTloRLSapEcDJ9l+Ye1YIiIiItogjWEiou0eAzy5dhARERERbZFy0IhoFUkzmTMSYjqwEtkPGBEREbHAUg4aEa3SjIToeRD4azqDRkRERCy4JIER0TqSNgW2oawInmf7ysohRURERLRG9gRGRKs0IyKOAVYAVgS+Iek/60YVERER0R5ZCYyIVpH0a+AZtu9tHi8JXGH7qXUji4iIiGiHrARGRNvcTJkP2PNo4Hd1QomIiIhon3QHjYhWkHQYZQ/gfcB1kn7WPN4BOK9mbBERERFtknLQiGhQaRvDAAAGwElEQVQFSXuP9bztYyYrloiIiIg2SxIYERERERExRLInMCJaRdJLJF0p6U5J/5Q0S9I/a8cVERER0RZZCYyIVpF0A/AKYKbzBhYRERExblkJjIi2uQW4NglgRERExMLJSmBEtIqkzYGPAWdTOoUCYPtz1YKKiIiIaJGMiIiItvkE8C/KrMDFK8cSERER0TpJAiOibR5ne8faQURERES0VfYERkTbnCkpSWBERETEQsqewIhoFUmzgKUo+wEfAATY9rJVA4uIiIhoiSSBERERERERQyR7AiOiVSRtO9px2+dMdiwRERERbZSVwIhoFUkn9z1cAtgCuNz29pVCioiIiGiVrARGRKvYfmn/Y0lPAj5dKZyIiIiI1kl30Ihouz8CG9YOIiIiIqItshIYEa0i6TCgV8c+DXg6cHW9iCIiIiLaJXsCI6JVJO3d9/BB4Gbb59eKJyIiIqJtkgRGREREREQMkZSDRkQrSJrJnDLQuZ6iDIvfeJJDioiIiGilrARGRCtIWmOs523/frJiiYiIiGizJIER0TqSVgY2bx5eYvu2mvFEREREtElGREREq0jaA7gE2B3YA7hY0m51o4qIiIhoj6wERkSrSLoa2KG3+idpJeBM25vUjSwiIiKiHbISGBFtM21E+ecd5L0sIiIiYoGlO2hEtM1pkk4Hvts83hM4tWI8EREREa2SctCIaB1JrwC2oYyHOMf2iZVDioiIiGiNJIER0QqS1gFWtn3+iOPbAn+y/bs6kUVERES0S/bRRERbfAGYNcrxe5rnIiIiImIBJAmMiLZY0/Y1Iw/avgxYc/LDiYiIiGinJIER0RZLjPHckpMWRURERETLJQmMiLa4VNJ+Iw9K2ge4vEI8EREREa2UxjAR0QqSVgZOBO5nTtK3GbA4sKvtv9SKLSIiIqJNkgRGRKtI2g7YsHl4ne1f1IwnIiIiom2SBEZERERERAyR7AmMiIiIiIgYIkkCIyIiIiIihkiSwIiImNIkPSTpqr6P943z+2+WtOICfu3rJa0y3q+TdKSkDcYTV0RERC2L1Q4gIiJiPv5t++mT9G+9HrgW+PN4vs72vhMaVURExABlJTAiIlqpWeH7iKQrJM2UtH5zfAVJZ0i6UtLXAI3yvdMlfUPStc33HihpN8rYkWObFcclJX1Y0qXN1x2uYrSvO0vSZs3fPaP5O6+V9Km+f/Nfkj4h6WpJFzVjT0bG9V+Sjmr+vhsl7d/33I8kXS7pOklvGvH3fqp57kxJW/R9/y59r/czzWu5RtKbm+NPlHRO8zqulfScAf3viYiIKSxJYERETHVLjigH3bPvudttbwr8L/Du5tjBwHm2nwGcBKw+yt/5dGBV2xva3gg42vYJwGXAq20/3fa/gS/b3tz2hsCSwEvm8XUANCWinwK2b/6NzSW9vHl6KeAi25sA5wD7zeP1rg+8ENgCOFjSo5rjb7T9TEoCur+kFfr+3rOa52YBHwd2AHYFPtp8zT7AP2xvDmwO7CdpLeBVwOnNSusmwFXziCkiIjok5aARETHVjVUO+sPmz8uBVzSfb9v73PZPJP19lO+7EXiypMOAnwBnzOPv307Se4DHAI8DrgNOHiPWzSkJ2d8AJB3bxPMj4H7glL54d5jH3/ET2/cB90m6DVgZ+CMl8du1+ZonAesCdzR/72nN8ZnAfbYfkDQTWLM5viOwcbOKCbBc8/2XAkc1ieaPbCcJjIgYAlkJjIiINruv+fMh5r6xOeYQXNt/p6x8nQW8FThy5NdIWgL4H2C3ZrXwCGCJ+cTziNLTPg94znDekfH2u6/v84eAxSQ9D3gBsGWzknhlXyz9f+/Dve+3/XDfvyHg7c3K5dNtr2X7DNvnUJLUPwHfkvS6+by+iIjogCSBERHRNecArwaQtBOw/MgvaLqFTrP9A+BDwKbNU7OAZZrPe0nW7ZKWBnbr+yv6v67fxcBzJa0oaTowAzh70V4OUFbu/m77nmbv47PH+f2nA/+vV1oqaT1JS0laA7jN9hHA15nz3yEiIjos5aARETHVLSmpv0zxNNtjjYn4CPBdSVdQErA/jPI1qwJHS+rdDH1/8+c3gK9K+jewJWX1byZwM6V0knl8HQC2b5X0fuCXlNW3n9r+8YK8yPk4DXiLpGuA64GLxvn9R1JKQ6+QJOBvwMuB5wEHSXoA+BeQlcCIiCGgORUkERERERER0XUpB42IiIiIiBgiSQIjIiIiIiKGSJLAiIiIiIiIIZIkMCIiIiIiYogkCYyIiIiIiBgiSQIjIiIiIiKGSJLAiIiIiIiIIZIkMCIiIiIiYoj8f3ZYA24OgyBmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_end_station.plot.bar(figsize = (15,15))\n",
    "plt.xticks(rotation =90)\n",
    "plt.xlabel('End station names')\n",
    "plt.ylabel('Total number of times bikes rented')\n",
    "plt.title('Top Ten End Station')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of top 10 end stations.\n",
    "Grove St PATH, Sip ave, and Harborside places are the most popular stations where people return their bikes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q. How long does the average trip take?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "585.5905645582855"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['duration_sec'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The average trip takes 660 seconds which equals to 10 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>stop_time</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>gender</th>\n",
       "      <th>start_time_month_name</th>\n",
       "      <th>start_time_weekday</th>\n",
       "      <th>start_time_day</th>\n",
       "      <th>start_time_hour</th>\n",
       "      <th>rider_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19204</th>\n",
       "      <td>290697</td>\n",
       "      <td>2020-02-25 07:16:57.800</td>\n",
       "      <td>2020-02-28 16:01:55.520</td>\n",
       "      <td>Lincoln Park</td>\n",
       "      <td>JCBS Depot</td>\n",
       "      <td>42326</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1984</td>\n",
       "      <td>2</td>\n",
       "      <td>February</td>\n",
       "      <td>Tue</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6293</th>\n",
       "      <td>245410</td>\n",
       "      <td>2020-02-08 21:55:24.948</td>\n",
       "      <td>2020-02-11 18:05:35.899</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>Grove St PATH</td>\n",
       "      <td>42479</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1996</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12027</th>\n",
       "      <td>221936</td>\n",
       "      <td>2020-02-16 21:43:35.712</td>\n",
       "      <td>2020-02-19 11:22:32.337</td>\n",
       "      <td>Harborside</td>\n",
       "      <td>Dixon Mills</td>\n",
       "      <td>42369</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1990</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sun</td>\n",
       "      <td>16</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18466</th>\n",
       "      <td>197074</td>\n",
       "      <td>2020-02-24 10:03:17.788</td>\n",
       "      <td>2020-02-26 16:47:52.511</td>\n",
       "      <td>Newport Pkwy</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>42178</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Mon</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22840</th>\n",
       "      <td>157660</td>\n",
       "      <td>2020-02-29 16:48:59.735</td>\n",
       "      <td>2020-03-02 12:36:40.591</td>\n",
       "      <td>Marin Light Rail</td>\n",
       "      <td>Paulus Hook</td>\n",
       "      <td>42146</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1983</td>\n",
       "      <td>1</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22886</th>\n",
       "      <td>149410</td>\n",
       "      <td>2020-02-29 18:22:52.975</td>\n",
       "      <td>2020-03-02 11:53:03.380</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>Leonard Gordon Park</td>\n",
       "      <td>42444</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Sat</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8720</th>\n",
       "      <td>117897</td>\n",
       "      <td>2020-02-12 09:10:16.071</td>\n",
       "      <td>2020-02-13 17:55:13.933</td>\n",
       "      <td>Jersey &amp; 3rd</td>\n",
       "      <td>5 Corners Library</td>\n",
       "      <td>42586</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Wed</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12597</th>\n",
       "      <td>95085</td>\n",
       "      <td>2020-02-17 17:08:51.167</td>\n",
       "      <td>2020-02-18 19:33:37.104</td>\n",
       "      <td>Sip Ave</td>\n",
       "      <td>City Hall</td>\n",
       "      <td>42113</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Mon</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5409</th>\n",
       "      <td>90388</td>\n",
       "      <td>2020-02-07 16:52:42.455</td>\n",
       "      <td>2020-02-08 17:59:10.919</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>Warren St</td>\n",
       "      <td>42447</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Fri</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16170</th>\n",
       "      <td>89655</td>\n",
       "      <td>2020-02-21 16:21:03.700</td>\n",
       "      <td>2020-02-22 17:15:19.025</td>\n",
       "      <td>Hilltop</td>\n",
       "      <td>Hilltop</td>\n",
       "      <td>42621</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>February</td>\n",
       "      <td>Fri</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_sec              start_time               stop_time  \\\n",
       "19204        290697 2020-02-25 07:16:57.800 2020-02-28 16:01:55.520   \n",
       "6293         245410 2020-02-08 21:55:24.948 2020-02-11 18:05:35.899   \n",
       "12027        221936 2020-02-16 21:43:35.712 2020-02-19 11:22:32.337   \n",
       "18466        197074 2020-02-24 10:03:17.788 2020-02-26 16:47:52.511   \n",
       "22840        157660 2020-02-29 16:48:59.735 2020-03-02 12:36:40.591   \n",
       "22886        149410 2020-02-29 18:22:52.975 2020-03-02 11:53:03.380   \n",
       "8720         117897 2020-02-12 09:10:16.071 2020-02-13 17:55:13.933   \n",
       "12597         95085 2020-02-17 17:08:51.167 2020-02-18 19:33:37.104   \n",
       "5409          90388 2020-02-07 16:52:42.455 2020-02-08 17:59:10.919   \n",
       "16170         89655 2020-02-21 16:21:03.700 2020-02-22 17:15:19.025   \n",
       "\n",
       "      start_station_name     end_station_name bike_id   user_type  birth_year  \\\n",
       "19204       Lincoln Park           JCBS Depot   42326  Subscriber        1984   \n",
       "6293             Sip Ave        Grove St PATH   42479    Customer        1996   \n",
       "12027         Harborside          Dixon Mills   42369  Subscriber        1990   \n",
       "18466       Newport Pkwy            Warren St   42178    Customer        1969   \n",
       "22840   Marin Light Rail          Paulus Hook   42146    Customer        1983   \n",
       "22886            Sip Ave  Leonard Gordon Park   42444    Customer        1969   \n",
       "8720        Jersey & 3rd    5 Corners Library   42586    Customer        1969   \n",
       "12597            Sip Ave            City Hall   42113    Customer        1969   \n",
       "5409           Warren St            Warren St   42447    Customer        1969   \n",
       "16170            Hilltop              Hilltop   42621    Customer        1969   \n",
       "\n",
       "      gender start_time_month_name start_time_weekday  start_time_day  \\\n",
       "19204      2              February                Tue              25   \n",
       "6293       1              February                Sat               8   \n",
       "12027      1              February                Sun              16   \n",
       "18466      0              February                Mon              24   \n",
       "22840      1              February                Sat              29   \n",
       "22886      0              February                Sat              29   \n",
       "8720       0              February                Wed              12   \n",
       "12597      0              February                Mon              17   \n",
       "5409       0              February                Fri               7   \n",
       "16170      0              February                Fri              21   \n",
       "\n",
       "       start_time_hour  rider_age  \n",
       "19204                7         36  \n",
       "6293                21         24  \n",
       "12027               21         30  \n",
       "18466               10         51  \n",
       "22840               16         37  \n",
       "22886               18         51  \n",
       "8720                 9         51  \n",
       "12597               17         51  \n",
       "5409                16         51  \n",
       "16170               16         51  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_trips = df.nlargest(10, ['duration_sec'])\n",
    "top_trips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I will visualize two variables to see the differences or similarities between them. Therefore, I will be able to see the trends."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#visualization of two variables\n",
    "Quantitative vs Quantitative = Scatterplots\n",
    "Quantitative vs Qualitative  = Violin plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trip Duration vs Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Duration (sec)')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's do analysis on two variables (trip duration vs age)\n",
    "plt.figure(figsize = [15,10])\n",
    "sb.regplot(data = df, x = 'rider_age', y = 'duration_sec')\n",
    "plt.axis([10, 75, 500, 20000])\n",
    "plt.title('Trip duration vs Age')\n",
    "plt.xlabel('Rider Age')\n",
    "plt.ylabel('Duration (sec)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Those riders who are between 35 and 50 years old, take bikes for a long period of time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 22956 entries, 0 to 22961\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   duration_sec           22956 non-null  int64         \n",
      " 1   start_time             22956 non-null  datetime64[ns]\n",
      " 2   stop_time              22956 non-null  datetime64[ns]\n",
      " 3   start_station_name     22956 non-null  object        \n",
      " 4   end_station_name       22956 non-null  object        \n",
      " 5   bike_id                22956 non-null  object        \n",
      " 6   user_type              22956 non-null  object        \n",
      " 7   birth_year             22956 non-null  int64         \n",
      " 8   gender                 22956 non-null  category      \n",
      " 9   start_time_month_name  22956 non-null  object        \n",
      " 10  start_time_weekday     22956 non-null  object        \n",
      " 11  start_time_day         22956 non-null  int32         \n",
      " 12  start_time_hour        22956 non-null  int64         \n",
      " 13  rider_age              22956 non-null  int64         \n",
      "dtypes: category(1), datetime64[ns](2), int32(1), int64(4), object(6)\n",
      "memory usage: 3.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rider Age vs Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.95778739278402, 79.74953094134327)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15, 10])\n",
    "base_color = sb.color_palette()[0]\n",
    "\n",
    "# left plot: violin plot\n",
    "plt.subplot(1, 2, 1)\n",
    "ax1 = sb.violinplot(data = df, x = 'gender', y = 'rider_age', color = base_color)\n",
    "\n",
    "# right plot: box plot\n",
    "plt.subplot(1, 2, 2)\n",
    "sb.boxplot(data = df, x = 'gender', y = 'rider_age', color = base_color)\n",
    "plt.ylim(ax1.get_ylim()) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The median age for both Males (1) and females (2) is 35 while the unknown gender is 50."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Summary Statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29a43017730>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.violinplot(data = df, x = 'gender', y = 'rider_age', color = base_color,\n",
    "              inner = 'quartile')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The median age for both Males (1) and females (2) is 35 while the unknown gender is 50."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rides Taken During the Week  by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Rides')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 5])\n",
    "ax = sb.countplot(data = df, x = 'start_time_weekday', hue = 'gender')\n",
    "ax.legend(loc = 2, ncol = 3, framealpha = 1, title = 'Gender')\n",
    "plt.title('Rides during the Week by Gender')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Total Rides')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most males and females take Citi bikes on Wednesday or Tuesday while unknown gender takes bikes on Saturday or Sunday."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I have observed very interesting relationships between rider's age and gender, and total rides taken during the week by gender.\n",
    "- Most males and females tend to take Citi bikes on Wednesday. \n",
    "- Those riders who are between 35 and 50 years old, take bikes for a long period of time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizations of three of more variables\n",
    "- three numeric variables\n",
    "- two numeric variables and one categorical variable\n",
    "- one numeric variable and two categorical variables\n",
    "- three categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Riders' Age vs User Type by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29a43673730>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 10])\n",
    "g = sb.FacetGrid(data = df, col = 'gender', size = 4)\n",
    "g.map(sb.boxplot, 'user_type', 'rider_age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most males and females are subscribers who are between 30 and 45, and the median age is 35. On the other hand, the unknown gender is the customer (age 40-50). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Riders' Age and Gender by User Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29a4479c250>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [10, 10])\n",
    "g = sb.FacetGrid(data = df, col = 'user_type', size = 4)\n",
    "g.map(sb.boxplot, 'gender', 'rider_age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both females and males prefer to get the annual membership."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weekly usage of Citi Bike per user type and gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 763.5x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.catplot(data= df, x = 'start_time_weekday', col = 'user_type', hue= 'gender', kind = 'count', sharey= False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customers tend to take bike on Saturday and Sunday while subscribers tend to take bike on Wednesday"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hourly usage of Citi Bike per user type and gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 763.5x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.catplot(data= df, x = 'start_time_hour', col = 'user_type', hue= 'gender', kind = 'count', sharey= False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Male customers take more bikes from 12 to 8 pm while male subscribers take bikes from 7 pm to 9 pm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://www.citibikenyc.com/system-data\n",
    "- https://s3.amazonaws.com/tripdata/index.html\n",
    "- https://en.wikipedia.org/wiki/Citi_Bike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}