Exploratory data Analysis -- Part 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Ford GoBike System Data project**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling\n",
    "1. Importing libraries\n",
    "2. [Gather](#Gather)\n",
    "3. Assess\n",
    "4. [Clean](#cell22)\n",
    "5. [Exploration](#cell78)\n",
    "### \n",
    "* [Univariate Exploration](#cell89)\n",
    "* [Bivariate Exploration](#cell132)\n",
    "* [Multivariate Exploration](#cell175)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Gather <a id=\"Gather\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"cell2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'C:\\Users\\ilesanmi\\Desktop\\Udacity\\Vis Proj\\201902-fordgobike-tripdata.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Assess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Used spreadsheet for visual assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.1450</td>\n",
       "      <td>2019-03-01 08:01:55.9750</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.789625</td>\n",
       "      <td>-122.400811</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.7890</td>\n",
       "      <td>2019-03-01 06:42:03.0560</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>2535</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.2180</td>\n",
       "      <td>2019-03-01 05:24:08.1460</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.769305</td>\n",
       "      <td>-122.426826</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.786375</td>\n",
       "      <td>-122.404904</td>\n",
       "      <td>5905</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec                start_time                  end_time  \\\n",
       "0         52185  2019-02-28 17:32:10.1450  2019-03-01 08:01:55.9750   \n",
       "1         42521  2019-02-28 18:53:21.7890  2019-03-01 06:42:03.0560   \n",
       "2         61854  2019-02-28 12:13:13.2180  2019-03-01 05:24:08.1460   \n",
       "\n",
       "   start_station_id                                start_station_name  \\\n",
       "0              21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1              23.0                     The Embarcadero at Steuart St   \n",
       "2              86.0                           Market St at Dolores St   \n",
       "\n",
       "   start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0               37.789625              -122.400811            13.0   \n",
       "1               37.791464              -122.391034            81.0   \n",
       "2               37.769305              -122.426826             3.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  \\\n",
       "0                Commercial St at Montgomery St             37.794231   \n",
       "1                            Berry St at 4th St             37.775880   \n",
       "2  Powell St BART Station (Market St at 4th St)             37.786375   \n",
       "\n",
       "   end_station_longitude  bike_id user_type  member_birth_year member_gender  \\\n",
       "0            -122.402923     4902  Customer             1984.0          Male   \n",
       "1            -122.393170     2535  Customer                NaN           NaN   \n",
       "2            -122.404904     5905  Customer             1972.0          Male   \n",
       "\n",
       "  bike_share_for_all_trip  \n",
       "0                      No  \n",
       "1                      No  \n",
       "2                      No  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 183412 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   duration_sec             183412 non-null  int64  \n",
      " 1   start_time               183412 non-null  object \n",
      " 2   end_time                 183412 non-null  object \n",
      " 3   start_station_id         183215 non-null  float64\n",
      " 4   start_station_name       183215 non-null  object \n",
      " 5   start_station_latitude   183412 non-null  float64\n",
      " 6   start_station_longitude  183412 non-null  float64\n",
      " 7   end_station_id           183215 non-null  float64\n",
      " 8   end_station_name         183215 non-null  object \n",
      " 9   end_station_latitude     183412 non-null  float64\n",
      " 10  end_station_longitude    183412 non-null  float64\n",
      " 11  bike_id                  183412 non-null  int64  \n",
      " 12  user_type                183412 non-null  object \n",
      " 13  member_birth_year        175147 non-null  float64\n",
      " 14  member_gender            175147 non-null  object \n",
      " 15  bike_share_for_all_trip  183412 non-null  object \n",
      "dtypes: float64(7), int64(2), object(7)\n",
      "memory usage: 22.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1984.0\n",
       "1            NaN\n",
       "2         1972.0\n",
       "3         1989.0\n",
       "4         1974.0\n",
       "           ...  \n",
       "183407    1996.0\n",
       "183408    1984.0\n",
       "183409    1990.0\n",
       "183410    1988.0\n",
       "183411    1989.0\n",
       "Name: member_birth_year, Length: 183412, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.member_birth_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183412, 16)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subscriber    163544\n",
       "Customer       19868\n",
       "Name: user_type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.user_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      130651\n",
       "Female     40844\n",
       "Other       3652\n",
       "Name: member_gender, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.member_gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality\n",
    "\n",
    "1. start time, end_time should be in date-time format\n",
    "2. start_station_id, end_station_id, bike_id should be object datatype \n",
    "3. member_birth_year should be an int not float\n",
    "4. inconsistent decimal place of the longitudes and latitudes\n",
    "\n",
    "5. creating an age column\n",
    "6.  start_station_id, start_station_name, end_station_id, end_station_name, member_birth_year, member_gender have missing datas\n",
    "7. missing member birth year\n",
    "8. member birth year 1878 is an outlier\n",
    "9. add a column that changes duration_sec column to realtime\n",
    "10. the member_age 119 should be 19 --- igored cause there are quite a good number of age above 100 as shown below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
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       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123833</th>\n",
       "      <td>251</td>\n",
       "      <td>2019-02-11 09:04:30.3160</td>\n",
       "      <td>2019-02-11 09:08:41.5440</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Esprit Park</td>\n",
       "      <td>37.761634</td>\n",
       "      <td>-122.390648</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Esprit Park</td>\n",
       "      <td>37.761634</td>\n",
       "      <td>-122.390648</td>\n",
       "      <td>1677</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82276</th>\n",
       "      <td>607</td>\n",
       "      <td>2019-02-18 10:43:39.7090</td>\n",
       "      <td>2019-02-18 10:53:46.8340</td>\n",
       "      <td>296.0</td>\n",
       "      <td>5th St at Virginia St</td>\n",
       "      <td>37.325998</td>\n",
       "      <td>-121.877120</td>\n",
       "      <td>279.0</td>\n",
       "      <td>Santa Clara St at 7th St</td>\n",
       "      <td>37.339146</td>\n",
       "      <td>-121.884105</td>\n",
       "      <td>3824</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133467</th>\n",
       "      <td>523</td>\n",
       "      <td>2019-02-08 18:58:02.0640</td>\n",
       "      <td>2019-02-08 19:06:46.0510</td>\n",
       "      <td>310.0</td>\n",
       "      <td>San Fernando St at 4th St</td>\n",
       "      <td>37.335885</td>\n",
       "      <td>-121.885660</td>\n",
       "      <td>294.0</td>\n",
       "      <td>Pierce Ave at Market St</td>\n",
       "      <td>37.327581</td>\n",
       "      <td>-121.884559</td>\n",
       "      <td>1086</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec                start_time                  end_time  \\\n",
       "123833           251  2019-02-11 09:04:30.3160  2019-02-11 09:08:41.5440   \n",
       "82276            607  2019-02-18 10:43:39.7090  2019-02-18 10:53:46.8340   \n",
       "133467           523  2019-02-08 18:58:02.0640  2019-02-08 19:06:46.0510   \n",
       "\n",
       "        start_station_id         start_station_name  start_station_latitude  \\\n",
       "123833             126.0                Esprit Park               37.761634   \n",
       "82276              296.0      5th St at Virginia St               37.325998   \n",
       "133467             310.0  San Fernando St at 4th St               37.335885   \n",
       "\n",
       "        start_station_longitude  end_station_id          end_station_name  \\\n",
       "123833              -122.390648           126.0               Esprit Park   \n",
       "82276               -121.877120           279.0  Santa Clara St at 7th St   \n",
       "133467              -121.885660           294.0   Pierce Ave at Market St   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "123833             37.761634            -122.390648     1677    Customer   \n",
       "82276              37.339146            -121.884105     3824  Subscriber   \n",
       "133467             37.327581            -121.884559     1086  Subscriber   \n",
       "\n",
       "        member_birth_year member_gender bike_share_for_all_trip  \n",
       "123833             1992.0        Female                      No  \n",
       "82276              1992.0        Female                     Yes  \n",
       "133467             1993.0          Male                     Yes  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 183412 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype  \n",
      "---  ------                   --------------   -----  \n",
      " 0   duration_sec             183412 non-null  int64  \n",
      " 1   start_time               183412 non-null  object \n",
      " 2   end_time                 183412 non-null  object \n",
      " 3   start_station_id         183215 non-null  float64\n",
      " 4   start_station_name       183215 non-null  object \n",
      " 5   start_station_latitude   183412 non-null  float64\n",
      " 6   start_station_longitude  183412 non-null  float64\n",
      " 7   end_station_id           183215 non-null  float64\n",
      " 8   end_station_name         183215 non-null  object \n",
      " 9   end_station_latitude     183412 non-null  float64\n",
      " 10  end_station_longitude    183412 non-null  float64\n",
      " 11  bike_id                  183412 non-null  int64  \n",
      " 12  user_type                183412 non-null  object \n",
      " 13  member_birth_year        175147 non-null  float64\n",
      " 14  member_gender            175147 non-null  object \n",
      " 15  bike_share_for_all_trip  183412 non-null  object \n",
      "dtypes: float64(7), int64(2), object(7)\n",
      "memory usage: 22.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>duration_sec</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_latitude</th>\n",
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       "      <th>bike_id</th>\n",
       "      <th>member_birth_year</th>\n",
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       "      <th>count</th>\n",
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       "      <td>183412.000000</td>\n",
       "      <td>183412.000000</td>\n",
       "      <td>175147.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>726.078435</td>\n",
       "      <td>138.590427</td>\n",
       "      <td>37.771223</td>\n",
       "      <td>-122.352664</td>\n",
       "      <td>136.249123</td>\n",
       "      <td>37.771427</td>\n",
       "      <td>-122.352250</td>\n",
       "      <td>4472.906375</td>\n",
       "      <td>1984.806437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1794.389780</td>\n",
       "      <td>111.778864</td>\n",
       "      <td>0.099581</td>\n",
       "      <td>0.117097</td>\n",
       "      <td>111.515131</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.116673</td>\n",
       "      <td>1664.383394</td>\n",
       "      <td>10.116689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>37.317298</td>\n",
       "      <td>-122.453704</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1878.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>37.770083</td>\n",
       "      <td>-122.412408</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>37.770407</td>\n",
       "      <td>-122.411726</td>\n",
       "      <td>3777.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.398285</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>37.781010</td>\n",
       "      <td>-122.398279</td>\n",
       "      <td>4958.000000</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>796.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>37.797280</td>\n",
       "      <td>-122.286533</td>\n",
       "      <td>235.000000</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.288045</td>\n",
       "      <td>5502.000000</td>\n",
       "      <td>1992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85444.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>37.880222</td>\n",
       "      <td>-121.874119</td>\n",
       "      <td>6645.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec  start_station_id  start_station_latitude  \\\n",
       "count  183412.000000     183215.000000           183412.000000   \n",
       "mean      726.078435        138.590427               37.771223   \n",
       "std      1794.389780        111.778864                0.099581   \n",
       "min        61.000000          3.000000               37.317298   \n",
       "25%       325.000000         47.000000               37.770083   \n",
       "50%       514.000000        104.000000               37.780760   \n",
       "75%       796.000000        239.000000               37.797280   \n",
       "max     85444.000000        398.000000               37.880222   \n",
       "\n",
       "       start_station_longitude  end_station_id  end_station_latitude  \\\n",
       "count            183412.000000   183215.000000         183412.000000   \n",
       "mean               -122.352664      136.249123             37.771427   \n",
       "std                   0.117097      111.515131              0.099490   \n",
       "min                -122.453704        3.000000             37.317298   \n",
       "25%                -122.412408       44.000000             37.770407   \n",
       "50%                -122.398285      100.000000             37.781010   \n",
       "75%                -122.286533      235.000000             37.797320   \n",
       "max                -121.874119      398.000000             37.880222   \n",
       "\n",
       "       end_station_longitude        bike_id  member_birth_year  \n",
       "count          183412.000000  183412.000000      175147.000000  \n",
       "mean             -122.352250    4472.906375        1984.806437  \n",
       "std                 0.116673    1664.383394          10.116689  \n",
       "min              -122.453704      11.000000        1878.000000  \n",
       "25%              -122.411726    3777.000000        1980.000000  \n",
       "50%              -122.398279    4958.000000        1987.000000  \n",
       "75%              -122.288045    5502.000000        1992.000000  \n",
       "max              -121.874119    6645.000000        2001.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clean <a id=\"cell22\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. missing member birth year\n",
    "i'll be needing the the member_birth_age column to deal with age of riders. Hence the need to replace it by the mean birth year (approximate mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    175147.000000\n",
       "mean       1984.806437\n",
       "std          10.116689\n",
       "min        1878.000000\n",
       "25%        1980.000000\n",
       "50%        1987.000000\n",
       "75%        1992.000000\n",
       "max        2001.000000\n",
       "Name: member_birth_year, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['member_birth_year'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['member_birth_year'].fillna(value = 1985, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    183412.000000\n",
       "mean       1984.815159\n",
       "std           9.886200\n",
       "min        1878.000000\n",
       "25%        1981.000000\n",
       "50%        1987.000000\n",
       "75%        1992.000000\n",
       "max        2001.000000\n",
       "Name: member_birth_year, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['member_birth_year'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define\n",
    "8. Remove the birth year 1878"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27370</th>\n",
       "      <td>1474</td>\n",
       "      <td>2019-02-25 09:28:42.1610</td>\n",
       "      <td>2019-02-25 09:53:17.0680</td>\n",
       "      <td>15.0</td>\n",
       "      <td>San Francisco Ferry Building (Harry Bridges Pl...</td>\n",
       "      <td>37.795392</td>\n",
       "      <td>-122.394203</td>\n",
       "      <td>386.0</td>\n",
       "      <td>24th St at Bartlett St</td>\n",
       "      <td>37.752105</td>\n",
       "      <td>-122.419724</td>\n",
       "      <td>6436</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1878.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_sec                start_time                  end_time  \\\n",
       "27370          1474  2019-02-25 09:28:42.1610  2019-02-25 09:53:17.0680   \n",
       "\n",
       "       start_station_id                                 start_station_name  \\\n",
       "27370              15.0  San Francisco Ferry Building (Harry Bridges Pl...   \n",
       "\n",
       "       start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "27370               37.795392              -122.394203           386.0   \n",
       "\n",
       "             end_station_name  end_station_latitude  end_station_longitude  \\\n",
       "27370  24th St at Bartlett St             37.752105            -122.419724   \n",
       "\n",
       "       bike_id user_type  member_birth_year member_gender  \\\n",
       "27370     6436  Customer             1878.0        Female   \n",
       "\n",
       "      bike_share_for_all_trip  \n",
       "27370                      No  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['member_birth_year'] == 1878]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = df.drop(df.index[27370], axis = 0,inplace = True)\n",
    "df = df.drop(27370)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "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>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [duration_sec, start_time, end_time, start_station_id, start_station_name, start_station_latitude, start_station_longitude, end_station_id, end_station_name, end_station_latitude, end_station_longitude, bike_id, user_type, member_birth_year, member_gender, bike_share_for_all_trip]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['member_birth_year'] == 1878]\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "1. change start_time column to date-time\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['start_time'] = pd.to_datetime(df['start_time'])\n",
    "df['end_time'] = pd.to_datetime(df['end_time'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183411 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183411 non-null  int64         \n",
      " 1   start_time               183411 non-null  datetime64[ns]\n",
      " 2   end_time                 183411 non-null  datetime64[ns]\n",
      " 3   start_station_id         183214 non-null  float64       \n",
      " 4   start_station_name       183214 non-null  object        \n",
      " 5   start_station_latitude   183411 non-null  float64       \n",
      " 6   start_station_longitude  183411 non-null  float64       \n",
      " 7   end_station_id           183214 non-null  float64       \n",
      " 8   end_station_name         183214 non-null  object        \n",
      " 9   end_station_latitude     183411 non-null  float64       \n",
      " 10  end_station_longitude    183411 non-null  float64       \n",
      " 11  bike_id                  183411 non-null  int64         \n",
      " 12  user_type                183411 non-null  object        \n",
      " 13  member_birth_year        183411 non-null  float64       \n",
      " 14  member_gender            175146 non-null  object        \n",
      " 15  bike_share_for_all_trip  183411 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(7), int64(2), object(5)\n",
      "memory usage: 23.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define:\n",
    "2. changing member_birth_year from float to int"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.member_birth_year = df.member_birth_year.apply('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183411 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183411 non-null  int64         \n",
      " 1   start_time               183411 non-null  datetime64[ns]\n",
      " 2   end_time                 183411 non-null  datetime64[ns]\n",
      " 3   start_station_id         183214 non-null  float64       \n",
      " 4   start_station_name       183214 non-null  object        \n",
      " 5   start_station_latitude   183411 non-null  float64       \n",
      " 6   start_station_longitude  183411 non-null  float64       \n",
      " 7   end_station_id           183214 non-null  float64       \n",
      " 8   end_station_name         183214 non-null  object        \n",
      " 9   end_station_latitude     183411 non-null  float64       \n",
      " 10  end_station_longitude    183411 non-null  float64       \n",
      " 11  bike_id                  183411 non-null  int64         \n",
      " 12  user_type                183411 non-null  object        \n",
      " 13  member_birth_year        183411 non-null  int64         \n",
      " 14  member_gender            175146 non-null  object        \n",
      " 15  bike_share_for_all_trip  183411 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(6), int64(3), object(5)\n",
      "memory usage: 23.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96977</th>\n",
       "      <td>274</td>\n",
       "      <td>2019-02-15 09:31:57.725</td>\n",
       "      <td>2019-02-15 09:36:31.808</td>\n",
       "      <td>195.0</td>\n",
       "      <td>Bay Pl at Vernon St</td>\n",
       "      <td>37.812314</td>\n",
       "      <td>-122.260779</td>\n",
       "      <td>182.0</td>\n",
       "      <td>19th Street BART Station</td>\n",
       "      <td>37.809013</td>\n",
       "      <td>-122.268247</td>\n",
       "      <td>2965</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_sec              start_time                end_time  \\\n",
       "96977           274 2019-02-15 09:31:57.725 2019-02-15 09:36:31.808   \n",
       "\n",
       "       start_station_id   start_station_name  start_station_latitude  \\\n",
       "96977             195.0  Bay Pl at Vernon St               37.812314   \n",
       "\n",
       "       start_station_longitude  end_station_id          end_station_name  \\\n",
       "96977              -122.260779           182.0  19th Street BART Station   \n",
       "\n",
       "       end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "96977             37.809013            -122.268247     2965  Subscriber   \n",
       "\n",
       "       member_birth_year member_gender bike_share_for_all_trip  \n",
       "96977               1989          Male                      No  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "3. Columns: start_station_id,end_station_id, end_station_id, bike_id should be object and not float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['start_station_id','end_station_id', 'bike_id' ]] = df[['start_station_id','end_station_id', 'bike_id' ]].astype(object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183411 entries, 0 to 183411\n",
      "Data columns (total 16 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183411 non-null  int64         \n",
      " 1   start_time               183411 non-null  datetime64[ns]\n",
      " 2   end_time                 183411 non-null  datetime64[ns]\n",
      " 3   start_station_id         183214 non-null  object        \n",
      " 4   start_station_name       183214 non-null  object        \n",
      " 5   start_station_latitude   183411 non-null  float64       \n",
      " 6   start_station_longitude  183411 non-null  float64       \n",
      " 7   end_station_id           183214 non-null  object        \n",
      " 8   end_station_name         183214 non-null  object        \n",
      " 9   end_station_latitude     183411 non-null  float64       \n",
      " 10  end_station_longitude    183411 non-null  float64       \n",
      " 11  bike_id                  183411 non-null  object        \n",
      " 12  user_type                183411 non-null  object        \n",
      " 13  member_birth_year        183411 non-null  int64         \n",
      " 14  member_gender            175146 non-null  object        \n",
      " 15  bike_share_for_all_trip  183411 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(4), int64(2), object(8)\n",
      "memory usage: 23.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11865</th>\n",
       "      <td>569</td>\n",
       "      <td>2019-02-27 18:08:49.554</td>\n",
       "      <td>2019-02-27 18:18:18.715</td>\n",
       "      <td>280.0</td>\n",
       "      <td>San Fernando St at 7th St</td>\n",
       "      <td>37.337122</td>\n",
       "      <td>-121.883215</td>\n",
       "      <td>296.0</td>\n",
       "      <td>5th St at Virginia St</td>\n",
       "      <td>37.325998</td>\n",
       "      <td>-121.87712</td>\n",
       "      <td>5895</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_sec              start_time                end_time  \\\n",
       "11865           569 2019-02-27 18:08:49.554 2019-02-27 18:18:18.715   \n",
       "\n",
       "      start_station_id         start_station_name  start_station_latitude  \\\n",
       "11865            280.0  San Fernando St at 7th St               37.337122   \n",
       "\n",
       "       start_station_longitude end_station_id       end_station_name  \\\n",
       "11865              -121.883215          296.0  5th St at Virginia St   \n",
       "\n",
       "       end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "11865             37.325998             -121.87712    5895  Subscriber   \n",
       "\n",
       "       member_birth_year member_gender bike_share_for_all_trip  \n",
       "11865               1995        Female                     Yes  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define:\n",
    "4. Make the decimal place of the longitudes and latitudes column consistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude']] = df[['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude']].round(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37.78963</td>\n",
       "      <td>-122.40081</td>\n",
       "      <td>37.79423</td>\n",
       "      <td>-122.40292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37.79146</td>\n",
       "      <td>-122.39103</td>\n",
       "      <td>37.77588</td>\n",
       "      <td>-122.39317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.76931</td>\n",
       "      <td>-122.42683</td>\n",
       "      <td>37.78638</td>\n",
       "      <td>-122.40490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.77484</td>\n",
       "      <td>-122.44655</td>\n",
       "      <td>37.77331</td>\n",
       "      <td>-122.44429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.80456</td>\n",
       "      <td>-122.27174</td>\n",
       "      <td>37.79271</td>\n",
       "      <td>-122.24878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183407</th>\n",
       "      <td>37.78806</td>\n",
       "      <td>-122.39186</td>\n",
       "      <td>37.78830</td>\n",
       "      <td>-122.40853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183408</th>\n",
       "      <td>37.78963</td>\n",
       "      <td>-122.40081</td>\n",
       "      <td>37.77874</td>\n",
       "      <td>-122.39274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183409</th>\n",
       "      <td>37.33193</td>\n",
       "      <td>-121.90489</td>\n",
       "      <td>37.33366</td>\n",
       "      <td>-121.90859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183410</th>\n",
       "      <td>37.81135</td>\n",
       "      <td>-122.27342</td>\n",
       "      <td>37.81783</td>\n",
       "      <td>-122.27570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183411</th>\n",
       "      <td>37.78968</td>\n",
       "      <td>-122.39043</td>\n",
       "      <td>37.78500</td>\n",
       "      <td>-122.39594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>183411 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        start_station_latitude  start_station_longitude  end_station_latitude  \\\n",
       "0                     37.78963               -122.40081              37.79423   \n",
       "1                     37.79146               -122.39103              37.77588   \n",
       "2                     37.76931               -122.42683              37.78638   \n",
       "3                     37.77484               -122.44655              37.77331   \n",
       "4                     37.80456               -122.27174              37.79271   \n",
       "...                        ...                      ...                   ...   \n",
       "183407                37.78806               -122.39186              37.78830   \n",
       "183408                37.78963               -122.40081              37.77874   \n",
       "183409                37.33193               -121.90489              37.33366   \n",
       "183410                37.81135               -122.27342              37.81783   \n",
       "183411                37.78968               -122.39043              37.78500   \n",
       "\n",
       "        end_station_longitude  \n",
       "0                  -122.40292  \n",
       "1                  -122.39317  \n",
       "2                  -122.40490  \n",
       "3                  -122.44429  \n",
       "4                  -122.24878  \n",
       "...                       ...  \n",
       "183407             -122.40853  \n",
       "183408             -122.39274  \n",
       "183409             -121.90859  \n",
       "183410             -122.27570  \n",
       "183411             -122.39594  \n",
       "\n",
       "[183411 rows x 4 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['start_station_latitude', 'start_station_longitude', 'end_station_latitude', 'end_station_longitude']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>member_birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>183411.000000</td>\n",
       "      <td>183411.000000</td>\n",
       "      <td>183411.000000</td>\n",
       "      <td>183411.000000</td>\n",
       "      <td>183411.000000</td>\n",
       "      <td>183411.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>726.074358</td>\n",
       "      <td>37.771223</td>\n",
       "      <td>-122.352663</td>\n",
       "      <td>37.771427</td>\n",
       "      <td>-122.352249</td>\n",
       "      <td>1984.815742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1794.393822</td>\n",
       "      <td>0.099582</td>\n",
       "      <td>0.117097</td>\n",
       "      <td>0.099490</td>\n",
       "      <td>0.116674</td>\n",
       "      <td>9.883080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>37.317300</td>\n",
       "      <td>-122.453700</td>\n",
       "      <td>37.317300</td>\n",
       "      <td>-122.453700</td>\n",
       "      <td>1900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>325.000000</td>\n",
       "      <td>37.770080</td>\n",
       "      <td>-122.412410</td>\n",
       "      <td>37.770410</td>\n",
       "      <td>-122.411730</td>\n",
       "      <td>1981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>514.000000</td>\n",
       "      <td>37.780760</td>\n",
       "      <td>-122.398280</td>\n",
       "      <td>37.781010</td>\n",
       "      <td>-122.398280</td>\n",
       "      <td>1987.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>796.000000</td>\n",
       "      <td>37.797280</td>\n",
       "      <td>-122.286530</td>\n",
       "      <td>37.797320</td>\n",
       "      <td>-122.288050</td>\n",
       "      <td>1992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>85444.000000</td>\n",
       "      <td>37.880220</td>\n",
       "      <td>-121.874120</td>\n",
       "      <td>37.880220</td>\n",
       "      <td>-121.874120</td>\n",
       "      <td>2001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec  start_station_latitude  start_station_longitude  \\\n",
       "count  183411.000000           183411.000000            183411.000000   \n",
       "mean      726.074358               37.771223              -122.352663   \n",
       "std      1794.393822                0.099582                 0.117097   \n",
       "min        61.000000               37.317300              -122.453700   \n",
       "25%       325.000000               37.770080              -122.412410   \n",
       "50%       514.000000               37.780760              -122.398280   \n",
       "75%       796.000000               37.797280              -122.286530   \n",
       "max     85444.000000               37.880220              -121.874120   \n",
       "\n",
       "       end_station_latitude  end_station_longitude  member_birth_year  \n",
       "count         183411.000000          183411.000000      183411.000000  \n",
       "mean              37.771427            -122.352249        1984.815742  \n",
       "std                0.099490               0.116674           9.883080  \n",
       "min               37.317300            -122.453700        1900.000000  \n",
       "25%               37.770410            -122.411730        1981.000000  \n",
       "50%               37.781010            -122.398280        1987.000000  \n",
       "75%               37.797320            -122.288050        1992.000000  \n",
       "max               37.880220            -121.874120        2001.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define:\n",
    "5. add an age column -- 2019 - member_birth_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['member_age'] = 2019 - df['member_birth_year']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
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       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132272</th>\n",
       "      <td>783</td>\n",
       "      <td>2019-02-09 08:39:23.838</td>\n",
       "      <td>2019-02-09 08:52:26.967</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Valencia St at 16th St</td>\n",
       "      <td>37.76505</td>\n",
       "      <td>-122.42187</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.77643</td>\n",
       "      <td>-122.42624</td>\n",
       "      <td>5034</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1995</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "132272           783 2019-02-09 08:39:23.838 2019-02-09 08:52:26.967   \n",
       "\n",
       "       start_station_id      start_station_name  start_station_latitude  \\\n",
       "132272             98.0  Valencia St at 16th St                37.76505   \n",
       "\n",
       "        start_station_longitude end_station_id       end_station_name  \\\n",
       "132272               -122.42187           74.0  Laguna St at Hayes St   \n",
       "\n",
       "        end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "132272              37.77643             -122.42624    5034  Subscriber   \n",
       "\n",
       "        member_birth_year member_gender bike_share_for_all_trip  member_age  \n",
       "132272               1995          Male                      No          24  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define:\n",
    "6.  start_station_id, start_station_name, end_station_id, end_station_name, member_birth_year, member_gender have missing datas\n",
    "\n",
    "*  drop columns with missing start_station_id as we are only to track those in this particular station. And a missing start_id indicates its not of this StopIteration\n",
    "* I also observed that those same columns had their start_station_name, end_station_id, end_station_name empty... Hence dropping them is okay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df[df['start_station_id'].isna() == False]\n",
    "#df['start_station_id'].dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183214 entries, 0 to 183411\n",
      "Data columns (total 17 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183214 non-null  int64         \n",
      " 1   start_time               183214 non-null  datetime64[ns]\n",
      " 2   end_time                 183214 non-null  datetime64[ns]\n",
      " 3   start_station_id         183214 non-null  object        \n",
      " 4   start_station_name       183214 non-null  object        \n",
      " 5   start_station_latitude   183214 non-null  float64       \n",
      " 6   start_station_longitude  183214 non-null  float64       \n",
      " 7   end_station_id           183214 non-null  object        \n",
      " 8   end_station_name         183214 non-null  object        \n",
      " 9   end_station_latitude     183214 non-null  float64       \n",
      " 10  end_station_longitude    183214 non-null  float64       \n",
      " 11  bike_id                  183214 non-null  object        \n",
      " 12  user_type                183214 non-null  object        \n",
      " 13  member_birth_year        183214 non-null  int64         \n",
      " 14  member_gender            174951 non-null  object        \n",
      " 15  bike_share_for_all_trip  183214 non-null  object        \n",
      " 16  member_age               183214 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(4), int64(3), object(8)\n",
      "memory usage: 25.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hence now, only the member gender is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9. add a column that changes duration_sec column to realtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-34-c6fc9127901d>:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['duration_time'] = df_new['duration_sec'].apply(lambda i: str(datetime.timedelta(seconds=i)))\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "# # df['duration_time'] = str(datetime.timedelta(seconds=df['duration_sec']))\n",
    "\n",
    "# # df['duration_time'] = pd.to_datetime(df['duration_time'])\n",
    "\n",
    "# def change_to_datetime():\n",
    "#     x = []\n",
    "#     for i in range(len(df.duration_sec)):\n",
    "#         x.append(str(datetime.timedelta(seconds=i)))\n",
    "#     #print(x)\n",
    "#     try:\n",
    "#         df['duration_time'] = x\n",
    "#     except:\n",
    "#         return None\n",
    "\n",
    "#     #to date-time column\n",
    "#     try:\n",
    "#         df['duration_time'] = pd.to_datetime(df['duration_time'])\n",
    "#     except:\n",
    "#         return None\n",
    "\n",
    "# change_to_datetime()\n",
    "\n",
    "df_new['duration_time'] = df_new['duration_sec'].apply(lambda i: str(datetime.timedelta(seconds=i)))\n",
    "#error cause of the presence of '1 day...'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>duration_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>143601</th>\n",
       "      <td>235</td>\n",
       "      <td>2019-02-07 14:56:55.115</td>\n",
       "      <td>2019-02-07 15:00:50.624</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Market St at 10th St</td>\n",
       "      <td>37.77662</td>\n",
       "      <td>-122.41738</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Civic Center/UN Plaza BART Station (Market St ...</td>\n",
       "      <td>37.78107</td>\n",
       "      <td>-122.41174</td>\n",
       "      <td>1497</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1996</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>0:03:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127926</th>\n",
       "      <td>1699</td>\n",
       "      <td>2019-02-10 14:13:55.105</td>\n",
       "      <td>2019-02-10 14:42:14.478</td>\n",
       "      <td>311.0</td>\n",
       "      <td>Paseo De San Antonio at 2nd St</td>\n",
       "      <td>37.33380</td>\n",
       "      <td>-121.88694</td>\n",
       "      <td>310.0</td>\n",
       "      <td>San Fernando St at 4th St</td>\n",
       "      <td>37.33588</td>\n",
       "      <td>-121.88566</td>\n",
       "      <td>4035</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1998</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21</td>\n",
       "      <td>0:28:19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "143601           235 2019-02-07 14:56:55.115 2019-02-07 15:00:50.624   \n",
       "127926          1699 2019-02-10 14:13:55.105 2019-02-10 14:42:14.478   \n",
       "\n",
       "       start_station_id              start_station_name  \\\n",
       "143601             58.0            Market St at 10th St   \n",
       "127926            311.0  Paseo De San Antonio at 2nd St   \n",
       "\n",
       "        start_station_latitude  start_station_longitude end_station_id  \\\n",
       "143601                37.77662               -122.41738           44.0   \n",
       "127926                37.33380               -121.88694          310.0   \n",
       "\n",
       "                                         end_station_name  \\\n",
       "143601  Civic Center/UN Plaza BART Station (Market St ...   \n",
       "127926                          San Fernando St at 4th St   \n",
       "\n",
       "        end_station_latitude  end_station_longitude bike_id   user_type  \\\n",
       "143601              37.78107             -122.41174    1497  Subscriber   \n",
       "127926              37.33588             -121.88566    4035  Subscriber   \n",
       "\n",
       "        member_birth_year member_gender bike_share_for_all_trip  member_age  \\\n",
       "143601               1996          Male                      No          23   \n",
       "127926               1998          Male                     Yes          21   \n",
       "\n",
       "       duration_time  \n",
       "143601       0:03:55  \n",
       "127926       0:28:19  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#sns.countplot(df.duration_time)\n",
    "plt.hist(df.duration_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183411 entries, 0 to 183411\n",
      "Data columns (total 17 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183411 non-null  int64         \n",
      " 1   start_time               183411 non-null  datetime64[ns]\n",
      " 2   end_time                 183411 non-null  datetime64[ns]\n",
      " 3   start_station_id         183214 non-null  object        \n",
      " 4   start_station_name       183214 non-null  object        \n",
      " 5   start_station_latitude   183411 non-null  float64       \n",
      " 6   start_station_longitude  183411 non-null  float64       \n",
      " 7   end_station_id           183214 non-null  object        \n",
      " 8   end_station_name         183214 non-null  object        \n",
      " 9   end_station_latitude     183411 non-null  float64       \n",
      " 10  end_station_longitude    183411 non-null  float64       \n",
      " 11  bike_id                  183411 non-null  object        \n",
      " 12  user_type                183411 non-null  object        \n",
      " 13  member_birth_year        183411 non-null  int64         \n",
      " 14  member_gender            175146 non-null  object        \n",
      " 15  bike_share_for_all_trip  183411 non-null  object        \n",
      " 16  member_age               183411 non-null  int64         \n",
      "dtypes: datetime64[ns](2), float64(4), int64(3), object(8)\n",
      "memory usage: 25.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploration** <a id=\"cell78\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-38-e2688a207c26>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['start_month'] = df_new['start_time'].apply(lambda time: time.month)\n"
     ]
    }
   ],
   "source": [
    "df_new['start_month'] = df_new['start_time'].apply(lambda time: time.month)\n",
    "#mc = df['start_month_num'].apply(lambda x: calendar.month_abbr[x])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    183214\n",
       "Name: start_month, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['start_month'].value_counts()\n",
    "\n",
    "#just a month present -- february"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-b22b47388521>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['start_hour'] = df_new['start_time'].apply(lambda time: time.hour)\n",
      "<ipython-input-40-b22b47388521>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['day_time'] = 'morning'\n",
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n",
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n",
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "morning      79111\n",
       "evening      48532\n",
       "afternoon    39979\n",
       "night        15592\n",
       "Name: day_time, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # b. morning, afternoon, night of a day\n",
    "df_new['start_hour'] = df_new['start_time'].apply(lambda time: time.hour)\n",
    "df_new['day_time'] = 'morning'\n",
    "\n",
    "df_new.loc[(df_new['start_hour'] > 12) & (df_new['start_hour'] <= 16), 'day_time'] = 'afternoon'\n",
    "df_new.loc[(df_new['start_hour'] > 16) & (df_new['start_hour'] <= 19), 'day_time'] = 'evening'\n",
    "\n",
    "df_new.loc[(df_new['start_hour'] > 19) & (df_new['start_hour'] <= 123), 'day_time'] = 'night'\n",
    "\n",
    "df_new['day_time'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_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>...</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>duration_time</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>day_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.78963</td>\n",
       "      <td>-122.40081</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.79423</td>\n",
       "      <td>...</td>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>14:29:45</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.789</td>\n",
       "      <td>2019-03-01 06:42:03.056</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.79146</td>\n",
       "      <td>-122.39103</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.77588</td>\n",
       "      <td>...</td>\n",
       "      <td>2535</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>11:48:41</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.218</td>\n",
       "      <td>2019-03-01 05:24:08.146</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.76931</td>\n",
       "      <td>-122.42683</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.78638</td>\n",
       "      <td>...</td>\n",
       "      <td>5905</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1972</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>47</td>\n",
       "      <td>17:10:54</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.010</td>\n",
       "      <td>2019-03-01 04:02:36.842</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.77484</td>\n",
       "      <td>-122.44655</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.77331</td>\n",
       "      <td>...</td>\n",
       "      <td>6638</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1989</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>10:08:10</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.549</td>\n",
       "      <td>2019-03-01 00:20:44.074</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.80456</td>\n",
       "      <td>-122.27174</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.79271</td>\n",
       "      <td>...</td>\n",
       "      <td>4898</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1974</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>45</td>\n",
       "      <td>0:26:25</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>night</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time                end_time  \\\n",
       "0         52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "1         42521 2019-02-28 18:53:21.789 2019-03-01 06:42:03.056   \n",
       "2         61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "3         36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842   \n",
       "4          1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074   \n",
       "\n",
       "  start_station_id                                start_station_name  \\\n",
       "0             21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1             23.0                     The Embarcadero at Steuart St   \n",
       "2             86.0                           Market St at Dolores St   \n",
       "3            375.0                           Grove St at Masonic Ave   \n",
       "4              7.0                               Frank H Ogawa Plaza   \n",
       "\n",
       "   start_station_latitude  start_station_longitude end_station_id  \\\n",
       "0                37.78963               -122.40081           13.0   \n",
       "1                37.79146               -122.39103           81.0   \n",
       "2                37.76931               -122.42683            3.0   \n",
       "3                37.77484               -122.44655           70.0   \n",
       "4                37.80456               -122.27174          222.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  ...  \\\n",
       "0                Commercial St at Montgomery St              37.79423  ...   \n",
       "1                            Berry St at 4th St              37.77588  ...   \n",
       "2  Powell St BART Station (Market St at 4th St)              37.78638  ...   \n",
       "3                        Central Ave at Fell St              37.77331  ...   \n",
       "4                         10th Ave at E 15th St              37.79271  ...   \n",
       "\n",
       "   bike_id   user_type member_birth_year  member_gender  \\\n",
       "0     4902    Customer              1984           Male   \n",
       "1     2535    Customer              1985            NaN   \n",
       "2     5905    Customer              1972           Male   \n",
       "3     6638  Subscriber              1989          Other   \n",
       "4     4898  Subscriber              1974           Male   \n",
       "\n",
       "  bike_share_for_all_trip member_age  duration_time start_month  start_hour  \\\n",
       "0                      No         35       14:29:45           2          17   \n",
       "1                      No         34       11:48:41           2          18   \n",
       "2                      No         47       17:10:54           2          12   \n",
       "3                      No         30       10:08:10           2          17   \n",
       "4                     Yes         45        0:26:25           2          23   \n",
       "\n",
       "   day_time  \n",
       "0   evening  \n",
       "1   evening  \n",
       "2   morning  \n",
       "3   evening  \n",
       "4     night  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183214 entries, 0 to 183411\n",
      "Data columns (total 21 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183214 non-null  int64         \n",
      " 1   start_time               183214 non-null  datetime64[ns]\n",
      " 2   end_time                 183214 non-null  datetime64[ns]\n",
      " 3   start_station_id         183214 non-null  object        \n",
      " 4   start_station_name       183214 non-null  object        \n",
      " 5   start_station_latitude   183214 non-null  float64       \n",
      " 6   start_station_longitude  183214 non-null  float64       \n",
      " 7   end_station_id           183214 non-null  object        \n",
      " 8   end_station_name         183214 non-null  object        \n",
      " 9   end_station_latitude     183214 non-null  float64       \n",
      " 10  end_station_longitude    183214 non-null  float64       \n",
      " 11  bike_id                  183214 non-null  object        \n",
      " 12  user_type                183214 non-null  object        \n",
      " 13  member_birth_year        183214 non-null  int64         \n",
      " 14  member_gender            174951 non-null  object        \n",
      " 15  bike_share_for_all_trip  183214 non-null  object        \n",
      " 16  member_age               183214 non-null  int64         \n",
      " 17  duration_time            183214 non-null  object        \n",
      " 18  start_month              183214 non-null  int64         \n",
      " 19  start_hour               183214 non-null  int64         \n",
      " 20  day_time                 183214 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(4), int64(5), object(10)\n",
      "memory usage: 30.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "<a id=\"cell89\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "* The most common gender "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def myCountPlot(df, xVar, order = None, hueOrder = None, palette = None, figsize=[14,10]):\n",
    "    plt.figure(figsize=figsize)\n",
    "\n",
    "    c=sns.countplot(data=df, y = xVar, edgecolor = 'black', order = order, hue_order = hueOrder, palette=palette)\n",
    "    xVar = xVar.replace('_', ' ')\n",
    "    #hueVar = hueVar.replace('_', ' ')\n",
    "    plt.title(f'''Distribution of {xVar}'''.title(), fontsize = 14, weight = 'bold')\n",
    "    plt.ylabel(xVar.title(), fontsize=10, weight ='bold')\n",
    "    plt.xlabel('Frequency'.title(), fontsize = 10, weight = 'bold')\n",
    "\n",
    "myCountPlot(df_new, 'member_gender')\n",
    "# df_new.member_gender.value_counts().plot(kind = 'barh');\n",
    "# plt.title('Gender count')\n",
    "# plt.ylabel('Gender')\n",
    "# plt.xlabel('Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### **Observation:** We have more male than females probably because Males tends to be more interested in cycling than Females"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###\n",
    "* What period of the day do people bike alot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "morning      79111\n",
       "evening      48532\n",
       "afternoon    39979\n",
       "night        15592\n",
       "Name: day_time, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['day_time'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myCountPlot(df_new, 'day_time')\n",
    "\n",
    "\n",
    "# df_new['day_time'].value_counts().plot(kind = 'barh');\n",
    "# plt.title('day time people bike')\n",
    "# plt.xlabel('Count')\n",
    "# plt.ylabel('Time of the day')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observation:** During the day, there are more trips in the morning and afternoon than at night. It probably because of rush hours. There are more rides in the afternoon... this i believe is cause of students who return frm school and goes biking. And then, at night, peeps tend return home\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* what hour of the day do they bike mostly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.figure(figsize = (12,8))\n",
    "# df_new.start_hour.value_counts().plot(kind = 'bar')\n",
    "# plt.title('Hours people bike mostly')\n",
    "# plt.xlabel('Hours')\n",
    "# plt.ylabel('Count')\n",
    "\n",
    "\n",
    "myCountPlot(df_new, 'start_hour', palette='winter_r')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observation:** As seen, the most time of the day people bike is in the evening-- 5pm and morning -- 8am. This could be due to time they close from work and time they resumed work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's compare with the end station names and time\n",
    "### Are there more rides at the end station than at the start station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.78963</td>\n",
       "      <td>-122.40081</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.79423</td>\n",
       "      <td>-122.40292</td>\n",
       "      <td>4902</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1984</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.789</td>\n",
       "      <td>2019-03-01 06:42:03.056</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.79146</td>\n",
       "      <td>-122.39103</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.77588</td>\n",
       "      <td>-122.39317</td>\n",
       "      <td>2535</td>\n",
       "      <td>Customer</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time                end_time  \\\n",
       "0         52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "1         42521 2019-02-28 18:53:21.789 2019-03-01 06:42:03.056   \n",
       "\n",
       "  start_station_id                                start_station_name  \\\n",
       "0             21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1             23.0                     The Embarcadero at Steuart St   \n",
       "\n",
       "   start_station_latitude  start_station_longitude end_station_id  \\\n",
       "0                37.78963               -122.40081           13.0   \n",
       "1                37.79146               -122.39103           81.0   \n",
       "\n",
       "                 end_station_name  end_station_latitude  \\\n",
       "0  Commercial St at Montgomery St              37.79423   \n",
       "1              Berry St at 4th St              37.77588   \n",
       "\n",
       "   end_station_longitude bike_id user_type  member_birth_year member_gender  \\\n",
       "0             -122.40292    4902  Customer               1984          Male   \n",
       "1             -122.39317    2535  Customer               1985           NaN   \n",
       "\n",
       "  bike_share_for_all_trip  member_age  \n",
       "0                      No          35  \n",
       "1                      No          34  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 183214 entries, 0 to 183411\n",
      "Data columns (total 21 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   duration_sec             183214 non-null  int64         \n",
      " 1   start_time               183214 non-null  datetime64[ns]\n",
      " 2   end_time                 183214 non-null  datetime64[ns]\n",
      " 3   start_station_id         183214 non-null  object        \n",
      " 4   start_station_name       183214 non-null  object        \n",
      " 5   start_station_latitude   183214 non-null  float64       \n",
      " 6   start_station_longitude  183214 non-null  float64       \n",
      " 7   end_station_id           183214 non-null  object        \n",
      " 8   end_station_name         183214 non-null  object        \n",
      " 9   end_station_latitude     183214 non-null  float64       \n",
      " 10  end_station_longitude    183214 non-null  float64       \n",
      " 11  bike_id                  183214 non-null  object        \n",
      " 12  user_type                183214 non-null  object        \n",
      " 13  member_birth_year        183214 non-null  int64         \n",
      " 14  member_gender            174951 non-null  object        \n",
      " 15  bike_share_for_all_trip  183214 non-null  object        \n",
      " 16  member_age               183214 non-null  int64         \n",
      " 17  duration_time            183214 non-null  object        \n",
      " 18  start_month              183214 non-null  int64         \n",
      " 19  start_hour               183214 non-null  int64         \n",
      " 20  day_time                 183214 non-null  object        \n",
      "dtypes: datetime64[ns](2), float64(4), int64(5), object(10)\n",
      "memory usage: 34.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-49-2c9267051aa8>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['end_hour'] = df_new['end_time'].apply(lambda time: time.hour)\n",
      "<ipython-input-49-2c9267051aa8>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_new['return_time'] = 'morning'\n",
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n",
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n",
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    " # b. morning, afternoon, night of a day\n",
    "# df_new['end_hour'] = df_new['end_time'].apply(lambda time: time.hour)\n",
    "# df_new['return_time'] = 'morning'\n",
    "# df_new['return_time'][(df_new['end_hour'] > 12) & (df_new['end_hour'] <= 16)] = 'afternoon'\n",
    "# df_new['return_time'][(df_new['end_hour'] > 16) & (df_new['start_hour'] <= 19)] = 'evening'\n",
    "\n",
    "# df_new['return_time'][(df_new['end_hour'] > 19) & (df_new['end_hour'] <= 23)] = 'night'\n",
    "\n",
    "\n",
    "df_new['end_hour'] = df_new['end_time'].apply(lambda time: time.hour)\n",
    "df_new['return_time'] = 'morning'\n",
    "\n",
    "df_new.loc[(df_new['end_hour'] > 12) & (df_new['end_hour'] <= 16), 'return_time'] = 'afternoon'\n",
    "df_new.loc[(df_new['end_hour'] > 16) & (df_new['end_hour'] <= 16), 'return_time'] = 'evening'\n",
    "\n",
    "df_new.loc[(df_new['end_hour'] > 19) & (df_new['end_hour'] <= 16), 'return_time'] = 'night'\n",
    "\n",
    "#df_new['day_time'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'return time people bike')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,8))\n",
    "plt.subplot(1,2,1)\n",
    "df_new['day_time'].value_counts().plot(kind = 'barh');\n",
    "plt.title('day time people bike')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "df_new['return_time'].value_counts().plot(kind = 'barh');\n",
    "plt.title('return time people bike')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(morning      79111\n",
       " evening      48532\n",
       " afternoon    39979\n",
       " night        15592\n",
       " Name: day_time, dtype: int64,\n",
       " morning      144586\n",
       " afternoon     38628\n",
       " Name: return_time, dtype: int64)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['day_time'].value_counts(), df_new['return_time'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observation:** There are lesser rides at the end station than at the start station. Probably cause the data is only restricted to just san fracisco"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration_sec', 'start_time', 'end_time', 'start_station_id',\n",
       "       'start_station_name', 'start_station_latitude',\n",
       "       'start_station_longitude', 'end_station_id', 'end_station_name',\n",
       "       'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type',\n",
       "       'member_birth_year', 'member_gender', 'bike_share_for_all_trip',\n",
       "       'member_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "* What user type uses the bike mostly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_new['user_type'].value_counts().plot(kind = 'bar')\n",
    "# plt.title('Bike user types')\n",
    "# plt.ylabel('Count')\n",
    "# plt.xlabel('User types of the services')\n",
    "\n",
    "myCountPlot(df_new, 'user_type', palette='PuBu')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **observation:** so we have more of subscribers than customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_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>...</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>duration_time</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>day_time</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>return_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52185</td>\n",
       "      <td>2019-02-28 17:32:10.145</td>\n",
       "      <td>2019-03-01 08:01:55.975</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Montgomery St BART Station (Market St at 2nd St)</td>\n",
       "      <td>37.78963</td>\n",
       "      <td>-122.40081</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.79423</td>\n",
       "      <td>...</td>\n",
       "      <td>1984</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>14:29:45</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "      <td>8</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42521</td>\n",
       "      <td>2019-02-28 18:53:21.789</td>\n",
       "      <td>2019-03-01 06:42:03.056</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.79146</td>\n",
       "      <td>-122.39103</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.77588</td>\n",
       "      <td>...</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>11:48:41</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>evening</td>\n",
       "      <td>6</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61854</td>\n",
       "      <td>2019-02-28 12:13:13.218</td>\n",
       "      <td>2019-03-01 05:24:08.146</td>\n",
       "      <td>86.0</td>\n",
       "      <td>Market St at Dolores St</td>\n",
       "      <td>37.76931</td>\n",
       "      <td>-122.42683</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Powell St BART Station (Market St at 4th St)</td>\n",
       "      <td>37.78638</td>\n",
       "      <td>...</td>\n",
       "      <td>1972</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>47</td>\n",
       "      <td>17:10:54</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>morning</td>\n",
       "      <td>5</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36490</td>\n",
       "      <td>2019-02-28 17:54:26.010</td>\n",
       "      <td>2019-03-01 04:02:36.842</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.77484</td>\n",
       "      <td>-122.44655</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Central Ave at Fell St</td>\n",
       "      <td>37.77331</td>\n",
       "      <td>...</td>\n",
       "      <td>1989</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>10:08:10</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "      <td>4</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585</td>\n",
       "      <td>2019-02-28 23:54:18.549</td>\n",
       "      <td>2019-03-01 00:20:44.074</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frank H Ogawa Plaza</td>\n",
       "      <td>37.80456</td>\n",
       "      <td>-122.27174</td>\n",
       "      <td>222.0</td>\n",
       "      <td>10th Ave at E 15th St</td>\n",
       "      <td>37.79271</td>\n",
       "      <td>...</td>\n",
       "      <td>1974</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>45</td>\n",
       "      <td>0:26:25</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>night</td>\n",
       "      <td>0</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_sec              start_time                end_time  \\\n",
       "0         52185 2019-02-28 17:32:10.145 2019-03-01 08:01:55.975   \n",
       "1         42521 2019-02-28 18:53:21.789 2019-03-01 06:42:03.056   \n",
       "2         61854 2019-02-28 12:13:13.218 2019-03-01 05:24:08.146   \n",
       "3         36490 2019-02-28 17:54:26.010 2019-03-01 04:02:36.842   \n",
       "4          1585 2019-02-28 23:54:18.549 2019-03-01 00:20:44.074   \n",
       "\n",
       "  start_station_id                                start_station_name  \\\n",
       "0             21.0  Montgomery St BART Station (Market St at 2nd St)   \n",
       "1             23.0                     The Embarcadero at Steuart St   \n",
       "2             86.0                           Market St at Dolores St   \n",
       "3            375.0                           Grove St at Masonic Ave   \n",
       "4              7.0                               Frank H Ogawa Plaza   \n",
       "\n",
       "   start_station_latitude  start_station_longitude end_station_id  \\\n",
       "0                37.78963               -122.40081           13.0   \n",
       "1                37.79146               -122.39103           81.0   \n",
       "2                37.76931               -122.42683            3.0   \n",
       "3                37.77484               -122.44655           70.0   \n",
       "4                37.80456               -122.27174          222.0   \n",
       "\n",
       "                               end_station_name  end_station_latitude  ...  \\\n",
       "0                Commercial St at Montgomery St              37.79423  ...   \n",
       "1                            Berry St at 4th St              37.77588  ...   \n",
       "2  Powell St BART Station (Market St at 4th St)              37.78638  ...   \n",
       "3                        Central Ave at Fell St              37.77331  ...   \n",
       "4                         10th Ave at E 15th St              37.79271  ...   \n",
       "\n",
       "   member_birth_year member_gender bike_share_for_all_trip  member_age  \\\n",
       "0               1984          Male                      No          35   \n",
       "1               1985           NaN                      No          34   \n",
       "2               1972          Male                      No          47   \n",
       "3               1989         Other                      No          30   \n",
       "4               1974          Male                     Yes          45   \n",
       "\n",
       "  duration_time start_month  start_hour day_time  end_hour  return_time  \n",
       "0      14:29:45           2          17  evening         8      morning  \n",
       "1      11:48:41           2          18  evening         6      morning  \n",
       "2      17:10:54           2          12  morning         5      morning  \n",
       "3      10:08:10           2          17  evening         4      morning  \n",
       "4       0:26:25           2          23    night         0      morning  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Top ten start_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10 = ['Market St at 10th St','San Francisco Caltrain Station 2  (Townsend St at 4th St)','Berry St at 4th St','Montgomery St BART Station (Market St at 2nd St)','Powell St BART Station (Market St at 4th St)'\n",
    ",'San Francisco Ferry Building (Harry Bridges Plaza)','San Francisco Caltrain (Townsend St at 4th St)', 'Powell St BART Station (Market St at 5th St)','Howard St at Beale St','Steuart St at Market St']\n",
    "\n",
    "df_top_ten = df_new.loc[df_new['start_station_name'].isin(top10)]\n",
    "\n",
    "#df_top_ten\n",
    "st_stat_order = df_top_ten['start_station_name'].value_counts().index\n",
    "\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data=df_top_ten, y='start_station_name', color=base_color, order=st_stat_order)\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('Start Station Name')\n",
    "plt.title('Top 8 Stations with the most trips in SF');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Market St at 10th St                                         3904\n",
       "San Francisco Caltrain Station 2  (Townsend St at 4th St)    3544\n",
       "Berry St at 4th St                                           3052\n",
       "Montgomery St BART Station (Market St at 2nd St)             2895\n",
       "Powell St BART Station (Market St at 4th St)                 2760\n",
       "San Francisco Ferry Building (Harry Bridges Plaza)           2709\n",
       "San Francisco Caltrain (Townsend St at 4th St)               2703\n",
       "Powell St BART Station (Market St at 5th St)                 2327\n",
       "Howard St at Beale St                                        2293\n",
       "Steuart St at Market St                                      2283\n",
       "Name: start_station_name, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['start_station_name'].value_counts().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Top ten end station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "San Francisco Caltrain Station 2  (Townsend St at 4th St)    4857\n",
       "Market St at 10th St                                         3973\n",
       "Montgomery St BART Station (Market St at 2nd St)             3647\n",
       "San Francisco Ferry Building (Harry Bridges Plaza)           3368\n",
       "Powell St BART Station (Market St at 4th St)                 2997\n",
       "San Francisco Caltrain (Townsend St at 4th St)               2947\n",
       "Berry St at 4th St                                           2872\n",
       "The Embarcadero at Sansome St                                2512\n",
       "Powell St BART Station (Market St at 5th St)                 2353\n",
       "Steuart St at Market St                                      2338\n",
       "Name: end_station_name, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['end_station_name'].value_counts().nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10 = ['San Francisco Caltrain Station 2  (Townsend St at 4th St)','Market St at 10th St','Montgomery St BART Station (Market St at 2nd St)','San Francisco Ferry Building (Harry Bridges Plaza)','Powell St BART Station (Market St at 4th St)',\n",
    "'San Francisco Caltrain (Townsend St at 4th St)','Berry St at 4th St','The Embarcadero at Sansome St','Powell St BART Station (Market St at 5th St)','Steuart St at Market St']\n",
    "\n",
    "df_top_ten_2 = df_new.loc[df_new['end_station_name'].isin(top10)]\n",
    "\n",
    "#df_top_ten\n",
    "st_stat_order = df_top_ten_2['end_station_name'].value_counts().index\n",
    "\n",
    "base_color = sns.color_palette()[0]\n",
    "sns.countplot(data=df_top_ten_2, y='end_station_name', color=base_color, order=st_stat_order)\n",
    "plt.xlabel('Count')\n",
    "plt.ylabel('End Station Name')\n",
    "plt.title('Top 8 Stations with the most trips in SF');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observation:** San Francisco Caltrain Station 2  (Townsend St at 4th St) seems to be a veery busy place. It tops the return location and seconds the start_ride_location. But still the top 10 start station and end station are about the same stations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## *Statistics of the ride duration*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    183214.000000\n",
       "mean        725.897934\n",
       "std        1795.082702\n",
       "min          61.000000\n",
       "25%         325.000000\n",
       "50%         514.000000\n",
       "75%         796.000000\n",
       "max       85444.000000\n",
       "Name: duration_sec, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['duration_sec'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics of the member age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    183214.000000\n",
       "mean         34.187404\n",
       "std           9.884882\n",
       "min          18.000000\n",
       "25%          27.000000\n",
       "50%          32.000000\n",
       "75%          38.000000\n",
       "max         119.000000\n",
       "Name: member_age, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['member_age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_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>...</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>duration_time</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>day_time</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>return_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147632</th>\n",
       "      <td>552</td>\n",
       "      <td>2019-02-07 07:31:39.989</td>\n",
       "      <td>2019-02-07 07:40:52.283</td>\n",
       "      <td>169.0</td>\n",
       "      <td>Bushrod Park</td>\n",
       "      <td>37.84652</td>\n",
       "      <td>-122.2653</td>\n",
       "      <td>252.0</td>\n",
       "      <td>Channing Way at Shattuck Ave</td>\n",
       "      <td>37.86585</td>\n",
       "      <td>...</td>\n",
       "      <td>1985</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>0:09:12</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>morning</td>\n",
       "      <td>7</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "147632           552 2019-02-07 07:31:39.989 2019-02-07 07:40:52.283   \n",
       "\n",
       "       start_station_id start_station_name  start_station_latitude  \\\n",
       "147632            169.0       Bushrod Park                37.84652   \n",
       "\n",
       "        start_station_longitude end_station_id              end_station_name  \\\n",
       "147632                -122.2653          252.0  Channing Way at Shattuck Ave   \n",
       "\n",
       "        end_station_latitude  ...  member_birth_year member_gender  \\\n",
       "147632              37.86585  ...               1985          Male   \n",
       "\n",
       "       bike_share_for_all_trip  member_age duration_time start_month  \\\n",
       "147632                      No          34       0:09:12           2   \n",
       "\n",
       "        start_hour day_time  end_hour  return_time  \n",
       "147632           7  morning         7      morning  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bivariate Exploration\n",
    "<a id=\"cell132\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **What is the Relationship between ride duration and member_age?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'age')"
      ]
     },
     "execution_count": 62,
     "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.scatter(df_new.member_age,df_new.duration_sec, alpha = 0.4)\n",
    "plt.title('relationship between duration_sec and age')\n",
    "plt.ylabel('duration in sec')\n",
    "plt.xlabel('age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A quick check on Which gender rides most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      130500\n",
       "Female     40804\n",
       "Other       3647\n",
       "Name: member_gender, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.member_gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'age')"
      ]
     },
     "execution_count": 64,
     "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": [
    "markers = [['Male', 'o'],\n",
    "               ['Female', 's'], ['Other', '^']]\n",
    "\n",
    "for c, marker in markers: #a loop created\n",
    "    df_c = df_new[df_new['member_gender'] == c]\n",
    "    plt.scatter(data = df_c, x = 'member_age', y = 'duration_sec', marker = marker)\n",
    "plt.legend(['male','female','others'])\n",
    "plt.title('relationship between duration_sec and age for each gender')\n",
    "plt.ylabel('duration in sec')\n",
    "plt.xlabel('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    183214.000000\n",
       "mean         34.187404\n",
       "std           9.884882\n",
       "min          18.000000\n",
       "25%          27.000000\n",
       "50%          32.000000\n",
       "75%          38.000000\n",
       "max         119.000000\n",
       "Name: member_age, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.member_age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_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>...</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>duration_time</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>day_time</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>return_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>148</td>\n",
       "      <td>2019-02-28 19:29:17.627</td>\n",
       "      <td>2019-02-28 19:31:45.967</td>\n",
       "      <td>158.0</td>\n",
       "      <td>Shattuck Ave at Telegraph Ave</td>\n",
       "      <td>37.83328</td>\n",
       "      <td>-122.26349</td>\n",
       "      <td>173.0</td>\n",
       "      <td>Shattuck Ave at 55th St</td>\n",
       "      <td>37.84036</td>\n",
       "      <td>...</td>\n",
       "      <td>1900</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>119</td>\n",
       "      <td>0:02:28</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>evening</td>\n",
       "      <td>19</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10827</th>\n",
       "      <td>1315</td>\n",
       "      <td>2019-02-27 19:21:34.436</td>\n",
       "      <td>2019-02-27 19:43:30.008</td>\n",
       "      <td>343.0</td>\n",
       "      <td>Bryant St at 2nd St</td>\n",
       "      <td>37.78317</td>\n",
       "      <td>-122.39357</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.77484</td>\n",
       "      <td>...</td>\n",
       "      <td>1900</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>119</td>\n",
       "      <td>0:21:55</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>evening</td>\n",
       "      <td>19</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16087</th>\n",
       "      <td>1131</td>\n",
       "      <td>2019-02-27 08:37:36.864</td>\n",
       "      <td>2019-02-27 08:56:28.022</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.77484</td>\n",
       "      <td>-122.44655</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Folsom St at 3rd St</td>\n",
       "      <td>37.78383</td>\n",
       "      <td>...</td>\n",
       "      <td>1900</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>119</td>\n",
       "      <td>0:18:51</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>morning</td>\n",
       "      <td>8</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19375</th>\n",
       "      <td>641</td>\n",
       "      <td>2019-02-26 17:03:19.855</td>\n",
       "      <td>2019-02-26 17:14:01.619</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Broadway at Battery St</td>\n",
       "      <td>37.79857</td>\n",
       "      <td>-122.40087</td>\n",
       "      <td>30.0</td>\n",
       "      <td>San Francisco Caltrain (Townsend St at 4th St)</td>\n",
       "      <td>37.77660</td>\n",
       "      <td>...</td>\n",
       "      <td>1900</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>119</td>\n",
       "      <td>0:10:41</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>evening</td>\n",
       "      <td>17</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21424</th>\n",
       "      <td>1424</td>\n",
       "      <td>2019-02-26 08:58:02.904</td>\n",
       "      <td>2019-02-26 09:21:47.749</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.77484</td>\n",
       "      <td>-122.44655</td>\n",
       "      <td>343.0</td>\n",
       "      <td>Bryant St at 2nd St</td>\n",
       "      <td>37.78317</td>\n",
       "      <td>...</td>\n",
       "      <td>1900</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>119</td>\n",
       "      <td>0:23:44</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>morning</td>\n",
       "      <td>9</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_sec              start_time                end_time  \\\n",
       "1285            148 2019-02-28 19:29:17.627 2019-02-28 19:31:45.967   \n",
       "10827          1315 2019-02-27 19:21:34.436 2019-02-27 19:43:30.008   \n",
       "16087          1131 2019-02-27 08:37:36.864 2019-02-27 08:56:28.022   \n",
       "19375           641 2019-02-26 17:03:19.855 2019-02-26 17:14:01.619   \n",
       "21424          1424 2019-02-26 08:58:02.904 2019-02-26 09:21:47.749   \n",
       "\n",
       "      start_station_id             start_station_name  start_station_latitude  \\\n",
       "1285             158.0  Shattuck Ave at Telegraph Ave                37.83328   \n",
       "10827            343.0            Bryant St at 2nd St                37.78317   \n",
       "16087            375.0        Grove St at Masonic Ave                37.77484   \n",
       "19375              9.0         Broadway at Battery St                37.79857   \n",
       "21424            375.0        Grove St at Masonic Ave                37.77484   \n",
       "\n",
       "       start_station_longitude end_station_id  \\\n",
       "1285                -122.26349          173.0   \n",
       "10827               -122.39357          375.0   \n",
       "16087               -122.44655           36.0   \n",
       "19375               -122.40087           30.0   \n",
       "21424               -122.44655          343.0   \n",
       "\n",
       "                                     end_station_name  end_station_latitude  \\\n",
       "1285                          Shattuck Ave at 55th St              37.84036   \n",
       "10827                         Grove St at Masonic Ave              37.77484   \n",
       "16087                             Folsom St at 3rd St              37.78383   \n",
       "19375  San Francisco Caltrain (Townsend St at 4th St)              37.77660   \n",
       "21424                             Bryant St at 2nd St              37.78317   \n",
       "\n",
       "       ...  member_birth_year member_gender bike_share_for_all_trip  \\\n",
       "1285   ...               1900          Male                     Yes   \n",
       "10827  ...               1900          Male                      No   \n",
       "16087  ...               1900          Male                      No   \n",
       "19375  ...               1900          Male                      No   \n",
       "21424  ...               1900          Male                      No   \n",
       "\n",
       "       member_age duration_time start_month  start_hour day_time  end_hour  \\\n",
       "1285          119       0:02:28           2          19  evening        19   \n",
       "10827         119       0:21:55           2          19  evening        19   \n",
       "16087         119       0:18:51           2           8  morning         8   \n",
       "19375         119       0:10:41           2          17  evening        17   \n",
       "21424         119       0:23:44           2           8  morning         9   \n",
       "\n",
       "       return_time  \n",
       "1285       morning  \n",
       "10827      morning  \n",
       "16087      morning  \n",
       "19375      morning  \n",
       "21424      morning  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[df_new['member_age'] == 119].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Further look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.distplot(df_new['member_age']);\n",
    "plt.title('member age')\n",
    "plt.xlabel('age')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "bb = np.arange(15, df_new['member_age'].mean()+20, 5)\n",
    "plt.hist(df_new['member_age'], bins = bb);\n",
    "plt.title('member age')\n",
    "plt.xlabel('age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### good number of the memmber_ages are between 20 and 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'age bewteen 20 and 50')"
      ]
     },
     "execution_count": 68,
     "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.figure(figsize = (16,12))\n",
    "active_ages = df_new.query('member_age > 20 & member_age<50')\n",
    "#active_ages\n",
    "\n",
    "plt.scatter(active_ages['member_age'], active_ages['duration_sec'])\n",
    "plt.title('relationship between duration_sec and age between 20 adn 50')\n",
    "plt.ylabel('duration in sec')\n",
    "plt.xlabel('age bewteen 20 and 50')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **observation:** More duration time on the left side of age 35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_below_100 = df_new[df_new['member_age']<= 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'age less than 100')"
      ]
     },
     "execution_count": 70,
     "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.scatter(age_below_100['member_age'],age_below_100['duration_sec'])\n",
    "plt.title('relationship between duration_sec and age lesser than 100')\n",
    "plt.ylabel('duration in sec')\n",
    "plt.xlabel('age less than 100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Final Observation:** There's a slight negative correlation between duration and member_age. \n",
    "* And this is normal because the duration really has nothing to do with age though (as seen too), younger people tend to ride longer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average age for genders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Gender')"
      ]
     },
     "execution_count": 71,
     "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": [
    "df_new.groupby('member_gender')['member_age'].mean().plot(kind = 'bar')\n",
    "plt.title('average age for the gender')\n",
    "plt.ylabel('count')\n",
    "plt.xlabel('Gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member_gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>40804.0</td>\n",
       "      <td>33.221057</td>\n",
       "      <td>9.268773</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>130500.0</td>\n",
       "      <td>34.453617</td>\n",
       "      <td>10.345189</td>\n",
       "      <td>18.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>3647.0</td>\n",
       "      <td>35.897998</td>\n",
       "      <td>10.098447</td>\n",
       "      <td>19.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       mean        std   min   25%   50%   75%    max\n",
       "member_gender                                                               \n",
       "Female          40804.0  33.221057   9.268773  18.0  27.0  31.0  37.0  119.0\n",
       "Male           130500.0  34.453617  10.345189  18.0  27.0  32.0  39.0  119.0\n",
       "Other            3647.0  35.897998  10.098447  19.0  29.0  34.0  40.0  109.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.groupby('member_gender')['member_age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **observation:** they are about the same mean age even with more of male than others and spanning ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration_sec', 'start_time', 'end_time', 'start_station_id',\n",
       "       'start_station_name', 'start_station_latitude',\n",
       "       'start_station_longitude', 'end_station_id', 'end_station_name',\n",
       "       'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type',\n",
       "       'member_birth_year', 'member_gender', 'bike_share_for_all_trip',\n",
       "       'member_age', 'duration_time', 'start_month', 'start_hour', 'day_time',\n",
       "       'end_hour', 'return_time'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **For the top 10 stations, how are the rides for each time of the day**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of trips for each day time -- morning, afternoon, evening and night"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Ordering the days\n",
    "\n",
    "# # days\n",
    "# day_ = ['Morning', 'Afternoon', 'Evening', 'Night']\n",
    "\n",
    "\n",
    "# day_t = pd.api.types.CategoricalDtype(ordered=True, categories=day_)\n",
    "# return_t = pd.api.types.CategoricalDtype(ordered=True, categories=day_)\n",
    "\n",
    "# # Use pandas.astype() to convert the \"days\" column from a plain object type into an ordered categorical type \n",
    "# df_top_ten['return_time'] = df_top_ten['return_time'].astype(return_t);\n",
    "# df_top_ten['day_time'] = df_top_ten['day_time'].astype(day_t);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def myCountPlotH(df, xVar, hueVar, order = None, hueOrder = None, palette = None, figsize=[14,10]):\n",
    "    plt.figure(figsize=figsize)\n",
    "\n",
    "    c=sns.countplot(data=df, y = xVar, hue = hueVar, edgecolor = 'black', order = order, hue_order = hueOrder, palette=palette)\n",
    "    xVar = xVar.replace('_', ' ')\n",
    "    hueVar = hueVar.replace('_', ' ')\n",
    "    plt.title(f'''Distribution of {xVar} by {hueVar}'''.title(), fontsize = 14, weight = 'bold')\n",
    "    plt.ylabel(xVar.title(), fontsize=10, weight ='bold')\n",
    "    plt.xlabel('Frequency'.title(), fontsize = 10, weight = 'bold')\n",
    "\n",
    "day_time_order = ['morning', 'afternoon', 'evening', 'night']\n",
    "\n",
    "myCountPlotH(df_top_ten, 'start_station_name', 'day_time', hueOrder=day_time_order, palette = 'PuBu')\n",
    "\n",
    "# fig = plt.figure(figsize=(8,10))\n",
    "# #plt.subplots_adjust(top=0.95)\n",
    "# fig.suptitle('Count of trips for each day time', fontsize=14)\n",
    "\n",
    "\n",
    "# sns.countplot(data=df_top_ten, y='start_station_name', hue='day_time')\n",
    "# plt.legend(loc='center left', bbox_to_anchor=(1,0.5))\n",
    "# plt.ylabel('Start Station Name')\n",
    "# plt.xlabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### checking the return time "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "day_time_order = ['morning', 'afternoon', 'evening', 'night']\n",
    "\n",
    "myCountPlotH(df_top_ten_2, 'end_station_name', 'return_time', hueOrder=day_time_order, palette = 'PuBu')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observation:** So, aside morning, most of the rides are between 5pm and 7pm --- evening and also, sanfrancisco seems to be a very busy industrious area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Usertypes for the top10 stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_t = ['Subscriber', 'Customer']\n",
    "\n",
    "myCountPlotH(df_top_ten, 'start_station_name', 'user_type', hueOrder=user_t, palette = 'PuBu')\n",
    "\n",
    "# fig = plt.figure(figsize=(10,10))\n",
    "# fig.suptitle('count of user type per station' , fontsize=14)\n",
    "\n",
    "\n",
    "# sns.countplot(data=df_top_ten, y='start_station_name', hue='user_type')\n",
    "# plt.legend(loc='center left', bbox_to_anchor=(1,0.5))\n",
    "# plt.ylabel('Top 10 start Station Name')\n",
    "# plt.xlabel('Count');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### End station check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myCountPlotH(df_top_ten_2, 'end_station_name', 'user_type', hueOrder=user_t, palette = 'PuBu')\n",
    "\n",
    "\n",
    "# fig = plt.figure(figsize=(10,10))\n",
    "# fig.suptitle('count of user type per station' , fontsize=14)\n",
    "\n",
    "\n",
    "# sns.countplot(data=df_top_ten_2, y='end_station_name', hue='user_type')\n",
    "# plt.legend(loc='center left', bbox_to_anchor=(1,0.5))\n",
    "# plt.ylabel('Top 10 start Station Name')\n",
    "# plt.xlabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observation:** There are places with good high number of customers like San francisco Ferry building and the The Embarcadero at Sansome St "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### \n",
    "*Generally, we know we have more subscribers than customers... but san fransisco seems to have a goog number of Customers compared to other stations. \n",
    "There could be reasons why"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_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>...</th>\n",
       "      <th>member_birth_year</th>\n",
       "      <th>member_gender</th>\n",
       "      <th>bike_share_for_all_trip</th>\n",
       "      <th>member_age</th>\n",
       "      <th>duration_time</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_hour</th>\n",
       "      <th>day_time</th>\n",
       "      <th>end_hour</th>\n",
       "      <th>return_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115748</th>\n",
       "      <td>695</td>\n",
       "      <td>2019-02-12 08:23:11.255</td>\n",
       "      <td>2019-02-12 08:34:46.544</td>\n",
       "      <td>109.0</td>\n",
       "      <td>17th St at Valencia St</td>\n",
       "      <td>37.76332</td>\n",
       "      <td>-122.42190</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Townsend St at 7th St</td>\n",
       "      <td>37.77106</td>\n",
       "      <td>...</td>\n",
       "      <td>1989</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>0:11:35</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>morning</td>\n",
       "      <td>8</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154149</th>\n",
       "      <td>721</td>\n",
       "      <td>2019-02-06 10:18:23.024</td>\n",
       "      <td>2019-02-06 10:30:24.873</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Howard St at Beale St</td>\n",
       "      <td>37.78976</td>\n",
       "      <td>-122.39464</td>\n",
       "      <td>101.0</td>\n",
       "      <td>15th St at Potrero Ave</td>\n",
       "      <td>37.76708</td>\n",
       "      <td>...</td>\n",
       "      <td>1987</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>0:12:01</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>morning</td>\n",
       "      <td>10</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14546</th>\n",
       "      <td>601</td>\n",
       "      <td>2019-02-27 11:58:42.986</td>\n",
       "      <td>2019-02-27 12:08:44.761</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Esprit Park</td>\n",
       "      <td>37.76163</td>\n",
       "      <td>-122.39065</td>\n",
       "      <td>284.0</td>\n",
       "      <td>Yerba Buena Center for the Arts (Howard St at ...</td>\n",
       "      <td>37.78487</td>\n",
       "      <td>...</td>\n",
       "      <td>1989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>0:10:01</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>morning</td>\n",
       "      <td>12</td>\n",
       "      <td>morning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                end_time  \\\n",
       "115748           695 2019-02-12 08:23:11.255 2019-02-12 08:34:46.544   \n",
       "154149           721 2019-02-06 10:18:23.024 2019-02-06 10:30:24.873   \n",
       "14546            601 2019-02-27 11:58:42.986 2019-02-27 12:08:44.761   \n",
       "\n",
       "       start_station_id      start_station_name  start_station_latitude  \\\n",
       "115748            109.0  17th St at Valencia St                37.76332   \n",
       "154149             22.0   Howard St at Beale St                37.78976   \n",
       "14546             126.0             Esprit Park                37.76163   \n",
       "\n",
       "        start_station_longitude end_station_id  \\\n",
       "115748               -122.42190           90.0   \n",
       "154149               -122.39464          101.0   \n",
       "14546                -122.39065          284.0   \n",
       "\n",
       "                                         end_station_name  \\\n",
       "115748                              Townsend St at 7th St   \n",
       "154149                             15th St at Potrero Ave   \n",
       "14546   Yerba Buena Center for the Arts (Howard St at ...   \n",
       "\n",
       "        end_station_latitude  ...  member_birth_year member_gender  \\\n",
       "115748              37.77106  ...               1989        Female   \n",
       "154149              37.76708  ...               1987          Male   \n",
       "14546               37.78487  ...               1989          Male   \n",
       "\n",
       "       bike_share_for_all_trip  member_age duration_time start_month  \\\n",
       "115748                      No          30       0:11:35           2   \n",
       "154149                      No          32       0:12:01           2   \n",
       "14546                       No          30       0:10:01           2   \n",
       "\n",
       "        start_hour day_time  end_hour  return_time  \n",
       "115748           8  morning         8      morning  \n",
       "154149          10  morning        10      morning  \n",
       "14546           11  morning        12      morning  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['duration_sec', 'start_time', 'end_time', 'start_station_id',\n",
       "       'start_station_name', 'start_station_latitude',\n",
       "       'start_station_longitude', 'end_station_id', 'end_station_name',\n",
       "       'end_station_latitude', 'end_station_longitude', 'bike_id', 'user_type',\n",
       "       'member_birth_year', 'member_gender', 'bike_share_for_all_trip',\n",
       "       'member_age', 'duration_time', 'start_month', 'start_hour', 'day_time',\n",
       "       'end_hour', 'return_time'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate \n",
    "<a id=\"cell175\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another look into the number of rides per daytime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2016x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# g = sns.FacetGrid(data = df_top_ten, col = 'day_time', size = 7 );\n",
    "# g.map(sns.countplot, 'start_station_name');\n",
    "# g.set_xticklabels(rotation = 90, fontsize = 15)\n",
    "# plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(28,10))\n",
    "g = sns.FacetGrid(data = df_top_ten, col = 'day_time', col_wrap=2);\n",
    "g.map(sns.countplot,'start_station_name', order = None);\n",
    "g.set_xticklabels(rotation = 90, fontsize = 15)\n",
    "g.fig.set_size_inches(24,10);\n",
    "plt.tight_layout();\n",
    "#plt.title('Facet look into the start station name, time and ride')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####\n",
    "* more evident of how there are more rides in the morning and evening\n",
    "\n",
    "#### \n",
    "* So, the order is morning > evening > afternoon > night"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### checking for return_ride"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ilesanmi\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:64: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  self.fig.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2016x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sJEmSJEmSJEnjsLZ3cB0D7Ns+3hf41MDyvZPcNMm2wPbASe1whpcnuXeSAPsMvEaSJEmSJEmSJElatDXewZXkw8CDgE2SnA8cBLwBOCrJfsB5wF4AVfWdJEcBZwPXAAdU1bXtt3oecDiwAfDZ9kuSJEmSJEmSJElakjV2cFXVUxdY9dAFtj8EOGSe5acAd11SdZIkSZIkSZIkSdKQtZ2DS5IkSZIkacmcs7jhnMWSJEmjWds5uCRJkiRJkiRJkqSpsINLkiRJkiRJkiRJvWIHlyRJkiRJkiRJknrFDi5JkiRJkiRJkiT1ih1ckiRJkiRJkiRJ6hU7uCRJkiRJkiRJktQr6027AEmSJGk52enAI6ddwrJw6qH7TLsESZIkSZIWZAeXJEmaGjsSGnYkSJIkSZIkLY1DFEqSJEmSJEmSJKlX7OCSJEmSJEmSJElSrzhEof7IYaIaDhMlSZIkSZIkSdLy5h1ckiRJkiRJkiRJ6pWROriS/DjJmUlOT3JKu2zjJMcl+WH7760Htn9lknOSfD/JI0ctXpIkSZIkSZIkSStPF0MUPriqLh14/grg+Kp6Q5JXtM9fnmQHYG/gLsAWwBeT3LGqrh3lhzusXsNh9SRpcmx7GrY9kiRJkiRJmpZxDFG4B3BE+/gI4PEDyz9SVVdV1bnAOcCuY/j5kiRJkiRJkiRJmmGjdnAV8IUkpybZv122WVVdCND+e5t2+W2Bnw689vx22Y0k2T/JKUlOueSSS0YsUZIkSZJmh3lJkiRJkkbv4LpvVd0TeBRwQJIHrGbbzLOs5tuwqt5TVTtX1c6bbrrpiCVKkiRJ0uwwL0mSJEnSiB1cVXVB++/FwCdphhy8KMnmAO2/F7ebnw9sNfDyLYELRvn5kiRJkiRJkiRJWnnWW9sXJrkZsE5VXd4+fgRwMHAMsC/whvbfT7UvOQb4UJK3AFsA2wMnjVC7pBVgpwOPnHYJy8Kph+4z7RIkSZIkSZIkadlY6w4uYDPgk0nmvs+HqupzSU4GjkqyH3AesBdAVX0nyVHA2cA1wAFVde1I1UuSJEmSJEmSJGnFWesOrqr6EXD3eZb/AnjoAq85BDhkbX+mJEmSJEmSJEmSNNIcXJIkSZIkSZIkSdKk2cElSZIkSZIkSZKkXrGDS5IkSZIkSZIkSb1iB5ckSZIkSZIkSZJ6xQ4uSZIkSZIkSZIk9YodXJIkSZIkSZIkSeoVO7gkSZIkSZIkSZLUK3ZwSZIkSZIkSZIkqVfs4JIkSZIkSZIkSVKv2MElSZIkSZIkSZKkXrGDS5IkSZIkSZIkSb1iB5ckSZIkSZIkSZJ6xQ4uSZIkSZIkSZIk9crEO7iS7J7k+0nOSfKKSf98SZIkSZIkSZIk9dtEO7iSrAv8G/AoYAfgqUl2mGQNkiRJkiRJkiRJ6rdJ38G1K3BOVf2oqq4GPgLsMeEaJEmSJEmSJEmS1GOpqsn9sGRPYPeqelb7/OnAvarq+UPb7Q/s3z69E/D9iRW5djYBLp12ETPCfdkd92V33JfdcV92x33ZHfdld9yX3XFfdqcv+/LSqtp9MRual1Y092V33JfdcV92x33ZHfdld9yX3XFfdsd92Z2+7Mt589J6Ey4i8yy7UQ9bVb0HeM/4y+lGklOqaudp1zEL3JfdcV92x33ZHfdld9yX3XFfdsd92R33ZXdmcV+al1Yu92V33JfdcV92x33ZHfdld9yX3XFfdsd92Z2+78tJD1F4PrDVwPMtgQsmXIMkSZIkSZIkSZJ6bNIdXCcD2yfZNslNgL2BYyZcgyRJkiRJkiRJknpsokMUVtU1SZ4PfB5YF3h/VX1nkjWMSW+GB+kB92V33JfdcV92x33ZHfdld9yX3XFfdsd92R335fT5f9Ad92V33JfdcV92x33ZHfdld9yX3XFfdsd92Z1e78tU3WgKLEmSJEmSJEmSJGnZmvQQhZIkSZIkSZIkSdJI7OCSJEmSJEmSJElSr9jBJUmSJEmSJEmSpF6xg0uSJEmSJEmSJEm9YgeXJEmSJEmSJEmSesUOLkmSJEmSJEmSJPWKHVySJEmSJEmSJEnqFTu4JEmSJEmSJEmS1Ct2cEmSJEmSJEmSJKlX7OCSJEmSJEmSJElSr9jBJUmSJEmSJEmSpF6xg0uSJEmSJEmSJEm9YgeXJEmSJEmSJEmSesUOLknSxCV5TZKXdvj9npFki4Hn70uyQ1ffv0tJtkjysUVs99sFlj9+uf5ukiRJkua3wjPQTZN8McnpSZ6S5FXTrkmSNBvs4JIkzYJnAH8Md1X1rKo6e3rlLKyqLqiqPUf4Fo8HlmVwlSRJkjQxz6AnGQi4B7B+Ve1YVR8FltzBlWTd7suSJPWdHVySpIlI8ndJvp/ki8CdBpY/O8nJSb6d5ONJNkyyUZJzk6zfbnOLJD+eez70ffcEdgY+2F4RuEGSE5Ps3K7/bZI3Jjm1vWpw13b9j5I8rt1m3SSHtnWckeQ5Hfy+hyc5LMn/tD9rz3b5NknOah9vmOSo9md+NMm35upu1x/S7pdvJtksyX2AxwGHtr/rdqPWKUmSJGk8VmAGemybaf63/bmbJbkN8J/Ajm2t/wVs0D7+YPu6/5fkpHbZu+c6s9rf4+Ak3wJ2a5/fICO1290uyfHt73F8kq3XsHzerCZJ6h87uCRJY5dkJ2Bvmiv3ngjsMrD6E1W1S1XdHfgusF9VXQ6cCPxFu83ewMer6g/D37uqPgacAjytvSLw90Ob3Aw4sap2Ai4H/hF4OPAE4OB2m/2A31TVLm1tz06y7Ty/x1fb0DX89bAFfvXNgfsBjwHeMM/6vwZ+VVV/DrwO2Gmo7m+2++UrwLOr6n+AY4AD29/1/xb4uZIkSZKmaIVmoK8B966qewAfAV5WVRcDzwK+2ta6F/D79vHTkvwZ8BTgvlW1I3At8LSB3+OsqrpXVX2NeTJSu92/Ake2ueqDwGFrWA5rzmqSpB5Yb9oFSJJWhPsDn6yqKwCSHDOw7q5J/hG4FXBz4PPt8vcBLwOOBp7J9eFlqa4GPtc+PhO4qqr+kORMYJt2+SOAPx+4cu+WwPbAuYPfqKruv8SffXRVXQecPXd14ZD7Af/Sfu+zkpwxVPen28en0gRSSZIkSf2wEjPQlsBHk2wO3GT4ey3goTQX+p2cBGAD4OJ23bXAxwe2XSgj7UbTiQjwH8Cb1rAc1pzVJEk9YAeXJGlSaoHlhwOPr6pvJ3kG8CCAqvp6O5zfA4F1q+qstfy5f6iquZ99HXBV+/2vSzLXDgZ4QVV9fr5vMCfJV4GN5ln10qr64jzLrxp8+XzfcpF1X4tttiRJktQ3Ky0DvR14S1Udk+RBwGsWUWuAI6rqlfOsu7Kqrh14vtiMtNB+H1y+pqwmSeoBhyiUJE3CV4AntGPDbwQ8dmDdRsCF7djyTxt63ZHAh4EPrOH7X878oWuxPg88b2C8+zsmudnwRlV1/3YojeGv+Tq3FuNrwJPbn7kDcLdFvGbU31WSJEnS+K3EDHRL4Gft431X87P/MDC32PHAnu1cXSTZOMntlvi7/A/NkI7Q7M+vrWG5JGlG2MElSRq7qjoN+ChwOs0QE18dWP0PwLeA44DvDb30g8CtaQLe6hwOvGtuguW1KPF9wNnAaUnOAt7NZO6YegewaTs04cuBM4DfrOE1HwEObCdu3m7cBUqSJElauhWagV4D/Fd719elq9nuPcAZST5YVWcDfw98oc1Fx9HMj7UULwSe2b7+6cCL1rBckjQjcv2dvZIkLS/tePB7VNXTp13LOCRZF1i/qq5sO6uOB+5YVVdPuTRJkiRJUzDrGUiSpC45n4ckaVlK8nbgUcCjp13LGG0InNAOzxHgeXZuSZIkSSvTCslAkiR1xju4JEm9keTfgPsOLf6XqlrT+PSSJEmS1DtmIEmSFmYHlyRJkiRJkiRJknpl2Q9RuPvuu9fnPve5aZchSZIkSeOStX2heUmSJEnSCjBvZlpn0lUs1aWXXjrtEiRJkiRpWTIvSZIkSVqpln0HlyRJkiRJkiRJkjTIDi5JkiRJkiRJkiT1ih1ckiRJkiRJkiRJ6hU7uCRJkiRJkiRJktQrdnBJkiRJkiRJkiSpV+zgkiRJkiRJkiRJUq/YwSVJkiRJkiRJkqResYNLkiRJkiRJkiRJvWIHlyRJkiRJkiRJknpljR1cSbZKckKS7yb5TpIXtcs3TnJckh+2/9564DWvTHJOku8neeTA8p2SnNmuOyxJxvNrSZIkSZIkSZIkaVYt5g6ua4CXVNWfAfcGDkiyA/AK4Piq2h44vn1Ou25v4C7A7sA7kqzbfq93AvsD27dfu3f4u0iSJEmSJEmSJGkFWGMHV1VdWFWntY8vB74L3BbYAzii3ewI4PHt4z2Aj1TVVVV1LnAOsGuSzYFbVNU3qqqAIwdeI0mSJEmSJEmSJC3KkubgSrINcA/gW8BmVXUhNJ1gwG3azW4L/HTgZee3y27bPh5eLkmSJEmSJEmSJC3aoju4ktwc+DjwN1V12eo2nWdZrWb5fD9r/ySnJDnlkksuWWyJkiRJkjTzzEuSJEmStMgOriTr03RufbCqPtEuvqgddpD234vb5ecDWw28fEvggnb5lvMsv5Gqek9V7VxVO2+66aaL/V0kSZIkaeaZlyRJkiRpER1cSQL8O/DdqnrLwKpjgH3bx/sCnxpYvneSmybZFtgeOKkdxvDyJPduv+c+A6+RJEmSJEmSJEmSFmW9RWxzX+DpwJlJTm+XvQp4A3BUkv2A84C9AKrqO0mOAs4GrgEOqKpr29c9Dzgc2AD4bPslSZIkSZIkSZIkLdoaO7iq6mvMP38WwEMXeM0hwCHzLD8FuOtSCpQkSZIkSZIkSZIGLWoOLkmSJEmSJEmSJGm5sINLkiRJkiRJkiRJvWIHlyRJkiRJkiRJknrFDi5JkiRJkiRJkiT1ih1ckiRJkiRJkiRJ6hU7uCRJkiRJkiRJktQrdnBJkiRJkiRJkiSpV+zgkiRJkiRJkiRJUq/YwSVJkiRJkiRJkqResYNLkiRJkiRJkiRJvWIHlyRJkiRJkiRJknrFDi5JkiRJkiRJkiT1ih1ckiRJkiRJkiRJ6hU7uCRJkiRJkiRJktQra+zgSvL+JBcnOWtg2WuS/CzJ6e3XowfWvTLJOUm+n+SRA8t3SnJmu+6wJOn+15EkSZIkSZIkSdKsW8wdXIcDu8+z/K1VtWP7dSxAkh2AvYG7tK95R5J12+3fCewPbN9+zfc9JUmSJEmSJEmSpNVaYwdXVX0F+OUiv98ewEeq6qqqOhc4B9g1yebALarqG1VVwJHA49eyZkmSJEmSJEmSJK1go8zB9fwkZ7RDGN66XXZb4KcD25zfLrtt+3h4+byS7J/klCSnXHLJJSOUKEmSJEmzxbwkSZIkSWvfwfVOYDtgR+BC4M3t8vnm1arVLJ9XVb2nqnauqp033XTTtSxRkiRJkmaPeUmSJEmS1rKDq6ouqqprq+o64L3Aru2q84GtBjbdErigXb7lPMslSZIkSZIkSZKkJVmrDq52Tq05TwDOah8fA+yd5KZJtgW2B06qqguBy5PcO0mAfYBPjVC3JEmSJEmSJEmSVqj11rRBkg8DDwI2SXI+cBDwoCQ70gwz+GPgOQBV9Z0kRwFnA9cAB1TVte23eh5wOLAB8Nn2S5IkSZIkSZIkSVqSNXZwVdVT51n876vZ/hDgkHmWnwLcdUnVSZIkSZIkSZIkSUPWaohCSZIkSZIkSZIkaVrs4JIkSZIkSZIkSVKv2MElSZIkSZIkSZKkXrGDS5IkSZIkSZIkSb1iB5ckSZIkSZIkSZJ6xQ4uSZIkSZIkSZIk9YodXJIkSZIkSZIkSeoVO7gkSZIkSZIkSZLUK3ZwSZIkSZIkSZIkqVfs4JIkSZIkSZIkSVKv2MElSZIkSZIkSZKkXrGDS5IkSZIkSZIkSb1iB5ckSZIkSZIkSZJ6Zb01bZDk/cBjgIur6q7tso2BjwLbAD8GnlxVv2rXvRLYD7gWeGFVfb5dvhNwOLABcCzwoqqqbn8dSZIkSZIkaWl2OvDIaZewLJx66D7TLkGSpEVbzB1chwO7Dy17BXB8VW0PHN8+J8kOwN7AXdrXvCPJuu1r3gnsD2zffg1/T0mSJEmSJEmSJGmN1tjBVVVfAX45tHgP4Ij28RHA4weWf6Sqrqqqc4FzgF2TbA7coqq+0d61deTAayRJkiRJkiRJkqRFW+MQhQvYrKouBKiqC5Pcpl1+W+CbA9ud3y77Q/t4ePm8kuxPc7cXW2+99VqWKEmSJEmzx7ykvnMouIZDwUmSJI1mMUMULkXmWVarWT6vqnpPVe1cVTtvuummnRUnSZIkSX1nXpIkSZKkte/guqgddpD234vb5ecDWw1styVwQbt8y3mWS5IkSZIkSZIkSUuyth1cxwD7to/3BT41sHzvJDdNsi2wPXBSO5zh5UnunSTAPgOvkSRJkiRJkiRJkhZtjXNwJfkw8CBgkyTnAwcBbwCOSrIfcB6wF0BVfSfJUcDZwDXAAVV1bfutngccDmwAfLb9kiRJkiRJkiQNcc7ChnMWSlrIGju4quqpC6x66ALbHwIcMs/yU4C7Lqk6SZIkSZIkSZIkacjaDlEoSZIkSZIkSZIkTYUdXJIkSZIkSZIkSeqVNQ5RKEmSJEmSpOXH+Xkazs8jSdLK5B1ckiRJkiRJkiRJ6hU7uCRJkiRJkiRJktQrdnBJkiRJkiRJkiSpV5yDS5IkSZIkSZI0s5yzsOGchZo13sElSZIkSZIkSZKkXvEOLkmSJEmS1sArvxte+S1JkqTlwju4JEmSJEmSJEmS1CvewSVJkiRJkiRJkjRBjhDQGGWEADu4JEmSJEmSJHXCD2wbDukqSePnEIWSJEmSJEmSJEnqlZE6uJL8OMmZSU5Pckq7bOMkxyX5YfvvrQe2f2WSc5J8P8kjRy1ekiRJkiRJkiRJK08Xd3A9uKp2rKqd2+evAI6vqu2B49vnJNkB2Bu4C7A78I4k63bw8yVJkiRJkiRJkrSCjGOIwj2AI9rHRwCPH1j+kaq6qqrOBc4Bdh3Dz5ckSZIkSZIkSdIMW2/E1xfwhSQFvLuq3gNsVlUXAlTVhUlu0257W+CbA689v112I0n2B/YH2HrrrUcsUZIkSZJmx1Ly0k4HHjmJkpa9Uw/dZ9olSJIkSerYqHdw3beq7gk8CjggyQNWs23mWVbzbVhV76mqnatq50033XTEEiVJkiRpdpiXJEmSJGnEDq6quqD992LgkzRDDl6UZHOA9t+L283PB7YaePmWwAWj/HxJkiRJkiRJkiStPGvdwZXkZkk2mnsMPAI4CzgG2LfdbF/gU+3jY4C9k9w0ybbA9sBJa/vzJUmSJEmSJEmStDKNMgfXZsAnk8x9nw9V1eeSnAwclWQ/4DxgL4Cq+k6So4CzgWuAA6rq2pGqlyRJkiRJkiRJE+Ecrw3neF0e1rqDq6p+BNx9nuW/AB66wGsOAQ5Z258pSZIkSZIkSZIkjXIHl2aMve8Ne98lSZIkSZIkSVre1noOLkmSJEmSJEmSJGka7OCSJEmSJEmSJElSr9jBJUmSJEmSJEmSpF6xg0uSJEmSJEmSJEm9YgeXJEmSJEmSJEmSesUOLkmSJEmSJEmSJPWKHVySJEmSJEmSJEnqFTu4JEmSJEmSJEmS1Ct2cEmSJEmSJEmSJKlX1pt2AZK0OjsdeOS0S1gWTj10n2mXIEnSktmON2zHJUmSJKl7dnBJY+CHOQ0/zJG0Jh4vGx4vJUmSJEmSlsYOLkmSpBlgZ2HDzkJJkiRJklaGiXdwJdkd+BdgXeB9VfWGSdcgSSuRH343/PBb0pp4vGx4vJQkSZIkLWcT7eBKsi7wb8DDgfOBk5McU1Vnr+339AOIhh9ASNLk2PY0bHskSZIkSZI0LetM+OftCpxTVT+qqquBjwB7TLgGSZIkSZIkSZIk9ViqanI/LNkT2L2qntU+fzpwr6p6/tB2+wP7t0/vBHx/YkWunU2AS6ddxIxwX3bHfdkd92V33JfdcV92x33ZHfdld9yX3enLvry0qnZfzIbmpRXNfdkd92V33JfdcV92x33ZHfdld9yX3XFfdqcv+3LevDTpObgyz7Ib9bBV1XuA94y/nG4kOaWqdp52HbPAfdkd92V33JfdcV92x33ZHfdld9yX3XFfdmcW96V5aeVyX3bHfdkd92V33JfdcV92x33ZHfdld9yX3en7vpz0EIXnA1sNPN8SuGDCNUiSJEmSJEmSJKnHJt3BdTKwfZJtk9wE2Bs4ZsI1SJIkSZIkSZIkqccmOkRhVV2T5PnA54F1gfdX1XcmWcOY9GZ4kB5wX3bHfdkd92V33JfdcV92x33ZHfdld9yX3XFfTp//B91xX3bHfdkd92V33JfdcV92x33ZHfdld9yX3en1vkzVjabAkiRJkiRJkiRJkpatSQ9RKEmSJEmSJEmSJI3EDi5JkiRJkiRJkiT1ih1ckiRJkiRJkiRJ6hU7uCRpQJIfJbn7AuvumuRHk65J8n0pzbYkD0hy8wXW3TzJAyZdkyRJ8/G8VMuR70tptpmXtDrrTbuAvklyLbBbVZ00z7qdgJOqat3JV9YvSdYBHgk8DNgV+FNgFfBL4AfA14FPVNV5UyuyR5LsADyU+ffl14AvVNXvp1dhr2wD3HSBdRsCW06ulNmQ5CbAprTvy6r61ZRL6qNt8H3ZCY+X3bAd79wJwG7Ajc4vgTu16z2/XKQkd2P+9+X/2AaNn3mpGx5nu2X736lt8Ly0U+alTmyD78tOeLzshu1458xLHZq1vGQH19JlNevWB66ZVCF9lGQj4G+B5wJ/AnwH+DbwTeAq4FY0JyavAP45yZeA11XVV6dR73KWJMDTgRcAOwG/As4ALuX6ffkomv39uyQfBV5fVedOpeBlLMktaPbXnD9NsvXQZquAvYGfTaquPktyF2AfmpO5uzFwopHkF8D/AB8DPu7J8fx8X3bH42V3bMfHZnXnlzcHrphUIX2V5PbA84CnAZsB1wG/5vr35YbAdUm+DLwP+GhVXTeVYmefeWkEHme7Y/vfHc9Lu2deGp3vy+54vOyO7fjYmJdGNMt5yQ6uRWgbyG0GFt0jyaqhzVYB+wIe3FfvXOB/gQOBY6rq8oU2THIP4MnAJ5K8tqr+dUI19sV323//A3h6VX1vvo2SbEhz1chewJlJnltV/zmhGvvib4GDgGq/PrnAdgFeMqmi+ijJfYF/BB4AnAx8GTiMG54YbwPsDLwVeHuStwBvrarfTqHk5cz3ZXc8XnbHdrwj7TAaDxpY9Kwkuw9ttgr4C+DMSdXVR0neRxPUvgYcTPOh4Heq6tqBbTYBdqH5G38T8Jok+1XV16ZQ8swxL3XK42x3bP+743lpR8xLnfJ92R2Pl92xHe+Ieak7s56XUlXTrmHZS3IQ1zeasHCv8e+BZ1XVhydSWA8l2amqTl3iazYEtl6ogV2pkuxJczXXov+Ik2wJbFlV3xxfZf2TZHvgjjR/28cALwW+P7TZ1cD3vX189ZJcRBPQjqiq89ew7bo0Vyz+Dc1t0K8bf4X94fuyOx4vu2M73p0kBwIva59uDFzGje9suRr4HnBgVZ02wfJ6Jcm/AodW1U8Wuf06NB/MUFUfHWdtK4V5qTseZ7tj+98dz0u7Y17qju/L7ni87I7teHfMS92Z9bxkB9ciJNkUuA1No3kGTY/nGUObXQ2cV1VXTbg8SR1K8kDgVK+OWztJVlXVlZN63Urh+1KabUnOBR5fVd+edi3S2jAvSSuH56WjMS+Nh+9LabaZl7Q6dnAtUZLbARdW1dXTrqXvnIBafZDkscCdgZ8DR6/u9nLdUHs7+WnzhYwkNwfuWVVfmXxl/ef7cu0l+RHwhPlOjJPclWYYidtPvrL+sR3XcpTk/TTzGNxoGLz2PP6gqvqryVe2cpiXuuNxVn3geenaMy+Nj+/LtWde6o7tuJajWcxL60y7gL6pqp/MhbUkGyZ5QZJ/S/IP7ZtAi+cE1B1K8pQkX0xyXpKLh7+mXd9yluTlSb46tGz9JF8BjgbeCBwBnJVkiymU2FcnADsssO5O7XotwPfl2GwD3HSBdRsCW06ulN6zHR9Bki2S7DbP8h2TfDzJd5J8KckTplFfjz0D2HSBdZvQzAGlMTIvdcrjbIfMS2vP89KxMS+NwPfl2GyDeakrtuMjMC+NzTOYsby03rQL6IMkbwYeW1V3HFi2Ec2EoNsDvwJuCbwkya5V9YPpVLr8xQmoxyLJXwLvBw4HHtI+Xgd4HPBr4Mhp1dYTTwBOHFr2QuB+wOuAQ2nG9v4Y8HfAAZMsrsdWdzJ3c+CKSRXSU74vO5LkFjQTds/507Y9GrQK2Bv42aTq6iPb8U4dQvPh1X3mFrRzSXwVuA44DtgO+FiSR1TV8VOpsp8WGqLirsAlkyxkpTAvdcfj7HiYl0bmeel4mJdG4/uyI+al7tiOd8q8ND4zlZfs4FqcBwP/ObTspTQN5bOq6v3tuPPHAf8APH3C9fXJM7l+AuoC3rnAdr8HnjWpombAgTQncG8A9gfeUVWntR8sHIcnxmuyHc2+G7Q3cG5VHdQ+Py3JG2j+9rWAdpiNBw0selaS3Yc2WwX8BXDmpOrqKd+X3flbbtj2fHKB7QK8ZFJF9ZTteHfuSzPJ/KAX01wxu3NVnQGQ5GjgFYCBbQFJXgS8qH1awNFJhud5WgVsRvPhtrpnXuqOx9nxMC+NxvPSjpiXOuX7sjvmpe7YjnfHvNSRWc9LdnAtzjbAqUPLngScXVXvB6iqS9orF1874dr65h00V884AXW3tge+XlXXtmP83gKgqi5P8kbgrcA/T7PAZW5Dmis3gT+Od34P4N+HtvsecNvJldVL9wJe0D4uYC9ufNv91TT78sAJ1tVHvi+78yHgFJq25xiagPv9oW2uBr5fVedNuLa+sR3vzhbAd4eWPQ74xlxYa70feNfEquqns4GP07wvX0wzpNOFQ9vMtT1HTba0FWMbzEtd8Tg7Hual0Xhe2h3zUnd8X3bHvNQd2/HumJe6M9N5yQ6uxVkPuHLuSZKNgT8D/m1oux8Dfzq5svqnqi6hvdUxybY4AXVXfsP1YyT/jOb9eWL7PMCfTKGmPvkRsCvX77OH0+y34as/bglcNrmy+qeqDqUZCoIk5wKPn29yWi2K78uOVNUPgR8CJHkwcOp8k3lrzWzHO3UFsMHck3Z/bk4T0Ab9ihsOGaMhVXUczR0YJLkceG9VXTDdqlYc81JHPM6OjXlpNJ6XdsS81Cnflx0xL3XHdrxT5qWOzHpesoNrcX5Acwv5XCP5mPbfzw9tdxvglxOqqfeq6idzj5NsCOwH3Bn4OXDk4Hqt0SnAn9O8J48BXp3kGpre91cD35pibX3wAeA17T67iObK4kuAzwxt92BufBWTFlBV2067hp7zfTkGVfXlwedJHsv1bc/RVXX5VArrIdvxkZ1OM0zbp9vnT6O5kvvTQ9ttx42vrtMCquoGdwcluTVwB+Bi35NjZV4aA4+znTIvjcbz0jEwL43M9+UYmJe6Yzs+stMxL3VuFvOSHVyL86/Ae5PckqbRfCHNRIBfGNruEcBZE66tV5yAemxeD9yuffzq9vE7gHVp9u1zplRXXxxGM3Hl64H1gZ8CT62q381t0P797wu8cSoV9kSSLYDbVdU3hpbvSDPnxp1pjqNvr6qFxvVWw/dlR5K8HHhMVd1/YNn6NB/E3pfrJ/j+aZLdZulKpq7ZjnfqYOCEJN8GLgYeApxQVcMfsj4JP3hdrSR7AA+pqhcNLT+EZninddvn/w3sXVVX3vi7aETmpY54nB0b89JoPC/tiHmpU74vO2Je6o7teKfMSx2Z9by0zrQL6IOqOpzmJPiJwCtprvx4QlX9YW6bdtLkPYBPTaPGHlndBNTPrqpNaMZY/THNyZ0Woaq+WVUfbR//uqr2AG4O3Kqq7lVV/zfdCpe3qrqmqp5Dc0vzbarqdlV1wtBmv6N5n7510vX1zCHAmwcXJNke+CrwMJrxk28NfCzJQydfXn/4vuzUE4CvDy17IXA/4B9p5uHYGbgW+LvJltY7tuMdqaqv0ezPM4GraI6fTxjcpj2/vI4eTvQ7Yc+j+aDgj5LsSXPe/kWac/QDadqhF068uhXAvNQpj7NjYF4ajeelnTIvdcT3ZafMS92xHe+IealTM52XUlXTrkErSJJfAk+vqs8MLDsLoKruOrDs6cBrq+r2k6+yf5LsA/xw+Cqwdt0mwKOr6sjJV6aVJskPgMOq6l8Hlr2T5lb8necmAk1yNHCzqnr4VArVipLkEpowcfTAspOBjatqu4Fl+wMvHbzaTjdkO67lKMnPgAOr6kMDyz5LMy/HllX1+3bZPwB7VtXdp1OptGYeZ8fDvKTlwryk5ci81B3bcS1Hs56XvINLk7bQBNRfGtruxzgB9VIcDnw1yd/Ps247mrGppUnYguaqw0GPA74xF9Za7wfuMrGqtNJtCPx67kmSmwP3oLlSadD3gNtOrqxesh3XcrQxcP7ckyTrAg8EjpsLa62vA9tMtjRpyTzOjsfhmJe0PJiXtByZl7pjO67laKbzkh1cmrS5CajnOAF1d/4NeFWSo9vxfaVpuALYYO5Jkm2BzYGvDG33K5qhJKRJ+BHNlUlzHk4zjvzxQ9vdErhsUkX1lO24lqMLgMGrX+8NrAJOHNpuHZqhdaTlzOPs+JiXtByYl7QcmZe6Yzuu5Wim89J60y5AK44TUI/PB4EjgE8AJyd5QlUNXxkmjdvpwNOBT7fPnwbUwPM52wEXTq4srXAfAF6T5Bqatue1wCXAZ4a2ezDNvDFamO24lqPPAn+X5Aya9+VBwNXceK6nXWiulpWWM4+z42Ne0nJwOuYlLT/mpe7Yjms5mum8ZAeXJqqqDk+yOXAAzdVIpwEHLDAB9WunUmSPVdVpSXYBjgK+leSvgJ9OuSytLAcDJyT5NnAx8BDghKr61tB2TwKGl0njchhwJ+D1wPo0x8WnVtXv5jZoA8i+wBunUmFP2I5rmTqIZoiNk9vnRTM/xB8/GGyH4XgmcPTEq5OWwOPseJmXtAyYl7QcmZc6YjuuZWqm81Kqato1SBpRkuuAe1fVSe3zdYE3Ay+g6aV/VFWtO8UStYIkuR/wXK4/mTu0qi4fWL8p8D7g36pq+ComzSPJl4C/rqrvzbPujsC7quohk6+sX5JsQDNZ96XzrFuPdsiNwfAhqR/av+EH0bQ9p1fVOUPrb03zIeK3qur8G30DSTPNvKTlxLzUPfNSN8xL0uya5bxkB9daSrIDsBOwFfD+qvp5kjsAFw2emEiTMBzYBpY/HXg3cFMD2+Il2RN4IrAlzZi0N1BVu97oRdIYLfQ33q7bGfhmVXlXttRzSW5KM3H3fG3P2ZOvSFp75iUtJ+albpmXtNyYl6SVwbyk+XhwX6IkNwfeT3O7+DU0+/BzwM+BfwLOA146tQK1IlXVOgss/48kX6f5YEGLkOQ1wKuBbwNn04xJKy0HN7oiJclNaK6w+fnky5HUlSRbAO8BHjXfapq/fz94VS+Yl7QcmZe6Y17SMmZekmaUeUmrYwfX0r0FuA/wMODrwJUD646lCWsGNi0bVfUj4EfTrqNH9gPeUFWvmnYhWtmSHETz4QE0J2vfTLLQ5odOpChJ4/I+4J7Ai/HDQvWfeUm9Yl5aMvOSlgXzkrSimJe0IDu4lu6JwIuq6oR23O5BPwFuN4WaJJLsRhM27ojDRIxiI+D4aRch0XwIeCnN1UiH0cwT8eOhba4GvldVX51saZI6dl/g2VV11LQLkTpgXtKyZF7qjHlJy4V5SVo5zEtakB1cS7cB8IsF1m0EXDvBWiQAkjyc5uTueOB+NBMlb0DTAJwPfHl61fXOR4DdMbRpyqrqZOBkgCSXA5+Zb7JfSTPhYuD30y5C6oh5ScuOealT5iUtC+YlaUUxL2lBqbrRELVajSQnAhdU1V+2VyT+Adi5qk5LciSwSVU9eqpFasVJ8g2aIWBezg3fk7cDPg/8U1UdOc0al7Mkg3+zGwJvogm5xwG/Ht6+qo6dTGXSDSXZAdiJZp6I91fVz5PcAbioqi6fbnWS1laSpwIHAI+uqsumXY80CvOSliPz0mjMS+oL85I0m8xLWh07uJYoyf2ALwJfA/4LeAdwEHAnYE/gAe1VJFqDJOsDL6IZxmRL5h8m4jaTrquPkvyGZj9+iWYy7wfN3YKfZG/gtVV1pymWuKwluY5mzO4FB+weUFXlxJVLkOSmwG2Z/2/87MlX1D9JbgZ8gKad+QPNHdi7tB/MHAWcV1XOZ7JISfZk9W2PQxQtgu34aNq/3UH3prm75WRu/GFhVdVTJlFXHyV59Zq3ul5VHTyuWmRe6pLH2e6Yl0ZjXhov89LozEvdMi91w3Z8NOal7sx6XnKIwiWqqq8leSjwBuBfaU7wXgt8E3iYYW1J3go8B/g0cAJOEDiKK4F1qqqSXAhsB8yNMX0ZTUOqhW077QJmUZItgPcAj5pvNU1INvwuzluB+wAPpbn6+MqBdccCL22/tAZJXkMzGfW3cXLaUdmOj2bToef/1/67/jzrtHovGHq+Ac0dBgC/BW7ePr6i/epVYOsb81KnPM52x7w0GvPSGJiXOmVe6oh5qVO246MxL3VnpvOSHVxroaq+Dtw/yQbArYFfV9UVUy6rj/YCXlFVb552ITPg2zRXxR5HMxb6K5P8jKbxPBg4c4q1LXtV9ZO5x0m2Bi6sqj8Mb5dkPWCLSdbWc+8D7gm8GE+MR/VE4EVVdUI73NOgnwC3m0JNfbUf8IaqetW0C5kBtuMjqKoHT7uGWVFVfwy4SXYDPgj8PfCJqroyySrgScDrgKdNp8qVxbzUGY+z3TEvjcC8NDbmpe6Yl7pjXuqO7fgIzEvdmfW8ZAfXCKrq9zjB3SgCnDHtImbE27j+qrpXAf9NM5Y8NJMmP2EKNfXVucBuwEnzrLt7u9yr6BbnvsCzq2r4tnIt3QbALxZYtxFw7QRr6buNcFL0rtiOd6QdMuJ9VXXBPOs2pzmW9uoquik6jGYunQ/NLaiqK4EPtsMX/RvNh4maAPPSyDzOdudtmJe6Yl7qjnmpO+al7piXumM73hHzUqdmLi/ZwbUWkuxKcwI83xjJjvm5eO8FnkpzFZ1GMDiJb1X9LMlOwB1oTvK+V1VeCbZ4qxtXfhVw1aQKmQEX44daXTkZ2Af43Dzr9gT+Z7Ll9NpHgN0xtHXBdrw7B9H8fd8osNFcCX8QPRsmYoruyvz7EeBnwJ9NsJYVy7zUGY+zHTEvdcq81B3zUnfMS90xL3XHdrw75qXuzFxesoNriZL8LfBm4CLgR3gL+ZIk+euBpz8HnpbkBJqD/a+HNq+qeuekapslVVXAD6ddR18k+XNgx4FFj05y56HNVgFPBn4wqbpmwKuBlyf5clVdNu1ieu7vgS8m+SLwXzTj8T+6bZP2BB4wzeKWuySPHnj6ReBNSTZh/rbnBh+C6YZsx8dmbp6N+WwJ/GqCtfTdD4AXJzm+qv74IWs77MaLge9PrbIVwrw0Go+zk2FeWhrz0tiYl7pjXhqBeak7tuNjY17qzszlpTTndVqsJOfTNJYvLnfekiW5bgmbV1U5tMEChhrNNbHRXI0kB9Fc7QFNg7nQVYnnAs+pqi9OpLAeSjI8vMa9aYY4OJn5T+a8gnuRktwXeAPNPl2X5r36TeBl7VwnWkDb9qzub3uQbc9q2I53J8m+wL7t0wcC/wsMf7i1Crgb8IWqetIEy+utJA+gmUz+9zQfJFwM3AZ4OM1Eyo+qqq9Mr8LZZ14ajcfZ7piXumNe6o55aXzMS2vPvNQd2/HumJfGYxbzkh1cS5TkEuCpnrBp2mw0u5NkfeAmNCdzlwEPoQkYg66ebyJl3VB7ZdKiOWno4iRZ1Y6JTJINgFsDv66qK9plt62qn02zxuUsyZImlR6cSF0alyR70VzpDs2EvicAvxza7Grge8A7qmqheSU0pB2H/2+BXYA/pbl69mTgbfON269umZe0XJiXumNe6o55aTzMS6MxL2k5Mi+Nz6zlJTu4lijJocDNqmopV4NpHm2P8WlV9dt51t0M2KlvPcaSNA5JvgA8Zr75IZLcATiuqra98Ss1LMnWwIXzfQCTZD1gi6o6b/KV9Y/teHeSfAA4uKrOnXYt0qjMS93xOCtJi2Ne6o55qTu2490xL2l17OBaoiTrAP8K3BH4Eo6futaSXAvsVlUnzbNuJ+Akr6JbnCR/AvzSYWBGk2Q7mslU70xzxVfR/I1/D/hcVf3f9KrrnySvBt4339Uf7dUiz64qJwFdhCTfpxkH+YlVdc3A8rvQ3FJ+RlXtPq36+sS2pzvuy/FKsgVwiVfDq2/MS93xONsd81I3zEvdMi91x7zUHdue7rgvx8u8pDnrTbuAHnoI8DSaMZIfMs/6Agxsi7O6sX1vDlwxqUL6qB0m4hDgucDNgKuTHAM8v6oumWpxPdMOYfBu4C+BPwD/RzNBZYBbAfsB/5LkgzRjyl85pVL75iDgc8B8tzdv0a43sC3OQ4AvAx9KsndVXZdkZ5r9+1XAsfkXb3VtzyrgqtWs1w3Zjo9JklsCPwUeRPM3rkVqz49eBDyRZsLpVcPbVNVtJl3XCmNe6o7H2RGYl7pjXhob81J3zEvdMS91x3Z8TMxLa28W85IdXEv3DuBbNG+Ec+wlXpr29twHDSx6VpLhq2hWAX8BnDmpunrq5TTjpR4OnApsCzwHeC/w+KlV1U9vAh4BPBU4evjvuj347wG8vd32hROvsJ9C8yHWfLakCcVahKr6WZKH0YS2I5L8O/Ap4NPAPlV17VQLXOaS/Dmw48CiRye589Bmq2jG9/7BpOrqI9vx7iR502pW35TmGPrXSR4LUFUvm0hh/fdWmvOhT9OM03+joYo0dualEXic7ZR5qTvmpfEwL3XEvDQa81J3bMe7Y14am5nLSw5RuERJfgs83kmT106SA4G5A87GNBPUXjO02dwEgQdW1WkTLK9XknwX+PDgkAVJHkNzEneLqvrd1IrrmXYy9BdW1YfXsN1TgcOqatPJVNY/SfYF9m2fPhD4X5q/80GrgLsBX6iqJ02wvN5rh4X5Cs0koO+tqudOuaReSHIQzRWw0HyIsNCVdOfSXHVsG78A2/HuJLkO+A03Hr4NYB2aD7YuBq6kGdLt9pOrrr+SXAS8qarePO1aVirz0mg8znbHvNQd81J3zEvjZV5aO+al7tiOd8e8NB6zmJe8g2vpvgjcvf1XS1RVhwKHAiQ5lyb8fnu6VfXW7Wl62gcdT3Misg3wnUkX1GOrWNzVcb+muUpEC7sC+EX7ODQnI78c2uZq4LM0V3hrAau5WulUYDfgsoFtqqpePpnKeumfgH+meU9eRjOEyclD21ztXQZrZjveqcOAZwJHAm+oqt/PrUhyK5pj51OceHrJApwx7SJWOPPSCDzOdsq81B3zUnfMSx0xL3XKvNQR2/FOmZfGY+bykndwLVGShwDvovnjmm/SZKrq7AmXpRWovZLh3oOTVSZZl2ZM9J29CmTxknwS2IpmQtrzFthmK+CTwHlV9cRJ1tdXST4AHFxV5067lj5qT4YXy6uVpB5KsgPwL8CdgZdX1Yfa5bek+SDxQQa2pUlyCLB5Vf3VtGtZqcxLWi7MS90xL42HeWk05iVp9pmXujeLeckOriVqT5LnDO+80DSa606wpJnQjvd7T5p9empVnTXlkpa99r34QeDCwcXAS4D/AC4aWO7VSquRZAvgC8CdaK5S+h7NhzFFM2nynYFd2uW7V9V8kwBrEdp9fYlXfmm5SLIhcDTwkqpyDPQRJLk5cBrwtKoavuJTq5FkT5qrZi+gmbfoBxjY1kqSFwIvphlC5zhu3LlSVfXOSde1kpiXxsO8tHTmpe6YlybHvKTlxrzUHfPS2jMvdWcW85IdXEuU5IFr2qaqvjyJWvooyYeAv6+qH7XPVwEfBh7H9WP8FvBxmgO+J3ULSPJjFp6QdphXK61BezXnk4HdaQLardtVv6IJap8FPlZVw2Mna5HaK2x+SXMC8tVp19MX7XHyGOCfqurEKZfTS+1VXwvZCPgG8AzgFPDOgtVJ8ujVrL4Z8FHgFcBZAFV17CTqmgXt3/qraD54PRZ4IvBgA9vSDHWuzMfOlTEzL43GvNQd81K3zEvjZ15aO+al0ZmXumNeGh/zUjdmMS/ZwaWJGh4mIsmbgefR9Bx/jCaA7AW8BXhjVb12WrVKWrrVjIMOzXj8L6A5ofspQFW9bDXbq5XkV8CeVXX8tGvpo7btWd0JT7h+MuXencxN0sC+XGji6cF17su1kGRb4A3AHYH9HEJLWlnMS9JsMy+Nh3lpNOal7piXxs+8pGF2cK2lJPcC7gdsTHOFzdeq6lvTrWr5myewXQC8q6oOHtruYGDvqrrjFMqUtJbav/HfMM98G8A6wJbAxcCVeKXsoiU5Arisql4w7Vr6KMn5NO+/1wDfH1q9Ec0Vn38LnA7eWbA6SU4FtqC5cu5LQ6tvSbMP9wa+BVBVP5lkfZKWD/PS2jEvSbPNvDQe5qXRmJe6Y16SJm+9aRfQN0luBvwXzW351wC/AP4EWDfJ54C9quqKKZbYN7cBTpxn+ZcBr1TSspLkAcBrquoh065lGTsMeCbNxPJvqKrfz61IciuaD7ie4i3kS/Z54NAkm9Pcin8RQ1fYObTBat0R+HvgzcB7aCbz/g38cSgYgP/1fbkoOwPPBt4IfBV46dzE6AP78ucGNU1DkvWBW1TVLxZYvxFwD//Wx8u81DnzknrDvLQo5qXxMC+NxrzUHfOSlq1ZzUvrTLuAHnoTsBvwFGBVVW0OrKLpfd+N5gCm1btPkke349L+ArjFPNvcAvj9PMuladoUWOO8EitZVf0NzbHwPsAPkvzl4OqpFDUb/hPYnGaM6fcB/w18euDrv6dX2vJXVVdU1auAe9CEtx8meU6ShYaN0AKq8R6a/XghcEaS17cfaEtTkWSdJIfSXA1/cZKLkvxDkuGL+XYATph4gSuPeWl05iX1lXlpDcxLY2NeGoF5qTvmJS1Hs56XvINr6Z4EvLyq/mtuQVVdB/xXklsDB9OMmayFvWXo+SNoTjgG3Rf44WTK0UqXZJ9FbrrLWAuZEe2Esw9Psifwz0meD7wI+MF0K+u1baddwCyoqnOAx7YfGL4N+GuaYTj8MGGJqurXwPOTvIfmSuQf0nxo7b7UNDyP5vz7zcD/0pxHvhJ4VJI9quqSaRa3ApmXRmde0rJiXuqWeWkszEsdMC91x7ykZWam85IdXEt3S9rJPufxU+a/uk7Xm++k46p5ll1O80cnTcLhrH4S0EGejCxSVX0syadpxp4+kWaoCPffWnD4gm5V1bFJjgNeAhzB4v72NY+qOgN4UJK9ae7acF9qGp5LM5TOP7XPP5bkvcDHgW8k2b39wEaTYV4ajXlJy9HhmJc6Z17qjnmpW+al7piXtEzMdF5KlW3nUiT5Js2En3vUwM5rb9v9FLBpVe02rfqkQUlu1V41otVoJ+/+b+DFa9j0CcARVbXu+KuaLUm2Bd5Ac5v+flV12pRL6qX29vGtaYZ6uoH2SlAtUZJNgNsC51TV76ZdT5+178+bAb+tqmunXU+ftFfGf2a+sdCTbAw8pqqOnHxl/ZDkd8CjhseKb+cyOQb4M+Ax7eL/sR0fL/OS+sS8tDjmpfEzL3XDvNQ981J3zEtrz7w0mlnPS97BtXSvAj4LfC/JJ2kmrrwNzYncNsCjpleaVqokzwM2qqo3tc93pBnGZPMkp9N8wHD+9Cpc9r4B7LSmk7UkznOwltpJVZ8y7Tr6qp0I9DBgX+CmC2zWqxOQ5aKqLgUunXYds6CqrgF+M+06euoDNPNxzDfZ77btegPbwi4BthpeWFW/TvJw4MPA8TRD7Wj8zEtadsxLIzMvjZl5aTTmpfExL3XHvDQS89JoZjovrTPtAvqmqr5EM+ni/wJ7AYcATwZOA+5ZVb2biE0z4QXAZQPPDwMuAJ5G83f+hmkU1SMfBX60iO3Oppk3Qpq0V9NcTbMfzZAGzweeSXMC8mPgsVOrTFIXVjdUyZ9wwzZeN/ZNFvhQsKquAvYEPkjT8aIxMy9pmTIvjca8pOXOvCTNNvPSaGY6LzlEoTQDkvwWeGxVnZBkU+DnwEOr6sQkTwT+taq2mG6VktZWku/TjNd9OPAHYJeqOrVddwRwZVU9Z3oVSlqqJHsAe7RPnwF8hubKukGrgPsD362qR0yuun5J8lBgf+B5VfXL1Wz3KuDhVfXgiRUnaVkwL0mzzbwkzR7zUndmPS85RGEHktwZuDNwUlVdMO16tCJdBdykffxg4Argq+3zXwK3mkJNkrqzFfCDqro2yZXArQfWfRD4EGBgk/rlNsDdBp5vB/zp0DZXA18A/nFSRfVRVR1Pc4X2mrb7J+Cf1rSdumde0jJgXpJmm3lJmj3mpY7Mel6yg2uJkrwbqKp6bvv8KTSN5TrAb5PsXlX/M80a+yLJ1sCFVfWHedatB2xRVedNvrJeOgk4IMn5wAuBzw1MWHl7muE3JPXXhVz/wcu5wAOAL7bPt5tGQZLt+Giq6r3AewGSnEBzNd33pluVNDrzUnc8znbKvCTNNvOSlh3b8dGYl7RYzsG1dLsDXxl4/jqaK0G2AD7fPtfinEszPv987t6u1+K8BNgBOJPmyqW/G1j3FODr0yhKK1uSfZL8yQLrNk6yz6Rr6rETaW67h+YE71VJPpTkA8CbgU9Nq7C+SXJtkl0XWLdTkmvnW6d52Y53pKoebFjTDDEvdcfjbHfMS1p2zEudOhHzUifMS52yHe+IeUmr4x1cS3cb4KcASbYH7gA8sap+nuQ9NJOvanFWN0HgKpphJLQIVXU2cIf25PiXdcPJ9V5KM8a8NGkfAHYDfjHPum3b9UdOtKL++jtgE4CqeluS0EwCugHwdpzMeylW1/asD1wzqUJmgO14h5JsRDPG/B1p9t8NVNXLJl6UtHbMS93xONsR85KWKfNSd8xL3TEvdcd2vEPmJS3EDq6l+yWwWfv4YcDPq+qs9nmAdadSVU8k+XNgx4FFj27H5B+0Cngy8INJ1TVDfglsmWQr4NtV9buqOnPaRWnFWt3J3J8Al02qkL6rqp8z8MFLVb0VeOv0KuqXdmiIbQYW3SPJ8AnxKmBfvIputWzHxyPJdjR3D2wI3Ixm8uSNac7VfwX8BjCwqS/MSyPwODt25iUtJ+aljpiXRmNe6o7t+HiYl7Q6dnAt3WeBg5NsRvOHc9TAursCP55GUT3yBOCg9nEBr15gu3NxAtAlSfLXwN/TTLhYwC7AaUk+AXylqt42xfJ6I8mrgffNNwF6ks2BZ1eVV38tIMkeNFfUzPmHJJcMbbaKZviIkydWmFa6Z9K0PdV+vXOB7X4PPGtSRfWU7fh4vBU4BdgL+B3waODbNMNmvb79V+oL89JoPM6OiXmpG+al0ZiXtEyZl7pjOz4e5iUtKDe8M19rkuSWNH9UuwCnAwdU1WXtuq8C/1NVL59ehctbkvWBm9BcqXQZ8BBufNJ29XwTMGphSQ6kmc/gjcAJwJeAnavqtCQvBJ5aVbtNs8a+aMeT3q2qTppn3U7ASVXllccLSPJsYP/26U7A92hOPgZd3S7/x6ry6q8FJPnSUravqoeMq5a+S7IpzZBZAc4Antb+O+hq4LyqcpiI1bAdH48kP6f5sOBYmmFf7lNV32zXvRDYu6ruM8USe8N2fPrMS6PxODse5qXueJwdjXmpO+al7piXumM7Ph7mpe7MYjvuHVxLVFW/Af5qgXX3n2+5rtcewOcO4utMs5YZcwDw6qp6U5Lhg9D3acan1eKE5iqb+WxJc+uzFlBV76WZ1JckJwDPcyLQtfYg4HKaCZOvnmolPVdVl9AMYUCSbYELq8p9uhZsx8dmFXBZVV2X5JfAFgPrzqKZhFqL47wRU2ZeGo3H2bExL3XHvDQC81KnHoR5qRPmpe7Yjo+Neak7M5eX7ODS1CXZkoUnCDx28hX10p8Cpy6w7jrm2be6XpJ9acaShvZ2/CTD452vAu4GfGGStfVZVT142jX03AeAxwMPAI4GPgx8saqum2JNvVdVPwFIsh6wNfO3PWdPuq4+sx3vxA+A27WP/xd4bpJjgWuB/YAbDQOl6zlvhGadx9lOmJdGYF4aD/PSyMxLY2Be6p7teCfMSyOY9bxkB9cStbeavgh4Is3VSfMdnG4z6br6KMlGNGPyP2JuUfvv4NVgvbolcorOAR4IHD/PugcAnnys3hXAL9rHoZmc8pdD21xNM6fEOyZYV++1f+d7sPDJnJOALqCq9kvyXOCRwN7Ax4DfJ/k48OGq+upUC+ypth0/jObE7aYLbGbbswi24536CM1k1P8B/APweZohTa6jOV9/xrQK6wnnjVhGzEvd8TjbKfPSaMxLY2JeWnvmpfEwL3XHdrxT5qXRzHRecg6uJUryrzSTAH6a5iT4RrfsVtVrJ11XH7X78sHAs4Gv0UzE+Cvg/9GMUfvUqnJS1UVI8iyaIHEwzUnd2TQTLt6W5sTk2VX1oelV2B9JPgAc7Fjno0uyHfB1YEPgZjRDHmxMc/LxK+A3VXX76VXYL+3VNY+jmTz1UTQfKhxSVQudmGgeSV5Hc/L7MuCDNEMW/Y6m7dkOeIFX0S2O7fj4JNkK2B3YAPhSVZ015ZKWNeeNWF7MS93xONsd81J3zEvdMS91y7zUDfNSd2zHx8e8tDSznpfs4FqiJBcBb6qqN0+7lr5L8iPg74GP0oxPe6+5A3uSNwNbVdWTp1hir7QTJ7+a5uR47qqQK4DXVtWhUytMK1aSY2jGnN6L5oR4Z+DbNIHj9cCenswtXZK70Hxw+NfAMVX1xCmX1CtJvg+8CTicpu3ZpapObdcdAVxZVc+ZXoX9YTuu5SjJ7XDeiKkyL3XH42y3zEtabsxL42FeGo15qTu241qOZjEvOUTh0s31dGp0mwE/raprk/yO5kqlOccCH59OWf1UVYcmeRewG7AJzdVK32gn+tYSJNmG5oqahYaJ8ARkcXalubV57uqPm1TVtcCHkmwC/Atwn2kV1yft1Z17t193prn66/k0VyBrabYCftC2PVcCtx5Y90HgQzSBWGtmO65lx3kjlgXzUnc8znbIvNQd81JnzEsdMS91yrzUHdtxLTuzmJfs4Fq69wJPBY6bdiEz4Kc0wQLgh8BjaMZQBbgXcOU0iuqzqrocJ/UdSZKdgC/TvD/vSPMBzS1pJmM8n2b8fi3OKuCyqrouyS+BLQbWnQXcfTpl9UOS29Jcvbk3sBNwMvDvwFFV5QSqa+9C4Fbt43Np5t34Yvt8u2kU1GO241p2nDdiWTAvdcfjbMfMS6MzL3XKvDQC89LYmJe6YzuuZWcW85IdXEt3EfC0JCfQhLZfD60vx/ddtOOAhwGfBN4KHNGeLF9F04A6rMkiJdlnNauvo5l48dtzvfRarUNprqL5K5pbyPerqtOS3Af4MM2t+lqcHwC3ax//L/DcJMcC1wL7AYaO1fsJzVAl/00zX8SP2uW3SnKr4Y37doXNFJ0I3J9mv74X+Ockd6Bpe55C83euxbEd13L0apoPD/ZjgXkjplfaimFe6o7H2Y6YlzplXuqOeWk05qXxOBHzUldsx7UczVxecg6uJUpy3Ro2qarqVS/ntCTZENiwqi5tnz8B2JNmgsDjgHdX1Zr2t/jj+3LujzkDqwaXFc1k30+rqt9OsLxeaa+ceyrNlZ3XAverqv9p1/0V8MKq2nF6FfZHkhcDt62qlyS5N82VShvQfIiwHvCMqvrPada4nA21N6trrINtz6Il+VNgk7lJaJP8LTdsew6uqt9NscTesB3XcuS8EdNnXuqOx9numJe6Y17qjnlpNOal8TAvdcd2XMvRLOYlO7ikGZBkR5pJK98HHANcAmwK7EEzpvdzaYY7OAz4cFUdMJ1Kl78kv6CZzPeEJD8H/raqPtyuezhwdFXdbKpF9lSSrYDdaU7mvjR3wqz5JXngUravqi+PqxZJ45Xk1cD75htOJ8nmwLOr6uDJV9Y/Sa4AHllVX20fP66qvtiuewTwoaraZLXfRNLMMS91x7w0PualpTEvSSuHeak7s5iXHKJQmg1vBt5RVf8ysOyXwJuSXA0cVFUPTLIZ8BKa2081v7Npbsk9AfgG8LdJTgGuBl4G/N8Ua+u1qvopzRAHWgQDmLSiHAR8jvmHItqiXW9gWxznjZA0H/NSd8xLY2JeWhrzkrSimJe6M3N5yQ6utZRkS5oJVVcNr6uqYydfkVa43YA3LrDuu8A/tY9PBf5kIhX113u4fhz0V9EMvfG99vnvaG4nlySpS3NDY81nS+BXE6yl707EeSOWBfOSlhnzUnfMS5KkSTMvdedEZiwv2cG1REk2Ao4CHjG3qP138I/McX01aecDz6AJF8Oe2a4HuDXwiwnV1EtV9R8Dj7+b5M9oAvEGwDer6uKpFSdJmhlJ9gX2bZ8W8M4klw1ttgq4G/O375rf3wGbAFTV25KE6+c6eDte2Tl25iUtU+aljpiXJEmTYF4am5nLS3ZwLd3rga1pejq/BjyBppf4/wEPoZlsVZq0vwM+nOSuND3wc2PKPxbYAdi73e7hwFenUmFPtRNMHzftOiRJM+cKrv8QNcBvaIbLGnQ18FngHROsq9eq6ufAzweevxV46/QqWpHMS1qOzEtjYl6SJI2JeWkMZjEvpWqhu/s0nyQ/Av6eZoLaPwD3qqqT23VvBraqqidPscTeSPIA4LT2hHh43c2Be1bVVyZfWT8l2Rl4ObAz8Kc0B6uTgTdW1anTrE2SloskWwMXVtUf5lm3HrBFVZ03+cr6x3a8O0k+ABxcVedOuxZpVOal7nic7ZZ5SZLWzLzUHdvx7piXtDrrTLuAHtoM+GlVXUszvvTGA+uO5fqhOLRmJ9BcLTefO7XrtUhVdUpV7VVV21bVBu2/TzasaVqSvDrJFgus2zzJqyddU18l2TrJ+gusW68NIVqcc4F7LLDu7u16LY7teEeq6pnDYS3JpkluMq2apBGYl7rjcbZD5iUtN+al7piXOmVe6o7teEfMS1odO7iW7qe041QCPwQeM7DuXsCVE6+ov7KadTenuRVVUn8dRDPZ53y2aNdrcQwZ3Vld27OKZmJVLY7t+AiS3C/JgfMs3z/JpTR3Fvw6yduSeM6uPjEvdcfjrDTbzEvdMS91x7zUHdvxEZiXtFjOwbV0xwEPAz5JMz7lEUl2ojnAPwB48xRrW/ba23MfNLDoWUl2H9psFfAXwJmTqquPknxpKdtX1UPGVYu0gHDDCeUHbUkzH4cWx5AxgiR/Duw4sOjRSe48tNkq4MnADyZVVx/ZjnfqpcC1gwuSPBR4J3A68BrgjsABwNnAeyZbnrTWzEsj8DjbHfOSesC81B3z0gjMS92xHe+UeUmLYgfX0r0c2BCgqv4jyW+BPYENgOcD755ibX1wL+AF7eMC9gKuGdrmauB7wI166XUDvxh6vhvNkDCnAhcDtwHuCVwEfGOypfVXkvcDr5tvXN8ktwMOqqq/mnxl/ZBkX2Df9mkB70xy2dBmq4C7AV+YZG19Y8jo1BO4/grYAhYa7uVc4DkTqai/bMe7c0/gH4aWPY/mSs6HV9UvAZJcCTwbA9uiOG/EsmBeGo3H2e6Yl8bAvDQa81J3zEudMi91x3a8O+alMZjFvJSqhS4WkcYrybnA46vq29Oupe+S7Ae8CHjM4EGoPWh9Gnh7Vb13WvX1SZLrgHtX1UnzrNsJOKmq1p18Zf2QZC+aAAHwJJoxpX85tNncydw7qmr4gwe1khzEDUPGQlclngs8p6q+OJHCeqgdj/8mNPvwMuAhNJPKD7p6vhM8Lcx2fDRJfg/sXlVfbp8H+DVwQlU9fmC7RwAfqaqN5/s+uqEk1wK72Y5rFnic7Y55qTvmpdGYl7pjXuqOeWk8bMdHY14aj1nMS97BNaIkjwXuTDPu59FVdfmUS+qNqtp22jXMkL8DXjzcw15V57UnfW8BDGyLt1DP/12BSyZZSN9U1X8B/wWQ5APAwfNd3alF+SfgnzFkjKzdR3P7ybG5O2I7PrKLaebXmHMPYCPgK0Pb/QHoVcCYMocoWmbMS2vP42ynzEvdMi+tJfNSp8xLHTEvjYft+MjMS+Mxc3nJDq5FSPJymiu97j+wbH3geOC+XP/G+GmS3arqgimU2QtJtgBuV1XfGFq+I81tp3emGSLi7VX1yclX2Ft/Ctx0gXU3pRl+QwtI8iKaKzqhCWtHJxk+oK+iGdLk8AmW1mtV9czhZUk2BX5TVVdPoaReMWR0J8mGwK2G2+e2TXoJ17c9766qb02hxN6wHe/UV4CXJPkCzRwbLwWuA44e2u7uwE8nW1q/OETR9JmXuuNxdmzMSyMwL42HeWk05qXumJe6YzveKfNSR2Y9LzlE4SIk+SZwYlW9YmDZS4BDgde1/94R+Bjw2ao6YCqF9kB7hdKdquo+A8u2B06jOUgdB2wH/DnwiKo6fiqF9kySY4EdgD2r6pSB5bvQvC+/U1WPnlZ9y12ShwOPoPnw5cXAh4ALhzabGybiqKrq3dUMk5LkfjS3Oh86tHx/mivsbk1zNch7aK6ivW7yVfZXki1p2ptVw+uq6tjJV9QPSd4J3K+q7jawbDPg28CftP9uCdwKuG9VnTqNOvvAdrw77bBY3wI2pWljVgFvqaqXDm13GvD1qnrBjb+LwCGKlgPzUnc8zo6HeWk05qXumJfGy7y0dsxL3bEd7455qTuznpfs4FqEJJcAz66qoweWnQxsXFXbDSzbH3hpVd1x8lX2Q5IfAIdV1b8OLHsnsB+wc1Wd0S47GrhZVT18KoX2THsSdwzNVQsXcf2kyZsBZwCPrarzp1dhf7QH/fd6ZfHaaf92r62qJw0seyjNBMmnAx+gCRzPAw6oKicBXYQkGwFH0XywANefjPyxEe/bGMmTlOQs4PCq+ueBZW8G/oZmTO/jkqwCPg/8anA8b92Q7Xi3ktyKZuLpWwKnVdWXhtZvQjMR/bFV9d3JV9gPzhsxfeal7nicHQ/zUnfMS6MxL42HeWk05qXu2I53y7zUjVnPSw5RuDgb0kxiB0CSm9OM+/nvQ9t9D7jt5MrqpS2A4QPO44BvzB3kW+8H3jWxqnquDWP3TPJoYBeaITh+DpzsVUpLU1WvHXye5NbAHYCLq+on06mqV+5Jc9v9oOcBVwAPr6pfAiS5Eng2zZWJWrPXA1sD9we+BjyB5hb9/0dzYvLU6ZXWC1vRfHg16PHAqVV1HEBVXZnk7TRzcGhhtuMdqqpfs5o5X6rqUuDNEyuopxyiaFkwL3XH4+wYmJe6Y14amXlpPMxLozEvdcd2vEPmpW7Mel6auV9oTH4E7Drw/OE0PZ7Dt5HekqYXVAu7Athg7kmSbYHNufEEgb+iufVZS1BVx1bVa6vqee2/hrVFSLJHkn+ZZ/khNFd4fhP4UZKj26uWtLBNgR/PPUkSmmPm8XNhrfVFmtvytTiPBg6huT0f4IKq+kpV7Q98CjhwapX1w3UM3IKfZHNgW+DEoe1+TvMe1sJsx7XsJNmwne9gePkWSd6c5DNJ3p/kXtOob4UwL3XH4+wYmZfWjnmpU+al8TAvjca81B3bcS07s56X7OBanA8Af5/kxUmeRjOG/CXAZ4a2ezDw/UkX1zOnA08feP40mlvGPz203XbceExvDWgnAf3j4zV9TbPWHngezQcuf5RkT+CVNMFiD5oT4ocBL5x4df1yMc0VS3PuAWzEjU/m/gA4RMTibQb8tKquBX4HbDyw7liuH4pD8/sOzVVzc55I0/Z8dmi7rWjew1rY6diOa/l5M82QOX/UzhtxGk27vRnNB19fTrLT5MtbEcxL3Tkdj7OdMC91yrzUHfPSeJiXRmNe6s7p2I5r+ZnpvOQQhYtzGHAnmlue1wd+Cjy1qn43t0GSW9KM+fnGqVTYHwcDJyT5Nk2j+BDghKr61tB2T+L6K280v8uT7FZVJwG/ZWBs6QV4crywuwFHDi3bj+aKmidV1e8BktyM5uTkTZMtr1e+ArwkyRdo9t9Laa4GO3pou7vTHEu1OD8FNmkf/xB4DNefnNwLuHIaRfXIG4FPtZPU/hz4S5qJkk8c2u6xNCd4WpjtuJaj+wOHDy17Gc0VxsPzRvwDzZA76pZ5qTseZ7tjXuqOeak75qXxMC+NxrzUHdtxLUcznZfs4FqEqroGeE6Sv6GZAPDSeTb7Hc1EoA65sRpV9bUkDwaeS3Mr7iE0V3j+UZJNaU7wDp90fT3zV8D/tY+fOc1CZsDGwB8nlU6yLvBA4Ji5sNb6Ok0A0cL+juYk7SLgamAV8Jaq+tHQdvsAJ0y4tj47juaK2E8CbwWOaK+quQp4AI45vVpV9d/tHQUH0Ay18XHgVVX1xw+62rbnzvjB62rZjmuZct6IKTMvdcfjbKfMS90xL3XHvDQe5qURmJe6YzuuZWqm81IGjlWSeijJ+jRzHpxbVRdMu54+SvJ/wOuq6vD2+X2BrwJ/XVXvGtjuYcBRVbXxvN9IACS5FbAXzTAmp1XVl4bWb0JzBfexVTU8+arm0Q6bs+HcB4ZJngDsSTO293HAu6vquimWKGkESe5aVWdNu46+SvIrYO+q+nz7fHPgZ8A/V9XLBra7H3BcVW0w/3eSNIvMS6MzL3XLvNQ985I028xLo5n1vOQdXFL/XQt8iWasVAPb2vks8HdJzqC5ku4gmqvpPjW03S4MTAis+VXVr4H3rmb9pXgF3ZJU1RU0k9XOPf8kzdWJkmbDGUlOBd4PfLg9jmrx5uaNmBuKyHkjJA0yL43OvNQh81L3zEvSzDMvjWam89I60y5A0mjaq5B+SDMhoNbOQTRjcp8MnEczRvIrquqPE362w3A8k2YSZUmSuvRQ4GyaOUsuSPLhJA9PkinX1RdvBJ6X5L+TvJdmPzpvhCTAvNQR85IkaZrMS6OZ6bzkEIXSDEiyB83Baq+qOnPa9fRRkvWAB9GMkXx6VZ0ztP7WNEHuW1V1/o2+gSRJI0pyM+ApwDOA+9HMd3IEcHhV/d9qXrriJXkqzbwRt6IJZa8abK/buQ4+D7yxqj46lSIlTY15aXTmJUnStJmX1t4s5yU7uKQZkORkYBuayX9/RjNsxA3+uKtq18lXJkmS1kaSO9AMwXHfdtHXgLe1Q+5IkpbAvCRJ0mwxL2mOQxQuUZIvJbnzAuvumORL863TjSXZup3wd7516yXZetI19dhZwKeBI4Hj2+ffGfqSJKkztuPjkWSbJK8BvgDsBhwL7E/zYexHk7x1iuVJa2Re6o7H2U6ZlyRJE2U7Ph7mJQ3zDq4lSnIdcO+qOmmedTsD36yq9SZfWf8kuRbYbYF9uRNwUlWtO/nKJGl5SfIA4LSq+u08624O3LOqvjL5yrSS2Y53J8mGwJ40c5fcHziX5mrEw4fmN3km8C9VdYupFCotgnmpOx5nJWlxzEtajmzHu2Ne0uoYLNbOjXoFk9yEZrzpn0++nN5a3USAq4CrJlWIpG4k2Wcp21fVkeOqZcacQHNl0o1OjIE7tes9MV5A+2Hroq/oMWQsmu14d35Oc17+ceBhVXXiAtudDPxiUkVJIzAvdcPjrDRjzEtjY14agXlpbGzHu2Ne0oLs4FqEJAcBr26fFvDNZMFj1KETKaqnkvw5sOPAokfPM4TJKuDJwA8mVVdfJbklzUnc+sCXq+qyJHeleb/+GfBT4F1VdcwUy9TKcvjQ87mT5MyzDJphYrRmqzsxvjlwxaQK6akXcv37bn3gJcBvgU8BFwObAXsANwPePI0C+8J2fGxeAXywqn6zuo2q6ixg28mUJC2eeak7Hme7ZV7SMnT40HPzUjfMS6MxL3XEdnxszEtakEMULkKSXYBdaRrMw2gO5j8e2uxq4HtV9dXJVtcvbfg9qH1aLHwSci7wnKr64kQK66G20fwc8KftokuBvYCjgZ8A3wN2AO4CPKmqjp58lf2T5NXA+6rqgnnWbQ48u6oOnnxl/ZDkZgNP7wwcBfw78AmaE+PbAE8C/gp4clWdOvEie6IdZuNB7dPXAO8Dzh/abBXwF8Dvquo+Eyuux5K8Bdga2KsGToLSfBL7X8DPqupF06pvubMd716SVcAZwAur6nPTrkdaG+al7nic7Y55aTzMS6MxL3XHvDQe5qXR2I53z7ykNbGDa4mS7At8pqounXYtfdROrngTmgP8ZTTDlJw8tNnVVfWHSdfWN0k+A2wK7EtzZc2bgEcAn6qqvxrY7j+A7TyZWxzHSO5OkhOBY6rqLfOsewnwuKp64MQL64kkBwIva59uTHPMvGZos6tpPpw5sKpOm2B5vZXkUuBpVfX5edY9EvhQVf3J5CvrB9vx8UhyMfD/quoL066l72zHp8+8NBqPs90xL42Hx9numJdGY14aD/PSaGzHx8O81J1ZbMcdonCJquqIadfQZ+0BfO4gvs40a5kBuwD7V9V3AZK8DHgK8KGh7T44zzItLCw89vSWwK8mWEvf7Qq8foF1ZwGvm2AtvVNVh9IO45TkXODxVfXt6VY1E9alGZLoRoGN5gpu26bVsB0fmw/STJhsYBvd6oYoWp8bf/CljpmXRuNxtlPmpfEwL3XHvDQC89LYmJdGYDs+Nual7sxcXrKDay0keQrwbOCONLc730BV3WbiRfVUktvQjO27M7AV8ISq+k6SF9H0GH9jqgUub5sAg8NCzD3+5dB2vwJuOZGKeqq90njf9mkB70xy2dBmq4C7YWO6FD+lOQGZ78R4P248fIQWUFWOId2dDwL/lGQ94BiuHwpmD+BgmiFitEi24505D3hyklOAY4GLuOGHh1VV75xKZT2QZGtgm4FF92iHMhm0iqatP3dSda1k5qXueJwdiXmpI+alsTEvdcS81CnzUodsxztjXhrBrOclO7iWKMlfAu+nmRj0Ie3jdYDHAb/GCUAXLcmuwBdpGssv04ydfNN29eY0DcCeUylOK80VwC/axwF+w42D79XAZ4F3TLCuvnsV8JEkZ3HDE+PH0Yw3/5Qp1rbsJdkCuN3wCW+SHYF/oNmHFwFvr6pPTr7C3noxzRV1BwNvHFh+FfBurh/mRGtgO96pucm6NwfuOc/6AgxsC3smzVwHxer31e+BZ02qqJXKvNQdj7NaRsxL42FeGoF5aWzMSx2xHe+UeWk0M52XnINriZL8L/Ax4A00B/ydq+q0JBsBxwEfq6p/nmaNfZHk68AlwBNpQu/VXL8/nwi8raq2nmaNy1mS62iurLlwbhFN4/gfNCdxczYH/rJv46dOS5IPAK+rqh9Nu5ZZkOSewCtohoj5U+DnNONPv9EJk1evfS/eaXA+iCTbA6cB19G0OdsBfw48oqqOn0qhPZVkY5qrjOfel2dW1fAHNVoN23EtF0k2pflAMDQTUD+t/XfQ1cB5VXXVhMtbccxL3fE4Oxrz0niYl7plXlp75qXxMi+NznZcy8Ws5yXv4Fq67YGvV9W17aRstwCoqsuTvBF4K2BgW5x7AntU1XVJhsf//AXNH54Wdh5wv6FlPwEesMC2WoSqeiZA+57ckuYW8m9X1e+mWlhPtRP5PnnadfTUfYHDhpa9mOaKr52r6gyAJEfThGID2xK04ezL066j52zHO9AODXEG8MKq+ty06+mjqrqE5sMDkmwLXFhVV0+3qhXNvNQdj7OjMS+NgXmpW+alkZiXxsi81Anb8Q6Yl0Y363nJDq6l+w3X3076M5qJF09snwf4kynU1Fe/ATZdYN3tueFVdRpSVdtMu4ZZleSvgb+nuVKpaK6mOy3JJ4CvVNXbplieVo4tgO8OLXsc8I25sNZ6P/CuiVU1A9oT5AfQfCgzPO60Y3cvnu14B6rqyiS3ornSWCOqqp8kuWmS53H9XAcHVNUP23mhzqiq4WOrumVe6o7H2RGYl8bHvKRlwrw0JualztiOd8C81K1ZzEt2cC3dKTS3N3+eZozkVye5huY2vlcD35pibX3zKeC1Sb5BcyUdQCXZBHgp8ImpVaYVK8mBwOtoxpo+AfjSwOoTgacCb5t4YT2VZE+a2/HnOzGmqnadeFH9cQWwwdyT9iqbzWkC2qBfAbeaXFn9luR+NO3LJgts4tjdi2c73p0P0oyL/oVpF9J3Se5IMyTRLYFTaeY62KhdfX/gL4B9plLcymFe6o7HWS075qVumZdGYl4aA/NSp2zHu2Ne6sgs5iU7uJbu9cDt2sevbh+/A1iXZpzk/adUVx/N3SJ+Ns0fFDRX1dwBOJdm/0qTdgDw6qp6U5Lhcfi/D9xxCjX1UpLX0Pwdf5vm73xmbn+ekNOBpwOfbp8/jSZMfHpou+24fm4JrdlhwP8BDwfOrqo/TLmePrMd7855wJOTnAIcS3M15+BEuV4pu3iH0ezPxwK/5YZtz5e54WTpGg/zUnc8zmo5Mi91xLw0stMxL42Deak7tuPdMS91Z+byUqpqzVtptZLcFLhpVV027Vr6JslNaE5IHkpzdcgvaQ7+R/ZxUjv1X5Irgb+oquPbwDY4OfrDgaOr6mbTrbIfkvwU+I+qetW0a+mj9sq5E2hOhi8GHgKcUFUPG9ruv4HLq+ovJ19l/yT5HfDEqvr8tGuZBbbj3UiypuE2qqqGP0TUPNq/8b2q6th52vEHAJ+vqg1W/13UNfPS2vM4q+XGvNQd89JozEvjYV7qlu14N8xL3ZnFvOQdXB1oD0gelJYoyS5VdTLw7+3X8Pp9qurIyVemFe4c4IHMPwHtA2hOnrU4G+FEvmutqr6W5MHAc2mG1DgEOHRwmySb0oxDffik6+uxM2jmi9CIbMe7U1XrTLuGGXIlA8MVDbkt8OvJlaI55qW143FWy5R5qTvmpRGYl8bGvNQR2/HumJc6NXN5yTu4NDVJfgE8eGjyz7l1zwfeWlXrT74yrWRJnkUzjM7BwMdoAtqjaQ7yhwHPrqoPTa/C/kjyLpor5Q6cdi3SnCQ70gTcF1XVl6dbTb/Zjms5SvIRYHuaq7h/S3NF4k407fkJwHerar/pVSgtnsdZLUfmpe6Yl7QcmZe6Yzuu5WgW85IdXJqaJG8Hngw8sKq+N7D8VcBrgedU1fDkoJpHkscAx1bVmm7Z1SK0Eye/GtgQSLv4CuC1VXXogi/UDSTZi2bs3i/TTGD56+FtqurYCZelFS7JJTR/26toTuRuNFxWVd1m0nX1ke14t5LcBngJsDOwFfCEqvpOkhcBJ1XVN6ZaYE8k2Qr4Os1ViccBTwGOAe4C3AS4d1X9fHoVSovncbY75qVumZe6YV7ScmRe6o7teLfMS92YxbxkB5emKsl7gb8A7l9V/5fk9cCLgX2q6qPTra4/klxLM+b0kcDhVfXdKZfUe0k2Anbj+jGSv1FVv0myXlVdM93q+sExkrUctZN5r/bkp6peO5lq+s92vBtJdqUJF5fQfMj1DGCXdhz0NwB3qKo9p1hiryS5Nc37cHiug7dU1S+mWZu0VB5nu2Fe6p55aXTmJS1H5qVu2Y53w7zUrVnLS3ZwaaqShCZkPAD4ErA3zUR3n55qYT2TZBvgmcA+wNbAScD7gY86mffSJPnHqvr7BdZtAHy8qh494bJ6Kcnt1rRNVf1kErVIGg/b8W4k+TpNWHsisA5wNddP9PtE4G1VtfU0a+yLJLevqh+tZv1Dq8r5TtQbHme7YV7qjnmpO+YlafbZjnfDvNSdWcxLdnCtpSRb0FyttDHXX610wXSr6qck6wAfAXYH9qiqE6ZcUq8leQhNeHsCzXARnwDe735dnCSXAW+sqkOGlt8c+Axwu6raZhq1SepOe8XSXWmGNvhsVf0qySrgaocvWhrb8dEl+T3NvvtCknVphoOZC2wPBD5fVaumW2U/JPkxcL+qOn+edXsAH66qDSde2ApkXuqOx9lumZdGY16SVgbzUndsx0dnXurOLOal9aZdQN+0f0RvB54NDN4qfm2S9wAv8EC/sHYs3/l6VdejGefzo83FDQ3H9V26qvoS8KX2Q4WPAE8D/jLJeTST/r7dISNW63HAZ5JcUVVvhT+e2H0BuBXNVTdapCQ3Bf6K68dIPqCqfpjkKcAZDg+zOEm2Bi6sqj/Ms249YIuqOm/ylfVPu7/+CTiAZszpAnYBfgV8HDgFOGhqBS5ztuNj8xtg0wXW3R64aIK19N1pwAlJ7j84dnySpwEfAF4/tcpWCPPSaDzOjp95aWTmpQ6Zl7phXuqOeWk0tuNjY17qzszlJTu4lu61NCcfrwI+SvMHtBnNhGwHA7+gmWxV8/s31jCWr0bTXrnwTOBJNFc0/BtwNPBImvfvLsBfTqu+5a6qTmxvbz66vULkaOCLNO/bGxz8tXpJ7kgzRvItgVOBBwEbtavvTzMO9T5TKa5/zqW5Cv6kedbdvV3u+PyLcwjNh67PB04ABm/N/xTwXAxsq2M7Ph6fAl6b5BvA3FBElWQT4KU0dxdocZ5C03Z/KckDqurSJM+l6XB5ZVX981SrWxnMS6PxODtm5qXRmJe6Y17qlHmpO+al0diOj4d5qTszl5cconCJ5q7qmu8/O8lLgRc65ueatbfnbg78pqp+O+16+q4du3vf9msb4ETgfcAnquqqge2eAPxnVd1sCmX2SpLHAUfRfAjzM+CRVfWr6VbVL0k+B9wMeCzwW244RvJeNEOb3H6aNfZFOwH1vavqRoEtyX2B4/p2C/m0JLkQeE1VvXueoQ0eSjNvxK2mWmQP2I53q73y/XhgB5oPuHYDTgbuQPOBzYOr6vLpVdgv7dXwn6G5yvMT8P/Zu/N4X8t5/+Ovzy7ZRUrDTqRBhH6GnJRSEhkSTgqVqZJZjrGjwxEVJzPHmCGUY0imEkmpJEKpoyTJoVmT0jzshs/vj+veWq3W2nutve7vPX1fz8djP9b3e993+3zs813Xdb+/13VfF/sAb8jMz7Va2JgwL9XDdrZe5qX6mZfmzrxUH/NSfcxL9bAfr5d5qV5Dy0s+wTV7C4Azpzl3ZnVeSzYPOJ9yI3d0u6UMwl+BvwEHU9aPP2+a6/7A1DOaxlpETLUJ8u3ANyif0Y8Bmy96jDwzj2quul57EmXz1GuqG+OJLqfc7GkaEfEYYKMJh7aLiEdMumw+sBNwblN1DcDKwF+mObcczuycKfvxGlV7GmwGvAzYBriRsmfRQcBXJ375qiXLzFsjYtFn8z+B3TLz6y2XNU7MS/Wwna2XeWkOzEsjY16aA/PSyKyMeakO9uM1Mi/Va2h5yQGu2TsX2IWyvvRkuwB/aracfsrM2yPiAsAZNPV4LnD0kvYzyMxzgac0U1Kv/JDyCHlMc/4bE14n3tDN1C2UNbun8iDgmuZK6aUduGvph2T65ZzOA17TSEXDcBawPWUpncmeRVmPWktgP16/zFwIfKn6o1mIiFOZeimY+wI3AW+KiDctOpiZmzZV25gyL9XAdrZ25qW5MS+NhnlpbsxLo2FeqoH9eP3MS0tv6HnJAa7Zex9waLWB5Xcos2oWAC+k3Ajv0mJtffNB4D8j4qTMvLLtYvoqIuYD36fMSjqi5XL6ar22CxioY4F3RsRPKUtuQFkj+d7AvwHO7Fy8A4CPUL5IuI7Sx/x20jULp9pIWYv1PuC7EbE88G3KTd5G1ZJEr6FsnK6ZsR8fkYhYGVgfuCgzr2i5nD74A+510CXmpfrYztbAvFQL89JomJfmxrw0Gual+tiPj4h5adYGnZfcg2spRMQzKJvP/gtwL8p6tKcB78nMY9usrU8i4tvAFty1oerl3P2XLTNz5zZq65uIuAh4XWb+sO1a+qwKEnsBP8zMM9qup+8i4sHALymzEo+lbGT5A+D/UZY22MxNqJes+lLm08BBmfnrtusZgojYCfgQMHEPmEuAt2XmYe1U1T/243MXEbsAz6PcT34vM78eEe8G3kFpJ6FsALxrZt7YSpHSUjAv1cN2tj7mpXqYl+plXqqHeal+5qV62I/PnXlJM+EA1yxUN3MvAE7JzD9XGwauBvx9SUsd6J4i4oQlXZOZLg8xAxHxLmAr4NnOTpqbiLgJeFZmnth2LUNQbQT6VsoayatR1kg+DvhYZl7VZm19EhHXA8/NzJ+1XcuQRMQG3PW5/FN6UzQr9uNzExGvAj5P2Rz5eko//hlgd+C9wNnAoylron82M9/ZTqX9UX3BdS2wc2Ye3nI5Y8m8VC/b2fqYl+pjXqqXeake5qXRMC/Njf343JiX6jfUvOQA1yxFxM3Att7MqUsi4iPAiykzQY5j6lkhe7dRW99UNyBHZubH2q5FWiQijgB+l5nvWeLFWioRsXJmXtN2HRofEXEm8NPMfGv1/qXAIcCbMvPTE657C/DazHx4O5X2i09ptM+8pC4yL9XHvKQuMi+NnnlJTTMvjcYQ85J7cM3e74ENAAObuuQFwK3V6ydNcT4BA9vMvB34RkQspKx5Pjn8kpk3tVFY30TEssAymXnrhGPPADYEfp6Zbk47c58BDoqI+zD95/LsNgrrm4h4HbBiZn6oer8RZeP0NSPid8D2mXlxexVqjKwPvGnC+yMoe0icNum63wLrNFXUAHweeGNE/MSnNFpjXlIXmZfqY16qiXmpVualmpiX1CHmpdEYXF7yCa5ZiogtgIOBtwBHZ+bt7VbUbxGxIrA9JQTPn3w+M9/eeFEaaxExcfmcKRvIzFymoXJ6LSK+C1ybmXtU798I/Dfly4VlgB2HNGNklCZ9LuHun82gzDr2czkDEXE28MnM/Fz1/ueU/udjlC+2/pCZL22xxF6xH1961e/1Zpl5SvV+Gco+RY+f+IVWRDwBONnf8ZnxKY32mZfqZTurrjEv1ce8VB/zUn3MS/WyH1965qXRGGJe8gmu2TscWIEyapwR8Q/uOStkQQt19U5ErE/ZUHUF4D7AlcAqlM/lPyhrgtrQL8GE9VN3yswj2q5nAPZgmqCmWduMu8+2+Xfgo5n57xHxWco6yQa2mXFd7vqsDfwJICJWp2z6u01m/qyaifzpxf3Huov9eC2m6m/sg+bm+fiURtsOx7xUC9vZepiXamdeqo95qT7mpfqYl2piP14L81L9BpeXHOCavc/gL1JdPk55jPSFwI3AdsAZwM7A+6ufWoLMvCUirgDuaLuWIcjMg9uuYUBWBS4DiIhHAw8EPled+zbwkpbq6h33ManVrcBy1eunADcBJ1XvrwZWbqGmvrIfn7ufRMTkp1uOm3TM+/VZyMz12q5B5qUa2c7WwLxUL/NSrcxLNTEv1cq8VB/78bkzL9VsiHnJD8AsZea+bdcwIJsCr+SuUePlMvMOynreqwGfAJ7YVnE9M7j1UzUIlwPrAr8AtgUuyMy/VOeWByYvI6EZiIh5TL20gXsdzMwpwJ4RcTHwRsryWYu+8HoI8LfWKusf+/G52a/tAqRRMC/Vyna2PuYldZF5aQTMS3NmXqqP/fjcmJc0Iw5wLaWIuD/wKODBwI8z8x/V0gcLM9ObkJmZD1yXmXdGxNWU2UqLnAU8tp2yemllyufx/IgYxPqpbYqInYFXMf0ayS6rMzPfBj4YEY8FXs7dlzJ4HPDnVqrqoYgIytIFrwKmm23jetMz8zbgB8DvgYsoy+wssjNlCQnNjP34HGSmgW1EqjZzC6bvxz/beFFjyLxUC9vZ+qyMeak25qXamJdqYl6qlXmpPvbjc2BeGp2h5SUHuGYpIpYFDgD2pMyoSWATytqp36U8evqe1grsl3OBdarX/wu8NiKOoiwd8QqcFTIbg1s/tS0R8WLgy5TN0Z9avZ4H/CtwDfDVtmrrof8ArqO0kQdSHsFfZGPgW20U1VNvpPx7fgj4L+B9lLZyF8ryEQe0V1q/ZObZwEMjYlXg6syc+OXWXlTLxGhG7MfVORGxBmWz5A0p9z9RnZr4u96rwNY35qVa2c7Wx7xUE/NSrcxL9TEv1cS8VCv7cXXOEPNS3L2d0pJExAeBVwNvBU4A/go8PjNPj4hXA6/NzH9ps8a+iIi3Ag/KzLdFxGbAT7jrMfxlgd0z82tt1qjxExH/C3wH+ABwG3f9fq8IHAt8JzM/0maNGj8RcRbwBcq+JhM/l/OAI4HfZ+Z/tFmjxo/9uLooIr5Gmbm9E2XW8RMoT2q8FNgVePaE5Z80Aual+tjOqovMS+oi85K6yH5cXTTEvOQA1yxFxKXAvpn5+YhYhrt3nNsA383MlVstsqci4sHAsyiPRh6fmWe1XJLGUETcADwnM38WEbcBT8/Mn1XndgA+npnrtlhiL1WzuZebfNx10GcmIm4EnpWZP4+IW6vXx1fnng0clJlrtlpkj0TE44EdgbWY+nH8nRovagDsx9UFEXER8CbgcOB2YLPMPKU69y7gSZn5zPYqHD7z0ujYzqoLzEujYV6aG/NSvcxLo2E/ri4YYl5yicLZWxmYbhRzOVzTd6ll5kWUGTdaCkNbP7VF1wL3rl5fAjwS+Fn1PoBVW6iplyLifpSlIHYEFnDXY88T2WbOzFXAfavXF1LW5D++en9/yiwwzUBEvI6yv8FVlH0NFrZb0XDYj6sjVgaurPY6uI7S/yxyMi5B1oSVMS+NhO3s3JiXamNeqol5qVbmpZqYl0bHflwdsTIDy0sOcM3eWcD2wE+nOPcs4PRmy+mXiFh7Ntdn5oWjqmVIhrh+aot+CzyG8uj4D4B3R8TtlJu6dwO/abG2vvk88BzgIOBsvDGei19S1uY/CvgGsG9ErEL5N92T8vuvmdkL+Apliazb2y6mb+zH1QPnAYtmaP8BeAnww+r9c4Gr2yhqzJiX5sB2djTMS7UyL9XHvFQf81J9zEtzYD+uHhhcXnKAa/beB3w3IpYHvk25Id6oehT/NZSNVTW982Z4XVD+bZ2tNDMfpcykezDTrJ/aXmm9837u2gT03dXrz1I+i6dS9pTQzDwTeEtmHtR2IQOwL/Cg6vUBlBk3u1NmIh4L/FsbRfXUAuCbhrWlZj+urvsR8AzgMMp9+xERcTFlmby16eGMxB4yL82N7exomJfqY16qj3mpPvtiXqqLeWlu7MfVdYPLS+7BtRQiYifgQ5T/py9yCfC2zDysnar6ISLuBG4AjqDM9lrsetKZ+aMm6uq7Ia6f2iURcW/g3pl5Xdu19En1uXxVZh7ddi3SIhFxGPC7zDyg7Vr6yH5cfVPtIbED1RdcmfnjlksaC+alpWc7OxrmpdEyLy0d85K6yLw0N/bj6psh5CUHuOYgIjYAVqM8uven9B9ziSJiS2AX4AXAfYAjgUOBH2fmbW3W1mcRcT2wXWaeFBHXAC/NzB9W554KHJGZK7ZZo8ZPRLwZeCrwvMy8s+VyJAAi4smUdc+/QZnNec3kazLz7IbL6g37cUmzYV6aPdvZ0TAvqYvMS+oi89Lc2I9LzXOAS62IiHnANpRGf4fq8OGURv+n3tzNTkScCbwvMw+LiF8CF2bmi6pzHwd2zMx1FvuXjLGIePcsLs/MfO/IihmQiPgwsBNl3fMTuOeNcWZm7x59bkNEnMrd94i4h8zctKFyeq2aUbfI5H/ToHwuXSZiCezHJWm0bGfrZV6aG/PSaJiX6mNeqo95qR7241JzHOCagVnezJGZ+4+qliGKiHsB2wIvpsxw+M6isKGZiYj3A6tn5isj4lmUR6GvYML6qZn5kTZr7LLqBu5m4Ebu2nB6OpmZC0ZfVf9FxJLWns7MfEgjxfRcRBzMPcPFKsDmlM/ucZm5R9N19VFEbM2Sw++JjRQzEPbjaltE3DGb6/1Spn7mpdGynZ0789LcmJdGw7xUH/NSfcxL9bMfV9uGnpeWbbuAnpi8GeXywArV6xuA+1avb6r+GNhm5zHAVsAWwB3An9otp38y8x0TXv84Ip5Iz9dPbdhfKcH2NMpsmu+7fvzcZeZ6bdcwFJm5+1THI+K+lHW9T260oB7LzJ+1XcMA2Y+rbQFcT2kPf91yLePKvDRatrNzZF6aM/PSCJiX6mNeqo95aSTsx9W2Qecln+CapYjYHPg68C7ge5l5S0TMB54PvBd4SWb+qs0a+yAiNqQ8pvsiYB3geMqN8ve8UVYbqk0Vd6EsEbEacDTwTeCHmXlzm7X1VUTMz8xb2q5j6CLi2cCnDcgzExH/A/wc+EVm/rHtevrKflxdEhEvpXwenw5cDHwLODQzz2y1sDFlXqqH7ay6xrxUP/NSM8xLs2Neqof9uLpk6HnJAa5Zqtb1/XxmHjTFuVcDr83Mf2m+sn6IiHdQGvcNgV9QGvfvZObfWy2shyJihSVfdZfMvGlUtQxNRGxFafifT5l9/APK7/3PWy2sZyLiVsosz5OqP7/MzH+0W9XwRMRLgM9k5spt19IHEXEk8ERgZeBqSl+06DN6embO6tH9cWM/ri6LiPtTln3ZBXgycC7lM3poZp7bZm3jxLw0N7az9TEvjY55qR7mpWaYl2bHvDQ39uPqsqHmJQe4Zikibgaen5lHTXHu2ZRGa/nmK+uHau3u64EfApcs4XI3VF2M6t9yxr/AfVs/tQsiYjngv4C3AD/IzB1bLqlXIuL5wJOqP4+hPBJ9NnfdHP8iMy9ur8L+iIjtpji8HPBIyufzl5m5wxTXaBoRsWiZiC0pn9EHUJbN+nVmPr3N2rrMflx9EREPAF5ICW9PoHzxume7VY0H89Lc2M7Wx7w0eualuTEv1ce8VD/z0tKxH1dfDCkvOcA1SxFxBnAl8OzMvHXC8fnAj4BVM3OjlsrrvIg4n5mHDDdUXYyI2J27/1suAxwE7AecP/n6zDykkcIGICK2oDTwLwBWpNyYHOhGqksvIlakrDe9FbAN8HjK77h7Qc7AhC9oJm/qfRtlk/Q3ZOYVjRc2ABGxLuVzuUf1M/2Ca3r24+qLiFhACWwvomww//nMfH27VY0H89Lc2M7Wx7w0Oual+pmX5sa8NDrmpdmxH1dfDCkvOcA1S9Wj+EcBNwPHAlcACyhrWK4APMvH8tWGiFiGcvP2+Mw8ve16+iYi/oUS0nYG1qCsKX8oZSaiy5XMQbU8zOaUWV9bUWaG3AacnJlTzbTTJBGxzhSHbwGuSDvyWYmIR3HXTNmtKL/vZ3LXTNmTDL9SP0XESpTlsl4MbA38mbvWlz+nxdLGinlJXWVemhvz0uiYl+bOvFQf85I0XEPNSw5wLYWIWJPyiPMmlEd0LwNOBf47M//WZm0aXwa2pRcRfwLWw00/axURH6bcED8OuIq71u7+OXCGQUNtqGZ33gx8BTiS8sXB9e1WJWkuIuJFlJmHzwD+BhxGCWm/a7OucWZeUheZl5aeeWk0zEvqIvOSNDxDz0sOcKlREfGkzDxplv/NSsDamfn7EZU1CAa2pVfdwN0C3MgMHiXPzAUjL2oAJtwYfwk4KDPPbLmkXqseH38bZbmSBwM7ZOYfIuJNwCmZ+atWC+yJiPgmZR35NYEzuOtLhJ+78e+S2Y+riybsdXAk8CsW35dnZh7YSGHSUrCdHR3z0tIzL42Geale5qV6mJfmxn5cXTT0vOQAlxoVEZcCf6XcwH0/M/+xmGsXrev9MuDtmfmFZqrsJwPb0ouI98zm+szcb1S1DElEPIMyI/FJwKaUDWl/SXVzDJyWmXe0V2F/RMSmlGWergROBHYHNsnM0yPiA8BDM/MFLZbYOxGxHnd9PrcC1gf+BJyYma9rs7Yusx9XF1WBbabcN0KdZjs7OualpWdeGg3zUn3MS/UzLy0d+3F10dDzkgNcSyEidgZeBWwAzJ983tlK04uI5YE3Aq8HHgicC5wF/B24FViZsvTB44DlKev3/1dm/raNerssIjacdGgZyuyanYCzJ1+fmfc4JjUlIpajhLatgGcBTwRuzMz7tVpYT0TELylhbUdgHrCQ6suZiNiRsuTT2m3W2FfVGvNbUdpON01eAvtxSTNhXlp6trP1MS+pT8xLc2NeGh3z0uzYj0vNc4BrliLixcCXgYOBV1ev5wH/ClwDfDUz92+rvr6IiAC2AZ4KbExZm38+cDVlRsgvgSPcuHJ61ej75F/gqH7mpGPegKg1EbEqZYmDRTO/Hkf5guGczJz8xYOmEBE3A9tn5jGTZx9HxJOBn2TmPb5A1D1FxGbctWnyFpSAcTVwMmWm7EmZeUprBfaE/bik6ZiX6mE7O3fmJfWFeWnuzEv1MS/Vw35cas6ybRfQQ/8OvBf4ACWwfbbqMFekPA59U5vF9UW1WepPqz9aOk9puwBpcSLiQEpAewRwJ/A7yvrd76fcFLt+98xdC6w+zbmHAJc3WEvfnQxcQvksvouylvwf2i2pf+zH1SURsW5mnj/L/2Ye8KDMvGg0VY0181INbGdrYV5Sp5mXamVeqo95qQb24+qSoeclB7hm72HALzPzjoi4A7gfQGZeHxEfBD4OfKTNAjUeMvPEtmuQlmBD4HuUG+OTM/OGluvpsyOA/SLiV8AF1bGMiNWAvSj/zpqZ9TPzvLaLkFSr30fEDyh7HZyQi1miIiLWAnYG9gQOBD7cTIljxbykTjAvqQfMS/UxL9XHvCQNz6DzkgNcs3ctcO/q9SXAI4GfVe8DWLWFmjSGIiIW1yDV/d9Js5WZT267hgH5D+A4yn4Rp1XHPgc8FDgPeHdLdfXOorBW7XPwaGAVyjIRv8/MhW3WJmmpPYIyw/hI4KaIOIWp9zrYmPJl4pmUjby/00q1w2deUieYl9R15qVamZdqYl6SBmnQeck9uGYpIo4AfpGZH46ITwIvpHSUC6uf52Xm09qsUeMhIv4MfBD4ZmbeOIPrNwbeBPwxM98/6vokgIhYFng+ZU35RTfGJwHfy8zb26ytb6qA8TLKOt6rUf4tj6PsZXJrm7X1TUS8HXgH5amCoOzDcR1wQGZ2fnaSpKlVS+DtzOL3OvheZp427V+iOTMvqSvMS+oD81J9zEv1MS9JwzTUvOQA1yxVmy2uk5nfioiVgUOA7SgbgJ4KvCgz/9piiRoTEfF8YF/KetLHUNZJnm70fVvgwcBBwPsy0/WnNXIRsYDy2XwMcD5l3fM1gHWBM4BnZOaVbdWn8RQRbwY+SpnR+S3u+lzuDLwGeGtmfrK1AiWp58xL6grzkrrOvKQuMi9J6hsHuGoQEfcG7p2Z17Vdi8ZLRATwNGBXyuj7mpSZNVBm2SykPJ7/PeB/MvOKNurUeIqIrwFPBnbMzFMnHN8E+C5wYma+rK36+qTaw2TzzDxlinMbA6dk5jLNV9Y/1WzuwzLzP6c491/Azpn50OYrk6ThMi+pLeYldZl5qT7mpfqYlyT1jXtwzVL1KN99M/PSRceqR51vjYg1gevdGFRNqdaGP7b6Q/UZnPh46XmukawWbQe8YWJYA8jMUyPiHcCn2imrl2Ix5+4FuHzJzD0YOGGacz8D3tZcKZI0POYldYl5SR1nXqqPeak+5iVJveIA1+x9ibJx8qumOLcvsBKwS5MF9VlEvADYEViLEjLuJjM3bbyoHqu+SLh0iRdKzbg3cP00564Hlmuwlt6JiLUpy5Ms8riImNxOzgd2o2ycrJm5EHgG8NMpzj29Oq8Zsh+XNAXzUo1sZ+tlXlLHmJfmwLw0MualGtmPS6PnANfsbQW8dppzRwEHNlhLr0XEvpSNps8AzqYsDyFpOH4N7B0Rx0/c2Dsi7gPsXZ3X9F4OvIeyjE4yff9yM/DKporqo4jYFfhRZl4FfBL4ZESsAnyHsqb8AuCFwO7AG9uqs2/sxyVNw7xUE9tZafDMS3NjXqqJeWk07MelZrgH1yxFxM3A9pl5zBTnngkcnpnLN19Z/0TERZR1zt/Zdi2S6hERxwOvz8xzImIjytIGSdk8edGN8TMpS0hsnZlntFVr10XE6pR/rwDOBF5S/ZxoIXBhtfSTpjF5Tf6IeBUlDD+Q8vkM4G/Avpl5UGuF9oz9uKSpmJfqYzsrDY95qT7mpfqYl0bDflxqhk9wzd6fgWdTbj4m2w74S7Pl9NqKwHFtFyGpVlsD9wPIzN9FxMOAvYBNgMdQloT5HPCxzPx7W0X2QWZeCVwJEBHrAZe6R8RSu9ua/Jn5xYg4iLJMxJqUz+XF6ayf2bIflzQV81J9bGel4dka81ItzEu1Mi+Nhv241AAHuGbvU8DnImIhcDClkV+TsqbvnsDr2iutdw4FtsXGXhqsKpT9R9t19F1mXrDodUSsALwCeARwGfDViec1M1U4u6j6o6VjP65OiojHM/1eB5mZOzdf1VgxL9XHdlYaOPNSPcxL9TMv1cJ+XJ00tLzkANcsVbMY1gDeAbx1wqlbgHdl5hfbqayXjgM+GBGrAccC10y+IDOParooSXPmrK4aRMRHgedm5gYTjq0InAo8DPgHsBLwtojYNDPPbafS3nhlRGw7g+syM9878mqGwX5cnRMRrwM+DVxFeZLImdwNMy/VynZWGibzUg3MS7UzL9XPflydM8S85B5csxAR8yizD68FlgE2B1alfCB+lZnXtlhe70TEnUu4JDNzmUaK6blqQ9Dp3AlcB5zhrCWNWvV7fSkwozXOM/Mho62ovyLidMo+JftPOLYfsA/wysz8crXu/LHA7zPzZS2V2nnV5/Ja4PYZXJ6ZuWDEJQ2C/bi6KCL+QtnP5LWZOZPfedXIvFQv29n6mJfUFeal+piX6mNeGg37cXXREPOST3DNzjzgfMoMkaOBo9stp/fWa7uAATmYu2aBTVw7eeKxjIgfAi/JzBsarE3j5wRKaNPcrAucNunY84GzM/PLUNadr2Yu7tdwbX30zEWbJqs29uPqogXAN4cS1nrIvFQv29n6HIx5Sd1hXqrHupiX6mReqp/9uLpocHnJAa5ZyMzbI+ICYIW2axkCZ8fV6l+AbwEHAT+gbLS6OrA98ErgtcADgU8CH6TsfyCNyie9Ma7FspTlnACIiFWARwKfmXTd+cADmitLKuzH1VE/Bp6Aex20wrxUL9vZWpmX1CXmpXqYl9Rp9uPqqMHlJQe4Zu+DwH9GxEmZeWXbxfRdRCxLmWGzJbAKcDVwEvC9IY0kN+CjwGcz8xMTjl0NfKja4Ps9mfnkaj+Et2Fgk/rgXGBr7rrpeE718yeTrltA+X2XGmc/rg76DPCFiLgX0+91cHbTRY0Z81KNbGdrY16Shse8pM6zH1cHDS4vuQfXLEXEt4EtKBtVngZczt03CM3M3LmN2vomIhYAxwCPocyouRxYg/KY+RnAMwzFMxMRNwHPy8xjpjj3TOD7mblCRGwN/CQz791wiRoT1RrTmzkjce4iYnfgi8CBlPbxjcD1wCMz87YJ130eWCczZ7Ih8FiKiPMobeQZbdcyJPbj6qJJex1MDjqBex2MnHmpPraz9TEvqSvMS/UxL9XHvDQa9uPqoiHmJZ/gmr3VgD9Neq+l8zHKptNPyMxTFx2MiE2A71bn3QR0Zi4Gdqd0nJO9vDoPcH/KJt/SqOzHXZ83zUFmHhwRa1JmEK8MnA7sOSmsLVpaxzXlFyMzXft8NOzH1UVPabsAmZdqZDtbH/OSusK8VBPzUn3MSyNjP64uGlxe8gkutSYirgbekJnfmOLcS4BPZeYqzVfWPxHxQuCbwNnAkdy1pvxzgQ2BXTLzOxHxWWBVZ81qVCJi3cw8f5b/zTzgQZl50Wiq0riLiOOA92fmT2d4/QLgDcCVmfmpkRbXY/bjkjRatrP1MS+pK8xL6iLz0mjYj0vN8AkutenelMfHp3I9sFyDtfRaZn67eqR8b+DFlA1ULwNOBV6emadV172+vSo1Jn4fET8AvgSckIuZRRERawE7U2bcHQh8uJkSNYaOAr4eEbcA3wNOBs4C/g7cSpnxuR6wMfAs4MmUtfvf1kaxPWI/rs6KiCdw970OfpGZv2m3KmnWbGdrYl5Sh5iX1EXmpdGwH1dnDSkv+QTXUoiIFSmPOG8AzJ98PjPf3nhRPVTNELk38MzMvHHC8ftQlo64OTOf1lZ9kmYvIh4EvAvYFbgJOIXpb4w3BM4EDsjM77RRr8ZH1be8mPLZfAIweU3pAC6lBLovZebvGi2wh+zH1UXV5+/bwLbA7ZSlxlal/M4fDbwwM29qr8LxYF6qh+2sNDzmJXWVeal+9uPqoiHmJQe4Ziki1gd+CawA3IeytMEqlKfh/gFcm5kPaa/C/oiIjYATKBvaHUPZbHEB8ExKx7m1G1xK/VR9sbUz8FRKOHsA5Quuqyn7cvwS+N6i2bJSkyJiBeCxTPpczna5mHFnP64uiojPUL6ceTXw3cy8s1ra6fnA54GvZ+a/tVnj0JmX6mM7Kw2XeUldZl6qh/24umiIeckBrlmqHiWfB7wQuBF4PHAG5cbk/cALJm4cqMWLiNWAvYBNgDUps0F+A3wsM//eZm19ExEvAHYE1mLqmbKbNl6UJGnQ7MfVNRFxGfDuzPzCFOdeDeyfmQ9ovrLxYV6ql+1sfcxLkqSm2Y+ra4aYl9yDa/Y2BV5JeXQcYLnMvAP4RtVofQJ4YlvF9U3VmP9H23X0XUTsC7yb8uXB2cDCVguSJI0F+3F10ErARdOcuwi4X4O1jCvzUo1sZ+thXpIktcF+XB00uLzkANfszQeuqx7fuxp44IRzZ1Ee4ZWa9grgA5n5zrYLkSRJatEZwOsi4uicsFRFRATwuuq8Rsu8pC4yL0mSJA0wLznANXvnAutUr/8XeG1EHAXcQblp/ltbhfVBRJwC7J6ZZ0fEqZR1aKflMhEztiJwXNtFSJKGzX5cPfBO4MfAORHxfe7a62AHYF3gWe2VNjbMS3NgOzsy5iVJ0sjZj6sHBpeXHOCavUOBjYD/AfYBfgJcB9xJ+ffcva3CeuIPwM0TXrsJXD0OBbbF0CZJGi37cXVaZh4fEY+jLEX2Qu6+18GOmXl2m/WNCfPS3NjOjoZ5SZLUBPtxddoQ81JMeBJNSyEiHkwZ2ZwPHJ+ZZ7VcksZQRLwQ+CBwInAscM3kazLzqIbLkiRJ0pgzL6kLzEuSJEnD5ACXWhMRXwbem5nnTXFuHeA9mblH85X1T0TcuYRLMjOXaaQYSeqBiNgAWIvyhevd+AXXzNiPS9Jo2c7Wx7wkSbNjXpo7+3GpGQ5wLYWIWI6ytMam3P0xvkMyc2GLpfVKFTI2y8xTpji3MXCKIWNmqo5xsTLzgiZqkaQui4gNgW8BGwIxxSV+wTVD9uPqiog4DHhHZv6ler04mZk7N1HXODMv1cN2tj7mJUmaGfNSfezH1RVDz0vuwTVLEfFI4GjggcBpwBXAo4BdgX0iYts+rlXZoulGWB8FXNlkIX1mGJOkGfs8sBywI3A24Betc2M/ri5YHbhX9XoB7nXQKvNS7Wxna2BekqQZMy/Vy35cXTDovOQTXLMUEScBKwHPycwLJxxfG/gR8I/M3Kqt+rouIt4EvKl6uw5wGXDrpMvmA2sAB2fmKxosr1ciYoXMvGnR6yVdv+haSRpnEXEDsEtm/rDtWvrIflzSkpiX5sZ2tj7mJUmaPfPS3NiPS83zCa7ZezzwoolhDSAzL4yIdwPfaKes3jgb+C7lMee3AidQliyZaCFwDrCkRybH3fURsXn1qPMNLHn03ceeJQn+whTryGvG7MfVadX9+EGZ+bcpzq0JvCoz92++srFiXpob29n6mJckafbMS3NjP65OG2JecoBr9s5n+oZ+PnDhNOcEZOaxwLEAEXE95Rfqknar6q09KDcei177OKYkLdnbgA9FxOmZ+de2i+kb+3H1wHsoy+PdI7BRlsx7D9CrwNZD52NeWmq2s7UyL0nS7JmX5sB+XD0wuLzkEoWzFBHbAx8FXpKZv5lwfDPgf4B/z8zDWypPkloXEY8D3kBZ1/cPwJcy88+TrtkI+F5mPqT5CjXOIuJUYG3g/pQvYa+ZfE1mbtpsVZLqUm3m/YTMPHWKc9tT+qTVmq9sfJiXJGnxzEvqMvOSNGxDzEs+wTUDVeM+cSTwfsDJEXEFZdPkBdWfq4B3Aoc3XWNfRcTmwCuADZhipqedptQvEfF44BeUR/D/AOwGvCki3p6Zn5pw6b0p61FLTTur+qMa2I+rCyJiN0p/A+We/cCIuG7SZfOBRwPHNFnbuDAvjY7trDQs5iX1gHmpRvbj6oKh5yUHuGbmD9w9sP2hrUKGJCKeDhwFHAdsCfwYWB7YArgYOLG96rovIs5jFstsOPNLDfkv4KfADpl5W0QsB7wL+HhEbAC8MX10WC3KzJe3XcNQ2I+rQ26iDJxA2e/gWuDqSdcspHxGP9tgXePEvDQCtrNzY15SR5mX1GnmpfrYj6tDBp2XXKJQrYmIXwG/BPYGbgMen5mnR8Q6wE+AAzLzq23W2GUR8RHuHth2AVagrPW7aKbs04EbgUMz8+2NF6mxExF/pyxJ9JNJx58NHEr5fL4YeCxwcma6mbfUU/bj6qKI+Aqwf2ae13Yt0lzZzs6NeUldZF6Sxof9uLpoiHnJJ7jUpg0pM5XupASP+wBk5gURsS+wH2BDP43M3GvR64h4J2UD5Wdn5o0Tjt8X+CEw+bFTaZRi8oHM/FFEPJXyefwpZeai1IiI+BDwycy8uHq9WH7BNWP24+ocZx1rYGxn58C8pA4zL6lTzEsjYz+uzhliXnKAaylExKbADsCDmHr91J0aL6qfbgHmZWZGxKXA+sBJ1bnrgLVaq6x/9gRePTGsAWTmDdXMxS8C72ulMo2bs4FtgKMnn8jMUyPiSdW5rzddmMbaCymfuYur14uTgIFtZuzH1UkRsS7wUqbf68B79REzL9XGdrY+5iV1hXlJXWReGg37cXXS0PKSA1yzFBFvAT4KXA78lbI+pZbOGcDDKY/gHwe8IyIuofyb7g/8vsXa+mYlYI1pzj0AuG+DtWi8/QjYOyLel5nXTj6ZmedGxBaUdX0f3Xh1GkuZud5UrzVn9uPqnIjYmLKfwUWUwHYm5T5pXcqXNv/XWnFjwrxUK9vZ+piX1BXmJXWOeWlk7MfVOUPMS+7BNUsRcTHwbeCtbvw5NxGxHbBeZn4mIh4EHAlsVJ2+mLLp6mlt1dcnEfEN4JnAa4AjM/PWiLg38K/A54CfZOaL26xR4yEi5lE2Tb05M+9czHXzgTUy84LGipNUK/txdVFEHE8Ja3tw970Ongh8E3hNZt5j1rzqY16qj+1sfcxL6grzkjQ+7MfVRUPMSw5wzVJEXAm8KDN/2nYtQxMRATyUcrN3TmY623OGImIl4GBge8rj4tcDK1LW9v4BsNtUs8MkaRxV/c0WTP84/mcbL2oA7MfVBRFxNfAi4BjgDmDLzDy5OrcH8MbM3Ki9CofPvDQ6trNLz7wkSTNnXhoN+3F1wRDzkksUzt7BwI6UTT81BxHxbuCgzPwbQDXD88/VuTUj4lWZuX+bNfZFFcZ2iIgNgU0oy2xcBpyamWe3WpwkdUhErEFZHmJDyhdcizb5njjjx8A2A/bj6qgEFlZ7HVwBrAOcXJ27CHhYa5WNj4MxL9XCdrY+5iVJmhnzUn3sx9VRg8tLPsE1S9Xj5J+mzGI4Hrhm0iWZmQc2XVcfRcQdwOaZecoU5zYGTsnMZZqvTJI0VBHxNWA9YCfKzdsTKPvEvBTYFXh2Zv6lvQr7w35cXRQRJwGHZOZBEfF94EHASyh7HRxEWe7pMW3WOHTmpfrYzkqSmmZeqo/9uLpoiHnJJ7hm76mU/6evWL2eLAED28wEd58BMtFawD8arKV3qtmHM+bMREkC4MnAm4BLq/eRmRcCB1Rfyn6WskeHlsx+XF30BcosRIB3UpbeOKd6fyPwgjaKGjPmpfrYzs6BeUmSlop5qT724+qiweUlB7hm77PAbyiN/f9l5m0t19MrEbEbsFv1NoEDI+K6SZfNBx5N+QXT9M5i+o5yokUdqrNCJAlWBq7MzDur/mfBhHMnA3u3UlVP2I+r6zLzfya8/mNEPBLYnLLXwa8z84rWihsf5qU5sJ2tlXlJkmZvZcxLS81+XF03xLzkANfsPRB4fWb+se1Ceuom4KrqdQDXAldPumYh8GNc03dJntJ2AZLUQ+cBa1av/0B5yuCH1fvncs8+SXdnP65Oi4hdgR9l5lUAmXkDcGx1bpWI2DUzv9pmjWPAvDQ3trP1MS9J0uyZl+bGflydNsS85B5csxQRhwMnZeZH266l7yLiK8D+mXle27VIGo2IeDxlo/m1KLOUJsrM3Ln5qjTOIuIDwGqZ+cqIeBZwBHAFcBuwNrB3Zn6kzRr7wn5cXeReB+0zL9XHdlYaPvOSusa8VB/7cXXREPOSA1yzFBFPBT4HfJWpN0127e45iIjVgWszc2HbtUiam4h4HWWT+auAP1NmKd1NZjqzVq2qvlTYgfI4/rGZ+eOWS+o1+3G1LSLuBDabJrA9A/hWZt6/+crGh3lptGxnpeEwL6kPzEv1sh9X24aYlxzgmqXqQ7DI5H+8oMyw6dUoZ5MiYkvKKPGHJx1/NXAAcH/gVsqGd2/NzDvv+bcIICKuAJ6Zmf8bEVeyhPXlM3PB4s5LdYuIvwAnAK/NzNvbrkeKiHsDewE/zMwz2q6nj+zH1UURsT2wffV2d+BHwJWTLpsPPAn4Y2Y+o7nqxo95aW5sZ+tjXlLXmZfUNealubMfVxcNPS+5B9fsOXtmbvYC7ph4ICK2AQ4EfgfsC2wA7AmcTWnwNbXPAJdPeO1otbpmAfBNw5q6IjNvjYj/BH7Rdi09Zj+uLlpA2ah7kfWBB0y6ZiFlI+/3NVXUGDMvzY3tbH3MS+o685I6xbxUC/txddGg85JPcKlREXEhsE9mHjLh2HeAZwLrZObV1bEPAU/JzE3aqVTSXEXEYcDvMvOAtmuRFomIE4AjM/NjbdfSR/bj6rrqd/x1mXlO27VIS8N2Vhof5iV1kXlpbuzH1XVDzEs+wTUDEfFE4IzMvHEJ161KWQLhG81U1kurA+cvehMRATwdOG5RI1/5KfDKZkuTVLPPAF+IiHsBx+IeHOqGtwPfiIiFwFGUmd13m+2TmTe1UVhP2I+r09yrpB3mpVrZzkrjw7ykLjIvzY39uDptiHnJAa6ZOQnYHDgFICKWoTy2t0lmnj7huocC/wMY2KZ3BfDACe8fB6wI/HzSdbcBrs0/QxFxKkteU37ThsqRFjmh+vke4N2TzgXlM+vvuZr2m+rnJ4FPTHONn8vp2Y+rcyJiQ+AxmXnopOPPoiyx8QjKlzOfzMz/br7CsWBeqo/t7AiYl9RR5iV1kXlpbuzH1TlDz0sOcM1MzPCYluznwNsi4hjgH5S1ae8EDp903WOBi5otrdf+wD0D2yqULxpuBo5rvCLJPTjUTXvgHhxzYT+uLtoHWBX4Z2CLiI2BI4BLKXsebAB8NCIuycxvt1LlsJmX6mM7OxrmJXWReUldZF6aG/txddGg85IDXGraf1Jmg1xOmdU5H/hYZv510nW7ctdsJi1BZu4+1fGIuC/wA+DkRguSgMw8se0apMky8+C2a+g5+3F10ROA90869ibKBt9PyswLASLioOp4rwKbxo7t7AiYl9RF5iV1kXlpzuzH1UWDzksOcKlRmXlhRDwSeCGwEnB6Zh4/8ZqIWA34OmWtX81BZt4QER8FPg0c1HY9Gk8R8QRgS8os2auBX2Tmbxb/X0mjERF/BXbIzDOmOPco4AeZ+ZDmK+sH+3F11AOAP086th1w4qKwVvkucAhSh9nONsu8pC4wL6lLzEtzYz+ujhp0XnKAa+amejzXR3aXQmZeA3xxMef/Dny0sYKGb2Xg/m0XofETEfehzPrYFrgduIrySPQyEXE08EI3p1UL1gXuPc25FYC1miuln+zH1UHXMOFep/pSYRXuudfBjZTfc42GeakmtrONWxnzklpgXlJHrYt5aU7sx9VB1zDgvOQA18wdHBE3Tjr2PxEx8WbjPk0WJC0SEdtNcXg54JHAW/CxZ7XjQ5R9DXYGvpuZd0bEPOD5wOeBDwL/1mJ9GhMRcT/Kl1eLPCAi1p502XxgF+CSpuqSVJvfAHtGxJGZeTvwGsrAyg8mXfdI/B0fJfOSOsu8pI4yL6kTzEvS4A06L0Wmk+qWJCK+MpvrM/Plo6pFmkpE3ElpmCZv5n0bZcPAN2TmFY0XprEWEZcB787ML0xx7tXA/pn5gOYr07iJiPcA72HJTxIE8LbM/Pjoq5JUl2q5nF8Bt1BmJ64PfCszXzTpuhOB/8vMVzRe5MCZl9R15iV1kXlJXWFekoZt6HnJJ7hmwACmHlhvimO3AFeko9hqz0rARdOcuwi4X4O1aLx9A/gtJZD9ANgL+NOkaxYCf5q0/rSkHsjMsyJiI2APqr0OmLR2fESsDpwBfK3xAseAeUk9YF5SF5mX1BXmJWnAhp6XfIJLkjQSEfFr4Apg+4lfHEREUGbKrp6Zm7dVn8ZTRDyZstHv9W3XIkmSpPFlXlIXmZck9Y0DXGpURBwPvD4zz2m7liGr1ph/JGXW14+9MVEbIuKpwI+B84HvA5cDC4AdKBvXPisz3e9A6hH7cUkaLdvZZpiX1AXmJWl47Mel5rlEoZq2NT5mX4uIWAH4GPA84F7A94A3U2Z6PXXCpedHxDaZeV7TNWq8ZebxEfE44N3AC4E1gUspm1vumJlnt1mfxkdEXMmS15P/p8xcMMJy+m5r7MclaZS2xna2FuYldZ15SV1hXqrV1tiPS41ygEvqr/cALwE+C9wAvBo4ClgdeDxwNvBo4H+A9wIvbadMjbMqlO3Sdh0ae59hFoFNkiQNgnlJnWdeUkeYlyT1lgNcaoOdZj1eALwzMz8F/3wM+iRgl8w8vbrm1Ih4H3BASzVKUusyc9+2axgY+3FJGi3b2XqYlyRpBsxLtbMflxrkANdSioj7A48CHkxZs/sfETEfWJiZd7ZbXecdHhG3zuTCzHzIqIvpsQcDp094f1r186+TrvsL8MBGKtLYi4jDgHdk5l+q14uTmblzE3VJqpX9uDolIr4MvNflxbrFvDQntrP1MC+pc8xL0liwH1enDD0vOcA1SxGxDPB+YE9gecqo/CbAP4DvAr+lLIWg6Z1AWVdac7MscNuE94teT/7C4E4gGqlIKku+3Kt6vQBnLqmDImJz4BXABsD8yeczc9PGi+oX+3F1ze7A54BBBra+MS/Vwna2HuYldZF5SZ1nXpoz+3F1ze4MOC85wDV7BwCvAt5AabAmzv46AngtBrYl+WRmntJ2EQOxXkTcUL1epvr5kIi4ZcI1zgZRYzLzKRNeb91iKdKUIuLplP03jgO2BH5M+QJ2C+Bi4MT2qusN+3FJi2Nemjvb2fqYl9Qp5iV1nXmpFvbjUoMc4Jq9XYH/yMyvVLMTJ/oL3hyrWd+Y4thh3H0WWOCsMLUgIt4NHJSZf5vi3JrAqzJz/+Yr05jbH/gEsDdlJvc+mXl6RKwD/AT4WYu1SVp63ut0h3lJXWJeUmeZl9RR5iVpmAZ7r+MA1+ytTAlmU1mOu2aFaY4iYvnMvLntOjrsKUu+RGrVe4CjgXsENso+B++h3DxLTdoQeBdlOaIE7gOQmRdExL7AfsBXW6tuQOzH1bADI+K6mVyYmU8ddTFjbmXMS42wnV0i85K6zrykLjIvNcR+XA0bbF5ygGv2zgK2B346xblncfdNbHVP+1EeaZ5WRKwG/Bvwesr61JpCZvpYuLpucbNh16LsxSE17RZgXmZmRFwKrA+cVJ27jvLZ1PTsx9VV1wJXt12EAPPSXNnO1sS8pB4wL6mLzEtzYz+urhpsXnKAa/beB3w3IpYHvk25GdkoInYAXgP8a5vFdV1m7hcRW1aP4j+Ysib/JzPzzxHxAGAfysZ3ywJfa69SSUsjInYDdqveJlPPEJkPPBo4psnapMoZwMOBYynryr8jIi4BFlJmyP6+xdo6z35cHfYO9zroDPPSHNjOSsNmXlIPmJfmwH5cHTbYvBSZg11+cWQiYifgQ8DaEw5fArwtMw9rp6p+iIjtge9RZiL9H2XmxwqUxv3g6vVBwIcy88J2qpS0tCLihcBO1dvnUzaXnzxDZCFwDvDZzLyqwfIkImI7YL3M/ExEPAg4EtioOn0xsENmntZWfV1nP64uiog7gc2GGtj6yLy09GxnpWEzL6nrzEtzYz+uLhp6XnKAaw4iYgNgNcrNyJ/Sf8wliohfA38HXpiZN0dEUMLvm4E/Af+amX9tsURJNYmIrwD7Z+Z5bdciTafqhx4KLA+ck5kLWy6p0+zH1UVDD2x9Zl6aPdtZaXyYl9QH5qXZsR9XFw09LznApUZFxD+AF2Xm0ROOrQ5cDuyYmYe3VZskabgiYkXgtsy8ZZrz84F7Zeb1zVbWL/bj6qKZfEEYEfcCXgrslZn/r7HipFmynZUktcG8VA/7cXXR0POSe3AthYh4IPAcymOm8yedzszcu/mqemMl7vn4/aL3Ppq7lCJimcy8o+06pMkiYl1KB7kB92wvycydJh+T6hYRTwB+ATwNmG7D+ScAx0bE0zLz540V1z/24+qczHx5RKwfEf/BXXsdHJyZV1X7QL2BMmt2TcpSUBox89Kc2M6OgHlJXWVeUheYl2plP67OGXpecoBrlqrNkb8JLANcQVkbeaIEDGyLt15E3DDh/TLVz4dExN1mimTm2c2V1WuXRMRXga9k5h/bLkYCiIiNKTfHF1EC25mUm711KWt3/19rxWnc7AV8KzOnC2tk5okR8U3gTYCBbfHsx9UpEfEk4GjKF4NXAqsAb6j2OTkUeAhwFPCCzPxVa4WOCfNSLWxn62deUueYl9Qh5qV62Y+rU4ael1yicJYi4o/An4HdM3PyiLyWoFrzc6oPXVQ/c8L7zMxlprhWk0TEfsDLgHWA3wJfAg7NzOtaLUxjLSKOp4S1PYDbgMdn5ukR8UTKF1+vmfjYvjQqEXEF5fP2/SVc9zzg85m5RiOF9ZD9uLqo6m/uAzwvMy+NiPsCnwN2oMyYfVFm/qLNGseJeWlubGdHw7ykLjIvqSvMS/WxH1cXDT0vOcA1S9UI/PMy86dt19JHEfHk2Vy/uNkjuqeIeCqwO7AjpbM8nDJL0c+rGhcRVwMvAo4B7gC2zMyTq3N7AG/MzI3aq1DjIiJuBbZZ0g1bRGwJHJeZ926msv6xH1cXRcSVwCsy8wcTjj2I8qXhSzLzm60VN4bMS3NjOzta5iV1iXlJXWFeqo/9uLpo6HnJJQpn72Tg4YA3wEthNg13tbmdZiEzjweOj4jXAzsBrwd+EhEXAQcDX8jMv7VYosZLAgszM6sZYetQ2lAonejDWqtM4+ZyYH3KuvKL85DqWk3DflwdtSpw2aRji96f23AtMi/Nie3saJmX1DHmJXWFeakm9uPqqEHnpXltF9AHEbHCoj/AW4FXR8RuEfHAiecmXKOlFMVTI+KL3PMXTzP3eGAr4BHAP4CTgFcC/xcRL22zMI2Vsyk3yQC/At4SEQ+LiHWAtwN/aa0yjZtjgT0jYtqJPdW5PSkzaLWU7MfVovmT7sfvUx2/t/fqo2deao7tbG3MS+oC85K6wrzUEPtxtWiwecklCmdgivVTJ6+bejeunzp7EfEEyqP5OwFrUNb/PCwz92y1sB6pboJ3B3albEr7U8ra8odn5sKIWAb4CLBLZq7ZVp0aHxHxMmCdzHxfRDySciP8wOr0jZTNK7051shFxEOA04HfAG+ZvJFv9fn8OLAZ8C+Z+dfmq+w3+3G1aQl7HdzjuPfq9TMvjZ7t7NyZl9Q15iV1hXlp9OzH1aah5yUHuGYgInabzfWZecioahmSiHgUpXHfhRIwFgLLUWZ9fiYzb2+vun6pNgvcCriYsrTGVzLzgimu2wT4TWb69KYaV21iuTmwPPDrzLyi5ZI0RiJiC+BQypcGfwMupNzIrQ08qDq286J9D7Rk9uPqCu/V2+f/D0bDdrY+5iX1gXlJbTIv1c9+XF0x9Ht1B7hmICK2Ak7PzBvarqXvqlkhu1Aa+A2B2ymPQn8TOJHSgW6dmT9vrcgeiojDgIOAY3Mxv9TV+r4PnCrMSXWLiF2BH2XmVVOcWwV4TmZ+tfnKNK4iYj5lxtxWlJAGcAnwM+A7mXlLS6X1hv24pKmYl+pjOzsa5iV1kXlJXWNemjv7cal5DnDNQETcAWyemae0XUvfTXgk8jfAl4HvZuY/qnMrUdY/t6GfheoG5FPAlzLz123XIy2yuLYzIjYGTunbY8/SuLMfV19FxPpUXzZk5qParmdozEv1sZ2tn3lJXWVekobHflx91ee85GP3MxNLvkQzdAHl3/NRwNbAExe3iaWWrJpBswswv+1apEkW13auClzXVCGSamM/rt6IiDUj4s0R8RvgXOA9lM+w6mdeqo/tbM3MS+ow85I0PPbj6o2h5CV/wdSozFwvIjYHXgy8oPr5j4j4HvBjptmIWkt0PPAUymPjUmsiYntg+wmH9omIKyddNh94EnBqY4VJqoX9uLouIu5P+Wy+iLK8zjzK5/LDwEczc3KfJHWK7ezImJfUCeYladjsx9V1Q8xLLlE4A9XjpfsDf53J9a6RPDMRMQ/YhvIL9TxgZcov1DeAT2Tmb1srrmci4hmUNeUPA44CLmdSp5mZZ7dQmsZMRLwKeHX1dmPgHODGSZctrI6/LzPPa7A8STWyH1dXRMR9KF8Wvgh4BnAv4GzKXgdHU74gdCmYETIvjYbtbH3MS+oK85I0PuzH1RVDz0sOcM1AFdhmKl0jefYiYjlgO8rSEc8BlgfOzcxHtlpYT0zxGZ34ix34uVQLIuIE4HWZeU7btUgaLftxtSkibqB85s4DvgV8MzPPqs6510EDzEujZzs7N+YldZF5SRof9uNq09DzkksUztxTAEfWRyQzFwKHA4dXo8rPozT6mpmntF2ANFlm+rmUxoT9uFoW3LWPSeLSL20xL42Q7eyceV+qzjEvSePDflwtG3Re8gmuGahme22Wmae0XYs0WUTMBz4FfCkzf912PRpvEbEh8JjMPHTS8WcB7wMeQVkS5pOZ+d/NVyhBRKwMvAbYElgFuBo4CfhCZl7TXmWSlkZErMBdS248kzKJ74+UJTd+ApxCj2ck9oF5SV1mXlKXmJfUB+YlaViGnpfmtV2Axk9ErBQR20bEcyPiftWxR0XEYRHx+4g4KiL+te06+yIzb6HM+pjfdi0SsA+wx8QDEbExcASwGnAgcBbw0Yh4YfPladxFxPrA7yl7xdwHuLD6uT9wZnVei2E/rq7JzJsy85uZ+a/AGsBrKV8O7gf8hjJD8bkR8YAWy5RmzHa2XuYldYx5SZ1mXpo7+3F1zdDzkk9wzYAzEusTEY+hbF636Bfm78ALKY/pXkDZSHVD4P8Bz8/Mw5uvsn8i4gjgd5n5nrZr0XiLiL8C78/ML0449lXK7/nDM/PC6thBwCMyc8t2KtW4iogfAOsB22bmJROOPwj4MXBeZm7fVn1dZz+uPqkC2i7Vn02BO4CfZuazWi1sgMxL9bGdHQ3zkrrCvKSuMy/Njf24+mQoeckBLjUqIn4ErA7sBtwAfAh4BnBEZu4x4br/AdbPzCe2UmjPRMQzgIOAw4CjKKPwd/vlzsyzWyhNYyYibgK2y8yfTTj2d+C3mbnthGPPAg7JzAXNV6lxFhHXAbtl5venOPd84CuZeb/mK+sH+3H1VUSsB7wY2CUzH912PdJ0bGdHw7ykrjAvqevMS3NjP66+6nNecolCNW0T4IDM/GNmXgS8Hbg/8I1J132dsva0ZuZoYC3grcBPgTMpj5T/nrK8we/bK01j5hrK7zQAEfFIyprdk9fxvRFYobmypH9KYJlpzs1jYJutjoD9uHopM8/LzP/qW1jTWLKdHQ3zkrriGsxL6jbz0tzYj6uX+pyXlm27AI2d1YC/TXi/6PXVk677B7BSIxUNw1PaLkCq/AbYMyKOzMzbKRvTJvCDSdc9Erhk8n8sNeAE4L0RcWpmXrDoYESsQ1lX/rjWKusH+3F1UkTMAzYA7gWclZkZEWsCb6H0ORcBB2Xm6S2WKc2E7exomJfUFeYldZ15aW7sx9VJQ85LDnBJA5CZJ7Zdg1TZB/gVcGlEXAOsD3wrM8+adN2LgV80XJsE8GbgeODPEXE6ZYmiBcDGlBu6t7ZXmqSlERHrAj+kBDOAcyPiBZR9IuYDfwU2A14REU/PzMmz5CUNnHlJHWJeUte9GfOSNChDz0sOcKkN/xYRl1avo/r5poi4fMI1azZc0yBU63Q/Hngw8L7MvDAitgL+LzP/tvj/Wpq7zDwrIjYC9qDMRjodOGTiNRGxOnAG8LXGC9TYy8zzI+IRlM/oJpT+5mzgK8DBmbmwzfp6wn5cXfMBYDngOZS9Dval7LFzBmXz7oURMR/4PvAeYJuW6pRmynZ2RMxLapt5SV1nXqqF/bi6ZtB5KTJdOlXNiYjzmcV6vZm53uiqGY6IWIOypMHGwPnAesAmmXl6RHwFuCUzX9diiZKkAbAfVxdFxCXA2zLz0Or9BsA5wHaZefSE654DfDkzF7RTqbRktrOjYV6SJDXBflxdNPS85BNcalRmrtt2DQP1KeC+lA0qzwcmzqj5KWX0XZLGXkRsAzw4Mw+e4tzuwAWZeULTdfWF/bg66gHAeRPe/7X6ecWk664EVm2kImkp2c6OjHlJkmbAvDQ39uPqqEHnpXltFyCpFtsC78rM/+OeM0UuBh7UfEmS1En/BawxzbnVgAMarEVSPYK73/+4RIWkycxLkjQz5iVpeAadl3yCSxqOO6Y5vhpwc5OFSFKH/T/gP6c597+Ujb8l9c/zI+Lx1et5lND2wojYbMI16zZelaQuMS9J0pKZl6RhGmxecoBLGoaTKJtY/mjCsUWj8XsAxzdfkiR10u3AKtOc692j+JL+6d+nOLb3FMcGNVtR0oyZlyRpZsxL0jANNi9FZu9qljRJRDwK+AVwKfB9SgP1eeBR1Z/NMvPc9iqUpG6IiCMpS25smZkLJxxfjvLl15WZ+Zy26pMkSfUzL0nSzJiXJPWNA1zSQETE+sC+wDaUZTauBo4D9s3MP7dYmsZcRGwArAXMn3wuM49qviKNs4h4DOULrmuAb1G+6FoT2AlYiRLkzmqtQEmSNBLmJXWVeUldYl6S1DcOcKk1EfFu4KDM/NsU59YEXpWZ+zdfWT9ExAOBKzLz9rZrkaYSERtSbog3pGxoOVlm5jLNViVBRDyc8gXXUyjLbFxF+YJrP2dvz5z9uCSNlu3s3JiX1HXmJXWVeake9uNSMxzgUmsi4g5g88w8ZYpzGwOneDM3vcn/fhERwCHAPpl5QavFSUBEnAQsAN4OnA0snHyNn1Wpv+zHJWm0bGfnxrykrjMvScNmPy41Y9m2C9BYC6bfuG4t4B8N1tJHk2d4zQNeCvw34E2wuuBxwC6Z+cO2C5EWJyIeATyCEjDuMbtO07Ifl6TRsp2dG/OSus68pF4wLy01+3GpAQ5wqVERsRuwW/U2gQMj4rpJl80HHg0c02Rtkmr3F6ZYR15qU0R8nrLcy2ur9zsDX6d86XVDRGybmSe3WWOX2Y9L0mjZzkpjxbykzjEvzY39uNS8eW0XoLFzE2Xt3qsoMxmunfB+0Z/zgA8Br26pRkn1eBvwzoh4SNuFSBNsC/x8wvv3At8AHgj8pHqv6dmPq9MiYteIWHWac6tExK5N1yTNku2sND7MS+oi89Lc2I+r04aYl9yDS62JiK8A+2fmeW3X0kcRcSfwVmDRBp/zgCOAN1Nmgt1NZh7VWHESEBGnAmsD9wfOB66ZfE1mbtpsVRp3EXEz8IzMPCkiHgb8CXhMZp4VEU8HvpWZq7RbZT/Yj6uL3OtAQ2I7OzfmJXWdeUldZF6qj/24umiIecklCtWazHz55GMRsTpwbWbeY3NVTeljUxz7xBTHEuhV46RBOKv6I3XJ1cAa1eunAZdl5qLPaWBbOWP24+qoyXvuTLQqMHmJGKmzbGdrYV5Sl5mX1EXmpZrYj6ujBpeXHOBSoyJiS8oo8YcnHX81cABl5tKtEfEF4K2ZeWcLZfbFem0XIC3OVDdzUgf8GNg/ItYA3g4cNuHcoyizZzUN+3F1UURsD2w/4dA+EXHlpMvmA08CTm2sMGkp2M7WyrykTjMvqaPMS3NgP64uGnpecoBLTdsLuGPigYjYBjgQ+B2wL7ABsCdwNvCFZsvrj8y8oO0aJKmH3gZ8HHgtZW35d084twNwdBtF9Yj9uLpoAWWj7kXWBx4w6ZqFlI2839dUUdJSsp2tiXlJkpaKeWlu7MfVRYPOS+7BpUZFxIXAPpl5yIRj3wGeCayTmVdXxz4EPCUzN2mnUklLo/rd/WRmXly9XqzMfHsDZUmqif24ui4iTgBel5nntF2LtDRsZ6VhMy9Jw2Y/rq4bYl7yCS41bXUmPM4cEQE8HThuUSNf+SnwymZLk1SDFwJfBy6uXi9OUpY8kNQf9uPqtMx8Sts1SHNkOysNm3lJGjb7cXXaEPOSA1xq2hXAAye8fxywIuWx54luw40rpd7JzPWmei21KSJOAXbPzLMj4lTKlwXTysxNm6msl+zH1TkRsSHwmMw8dNLxZ1GW2HgEcDllxvx/N1+hNCu2s9KAmZfURealWtmPq3OGnpcc4FLTfg68LSKOAf5BWZv2TuDwSdc9Frio2dIkSQP1B+DmCa9dn3np2Y+ri/YBVgX+GdgiYmPgCOBSyp4HGwAfjYhLMvPbrVQpzYztrCSpaeal+tiPq4sGnZfcg0uNioi1gd9QHtldCMwHPpaZe0267nTgl5n5b81XKaku1eP4W1A6yvmTz2fmZxsvStJSsx9XF0XEX4H3Z+YXJxz7KmXpp4dn5oXVsYOAR2Tmlu1UKi2Z7aw0XsxL0rDYj6uLhp6XHOBS4yJiZcov0ErA6Zl5/KTzqwG7AUdl5h+br7Cfqn/X1wBbAqsAVwMnAV/IzGvaq0zjKiLWAI4DNqTMAIvq1D87nsz0kXw1KiJWBO6bmZdOcW5N4PrMvKH5yvrDflxdExE3Adtl5s8mHPs78NvM3HbCsWcBh2TmguarlGbOdnY0zEvqGvOSusi8NHf24+qaoeclB7ikAYiI9YGfAQuAX1LWTV0DeCJl/d+nZOZfWitQYykivgasB+xEefT+CZTP5kuBXYFn+7lU0yLiMODazHzVFOc+D6yUmbs0X5mkpRURfwP2zMzvV+8fSVle512ZecCE67aifJFw33YqldQW85K6yLykLjIvScMz9Lw0r+0CJNXi48A1wEMy86mZ+aLMfCqwfnX8Yy3WpvH1ZOCjlPV8oUyquLDqPL8GuNyG2rAV8KNpzh1VnZfUL78B9oyIRfsLv4Yy+/0Hk657JHBJk4VJ6gzzkrrIvKQuMi9JwzPovLTski+R1ANbA7tl5t0aocy8JCL2A77SSlUadysDV2bmnRFxHWXG7CInA3u3UpXG3UrATdOcuwW4f4O1SKrHPsCvgEsj4hrKF9bfysyzJl33YuAXDdcmqRu2xryk7lkZ85K6x7wkDc+g85JPcEnDkMB0a3PPY8Ia3lKDzgPWrF7/AXjJhHPPpex7IDXtz8Czpzm3HeAyMFLPVMFsI+ALwE+AV1KWd/qniFgdOAP4fNP1SeoE85K6yLykLjIvSQMz9LzkHlzSAETE4cDDgW0z84IJx9cBjgb+mJk7tlSexlREfABYLTNfWW1UeQRlj4PbgLWBvTPzI23WqPETEa8CPkdZiuhgypIwa1I2+X0r8LrM/GJrBUqSpNqZl9RF5iV1kXlJUt84wCUNQESsCxwPrAWcTtmYdgGwMWWz2m0y8/y26pMAIuLxwA7A8sCxmfnjlkvSmIqIdwHvAOZPOHwL8N7M/EA7VUmSpFExL6kPzEvqCvOSpD5xgEuNiojtZnN9Zh41qlqGJiKWA/YANqHMrrmUsongwZm5sM3aNH4i4t7AXsAPM/OMtuuRJouIlYDNgVWBq4BfZea17VbVffbjkjRatrOjY15Sl5iX1HXmpaVjPy41zwEuNSoi7qSsbx4zuDwzc7p10iV1XETcBDwrM09suxZJ9bAfl6TRsp2Vxod5SRoe+3Gpecu2XYDGznptFzBEEbEN8ODMPHiKc7sDF2TmCU3XpbH3G8qyLwY2dUpEzAe2oixTNH/S6czMA5uvqjfsxyVptGxnR8C8pI4yL6mTzEtzYj8uNcwBLjVq4oa+qtV/Ad+f5txqwGsoj5ZLTXo78I2IWAgcRdnr4G6PDWfmTW0UpvEVEVsC36O0jVNJwMA2DftxdV1ErJKZV7ddh7S0bGdHxrykLjIvqXPMS3NjP66uG2JecolCNSoiVpjN9d7MzUxEXA88LzOPm+LcNsD3MnOl5ivTOKsezV9kys7Gx/HVtIg4HbgVeC1wdmbe1nJJvWI/rq6LiFuBI4CvAD/JzDuX8J9InWI7OxrmJXWReUldZF6aG/txdd0Q85JPcKlpNzDNjds0vJmbmduBVaY5t2qThUgT7MHsft+lJjwc2NHNvJea/bi67rXAbsAPgcsi4hDgkMz8U7tlSTNmOzsa5iV1kXlJXWRemhv7cXXd4PKST3CpUdX65jP+0GXmIaOrZjgi4khgDWDLzFw44fhywEnAlZn5nLbqk6SuiIhfAZ+zf1k69uPqi4h4CLA78DJgbeDXwJeBb2XmDS2WJi2W7exomJckaWbMS3NjP66+GFJecoBLGoCIeAzwC+Aa4FvApcCawE7ASpQgd1ZrBWosRcRfgR2mmvkVEY8CfpCZD2m+Mo2ziNgIOBh4U2a6obc0BiLiqcC+wBbAzcB3gE9m5ult1iWpOeYldZF5SV1kXpLGT9/zkgNcal1EbAhsDDwY+HJmXhYRDwUuz8zr262uPyLi4ZTG6CmUZTauAo4D9svMc1ssTWOqWlN+s8w8ZYpzmwK/yMzlmq9M4ywirgRWAOYDtwHXTb4mMxc0XVef2Y+rq6o9EHaizEzcCvgDcDjwDODxwH9k5ofbqk+aKdvZepiX1DXmJXWReal+9uPqqqHkJQe41JqIuC/l0ccXUDrNZYFNMvP0iDgMuDAz92qzRkmzExH3A1au3p4PPA/43aTL5lPW/N0hM9drqDQJgIjYlyUsGZGZ+zVTTb/Zj6urImIr4OXA8ym/74cCX5r4BWJEvB3YOzPde0edZTsrDY95SV1nXqqP/bi6amh5adm2C9BY+xjwRGAb4JfALRPOHQXsVf3RUoiIRwCPAE7JzL+1XY/GxluA91A6yAS+P811AbytqaKkRTJz37ZrGBD7cXVORPwFWBc4GXgjcFhm3jTFpccBH2iwNGlp2M6OkHlJLTEvqdPMS7WyH1fnDDEvOcClNu1IWdP3hIhYZtK5C4B1WqiplyLi80Bm5mur9zsDXwfmATdExLaZeXKbNWpsfAP4LSWQ/YBys/anSdcsBP6UmRc2XJuketmPq4u+Cxy0pOXGMvM0yn2S1GW2szUxL6lDzEvS+LAfVxcNLi85wKU2LU9Z93wqKwJ3NFhL320LvGPC+/dSbpzfDnyqer9NC3VpzGTmn4E/A0TEU4DTXVNaXRMRmwOvADagLAFzN5m5aeNF9ZP9uDolIuYDGwEPBNxPR0NgO1sf85I6wbykPjAv1cZ+XJ0y1LzUi1E4DdapwK7TnHsB5VFJzcwC4CKAiHgY8FDgQ5l5GfAF4HEt1qYxlZknGtbUNRHxdODnwFrAlsCVwA3AYykbzp/VXnW9Yz+uTsnMW4BNgMkzZKW+sp2tj3lJnWNeUheZl2plP65OGWpe8gkuteldwE8j4qfAtynrT28XEW+hNPRbtVlcz1wNrFG9fhpwWWYuuukIBtZwqbsi4kqWsCHtRJm5YITlSFPZH/gEsDdlo999qk1+1wF+Avysxdr6xn5cXfQD4HmUNeOlvrOdrY95SZ1gXlIPmJfqYz+uLhpcXnKAS63JzF9ExDaUDes+TQkW+wG/Bp6Wmae2WV/P/BjYPyLWoCyzcdiEc48Czm+jKI2lzzCLwCa1YENK0LiT8lm9D0BmXhAR+1L6oa+2Vl2P2I+ro34CfDgi1qRs3n05k/qlzDyqjcKk2bKdrZV5SV1hXlLXmZdqYj+ujhpcXopM+1W1LyKWB+4PXJOZN7VdT99ExErAxymPmf4O2DMzr6vOnQScnJl7t1ehJHVDRFwOvDQzj42Ii4F3ZebB1bntgG9n5n3arLGP7MfVFRFx5xIuycz0SQ31ju3s3JiXJGlmzEujYT+urhhiXnKAS62pZjH8yoZdktSUiDgG+EFmfjoiDgE2A94ALAQ+CtyemZu1WWNf2I+ri6rlcxYrMy9oohZprmxnJUlNMy/Vx35cXTTEvOQAl1oTEXcAdwD/C5xU/flFZl7VamGSahMRmwOvADYA5k8+n5mbNl6Uxlo163C9zPxMRDwIOBLYqDp9MbBDZp7WVn19Yj8uSaNlOysNn3lJXWNeqo/9uNQMB7jUmohYlbKh4pbVz42AecA5VA1/Zn69tQI7LiJOAXbPzLMj4lSWsI63N8ZqWkQ8nbKe73HAMyh7HywPbEG5MT4xM/dor0IJIiKAh1I+m+dk5sKWS+oN+3F1VUTcG9gDeDzwYMpSZH+OiJ2BMzPzj60WKM2Q7ezcmJfUdeYl9YF5aenZj6urhpaXHOBSZ0TEfYCnAm+jNPy9W/OzSRHxFWD/zDwvIg5myYHt5Y0UJlUi4lfAL4G9gduAx2fm6dXj0D8BDshMN6dVYyJiPnAm8MbMPLrteobGflxdEBEbAMcCKwGnAVsDm1T9z6eB+2Xmri2WKC0129nZMS+p68xL6hrz0mjZj6sLhpiXlm27AI23iLgv8ETgSdWfTYFbgB9RZjNoGhMDWGbu3mIp0nQ2BN4F3En5QuE+UNbyjYh9gf0AA5sak5m3RMTKlM+kamA/rg76JHAh8FzgBsp+EYucCHywjaKkpWU7u/TMS+oB85I6xbxUP/txddDg8pIDXGpNtUzEY4ErKI36t4E3Ar9PHy2clYhYEbhvZl46xbk1gesz84bmK9OYuwWYl5kZEZcC63PXDdx1wFqtVaZx9nXg5cAxbRfSd/bj6qgnAS/MzGsiYvKM2MuBNVuoSVoqtrP1MS+po8xL6iLzUk3sx9VRg8tLDnCpTRtRHsP/FXAy5dF8G/ml8yXgWuBVU5zbl/LY6S5NFiQBZwAPpzz6fBzwjoi4hDI7ZH/g9y3WpvF1IbBTRPyWsufB5dx9yaLMzANbqax/NsJ+XN1zC2WPiKk8CLimuVKkOdsI29m6mJfUReYldZF5qT4bYT+u7hlcXnIPLrUmIlbgrsd0t6I8pruQ0uD/HPh5Zv66vQr7IyIuA16bmYdPcW574MDMfGDjhWmsRcR2wHqZ+ZmIeBBwJOUGD8qmyTtk5mlt1afxFBFLWm7DddBnyH5cXRQRhwIPo+xvcAPlS4WNgbOBE4A/ZuYr2qtQmjnb2fqYl9RF5iV1kXmpPvbj6qIh5iUHuNQZEXEvYBvgP3CzxVmJiJuB7TPzHo+QR8QzgcMzc7rReakRERHAQykzRc7JzIVL+E8k9Yj9uLogIh5M+dJgecqM+J2BHwD/D1gO2CwzL2uvQmnp2c4uPfOS+sC8JA2b/bi6YIh5ySUK1aqIWJ27Nlp8EmVt2nnAH3Czxdn4M/Bspl4jeTvgL82Wo3FW7XFwW2beMvF49Rj+nyNiPnBv7r6RpTQyEXEu8ILMPHPCsT0oX2Zd3V5l/Wc/rq7JzIsi4rHAWylfIPyFso78t4GPZeZVbdYnzZbtbG3MS+oM85K6xrw0Ovbj6poh5iWf4FJrIuIcyiORdwD/S3k89yTgF3agsxMRrwI+B3wMOBi4lNI47UZpsF6XmV9srUCNjYh4AvAL4GmZeeI01zyZMkvkaZn58ybr03iqltnYLDNPqd4vQ/nCYJPMPL3V4nrMflySRst2tj7mJXWFeUldZF4aDftxqRk+waU2fYvSuP8qM29qu5g+y8wvRsQawDsoAW2RW4B3GdbUoL2Ab00X1gAy88SI+CbwJkobILUh2i5gAOzHJWm0bGdrYl5Sh5iX1BfmpbmzH5ca4BNc0oBExErA5sCqwFWUTvTadqvSOImIK4DXZOb3l3Dd84DPZ+YajRSmsTbNjMTbgMc7I1EalohYDngzsAPwIGD+5Gsyc0HDZUnqCPOS2mZeUheZl6TxMcS85BNcalVEPAT4d2BLYBXgasrjuh/JzL+2WVsfVeHs6Lbr0FhbCbhyBtf9HVh5tKVIdzM/IlaoXi87xbF/cnbdzNmPq4MOBF4CHAEcj/uXqOdsZ+tlXlIHmJfUVealEbAfVwcNLi/5BJdaExEbAydQloX4IXA5sAZl89/5wFOcKTJz1Ua0WwFrcc/R98zMA5uvSuMmIi4E9snMQ5Zw3a7A+zJz7WYq0zirZiROvuGJKY4BkJnLjLyoAbAfVxdFxD+Ad2Tm59quRZor29l6mZfUBeYldZF5aTTsx9VFQ8xLDnCpNRFxAjAPeNbE2R/V7JCjgDsz86lt1dcnEbEl8D1gtWkuSW9A1ISI+BLwaOCJmXn7NNcsC/wS+H1mvrLJ+jSeImK32Vy/pC8cVNiPq4si4i/Av2XmUW3XIs2V7Wx9zEvqCvOSusi8NBr24+qiIeYlB7jUmoi4EdgpM380xbnnUDZevU/zlfVPRJwO3Aq8Fjg7M29ruSSNqerx+9OB3wBvycyzJ51/JPBxYDPgX3wkX+ov+3F1UfUFza7Av2bmjW3XI82F7Wx9zEvqCvOSND7sx9VFQ8xL7sGlNt1M2dx3KqtQHuHVzDwc2DEzz2i7EI23zPxrRDwbOBT4fUT8DbiQsrTB2pQNLP8GbGdYk3rPflydk5mHRMSGwIURcRpwzT0vyZ2br0xaKraz9TEvqRPMS9JYsR9X5wwxLznApTb9CPhARPw1M3+x6GC1fMT7gSNbq6x/zgQe0HYREkBm/jIiHgbsRNnn4EHVqWOAnwHfyUxv5KT+sx9X50TE2ygbeV8G3Ae4V7sVSXNiO1sf85I6w7wkjQ37cXXOEPOSSxSqNRGxKnAEsDlwJWWzxQXVn5OB52XmVe1V2B8RsRFwMPCmzDyx3WokSePAflxdFBFXAl+nLPtk0FGv2c7Wx7wkSWqa/bi6aIh5yQEutS4itgU2AdYELgV+k5nHtFtVv1SN0wrAfOA24LrJ12TmgqbrkiQNn/24uiQi/g7skpk/bbsWqS62s3NnXpIktcV+XF0yxLzkEoVqXEQsD2wHrEtp2I/LzKNbLar/PkNZs1uSpJGyH1fHHQw8HxhMYNP4sZ0dCfOSJKkR9uPquIMZWF7yCS41KiIeQvkFWnfC4euAnZy9IElqUkQsCyw3+Xhm3tRCOb1gP66ui4g3A28FzgWOZ+pNkw9suCxpxmxnJUldYV6aPftxdd0Q85IDXGpURHwH2AjYDTgNWA/4LLBuZq7XYmmSpDEQEfcDDgB2pKx9HpOvycxlmq6rL+zH1XURcecSLkl/x9VltrOSpDaZl+bGflxdN8S85ACXGhURlwBvy8xDJxzbAPgjsFZmXtpacT0XEZsDrwA2oKwtfzeZuWnjRUlSx0TEN4HnAAcBZwMLJ1+TmYc0XVdf2I9L0mjZzo6OeUmSlsy8NDf241Lz3INLTVsT+OukY3+hzAh5AGVtWs1SRDwdOAo4DtgS+DGwPLAFcDFwYnvVaZxFxMrAayify1WAq4GTgC9k5jXtVaYx9kzgLZl5UNuF9JT9uCSNlu3sCJiX1FXmJXWQeWlu7Melhs1ruwCNJR8brN/+wCeAZ1fv98nMp1JmJ94G/KylujTGImJ94PeUz+d9gAurn/sDZ1bnpabdSPkiS0vPflydFhErR8TeEXFkRPyy+vn26ktEqQ9sZ+tnXlLnmJfUUealubMfV6cNLS+5RKEaVa3zeQ1w+6RTq011PDMXNFJYz0XEtZT1kY+n/BtunZknVed2AfbLzIe3WKLGUET8gLLe9LaZecmE4w+izJo9LzO3b6s+jadqQ9WnAs/LzCWtPa1J7MfVddWXgT+j7BnxS+ByYA3gicAVwFMy8y+tFSgtge3saJiX1EXmJXWReWlu7MfVdUPMSy5RqKbt13YBA3ULMC8zMyIuBdanLGsAcB2wVmuVaZxtDew2MawBZOYlEbEf8JVWqtK4exDwWOBPEXECJWRMlJm5d+NV9Yf9uLru45Tf682m+bLwY4BfFqrLbGdHw7ykLtoa85K6x7w0N/bj6rrB5SUHuNSozLShH40zgIcDx1LWlX9HtbHlQsryBr9vsTaNrwSWmebcPHxsX+14AXAn5R7o6VOcT8DANg37cfXA1vhloXrMdnZkzEvqIvOSusi8NAf24+qBrRlYXnKASxqG/6YsbQDwTuBI4CfV+4uBHVqoSToBeG9EnJqZFyw6GBHrUL5IOK61yjS2MnO9JV8lqcf8slDSVP4b85K6x7ykzjEvSYM3uLzkHlzSAEVEAA8FlgfOycyFLZekMRQR61L2OVgLOJ2yru8CYGPgImCbzDy/rfokScMTEYdTntLYdoovC48G/piZO7ZUnqSOMC+pC8xLkqSmDTEvOcAl9VxEzAfOBN6YmUe3XY80UUQsB+wBbAKsCVwK/AY42C8S1JbqS60tgA2A+ZPPZ+ZnGy9KUi38slDSZOYldZl5SV1kXpKGa4h5yQEuaQAi4grgpZl5TNu1SFKXRcQalOVeNqQ8eh/VqX/eEGXmdI/rS+oBvyyUNJl5SZJmxrwkDd/Q8pIDXNIARMTHgQdk5ovarkVaJCK2AR6cmQdPcW534ILMPKHpujTeIuJrlD04dqLMTnoCZcbSS4FdgWdn5l/aq1CSJNXNvKQuMi+pi8xLkvpm2bYLkFSLC4GdIuK3wFGUm4+Jo9eZmQe2UpnG2X8B35/m3GrAa4DNmytHAuDJwJsoM5SgTPa5EDggIuYBnwWe2VZxkiRpJMxL6iLzkrrIvCSpV3yCSxqAiLhzCZekj5CraRFxPfC8zDxuinPbAN/LzJWar0zjrPpcbpeZJ0XENZTlin5YnXsqcERmrthmjZKWXnVPNF3ASeA64Azgk5k53ZeKkgbGvKQuMi+pi8xL0rANMS/Na7sASXOXmfOW8MewpjbcDqwyzblVmyxEmuA8yhrTAH8AXjLh3HOBqxuvSFKd3gpcAvwR+BDw78CHgXOAvwGfAO4AvhMRL22rSEnNMi+po8xL6iLzkjRsg8tLPsEl9VREnAu8IDPPnHBsD+DwzPSGQ62LiCOBNYAtJ25SWW1meRJwZWY+p636NJ4i4v3A6pn5yoh4FnAEcAVwG7A2sHdmfqTNGiUtvYj4EGU/k3vssxMRhwKXZeabI+KrwEaZ+ZjGi5TUCPOSus68pC4yL0nDNsS85ACX1FPVI6WbZeYp1ftlgIXAJpl5eqvFSUBEPAb4BXAN8C3KGt5rUjarXYkS5M5qrUAJiIhNgB2A+cCxmfnjlkuSNAcRcSXwksw8ZopzzwS+kZmrRsSzge9k5vKNFympEeYldZ15SX1gXpKGZYh5adm2C5BUq2i7AGmRzDyzuhneF3gZZZmNq4DjgP0y89wWy5MAyMxTgVPbrkNSbZYFHgHcI7ABj+SuJdoXArc0VZSkzjAvqTPMS+oD85I0OIPLSw5wSZJGJjP/BNzjsWepCyJiBeAVlJu7y4CvZuYF7VYlaY4OBd4fEcsCRwJXAqsD2wP7A1+prvsXyjrzkiS1xrykLjMvSYM0uLzkEoVST1VLbmwN/LY6tCxlaYMtgd9Nvj4zb2qoNGlaEfEIys3xKZn5t7br0XiIiI8Cz83MDSYcW5EyE/FhwD8oy8DcCGzqbFmpv6p9Sz4MvAq494RTtwJfBN6embdGxNbADZn523v8JZIGwbykPjIvqQ3mJWl8DDEvOcAl9VQV2Cb/AscUxwDIzGVGXpQ0QUR8HsjMfG31fmfg65THnW8Ats3Mk1ssUWMiIk6nbCi//4Rj+wH7AK/MzC9HxOrAscDvM/NlLZUqqSYRsQrwaOABlBnHv8/Mq9utSlKTzEvqOvOSusK8JI2fIeUlB7iknoqI3WZzfWYeMqpapKlExAXAOzLzG9X7c4FfA28HPgWskpnbtFiixkREXA28LDN/NOHYWQCZ+agJx15G2e/gIc1XKakOEbEr8KPMvGqKc6sAz8nMrzZfmaSmmZfUdeYldYV5SRofQ8xL7sEl9ZQBTD2wALgIICIeBjwU2DEzL4uILwDfarM4jZVlmbA5anXT9kjgM5OuO58ye0lSf30F2By4R2AD1qvO9yqwSVo65iX1gHlJXWFeksbH4PLSvLYLkCQN1tXAGtXrpwGXZeZZ1fsAXAZGTTmXsgfHIs+pfv5k0nULKJ9bSf0Vizm3KnBdU4VIkrQE5iV1hXlJGh+Dy0s+wSVJGpUfA/tHxBqUZTYOm3DuUZTZX1ITPg18MSJWAi4H3gicBxwz6bpnAGchqVciYntg+wmH9omIKyddNh94EmWzdEmSusC8pK4wL0kDNvS85ACXJGlU3gZ8HHgt8HPg3RPO7QAc3UZRGj+ZeXBErAnsCawMnA7smZm3Lbqm2jR5e2C/VoqUNBcLKBskL7I+91w+ZyHlS5r3NVWUJElLYF5SJ5iXpMEbdF6KzGy7BkmSJEmas4g4AXhdZp7Tdi2SJEmS1CVDzEsOcEmSJEmSJEmSJKlXXKJQGpiIWBZYbvLxzLyphXI0ZiLiFGD3zDw7Ik4FFjuLIjM3baYySdK4iIgHUjZHX4uylvxEmZl7N1+VpK4wL6lN5iVJUtuGlpcc4JIGICLuBxwA7EhZVzWmuGyZRovSuPoDcPOE1z4mLElqTETsAHyTct9zBWUt+YkS6FVgkzR35iV1iHlJktSaIeYllyiUBiAivkkZeT8IOJt7Nk5k5iFN1yVJktSkiPgj8GfK7Pir265HUjeYlyRJkoaZlxzgkgYgIq4G3p6ZB7Vdi7RIRKwI3DczL53i3JrA9Zl5Q/OVSZKGKiJuAJ6XmT9tuxZJ3WFeUheZlyRJTRtiXprXdgGSanEjcHHbRUiTfAnYf5pz+1Jm0EqSVKeTgYe3XYSkzjEvqYvMS5Kkpg0uLznAJQ3DR4HXR4S/0+qSrYAfTXPuqOq8JEl1eivw6ojYLSIeGBErTP7TdoGSWmFeUheZlyRJTRtcXlq27QIk1eJBwGOBP0XECcA1k85nZvZqg0ANwkrATdOcuwW4f4O1SJLGw5nVz69QNkieyjIN1SKpO8xL6iLzkiSpaYPLSw5wScPwAuBOyu/006c4n4CBTU37M/Bs4Jgpzm0H/KXZciRJY2APpg9qksaXeUldZF6SJDVtcHnJAS5pADJzvbZrkKbwKeBzEbEQOBi4FFgT2A3YE3hde6VJkoYoMw9uuwZJ3WNeUkeZlyRJjRpiXorMQQ3YSZI6JCLeBbwDmD/h8C3AezPzA+1UJUkauoh4ILA5sApwNfCrzPxbu1VJknR35iVJUhuGlJcc4JIGIiIC2ALYgLvfHAOQmZ9tvCgJiIiVKJ3mqsBVlE7z2narkiQNUUQsQ5kR/yruvnb8HcAXgH/LzDvbqE1Su8xL6irzkiSpKUPMSw5wSQMQEWsAxwEbUtZRjerUP3/BM7NXGwRKkiTNVkS8D9gL2Af4FnA5sAawM7A/8OHMfHd7FUpqg3lJkiRpmHnJAS5pACLia8B6wE7ARcATKA3US4FdgWdnphvUqnERMR/YCliLe86Uzcw8sPmqJElDFREXAp/MzI9McW4v4I2ZuXbzlUlqk3lJXWVekiQ1aYh5adm2C5BUiycDb6JsSgtl8PpC4ICImAd8FnhmW8VpPEXElsD3gNWmuSQBA5skqU4LgDOnOXdmdV7S+DEvqXPMS5KkFgwuL81ruwBJtVgZuLJaI/U67t4YnQw8sY2iNPY+CfwFeBxw78ycN+mPy8BIkup2LrDLNOd2Af7UYC2SumNlzEvqHvOSJKlpg8tLPsElDcN5wJrV6z8ALwF+WL1/LnB1G0Vp7D0c2DEzz2i7EEnS2HgfcGhErA18h7IE2QLghcBTmD7MSRo285K6yLwkSWra4PKSA1zSMPwIeAZwGKWhOiIiLgZuA9YG9m6xNo2vM4EHtF2EJGl8ZOZhEXENsB/wCeBelPuh04BtM/PYFsuT1B7zkrrIvCRJatQQ81JkZts1SKpZRGwC7EDZpPbYzPxxyyVpDEXERsDBwJsy88R2q5EkjZtqX53VgL9Xy5JJEmBeUjeYlyRJbRpKXnKAS5I0EhFxJbAC5YuD2yj7HdxNZvZu80pJkiRJmivzkiRJc+cShdLARMQKwCuARwCXAV/NzAvarUpj6jOAsygkSSMVEe+exeWZme8dWTGSOs+8pA4xL0mSRm7oecknuKSeioiPAs/NzA0mHFsROBV4GPAPYCXgRmDTzDy3lUIlSZJGKCLuBG6m3PPEEi5PZ8NL48G8JEmSNPy8NK/tAiQttacAX5t0bC9gA+BVmbka8EDgfGCfZkuTJElqzF8pmyOfRrkXWj8zV5/mT6/CmqQ5MS9JkiQNPC/5BJfUUxFxNfCyzPzRhGNnAWTmoyYcexmwX2Y+pPkqNe4iYnPKEjAbUNaWv5vM3LTxoiRJgxMRjwd2AXaibJR8NPBN4IeZeXObtUlqh3lJfWBekiQ1Ych5ySe4pP5aFrhl0ZuIWAV4JHD8pOvOBx7QXFlSERFPB34OrAVsCVwJ3AA8FlgVOKu96iRJQ5KZv83MvTJzbWBbyr46nwauiIivR8RW7VYoqQXmJXWaeUmS1JQh5yUHuKT+OhfYesL751Q/fzLpugXA1U0UJE2yP/AJ4NnV+30y86mU2Ym3AT9rqS5J0oBl5s8z8/XAg4HPATsDb261KEltMC+p68xLkqTGDS0vLdt2AZKW2qeBL0bESsDlwBuB84BjJl33DJz5pXZsCLwLuBNI4D4AmXlBROwL7Ad8tbXqJEmDFBFbUJbfeAGwIvAd4MBWi5LUBvOSus68JElq3NDykgNcUk9l5sERsSawJ7AycDqwZ2betuiaiFgd2J5yYyw17RZgXmZmRFwKrA+cVJ27jrIUhyRJcxYR/0IJaTsDa1DWlH8L8IPMvKnN2iS1w7ykHjAvSZIaMeS8FJnZdg2SpAGKiGMoHeWnI+IQYDPgDcBC4KPA7Zm5WZs1SpL6LyL+BKxH2VfnUOB7mXldu1VJkrR45iVJUhOGnpcc4JIkjUREbAesl5mfiYgHAUcCG1WnLwZ2yMzT2qpPkjQMEXEnZRb8jZQlnhYrMxeMvChJkpbAvCRJasLQ85IDXJKkRkREAA8FlgfOycyFLZckSRqAiHjPbK7PTJcikyR1jnlJkjQKQ89LDnBJkmoXEfOBM4E3ZubRbdcjSZIkSV1hXpIkqR7z2i5AkjQ8mXkLZTPvO1suRZIkSZI6xbwkSVI9HOCSJI3K14GXt12EJEmSJHWQeUmSpDlatu0CJEmDdSGwU0T8FjgKuJy7b2aZmXlgK5VJkiRJUrvMS5IkzZF7cEmSRiIilrTcRmbmMo0UI0mSJEkdYl6SJGnuHOCSJEmSJEmSJElSr7gHlySpNhFxbkQ8ZtKxPSJilbZqkiRJkqQuMC9JklQvB7gkSXV6KDB/0ZuIWAb4IrBuWwVJkiRJUkeYlyRJqpEDXJKkUYu2C5AkSZKkjjIvSZK0lBzgkiRJkiRJkiRJUq8s23YBkqTBmR8RK1Svl53i2D9l5k3NlSVJkiRJrTMvSZJUk8jMtmuQJA1ERNwJTO5YYopjAGTmMiMvSpIkSZI6wLwkSVK9fIJLklSnl7ddgCRJkiR1lHlJkqQa+QSXJEmSJEmSJEmSemVe2wVIkiRJkiRJkiRJs+EAlyRJkiRJkiRJknrFAS5JkiRJkiRJkiT1igNckiRJkiRJkiRJ6hUHuCRJkiRJkiRJktQry7ZdgCRp+CJiWWC5yccz86YWypEkSZKkzjAvSZK0dHyCS5I0EhFxv4j4dET8DbgFuH6KP5IkSZI0dsxLkiTNnU9wSZJG5fPAc4CDgLOBhe2WI0mSJEmdYV6SJGmOIjPbrkGSNEARcTXw9sw8qO1aJEmSJKlLzEuSJM2dSxRKkkblRuDitouQJEmSpA4yL0mSNEcOcEmSRuWjwOsjwr5GkiRJku7OvCRJ0hy5B5ckaVQeBDwW+FNEnABcM+l8ZubejVclSZIkSe0zL0mSNEfuwSVJGomIOG8Jl2RmPqSRYiRJkiSpQ8xLkiTNnQNckiRJkiRJkiRJ6hXX+ZUkSZIkSZIkSVKvuAeXJGlkIiKALYANgPmTz2fmZxsvSpIkSZI6wLwkSdLcuEShJGkkImIN4DhgQyCBqE79s+PJzGVaKE2SJEmSWmVekiRp7lyiUJI0Kh8FrgUeTAlrTwDWBfYB/kyZpShJkiRJ48i8JEnSHLlEoSRpVJ4MvAm4tHofmXkhcEBEzAM+CzyzreIkSZIkqUXmJUmS5sgnuCRJo7IycGVm3glcByyYcO5k4IltFCVJkiRJHbAy5iVJkubEAS5J0qicB6xZvf4D8JIJ554LXN14RZIkSZLUDeYlSZLmyCUKJUmj8iPgGcBhwPuAIyLiYuA2YG1g7xZrkyRJkqQ2mZckSZqjyMy2a5AkjYGI2ATYAZgPHJuZP265JEmSJEnqBPOSJEmz5wCXJEmSJEmSJEmSesUlCiVJIxcRKwCvAB4BXAZ8NTMvaLcqSZIkSWqfeUmSpKXjE1ySpNpExEeB52bmBhOOrQicCjwM+AewEnAjsGlmnttKoZIkSZLUMPOSJEn1mtd2AZKkQXkK8LVJx/YCNgBelZmrAQ8Ezgf2abY0SZIkSWqVeUmSpBo5wCVJqtO6wGmTjj0fODszvwyQmVcCHwW2aLY0SZIkSWrVupiXJEmqjQNckqQ6LQvcsuhNRKwCPBI4ftJ15wMPaK4sSZIkSWqdeUmSpBo5wCVJqtO5wNYT3j+n+vmTSdctAK5uoiBJkiRJ6gjzkiRJNVq27QIkSYPyaeCLEbEScDnwRuA84JhJ1z0DOKvh2iRJkiSpTeYlSZJq5ACXJKk2mXlwRKwJ7AmsDJwO7JmZty26JiJWB7YH9mulSEmSJElqgXlJkqR6RWa2XYMkSZIkSZIkSZI0Y+7BJUmSJEmSJEmSpF5xgEuSJEmSJEmSJEm94gCXJEmSJEmSJEmSesUBLkmSJEmSJEmSJPWKA1ySpM6KiBuW4r9559JcFxEnz/b/liRJkiS1xbwkSRp3kZlt1yBJ0pQi4obMvO8o/pul+bslSZIkqSvMS5KkcecTXJKkkYqIl0bEKRHxu4j4fEQsExE3RMR/RcQZEfHriFijuna9iPhVRJwaEe9dwt+7ZkT8vPp7z4qIJ0XEB4Dlq2Nfr647PCJOi4g/RMSrq2NTXXdD9TMi4sPV3/n7iNi5Or51RPwsIr4TEedExNcjIhZT3/kRsV9EnF79PY+ojm8aESdHxP9WPx9eHd+9qvXIiDgvIt4QEW+trvt1RKxSXbd+RBxd/W86adHfK0mSJKl/zEvmJUnS0nOAS5I0MhHxSGBnYIvM3Ai4A3gJcB/g15n5WODnwKuq/+QTwIGZuQlw2RL++hcDP6n+3scCv8vM/wBuzsyNMvMl1XV7ZObGwOOBN0bEqtNct8iOwKK/82nAhyNizerc44A3AxsCDwG2WEKNf8/MfwEOBPaqjp0DbJWZjwPeDRww4fpHVf+7NgX+C7ipuu5XwK7VNV8A/q3637QX8Nkl1CBJkiSpg8xL5iVJ0tws23YBkqRB2wbYGDi1mry3PHAFsBD4YXXNacDTq9dbAM+vXv8P8MHF/N2nAl+OiHsBh2fm76a57o0RsUP1+sHAw4CrFvP3bgl8MzPvAC6PiBOBTYDrgFMy82KAiPgdsC7wi8X8Xd+rfp5GCYIAKwGHRMTDgATuNeH6EzLzeuD6iLgWOLI6/nvgMRFxX+CJwLcnTIa892L+70uSJEnqLvNSYV6SJC0Vn+CSJI1SAIdUM/82ysyHZ+a+wG151yaQd3D3CRcz2hwyM38ObAVcAvxPROw6+ZqI2Joyq3Dzavbj/wLzZ1DzdG6d8Hpy3Yu7fuK176UEs0cBz51Uz8S//84J7++s/vt5wDUT/j03ysxHLqEGSZIkSd1kXrrnteYlSdKMOcAlSRql44AXRMQCgIhYJSLWWcz1vwR2qV5PXgrjbqq/54rM/CLwJeBfqlO3VbMUocz++0dm3lStvb7ZhL9i4nUT/RzYuVr7fnVKKDxlcbXM0kqUkAmw+2z+w8y8DjgvIl4I/1z//rE11iZJkiSpOealezIvSZJmzAEuSdLIZObZwLuAY+L/t3fHKA0FQRiA/0EvYekBrG29gQewtLSw0wNYCHa2th4grSLYCYKFJuBhrNYiD3wESVTE5yPfVw477LTDsLNVsyR3SbaWpBwnOaqqp8wbm2X2krxU1XPmazouu/hVkln3GfJNks3u7rMkj738/rm+SZJZkmmS+yQnrbVV++2/4yLJeVU9JNn4Qf5BksOqmiZ5TbL/i7UBAAB/RL/0Kf0SAF9WHy+eAQAAAAAA4P/zggsAAAAAAIBRWfXZIwAMqqp2klwvhN9aa7tD1LOoqiZJthfCp6212yHqAQAA1od+CYB1ZkUhAAAAAAAAo2JFIQAAAAAAAKNiwAUAAAAAAMCoGHABAAAAAAAwKgZcAAAAAAAAjMo7TqiCxSO8tp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(28,10))\n",
    "g = sns.FacetGrid(data = df_top_ten_2, col = 'day_time', col_wrap=2);\n",
    "g.map(sns.countplot,'end_station_name', order = None);\n",
    "g.set_xticklabels(rotation = 90, fontsize = 15)\n",
    "g.fig.set_size_inches(24,10);\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observation:** \n",
    "* We see a great increase in the evening compared to morning\n",
    "* So the order is evening > morning > afternoon > night"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Having done all of the necessary exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *Saving the dataframe for explanation*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.to_csv('cleaned_data.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.8.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "f1b1584471b4390c269948253bc0abe58a202cc753e11f035e3dc25ef1e1ef6e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}