notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H9wADwK78DCz"
   },
   "source": [
    "# Proyek Analisis Data: Bike Sharing Dataset\n",
    "- **Nama:** Dimas Firmansyah\n",
    "- **Email:** foreirn@gmail.com\n",
    "- **ID Dicoding:** dfirmansyah"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eE0raob58DC0"
   },
   "source": [
    "## Menentukan Pertanyaan Bisnis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GmQeQ5YF8DC0"
   },
   "source": [
    "- Bagaimana petumbuhan transaksi pada tahun 2012 dibandingkan 2011?\n",
    "- Bagaimana jumlah bulanan transaksi rental sepeda?\n",
    "- Bulan apa transaksi paling sering terjadi?\n",
    "- Hari apa transaksi paling sering terjadi?\n",
    "- Jam berapa transaksi paling sering terjadi?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H-z4QGlO8DC1"
   },
   "source": [
    "## Import Semua Packages/Library yang Digunakan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "FVYwaObI8DC1"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_Sh51Xy8DC1"
   },
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sXU2GBYu8DC1"
   },
   "source": [
    "### Gathering Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
       "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
       "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
       "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
       "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/Bike-sharing-dataset/hour.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FHSiqaZp8DC1"
   },
   "source": [
    "### Assessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "id": "ax-3tEjc9Cj1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   instant     17379 non-null  int64  \n",
      " 1   dteday      17379 non-null  object \n",
      " 2   season      17379 non-null  int64  \n",
      " 3   yr          17379 non-null  int64  \n",
      " 4   mnth        17379 non-null  int64  \n",
      " 5   hr          17379 non-null  int64  \n",
      " 6   holiday     17379 non-null  int64  \n",
      " 7   weekday     17379 non-null  int64  \n",
      " 8   workingday  17379 non-null  int64  \n",
      " 9   weathersit  17379 non-null  int64  \n",
      " 10  temp        17379 non-null  float64\n",
      " 11  atemp       17379 non-null  float64\n",
      " 12  hum         17379 non-null  float64\n",
      " 13  windspeed   17379 non-null  float64\n",
      " 14  casual      17379 non-null  int64  \n",
      " 15  registered  17379 non-null  int64  \n",
      " 16  cnt         17379 non-null  int64  \n",
      "dtypes: float64(4), int64(12), object(1)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jumlah duplikat:  0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "instant       0\n",
       "dteday        0\n",
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "hr            0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "casual        0\n",
       "registered    0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Jumlah duplikat: \", df.duplicated().sum())\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>2.501640</td>\n",
       "      <td>0.502561</td>\n",
       "      <td>6.537775</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5017.0295</td>\n",
       "      <td>1.106918</td>\n",
       "      <td>0.500008</td>\n",
       "      <td>3.438776</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4345.5000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8690.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13034.5000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17379.0000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant        season            yr          mnth            hr  \\\n",
       "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
       "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
       "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
       "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
       "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
       "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
       "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
       "\n",
       "            holiday       weekday    workingday    weathersit          temp  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
       "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
       "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
       "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
       "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
       "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
       "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
       "\n",
       "              atemp           hum     windspeed        casual    registered  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
       "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
       "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
       "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
       "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
       "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
       "\n",
       "                cnt  \n",
       "count  17379.000000  \n",
       "mean     189.463088  \n",
       "std      181.387599  \n",
       "min        1.000000  \n",
       "25%       40.000000  \n",
       "50%      142.000000  \n",
       "75%      281.000000  \n",
       "max      977.000000  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dtxhAPrkhPL"
   },
   "source": [
    "#### Insight\n",
    "- Data tidak memiliki duplikat\n",
    "- Tipe kolom belum sesuai dengan deskripsi data (`data/Bike-sharing-dataset/Readme.txt`)\n",
    "- Kolom temperatur (`temp`, `atemp`) dalam format normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EhN5R4hr8DC1"
   },
   "source": [
    "### Cleaning Data\n",
    "\n",
    "#### Drop kolom yang tidak dibutuhkan\n",
    "\n",
    "- `instant`, karena hanya index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "id": "jVnYpprE9Evz"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "0  2011-01-01       1   0     1   0        0        6           0           1   \n",
       "1  2011-01-01       1   0     1   1        0        6           0           1   \n",
       "2  2011-01-01       1   0     1   2        0        6           0           1   \n",
       "3  2011-01-01       1   0     1   3        0        6           0           1   \n",
       "4  2011-01-01       1   0     1   4        0        6           0           1   \n",
       "\n",
       "   temp   atemp   hum  windspeed  casual  registered  cnt  \n",
       "0  0.24  0.2879  0.81        0.0       3          13   16  \n",
       "1  0.22  0.2727  0.80        0.0       8          32   40  \n",
       "2  0.22  0.2727  0.80        0.0       5          27   32  \n",
       "3  0.24  0.2879  0.75        0.0       3          10   13  \n",
       "4  0.24  0.2879  0.75        0.0       0           1    1  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=[\"instant\"], inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Menggabungkan kolom `dteday` dan `hr` menjadi `datetime`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
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       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
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       "      <th>cnt</th>\n",
       "      <th>datetime</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>2011-01-01 00:00:00</td>\n",
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       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  hr  holiday  weekday  workingday  weathersit  \\\n",
       "0  2011-01-01       1   0     1   0        0        6           0           1   \n",
       "1  2011-01-01       1   0     1   1        0        6           0           1   \n",
       "2  2011-01-01       1   0     1   2        0        6           0           1   \n",
       "3  2011-01-01       1   0     1   3        0        6           0           1   \n",
       "4  2011-01-01       1   0     1   4        0        6           0           1   \n",
       "\n",
       "   temp   atemp   hum  windspeed  casual  registered  cnt            datetime  \n",
       "0  0.24  0.2879  0.81        0.0       3          13   16 2011-01-01 00:00:00  \n",
       "1  0.22  0.2727  0.80        0.0       8          32   40 2011-01-01 01:00:00  \n",
       "2  0.22  0.2727  0.80        0.0       5          27   32 2011-01-01 02:00:00  \n",
       "3  0.24  0.2879  0.75        0.0       3          10   13 2011-01-01 03:00:00  \n",
       "4  0.24  0.2879  0.75        0.0       0           1    1 2011-01-01 04:00:00  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"datetime\"] = (\n",
    "    df[\"dteday\"] + \" \" + df[\"hr\"].astype(str).str.pad(2, \"left\", \"0\") + \":00:00\"\n",
    ")\n",
    "df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Series name: datetime\n",
      "Non-Null Count  Dtype         \n",
      "--------------  -----         \n",
      "17379 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1)\n",
      "memory usage: 135.9 KB\n"
     ]
    }
   ],
   "source": [
    "df[\"datetime\"].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mapping data kategorikal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
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       "      <td>32</td>\n",
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       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  hr  holiday   weekday  workingday  \\\n",
       "0  2011-01-01  Spring   0     1   0        0  Saturday           0   \n",
       "1  2011-01-01  Spring   0     1   1        0  Saturday           0   \n",
       "2  2011-01-01  Spring   0     1   2        0  Saturday           0   \n",
       "3  2011-01-01  Spring   0     1   3        0  Saturday           0   \n",
       "4  2011-01-01  Spring   0     1   4        0  Saturday           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "0           1  0.24  0.2879  0.81        0.0       3          13   16   \n",
       "1           1  0.22  0.2727  0.80        0.0       8          32   40   \n",
       "2           1  0.22  0.2727  0.80        0.0       5          27   32   \n",
       "3           1  0.24  0.2879  0.75        0.0       3          10   13   \n",
       "4           1  0.24  0.2879  0.75        0.0       0           1    1   \n",
       "\n",
       "             datetime  \n",
       "0 2011-01-01 00:00:00  \n",
       "1 2011-01-01 01:00:00  \n",
       "2 2011-01-01 02:00:00  \n",
       "3 2011-01-01 03:00:00  \n",
       "4 2011-01-01 04:00:00  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons = [\"Spring\", \"Summer\", \"Fall\", \"Winter\"]\n",
    "df[\"season\"] = df[\"season\"].replace(dict(enumerate(seasons, start=1)))\n",
    "df[\"season\"] = pd.Categorical(df[\"season\"], seasons, ordered=True)\n",
    "\n",
    "days = [\"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\"]\n",
    "df[\"weekday\"] = df[\"weekday\"].replace(dict(enumerate(days)))\n",
    "df[\"weekday\"] = pd.Categorical(df[\"weekday\"], days)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 17379 entries, 0 to 17378\n",
      "Data columns (total 17 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   dteday      17379 non-null  object        \n",
      " 1   season      17379 non-null  category      \n",
      " 2   yr          17379 non-null  int64         \n",
      " 3   mnth        17379 non-null  int64         \n",
      " 4   hr          17379 non-null  int64         \n",
      " 5   holiday     17379 non-null  int64         \n",
      " 6   weekday     17379 non-null  category      \n",
      " 7   workingday  17379 non-null  int64         \n",
      " 8   weathersit  17379 non-null  int64         \n",
      " 9   temp        17379 non-null  float64       \n",
      " 10  atemp       17379 non-null  float64       \n",
      " 11  hum         17379 non-null  float64       \n",
      " 12  windspeed   17379 non-null  float64       \n",
      " 13  casual      17379 non-null  int64         \n",
      " 14  registered  17379 non-null  int64         \n",
      " 15  cnt         17379 non-null  int64         \n",
      " 16  datetime    17379 non-null  datetime64[ns]\n",
      "dtypes: category(2), datetime64[ns](1), float64(4), int64(9), object(1)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mengubah data temperatur ke non-normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.02</td>\n",
       "      <td>13.635</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>Spring</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.84</td>\n",
       "      <td>14.395</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  hr  holiday   weekday  workingday  \\\n",
       "0  2011-01-01  Spring   0     1   0        0  Saturday           0   \n",
       "1  2011-01-01  Spring   0     1   1        0  Saturday           0   \n",
       "2  2011-01-01  Spring   0     1   2        0  Saturday           0   \n",
       "3  2011-01-01  Spring   0     1   3        0  Saturday           0   \n",
       "4  2011-01-01  Spring   0     1   4        0  Saturday           0   \n",
       "\n",
       "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \\\n",
       "0           1  9.84  14.395  0.81        0.0       3          13   16   \n",
       "1           1  9.02  13.635  0.80        0.0       8          32   40   \n",
       "2           1  9.02  13.635  0.80        0.0       5          27   32   \n",
       "3           1  9.84  14.395  0.75        0.0       3          10   13   \n",
       "4           1  9.84  14.395  0.75        0.0       0           1    1   \n",
       "\n",
       "             datetime  \n",
       "0 2011-01-01 00:00:00  \n",
       "1 2011-01-01 01:00:00  \n",
       "2 2011-01-01 02:00:00  \n",
       "3 2011-01-01 03:00:00  \n",
       "4 2011-01-01 04:00:00  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"temp\"] = df[\"temp\"] * 41\n",
    "df[\"atemp\"] = df[\"atemp\"] * 50\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gp-Y6wU38DC1"
   },
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumlah per jam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01 00:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 01:00:00</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 02:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 03:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01 04:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     casual  registered  cnt\n",
       "datetime                                    \n",
       "2011-01-01 00:00:00       3          13   16\n",
       "2011-01-01 01:00:00       8          32   40\n",
       "2011-01-01 02:00:00       5          27   32\n",
       "2011-01-01 03:00:00       3          10   13\n",
       "2011-01-01 04:00:00       0           1    1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_hour_df = df[[\"datetime\", \"casual\", \"registered\", \"cnt\"]]\n",
    "per_hour_df.set_index(\"datetime\", inplace=True)\n",
    "per_hour_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "      <td>189.463088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "      <td>181.387599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>142.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>977.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             casual    registered           cnt\n",
       "count  17379.000000  17379.000000  17379.000000\n",
       "mean      35.676218    153.786869    189.463088\n",
       "std       49.305030    151.357286    181.387599\n",
       "min        0.000000      0.000000      1.000000\n",
       "25%        4.000000     34.000000     40.000000\n",
       "50%       17.000000    115.000000    142.000000\n",
       "75%       48.000000    220.000000    281.000000\n",
       "max      367.000000    886.000000    977.000000"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_hour_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "per_hour_df.boxplot(vert=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumlah per hari"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "id": "e9CQCZjk8DC2"
   },
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-02</th>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-05</th>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casual  registered   cnt\n",
       "datetime                            \n",
       "2011-01-01     331         654   985\n",
       "2011-01-02     131         670   801\n",
       "2011-01-03     120        1229  1349\n",
       "2011-01-04     108        1454  1562\n",
       "2011-01-05      82        1518  1600"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_agg = {\n",
    "    \"casual\": \"sum\",\n",
    "    \"registered\": \"sum\",\n",
    "    \"cnt\": \"sum\",\n",
    "}\n",
    "\n",
    "sum_per_date_df = df.resample(rule=\"D\", on=\"datetime\").agg(sum_agg)\n",
    "sum_per_date_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casual   registered          cnt\n",
       "count   731.000000   731.000000   731.000000\n",
       "mean    848.176471  3656.172367  4504.348837\n",
       "std     686.622488  1560.256377  1937.211452\n",
       "min       2.000000    20.000000    22.000000\n",
       "25%     315.500000  2497.000000  3152.000000\n",
       "50%     713.000000  3662.000000  4548.000000\n",
       "75%    1096.000000  4776.500000  5956.000000\n",
       "max    3410.000000  6946.000000  8714.000000"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_per_date_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_per_date_df.boxplot(vert=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumlah per bulan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-01-31</th>\n",
       "      <td>3073</td>\n",
       "      <td>35116</td>\n",
       "      <td>38189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-28</th>\n",
       "      <td>6242</td>\n",
       "      <td>41973</td>\n",
       "      <td>48215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>12826</td>\n",
       "      <td>51219</td>\n",
       "      <td>64045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-30</th>\n",
       "      <td>22346</td>\n",
       "      <td>72524</td>\n",
       "      <td>94870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-31</th>\n",
       "      <td>31050</td>\n",
       "      <td>104771</td>\n",
       "      <td>135821</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casual  registered     cnt\n",
       "datetime                              \n",
       "2011-01-31    3073       35116   38189\n",
       "2011-02-28    6242       41973   48215\n",
       "2011-03-31   12826       51219   64045\n",
       "2011-04-30   22346       72524   94870\n",
       "2011-05-31   31050      104771  135821"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_per_month_df = df.resample(rule=\"ME\", on=\"datetime\").agg(sum_agg)\n",
    "sum_per_month_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25834.041667</td>\n",
       "      <td>111360.916667</td>\n",
       "      <td>137194.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13528.543135</td>\n",
       "      <td>40136.833621</td>\n",
       "      <td>52267.138331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3073.000000</td>\n",
       "      <td>35116.000000</td>\n",
       "      <td>38189.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13140.250000</td>\n",
       "      <td>87474.500000</td>\n",
       "      <td>100811.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27693.500000</td>\n",
       "      <td>106369.000000</td>\n",
       "      <td>136256.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36953.000000</td>\n",
       "      <td>139733.500000</td>\n",
       "      <td>179634.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44235.000000</td>\n",
       "      <td>174795.000000</td>\n",
       "      <td>218573.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             casual     registered            cnt\n",
       "count     24.000000      24.000000      24.000000\n",
       "mean   25834.041667  111360.916667  137194.958333\n",
       "std    13528.543135   40136.833621   52267.138331\n",
       "min     3073.000000   35116.000000   38189.000000\n",
       "25%    13140.250000   87474.500000  100811.250000\n",
       "50%    27693.500000  106369.000000  136256.000000\n",
       "75%    36953.000000  139733.500000  179634.250000\n",
       "max    44235.000000  174795.000000  218573.000000"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_per_month_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_per_month_df.boxplot(vert=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumlah per tahun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-12-31</th>\n",
       "      <td>247252</td>\n",
       "      <td>995851</td>\n",
       "      <td>1243103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31</th>\n",
       "      <td>372765</td>\n",
       "      <td>1676811</td>\n",
       "      <td>2049576</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            casual  registered      cnt\n",
       "datetime                               \n",
       "2011-12-31  247252      995851  1243103\n",
       "2012-12-31  372765     1676811  2049576"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_per_year_df = df.resample(rule=\"YE\", on=\"datetime\").agg(sum_agg)\n",
    "sum_per_year_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>310008.500000</td>\n",
       "      <td>1.336331e+06</td>\n",
       "      <td>1.646340e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>88751.093427</td>\n",
       "      <td>4.815114e+05</td>\n",
       "      <td>5.702625e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>247252.000000</td>\n",
       "      <td>9.958510e+05</td>\n",
       "      <td>1.243103e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>278630.250000</td>\n",
       "      <td>1.166091e+06</td>\n",
       "      <td>1.444721e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>310008.500000</td>\n",
       "      <td>1.336331e+06</td>\n",
       "      <td>1.646340e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>341386.750000</td>\n",
       "      <td>1.506571e+06</td>\n",
       "      <td>1.847958e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>372765.000000</td>\n",
       "      <td>1.676811e+06</td>\n",
       "      <td>2.049576e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              casual    registered           cnt\n",
       "count       2.000000  2.000000e+00  2.000000e+00\n",
       "mean   310008.500000  1.336331e+06  1.646340e+06\n",
       "std     88751.093427  4.815114e+05  5.702625e+05\n",
       "min    247252.000000  9.958510e+05  1.243103e+06\n",
       "25%    278630.250000  1.166091e+06  1.444721e+06\n",
       "50%    310008.500000  1.336331e+06  1.646340e+06\n",
       "75%    341386.750000  1.506571e+06  1.847958e+06\n",
       "max    372765.000000  1.676811e+06  2.049576e+06"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_per_year_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insight\n",
    "- Dari nilai diatas, dapat dilihat bahwa jumlah transaksi sangatlah volatile untuk per jam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zsyZjqak8DC2"
   },
   "source": [
    "## Visualization & Explanatory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertanyaan 1: Bagaimana petumbuhan transaksi pada tahun 2012 dibandingkan 2011?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_per_year_df.index = pd.to_datetime(sum_per_year_df.index).strftime(\"%Y\")\n",
    "\n",
    "sum_per_year_df[[\"casual\", \"registered\"]].plot.bar()\n",
    "plt.ticklabel_format(axis=\"y\", style=\"plain\")\n",
    "plt.legend([\"Casual\", \"Registered\"])\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari grafik diatas dapat dilihat bahwa terdapat pertumbuhan transaksi pada tahun 2012 dari tahun 2011."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DgHI7CiU8DC2"
   },
   "source": [
    "### Pertanyaan 2: Bagaimana jumlah bulanan transaksi rental sepeda?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "Go0lCsvO8DC2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y6n6XVV/2CzKOX75OVkni6Q99P4IB34FPNTi2FT7tAcsmgS3d7OikCBTZYsQiIiIiIvl25iD8PhZ2zDf2/YPh5regyS0X2+RVRr2ka9ILgjvCr0/A9nnw12uwYwHc/jFUbWB2dFKIlGSJiIiIiHlsaRD1ISx9E9LOgdUVwkZCt2fA3Sdre6sLhHQt/jgLi09VuPsb2PgD/PYMHF5rDIu88VVo/yBYNdCsLFCSJSIiIiLm2B8F88cYw+cAaneGW9+B6k3NjauoWSzQcgDU7QK/jIC9S+D3p43erb4fgX8tsyOUq6RUWURERESKV9JJI7n4MsJIsLyrwG0fwf2/lf0E61L+teC+n+GW/4GrF8QshY86w4b/M0rXS6mlJEuKxPTp06lYsaLZYeRbaYtXRESkVLLbYd1X8EFbWP+NcazNIBi5Flrfa/TwlDdWK3R4CIb9A7XaQ0o8zBkGP9wHSScutrPbIGa5sTBzzHJjX0osJVklQTG/aYYMGYLFYsFiseDm5kZISAjPPPMMycnJhfYcd999Nzt37sxXWyU4IiIi5cDRLUbP1dzH4PxpqH4NPPAn9HkfvCubHZ35qjaA+xfADePA6mYMHfywI2ybB1vnwuRmMONW+HGocT+5mXFcSiTNyTLb1rmwYCwkHLl4zC8IIt4s0lKkERERfPnll6SlpREdHc3gwYOxWCy8+eabhfL4Xl5eeHl5Fcpj5ZfNZsNisWDVhFEREZGSIyURlk6EqI/AYQM3H+jxHHQcBi5uZkdXsri4QtcnoeFN8NMjcGwL/HBv9m0TYmHmoJJfvr6c0qdRM22da7w5Lk2w4OKbpgi/nfDw8CAwMJDg4GD69u1Lz549WbhwIQB2u50JEyYQEhKCl5cXLVu2ZPbs2Zmunzt3Lg0bNsTT05MePXowY8YMLBYLZ86cAbL2Tv3777/06NGDChUq4OfnR9u2bVm7di1Llizh/vvvJz4+3tm7Nn78eABSUlJ46qmnqFmzJj4+PnTs2JElS5Y4HzPjOebOnUtoaCgeHh4cOHAgz+syrq1duzbe3t7cfvvtnDx5srB/xCIiIuWbwwHbfjV6YyLfNxKspr1h5Gro/JgSrNwENoeH/4bOj+fS6MKcrQXPauhgCaSerMLkcBilR/PDboPfn8H5Bsn8QIDF6OGq1z1/az+4eV/xOObNmzcTGRlJnTp1AJgwYQLffPMN06ZNo2HDhixbtoz77ruPatWq0a1bN2JiYujfvz9PPPEEDz74IOvXr+epp57K9TnuvfdeWrduzdSpU3FxcWHDhg24ubnRuXNnJk+ezLhx49ixYwcAvr6+AIwcOZKtW7fy/fffExQUxM8//0xERASbNm2iYcOGAJw7d44333yTzz77jCpVqlC9evU8r1u1ahVDhw5lwoQJ9O3blwULFvDyyy9f0c9OREREsnF6v/E5Z+cCY79ibaO4Q6Nwc+MqTVw9jB6tyPdyaeSAhMPGumGluax9GaQkqzClnYM3ggrpwRxGD9fE4Pw1f/5I9mtJ5GDevHn4+vqSnp5OSkoKVquVDz74gJSUFN544w0WLVpEWFgYAPXq1eOff/7h448/plu3bnz88cc0btyYSZMmAdC4cWM2b97M66+/nuPzHThwgKeffpomTZoAOJMkAH9/fywWC4GBgZnaf/nllxw4cICgIONn+tRTT7FgwQK+/PJL3njjDQDS0tL46KOPaNmyZb6vmzJlChERETzzzDMANGrUiMjISBYsWJDvn5+IiEi5ZrdlvyBweipEvQ9LJ0H6eWNu0bWPQ9enwN3b7KhLn8SjhdtOio2SrHKqR48eTJ06laSkJN59911cXV3p168fW7Zs4dy5c9x4442Z2qemptK6dWsAduzYQfv27TOd79ChQ67PN2bMGB588EG+/vprevbsyZ133kn9+vVzbL9p0yZsNhuNGjXKdDwlJYUqVao4993d3WnRokWBrtu2bRu33357pvNhYWFKskRERPIjp/nkbYfAph/hhDEyhbpdodfbUK2xKWGWCb4BhdtOio2SrMLk5m30KOXH/kj4tn/e7e6dbXw7lJ/nLgAfHx8aNGgAwBdffEHLli35/PPPadasGQDz58+nZs2ama7x8PAo0HNcavz48dxzzz3Mnz+f33//nZdffpnvv/8+S7KTITExERcXF6Kjo3FxyTxcMmM4IRgFNiyXDJPM73UiIiJyBTLmk18+3SHhCPxtjDLBuyqEvw4t7i6fJdkLU53ORgKbEEv2U0wsxvn8fFaUYqUkqzBZLPkfslf/+vy9aepfn785WVfBarXy/PPPM2bMGHbu3OksINGtW7ds2zdu3Jjffvst07E1a9bk+TyNGjWiUaNGjB49moEDB/Lll19y++234+7ujs2WecJm69atsdlsHDt2jK5d8z/GOD/XNW3alFWrVmU6tnLlynw/h4iISLlktxk9WNl+brnAzQdGrAKfqsUWVplmdTEqTs8cBFjI9mcfMbHIPytKwam6oFky3jSA8aa51IX9YnzT3Hnnnbi4uPDxxx/z1FNPMXr0aGbMmMGePXtYt24d77//PjNmzADgkUceYfv27YwdO5adO3cyc+ZMpk+fbkSezTdW58+fZ+TIkSxZsoT9+/ezYsUK1qxZQ9OmxorudevWJTExkcWLF3PixAnOnTtHo0aNuPfeexk0aBA//fQTMTExrF69mgkTJjB//vwcX0d+rnv88cdZsGAB//vf/9i1axcffPCBhgqKiIjkxG6DUzFGAYbLKyJfLi0Jjm0rnrjKi9A+Rpl2vxqZj1td4c4ZKt9eQqkny0wZb5ps18maWKxvGldXV0aOHMlbb71FTEwM1apVY8KECezdu5eKFSvSpk0bnn/+eQBCQkKYPXs2Tz75JFOmTCEsLIwXXniB4cOHZzuk0MXFhZMnTzJo0CCOHj1K1apVueOOO3jllVcA6Ny5M8OGDePuu+/m5MmTvPzyy4wfP54vv/yS1157jSeffJLDhw9TtWpVOnXqxK233prra8nruk6dOvHpp5/y8ssvM27cOHr27MmLL77If//730L+qYqIiBShnIpPXKmUs3Bil3E7uQtO7LywvQdsKfl/HBVhKHyhfaBJL+Pf+3QM/PaMUVjErXjXJJX8szgcjlz6fMu3hIQE/P39iY+Px8/PL9O55ORkYmJiCAkJwdPT8+qeqLB/SZrg9ddfZ9q0aRw8eNDsUIpMof6bi4iIXI2cik9EvJn7l7R2OyQcuphAnbiQTJ3cDWdjc77OxQMqBMKZ/XnHNnieyokXtT9egKgPjOIiQ+aZHU25kVtucDn1ZJUEVpdS98voo48+on379lSpUoUVK1YwadIkRo4caXZYIiIiZV+OxSdijeN3fWXM6T65+7Jeqd3GsfTzOT+2T3Wo2giqNjDuqzSEqg2Nda4AJjdTEYaSoNNwWDUN9i2HI+shqLXZEclllGTJFdm1axevvfYap06donbt2jz55JM899xzZoclIiJStuVafOLCsVlDwGHL5vwFVjeoUt9Inqo0vJBUNYQqDcCrYu7Pn2MRhuKfT16u+deCa+6ATTMh8gPo/7nZEcllNFwwF8U2XFBKBf2bi4iI6WKWw4zc5yY7eVfNoVeqDrhcxffs2Q5VrFns88nLvdiN8HFXsLjAExsu9jZKkdFwQREREZGyKD9zogBunQLthhRNDJcWYSjF88lLvRotoF532LsEVk6FiAlmRySXUAl3ERERkZLOlgZrPoM/Xsxf+yr1izaejPnkzfsb90qwzNH5MeM+egacP21uLJKJkqyrZLfbzQ5Bion+rUVEpNjZ7bBpNnzQHuY/CcmnjeFhObIYQ/dUfKJ8qH8DVA811ieLnm52NHIJDRe8Qu7u7litVo4cOUK1atVwd3fPdiFeKf0cDgepqakcP34cq9WKu7u72SGJiEhZ53DAnsWw6BWI22gc86kO3Z4B7yow+4GMhpdcpOIT5Y7FYvRmzRkOK6dBpxHgqs8pJYGSrCtktVoJCQkhNjaWI0fyWP1cygRvb29q166N1aoOYBERKUKH1sKi8UZ5bgD3CnDtE0bZbg9f45jVNYd1slR8otxp1h8Wv2qsc7Z5NrS6x+yIBFUXzFV+Kog4HA7S09Ox2XIplSqlnouLC66uruqtFBGRonN8J/z1Kmz71dh3cYcOD0OXMeBTJWt7u03FJ8Twz7tGYl49FIZHGj1cUuhUXbAYWSwW3NzccHNzMzsUERERKY3iD8PSibD+G3DYwWKFlvdA92ehYnDO12UUnxBpez8s+x8c2wq7F0PDnmZHVO4pyRIRERExw7lTRg/E6k8gPdk41rgX3PASVG9qbmxSunhVhDaDYeWHEPmekqwSQEmWiIiISHFKTYJV0+CfKZASbxyrcy30HA/BHUwNTUqxTsOM/1cxSyH2X6jR0uyIyjXN4BcREREpDrY0WPM5vNfaKFSQEg8BzeCeWTBkvhIsuToVa8M1txvbkR+YG4soyRIREREpUnY7bP4RPuwA88cYhSoq1oE7PoVHlkOjm1SoQApH55HG/eYfIf6QubGUcxouKCIiInI1cqry53DAnr9g8SvG8C0An2pw3TPQdojWM5LCF9Qa6nY1yv+vnArhr5sdUbmlJEtERETkSm2dm/16VR0eNhKsmGXGMfcKxqKxYSMurnUlUhSufcJIsqJnGItXe/qbHVG5pCRLRERE5EpsnQszBwGXLTmacMRYswiMta7aPwhdnwSfqsUdoZRHDXpCtSZwfDtETzeSLil2mpMlIiIiUlB2m9GDdXmCdSk3bxixGiImKMGS4mOxGL2mACunQXqqufGUU0qyRERERApqf2TmIYLZSTun4gNijuZ3GvMDzx6BLT+ZHU25VOAka9myZfTu3ZugoCAsFgtz5szJdH7IkCFYLJZMt4iIiExtTp06xb333oufnx8VK1Zk6NChJCYmZmqzceNGunbtiqenJ8HBwbz11ltZYpk1axZNmjTB09OT5s2b89tvv2U673A4GDduHDVq1MDLy4uePXuya9eugr5kERERkcwSjxZuO5HC5OoBHR8xtiPfN4qwSLEqcJKVlJREy5Yt+fDDD3NsExERQWxsrPP2f//3f5nO33vvvWzZsoWFCxcyb948li1bxsMPP+w8n5CQwE033USdOnWIjo5m0qRJjB8/nk8++cTZJjIykoEDBzJ06FDWr19P37596du3L5s3b3a2eeutt3jvvfeYNm0aq1atwsfHh/DwcJKTkwv6skVEREQu8g0o3HYiha3dA+DmA0c3w96/zY6m3LE4HFee2losFn7++Wf69u3rPDZkyBDOnDmTpYcrw7Zt2wgNDWXNmjW0a9cOgAULFnDLLbdw6NAhgoKCmDp1Ki+88AJxcXG4uxvlTZ999lnmzJnD9u3bAbj77rtJSkpi3rx5zsfu1KkTrVq1Ytq0aTgcDoKCgnjyySd56qmnAIiPjycgIIDp06czYMCALLGlpKSQkpLi3E9ISCA4OJj4+Hj8/Pyu9MckIiIiZc3BNfD5jeQ8J8tiVBkctcko5y5iht+fhVVToV4PGDTH7GhKvYSEBPz9/fOVGxTJnKwlS5ZQvXp1GjduzPDhwzl58qTzXFRUFBUrVnQmWAA9e/bEarWyatUqZ5vrrrvOmWABhIeHs2PHDk6fPu1s07Nnz0zPGx4eTlRUFAAxMTHExcVlauPv70/Hjh2dbS43YcIE/P39nbfg4OCr/EmIiIhImRO3Gb7tz8UE6/KFhC/sR0xUgiXm6jQcLFajJytuk9nRlCuFnmRFRETw1VdfsXjxYt58802WLl3KzTffjM1mAyAuLo7q1atnusbV1ZXKlSsTFxfnbBMQkLl7PWM/rzaXnr/0uuzaXO65554jPj7eeTt48GCBX7+IiIiUYcd3wFe3QfIZqNUe7vgE/GpkbuMXBHd9BaF9TAlRxKlSHQjta2xHfmBqKOVNoa+TdekwvObNm9OiRQvq16/PkiVLuOGGGwr76QqVh4cHHh4eZochIiIiJdHJPTCjD5w7ATVawr2zwasiNOtvVBtMPGrMwarTWT1YUnJ0fsyoMLh5NtwwDvxrmh1RuVDkJdzr1atH1apV2b17NwCBgYEcO3YsU5v09HROnTpFYGCgs83Ro5mr8WTs59Xm0vOXXpddGxEREZF8Ob3fSLAS46B6KPxnjpFggZFQhXSF5v2NeyVYUpLUbAN1uoA9HVZNMzuacqPIk6xDhw5x8uRJatQwutLDwsI4c+YM0dHRzjZ//fUXdrudjh07OtssW7aMtLQ0Z5uFCxfSuHFjKlWq5GyzePHiTM+1cOFCwsLCAAgJCSEwMDBTm4SEBFatWuVsIyIiIpKnhCPwVR9IOARVGsKgX8C7stlRieRfxuLE0dMhOcHUUMqLAidZiYmJbNiwgQ0bNgBGgYkNGzZw4MABEhMTefrpp1m5ciX79u1j8eLF3HbbbTRo0IDw8HAAmjZtSkREBA899BCrV69mxYoVjBw5kgEDBhAUFATAPffcg7u7O0OHDmXLli388MMPTJkyhTFjxjjjeOKJJ1iwYAFvv/0227dvZ/z48axdu5aRI0cCRuXDUaNG8dprrzF37lw2bdrEoEGDCAoKylQNUURERCRHiceMHqzT+6BSXRg8F3yr53WVSMnS8Cao2ghSEmDdDLOjKR8cBfT33387MMrpZLoNHjzYce7cOcdNN93kqFatmsPNzc1Rp04dx0MPPeSIi4vL9BgnT550DBw40OHr6+vw8/Nz3H///Y6zZ89mavPvv/86unTp4vDw8HDUrFnTMXHixCyxzJw509GoUSOHu7u745prrnHMnz8/03m73e546aWXHAEBAQ4PDw/HDTfc4NixY0e+X2t8fLwDcMTHxxfgJyQiIiJlQuIJh+PDTg7Hy34OxzvXOByn95sdkciVi55h/F9+u6nDkZ5qdjSlUkFyg6taJ6usK0gtfBERESlDzp+BGb0hbiP4BsL9v0GV+mZHJXLl0pJhcnNIOgZ3fAot7jI7olLH9HWyREREREqtlLPGOlhxG8G7qjFEUAmWlHZuntDxYWM78j1QP0uRUpIlIiIikiH1HHx3NxxaA16VjCIX1RqbHZVI4Wg3FNy8jYWJY5aaHU2ZpiRLREREBIzhVN8PhP0rwMMP/vMzBDYzOyqRwuNdGVrfZ2xHvm9uLGWckiwRERGR9FSYOQj2LgE3H2Oh4aDWZkclUvg6PQoWK+xeBEe3mB1NmaUkS0RERMo3Wzr8OBR2/QGunnDPD1C7o9lRiRSNyiHQtI+xHfmBubGUYUqyREREpPyy22DOMNg2F1zcYcB3ENLV7KhEilbG4sSbZhmLbUuhU5IlIiIi5ZPdDr8+bnzQtLrCXV9BgxvMjkqk6NVqB7U7gz0NVn1sdjRlkpIsERERKX8cDvjtKVj/jTE/pd9n0Phms6MSKT4ZvVlrvzSWLZBCpSRLREREyheHA/58EdZ+Dlig7zS45nazoxIpXo0ioEoDSImHdV+bHU2ZoyRLREREype/XoOoCxP+e0+BlnebG4+IGaxWCBtpbK/8CGxp5sZTxijJEhERkfJj2SRY/j9j++ZJ0HawufGImKnlAPCuCvEHYesvZkdTpijJEhERkfIh8gOjFwvgxleh48PmxiNiNjcv6PiIsR35njGUVgqFkiwREREp+1Z/Cn++YGx3fx6ufcLceERKinZDwdULYv+FfcvNjqbMUJIlIiIiZdu6r41KggBdRkO3Z8yNR6Qk8akCre81tiPfNzeWMkRJloiIiJRdG2fB3AulqjsOhxteBovF3JhESppOjwIW2PUnHNtmdjRlgpIsERERKZu2/gI/PwI4oN0DEDFBCZZIdqrUh6a3GtsZlTflqijJEhERkdLPboOY5bBptnG//XeYPRQcNmh1L9zythIskdx0fty43zgTzsaZG0sZ4Gp2ACIiIiJXZetcWDAWEo5kPdesH/R531gTSERyFtwBgjvBwZWw6mPo+bLZEZVq+o0jIiIipdfWuTBzUPYJFkCT3mB1Kd6YREqrzhfmL679HFISzY2llFOSJSIiIqWT3Wb0YJHT2j4Wo2y73VacUYmUXo1vhsr1ITke1n9jdjSlmpIsERERKZ12Lcy5BwsAByQchv2RxRaSSKlmdYGwEcb2yg/Blm5uPKWY5mSJiIhI4bDbjIQm8Sj4BkCdzoUzVC81CY5vN0pLH9t2cTvhcP6uTzx69TGIlBctB8Lfr8OZA7BtLjS7w+yISiUlWSIiInL1sis+4RcEEW9CaJ/8PUbaeTixE45th2NbLyZTZ/ZfXWy+AVd3vUh54u4N7R+CpRMh8j245nZV5rwCSrJERETk6mQUn7h8blRCrHH8rq8yJ1rpqXByd+ZE6tg2OB0DDnv2z+FTHao3geqhUO3CfZUG8HEX43mynZdlMRK9Op0L6YWKlBMdHoIVk+HIeti/Aup2MTuiUkdJloiIiFy5XItPXDj26xNwdDMc32EkU6f2gD2HuR5elS5JpJoat2pNwadK9u0j3ryQ4Fkui+HCN+8RE1VdUKSgfKpCq3tg7RcQ+b6SrCtgcTgcOZXkKfcSEhLw9/cnPj4ePz8/s8MREREpeWKWw4xbC36dh9+FBOqyZMq3esGHJmU7VLGmkWDld6iiiGR2Yjd80A5wQN+p4OJeuHMtS6GC5AbqyRIREZErl9+iEnWuNcpDZyRTfkGFN88jtA806VU0RTdEyquqDaBmGzgcDXOGXzxe0LmW5ZSSLBEREbly+S0q0f05COladHFYXYr28UXKm61zjQTrcjnNtZRMtE6WiIiIXLk6nY1vtsmpV8piDN1T8QmR0sM51zI7F2YaLXhWC33nQkmWiIiIXDmrizF0KFsqPiFSKu2P1ELfV0lJloiIiFyd0D7Q7oGsx/2CNKRIpDTK71xLLfSdI83JEhERkasXu8G4b3u/Ue5ZxSdESq/8zrXUQt85UpIlIiIiVyduszFB3uoGPV4A32pmRyQiVyNjrmWOC32juZZ50HBBERERuTrrZhj3TW5RgiVSFmSaa5lDUZvrX1JPdS6UZImIiMiVSzsPG38wttsMNjcWESk8oX2MOZV+NTIft1xIrHb+Do4cerlEwwVFRETkKmz9BZLjwb821OthdjQiUpiyW+jb1QO+vNl476/7Ctrqy5XsKMkSERGRK7fuK+O+zX/AqgEyImVOdgt9X/8SLHrZWCurdhhUa2RObCWYfhuKiIjIlTmxC/avAIsVWt1rdjQiUlw6Pw71ukPaOfjxAUhPMTuiEkdJloiIiFyZjIIXDW8C/5rmxiIixcdqhds/Bu8qELcJFo03O6ISp8BJ1rJly+jduzdBQUFYLBbmzJnjPJeWlsbYsWNp3rw5Pj4+BAUFMWjQII4cybxidN26dbFYLJluEydOzNRm48aNdO3aFU9PT4KDg3nrrbeyxDJr1iyaNGmCp6cnzZs357fffst03uFwMG7cOGrUqIGXlxc9e/Zk165dBX3JIiIicrn0VNjwf8a2Cl6IlD8VAuG2j4ztlR/BroXmxlPCFDjJSkpKomXLlnz44YdZzp07d45169bx0ksvsW7dOn766Sd27NhBnz5ZV3p/9dVXiY2Ndd4ee+wx57mEhARuuukm6tSpQ3R0NJMmTWL8+PF88sknzjaRkZEMHDiQoUOHsn79evr27Uvfvn3ZvHmzs81bb73Fe++9x7Rp01i1ahU+Pj6Eh4eTnJxc0JctIiIil9oxH86dgAo1jJ4sESl/GkdAh0eM7Z+Hwdmj5sZTglgcjiuvvWixWPj555/p27dvjm3WrFlDhw4d2L9/P7Vr1waMnqxRo0YxatSobK+ZOnUqL7zwAnFxcbi7uwPw7LPPMmfOHLZv3w7A3XffTVJSEvPmzXNe16lTJ1q1asW0adNwOBwEBQXx5JNP8tRTTwEQHx9PQEAA06dPZ8CAAXm+voSEBPz9/YmPj8fPzy8/PxIREZHy4au+sPdv6PoU3PCS2dGIiFnSkuGzG+DoZqh/Pdz7Y5ktglOQ3KDIfwLx8fFYLBYqVqyY6fjEiROpUqUKrVu3ZtKkSaSnpzvPRUVFcd111zkTLIDw8HB27NjB6dOnnW169uyZ6THDw8OJiooCICYmhri4uExt/P396dixo7PN5VJSUkhISMh0ExERkcuc3mckWGBUFRSR8svNE/p9Dq5esOcvWJl1tFt5VKRJVnJyMmPHjmXgwIGZsr3HH3+c77//nr///ptHHnmEN954g2eeecZ5Pi4ujoCAgEyPlbEfFxeXa5tLz196XXZtLjdhwgT8/f2dt+Dg4Ct52SIiImXbuq+N+3o9oFJdU0MRkRKgehOIeMPYXvQKHFlvbjwlQJElWWlpadx11104HA6mTp2a6dyYMWPo3r07LVq0YNiwYbz99tu8//77pKSYW/7xueeeIz4+3nk7ePCgqfGIiIiUOLZ02PCtsa1FSEUkQ9v7oWlvsKfB7KGQkmh2RKYqkiQrI8Hav38/CxcuzHPMYseOHUlPT2ffvn0ABAYGcvRo5olzGfuBgYG5trn0/KXXZdfmch4eHvj5+WW6iYiIyCV2L4SzsUbp5sa3mB2NiJQUFgv0fg/8asKpPfD7WLMjMlWhJ1kZCdauXbtYtGgRVapUyfOaDRs2YLVaqV69OgBhYWEsW7aMtLQ0Z5uFCxfSuHFjKlWq5GyzePHiTI+zcOFCwsLCAAgJCSEwMDBTm4SEBFatWuVsIyIiIgUUfWFtrJYDwdXD3FhEpGTxrgx3fAJYYMM3sPlHsyMyTYGTrMTERDZs2MCGDRsAo8DEhg0bOHDgAGlpafTv35+1a9fy7bffYrPZiIuLIy4ujtTUVMAoWDF58mT+/fdf9u7dy7fffsvo0aO57777nAnUPffcg7u7O0OHDmXLli388MMPTJkyhTFjxjjjeOKJJ1iwYAFvv/0227dvZ/z48axdu5aRI0cCRuXDUaNG8dprrzF37lw2bdrEoEGDCAoKyrUaooiIiOQg4Qjs+sPY1tpYIpKdul3gOqOyN7+OgtP7TQ3HNI4C+vvvvx1AltvgwYMdMTEx2Z4DHH///bfD4XA4oqOjHR07dnT4+/s7PD09HU2bNnW88cYbjuTk5EzP8++//zq6dOni8PDwcNSsWdMxceLELLHMnDnT0ahRI4e7u7vjmmuuccyfPz/Tebvd7njppZccAQEBDg8PD8cNN9zg2LFjR75fa3x8vANwxMfHF/THJCIiUvYsecvheNnP4fg8wuxIRKQkS09zOD7tafy++LSnsV8GFCQ3uKp1sso6rZMlIiJygd0O77WEMwfg9o+hZd7rTYpIOXZ6H0zrCikJcN0zcP0LZkd01UrUOlkiIiJSBuz920iwPP0h9DazoxGRkq5SXbj1XWN7+f9g3wpTwyluSrJEREQkb+suFLxocTe4eZkbi4iUDs37Q8t7wGGHnx6Cc6fMjqjYKMkSERGR3CUeh+2/GdsqeCEiBXHLW1C5HiQchl8fh3IyU0lJloiIiOTu3++MBUZrtoXAZmZHIyKliUcF6Pc5WN1g268QPd3siIqFkiwRERHJmcMB674yttsMMjcWESmdaraBG14ythc8B8e2mxtPMVCSJSIiIjnbHwknd4ObDzTrZ3Y0IlJahT0G9XpA+nn4cSikJZsdUZFSkiUiIiI5yyh40byfMexHRORKWK1w+zTwrgJHN8Oil82OqEgpyRIREZHsnT8NW38xttsMMTUUESkDKgRC36nG9qppsPMPc+MpQkqyREREJHsbZ0J6MgQ0M+ZUiIhcrUbh0HGYsT3nUTgbZ248RURJloiIiGTlcED0haGCbQaDxWJuPCJSdvR8BQKaw7kT8PMwsNvNjqjQKckSERGRrA5Hw7Et4OoJLe40OxoRKUvcPKH/5+DqBXv/hqgPzI6o0CnJEhERkawy1rIJ7QtelcyMRETKomqNIWKCsb34VTiy3tx4CpmSLBEREcks5Sxs/snYbjvY3FhEpOxqOwSa9jYWO589FFISzY6o0CjJEinv7DaIWQ6bZhv3dpvZEYmI2Tb/CGlJUKUh1A4zOxoRKassFuj9HvjVhFN74PdnzI6o0LiaHYCImGjrXFgwFhKOXDzmFwQRb0JoH/PiEhFzOQteDFLBCxEpWt6V4Y5PYcatsOFbqH89NO9vdlRXTT1ZIuXV1rkwc1DmBAsgIdY4vnWuOXGJiLniNsGRdWB1g1b3mB2NiJQHda+Frk8Z2/NGw+l9poZTGJRkiZRHdpvRg4Ujm5MXji14VkMHRcqjjF6sJr3Ap6q5sYhI+dFtLAR3hJQE+PEhsKWbHdFVUZIlUh7tj8zag5WJAxIOG+1EpPxIPWcsQAwqeCEixcvF1Rg26OEPh1bD0olmR3RVlGSJlEcnduavXeLRoo1DREqWrb9ASjxUrAMh3c2ORkTKm0p1oPe7xvay/8HepaW2OJcKX4iUJ6lJEPkB/PNO/tqX8q56ESmgdRkFL/4DVn0PKyImaNYPdv8FG76Br/uCw37xXCkqzqXfoCLlgS3dmGfxXhtY8gakJxuT2vMyZzjMfQziDxV9jCJiruM74EAUWFyg1X1mRyMi5Vm9bsb9pQkWlKriXEqyRMoyhwN2LIBp18Kvj0NinDEMqP8X0P9zwHLhdqkL+zVaAXZY9xW81xp+fxYSjxdr+CJSjNZ9Zdw3Cge/GubGIiLll90Gi17O4WTpKc6lJEukrDq8Dmb0hv+7G45vB69KEDERRq4xuuJDb4O7vsr6YcovCO76Gh5ZCg/8CXW6gC0VVk2FKS1h8X/h/BlTXpKIFJH0FNjwnbHdZpC5sYhI+VZGinNpTpZIWXN6n5EIbZ5t7Lt4QKfh0GU0eFXM3Da0j1GmeX+kUeTCNwDqdAari3G+dkcYMg/2/g2LX4Uj62H5/2DNp3DtKOj4CLj7FOOLK0R2W86vW6S82T4fzp+CCjWgwY1mRyMi5Vl+i26V8OJcSrJEyopzp2D527D6E6PnCQu0HAA9XoCKwTlfZ3WBkK45n7dYjNXX6/WA7fPgr9fh+DZY/AqsnArXPW2Uenb1KPSXVGS2zjXWCbv0m7JSNJlWpNBlFLxofZ9RRllExCy+AYXbziQWh8OR3WqkAiQkJODv7098fDx+fn5mhyOSvbRkI7Fa/j9IjjeO1esON74KNVoW/vPZbUYp1b9fhzP7jWP+taH7s9Di7pL/AW3rXGPSbJaFmC/MRbvrKyVaUr6cioH3WgEWeOJfo4SyiIhZ7DaY3MwocpHlbzWAxfhidNSmYh+BUpDcQHOyREoru91YNPSD9rDwJSPBqn4N3PcjDPqlaBIsMH6htbwbRq6FXu+AbyDEH4BfHoWpYbBljhFbSWS3GT1Y2f7SLj2TaUUK1fqvjfv6PZRgiYj5rC7GyBIgx+JcERNL/BB/JVkipdHepfBpd/jpISPBqRAEt30Ew5ZDg57FE4OrO7QfCo+vhxv/axTWOLETZg02Ytu1yKhuWJLELC8Tk2lFCo0tHdZ/a2y3GWxuLCIiGUL75FKcq3SMOCnh43pEJJOjW2Dhy7B7obHvXgG6joZOj4KblzkxuXvDtY9D2yEQ9SFEfQCx/8K3/aB2Z7jhJaOoRHFLS4ZjW41Y4jYa97Eb83dtCZ9MK1Jodv1hLO3gXRUa32J2NCIiF+VVnKuEU5IlUhLkVeku4YgxB2rDd8bCfFZXaP+gUXTCp6p5cV/K0w96PAcdHoZ/3oHVn8KBSPjyZqNa2fUvQlCrzNcUVoW/lLMQt8lIojKSquPbwZ5+Za/l1F5jyKNVnf1SxkVfKHjR6h6jd1pEpCTJqzhXCabCF7lQ4QspFrlVuqvXHVZMMXqI0s8b50L7wg3joEp9M6LNv/jDsGySMd8jI9kJ7WtUO6zW6Mor/CWdhLh/L/ZMxf5rJEXZzbPyrgKBLYz5aTVaQEBz+Pq2XCbTXqJqY+gyCprfCS5uBXvtIqVB/GFjcrnDDiOjoWoDsyMSESnRCpIbKMnKhZIsKXK5VrpzGMMBU88ah4I7wU2vQXD7Yg7yKp3cA0smwqZZgAMsVmOB433LybXCX9PeRgJ2+XC/hEPZP49fTSOZujSp8qtplKC/lPNnzmXPf6Fd097GnLeUC5Ua/WpB58eMBVrdva/4xyBS4ix5E5a8AXWuhft/MzsaEZEST0lWIVGSJUXKWaI0t0IMQOX6Rjn2Jr2yJgylydGtxpDH7fPybuviYSxyfP5U9ucr1zeSqEuTqoIMm8y2F62mUa0otA8kJ8DaL2DlRxfnZ3lXgY7DoMNDRpEPkdLMboMpLSH+INz+iVExVEREcqUkq5AoyZIiFbMcZtyad7v/zDFKK5cVqz+D357MX1uLC1RrcrFnqkZLCGhmzP+6WvmZD5aWDP9+ZwzZPL3POObuaxT5CBuZteqRSGmxexF80w88/eHJHeYVzhERKUUKkhuo8IWIWfJbwe7cyaKNo7h5Vcxfux4vGMP0iurDX34m07p5QrsHoPUg2DoH/pkMRzcZFRRXfwItB8C1o0r+/DiRy2UUvGgxQAmWiEgRUOksEbP4BhRuu9Iiv6+ndljJ+fDn4grN+xvrkN072yhNb0uFdV/B+21h5mA4ssHsKEXyJ/EY7LgwB6ut1sYSESkKSrJEzFKns1FNL8tq5hksxjwhM9aYKkql+XVbLNDwRnjgd3jgD2gUATiMXq5PusHXtxvDQDUKW0qyDd8ZFT9rtoOAa8yORkSkTFKSJWIWq4tRrjzbUuIXEpCIiaVm0b18c75uyJpolaLXXbsT3PMDDI+E5ncZ88f2/GXMs/usJ2yfb6y1dTm7zUjENs027u224o9dyi+Hw+iBBfViiYgUIRW+yIUKX0ix+LgbxG7IfOzSSndlVV4V/kqb0/sg8n1Y/w2kJxvHqjUx5mw172+stXWla4OJFJaMgjvuvkbBCw9fsyMSESk1VF2wkCjJkiIXtxmmXQtY4PaPjd6bnCrdlUX5qfBX2iQeg5VTYc1nkJJgHPMPNhaWXv8Nua4NpkRLitqPDxpr1rUdAr2nmB2NiEipUpDcoMDDBZctW0bv3r0JCgrCYrEwZ86cTOcdDgfjxo2jRo0aeHl50bNnT3bt2pWpzalTp7j33nvx8/OjYsWKDB06lMTExExtNm7cSNeuXfH09CQ4OJi33norSyyzZs2iSZMmeHp60rx5c377LfNiivmJRcRU/7xr3F9zu7FOTfP+RsW70p5o5FdGhb+y9Lp9q0PPl2H0Zug5HnyqG2sRrf+a7IeGXji24FkNHZSide6U0ZsKxuLaIiJSZAqcZCUlJdGyZUs+/PDDbM+/9dZbvPfee0ybNo1Vq1bh4+NDeHg4ycnJzjb33nsvW7ZsYeHChcybN49ly5bx8MMPO88nJCRw0003UadOHaKjo5k0aRLjx4/nk08+cbaJjIxk4MCBDB06lPXr19O3b1/69u3L5s2bCxSLiGlO7oEtPxnbXceYG4sUPk9/6DIaRm2ETo/m0dgBCYeNXj2RorJxJthSIKA5BLUxOxoRkTLtqoYLWiwWfv75Z/r27QsYPUdBQUE8+eSTPPXUUwDEx8cTEBDA9OnTGTBgANu2bSM0NJQ1a9bQrl07ABYsWMAtt9zCoUOHCAoKYurUqbzwwgvExcXh7u4OwLPPPsucOXPYvn07AHfffTdJSUnMmzfPGU+nTp1o1aoV06ZNy1csedFwQSlScx+HdTOg4U1w7yyzo5GitGk2/Dg073YBzYyFp6s1hepNoVpjcPcp+vik7HM4YGpnOLYVbvkfdHjI7IhEREqdIh0umJuYmBji4uLo2bOn85i/vz8dO3YkKioKgKioKCpWrOhMsAB69uyJ1Wpl1apVzjbXXXedM8ECCA8PZ8eOHZw+fdrZ5tLnyWiT8Tz5ieVyKSkpJCQkZLqJFImEI/Dv/xnbXZ80NxYpevldG+zoZqN4xi+Pwqc94I2aMLkFfHc3LHwZ/v0BYv+FtPNXFocqG5Zfh9YaCZarFzS/0+xoRETKPNfCfLC4uDgAAgIyf6AICAhwnouLi6N69eqZg3B1pXLlypnahISEZHmMjHOVKlUiLi4uz+fJK5bLTZgwgVdeeSV/L1bkakR9aCxmW7uzUQpcyraMtcESYsmxZL9PVeg2Fk7shGPb4Ph2SDoOZ/Ybt50LLmluhUp1L/Z4VW9qVDKs2hBcPbKPQZUNy7d10437a/qCV0UTAxERKR8KNckq7Z577jnGjLk4NyYhIYHg4GATI5Iy6dwpWPulsa1erPIhY22wmYMwqglemmhdqC7Y652syU7SiYsJ16X350/Bqb3Gbcf8Sx7KBSrXy5x4VW8Kx7bD7PvJkuAlxBoxqbJh2ZacAJsvzP9so7WxRESKQ6EmWYGBgQAcPXqUGjVqOI8fPXqUVq1aOdscO3Ys03Xp6emcOnXKeX1gYCBHjx7N1CZjP682l57PK5bLeXh44OGRw7fAIoVl1ceQlgSBLaDBDWZHI8UltI+RzGTbm5TD2mA+VY2qiyFdLx5zOIwy8ce3GcnTpffJ8XByl3HbNjcfQTkAi1HZsEmvslHdUbLaPBvSzkHVxuo5FxEpJoWaZIWEhBAYGMjixYudiUxCQgKrVq1i+PDhAISFhXHmzBmio6Np27YtAH/99Rd2u52OHTs627zwwgukpaXh5uYGwMKFC2ncuDGVKlVytlm8eDGjRo1yPv/ChQsJCwvLdywixS7lLKyaZmx3HQMWi7nxSPEK7WMkM1ezNpjFAhUCjFu97hePOxxwNjZrz1fcZkjPbQ7XJZUNL03mpOyInmHctxmk3zkiIsWkwElWYmIiu3fvdu7HxMSwYcMGKleuTO3atRk1ahSvvfYaDRs2JCQkhJdeeomgoCBnBcKmTZsSERHBQw89xLRp00hLS2PkyJEMGDCAoKAgAO655x5eeeUVhg4dytixY9m8eTNTpkzh3XffdT7vE088Qbdu3Xj77bfp1asX33//PWvXrnWWebdYLHnGIlLsoqdD8hmo0gCaanhWuZSxNlhhs1iMXjG/oMw9pBtnwU8P5n194tG820jpE/svxG4Aqxu0HGh2NCIi5UaBk6y1a9fSo0cP537GHKbBgwczffp0nnnmGZKSknj44Yc5c+YMXbp0YcGCBXh6ejqv+fbbbxk5ciQ33HADVquVfv368d577znP+/v78+effzJixAjatm1L1apVGTduXKa1tDp37sx3333Hiy++yPPPP0/Dhg2ZM2cOzZo1c7bJTywixSY9BSI/MLavHaWhWVI8KgTmr11+KyBK6ZLRi9X0VvCpYm4sIiLlyFWtk1XWaZ0sKVRrv4R5o8CvJjy+AVzd87pC5OrZbTC5WS6VDS8IGwE9XwUX1UMqM1LPwduNISUB/jPHWINNRESumGnrZIlIDmzpsGKKsd35MSVYUnwyKhsCzkqGTpfsR30I39xuVDSU0i1jPbQ/XzASLP/aENLN7KhERMoVJVkixWHrHDgdA16VjcnnIsUpo7KhX43Mx/2C4K6v4c4Z4OYDMcvgk+5weJ0pYUoh2DrX6LmccSus/cI4lnwats8zNy4RkXJG40JEiprDAcvfMbY7PQruPubGI+VTXpUNqzWBH+6Fk7vhiwi49R1ofZ+5MUvBbJ17YS22y4aFpiRqPTQRkWKmniyRorbzDzi2Bdx9oUM+qryJFJWMyobN+xv3lxZfqd4EHvoLGt8CthT4ZQTMGw3pqebFK/lntxlrsGU77+7CsQXPGu1ERKTIKckSKUoOByx/29huPxS8Kpkbj0huPP3h7m+hx4uAxRhuNv2WC0UzpETbH5l5kessLlkPTUREipySLJGitH8FHFoNLh7QaYTZ0YjkzWqFbk/DPTONpOvQGvj4On04L8nSkmHjD/lrq/XQRESKhZIskaKUMRer9X1QQesQSSnS6CZ4eAlUvwaSjsGM3rDqY6N3VkqG82eMnvLJzWH91/m7RuuhiYgUCyVZIkXlyHrYsxgsLnDt42ZHI1JwlevBgwuhWT+wp8Pvz8DPw4z1l8Q8CUfgzxfh3Waw+FUjCa5QEzz8yVqmP4PFWKOvTufijFREpNxSdUGRovLPu8Z98/5Qqa6poYhcMXcf6Pc51GwLf74EG783Crnc/Y3+Xxe34ztgxXvG0EB7mnGseihc+4SRCO/4/UJ1QQuZC2BcSLwiJmYudiIiIkXG4nBo7EdOCrKqs0gmx3fChx0ABzy6Eqo3NTsikasXsxxmDYFzJ4wiLv0+hwY3mB1V2XdwNfwzGXbMv3iszrVGctXwJrBc0nu1da5RZfDSIhh+NY0ES+XbRUSuSkFyAyVZuVCSJVdszqOw4Vto3AsGfmd2NCKFJ/4wzPwPHI4GLHDDS9BlTOYP+nL17HbY9SesmAwHoi4ctBhrnV07CoLb53KtLef10ERE5IoVJDfQcEGRwnbm4MVKX13HmBuLSGHzrwlDfoPfn4Z1Xxlzgg6vg9ungUcFs6Mr/dJTYfNsY1jg8W3GMasbtBwAnR+Hao3yfoyM9dBERMQ0SrJEClvk+0aRgJDroFY7s6MRKXxuntDnfWOe1m9Pw/Z58On1xhpb+UkCJKuURFg3A6I+NNazAnCvAO3uh06Pgl8Nc+MTEZECUZIlUpgSjxvf7gN0fdLcWESKWtshENAMfvgPnNhpJFq3T4Omt5odWcmR19C9xOOwahqs+QySzxjHfAOg03Bo94CxVpmIiJQ6SrJECtOqqZB+HoLaQEg3s6MRKXq12sEjS42CGPtXwA/3Gl8w9HhB84CyLUIRBBFvQmAzo9d7w3eQnmycq1zfWO6hxQCjt1BEREotFb7IhQpfSIEkx8O7zSEl3hg2pW/zpTyxpcHCcbDyI2O//g3Q7zPwrmxuXGbZOvdCOfWc/sReUma9ZlujmEWTXkpMRURKMBW+EDHDms+NBKtaE2h8i9nRiBQvFzeImGD04s59zFiI+5PuMOBbCGxevire2W1GD1aOCRbGufo9oetooxy7qjOKiJQpSrJECkPa+Yvf4HcZDVarufGImKXFnVC9CXx/L5zZD5/daMzd2vZL9sPmyuLaTfsjM7/WnHQZBXW7FHk4IiJS/PRJUKQwrP8Gko6Df21o1s/saETMFdgcHl4CDXoacxRXTc2adCTEGsPpts41JcQilXi0cNuJiEipoyRL5GrZ0ow1bcCYtO7iZm48IiWBd2UY8H/g7ptDgwtD6RY8awyvKyvSzsOev/PX1jegaGMRERHTKMkSuVqbZkP8AfCpBq3vMzsakZLj4CpITcylgcNYE2p/ZLGFVGQcDtj6C3zYATZ8k0djC/jVNOaliYhImaQkS+Rq2O3wz7vGdtgIcPMyNx6RkqS8DJs7ugVm9DaGP545ABWCjAWEsVy4XerCfsTEslv4Q0REVPhC5KrsmA8ndoCHP7QbanY0IiVLfofDbf4RaoeBf82ijaewnTsFf78Baz8Hhx1cPODaJ4yCFu4+xmvKdp2siWWz4IeIiDgpyRK5Ug4HLH/H2O7wEHhqLTWRTOp0NpKKhFhyLWe+4zfYvQjaDIauY4xrSjJbOkR/CX+/DudPG8ea9oGbXoNKdS62C+1jrH1VXkrXi4iIk5IskSu1dwkcWQeuXtBpuNnRiJQ8VhejTPvMQWRafBdwDpvr/hzELIX9K2DNp7DuK6Pke5fR4Fej+GPOS8wy+P1ZOLbF2K8eavRM1euWfXurC4R0Lb74RESkRNCcLJEr9c+FXqy2g8GnqrmxiJRUoX3grq+yJkx+Qcbx7mNhyHwY/CvU7gy2FFj9MUxpCb+PhbNx5sR9udP74Yf/GHOvjm0Br0pwy//gkeU5J1giIlJuWRwOR25L0pdrCQkJ+Pv7Ex8fj5+fhoLJJQ6thc9uAKsrPL4BKgabHZFIyWa35T1szuEwerX+ngAHVxrHXD2h3QNw7SioYELJ89Qk+GcyRL4H6clgsRrzL3s8b5SpFxGRcqMguYGGC4pciYy5WC0GKMESyY/8DJuzWKBedwjpZgzHXTLBKAO/8iNY+4WR3HQZBb7Viz5eh8MoyLFwnFFmHqBuV7j5TQi4puifX0RESjX1ZOVCPVmSraNbYWoYYIGRa6BqQ7MjEimbHA7Y85eRbB1aYxxz9YL2Q42eLd9qRfO8sf8aQxUPRBn7/rUh/DWjuIXl8pLsIiJSXqgnS6QoZayLFdpHCZZIUbJYoMENUP962LPYGEZ4eC1EfWD0bLUfCp2fKLxkK+kELH7VKL6BA9y8ocsY6DxSa+CJiEiBqCcrF+rJkixOxcD7bcFhg4eXQlArsyMSKT8cDqPU+99vGJU9wUiEOjwEnR+/8gI0tjRY/SksmQgp8caxZv3hxldL39pdIiJSZNSTJVJUIt8zEqz6NyjBEiluFgs0vBEa9IRdC2HJG3BkPayYAqs/uyTZqpL5utyKbuxeDAueMxYVBwhsATe/BXXCive1iYhImaKerFyoJ0syORsHk1sYJaaH/AZ1rzU7IpHyzeGAnX8Yc7ZiNxjH3H2hw8PQ+TGj+t/WubBgLCQcuXidXxBcO9qY77Xzd+OYd1W4YRy0vk+LBYuISLYKkhsoycqFkizJ5M+XjJ6s4I7wwB+aAC9SUjgcsHOBMYwwbqNxzN3XmMu17VcyL4J8GasrdHgEuj0DXhWLI1oRESmlNFxQpLCdP21MtAfo+qQSLJGSxGKBxjdDowjY8ZvRsxW3CbbNzf06Fw94eAkEhBZLmCIiUn5YzQ5ApFRY/SmkJkJAM2h4k9nRiEh2LBZo0gseWQ49Xsi7vS0Fzp0s+rhERKTcUZIlkpfUJFg51djuMlq9WCIlncUClevlr23i0aKNRUREyiUlWSJ5iZ4B509BpRAI7Wt2NCKSH74BhdtORESkAJRkieQmPRUi3ze2u4wCF01jFCkV6nQ2qgiSU8+zBfxqGu1EREQKmZIskdxs/B7OHoEKNaDlQLOjEZH8srpAxJsXdi5PtC7sR0xUuXYRESkShZ5k1a1bF4vFkuU2YsQIALp3757l3LBhwzI9xoEDB+jVqxfe3t5Ur16dp59+mvT09ExtlixZQps2bfDw8KBBgwZMnz49SywffvghdevWxdPTk44dO7J69erCfrlSltlt8M9kYztsJLh6mBqOiBRQaB+46yvwq5H5uF+QcTy0jzlxiYhImVfoY5/WrFmDzWZz7m/evJkbb7yRO++803nsoYce4tVXX3Xue3t7O7dtNhu9evUiMDCQyMhIYmNjGTRoEG5ubrzxxhsAxMTE0KtXL4YNG8a3337L4sWLefDBB6lRowbh4eEA/PDDD4wZM4Zp06bRsWNHJk+eTHh4ODt27KB69eqF/bKlLNr6C5zaA16VoO0Qs6MRkSsR2seoOLg/0ihy4RtgDBFUD5aIiBShIl+MeNSoUcybN49du3ZhsVjo3r07rVq1YvLkydm2//3337n11ls5cuQIAQHGhORp06YxduxYjh8/jru7O2PHjmX+/Pls3rzZed2AAQM4c+YMCxYsAKBjx460b9+eDz74AAC73U5wcDCPPfYYzz77bL5i12LE5ZjDAR93Ndba6f4cdM/f/xkRERERKZsKkhsU6Zys1NRUvvnmGx544AEsl5S9/vbbb6latSrNmjXjueee49y5c85zUVFRNG/e3JlgAYSHh5OQkMCWLVucbXr27JnpucLDw4mKinI+b3R0dKY2VquVnj17OttkJyUlhYSEhEw3Kad2LzISLDcf6PCw2dGIiIiISClSpKXS5syZw5kzZxgyZIjz2D333EOdOnUICgpi48aNjB07lh07dvDTTz8BEBcXlynBApz7cXFxubZJSEjg/PnznD59GpvNlm2b7du35xjvhAkTeOWVV6749UopZ7ddHFK0/B3jWLv7wbuyuXGJiIiISKlSpEnW559/zs0330xQUJDz2MMPX+wVaN68OTVq1OCGG25gz5491K9fvyjDydNzzz3HmDFjnPsJCQkEBwebGJEUm61zYcFYSDiS+XjVRubEIyIiIiKlVpElWfv372fRokXOHqqcdOzYEYDdu3dTv359AgMDs1QBPHr0KACBgYHO+4xjl7bx8/PDy8sLFxcXXFxcsm2T8RjZ8fDwwMNDFeTKna1zYeYgIJvpib8+YRS+UBUyEREREcmnIpuT9eWXX1K9enV69eqVa7sNGzYAUKOGUWI3LCyMTZs2cezYMWebhQsX4ufnR2hoqLPN4sWLMz3OwoULCQsLA8Dd3Z22bdtmamO321m8eLGzjQhgDBFcMJZsE6wMC5412omIiIiI5EORJFl2u50vv/ySwYMH4+p6sbNsz549/Pe//yU6Opp9+/Yxd+5cBg0axHXXXUeLFi0AuOmmmwgNDeU///kP//77L3/88QcvvvgiI0aMcPYyDRs2jL179/LMM8+wfft2PvroI2bOnMno0aOdzzVmzBg+/fRTZsyYwbZt2xg+fDhJSUncf//9RfGSpbTaH5l1iGAmDkg4bLQTEREREcmHIhkuuGjRIg4cOMADDzyQ6bi7uzuLFi1i8uTJJCUlERwcTL9+/XjxxRedbVxcXJg3bx7Dhw8nLCwMHx8fBg8enGldrZCQEObPn8/o0aOZMmUKtWrV4rPPPnOukQVw9913c/z4ccaNG0dcXBytWrViwYIFWYphSDmXeDTvNgVpJyIiIiLlXpGvk1WaaZ2sciBmOcy4Ne92g+dBSNeij0dERERESqQSs06WSIlXpzP4BeXSwAJ+NY12IiIiIiL5oCRLyjerCzSKyOHkhQW0IyYa7URERERE8kFJlpRvJ/fAvz8Y2x6Xdfv6BcFdX6l8u4iIiIgUSJEuRixSotnS4edhkJYEdbvCfT/DwZVGkQvfAGOIoHqwRERERKSAlGRJ+bXiXTi02ujB6jsVXN1U3EJERERErpqGC0r5dGQ9LJlobN/yP6gYbG48IiIiIlJmKMmS8iftPPz0MNjTIbQvtLjL7IhEREREpAxRkiXlz6LxcGIn+AbCre+CxWJ2RCIiIiJShijJkvJlz1+wapqx3fdD8K5sbjwiIiIiUuYoyZLy49wpmPOosd3+IWjQ09x4RERERKRMUpIl5cdvT8HZWKjSAG581exoRERERKSMUpIl5cOm2bD5R7C4wB2fgLu32RGJiIiISBmlJEvKvvhDMH+Msd1tLNRsa248IiIiIlKmKcmSss1uN+ZhJccbyVXXJ82OSERERETKOCVZUrat/hhiloKbN9z+Cbi4mh2RiIiIiJRxSrKk7Dq2HRa+bGzf9BpUbWBuPCIiIiJSLijJkrIpPRV+eghsKdDgRmj3gNkRiYiIiEg5oSRLyqalEyFuI3hVhts+AIvF7IhEREREpJxQkiVlz4GV8M+7xnbvKVAh0Nx4RERERKRcUZIlZUvKWfj5EXDYoeU9ENrH7IhEREREpJxRkiVlyx/Pw+l94F8bbp5odjQiIiIiUg4pyZKyY/tvsO4rwAK3TwVPf7MjEhEREZFySEmWlA2Jx2HuY8Z258egbhdz4xERERGRcktJlpR+Dgf8+jicOwHVr4HrXzQ7IhEREREpx5RkSem3/mvY8Ru4uMMdn4Crh9kRiYiIiEg5piRLSrdTe+H3Z43t61+CwGbmxiMiIiIi5Z6SLCm97Db4eRikJUGdLhA2wuyIRERERESUZEkptmIyHFwF7hWMaoJWF7MjEhERERFRkiWlVOy/8PcbxvYtk6BibXPjERERERG5QEmWlD5p5+Gnh8GeDk37QMsBZkckIiIiIuKkJEtKn8WvwvHt4BsAt04Gi8XsiEREREREnJRkSemydwms/MjYvu1D8KliajgiIiIiIpdTkiWlx/nTMOdRY7vdUGh4o7nxiIiIiIhkQ0mWlB6/PQ0Jh6Fyfbjpv2ZHIyIiIiKSLSVZUjpsmg2bZoHFBe74BNx9zI5IRERERCRbSrKk5Es4AvPHGNvXPQ212pkbj4iIiIhILpRkSclmtxvzsJLjIagNXPeU2RGJiIiIiOTK1ewARLKw22B/JCQehYOrYe/f4OplDBN0cTM7OhERERGRXCnJkpJl61xYMNYYInipFndB1YbmxCQiIiIiUgAaLiglx9a5MHNQ1gQLYN1XxnkRERERkRKu0JOs8ePHY7FYMt2aNGniPJ+cnMyIESOoUqUKvr6+9OvXj6NHj2Z6jAMHDtCrVy+8vb2pXr06Tz/9NOnp6ZnaLFmyhDZt2uDh4UGDBg2YPn16llg+/PBD6tati6enJx07dmT16tWF/XKlsNhtRg8WjpzbLHjWaCciIiIiUoIVSU/WNddcQ2xsrPP2zz//OM+NHj2aX3/9lVmzZrF06VKOHDnCHXfc4Txvs9no1asXqampREZGMmPGDKZPn864ceOcbWJiYujVqxc9evRgw4YNjBo1igcffJA//vjD2eaHH35gzJgxvPzyy6xbt46WLVsSHh7OsWPHiuIly9XaH5l9D5aTw1gja39ksYUkIiIiInIlLA6HI5eug4IbP348c+bMYcOGDVnOxcfHU61aNb777jv69+8PwPbt22natClRUVF06tSJ33//nVtvvZUjR44QEBAAwLRp0xg7dizHjx/H3d2dsWPHMn/+fDZv3ux87AEDBnDmzBkWLFgAQMeOHWnfvj0ffPABAHa7neDgYB577DGeffbZfL2WhIQE/P39iY+Px8/P72p+LJKXDf8Hc4bl3a7f59C8f9HHIyIiIiJyiYLkBkXSk7Vr1y6CgoKoV68e9957LwcOHAAgOjqatLQ0evbs6WzbpEkTateuTVRUFABRUVE0b97cmWABhIeHk5CQwJYtW5xtLn2MjDYZj5Gamkp0dHSmNlarlZ49ezrbZCclJYWEhIRMNykGuxfB4lfy19Y3IO82IiIiIiImKvQkq2PHjkyfPp0FCxYwdepUYmJi6Nq1K2fPniUuLg53d3cqVqyY6ZqAgADi4uIAiIuLy5RgZZzPOJdbm4SEBM6fP8+JEyew2WzZtsl4jOxMmDABf39/5y04OPiKfgaST/GH4If/wDf94GwsWHL772gBv5pQp3OxhSciIiIiciUKvYT7zTff7Nxu0aIFHTt2pE6dOsycORMvL6/CfrpC9dxzzzFmzBjnfkJCghKtopCeCis/hKVvQdo5sLhAx2FQoyX8/MiFRpeOYrUYdxETwepS3NGKiIiIiBRIka+TVbFiRRo1asTu3bu58cYbSU1N5cyZM5l6s44ePUpgYCAAgYGBWaoAZlQfvLTN5RUJjx49ip+fH15eXri4uODi4pJtm4zHyI6HhwceHh5X/FolH/Yuhd+eghM7jf3aYXDL/yCwmbHv5pV1nSy/ICPBCu1T/PGKiIiIiBRQka+TlZiYyJ49e6hRowZt27bFzc2NxYsXO8/v2LGDAwcOEBYWBkBYWBibNm3KVAVw4cKF+Pn5ERoa6mxz6WNktMl4DHd3d9q2bZupjd1uZ/Hixc42UswSYmH2A/BVHyPB8qkGfafB/b9fTLDASKRGbYbB84wiF4PnwahNSrBEREREpNQo9J6sp556it69e1OnTh2OHDnCyy+/jIuLCwMHDsTf35+hQ4cyZswYKleujJ+fH4899hhhYWF06tQJgJtuuonQ0FD+85//8NZbbxEXF8eLL77IiBEjnL1Mw4YN44MPPuCZZ57hgQce4K+//mLmzJnMnz/fGceYMWMYPHgw7dq1o0OHDkyePJmkpCTuv//+wn7JkhtbGqz6GJZMgNREY95V+wehxwvgVTH7a6wuENK1WMMUERERESkshZ5kHTp0iIEDB3Ly5EmqVatGly5dWLlyJdWqVQPg3XffxWq10q9fP1JSUggPD+ejjz5yXu/i4sK8efMYPnw4YWFh+Pj4MHjwYF599VVnm5CQEObPn8/o0aOZMmUKtWrV4rPPPiM8PNzZ5u677+b48eOMGzeOuLg4WrVqxYIFC7IUw5AitG+FMTTw2FZjv1Z7Y2hgUCtTwxIRERERKUqFvk5WWaJ1sq5Q4jH48yXY+L2x71UZbnwFWt0H1iIfoSoiIiIiUugKkhsUeeELKUds6bD2c/jrNUhJACzQdgjcMA68K5sdnYiISJGw2R2sjjnFsbPJVK/gSYeQyrhYLWaHJSImUpIlhePgapg/BuI2Gfs1WsGt70DNtqaGJSIiUpQWbI7llV+3Ehuf7DxWw9+Tl3uHEtGshomRSVFQQi35pSRLrk7SCVj0Mqz/xtj3rGj0XLUdojWtRESkTFuwOZbh36zj8nkXcfHJDP9mHVPva1OkiVZ5/cBv1utWQi0FoSRLrozdBtHTYfGrkHzGONb6Puj5CvhUNTMyERGRImezO3jl161ZEiwAB2ABXvl1KzeGBhZJAlBeP/Cb9brNTqil9FHhi1yU68IXdhvsj4TEo+AbAHU6X+yZOhwN85+EI+uN/YDm0OttqN3RvHhFRESKUdSekwz8dGWe7UKq+hBU0RN/Lzf8PN3w83LDz9PV2Hcec8103sPVisWSc2KW0wf+jCvK6gf+4n7dDoeDlHQ7icnp3PLeco6dTcm2nQUI9Pfkn7HXl4uexPJMhS/k6mydCwvGQsKRi8f8guD6l4y5V9HTAQd4+MH1L0K7oeCi/0oiIlL22e0Oovae5N2FO/LVPuZEEjEnkgr0HO4uViMB83J1Jl7+F5IzX09Xvlt5wLQetAzFPWQvr55DgBfnbKaChxupNjvn02ycT7VxPs1G8iXbWfftJF9yznk81UZyuo38dEU4gNj4ZFbHnCKsfpVCfNVSmumTsWS2dS7MHASX/xpLOAJzhl/cbzEAbnwVKmjdMRERKfsOnDzH7OiD/LjuMIfPnM/3dc+ENyKoojcJyWnEn0sjITmNhPPpxv75i/vx59M4m5yG3QGpNjsnElM4kZh9z0luMj7wD/wkitAgfwL8PAn09yCggicB/p4E+nni43F1H/+Kcsieze7gZFIKxxJSOH42hWNnkzmWkMLGQ/GZni87JxJTuffzVVf1/Ffj2Nnc45PyRUmWXGS3GT1Y2X5PdIHVFe77GepdV2xhiYiImCEpJZ3fNsUyO/oQq2JOOY9X8HSlV4saLNxylFNJqdn+1cwYQvZItwb57uGx2x0kpaaTkJx+SUKWZuyfN7Y3HDzN0p0n8nys1ftOs3rf6WzP+Xq4EuDnYSRgfp5U9/Mk0M+DQP+MbU+qVfDAzSXr2pZXOjcpJd12IWnKSKCSndvHMrbPpnAyMQX7VUxkCfDzoHoFT7zcXPB0d8HLzYqXmwte7i54urkY25fvu2d/73lhe8OB09z3+eo8n7uqr8eVBy5ljpIsuWh/ZOYhgtmxp0Mu48RFRERKM4fDGAY3K/oQv22K5VyqDTD+9HVpUJX+bWsRfk0gnm4udG9kJBwWMn89mfFX8uXeoQUaQme1Wqjg6UYFTzdqVvTKtk3UnpP5SrIGh9XBy92VYwnJxCUkczQhmaMJKSSmpBu34+nsOZ7zMEaLBar4eBDg50Ggn9ELVs3Xg+mR+3IdsvfM7I1EHzjNybOpFxInI4E6cy4tHz+BzM9dvYIH1f2M+9R0O3M25PEZBZh8d+tCH7IXVr8qNfw9iYtPzu1raN78fRuv3d6cFrUqFurzS+mkJEsuSjxauO1ERERKicNnzvNj9CFmRx/iwKlzzuN1q3jTv20t7mhTi6DLEp+IZjWYel+bLEPnAouw2l2HkMq5fuDP6EEb1/uabBO8xJR0I+GKT+bo2WTi4lMuJGAXE7GjCcmk2x3OIYtbjiTkO76E5HQ+XRaT7Tl3FyvVKnhQrcKlCZSnc7uaryfV/Tyo4uOO62W9aDa7g1Uxp/J83R1CKuc71vxysVp4uXdojgm1A/B0tbLxcAK3fbiCezrU5unwxlT0di/0WKT0UHXBXJSr6oIJsfD707Dt17zbDp4HIV2LPiYREZEidD7Vxh9b4pgdfYgVe044ixz4uLvQq0UN7mwXTLs6lXKt9AfFXwQiY8geZN+DdrVV9ux2B6fOpRIXn8yxSxKx1TEnidp7Ks/ruzWqRqd6VbIkUhW93fL8WeamqF93fp4/p7lobWpXYsLv2/l5/WEAKvu482xEE/q3rYVVFQfLjILkBkqyclEukqyEI/DPuxA9A2x5TbC1GFUGR23SQsMiIlIqORwO1h04w+zoQ8z79whnU9Kd5zrVq8ydbYO5uXkg3u4le7CPGetF5bds/f891KnIquyZvT5YXgn1yr0nGffLZnYeTQSgTe2K/LdvM64J8i/y2KToKckqJGU6yYo/ZCRX674CW6pxLLiTUdBi6aQLjbL5nuiuryC0T3FGKiIikqe8PvweTUjmp3WHmR19MNNcpJoVvejfthb929YiuLK3GaFfMTPKqHd58688h+wV9XpRxf26CyrNZmf6in1MXrSTpFQbVgsMCqvL6Bsb4e/lZnZ4chWUZBWSMplknTkI/7wD67+5mFzV7gzdx0JIN2O2abbrZNWEiIlKsEREpMTJqXfj+VuaYLVYmRV9kGU7jzur1nm6WbmlWQ36t6tFp5AqGs5VAGYP2StN4uKTeW3+VuZtjAWgqq87z93clDva1LyqYZNiHiVZhaRMJVmn919Irr4F+4UKP3W6QPdns59fZbcZ1QYTj4JvANTprCGCIiJS4uRUUjw77etWon/bWtzSvAYVPNWjcKXMHrJX2vyz6wTj5m5m74Ue1PZ1K/Hfvs1oEljKP1uWQ0qyCkmZSLJO74Plb8OG74zy6wB1uxrJVd0upoYmIiJyNTKGr+W2SK3VAo90q8dd7WoTUtWnGKMr20r6kL2SJjXdzmf/7OX9xbs5n2bDxWphSOe6jOrZUAl/KaIkq5CU6iTrVAws/x/8+/3F5Cqkm5Fc1elsbmwiF+iPtIhcjZJQiEGkIA6fOc9r87by++Y4AKpX8OCFXk3p0zJIQwhLgYLkBiW7dI4U3Mk9Rs/Vv9+Dw1hAkfrXQ7exULuTubGJXELDTUTkauyIO8s7C3fkq+2xszn3dIkUp5oVvZh6X1uW7DjG+Llb2HfyHE98v4HvVx/k1duuoWFABbNDlEKinqxclKierLzmSJ3YbfRcbZx5Mblq0NNIroI7mBOzSA5ymkOhidMikhuHw1iQ9uOle/h7x/F8X6eeLCmJktNsfLpsLx/8vZuUdDuuVgtDu4bw+PUN8fFQP0hJpOGChaTEJFnZVvsLgog3oVoTWDYJNs8Gh9041/AmI7mq1c6ceEVykdcciuIqASwipYfN7uDPLXFMW7aXfw+eAYy5VhHXBLIq5hSnklJNLSkucjUOnjrHK79uZdG2o4AxquOlW0O5uVmghhCWMBouWJZsnQszB8Hlfz4SjsDM/2Q+1igCuj0DNdsWW3giBbU65lSuk9QdQGx8MqtjTumbZ5FyLjnNxk/rDvPp8r3EnDAqs7m7WrmzbS0e6lqPulV9nD3jFrIvKf5y71AlWFKiBVf25rPB7Vi87Sjjf93CwVPnefTbdXRtWJVX+lxDvWq+gOYxlzbqycqF6T1ZdhtMbpa5Bys7jW421rkKal08cYlcgXOp6fy2KY6pS3ZnWgg0J1MGtOK2VjWLITIRKWniz6Xxzar9fLliHycSUwDw93JjUFgdBoXVpVoFj0ztNcdTyorkNBsfLdnDtKV7SE234+Zi4eHr6tEooAITf9+u/+Mm03DBQmJ6khWzHGbcmne7wfOyX+tKxGQOh4N1B04za+0h5m2MJTElPd/XRjQL4NU+zaju51mEEYpISXLkzHk+/yeG/1t9gHOpxvziIH9PHuxaj7vbB+c6T0Xf8ktZsu9EEuN/3cKSXOYeah5z8dNwwbIi8WjhthMpJscSkvlp/WFmrj3oXHwRoE4Vb/q1qck3Kw9w/GxKrouHLth8lL+2H+eeDrUZ1q0+gf5KtkTKqh1xZ/l42R7mbjhCut34zdAksAKPdKvHrS2CcHOx5vkYLlaLhhhLmVG3qg9fDmnPgs1xjPhuHfZs/mA6MBKtV37dyo2hgfpSoYRRklWS+QYUbjuRIpSabuev7ceYtfYgS3Yex3bhL4KXmwu3NK/BXe1q0SGkMhaLhUYBFXKdQ/HYDQ1Ysfsk0ftPMz1yH9+tPsDA9sEM795AyZZIGZFTpcCwelV4pFs9ujWqpkn/Uq5ZLBYqertnm2Bl0DzmkktJVklWp7NRRTAhliyFLwCwGOe1uLCYaEfcWWauPcic9Yc5mZTqPN62TiXualeLXi2C8L1siE9EsxpMva9NljkUgZeMLx/d08GK3SeZsngna/adZkbUfv5v9UHubh/M8O71CaroVWyvUUQKj83uYOHWOKYt3cuGC5UCLRa4uVkgD19Xn1bBFU2NT6Qkye8ab1oLruRRklWSWV2MMu0zB0FO3/lHTMy8XpZIMYg/n8bcf48we+1B/j0U7zxerYIH/drUon/bWjSo7pvrY0Q0q8GNoYE5zqGwWCx0aViVaxtUIWrPSSYv2sXqfaf4euV+flhzkLva1+LR7g2UbImUILnNi8pPpUARyax6hfyN3shvOyk+KnyRC9MLX2TIdp2smkaCFdrHvLik0JSGCdt2u4PIPSeZFX2QBZvjSEk31mVztVro2TSAO9vVolujarjmY+7ElXA4HETtPcmURbtYFXMKADcXC3e1C+bRHg2oqWRLxFQ5Vfh76qbGxCUk57tSoIhclLG2ZFx8co7zmP293Fj30o0l7nNDWaTqgoWkxCRZYJRz3x9pFLnwDTCGCKoHq0wwu/RwXgnewVPnmBV9iB+jD3H4zHnn8SaBFejftha3t65JFd/i/ZAUtccYRrhy78Vkq3/bYEb0qE+tSt7FGouI4FyrKq8PFEH+ngztWo8BeVQKFJGLMt5fkP3kEYAxNzbisesbaB5jEVOSVUhKVJIlZVJOH0yKqyxrTgneszc3we5wMHPNIaL2nnSe8/N05bZWNbmzXS2a1/Q3/Zf5qr0nmbJ4F5F7jBhdrRbubGcMIwyurGRLpDhkfNOe2yLjrlYLb/ZrTp9WNfNVKVBEMsvp73Xr2pX4bVMsAIPC6jC+9zVY1aNVZJRkFRIlWVKU8vPBpJqvBz880glvd1fcXa14uFpxd7XiarVcdYKT32+eLRbo0qAq/dvWIvyaQDzdSl4P6uqYU0xZvJMVuy8mW/3a1GJEjwbUrqJkS6QoRe05ycBPV+bZ7v8e6qTqZyJXIaeRJzMi9zH+1y04HHBrixq8c1cr3F31ZUZR0DpZIqXA6phTuSZYAMcTU7j+7aVZjlstXEi6XJyJl3GfeT/jfOY2VtxcrHwVtT/XBMvFAiOvb8id7WqV+CF4HUIq8+2DnVi77xRTFu9i+a4T/LD2ILPXHaJfm5qM7NEwS7JVGubBiZQGqn4mUjxyWgtucOe6VPJx58mZG5i3MZb482lMu6+thuSaTD99EZPk9wOHh4sFO5Bmu5gS2R2QnGYnOc1eRNGBzQGd6lUp8QnWpdrVrczXQzsSvf80UxbvYtnO48xce4gf1x3m9tY1GdmjAXWr+pg+D06kLFH1MxHz9WkZREUvN4Z9E83yXSe459OVfDGkfbHPmZaLNFwwFxouKEVl34kknpr1L2v3n86zbcYQG5vdQWq6ndR0OynpNlLS7RdutgvH7Jfd2zJtpzrb29kem8CyXSfyfO4pA1pxW6uahfGSTbHuwGmmLNrF0p3GQqcuVgvt61RmZczJLG2Lax6cSFljszu4duJi4hJSsj1vwVgD75+x16u3WKSIbTh4hvu/XM3pc2nUq+bDVw90KFVflpZ0Gi4oUkLFn0/jg792MT1yX6aeqexkfDDpEFIZMBIEL3cXvNxdALeriiNqz8l8JVml/ZvnNrUrMeOBDqw/cJr3Fu/i7x3Hs02wwKjYZAFe+XUrN4YG6sOgSD65WC20qFWRuK1Hs5zLeBe93DtU7ymRYtAquCKzhnVm8Ber2Xs8if5To/hqaAcaBVQwO7RyR7PiRIpBus3O1yv30+N/S/h0eQxpNgfdG1fj5d6hWLj4QSRDUX8w6RBSmRr+nlme99Lnr3FJglfata5diS/v78B/+zbLtZ0DiI1PZnUOiZiIZLX+wGkWbTMSrEremb8ACvT3VO+wSDFrUN2X2cPDaFjdl7iEZO6cFkX0/lNmh1XuqCdLpIgt23mc1+ZvZefRRMD45fdir6Z0b1wdMJKZy+cHBRbx/CAXq4WXe4cy/Jt1WMi87kZZ/ubZzzN/v/Lu/3INjWv4Ub+qD/Wr+1K/mg/1q/lSu4o3Hq5XV11RBTekLElOs/HUrH+xO+C2VkG8c1cr/f8WKQFq+Hsxa1gYD0xfw7oDZ7j3s1VMvbctPZpUNzu0ckNzsnKhOVlyNXYfS+SN37bx1/ZjgPEN7+gbG3FPh9q4XrZOjFkfvMtbAYj8lprOidUCtSt7U7+aL/UuJF5GEuZLZR/3PK8vbz9vKfsm/L6Nj5fupaqvBwtHX0elfLwPRKT4nE+18ei30fy94zguVguT+rfgjja1zA6r1NI6WYVESZZciTPnUpm8aBffrNxPut2Bq9XCkM51eez6hvh7X91cqqJQnnpWMtYmi4tPzrZ8vQUI8PfkyyHt2XciiT3HE9l73LjfczyJxJT0HB+7orebkXRV86FeNV/ndnBlb9xcrKYvPC1S2NYdOE3/qZHYHfDJf9py0zWBZockItlIs9l5ZvZGfl5/GIAXezXlwa71TI6qdFKSVUiUZElBpNnsfLNyP5MX7SL+fBoAPZsG8PwtTahXzdfk6CRDRrID2Q+TzCnZcTgcHDub4ky49hxLZO8J4/7wmfM5Pp+r1ULtyl4cPpNMSnr2JfeLq/paeUqopWglp9m45b3l7D2exO2ta/Lu3a3MDklEcmG3O3jjt2189k8MAMO61WdsRGMsFv0NKAhTk6wJEybw008/sX37dry8vOjcuTNvvvkmjRs3drbp3r07S5dmXmD1kUceYdq0ac79AwcOMHz4cP7++298fX0ZPHgwEyZMwNX14pyKJUuWMGbMGLZs2UJwcDAvvvgiQ4YMyfS4H374IZMmTSIuLo6WLVvy/vvv06FDh3y9FiVZkh8Oh4O/dxzjtfnb2Hs8CYAmgRV46dZQrm1Q1eToJDuFPWzvfKqNmBMZPV4Xe7/2Hk/ifJot34/TuX4VGgdWoKqvB1V93ani40EVX3eq+hr33u5XPo1WQxWlML3x2zY+WbaXahWMYYIVvTVMUKSkczgcfLxsLxN/3w7AXe1q8cbtzbNMYZCcmZpkRUREMGDAANq3b096ejrPP/88mzdvZuvWrfj4+ABGktWoUSNeffVV53Xe3t7OYG02G61atSIwMJBJkyYRGxvLoEGDeOihh3jjjTcAiImJoVmzZgwbNowHH3yQxYsXM2rUKObPn094eDgAP/zwA4MGDWLatGl07NiRyZMnM2vWLHbs2EH16nlP/FOSJXnZEXeW1+ZvZfmFcuhVfd158qbG3NUuWD0EJVxx9OrY7Q5iE5L5duV+Plqy56ofz8vNhaoVjOTr0iSsyiVJWcb5St5uzj+cGqoohSl6/2n6T4vE4YDPBrWjZ2iA2SGJSAHMXHOQZ3/aiN1hjLj54J7WeLpdXVGn8qJEDRc8fvw41atXZ+nSpVx33XWAkWS1atWKyZMnZ3vN77//zq233sqRI0cICDB+eU+bNo2xY8dy/Phx3N3dGTt2LPPnz2fz5s3O6wYMGMCZM2dYsGABAB07dqR9+/Z88MEHANjtdoKDg3nsscd49tln84xdSZbk5GRiCu8s3Mn/rT6A3QHuLlYe6BLCiB71qeBZ8uZdibnyW3Djvk618fVw42RiCieTUjmZmMKJxFROJKbkONQwJxYLVPJ2p7K3GwdOnSM1h3XZtFCsFERymo1bpixn74kk7mhdk3c0TFCkVPpzSxyP/d96UtLtdKhbmU8Ht8PfS59f8lKiFiOOj48HoHLlzOvtfPvtt3zzzTcEBgbSu3dvXnrpJby9jRWpo6KiaN68uTPBAggPD2f48OFs2bKF1q1bExUVRc+ePTM9Znh4OKNGjQIgNTWV6OhonnvuOed5q9VKz549iYqKyjbWlJQUUlIurlifkJBw5S9cyqSUdBszIvfx/uLdnL1QBOGW5oE8G9GU2lW0orpkL2NdstwKbgT6e/JKn2bZJjoOh4OkVJsz6bo8Cbu4ncLJxFROnUvF4YBTSamcSkrNNbaLa4OdIqx+lUJ5vVJ2vf3nDvaeSKJ6BQ9e7n2N2eGIyBW66ZpAvnqgAw9+tZbV+05x98dRfPVAB6r7eZodWplRpEmW3W5n1KhRXHvttTRrdnER0HvuuYc6deoQFBTExo0bGTt2LDt27OCnn34CIC4uLlOCBTj34+Licm2TkJDA+fPnOX36NDabLds227dvzzbeCRMm8Morr1zdi5ZSK7fhYw6Hgz+3HuWN37ax/+Q5AK4J8mPcraF0rKcPppK7q12XzGKx4Ovhiq+HK3Wq+OT5fDa7g9PnUjmZmMqv/x7mg7/zHqp47Gxynm2kfIvef8o5aX7CHc1LZLVUEcm/jvWq8MPDYQz+cjXb487Sb1okXz3QkZCqef+dkbwVaZI1YsQINm/ezD///JPp+MMPP+zcbt68OTVq1OCGG25gz5491K9fvyhDytVzzz3HmDFjnPsJCQkEBwebFo8Un9yKAgRX9ua/87aycq+xWnq1Ch48E96Yfm1qYdXwKsmniGY1mHpfm2JZeNrFarlQPMODU0nV8pdkJaTk2UbKr/OpNp6atRGHA/q1qcUNTTUPS6QsCA3y48dhnRn0xSr2nTzHndMimX5/B5rV9Dc7tFKvyJKskSNHMm/ePJYtW0atWrkvetaxY0cAdu/eTf369QkMDGT16tWZ2hw9ehSAwMBA533GsUvb+Pn54eXlhYuLCy4uLtm2yXiMy3l4eODh4ZH/FyllQk5FAWLjkxl2odQ3gIerlYe61mN49/r4eBT5SFspgyKa1eDG0MBiLaOe11DFDK//to3dxxJ5vldTjcuXLP735w5iTiQR4OfBuN6hZocjIoWodhVvZg3rzJAvV7PlSAIDPlnJJ4Pa0rm+KiRfjUKv2ehwOBg5ciQ///wzf/31FyEhIXles2HDBgBq1DC+yQ0LC2PTpk0cO3bM2WbhwoX4+fkRGhrqbLN48eJMj7Nw4ULCwsIAcHd3p23btpna2O12Fi9e7GwjYrM7eOXXrbl++ATo3aIGfz3VnafCGyvBkqviYrUQVr8Kt7WqSVj9KkVebCJjqCJcHJqYwXLh1r1RNQB+WHuQG99Zyp9b4oo0Jild1uw7xRcrjGGCE+9ooSRcpAyqVsGD7x/uRFi9KiSmpDPkizX8vikWMD4rRe05yS8bDhO15yQ2u5bYzY9Cry746KOP8t133/HLL79kWhvL398fLy8v9uzZw3fffcctt9xClSpV2LhxI6NHj6ZWrVrOtbMySrgHBQXx1ltvERcXx3/+8x8efPDBLCXcR4wYwQMPPMBff/3F448/nqWE++DBg/n444/p0KEDkydPZubMmWzfvj3LXK3sqLpg2Zffqm//91AnFQWQUi2vdbLW7DvF2Nkb2XvCWOutV4sajO99DdUqqHe/PDufauPmKcvYd/Ic/dvW4n93tjQ7JBEpQslpNkZ9v4EFW+KwWGBg+2D+3nFcayxeYGoJ95xWjv7yyy8ZMmQIBw8e5L777mPz5s0kJSURHBzM7bffzosvvpgp2P379zN8+HCWLFmCj48PgwcPZuLEiVkWIx49ejRbt26lVq1avPTSS1kWI/7ggw+cixG3atWK9957zzk8MS9Kssq+XzYc5onvN+TZbsqAVtzWqmbRByRShPJaGyw5zcaUxbv4ZNlebHYHFb3deLl3KH1b1czxd7uUba/+upUvVsQQ6OfJH6OvUy+WSDlgszt4cc5m/m/1gWzPl+c1FkvUOlmlmZKssi9y9wnu+WxVnu3UkyXlyebD8TwzeyNbY41lLLo3rsbrtzenZkUvkyOT4rQ65hR3fxKFwwFf3t+eHo2rmx2SiBSTdJudVq/+SWKKLdvz5XWNxYLkBoU+J0uktNh7PJHJi3bm2saC0S3eIaRyru1EypJmNf35ZeS1PB3eGHcXK0t2HOemd5by9cr92DUWv1w4l5rO07P/xeGAu9rVUoIlUs6s2Xc6xwQLMq+xKNlTkiXlTprNzkdLdhMxZTmr953G3cV4G2RXFAByX79IpKxyc7EyokcDfnuiK23rVCIp1cZLczYz4JOV7D2eaHZ4UsTeWrCD/SfPUcPfkxdvVTVBkfImv2snao3FnCnJknJl06F4bvtgBW8t2EFqup2uDauy+MluTLuvDYH+mVc5D/T3LJfjjUUu1aC6L7MeCWN871C83V1Yve8UN09ZzrSle0i32c0OT4rAqr0nmR65D4CJ/Vrg56l5WCLlTfUKnnk3wqgqLtnTnKxcaE5W2XE+1ca7i3by2fK92B1Q0duNcbeGcnvrixP68yoKIFLeHTx1jud/3sTyXScAaFbTj7f6tSQ0SL8fy4pzqelETF7OgVPnuLtdMG/2b2F2SCJiApvdQZc3/8pzjUWrBe5sG8zI6xsQXNm72OIziwpfFBIlWWXDit0neO6nTRw4dQ6APi2DGNc7lKq+Kk0tUlAOh4PZ0Yf477ytJCSn42q1MLx7fUZe3wAPVxezw5OrNH7uFqZH7iPI35MFo69TL5ZIObZgcyzDv1kHkCnRslzYvybIjy1HjAJJrlYLd7Yzkq2yXCRJSVYhUZJVusWfS+P137Yyc+0hwChg8VrfZtzQNO810kQkd8cSknnpl838seUoYAwrfLNfC9rWqWRyZHKlVu49yYBPjHUDv3qgA9ddWKRaRMqvvNZYXHfgNO8u3Okc4eDmYmFA+9o82qM+NfzLXrKlJKuQKMkqnRwOB79vjmPcL1s4kZiCxQL/6VSHZyKa4OvhmvcDiEi+/b4plpcuea8N6VyXp8Mb4+2u91ppkpSSTsSUZRw8dZ6BHYKZcIeGCYqIIT/TKdbsO8W7C3cSueckAO6uVu7pUJtHu9enul/+5neVBkqyComSrNInLt74dn3hVuPb9frVfHizXwva1VUJdpGicuZcKv+dt40f1xm9xrUqeTHxjhZ0aVjV2UZzHku2cb9s5quo/dSs6MWCUV2poGGCInIFovac5N1FO52l3T1crdzXqQ7DutWnWoXSP01DSVYhUZJVetjtDv5vzQEm/radsynpuLlYGN69ASN61Nc8EZFisnTncZ7/aROHz5wHjPWVXugVStSeE7kONxFzRe45wT2fGouyfz20A10bapigiFw5h8NB5J6TvLNwJ9H7TwPg6WZlcFhdHr6uHlVK8Zx4JVmFRElW6bD3eCLP/rTJ+a1Jq+CKvNmvBY0DK5gcmUj5k5iSzqQF25kRtR8AP09XEpLTs7TL6MPSMgnmSkpJJ3zyMg6dPs/ADrWZcEdzs0MSkTLC4XCwbNcJ3l24kw0HzwDg7e7CkM51eahrPSr5uJsb4BVQklVIlGSVbGk2O58s28uUxbtITbfj5ebC0+GNGdy5roYhiZhszb5TPDPrX2JOnsuxjQVjPbp/xl6v96xJXpqzma9XGsME/xh9neatikihczgcLNlxnHcW7mTT4XgAfD1cuf/aujzYpR7+3qVneLKSrEKiJKvk2njoDGN/3MS2WKN06HWNqvF632blYo0GkdJi6c5jDP5iTZ7t/u+hToTVr1IMEcmlInef4J7PjGGC3z7YkWsbVM3jChGRK+dwOFi07RjvLtzJ1guf3yp4uDK0awgPdAkpFUtGFCQ30FdWUqqcT7XxzsIdfP5PDHYHVPJ2Y1zvUPq2uriosIiUDGfOpeWr3bGzyXk3kkKVmJLO07M3AnBvx9pKsESkyFksFm4MDeCGJtX5c+tRJi/ayfa4s0xetIsv/onh4evqMeTakEw96qW5aJKSLClxcnpD/bPrBM//fHFR4dtaBfHSrVpUWKSkql4hf2V7S8efy7Jlwm/bOHzmPDUrevHcLU3NDkdEyhGr1UJEs0BuCg3g981xTF60k13HEvnfnzv57EKyNTisLst3HS/VRZM0XDAXGi5Y/LJb9C7Az4N6VX2J2musvRDk78lrtzfj+iZaVFikJLPZHXR58y/i4pPJ7Q+N1QK9WwbxyHX1CQ3S79qitmL3Ce69MEzwuwc70lm9WCJiIpvdwbyNR5iyeBd7jycBxpytxJSSVzRJc7IKiZKs4rVgcyzDv1mX64exwWF1eFqLCouUGhnvayDTe9tyYb9pjQpsiz3rPN69cTWGdatPx5DKGgJcBBJT0gl/dxmHz5znvk61ea2vqgmKSMlgszuY++9hJi/cyf5T53NsZ2bRpILkBtZiikkkVza7g1d+3ZprglXF151xva9RgiVSikQ0q8HU+9oQ6J956GCgvyfT7mvD709cx7zHunBrixpYLbBkx3EGfLKSO6ZG8seWOOx2fQ9YmN64MEywViUvnrtZwwRFpORwsVq4vXUtXr899y9/HEBsfLJz6Z6SSp9WpURYHXMq0xDB7JxMTGV1zClVIRMpZSKa1eDG0MAcJy83q+nPB/e0Yf/JJD5ZtpdZ0YdYf+AMj3wdTf1qPjzSrT59W9XE3VXfC16N5buO892qAwBM6t8SH31hJSIl0Mmk1Hy1K+lFk/QbVkyXbrOzaGtcvtqW9DeUiGTPxWrJ8wuSOlV8eP325jzRsyHTV+zj65X72XM8iWdmb+SdP3fyYNcQBnSord7sK3A2OY2xF6oJDgqroy+rRKTEym/RpPy2M4v+UolpzpxL5fs1B/k6aj+Hz+Q89vZSJf0NJSJXr3oFT56JaMLw7vX5btUBPv8nhriEZF6bv433/9rNoLA6DOlclyqqLJpvb/y2jSPxyQRX9mJsRBOzwxERyVGHkMrU8PfMsWhSxpysDiGVizu0AlHhi1yo8EXR2BF3lumR+/h5/SGS0+wAVPRyJc3uICnFlu01Zk5yFBFzpaTb+HndYT5Ztpe9J4zKU55uVu5qF8xDXetpEfI8LNt5nEFfrAa08LOIlA65FU0CVRcs9ZRkFR6b3cHibUeZHrmPyD0nnceb1vDj/mvr0qdlEEt2HCuRbygRKRlsdgd/bolj6tI9bDwUDxjDEG9tUYNh3erTtEbJ+z1txkKalz6nr4crL/68idiEFAaH1eGV25oV6XOLiBSW7Jb1MXudLCVZhURJ1tWLP5fGzLUHmRG1j0OnjSGBVgtENAtkSOcQ2tetlKlMc0l8Q4lIyeJwOIjac5KpS/ewfNcJ5/HujasxvFt9OlxW/t2MRAfM+X2W3XMCVPV1Z9kzPfB21ywBESk9zPr9nRMlWYVESdaV23XUGBL407rDnE8zhgBW9HZjQPva/CesDjUreuV4bUl7Q4lIybX5cDzTlu7ht02xZFR7b127IsO71adn0wD+3Bpnyhc3Oa37V5Q983mtNThNowFERK6KkqxCoiSrYGx2B39vP8b0yH38s/vit8tNAiswpHNdbmtVEy93FxMjFJGyat+JJD5dbpR/T0035noG+nkSl5C1ImlhJzoOh4OUdDspaXZS0m0kpdi48+NITiTmXIa4qq87Xwxpj4erC64uFlytFlxdrMa91YKr1YpLxnGrBRerJdfFmW12B13e/CvHpTA0r1VE5OopySokSrLyJyE5jVlrDzEjch8HTp0DjCGBN4YGMKRzCJ3qVc71w4GISGE5djaZ6Sv28VXkPhJTsy+kk8Hfy43RNzYkNd1O8oUEKTnNTnKajZR04z7jeEqaneR0W5ZzGfvFwUjELiRgVgtuLkby5Wq1km63czQhJc/HUOELEZErV5DcQIOzJVv5GbK3+1giX0XtY3b0Ic5d+DDj5+nKwA61ua9THVX8EpFil1H+vV2dSjwwY22ubePPpzF+7tZCfX6rxUiGUm15f3/p5+WKm9VKut1Bus1u3Nsd2OzZX5txHq48qdNagyIixUNJlmSR22Ttm0IDWbrzOF9G7mPZzuPO840CfBnSOYS+rYM0sVpETHc2JT1f7VoF+1Ovqi8ebi54uFrxdHPB082Kh6txn2Xf1SXXtm4uVqL2nGTgpyvzfO6P72uXba+Sw3Ex2Uqz2S/cG/vpdjvpNseFhOvi9r8HT/NyPhJGrTUoIlI89GlYMslp4nRcfDLDvllHNV93jl+YZ2CxwA1NArj/2rp0rl9FQwJFpMTIbzIxNqJpoQ+fu9qFNC0WYyigmwt4uuVvHmvzmv5MW7q31C/eKSJSVljNDkBKDpvdwSu/bs32D3TGseOJqfh6uPBglxCWPtWDzwa349oGVZVgiUiJkpHo5PSbyYLRQ18USYeL1cLLvUOdz3P58wK83Du0UAtQmPGcIiKSMyVZ4rQ65lSOlaku9f7ANrx4ayi1q2jOlYiUTGYnHRHNajD1vjYE+mfuUQv09yyyhdXNeE4REcmeqgvmojxUFzx+NoXo/adYu+80C7cdZf/Jc3leM2VAK25rVbMYohMRuTpmL3Buxrp/WmtQRKRoqLqgZMtud7DrWCJr958iet9p1u4/7Sy5XhCaOC0ipUVEsxrcGBpoWtLhYrUUe8l0M55TREQyU5JVSlzJN5PnUtPZcOAM0fuNhGrdgdOcTc5ccctigcYBFWhTpxJtgivy5oIdnEhM0cRpESkzlHSIiEhxU5JVCuR3uEts/HnW7jtN9H7jtjU2Ict6K97uLrQKrki7OpVoU6cSrWtXwt/LzXne19OV4d+swwKZEi1NnBYRERERyR/NycpFSZiTlVNJ9Ywk6O52wZxLsxG97xRHsilaUcPfk7Z1KtGuTiXa1a1Mk8AKuLrkXu/E7DkMIiIiIiIlTUFyAyVZuTA7ybLZHXR58698VfwDsFogNMiPtrUr0bZuZdrVqURQRa8rfm5NnBYRERERMajwRRmR35Lqd7atRd/WNWkVXBEfj8L5J9UcBhERERGRK6MkqwQ7djZ/PVhdGlb9//buP6aq+v8D+PMicAFBLK17uQqKK2eowcJgmE4rPqI5gmLLlR8ldKmFc6m5RSvv7A9/pFOqMS3nj7YykMp+SFkOBF2iFh5n/oiR07TJj3TxY6DI7n19//iMO678uuTh3HPO9/nYzh+e+/b9fvEcvvXFufctnnho5CBXQ0REREREvuB/Rqxjvh6VziPViYiIiIj04/9Fk1VQUICxY8ciJCQEycnJOHXqlL9L8klS7P2IigxBb5+EsuB/B1LwSHUiIiIiIv0wfZNVVFSEVatWwel04vTp04iPj0daWhoaGhr8XVq/hgRY4EyPA4BujRaPVCciIiIi0ifTN1lbt27FK6+8gpycHMTFxWHHjh0ICwvD7t27/V2aT2ZPisL2/z4Ge6T3WwLtkSHY/t/HeKQ6EREREZHOmPrgizt37qCqqgp5eXmeewEBAUhNTUVlZWW38e3t7Whvb/f8urm5WZM6+zN7UhT+E2fnkepERERERAZg6ibrxo0bcLlcsNlsXvdtNht+//33buM3bNiAdevWaVXegPBIdSIiIiIiYzD92wUHIi8vD01NTZ7r2rVr/i6JiIiIiIgMxtRPskaOHIkhQ4agvr7e6359fT3sdnu38VarFVarVavyiIiIiIjIhEz9JCs4OBiJiYkoLS313HO73SgtLUVKSoofKyMiIiIiIrMy9ZMsAFi1ahWys7MxZcoUJCUlIT8/H62trcjJyfF3aUREREREZEKmb7LmzZuHv//+G2vXrkVdXR0SEhJw6NChbodhEBERERERqcEiIuLvIvSqubkZkZGRaGpqwrBhw/xdDhERERER+clAegNTfyaLiIiIiIhIa2yyiIiIiIiIVMQmi4iIiIiISEWmP/jiXnR+XK25udnPlRARERERkT919gS+HGnBJqsPLS0tAIDo6Gg/V0JERERERHrQ0tKCyMjIPsfwdME+uN1uXL9+HREREbBYLAD+18FGR0fj2rVrPHFwkDFrbTFvbTFv7TFz7TBrbTFv7TFzbeklbxFBS0sLHA4HAgL6/tQVn2T1ISAgAKNHj+7xtWHDhvEPlUaYtbaYt7aYt/aYuXaYtbaYt/aYubb0kHd/T7A68eALIiIiIiIiFbHJIiIiIiIiUhGbrAGyWq1wOp2wWq3+LsX0mLW2mLe2mLf2mLl2mLW2mLf2mLm2jJg3D74gIiIiIiJSEZ9kERERERERqYhNFhERERERkYrYZBEREREREamITRYREREREZGK2GQRERERERGpyDRN1oYNG/D4448jIiICDz74IDIzM1FdXe015vbt28jNzcWIESMQHh6OrKws1NfXe41ZsWIFEhMTYbVakZCQ0G2d27dv4+WXX8bkyZMRGBiIzMxMn2ssLi7GhAkTEBISgsmTJ+P777/3ev2rr77CrFmzMGLECFgsFpw5c8bnubVihpy7WrZsGSwWC/Lz832eX0tmyNtisfR4bd682ec1tKL3vM+fP4+srCyMHTu2z+/bgoICjB07FiEhIUhOTsapU6d8mt8ftMq8vLwcGRkZiIqKwtChQ5GQkIDPPvvMpxr7y/Pjjz/GzJkzMWzYMFgsFjQ2Ng4oAy2ZIe9OIoI5c+bAYrHg66+/9mluLRk96ytXrvS6fxcXFw88EA3oPfOjR48iPT0dDoej1+9bEcHatWsRFRWF0NBQpKamoqamZkA5aEWrvKurq/Hkk0/CZrMhJCQE48aNw9tvv42Ojo5+a/Tn/m2aJquiogK5ubk4ceIEDh8+jI6ODsyaNQutra2eMStXrsR3332H4uJiVFRU4Pr163j++ee7zbVo0SLMmzevx3VcLhdCQ0OxYsUKpKam+lzf8ePH8eKLL2Lx4sVQFAWZmZnIzMzEuXPnPGNaW1sxbdo0bNq0aQBfubbMkHOnAwcO4MSJE3A4HD7PrzUz5F1bW+t17d69GxaLBVlZWQNIQht6z7utrQ3jxo3Dxo0bYbfbexxTVFSEVatWwel04vTp04iPj0daWhoaGhp8XkdLWmV+/PhxPProo/jyyy9x9uxZ5OTkYOHChTh48GCf9fmSZ1tbG2bPno233nrrX6agHTPk3Sk/Px8Wi2WACWjH6FlHR0d327/XrVuH8PBwzJkz5x6SGTx6z7y1tRXx8fEoKCjodcx7772HDz74ADt27MDJkycxdOhQpKWl4fbt2z6moB2t8g4KCsLChQvx008/obq6Gvn5+di5cyecTmef9fl9/xaTamhoEABSUVEhIiKNjY0SFBQkxcXFnjEXL14UAFJZWdnt9zudTomPj+9zjezsbMnIyPCpnhdeeEHmzp3rdS85OVmWLl3abezly5cFgCiK4tPc/mTUnP/66y8ZNWqUnDt3TsaMGSPbtm3zaX5/M2reXWVkZMhTTz3l0/z+pre8u+rt+zYpKUlyc3M9v3a5XOJwOGTDhg0DXsMftMi80zPPPCM5OTl9jhlInkeOHBEA8s8///i0vh4YNW9FUWTUqFFSW1srAOTAgQM+1eBPRs26q4SEBFm0aJFPNeiB3jLvqqfvW7fbLXa7XTZv3uy519jYKFarVT7//HOf5/YXLfNeuXKlTJs2rc8x/t6/TfMk625NTU0AgPvvvx8AUFVVhY6ODq+fGk+YMAExMTGorKwc9HoqKyu7/cQ6LS1Nk7UHkxFzdrvdWLBgAdasWYOJEycOek1qMmLeXdXX16OkpASLFy8e9NrUoLe8+3Pnzh1UVVV51RcQEIDU1FRd1OcLLTNvamryrNMTM+TZHyPm3dbWhpdeegkFBQW9PtHVIyNm3VVVVRXOnDljmP0b0Ffmvrh8+TLq6uq86ouMjERycrIh9hyt8v7jjz9w6NAhzJgxo9cxeti/Tdlkud1uvP7663jiiScwadIkAEBdXR2Cg4MxfPhwr7E2mw11dXWDXlNdXR1sNptf1h4sRs1506ZNCAwMxIoVKwa9HjUZNe+uPvnkE0RERPT4VgG90WPe/blx4wZcLpdh9xotM9+/fz9++eUX5OTk9DrG6Hn2x6h5r1y5ElOnTkVGRsa/rkdrRs26q127duGRRx7B1KlT/3VtWtJb5r7orMGIe44WeU+dOhUhISF4+OGHMX36dLz77ru9jtXD/m3KJis3Nxfnzp1DYWGh5mtfvXoV4eHhnmv9+vWa16AVI+ZcVVWF999/H3v37tX1e/l7YsS877Z7927Mnz8fISEhKleoPjPkbTRaZX7kyBHk5ORg586dnqfZx44d88rc14MDjMyIeX/77bcoKyvT7WFFvTFi1l3dunUL+/btM9RTLKNnbjRa5F1UVITTp09j3759KCkpwZYtWwDoN+9AfxegtuXLl+PgwYM4evQoRo8e7blvt9tx584dNDY2enXU9fX1qr7dwOFweJ0K2PnI1G63dztNRe21tWTUnI8dO4aGhgbExMR4Xne5XFi9ejXy8/Nx5coV1WpUk1Hz7urYsWOorq5GUVGRanUNFr3m3Z+RI0diyJAhhtxrtMq8oqIC6enp2LZtGxYuXOi5P2XKFK/MbTYbrFarYfPsj1HzLisrw6VLl7r9ZDwrKwvTp09HeXn5gGscbEbNuqsvvvgCbW1tXvPqmR4z90VnDfX19YiKivKqr6dT9/RCq7yjo6MBAHFxcXC5XFiyZAlWr16t2/3bNE+yRATLly/HgQMHUFZWhtjYWK/XExMTERQUhNLSUs+96upqXL16FSkpKarVERgYiIceeshzdf7jKCUlxWttADh8+LCqa2vB6DkvWLAAZ8+exZkzZzyXw+HAmjVr8OOPP6pWn1qMnndXu3btQmJiIuLj41WrS216z7s/wcHBSExM9KrP7XajtLRUt3uNlpmXl5dj7ty52LRpE5YsWeL1WmhoqFfmERERhsyzP0bP+8033+y2hwPAtm3bsGfPngHVN9iMnnVXu3btwrPPPosHHnhgQHVpTc+Z+yI2NhZ2u92rvubmZpw8eVKXe44//850u93o6OiA2+3W7/6t2hEafvbqq69KZGSklJeXS21tredqa2vzjFm2bJnExMRIWVmZ/Prrr5KSkiIpKSle89TU1IiiKLJ06VIZP368KIoiiqJIe3u7Z8z58+dFURRJT0+XmTNnesb05eeff5bAwEDZsmWLXLx4UZxOpwQFBclvv/3mGXPz5k1RFEVKSkoEgBQWFoqiKFJbW6tOSCowQ8530/PpgmbJu6mpScLCwmT79u33Hsog0nve7e3tnnFRUVHyxhtviKIoUlNT4xlTWFgoVqtV9u7dKxcuXJAlS5bI8OHDpa6uTp2QVKZV5mVlZRIWFiZ5eXle69y8ebPP+nzJs7a2VhRFkZ07dwoAOXr0qCiK0u/c/mCGvO8GnZ4uaJasa2pqxGKxyA8//KBSMoNH75m3tLR45gIgW7duFUVR5M8///SM2bhxowwfPly++eYbOXv2rGRkZEhsbKzcunVLxaTUoVXen376qRQVFcmFCxfk0qVLUlRUJA6HQ+bPn99nff7ev03TZAHo8dqzZ49nzK1bt+S1116T++67T8LCwuS5557r1sDMmDGjx3kuX77sGTNmzJgex/Rn//79Mn78eAkODpaJEydKSUmJ1+t79uzpcV6n03kv0ajKDDnfTc9Nllny/uijjyQ0NFQaGxv/dRZa0Hvenf+9w93XjBkzvMZ9+OGHEhMTI8HBwZKUlCQnTpy412gGjVaZZ2dn+5RdT/rL0+l09vs16IUZ8u7pa9Jjk2WWrPPy8iQ6OlpcLte9xKEJvWfeeUz43Vd2drZnjNvtlnfeeUdsNptYrVZ5+umnpbq6WqWE1KVV3oWFhfLYY49JeHi4DB06VOLi4mT9+vU+NZ7+3L8tIiIgIiIiIiIiVZjmM1lERERERER6wCaLiIiIiIhIRWyyiIiIiIiIVMQmi4iIiIiISEVssoiIiIiIiFTEJouIiIiIiEhFbLKIiIiIiIhUxCaLiIiIiIhIRWyyiIiIiIiIVMQmi4iIiIiISEVssoiIiIiIiFT0f+w5XmstXdywAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum_per_month_df.reset_index(inplace=True)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(\n",
    "    sum_per_month_df[\"datetime\"],\n",
    "    sum_per_month_df[\"casual\"],\n",
    "    label=\"Casual\",\n",
    "    marker=\"o\",\n",
    ")\n",
    "plt.plot(\n",
    "    sum_per_month_df[\"datetime\"],\n",
    "    sum_per_month_df[\"registered\"],\n",
    "    label=\"Registered\",\n",
    "    marker=\"o\",\n",
    ")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari grafik diatas dapat dilihat bahwa transaksi paling tinggi ada disekitar bulan Juli-Oktober\n",
    "dengan penurunan transaksi di sekitar bulan Januari."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertanyaan 3: Bulan apa transaksi paling sering terjadi?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_by_month_df = sum_per_month_df.copy()\n",
    "avg_by_month_df[\"month\"] = avg_by_month_df[\"datetime\"].dt.strftime(\"%B\")\n",
    "\n",
    "avg_by_month_df = avg_by_month_df.groupby(\"month\").agg({\"cnt\": \"mean\"})\n",
    "avg_by_month_df.sort_index(\n",
    "    key=lambda x: pd.to_datetime(x, format=\"%B\"),\n",
    "    ascending=False,\n",
    "    inplace=True,\n",
    ")\n",
    "\n",
    "max_val = avg_by_month_df[\"cnt\"].max()\n",
    "colors = avg_by_month_df[\"cnt\"].apply(\n",
    "    lambda x: \"#3A75B1\" if x == max_val else \"#D3D3D3\"\n",
    ")\n",
    "\n",
    "avg_by_month_df[\"cnt\"].plot.barh(color=colors)\n",
    "plt.title(\"Avg. monthly transaction by month\")\n",
    "plt.ylabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari grafik diatas dapat dilihat bahwa transaksi paling sering dilakukan pada bulan Agustus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertanyaan 4: Hari apa transaksi paling sering terjadi?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_by_day_df = sum_per_date_df.reset_index()\n",
    "avg_by_day_df[\"day\"] = pd.Categorical(avg_by_day_df[\"datetime\"].dt.strftime(\"%A\"), days)\n",
    "avg_by_day_df = avg_by_day_df.groupby(\"day\", observed=True).agg({\"cnt\": \"mean\"})\n",
    "avg_by_day_df.sort_index(ascending=False, inplace=True)\n",
    "\n",
    "max_val = avg_by_day_df[\"cnt\"].max()\n",
    "colors = avg_by_day_df[\"cnt\"].apply(lambda x: \"#3A75B1\" if x == max_val else \"#D3D3D3\")\n",
    "\n",
    "avg_by_day_df[\"cnt\"].plot.barh(color=colors)\n",
    "plt.title(\"Avg. daily transaction by day of week\")\n",
    "plt.ylabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari grafik diatas dapat dilihat bahwa transaksi paling sering dilakukan pada hari Jumat,    \n",
    "namun perbedaan dari hari lain hanya seberapa."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pertanyaan 5: Jam berapa transaksi paling sering terjadi?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_by_hour_df = df.groupby([\"hr\"]).agg({\"cnt\": \"mean\"})\n",
    "avg_by_hour_df.index = avg_by_hour_df.index.astype(str).str.pad(2, \"left\", \"0\")\n",
    "avg_by_hour_df.sort_index(ascending=False, inplace=True)\n",
    "\n",
    "max_val = avg_by_hour_df[\"cnt\"].nlargest(3).values\n",
    "colors = avg_by_hour_df[\"cnt\"].apply(lambda x: \"#3A75B1\" if x in max_val else \"#D3D3D3\")\n",
    "\n",
    "avg_by_hour_df[\"cnt\"].plot.barh(color=colors)\n",
    "plt.title(\"Avg. hourly transaction by hour\")\n",
    "plt.ylabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dari grafik diatas dapat dilihat bahwa transaksi paling sering dilakukan pada pukul 17, diikuti dengan pukul 18 dan 08.    \n",
    "Ini selaras dengan jam dimana orang-orang berangkat dan pulang melaksanakan aktifitas (sekolah, bekerja)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_WeHlCeX8DC2"
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZTcyR48Y8DC2"
   },
   "source": [
    "- Jumlah transaksi rental sepeda mengalami penaikan pada tahun 2012 dibanding tahun 2011.\n",
    "- Secara keseluruhan, jumlah transaksi per bulan mengalami penaikan, namun terjadi penurunan di sekitar bulan Januari.\n",
    "- Transaksi paling sering terjadi di bulan Agustus.\n",
    "- Transaksi paling sering terjadi di hari Jumat, namun hanya selisih kecil dari hari lainnya.\n",
    "- Transaksi paling sering terjadi di pukul 17, 18, dan 08. Ini selaras dengan jam orang-orang berangkat dan pulang melaksanakan aktifitas (sekolah, bekerja)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_per_year_df.to_csv(\"dashboard/sum_per_year.csv\")\n",
    "sum_per_month_df.to_csv(\"dashboard/sum_per_month.csv\", index=False)\n",
    "avg_by_month_df.to_csv(\"dashboard/avg_by_month.csv\")\n",
    "avg_by_day_df.to_csv(\"dashboard/avg_by_day.csv\")\n",
    "avg_by_hour_df.to_csv(\"dashboard/avg_by_hour.csv\")"
   ]
  }
 ],
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   "pygments_lexer": "ipython3",
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 },
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