2.video_games_data_visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "877895de",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Impressive... you've finished the first part of this workshop!\\\n",
    "It's time to apply what you've learned previously.\\\n",
    "You'll learn new things too, don't worry.\n",
    "\n",
    "In this part of the workshop, you will learn how to analyze a dataset of raw data in order to draw conclusions.\\\n",
    "You will have access to a dataset that contains information from over 15,000 video games.\n",
    "\n",
    "In this workshop:\n",
    "   - Reading a dataset\n",
    "   - Data analysis\n",
    "   - Data cleaning\n",
    "   - Data visualizations\n",
    "\n",
    "For this we will use the pandas library : [pandas](https://pandas.pydata.org/)\\\n",
    "we will also use seaborn to visualize our analysis : [seaborn](https://seaborn.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "017435c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d366a91b",
   "metadata": {},
   "source": [
    "## I) Reading dataset\n",
    "\n",
    "The routine ... create a dataframe with the pandas function that reads a csv.\\\n",
    "Then use the head method on the dataframe to display the first rows of the dataset.\n",
    "\n",
    "**Exercice :**\\\n",
    "create a dataframe with the pandas function that reads a csv.\\\n",
    "use the head method on the dataframe to display the first rows of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "515fc9fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year_of_Release</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "      <th>Critic_Score</th>\n",
       "      <th>Critic_Count</th>\n",
       "      <th>User_Score</th>\n",
       "      <th>User_Count</th>\n",
       "      <th>Developer</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.36</td>\n",
       "      <td>28.96</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.45</td>\n",
       "      <td>82.53</td>\n",
       "      <td>76.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>8</td>\n",
       "      <td>322.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.68</td>\n",
       "      <td>12.76</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.29</td>\n",
       "      <td>35.52</td>\n",
       "      <td>82.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>709.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.61</td>\n",
       "      <td>10.93</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.95</td>\n",
       "      <td>32.77</td>\n",
       "      <td>80.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>8</td>\n",
       "      <td>192.0</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name Platform  Year_of_Release         Genre Publisher  \\\n",
       "0                Wii Sports      Wii           2006.0        Sports  Nintendo   \n",
       "1         Super Mario Bros.      NES           1985.0      Platform  Nintendo   \n",
       "2            Mario Kart Wii      Wii           2008.0        Racing  Nintendo   \n",
       "3         Wii Sports Resort      Wii           2009.0        Sports  Nintendo   \n",
       "4  Pokemon Red/Pokemon Blue       GB           1996.0  Role-Playing  Nintendo   \n",
       "\n",
       "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  Critic_Score  \\\n",
       "0     41.36     28.96      3.77         8.45         82.53          76.0   \n",
       "1     29.08      3.58      6.81         0.77         40.24           NaN   \n",
       "2     15.68     12.76      3.79         3.29         35.52          82.0   \n",
       "3     15.61     10.93      3.28         2.95         32.77          80.0   \n",
       "4     11.27      8.89     10.22         1.00         31.37           NaN   \n",
       "\n",
       "   Critic_Count User_Score  User_Count Developer Rating  \n",
       "0          51.0          8       322.0  Nintendo      E  \n",
       "1           NaN        NaN         NaN       NaN    NaN  \n",
       "2          73.0        8.3       709.0  Nintendo      E  \n",
       "3          73.0          8       192.0  Nintendo      E  \n",
       "4           NaN        NaN         NaN       NaN    NaN  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the file video_games.csv and display head of dataframe\n",
    "df = pd.read_csv('video_games.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392f3d6f",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "<img src=\"./image/head.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104d7a76",
   "metadata": {},
   "source": [
    "The [info](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) method will allow you to retrieve the information related to the dataframe.\\\n",
    "It allows you to better understand the data, which will allow you to act accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e4cc7fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16719 entries, 0 to 16718\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Name             16717 non-null  object \n",
      " 1   Platform         16719 non-null  object \n",
      " 2   Year_of_Release  16450 non-null  float64\n",
      " 3   Genre            16717 non-null  object \n",
      " 4   Publisher        16665 non-null  object \n",
      " 5   NA_Sales         16719 non-null  float64\n",
      " 6   EU_Sales         16719 non-null  float64\n",
      " 7   JP_Sales         16719 non-null  float64\n",
      " 8   Other_Sales      16719 non-null  float64\n",
      " 9   Global_Sales     16719 non-null  float64\n",
      " 10  Critic_Score     8137 non-null   float64\n",
      " 11  Critic_Count     8137 non-null   float64\n",
      " 12  User_Score       10015 non-null  object \n",
      " 13  User_Count       7590 non-null   float64\n",
      " 14  Developer        10096 non-null  object \n",
      " 15  Rating           9950 non-null   object \n",
      "dtypes: float64(9), object(7)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#display informations about the dataframe\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe529247",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "<class 'pandas.core.frame.DataFrame'>\\\n",
    "RangeIndex: 16719 entries, 0 to 16718\\\n",
    "Data columns (total 16 columns):\\\n",
    " \\#   Column           Non-Null Count  Dtype  \\\n",
    "\\---  ------           --------------  -----\n",
    "\n",
    " 0   Name             16717 non-null  object \\\n",
    " 1   Platform         16719 non-null  object \\\n",
    " 2   Year_of_Release  16450 non-null  float64\\\n",
    " 3   Genre            16717 non-null  object\\\n",
    " 4   Publisher        16665 non-null  object\\\n",
    " 5   NA_Sales         16719 non-null  float64\\\n",
    " 6   EU_Sales         16719 non-null  float64\\\n",
    " 7   JP_Sales         16719 non-null  float64\\\n",
    " 8   Other_Sales      16719 non-null  float64\\\n",
    " 9   Global_Sales     16719 non-null  float64\\\n",
    " 10  Critic_Score     8137 non-null   float64\\\n",
    " 11  Critic_Count     8137 non-null   float64\\\n",
    " 12  User_Score       10015 non-null  object\\\n",
    " 13  User_Count       7590 non-null   float64\\\n",
    " 14  Developer        10096 non-null  object\\\n",
    " 15  Rating           9950 non-null   object \\\n",
    "dtypes: float64(9), object(7)\\\n",
    "memory usage: 2.0+ MB"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a029eaca",
   "metadata": {},
   "source": [
    "You will notice that the info() method informs you that your object is a dataframe.\\\n",
    "We also get the names of the columns and the number of values defined for each of them.\n",
    "\n",
    "You will notice that there are several functions that allow you to get information about a dataframe.\\\n",
    "In Data Science, it is a reflex to take, when you work with datasets we start by seeing how the data is composed.\n",
    "\n",
    "To continue in this direction there is an attribute that contains the names of all the columns.\n",
    "\n",
    "**Exercice :**\\\n",
    "Find and display the attribute that contains the names of the different columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1ca82b16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Platform', 'Year_of_Release', 'Genre', 'Publisher', 'NA_Sales',\n",
       "       'EU_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales', 'Critic_Score',\n",
       "       'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the name of columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7e20458",
   "metadata": {},
   "source": [
    "**Expected output:** Index(\\['Name', 'Platform', 'Year_of_Release', 'Genre', 'Publisher', 'NA_Sales',\n",
    "       'EU_Sales', 'JP_Sales', 'Other_Sales', 'Global_Sales', 'Critic_Score',\n",
    "       'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating'],\n",
    "      dtype='object')   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17fc02e2",
   "metadata": {},
   "source": [
    "## II) Data Analysis\n",
    "\n",
    "You have observed that the dataset contains several columns of data.\n",
    "\n",
    "However, a fairly common problem in Data Science is undefined data (NaN values).\\\n",
    "It is important to know how much undefined data a dataset contains.\n",
    "\n",
    "**Exercice :**\\\n",
    "Display the percentage of missing data for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3c9d957a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                0.011962\n",
       "Platform            0.000000\n",
       "Year_of_Release     1.608948\n",
       "Genre               0.011962\n",
       "Publisher           0.322986\n",
       "NA_Sales            0.000000\n",
       "EU_Sales            0.000000\n",
       "JP_Sales            0.000000\n",
       "Other_Sales         0.000000\n",
       "Global_Sales        0.000000\n",
       "Critic_Score       51.330821\n",
       "Critic_Count       51.330821\n",
       "User_Score         40.098092\n",
       "User_Count         54.602548\n",
       "Developer          39.613613\n",
       "Rating             40.486871\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the percentage of data missing\n",
    "df.isnull().sum()/len(df)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3305f3c",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "Name&nbsp;&nbsp;                0.011962\\\n",
    "Platform        &nbsp;&nbsp;    0.000000\\\n",
    "Year_of_Release     &nbsp;&nbsp;1.608948\\\n",
    "Genre         &nbsp;&nbsp;      0.011962\\\n",
    "Publisher  &nbsp;&nbsp;         0.322986\\\n",
    "NA_Sales       &nbsp;&nbsp;     0.000000\\\n",
    "EU_Sales       &nbsp;&nbsp;     0.000000\\\n",
    "JP_Sales    &nbsp;&nbsp;        0.000000\\\n",
    "Other_Sales   &nbsp;&nbsp;      0.000000\\\n",
    "Global_Sales    &nbsp;&nbsp;    0.000000\\\n",
    "Critic_Score    &nbsp;&nbsp;   51.330821\\\n",
    "Critic_Count   &nbsp;&nbsp;    51.330821\\\n",
    "User_Score     &nbsp;&nbsp;    40.098092\\\n",
    "User_Count  &nbsp;&nbsp;       54.602548\\\n",
    "Developer       &nbsp;&nbsp;   39.613613\\\n",
    "Rating        &nbsp;&nbsp;     40.486871\\\n",
    "dtype: float64"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5f2f86",
   "metadata": {},
   "source": [
    "You have obtained the percentages of missing values for each column.\\\n",
    "It may be interesting to see our analyses in different forms\n",
    "\n",
    "**Exercice :**\\\n",
    "Display the number of missing values for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "62758e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  2\n",
       "Platform              0\n",
       "Year_of_Release     269\n",
       "Genre                 2\n",
       "Publisher            54\n",
       "NA_Sales              0\n",
       "EU_Sales              0\n",
       "JP_Sales              0\n",
       "Other_Sales           0\n",
       "Global_Sales          0\n",
       "Critic_Score       8582\n",
       "Critic_Count       8582\n",
       "User_Score         6704\n",
       "User_Count         9129\n",
       "Developer          6623\n",
       "Rating             6769\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the count of data missing\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a9c04e9",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "Name        &nbsp;          2\\\n",
    "Platform       &nbsp;       0\\\n",
    "Year_of_Release   &nbsp;  269\\\n",
    "Genre        &nbsp;         2\\\n",
    "Publisher         &nbsp;   54\\\n",
    "NA_Sales  &nbsp;            0\\\n",
    "EU_Sales        &nbsp;      0\\\n",
    "JP_Sales     &nbsp;         0\\\n",
    "Other_Sales    &nbsp;       0\\\n",
    "Global_Sales    &nbsp;      0\\\n",
    "Critic_Score  &nbsp;     8582\\\n",
    "Critic_Count  &nbsp;     8582\\\n",
    "User_Score     &nbsp;    6704\\\n",
    "User_Count     &nbsp;    9129\\\n",
    "Developer     &nbsp;     6623\\\n",
    "Rating       &nbsp;      6769\\\n",
    "dtype: int64"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3da43a4",
   "metadata": {},
   "source": [
    "We observe that some columns contain a lot of undefined data.\\\n",
    "These columns are : ```Critic_Score```, ```Critic_Count```, ```User_Score```, ```User_Count```, ```Developer```, ```Rating```.\n",
    "\n",
    "We will deal with this problem later."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24e1b0eb",
   "metadata": {},
   "source": [
    "When analyzing data, one of the first things to do is to look at the correlations between the different columns.\\\n",
    "To do this, we use a heatmap that displays all the correlations of a dataframe.\n",
    "\n",
    "Obviously when we talk about correlations, we are talking about columns that have numerical values.\n",
    "\n",
    "**Exercice :**\\\n",
    "Select the numerical values of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "eb92a7a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year_of_Release', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales',\n",
       "       'Global_Sales', 'Critic_Score', 'Critic_Count', 'User_Count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#name of columns select numerical values\n",
    "df.select_dtypes(include = ['float64', 'int64']).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772c07cb",
   "metadata": {},
   "source": [
    "**Expected output:** Index(\\['Year_of_Release', 'NA_Sales', 'EU_Sales', 'JP_Sales', 'Other_Sales',\n",
    "       'Global_Sales', 'Critic_Score', 'Critic_Count', 'User_Count'],\n",
    "      dtype='object')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57613f7a",
   "metadata": {},
   "source": [
    "Now that we have selected the numerical data from the dataframe,\\\n",
    "we would like to use a heatmap to display the different correlations.\n",
    "\n",
    "For this, we will use [Seaborn](https://seaborn.pydata.org/), a library based on Matplotlib and which allows to visualize all kinds of analysis.\n",
    "\n",
    "**Exercice :**\\\n",
    "Display a heatmap of correlation of the numerical values of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0f9b62b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h7OwMfX191KtXD8uXL5cfy83Nxbhx42BtbQ19fX3Y2dmV+hmJiIhIGipcQaatrY05c+bgxx9/xJ07d5S2efr0KX7++Wd88cUX8t6ukydPlun81apVg5aWFnbt2oWCgoJi25mYmGDjxo24du0aFi9ejDVr1uCHH34otv3WrVsxY8YMzJ49G3FxcZgzZw6mT58u771bsmQJ9uzZg507d+L69evYunUr7O3ty5SZiIhIckQcsly2bBns7e2hr6+PFi1a4MyZMyW2DwsLQ926dWFgYABbW1tMmDABL168UOmaFXLZi169eqFx48YIDAzEunXrihzfsWMH6tSpg/r16wMAPv/8c6xbtw6tW7cu9dw2NjZYsmQJJk+ejODgYLi5uaF9+/YYNGgQHBwc5O2mTZsm/7O9vT0mTZqEHTt2YPLkyUrPGxgYiIULF6J3794AgFq1auHatWtYtWoVvL29kZqaijp16uCjjz6CTCaDnZ1diTlzcnKQk5OjsE8QCiGTVbganYiIpEikocbw8HD4+/tj5cqVaNGiBcLCwuDl5YXr16+jevXqRdpv27YN3377LdavX49WrVrhxo0b8jnrixYtKvN1K+xP33nz5mHTpk2Ii4srcmz9+vX44osv5K+/+OIL/Pzzz3j69GmZzj127FikpaVh69atcHd3x88//4z69evj8OHD8jbh4eHw8PCAlZUVjI2NMW3aNKSmpio9X3Z2NhITEzF8+HAYGxvLt1mzZiExMRHAyxsWYmJiULduXfj6+uLQoUMlZgwNDYWZmZnClpXzsEyfj4iI6H21aNEijBw5EsOGDYOLiwtWrlwJQ0PDYqcvnT59Gh4eHhg4cCDs7e3xySefYMCAAaX2qr2pwhZkbdq0gZeXFwICAhT2X7t2DdHR0Zg8eTJ0dHSgo6ODli1b4tmzZ9ixY0eZz29iYoJu3bph9uzZiI2NRevWrTFr1iwAQFRUFAYNGoQuXbpg7969uHjxIqZOnYrc3Fyl58rKygIArFmzBjExMfLtypUriI6OBgA0bdoUSUlJmDlzJp4/f45+/fqhb9++xeYLCAhAZmamwmasV6XMn4+IiOidUuOk/pycHDx58kRhe3OUCHg5H/v8+fPw9PSU79PS0oKnpyeioqKUxmzVqhXOnz8vL8Bu3bqF/fv3o0uXLip93Ao5ZPnK3Llz0bhxY9StW1e+b926dWjTpg2WLVum0HbDhg1Yt24dRo4cqfJ1ZDIZ6tWrh9OnTwN4WU3b2dlh6tSp8jYpKSnFvt/S0hI1atTArVu3MGjQoGLbmZqaon///ujfvz/69u2LTp06ISMjAxYWFkXa6unpQU9P742cFbY+JyIiqREEtZ0qNDQUwcHBCvsCAwMRFBSksO/BgwcoKCiApaWlwn5LS8til64aOHAgHjx4gI8++giCICA/Px8+Pj747rvvVMpYoQuyBg0aYNCgQViyZAmAl0tW/PTTTwgJCYGrq6tC2xEjRmDRokW4evWqfG6ZMjExMQgMDMTgwYPh4uICXV1dHD9+HOvXr8eUKVMAAHXq1EFqaip27NiBZs2aYd++ffj1119LzBocHAxfX1+YmZmhU6dOyMnJwblz5/Do0SP4+/tj0aJFsLa2RpMmTaClpYWff/4ZVlZWMDc3f7svEhERkYYLCAiAv7+/wr43OyX+q8jISMyZMwfLly9HixYtcPPmTfj5+WHmzJmYPn16mc9ToQsyAAgJCUF4eDgAYM+ePXj48CF69epVpJ2zszOcnZ2xbt26Eifp1axZE/b29ggODkZycjJkMpn89YQJEwAA3bt3x4QJEzBu3Djk5OTg008/xfTp04tU6q8bMWIEDA0NsWDBAnzzzTcwMjJCgwYNMH78eAAvh0jnz5+PhIQEaGtro1mzZti/fz+0tNjrRUREGkiNk/qVjQopU7VqVWhrayM9PV1hf3p6OqysrJS+Z/r06Rg8eDBGjBgB4GVnT3Z2NkaNGoWpU6eW+eewTBDU2CdIGs3a3EXsCCpJvblX7Agqyf99eemNJCRpTtEbXqTMtrNm/VOm66vacIbYfvRcKXYElVQtftUhSXqoLXYC1U1M3fJOz/98a9l7l0pjMGhmmdu2aNECzZs3x48//ggAKCwsxAcffIBx48bh22+/LdL+ww8/hKenJ+bNmyfft337dgwfPhxPnz6FtnbZ/nIrfA8ZERER0Sv+/v7w9vaGm5sbmjdvjrCwMGRnZ2PYsGEAgCFDhsDGxka++Hq3bt2waNEiNGnSRD5kOX36dHTr1q3MxRjAgkxlJ0+eROfOnYs9/uqOSCIiInoLIj2Dsn///vj3338xY8YMpKWloXHjxjhw4IB8on9qaqrCMOS0adMgk8kwbdo03L17F9WqVZOvsqAKFmQqcnNzQ0xMjNgxiIiI3m8iPoNy3LhxGDdunNJjkZGRCq91dHQQGBiIwMDAt7omCzIVGRgYwNHRUewYRERE77cKNsWdt+ARERERiYw9ZERERCQ9Ig5ZioEFGREREUlPBSvIOGRJREREJDL2kBEREZH0iLTshVhYkBEREZHkCIW8y5KIiIiIyhF7yIiIiEh6KtikfhZkREREJD0VbA4ZhyyJiIiIRMYeMiIiIpKeCjapnwUZydkZVhc7gkryf18udgSV6PQYI3YEleTP9BM7gkq0HazEjqAS4ekDsSOoJEemWT8cc2QysSOoxLRijc6VDeeQEREREYmsghVknENGREREJDL2kBEREZH0CJo1TP62WJARERGR9HDIkoiIiIjKE3vIiIiISHq47AURERGRyLhSPxERERGVJ/aQERERkfRwyJKIiIhIXALvsiQiIiKi8sQeMiIiIpIeDlkSERERiYx3WZIUBAUFoXHjxmLHICIiEkehoL5NA1S4gmzo0KGQyWRFtk6dOgEAZDIZfvvtN6Xv69mzZ5mukZSUhIEDB6JGjRrQ19dHzZo10aNHD8THx6vxkxAREdH7okIOWXbq1AkbNmxQ2Kenp6eWc+fl5aFjx46oW7cudu/eDWtra9y5cwd//vknHj9+rJZrEBERvfd4l+X7T09PD1ZWVgpb5cqV1XLuq1evIjExEcuXL0fLli1hZ2cHDw8PzJo1Cy1btpS3mzJlCpycnGBoaAgHBwdMnz4deXl5JZ577dq1cHZ2hr6+PurVq4fly5fLj+Xm5mLcuHGwtraGvr4+7OzsEBoaqpbPREREVO4q2JBlhewhe5eqVasGLS0t7Nq1C+PHj4e2trbSdiYmJti4cSNq1KiBy5cvY+TIkTAxMcHkyZOVtt+6dStmzJiBpUuXokmTJrh48SJGjhwJIyMjeHt7Y8mSJdizZw927tyJDz74ALdv38bt27ff5UclIiIiNamQBdnevXthbGyssO+7777Dd99999bntrGxwZIlSzB58mQEBwfDzc0N7du3x6BBg+Dg4CBvN23aNPmf7e3tMWnSJOzYsaPYgiwwMBALFy5E7969AQC1atXCtWvXsGrVKnh7eyM1NRV16tTBRx99BJlMBjs7uxJz5uTkICcnR2FfoVAILVmF7DQlIiKp4V2W77/27dsjJiZGYfPx8VHb+ceOHYu0tDRs3boV7u7u+Pnnn1G/fn0cPnxY3iY8PBweHh6wsrKCsbExpk2bhtTUVKXny87ORmJiIoYPHw5jY2P5NmvWLCQmJgJ4edNBTEwM6tatC19fXxw6dKjEjKGhoTAzM1PY/slSfn0iIqJyV8GGLCtkQWZkZARHR0eFzcLCAsDLocTMzMwi73n8+DHMzMzKfA0TExN069YNs2fPRmxsLFq3bo1Zs2YBAKKiojBo0CB06dIFe/fuxcWLFzF16lTk5uYqPVdWVhYAYM2aNQpF5JUrVxAdHQ0AaNq0KZKSkjBz5kw8f/4c/fr1Q9++fYvNFxAQgMzMTIWthvEHZf58REREpD4VcsiyJHXr1sX58+fh7e0t31dQUIDY2FiMGDHiP51TJpOhXr16OH36NADg9OnTsLOzw9SpU+VtUlJSin2/paUlatSogVu3bmHQoEHFtjM1NUX//v3Rv39/9O3bF506dUJGRoa82Hydnp5ekTtLOVxJRERSUdGeZVkhC7KcnBykpaUp7NPR0UHVqlXh7++P4cOHo169eujYsSOys7Px448/4tGjR2UqyGJiYhAYGIjBgwfDxcUFurq6OH78ONavX48pU6YAAOrUqYPU1FTs2LEDzZo1w759+/Drr7+WeN7g4GD4+vrCzMwMnTp1Qk5ODs6dO4dHjx7B398fixYtgrW1NZo0aQItLS38/PPPsLKygrm5+X/+OhEREYlGQ4Ya1aVCFmQHDhyAtbW1wr66desiPj4eAwYMgCAIWLRoEb799lsYGhriww8/xIkTJ2BpaVnquWvWrAl7e3sEBwcjOTkZMplM/nrChAkAgO7du2PChAkYN24ccnJy8Omnn2L69OkICgoq9rwjRoyAoaEhFixYgG+++QZGRkZo0KABxo8fD+DlEOn8+fORkJAAbW1tNGvWDPv374eWFnu9iIiIpE4mCELFKkGpWC1rtBM7gkqOzXQTO4JKdHqMETuCSuLc/MSOoBJHXyuxI6hEq+2nYkdQycLeP4sdQSVVC2RiR1CJJv7qPPLOlnd6/qxveqntXMYLSh6FkoIK2UNGREREElfBlr1gQaaikydPonPnzsUef3VHJBEREb0FziGjkri5uSEmJkbsGERERPQeYUGmIgMDAzg6Ooodg4iI6L0msIeMiIiISGQVrCDTxBs7iIiIiN4r7CEjIiIi6eFK/UREREQi45AlEREREZUn9pARERGR9FSwHjIWZERERCQ5Fe3JjhyyJCIiIhIZe8iIiIhIejhkSURERCQyFmRERERE4uKjk6jCOvcgQewIKkmaYyR2BJXkz/QTO4JKnM8tFjuCSrY0miF2BJX0ubpM7AgquSezEDuCSjJ1NGuKdEJhltgRVDZS7ADv0LJly7BgwQKkpaWhUaNG+PHHH9G8efNi2z9+/BhTp07F7t27kZGRATs7O4SFhaFLly5lviYLMiIiIpIekXrIwsPD4e/vj5UrV6JFixYICwuDl5cXrl+/jurVqxdpn5ubi44dO6J69erYtWsXbGxskJKSAnNzc5Wuy4KMiIiIpEeNT07KyclBTk6Owj49PT3o6ekVabto0SKMHDkSw4YNAwCsXLkS+/btw/r16/Htt98Wab9+/XpkZGTg9OnTqFSpEgDA3t5e5Yya1adLREREpKLQ0FCYmZkpbKGhoUXa5ebm4vz58/D09JTv09LSgqenJ6KiopSee8+ePXB3d8fYsWNhaWkJV1dXzJkzBwUFBSplZA8ZERERSY46J/UHBATA399fYZ+y3rEHDx6goKAAlpaWCvstLS0RHx+v9Ny3bt3C0aNHMWjQIOzfvx83b97EmDFjkJeXh8DAwDJnZEFGRERE0qPGgqy44Ul1KCwsRPXq1bF69Wpoa2vjww8/xN27d7FgwQIWZERERESqqlq1KrS1tZGenq6wPz09HVZWVkrfY21tjUqVKkFbW1u+z9nZGWlpacjNzYWurm6Zrs05ZERERCQ9hWrcykhXVxcffvghIiIi/j9GYSEiIiLg7u6u9D0eHh64efMmCgv//0I3btyAtbV1mYsxgAUZERERSZBQKKhtU4W/vz/WrFmDTZs2IS4uDl999RWys7Pld10OGTIEAQEB8vZfffUVMjIy4Ofnhxs3bmDfvn2YM2cOxo4dq9J1OWRJRERE9D/9+/fHv//+ixkzZiAtLQ2NGzfGgQMH5BP9U1NToaX1//1Ztra2OHjwICZMmICGDRvCxsYGfn5+mDJlikrXZUFGRERE0qPGdchUNW7cOIwbN07pscjIyCL73N3dER0d/VbXZEFGREREksNnWRIRERGJTcQeMjFwUj8RERGRyFiQSZRMJsNvv/0mdgwiIiJRCIXq2zQBC7L/GTp0KHr27Cn/s0wmg0wmg66uLhwdHRESEoL8/PwynWvNmjVo1KgRjI2NYW5ujiZNmih9ZhYREREVQ4R1yMTEOWTF6NSpEzZs2ICcnBzs378fY8eORaVKlRTWHlFm/fr1GD9+PJYsWYK2bdsiJycHly5dwpUrV8opOREREWka9pAVQ09PD1ZWVrCzs8NXX30FT09P7Nmzp9T37dmzB/369cPw4cPh6OiI+vXrY8CAAZg9e7a8zdmzZ9GxY0dUrVoVZmZmaNu2LS5cuFDieW/fvo1+/frB3NwcFhYW6NGjB5KTk+XHIyMj0bx5cxgZGcHc3BweHh5ISUn5z5+fiIhITByyJKUMDAyQm5tbajsrKytER0eXWAw9ffoU3t7e+OuvvxAdHY06deqgS5cuePr0qdL2eXl58PLygomJCU6ePIlTp07B2NgYnTp1Qm5uLvLz89GzZ0+0bdsWly5dQlRUFEaNGgWZTPafPy8REZGoOGRJrxMEARERETh48CC+/vrrUtsHBgaid+/esLe3h5OTE9zd3dGlSxf07dtXvrLvxx9/rPCe1atXw9zcHMePH0fXrl2LnDM8PByFhYVYu3atvMjasGEDzM3NERkZCTc3N2RmZqJr166oXbs2gJcPNi1JTk4OcnJyinxWFnFERETljz1kxdi7dy+MjY2hr6+Pzp07o3///ggKCir1fdbW1oiKisLly5fh5+eH/Px8eHt7o1OnTvIHj6anp2PkyJGoU6cOzMzMYGpqiqysLKSmpio9Z2xsLG7evAkTExMYGxvD2NgYFhYWePHiBRITE2FhYYGhQ4fCy8sL3bp1w+LFi3Hv3r0Sc4aGhsLMzExhEwqV99ARERGVt4o2ZMkesmK0b98eK1asgK6uLmrUqAEdHdW+VK6urnB1dcWYMWPg4+OD1q1b4/jx42jfvj28vb3x8OFDLF68GHZ2dtDT04O7u3uxQ6JZWVn48MMPsXXr1iLHqlWrBuBlj5mvry8OHDiA8PBwTJs2DYcPH0bLli2VnjMgIAD+/v4K+ypXqafSZyQiInpXNKWQUhcWZMUwMjKCo6OjWs7l4uICAMjOzgYAnDp1CsuXL0eXLl0AvJyw/+DBg2Lf37RpU4SHh6N69eowNTUttl2TJk3QpEkTBAQEwN3dHdu2bSu2INPT04Oenp7CPg5XEhGRVFS0goxDlmr21VdfYebMmTh16hRSUlIQHR2NIUOGoFq1anB3dwcA1KlTBz/99BPi4uLw999/Y9CgQTAwMCj2nIMGDULVqlXRo0cPnDx5EklJSYiMjISvry/u3LmDpKQkBAQEICoqCikpKTh06BASEhJKnUdGRERE0sCC7H8KCwtVHpZUxtPTE9HR0fjss8/g5OSEPn36QF9fHxEREahSpQoAYN26dXj06BGaNm2KwYMHw9fXF9WrVy/2nIaGhjhx4gQ++OAD9O7dG87Ozhg+fDhevHgBU1NTGBoaIj4+Hn369IGTkxNGjRqFsWPHYvTo0W/9eYiIiEQhyNS3aQAOWf7P/fv35UOUGzdu/M/n6dOnD/r06VNimyZNmuDs2bMK+/r27avwWhAUn3JvZWWFTZs2KT2fqakpfv311/+QloiISJo4ZFnBPHr0CHv37kVkZCQ8PT3FjkNEREQVUIUvyL788kv4+Phg4sSJ6NGjR6ntO3fuLF964s1tzpw55ZCYiIjo/ScUytS2aYIKP2Sp6lDf2rVr8fz5c6XHLCws1BGJiIiowqtoQ5YVviBTlY2NjdgRiIiI6D3DgoyIiIgkR9CQuyPVhQUZERERSU5FG7Ks8JP6iYiIiMTGHjIiIiKSHE25O1JdWJARERGR5LyxPvp7jwUZERERSU5F6yHjHDIiIiIikbGHjIiIiCSnovWQsSAjIiIiyaloc8g4ZElEREQkMvaQERERkeRwyJIqrHaWrmJHUIltZ83qz9Z2sBI7gkq2NJohdgSVfBEbInYEleTtWix2BJWYR/wrdgSVVCvUrAGg6jATO4LkVLRHJ2nWdywRERHRe4g9ZERERCQ5Fe1ZlizIiIiISHIKOWRJREREROWJPWREREQkORVtUj8LMiIiIpIcLntBREREJDKu1E9ERERE5Yo9ZERERCQ5HLIkIiIiEhmXvSAiIiKicsUeMiIiIpIcLntBREREJDLeZfme2LhxI8zNzcWO8Z/JZDL89ttvYscgIiKiciD5guz27dv48ssvUaNGDejq6sLOzg5+fn54+PChvI29vT3CwsLEC/mGNWvWoFGjRjA2Noa5uTmaNGmC0NBQsWMRERFpjEJBprZNE0h6yPLWrVtwd3eHk5MTtm/fjlq1auHq1av45ptv8OeffyI6OhoWFhblmikvLw+VKlUq9vj69esxfvx4LFmyBG3btkVOTg4uXbqEK1eulGNKIiIizVbR5pBJuods7Nix0NXVxaFDh9C2bVt88MEH6Ny5M44cOYK7d+9i6tSpaNeuHVJSUjBhwgTIZDLIZIp/gQcPHoSzszOMjY3RqVMn3Lt3T+H42rVr4ezsDH19fdSrVw/Lly+XH0tOToZMJkN4eDjatm0LfX19bN26tcTMe/bsQb9+/TB8+HA4Ojqifv36GDBgAGbPni1vc/bsWXTs2BFVq1aFmZkZ2rZtiwsXLpR43tu3b6Nfv34wNzeHhYUFevTogeTkZPnxyMhING/eHEZGRjA3N4eHhwdSUlJK+xITERGRBEi2IMvIyMDBgwcxZswYGBgYKByzsrLCoEGDEB4ejl9++QU1a9ZESEgI7t27p1BwPXv2DN9//z1++uknnDhxAqmpqZg0aZL8+NatWzFjxgzMnj0bcXFxmDNnDqZPn45NmzYpXO/bb7+Fn58f4uLi4OXlVWJuKysrREdHl1gMPX36FN7e3vjrr78QHR2NOnXqoEuXLnj69KnS9nl5efDy8oKJiQlOnjyJU6dOyQvM3Nxc5Ofno2fPnmjbti0uXbqEqKgojBo1qkhxSkREpCkEQX2bJpDskGVCQgIEQYCzs7PS487Oznj06BEKCgqgra0NExMTWFlZKbTJy8vDypUrUbt2bQDAuHHjEBISIj8eGBiIhQsXonfv3gCAWrVq4dq1a1i1ahW8vb3l7caPHy9vU5rAwED07t0b9vb2cHJygru7O7p06YK+fftCS+tl/fvxxx8rvGf16tUwNzfH8ePH0bVr1yLnDA8PR2FhIdauXSsvsjZs2ABzc3NERkbCzc0NmZmZ6Nq1q/yzFvd1eyUnJwc5OTkK+wqFQmjJJFujExFRBaIpc7/URfI/fYW3KG0NDQ3lBQoAWFtb4/79+wCA7OxsJCYmYvjw4TA2NpZvs2bNQmJiosJ53NzcynxNa2trREVF4fLly/Dz80N+fj68vb3RqVMnFBYWAgDS09MxcuRI1KlTB2ZmZjA1NUVWVhZSU1OVnjM2NhY3b96EiYmJPKeFhQVevHiBxMREWFhYYOjQofDy8kK3bt2wePHiIkOzbwoNDYWZmZnClvzkVpk/JxER0bskCDK1bZpAsj1kjo6OkMlkiIuLQ69evYocj4uLQ+XKlVGtWrViz/Hm5HuZTCYv8LKysgC8vCOyRYsWCu20tbUVXhsZGamc39XVFa6urhgzZgx8fHzQunVrHD9+HO3bt4e3tzcePnyIxYsXw87ODnp6enB3d0dubq7Sc2VlZeHDDz9UOn/t1effsGEDfH19ceDAAYSHh2PatGk4fPgwWrZsqfScAQEB8Pf3V9jX06WPyp+TiIiI3p5ke8iqVKmCjh07Yvny5Xj+/LnCsbS0NGzduhX9+/eHTCaDrq4uCgoKVDq/paUlatSogVu3bsHR0VFhq1Wrljo/ClxcXAC87JUDgFOnTsHX1xddunRB/fr1oaenhwcPHhT7/qZNmyIhIQHVq1cvktXMzEzerkmTJggICMDp06fh6uqKbdu2FXtOPT09mJqaKmwcriQiIqkQc9mLZcuWwd7eHvr6+mjRogXOnDlTpvft2LEDMpkMPXv2VPmakv4JvHTpUuTk5MDLywsnTpzA7du3ceDAAXTs2BE2NjbyOxft7e1x4sQJ3L17t8TC5k3BwcEIDQ3FkiVLcOPGDVy+fBkbNmzAokWL/nPmr776CjNnzsSpU6eQkpKC6OhoDBkyBNWqVYO7uzsAoE6dOvjpp58QFxeHv//+G4MGDSpy48LrBg0ahKpVq6JHjx44efIkkpKSEBkZCV9fX9y5cwdJSUkICAhAVFQUUlJScOjQISQkJJQ6j4yIiEiqBDVuqggPD4e/vz8CAwNx4cIFNGrUCF5eXvIpT8VJTk7GpEmT0Lp1axWv+JKkC7I6derg3LlzcHBwQL9+/VC7dm2MGjUK7du3R1RUlHwNspCQECQnJ6N27dolDmG+acSIEVi7di02bNiABg0aoG3btti4ceNb9ZB5enoiOjoan332GZycnNCnTx/o6+sjIiICVapUAQCsW7cOjx49QtOmTTF48GD4+vqievXqxZ7T0NAQJ06cwAcffIDevXvD2dkZw4cPx4sXL2BqagpDQ0PEx8ejT58+cHJywqhRozB27FiMHj36P38OIiKi90VOTg6ePHmisL15Y9srixYtwsiRIzFs2DC4uLhg5cqVMDQ0xPr164s9f0FBAQYNGoTg4GA4ODj8p4wy4W1mzdN7xdO25CU9pObXHtqlN5IQbQcbsSOoJHzh89IbScgXsSGlN5KQvF2LxY6gkjlz/xU7gkqqFUq6v6GIPLED/AeTUre80/OftlbfvOZDoxsgODhYYV9gYCCCgoIU9uXm5sLQ0BC7du1SGHb09vbG48eP8fvvvys9f2BgIC5duoRff/0VQ4cOxePHj1V+/KFkJ/UTERFRxaXOuyOV3cimp6dXpN2DBw9QUFAAS0tLhf2WlpaIj49Xeu6//voL69atQ0xMzFtl1KxfISSgc+fOCstkvL7NmTNH7HhERET0BmU3sikryFT19OlTDB48GGvWrEHVqlXf6lzsIVPR2rVri9z1+Up5P1eTiIjofVUowjWrVq0KbW1tpKenK+xPT08vsvg8ACQmJiI5ORndunWT73u15qiOjg6uX7+usB5qSViQqcjGRrPmAREREWkiAeW/oKuuri4+/PBDREREyOeQFRYWIiIiAuPGjSvSvl69erh8+bLCvmnTpuHp06dYvHgxbG1ty3xtFmRERERE/+Pv7w9vb2+4ubmhefPmCAsLQ3Z2NoYNGwYAGDJkCGxsbBAaGgp9fX24uroqvN/c3BwAiuwvDQsyIiIikpxCkdaA6N+/P/7991/MmDEDaWlpaNy4MQ4cOCCf6J+amip/NrU6sSAjIiIiySkUYcjylXHjxikdogSAyMjIEt+7cePG/3RNFmREREQkOWLMIRMTl70gIiIiEhl7yIiIiEhyxFj2QkwsyIiIiEhyOGRJREREROWKPWREREQkORyyJCIiIhJZRSvIOGRJREREJDL2kJFcwrN7YkdQia7vcrEjqER4+kDsCCrpc3WZ2BFUkrdrsdgRVFKpr5/YEVRSec4MsSOopEq+2AlU81hb7ATSU9Em9bMgIyIiIskprFj1GIcsiYiIiMTGHjIiIiKSHDGfZSkGFmREREQkOYLYAcoZCzIiIiKSHC57QURERETlij1kREREJDmFMs4hIyIiIhJVRZtDxiFLIiIiIpGxh4yIiIgkp6JN6mdBRkRERJLDlfqJiIiIqFyxh4yIiIgkp6Kt1K9xPWQymQy//fZbmdsPHToUPXv2fKtrJicnQyaTISYm5q3Oowp7e3uEhYWV2/WIiIikRFDjpgkkVZClpaXBz88Pjo6O0NfXh6WlJTw8PLBixQo8e/ZM7Hhl9uuvv6Jly5YwMzODiYkJ6tevj/Hjx4sdi4iIiCRKMkOWt27dgoeHB8zNzTFnzhw0aNAAenp6uHz5MlavXg0bGxt0795d7JilioiIQP/+/TF79mx0794dMpkM165dw+HDh8WORkREpDE4qV8kY8aMgY6ODs6dO4d+/frB2dkZDg4O6NGjB/bt24du3bopfd/ly5fx8ccfw8DAAFWqVMGoUaOQlZVVpF1wcDCqVasGU1NT+Pj4IDc3V37swIED+Oijj2Bubo4qVaqga9euSExM/E+f448//oCHhwe++eYb1K1bF05OTujZsyeWLVsmb5OYmIgePXrA0tISxsbGaNasGY4cOVLieR8/fowRI0bIP8PHH3+M2NhY+fHY2Fi0b98eJiYmMDU1xYcffohz5879p89AREQktkI1bppAEgXZw4cPcejQIYwdOxZGRkZK28iUPEIhOzsbXl5eqFy5Ms6ePYuff/4ZR44cwbhx4xTaRUREIC4uDpGRkdi+fTt2796N4OBghfP4+/vj3LlziIiIgJaWFnr16oXCQtX/Gq2srHD16lVcuXKl2DZZWVno0qULIiIicPHiRXTq1AndunVDampqse/57LPPcP/+ffz55584f/48mjZtig4dOiAjIwMAMGjQINSsWRNnz57F+fPn8e2336JSpUoq5yciIpICziETwc2bNyEIAurWrauwv2rVqjA2NoaxsTGmTJlS5H3btm3DixcvsHnzZri6uuLjjz/G0qVL8dNPPyE9PV3eTldXF+vXr0f9+vXx6aefIiQkBEuWLJEXXH369EHv3r3h6OiIxo0bY/369bh8+TKuXbum8mf5+uuv0axZMzRo0AD29vb4/PPPsX79euTk5MjbNGrUCKNHj4arqyvq1KmDmTNnonbt2tizZ4/Sc/711184c+YMfv75Z7i5uaFOnTr4/vvvYW5ujl27dgEAUlNT4enpiXr16qFOnTr47LPP0KhRI5XzExERUfmTREFWnDNnziAmJgb169dXKGheiYuLQ6NGjRR61Tw8PFBYWIjr16/L9zVq1AiGhoby1+7u7sjKysLt27cBAAkJCRgwYAAcHBxgamoKe3t7ACixx6o4RkZG2LdvH27evIlp06bB2NgYEydORPPmzeU3JmRlZWHSpElwdnaGubk5jI2NERcXV+z1YmNjkZWVhSpVqsgLVGNjYyQlJcmHVv39/TFixAh4enpi7ty5pQ655uTk4MmTJwqbIGhKxy4REb3vCmXq2zSBJAoyR0dHyGQyhSIKABwcHODo6AgDA4N3ev1u3bohIyMDa9aswd9//42///4bABTmmamqdu3aGDFiBNauXYsLFy7g2rVrCA8PBwBMmjQJv/76K+bMmYOTJ08iJiYGDRo0KPZ6WVlZsLa2RkxMjMJ2/fp1fPPNNwCAoKAgXL16FZ9++imOHj0KFxcX/Prrr8XmCw0NhZmZmcKW+fzf//x5iYiI1IlzyERQpUoVdOzYEUuXLkV2dnaZ3+fs7IzY2FiF95w6dQpaWloKw5+xsbF4/vy5/HV0dDSMjY1ha2uLhw8f4vr165g2bRo6dOgAZ2dnPHr0SD0f7H/s7e1haGgoz3nq1CkMHToUvXr1QoMGDWBlZYXk5ORi39+0aVOkpaVBR0cHjo6OClvVqlXl7ZycnDBhwgQcOnQIvXv3xoYNG4o9Z0BAADIzMxU2M4NqavvMREREVHaSKMgAYPny5cjPz4ebmxvCw8MRFxeH69evY8uWLYiPj4e2tnaR9wwaNAj6+vrw9vbGlStXcOzYMXz99dcYPHgwLC0t5e1yc3MxfPhwXLt2Dfv370dgYCDGjRsHLS0tVK5cGVWqVMHq1atx8+ZNHD16FP7+/v/5cwQFBWHy5MmIjIxEUlISLl68iC+//BJ5eXno2LEjAKBOnTrYvXs3YmJiEBsbi4EDB5Z4A4Gnpyfc3d3Rs2dPHDp0CMnJyTh9+jSmTp2Kc+fO4fnz5xg3bhwiIyORkpKCU6dO4ezZs3B2di72nHp6ejA1NVXYZDLJfDsQEVEFV9F6yCSzDlnt2rVx8eJFzJkzBwEBAbhz5w709PTg4uKCSZMmYcyYMUXeY2hoiIMHD8LPzw/NmjWDoaEh+vTpg0WLFim069ChA+rUqYM2bdogJycHAwYMQFBQEABAS0sLO3bsgK+vL1xdXVG3bl0sWbIE7dq1+0+fo23btli2bBmGDBmC9PR0VK5cGU2aNMGhQ4fkvXaLFi3Cl19+iVatWqFq1aqYMmUKnjx5Uuw5ZTIZ9u/fj6lTp2LYsGH4999/YWVlhTZt2sDS0hLa2tp4+PCh/JpVq1ZF7969Fe4kJSIi0iSChsz9UheZIAiackcovWN2VRqKHUElN6KWix1BJcLTB2JHUEnu8mWlN5IQnWYuYkdQSaW+fmJHUMmSpjPEjqASy3yxE6jmcdFBIMkbd3vLOz3/Stsv1HYun3ecVR0k00NGRERE9IqmDDWqCycNqcjHx0dh6YnXNx8fH7HjERERvRc4h4xKFBISgkmTJik9ZmpqWs5piIiI6H3AgkxF1atXR/Xq1cWOQURE9F6raBPcWZARERGR5GjKCvvqwoKMiIiIJEdT5n6pCyf1ExEREYmMPWREREQkORWth4wFGREREUlORZvUzyFLIiIiIpGxh4yIiIgkh3dZEhEREYmsos0h45AlERERkcjYQ0ZERESSU9Em9bMgIyIiIskprGAlGQsykqupX1XsCCr50XOl2BFUkiPTrH9c7sksxI6gEvOIf8WOoJLKc2aIHUElvhdCxI6gkitNJ4gdQSWnBBOxI5DIWJARERGR5FS0Sf0syIiIiEhyNGtM4e3xLksiIiKSnEI1bqpatmwZ7O3toa+vjxYtWuDMmTPFtl2zZg1at26NypUro3LlyvD09CyxfXFYkBERERH9T3h4OPz9/REYGIgLFy6gUaNG8PLywv3795W2j4yMxIABA3Ds2DFERUXB1tYWn3zyCe7evavSdVmQERERkeQUytS3qWLRokUYOXIkhg0bBhcXF6xcuRKGhoZYv3690vZbt27FmDFj0LhxY9SrVw9r165FYWEhIiIiVLou55ARERGR5Khz2YucnBzk5OQo7NPT04Oenp7CvtzcXJw/fx4BAQHyfVpaWvD09ERUVFSZrvXs2TPk5eXBwkK1O9XZQ0ZERETvtdDQUJiZmSlsoaGhRdo9ePAABQUFsLS0VNhvaWmJtLS0Ml1rypQpqFGjBjw9PVXKyB4yIiIikhx13mUZEBAAf39/hX1v9o6pw9y5c7Fjxw5ERkZCX19fpfeyICMiIiLJUec6ZMqGJ5WpWrUqtLW1kZ6errA/PT0dVlZWJb73+++/x9y5c3HkyBE0bNhQ5YwcsiQiIiICoKuriw8//FBhQv6rCfru7u7Fvm/+/PmYOXMmDhw4ADc3t/90bfaQERERkeSI9SxLf39/eHt7w83NDc2bN0dYWBiys7MxbNgwAMCQIUNgY2Mjn4M2b948zJgxA9u2bYO9vb18rpmxsTGMjY3LfF0WZERERCQ5Yq3U379/f/z777+YMWMG0tLS0LhxYxw4cEA+0T81NRVaWv8/wLhixQrk5uaib9++CucJDAxEUFBQma/LgoyIiIjoNePGjcO4ceOUHouMjFR4nZycrJZrsiAjIiIiyaloDxd/ryf1R0ZGQiaT4fHjxyW2s7e3R1hYWLlkIiIiotIVQlDbpgk0piBLS0vD119/DQcHB+jp6cHW1hbdunUr8dEErVq1wr1792BmZgYA2LhxI8zNzYu0O3v2LEaNGqWWnGvWrEGjRo1gbGwMc3NzNGnSROnic0RERFQ8QY2bJtCIIcvk5GR4eHjA3NwcCxYsQIMGDZCXl4eDBw9i7NixiI+PL/KevLw86OrqlrpuCABUq1ZNLTnXr1+P8ePHY8mSJWjbti1ycnJw6dIlXLlyRS3nVyY3Nxe6urrv7PxERET07mlED9mYMWMgk8lw5swZ9OnTB05OTqhfvz78/f0RHR0NAJDJZFixYgW6d+8OIyMjzJ49W2HIMjIyEsOGDUNmZiZkMhlkMpn87oc3hywfP36M0aNHw9LSEvr6+nB1dcXevXtLzblnzx7069cPw4cPh6OjI+rXr48BAwZg9uzZCu3Wr1+P+vXrQ09PD9bW1goTB1NTU9GjRw8YGxvD1NQU/fr1U1igLigoCI0bN8batWtRq1Yt+UrAjx8/xogRI1CtWjWYmpri448/Rmxs7H/9khMREYmqUI2bJpB8QZaRkYEDBw5g7NixMDIyKnL89SHIoKAg9OrVC5cvX8aXX36p0K5Vq1YICwuDqakp7t27h3v37mHSpElFzldYWIjOnTvj1KlT2LJlC65du4a5c+dCW1u71KxWVlaIjo5GSkpKsW1WrFiBsWPHYtSoUbh8+TL27NkDR0dH+bV79OiBjIwMHD9+HIcPH8atW7fQv39/hXPcvHkTv/zyC3bv3o2YmBgAwGeffYb79+/jzz//xPnz59G0aVN06NABGRkZpeYmIiKSGkGN/2kCyQ9Z3rx5E4IgoF69eqW2HThwoHzhNgC4deuW/M+6urowMzODTCYrcRjzyJEjOHPmDOLi4uDk5AQAcHBwKFPWwMBA9O7dG/b29nBycoK7uzu6dOmCvn37ytcsmTVrFiZOnAg/Pz/5+5o1awYAiIiIwOXLl5GUlARbW1sAwObNm1G/fn2cPXtW3i43NxebN2+WD7X+9ddfOHPmDO7fvy9/NMT333+P3377Dbt27VI6Py4np+iT7wuFQmjJJF+jExERvXck/9NXEMpe2f7XxxW8LiYmBjVr1pQXY6qwtrZGVFQULl++DD8/P+Tn58Pb2xudOnVCYWEh7t+/j3/++QcdOnRQ+v64uDjY2trKizEAcHFxgbm5OeLi4uT77OzsFOa9xcbGIisrC1WqVJGvDGxsbIykpCQkJiYqvVZoaKjCU+/NzMxw52nxPXtERETlqaINWUq+h6xOnTqQyWRKJ+6/SdmQpqoMDAze+hyurq5wdXXFmDFj4OPjg9atW+P48eNqKRiBop8zKysL1tbWRRarA6D0rlJA+ZPvvep1V0s+IiKit6Upy1Woi+R7yCwsLODl5YVly5YhOzu7yPHS1hh7na6uLgoKCkps07BhQ9y5cwc3btxQNapSLi4uAIDs7GyYmJjA3t6+2KU6nJ2dcfv2bdy+fVu+79q1a3j8+LH8PMo0bdoUaWlp0NHRgaOjo8JWtWpVpe/R09ODqampwsbhSiIiInFoxE/gZcuWoaCgAM2bN8cvv/yChIQExMXFYcmSJSU+ff1N9vb2yMrKQkREBB48eIBnz54VadO2bVu0adMGffr0weHDh5GUlIQ///wTBw4cKPX8X331FWbOnIlTp04hJSUF0dHRGDJkCKpVqybPGRQUhIULF2LJkiVISEjAhQsX8OOPPwIAPD090aBBAwwaNAgXLlzAmTNnMGTIELRt27bE3jVPT0+4u7ujZ8+eOHToEJKTk3H69GlMnToV586dK/PXh4iISCoq2jpkGlGQOTg44MKFC2jfvj0mTpwIV1dXdOzYEREREVixYkWZz9OqVSv4+Pigf//+qFatGubPn6+03S+//IJmzZphwIABcHFxweTJk0vtWQNeFkbR0dH47LPP4OTkhD59+kBfXx8RERGoUqUKAMDb2xthYWFYvnw56tevj65duyIhIQHAy6U7fv/9d1SuXBlt2rSBp6cnHBwcEB4eXuJ1ZTIZ9u/fjzZt2mDYsGFwcnLC559/jpSUFPnDUImIiDRJRVupXyaoMmue3mseNh+LHUElvbRriB1BJTkyzfpf7Z4sX+wIKjFH6UvTSEnlQo34fVjO90KI2BFUcqXpBLEjqOSUYCJ2BJWNu73lnZ5/tP1najvXquSf1Xaud0Xyk/qJiIio4tGUuyPVRbN+RRNZ586dFZaVeH2bM2eO2PGIiIjeG1wYloq1du1aPH/+XOkxCwuLck5DRET0/qpoPWQsyFRgY2MjdgQiIiJ6D7EgIyIiIsnRlKFGdWFBRkRERJJT0YYsOamfiIiISGTsISMiIiLJKaxgy6SyICMiIiLJqVjlGIcsiYiIiETHHjIiIiKSHE15BqW6sCAjIiIiyaloy15wyJKIiIhIZOwhIyIiIsmpaOuQsSAjuS461mJHUEnVfLETqCZHJhM7gkoydTSrA71aoWblraJh379Xmk4QO4JKXC/8IHYElfxb/zuxI0gO55ARERERiYxzyIiIiIioXLGHjIiIiCSHc8iIiIiIRCZUsEcncciSiIiISGTsISMiIiLJ4V2WRERERCKraHPIOGRJREREJDL2kBEREZHkVLR1yFiQERERkeRUtDlkHLIkIiIiEhl7yIiIiEhyKto6ZCzIiIiISHIq2l2WLMiIiIhIcirapP73bg5ZZGQkZDIZHj9+XGI7e3t7hIWFlUsmIiIiopJIuiBLS0vD119/DQcHB+jp6cHW1hbdunVDREREse9p1aoV7t27BzMzMwDAxo0bYW5uXqTd2bNnMWrUKLXkFAQBq1evRosWLWBsbAxzc3O4ubkhLCwMz549U8s1ymro0KHo2bNnuV6TiIhI3QohqG3TBJIdskxOToaHhwfMzc2xYMECNGjQAHl5eTh48CDGjh2L+Pj4Iu/Jy8uDrq4urKysSj1/tWrV1JZ18ODB2L17N6ZNm4alS5eiWrVqiI2NRVhYGOzt7VkgERERqaiiTeqXbA/ZmDFjIJPJcObMGfTp0wdOTk6oX78+/P39ER0dDQCQyWRYsWIFunfvDiMjI8yePVthyDIyMhLDhg1DZmYmZDIZZDIZgoKCABQdsnz8+DFGjx4NS0tL6Ovrw9XVFXv37i01586dO7F161Zs374d3333HZo1awZ7e3v06NEDR48eRfv27QEAhYWFCAkJQc2aNaGnp4fGjRvjwIED8vMoG2qNiYmBTCZDcnIygP/v7Tt48CCcnZ1hbGyMTp064d69ewCAoKAgbNq0Cb///rv880ZGRv73vwQiIiIqF5LsIcvIyMCBAwcwe/ZsGBkZFTn++hBkUFAQ5s6di7CwMOjo6ODWrVvyY61atUJYWBhmzJiB69evAwCMjY2LnK+wsBCdO3fG06dPsWXLFtSuXRvXrl2DtrZ2qVm3bt2KunXrokePHkWOyWQy+dDp4sWLsXDhQqxatQpNmjTB+vXr0b17d1y9ehV16tQp9TqvPHv2DN9//z1++uknaGlp4YsvvsCkSZOwdetWTJo0CXFxcXjy5Ak2bNgAALCwsCjzuYmIiKRCU4Ya1UWSBdnNmzchCALq1atXatuBAwdi2LBh8tevF2S6urowMzODTCYrcRjzyJEjOHPmDOLi4uDk5AQAcHBwKFPWhIQE1K1bt9R233//PaZMmYLPP/8cADBv3jwcO3YMYWFhWLZsWZmuBbwcll25ciVq164NABg3bhxCQkIAvCw2DQwMkJOTU+qwbU5ODnJychT25QsF0JGVXoQSERG9a7zLUgJUGTd2c3N76+vFxMSgZs2a8mJMFWXJ+uTJE/zzzz/w8PBQ2O/h4YG4uDiVrmdoaCgvxgDA2toa9+/fV+kcABAaGgozMzOF7XjmVZXPQ0RERG9PkgVZnTp1IJPJlE7cf5OyIU1VGRgY/Of3Ojk5lSlnabS0Xv5VvF7g5eXlFWlXqVIlhdcymew/TXwMCAhAZmamwtbWrL7K5yEiInoXCgVBbZsmkGRBZmFhAS8vLyxbtgzZ2dlFjpe2xtjrdHV1UVBQUGKbhg0b4s6dO7hx44aqUTFw4EDcuHEDv//+e5FjgiAgMzMTpqamqFGjBk6dOqVw/NSpU3BxcQHw/3d9vpqgD7zsuVNVWT4vAOjp6cHU1FRh43AlERFJhaDGTRNIsiADgGXLlqGgoADNmzfHL7/8goSEBMTFxWHJkiVwd3cv83ns7e2RlZWFiIgIPHjwQOm6YG3btkWbNm3Qp08fHD58GElJSfjzzz8V7oIsTr9+/dC/f38MGDAAc+bMwblz55CSkoK9e/fC09MTx44dAwB88803mDdvHsLDw3H9+nV8++23iImJgZ+fHwDA0dERtra2CAoKQkJCAvbt24eFCxeW+XO+/nkvXbqE69ev48GDB0p72YiIiEhaJFuQOTg44MKFC2jfvj0mTpwIV1dXdOzYEREREVixYkWZz9OqVSv4+Pigf//+qFatGubPn6+03S+//IJmzZphwIABcHFxweTJk8vU0ySTybBt2zYsWrQIv/32G9q2bYuGDRsiKCgIPXr0gJeXFwDA19cX/v7+mDhxIho0aIADBw5gz5498jssK1WqhO3btyM+Ph4NGzbEvHnzMGvWrDJ/zldGjhyJunXrws3NDdWqVSvSK0dERKQJxFwYdtmyZbC3t4e+vj5atGiBM2fOlNj+559/Rr169aCvr48GDRpg//79Kl9TJlS0ldeoWLPtBokdQSU2+TKxI6gkR7PiIlGn9F9IpMSmULOG3Kvki51ANfV1noodQSWuF34QO4JKjtX/TuwIKvskfcc7Pb+7TXu1nSvq7rEytw0PD8eQIUOwcuVKtGjRAmFhYfj5559x/fp1VK9evUj706dPo02bNggNDUXXrl2xbds2zJs3DxcuXICrq2uZryvZHjIiIiKquARBUNumikWLFmHkyJEYNmwYXFxcsHLlShgaGmL9+vVK2y9evBidOnXCN998A2dnZ8ycORNNmzbF0qVLVbouC7JSdO7cGcbGxkq3OXPmiB2PiIiISpGTk4MnT54obG+uxQkAubm5OH/+PDw9PeX7tLS04OnpiaioKKXnjoqKUmgPAF5eXsW2L44kF4aVkrVr1+L58+dKj3EVfCIiondDnSv1h4aGIjg4WGFfYGCg/HGKrzx48AAFBQWwtLRU2G9paVnsEldpaWlK26elpamUkQVZKWxsbMSOQEREVOGoc6X+gIAA+Pv7K+zT09NT2/nVgQUZERERvdf09PTKVIBVrVoV2traSE9PV9ifnp5e7CMJraysVGpfHM4hIyIiIskRY1K/rq4uPvzwQ0RERMj3FRYWIiIiotg1UN3d3RXaA8Dhw4dVWjMVYA8ZERERSZA655Cpwt/fH97e3nBzc0Pz5s0RFhaG7OxsDBs2DAAwZMgQ2NjYIDQ0FADg5+eHtm3bYuHChfj000+xY8cOnDt3DqtXr1bpuizIiIiIiP6nf//++PfffzFjxgykpaWhcePGOHDggHzifmpqqvz508DLBei3bduGadOm4bvvvkOdOnXw22+/qbQGGcCFYek1XBj23eLCsO8WF4Z9t7gw7LvFhWGLamLlobZzXUyT/lNr2ENGREREkiPWkKVYOKmfiIiISGTsISMiIiLJUec6ZJqABRkRERFJTmEFm+LOgoyIiIgkhz1kVGE9khWKHUEl+tqadVedqWZ9eZFQmCV2BJVUh5nYEVTyWLO+fXFKMBE7gkr+1bC7FttfnSN2BBIZCzIiIiKSHA5ZEhEREYmsog1ZctkLIiIiIpGxh4yIiIgkh0OWRERERCLjkCURERERlSv2kBEREZHkcMiSiIiISGQcsiQiIiKicsUeMiIiIpIcQdCwx5u8JRZkREREJDmFFWzIkgUZERERSY5QwSb1cw4ZERERkcjYQ0ZERESSwyFLIiIiIpFxyFKi2rVrh/HjxxfZv3HjRpibm5d7ntf98ssvaNeuHczMzGBsbIyGDRsiJCQEGRkZ5ZojKCgIjRs3LtdrEhER0dvTmIJMDHl5eaW2mTp1Kvr3749mzZrhzz//xJUrV7Bw4ULExsbip59+KoeURERE759CQVDbpgneq4IsMjISzZs3h5GREczNzeHh4YGUlBT58d9//x1NmzaFvr4+HBwcEBwcjPz8fPlxmUyGFStWoHv37jAyMsLs2bNLvN6ZM2cwZ84cLFy4EAsWLECrVq1gb2+Pjh074pdffoG3t7e87YoVK1C7dm3o6uqibt26CsVacnIyZDIZYmJi5PseP34MmUyGyMhI+WeTyWSIiIiAm5sbDA0N0apVK1y/fh3Ay57C4OBgxMbGQiaTQSaTYePGjW/x1SQiIhKPoMb/NMF7U5Dl5+ejZ8+eaNu2LS5duoSoqCiMGjUKMpkMAHDy5EkMGTIEfn5+uHbtGlatWoWNGzcWKbqCgoLQq1cvXL58GV9++WWJ19y6dSuMjY0xZswYpcdfDaX++uuv8PPzw8SJE3HlyhWMHj0aw4YNw7Fjx1T+nFOnTsXChQtx7tw56OjoyDP2798fEydORP369XHv3j3cu3cP/fv3V/n8REREVP7em0n9T548QWZmJrp27YratWsDAJydneXHg4OD8e2338p7rRwcHDBz5kxMnjwZgYGB8nYDBw7EsGHDynTNhIQEODg4oFKlSiW2+/777zF06FB54ebv74/o6Gh8//33aN++vUqfc/bs2Wjbti0A4Ntvv8Wnn36KFy9ewMDAAMbGxtDR0YGVlVWp58nJyUFOTo7CvnyhADoybZXyEBERvQuc1K+hLCwsMHToUHh5eaFbt25YvHgx7t27Jz8eGxuLkJAQGBsby7eRI0fi3r17ePbsmbydm5tbma9Z1m+WuLg4eHh4KOzz8PBAXFxcma/1SsOGDeV/tra2BgDcv39f5fOEhobCzMxMYTuTeU3l8xAREb0LhRDUtmkCjSnITE1NkZmZWWT/48ePYWZmBgDYsGEDoqKi0KpVK4SHh8PJyQnR0dEAgKysLAQHByMmJka+Xb58GQkJCdDX15efz8jIqMyZnJyccOvWrTJN/i+JltbLv4bXC7zizvl6b9yr4djCQtWf9xUQEIDMzEyFrbmZi8rnISIiorenMQVZ3bp1ceHChSL7L1y4ACcnJ/nrJk2aICAgAKdPn4arqyu2bdsGAGjatCmuX78OR0fHIturgkhVAwcORFZWFpYvX670+OPHjwG8HDo9deqUwrFTp07BxeVlAVStWjUAUOjRe32Cf1np6uqioKCgTG319PRgamqqsHG4koiIpEIQBLVtmkBj5pB99dVXWLp0KXx9fTFixAjo6elh37592L59O/744w8kJSVh9erV6N69O2rUqIHr168jISEBQ4YMAQDMmDEDXbt2xQcffIC+fftCS0sLsbGxuHLlCmbNmvWfMrVo0QKTJ0/GxIkTcffuXfTq1Qs1atTAzZs3sXLlSnz00Ufw8/PDN998g379+qFJkybw9PTEH3/8gd27d+PIkSMAAAMDA7Rs2RJz585FrVq1cP/+fUybNk3lPPb29khKSkJMTAxq1qwJExMT6Onp/afPRkREJCZNWa5CXTSmh8zBwQEnTpxAfHw8PD090aJFC+zcuRM///wzOnXqBENDQ8THx6NPnz5wcnLCqFGjMHbsWIwePRoA4OXlhb179+LQoUNo1qwZWrZsiR9++AF2dnZvlWvevHnYtm0b/v77b3h5eaF+/frw9/dHw4YN5TcQ9OzZE4sXL8b333+P+vXrY9WqVdiwYQPatWsnP8/69euRn5+PDz/8EOPHj/9PRWKfPn3QqVMntG/fHtWqVcP27dvf6rMRERGJpaL1kMkETUlK79wk+wFiR1CJdaFmDbGaqj7VT1T7tYrO2ZQyD5iJHUEl+vyX951yys0vvZGEtL86R+wIKqtU1eGdnr+ysaPazvUo66bazvWuaMyQJREREVUcmnJ3pLpozJClGHx8fBSWyXh98/HxETseERHRe6uiDVmyh6wEISEhmDRpktJjpqam5ZyGiIiI3lcsyEpQvXp1VK9eXewYREREFU5Fu8uSBRkRERFJjqY8FFxdOIeMiIiISGTsISMiIiLJ4ZAlERERkcg05e5IdeGQJREREZHI2ENGREREklPRJvWzICMiIiLJqWhDlizIiIiISHIqWkHGOWREREREImMPGREREUlOxeofAyAQvUMvXrwQAgMDhRcvXogdpUyY991i3neLed8t5qV3SSYIFWyQlsrVkydPYGZmhszMTI14IDvzvlvM+24x77vFvPQucQ4ZERERkchYkBERERGJjAUZERERkchYkNE7paenh8DAQOjp6YkdpUyY991i3neLed8t5qV3iZP6iYiIiETGHjIiIiIikbEgIyIiIhIZCzIiIiIikbEgIyIiIhIZCzIiIiIikbEgo3fi5s2bOHjwIJ4/fw4A4M28Fc/t27dx584d+eszZ85g/PjxWL16tYipiFRz4sQJ5OfnF9mfn5+PEydOiJCI3lcsyEitHj58CE9PTzg5OaFLly64d+8eAGD48OGYOHGiyOmoPA0cOBDHjh0DAKSlpaFjx444c+YMpk6dipCQEJHTFU/TCskLFy7g8uXL8te///47evbsie+++w65ubkiJive48ePsXbtWgQEBCAjIwPAy89x9+5dkZMV1b59e3nG12VmZqJ9+/YiJKL3FQsyUqsJEyZAR0cHqampMDQ0lO/v378/Dhw4IGIy1Tx+/FjsCCqRYt4rV66gefPmAICdO3fC1dUVp0+fxtatW7Fx40Zxw5VA0wrJ0aNH48aNGwCAW7du4fPPP4ehoSF+/vlnTJ48WeR0RV26dAlOTk6YN28evv/+e/n37u7duxEQECBuOCUEQYBMJiuy/+HDhzAyMhIhUek2b96MnJycIvtzc3OxefNmERJRWbAgI7U6dOgQ5s2bh5o1ayrsr1OnDlJSUkRKVbJ58+YhPDxc/rpfv36oUqUKbGxsEBsbK2Iy5TQlb15ennyF8CNHjqB79+4AgHr16sl7TqVI0wrJGzduoHHjxgCAn3/+GW3atMG2bduwceNG/PLLL+KGU8Lf3x9Dhw5FQkIC9PX15fu7dOkiqSHA3r17o3fv3pDJZBg6dKj8de/evdGjRw94eXmhVatWYsdUatiwYcjMzCyy/+nTpxg2bJgIiagsWJCRWmVnZyv0jL2SkZEh2cd3rFy5Era2tgCAw4cP4/Dhw/jzzz/RuXNnfPPNNyKnK0pT8tavXx8rV67EyZMncfjwYXTq1AkA8M8//6BKlSoipyuephWSgiCgsLAQwMu8Xbp0AQDY2triwYMHYkZT6uzZsxg9enSR/TY2NkhLSxMhkXJmZmYwMzODIAgwMTGRvzYzM4OVlRVGjRqFLVu2iB1TqeJ69e7cuQMzMzMRElFZ6IgdgN4vrVu3xubNmzFz5kwAgEwmQ2FhIebPny/Z+RZpaWnyAmfv3r3o168fPvnkE9jb26NFixYipytKU/LOmzcPvXr1woIFC+Dt7Y1GjRoBAPbs2SPvgZKiV4Xkp59+isOHD8u/l6VaSLq5uWHWrFnw9PTE8ePHsWLFCgBAUlISLC0tRU5XlJ6eHp48eVJk/40bN1CtWjUREim3YcMGAIC9vT0mTZok2eHJ1zVp0gQymQwymQwdOnSAjs7//4gvKChAUlKS/Bcjkh4WZKRW8+fPR4cOHXDu3Dnk5uZi8uTJuHr1KjIyMnDq1Cmx4ylVuXJl3L59G7a2tjhw4ABmzZoF4OVvmQUFBSKnK0pT8rZr1w4PHjzAkydPULlyZfn+UaNGKe1FlQpNKyTDwsIwaNAg/Pbbb5g6dSocHR0BALt27ZLkkFr37t0REhKCnTt3Anj5S1tqaiqmTJmCPn36iJyuqMDAQLEjlFnPnj0BADExMfDy8oKxsbH8mK6uLuzt7SX5NaaX+HBxUrvMzEwsXboUsbGxyMrKQtOmTTF27FhYW1uLHU2pcePGYe/evahTpw4uXryI5ORkGBsbY8eOHZg/fz4uXLggdkQFmpQ3Pz8fkZGRSExMxMCBA2FiYoJ//vkHpqamCj8spKagoKBIIZmcnAxDQ0NUr15dxGRl9+LFC2hra6NSpUpiR1GQmZmJvn374ty5c3j69Clq1KiBtLQ0uLu7Y//+/ZLriUpPT8ekSZMQERGB+/fvF1nCR0q/BL2yadMm9O/fX2GOHkkfCzKq8PLy8rB48WLcvn0bQ4cORZMmTQAAP/zwA0xMTDBixAiREyrSlLwpKSno1KkTUlNTkZOTgxs3bsDBwQF+fn7IycnBypUrxY5YLE0rJB8/foxdu3YhMTER33zzDSwsLHDhwgVYWlrCxsZG7HhKnTp1SuGXNk9PT7EjKdW5c2ekpqZi3LhxsLa2LjI3q0ePHiIlK11ubi7u378vn2P4ygcffCBSIioJCzJSqwMHDsDY2BgfffQRAGDZsmVYs2YNXFxcsGzZMoUeB3q/9ezZEyYmJli3bh2qVKmC2NhYODg4IDIyEiNHjkRCQoLYEZXStELy0qVL6NChA8zNzZGcnIzr16/DwcEB06ZNQ2pqqqSWOcjLy4OBgQFiYmLg6uoqdpwyMTExwcmTJ+V3smqChIQEfPnllzh9+rTC/leT/aXYq0e8y5LU7JtvvpFP2L18+TL8/f3RpUsXJCUlwd/fX+R0xfvpp5/w0UcfoUaNGvLlOcLCwvD777+LnEw5Tch78uRJTJs2Dbq6ugr77e3tJbkA6Ct+fn5wc3PDo0ePYGBgIN/fq1cvREREiJhMOX9/fwwbNkzyy0gAQKVKlfDBBx9oVEFga2urcU8aGTp0KLS0tLB3716cP38eFy5cwIULF3Dx4kVJTWmgNwhEamRkZCQkJSUJgiAIgYGBQp8+fQRBEITz588LlpaWIiYr3vLly4WqVasKs2bNEgwMDITExERBEARhw4YNQrt27UROV5Sm5DU3NxeuXr0qCIIgGBsby3OePHlSqF69upjRSmRhYSHEx8cLgqCYOykpSTAwMBAzmlKmpqbCzZs3BUFQzJucnCzo6emJGU2ptWvXCl26dBEePnwodpQyOXjwoPDJJ5/I/13TBIaGhkJcXJzYMUhF7CEjtdLV1cWzZ88AvFwT6ZNPPgEAWFhYKL3VXQp+/PFHrFmzBlOnToW2trZ8v5ubm8IjaaRCU/J+8sknCAsLk7+WyWTIyspCYGCgfK0sKSosLFTag3Pnzh2YmJiIkKhkmrKMxCtLly7FiRMnUKNGDdStWxdNmzZV2KSmf//+iIyMRO3atWFiYgILCwuFTYpcXFwkuQYdlYzLXpBaffTRR/D394eHhwfOnDkjX1H+xo0bRVbvl4qkpCT5xPjX6enpITs7W4REJdOUvAsXLoSXlxdcXFzw4sULDBw4EAkJCahatSq2b98udrxivSokXz27UuqFpKYtI/FqaQZN8fovFZpi3rx5mDx5MubMmYMGDRoUudPW1NRUpGRUEhZkpFZLly7FmDFjsGvXLqxYsUJ+h9eff/4p2QUJa9WqhZiYGNjZ2SnsP3DgAJydnUVKVTxNyVuzZk3ExsZix44duHTpErKysjB8+HAMGjRIYW6W1GhaIblw4UL07dsX1atXx/Pnz9G2bVv5MhKzZ88WO14RmrSuFwB4e3uLHUFlr+5Y7dChg8J+gZP6JY0FGanVBx98gL179xbZ/8MPP4iQpmz8/f0xduxYvHjxAoIg4MyZM9i+fTtCQ0Oxdu1aseMVoUl5dXR08MUXX4gdQyWaVkiamZnh8OHD+Ouvv+R5pbyMxCvnz59HXFwcgJdPR1DW6ysFqampJR6X4hISx44dEzsC/Qdc9oLemRcvXiA3N1dhn1S7yrdu3YqgoCAkJiYCAGrUqIHg4GAMHz5c5GTKSTXvnj17ytz21TMiqWK5f/8+Pv/8c0RGRsLc3BzAy3XU2rdvjx07dkhu3puWlpbS50K+wt4mUhcWZKRW2dnZmDJlCnbu3ImHDx8WOS71f7yePXuGrKwsjVmNXWp5tbTKdp+Q1IZNNK2QXLJkSZnb+vr6vsMkquvfvz9u3bqFzZs3y4fYr127Bm9vbzg6OkpuWDg2NlbhdV5eHi5evIhFixZh9uzZ6N27t0jJilfacidt2rQppySkChZkpFZjx47FsWPHMHPmTAwePBjLli3D3bt3sWrVKsydOxeDBg0SOyJREZpWSNaqVatM7WQyGW7duvWO06jGzMwMR44cQbNmzRT2nzlzBp988gkeP34sTjAV7du3DwsWLEBkZKTYUYpQ9v38ei+fFL6HqSjOISO1+uOPP7B582a0a9cOw4YNQ+vWreHo6Ag7Ozts3bpVMgVZkyZNShyGeJ0UFlLUtLya5s1Hy0hdUlKS2BH+s8LCQqXP16xUqZJG/T3UrVsXZ8+eFTuGUo8ePVJ4/apXb/r06ZK80YNeYkFGapWRkQEHBwcAL+eLZWRkAHi5HMZXX30lZjQFmnbrvablfSU7OxvHjx9HampqkfmEUhtKo/Lx8ccfw8/PD9u3b0eNGjUAAHfv3sWECROK3BUoBW+u8SYIAu7du4egoCDUqVNHpFQlMzMzK7KvY8eO0NXVhb+/P86fPy9CKioNCzJSKwcHByQlJeGDDz5AvXr1sHPnTjRv3hx//PGHfAKvFGjarfealhcALl68iC5duuDZs2fIzs6GhYUFHjx4AENDQ1SvXl3SBZmmFZJ37tzBnj17lOZdtGiRSKmUW7p0Kbp37w57e3vY2toCAG7fvg1XV1ds2bJF5HRFmZubF+mdFgQBtra22LFjh0ip/htLS0tcv35d7BhUDM4hI7X64YcfoK2tDV9fXxw5cgTdunWDIAjIy8vDokWL4OfnJ3ZEKift2rWDk5MTVq5cCTMzM8TGxqJSpUr44osv4OfnJ8nJ0EDphaTU5mRFRESge/fucHBwQHx8PFxdXZGcnAxBENC0aVMcPXpU7IhFCIKAI0eOID4+HgDg7Ows2WU6jh8/rvBaS0sL1apVg6OjI3R0pNmncenSJYXXr3r15s6di/z8fPz1118iJaOSsCCjdyolJQXnz5+Ho6MjGjZsKHYcpQoKCvDDDz9g586dSnsYXg27SoWm5DU3N8fff/+NunXrwtzcHFFRUXB2dsbff/8Nb29v+Q9jqdG0QrJ58+bo3LkzgoODYWJigtjYWFSvXh2DBg1Cp06dJDVVgMrHq6U63vzx3rJlS6xfvx716tUTKRmVhM+ypHfmxYsXsLOzQ+/evSVbjAFAcHAwFi1ahP79+yMzMxP+/v7o3bs3tLS0EBQUJHa8IjQlb6VKleR3e1WvXl2+wKaZmRlu374tZrQSxcTEYOLEidDS0oK2tjZycnJga2uL+fPn47vvvhM7XhFxcXEYMmQIgJcL8T5//hzGxsYICQnBvHnzRE5XlK+vr9JlO5YuXYrx48eXf6AySExMxNdffw1PT094enrC19dXvgagFCUlJeHWrVtISkpCUlISUlJS8OzZM5w+fZrFmISxICO1KigowMyZM2FjYwNjY2P58M706dOxbt06kdMpt3XrVqxZswYTJ06Ejo4OBgwYgLVr12LGjBmIjo4WO14RmpK3SZMm8rvQ2rZtixkzZmDr1q0YP348XF1dRU5XPE0rJI2MjOS9pNbW1gqFghQfMP3LL7/Aw8OjyP5WrVph165dIiQq2cGDB+Hi4oIzZ86gYcOGaNiwIf7++2/Ur18fhw8fFjueUnZ2dgqbra0t9PX1xY5FpRGI1Cg4OFhwcHAQtmzZIhgYGAiJiYmCIAjCjh07hJYtW4qcTjlDQ0MhJSVFEARBsLKyEs6fPy8IgiAkJiYKpqamYkZTSlPynj17Vjh69KggCIKQnp4ueHl5CSYmJkLTpk2FmJgYkdMVr2PHjsLWrVsFQRCEESNGCM2bNxe2bNkieHl5Cc2bNxc5XVE9evQQVq9eLQiCIEycOFFwdHQUZs2aJTRt2lTo0KGDyOmK0tPTExISEorsT0hIEPT09ERIVLLGjRsLU6ZMKbJ/ypQpQpMmTURIVDaRkZFC165dhdq1awu1a9cWunXrJpw4cULsWFQC9pCRWm3evBmrV6/GoEGDoK2tLd/fqFEjyc4ZqlmzJu7duwcAqF27Ng4dOgQAOHv2LPT09MSMppSm5HVzc0P79u0BvOxpOnDgAJ48eYLz58+jUaNGIqcr3pw5c2BtbQ0AmD17NipXroyvvvoK//77L1avXi1yuqIWLVqEFi1aAHg5nN2hQweEh4fD3t5ekr3Sjo6OOHDgQJH9f/75p3zJHCmJi4tT+kiyL7/8EteuXRMhUem2bNkCT09PGBoawtfXF76+vjAwMECHDh2wbds2seNRccSuCOn9oq+vLyQnJwuCIAjGxsbyHrKrV68KRkZGYkYr1pQpU4TZs2cLgvCyJ09HR0dwdHQUdHV1lf5mLDZNy/tKZGSksH//fiEjI0PsKCSidevWCQYGBsKMGTOEyMhIITIyUpg+fbpgaGgo7+mTkpo1awo7d+4ssj88PFywtbUVIVHp6tWrJyxatKjI/oULFwr16tUTIRGVBQsyUqumTZsKP/30kyAIigVZcHCw8NFHH4kZrcxOnz4tLFy4UNizZ4/YUcpEannnzp0rTJs2Tf66sLBQ8PLyEmQymSCTyQRLS0vhypUrIiZUjSYVks+fPxc2btwoLF++XOmwoFQsX75csLGxkX9P1KpVS9i0aZPYsZQKDg4WzM3Nhblz5wonTpwQTpw4IYSGhgrm5uZCSEiI2PGU0tXV1ahhYXqJBRmp1W+//SaYmZkJc+fOFQwNDYUFCxYII0aMEHR1dYVDhw6JHY/KQZMmTYQdO3bIX+/cuVMwMDAQ/vrrL+Hhw4fCp59+Knz22WciJlRO0wrJCRMmCOPGjZO/zsnJERo3bixUqlRJMDMzE4yMjITTp0+LmLB09+/fF54+fSp2jBIVFhYKixYtUiggbWxshLCwMKGwsFDseErVrl1bWLlyZZH9K1asEBwdHUVIRGXBgozU7sSJE4Knp6dQrVo1wcDAQPDw8BAOHjwodqwirl+/Lvz9998K+44cOSK0a9dOaNasmXxYUCo0Ja+5ublw7do1+euhQ4cKgwcPlr+OiooSatasKUa0EmlaIVm/fn3h999/l79ev369ULlyZSE5OVkoLCwUhg4dKnTp0kXEhGWjST2QT548EZ48eSJ2jFItX75c0NXVFXx8fITNmzcLmzdvFkaPHi3o6ekpLdRIGliQUYXVs2dPYfr06fLXt27dEgwMDIRPPvlE8PX1FYyNjYUffvhBvIBv0JS8rw9VC4Ig1K1bV1ixYoX8dUpKiqCvry9GtBJpWiFpYmKiMCz1+eefCyNHjpS/vnjxomBtbS1GNKU0rQfy2bNnwu+//660AMvMzBR+//134cWLFyIkK5vdu3cLHh4egoWFhWBhYSF4eHgIv/32m9ixqAQsyKjCqlmzpsKQzsyZM4VGjRrJX69du1bhtdg0JW+jRo2EDRs2CILwsviSyWTC1atX5cdPnTol2NjYiJSueJpWSJqZmQk3btyQv7a3txfWrVsnf52UlCSpvJrWAxkWFiZ8/PHHxR7v0KGDsHTp0nJMRO87LntBb61y5cqwsLAo0yYlDx48QM2aNeWvjx07hm7duslft2vXDsnJySIkU05T8o4dOxbjxo3D8OHD0blzZ7i7u8PFxUV+/OjRo2jSpImICZWrXbs2Tpw4AQBITU3FjRs30KZNG/nxO3fuoEqVKmLFK8LZ2Rl//PEHAODq1atITU2VLzMCvHxsmaWlpVjxikhKSlJ4Ysf+/fvRt29feHh4wMLCAtOmTUNUVJSICRW9WsS4OOPHj8emTZvKL1AZPHr0CD/++COePHlS5FhmZmaxx0gapPlkVNIoYWFhYkf4TywsLHDv3j3Y2tqisLAQ586dg7+/v/x4bm5ukWfBiUlT8o4cORLa2tr4448/0KZNGwQGBioc/+eff/Dll1+KlK54rwrJkydPIjo6WvKF5OTJk/H5559j3759uHr1Krp06YJatWrJj+/fvx/NmzcXMaGi/Px8hXXyoqKiFAqeGjVqSOrJAgkJCSWul9ewYUMkJCSUY6LSLV26FJcuXcLXX39d5JiZmRlOnjyJJ0+eYOrUqSKko1KJ3UVHJJaBAwcKXbt2FVJTU4WFCxcKxsbGQlZWlvz4rl27hIYNG4qYUJGm5S2r0NBQ4dGjR2LHEATh5RpZPXv2FHx8fIR79+4pHPvqq6+E3bt3i5RMuSNHjgjjx48X5s6dK2RnZyscCwoKEo4dOyZOMCU0bSjb2NhYOHfuXLHHz507JxgbG5djotI1atRIOHLkSLHHjxw5IjRu3LgcE5EqWJCR2t28eVOYOnWq8Pnnnwvp6emCIAjC/v37JTVhVxBezrFxdHQUZDKZoKOjIyxfvlzheI8ePYTx48eLlK4oTctbViYmJgpztzSFlArJsvjqq6+Ef//9V7Trr169WjAyMhK+/PJLwcXFRWjVqpXC8ZkzZwpdu3YVKV1RLVq0EObOnVvs8Tlz5ggtWrQox0SlMzY2lj9WTZmUlBTBxMSkHBORKliQkVpFRkYKBgYGgqenp6Crqyv/QRsaGir06dNH5HRF5eXlCTExMcLdu3eLHIuJiREePHggQqriaVresnhzMr2m0LRCUgp5NakHctWqVYKRkZHwxx9/FDm2Z88ewcjISFi1apUIyYpnZmYmREVFFXs8KipKMDMzK79ApBKZIEhg0gm9N9zd3fHZZ5/B398fJiYmiI2NhYODA86cOYPevXvjzp07Ykf8z0xNTRETEyPJ5+0poyl5X/8+0SSallvT8s6dOxc+Pj4wNzcXLcMXX3yBbdu2oV69eqhbty4AID4+Hjdu3EC/fv2wfft20bIp0759e7Ro0QJz585VenzKlCk4c+YMjh07Vs7JqCx4lyWp1eXLl9GrV68i+6tXry6pCbv/hab97qJpeYleN2fOHGRkZIiaYcuWLdixYwecnJxw48YNXL9+HXXr1sX27dslV4wBwLhx47Bw4UIsXboUBQUF8v0FBQX48ccf8cMPP2Ds2LEiJqSS8C5LUitzc3Pcu3dP4W4vALh48SJsbGxESkVEmkYqv1D069cP/fr1K7WdFHr0+vTpg8mTJ8PX1xdTp06V94beunULWVlZ+Oabb9C3b1/R8lHJ2ENGavX5559jypQpSEtLg0wmQ2FhIU6dOoVJkyZhyJAhYscjInonpNCjBwCzZ89GdHQ0hg4diho1asDa2hrDhg1DVFRUsUOZJA3sISO1mjNnDsaOHQtbW1sUFBTAxcUFBQUFGDhwINe+IaVat24NAwMDsWMQvRWp9OgBQPPmzcu0Bt2YMWMQEhKCqlWrlkMqKg17yEitdHV1sWbNGty6dQt79+7Fli1bEB8fj59++gk6Oppd/8tkMrEjqESsvIWFhZg3bx48PDzQrFkzfPvtt3j+/Hmx7ffv3w9ra+tyTFi8/Px8bN68Genp6aW21bRC8osvvoCpqanYMUhCtmzZwpX7JUSzf0KSZNna2sLW1lb+evfu3QgKCsKlS5dETPV2pPQbcFmIlXf27NkICgqCp6cnDAwMsHjxYty/fx/r168XJY8qdHR04OPjg7i4uFLb7t+/vxwSKafK/0evHle0YsWKdxWHNJSm/Zv2vmNBRmqzatUqHD58GLq6uvDz80OLFi1w9OhRTJw4ETdu3NC4OWRxcXFYt24dvv/+ewDAn3/+qVE3JoiVd/PmzVi+fDlGjx4NADhy5Ag+/fRTrF27Flpa0u+Ub968OWJiYmBnZyd2lGI1btwYMpms2B+or47JZDKFu+00iab1QBK9La5DRmoxd+5czJgxAw0bNkR8fDwEQcDUqVPx448/ws/PD6NHj0blypXFjlmq7Oxs7NixA+vWrUN0dDRcXFxw5coVsWMBAHr37q10v5mZGZycnDBixAhUq1atnFMVpaenh5s3byr0kOrr6+PmzZsKD0eXqp07dyIgIAATJkzAhx9+CCMjI4Xjrz8gWywpKSllbiu1wnL//v3Q1taGl5eXwv6DBw+isLAQnTt3FinZ29G0dd4Azcz8PmMPGanFhg0bsGbNGnh7e+PkyZNo27YtTp8+jZs3bxb5gSZFp06dwrp167Bz5048f/4cEyZMwPr161GvXj2xo8mZmZkp3f/48WOsWbMGCxYswIkTJ+Dq6lrOyRTl5+dDX19fYV+lSpWQl5cnUiLVfP755wAAX19f+T6p9ThJrchSxbfffqv0bj9BEPDtt99qbEHGHj16W+whI7UwMDDAjRs35L0ienp6OH36ND788EORkxXv/v372LhxI9avX4/MzEwMGDAAAwcOhLu7O2JjY+Hi4iJ2xDIrLCzEyJEjcf/+ffzxxx+iZtHS0kLnzp2hp6cn3/fHH3/g448/VijOd+/eLUa8UpXW+yTVYujatWtITU1Fbm6uwv7u3buLlEg5AwMDxMXFwd7eXmF/cnIy6tevj+zsbHGCFeN97dED2EMmNewhI7XIyclR6BXR1dWFhYWFiIlKZ2dnh759+2Lx4sXo2LGjRsxvKo6WlhZ8fX0l8cNhyJAhRe7w/OKLL0RKozqpFlzFuXXrFnr16oXLly8rzCt79XcghR6915mZmeHWrVtFCjKp9qZrWo9efn4+5syZgy+//LLUKQK881ZaWJCR2kyfPh2GhoYAgNzcXMyaNavIMNuiRYvEiKaUnZ0d/vrrL3zwwQews7OT1PDkf2FkZIRnz56JHQMbN24UO8Jb++mnn7By5UokJSUhKioKdnZ2CAsLQ61atdCjRw+x4ynw8/NDrVq1EBERgVq1auHMmTN4+PAhJk6cKL8hRUp69OiB8ePH49dff0Xt2rUBvCzGJk6cKLnePABISEhQ2lter1493Lx5U4REJdPR0cGCBQvKdBMV77yVFhZkpBZt2rTB9evX5a9btWqFW7duKbSR2jpe8fHx8rljzZo1g5OTk7wnR2pZy+Lw4cNwcnISO0axNx+8TkdHB1ZWVujYsSO6detWDqnKbsWKFZgxYwbGjx+P2bNny3uYzM3NERYWJrmCLCoqCkePHkXVqlWhpaUFLS0tfPTRRwgNDYWvry8uXrwodkQF8+fPR6dOnVCvXj15D86dO3fQunVrSRaQmtajBwAff/wxjh8/XiQzSRvnkBEByMrKwvbt27FhwwZER0ejbdu2GDhwIHr27CmJOxcBYM+ePUr3Z2Zm4vz581i7di3Wrl0rn5QulmHDhpXaprCwEPfv38fx48cxadIkhISElEOysnFxccGcOXPQs2dPhTk2V65cQbt27fDgwQOxIyqoXLkyLly4gFq1aqF27dpYu3Yt2rdvj8TERDRo0EASvaZvEgQBhw8fRmxsLAwMDNCwYUO0adNG7FhKjR49GlFRUUV69Pr06YNmzZph7dq1IicsauXKlQgODsagQYOU3iksxZ5IYkFGIjE1NUVMTIwkJ5Neu3YN69atw5YtW5CRkSGZuwOLm+NmYmKCunXrwt/fX/RiTFV79+7FmDFjkJqaKnYUOQMDA8THx8POzk6hIEtISEDDhg1LfOqAGFq3bo2JEyeiZ8+eGDhwIB49eoRp06Zh9erVOH/+vGSWbdFUmZmZ6NSpE86dO1ekR2/37t2iPky8OCXNh5XKncJUFIcsSRRS/j3AxcUFCxcuxLx584rtlRJDYWGh2BHU7qOPPoKbm5vYMRTUqlVL6cKwBw4cgLOzs0ipijdt2jT5nYkhISHo2rUrWrdujSpVqiA8PFzkdC8tWbIEo0aNgr6+PpYsWVJi29eXG5ECMzMznD59WmN69ID389+KioA9ZCQKKdxuraWlVepcMZlMhvz8/HJKVLIuXbpg+/bt8hsl5s6dCx8fH/lv6A8fPkTr1q1x7do1EVNqvrVr1yIoKAgLFy7E8OHDsXbtWiQmJiI0NFQSQ8JlkZGRgcqVK0tmLmStWrVw7tw5VKlSBbVq1Sq2nUwmKzL3lN7OixcviqwLSNLEgoxEIYWC7Pfffy/2WFRUFJYsWQJBECQzRKWlpYW0tDRUr14dQNFh3/T0dNSoUYPDEWqwdetWBAUFITExEQBQo0YNBAcHY/jw4SInK9nt27cBQOEpCaQ6Te7RA14udTJnzhysXLkS6enpuHHjBhwcHDB9+nTY29tL/vu4whKIRGBsbCwkJiaKHaOI+Ph4oWfPnoK2trYwZMgQITk5WexIcjKZTEhPT5e/fvNrmJaWJmhpaYkR7b2VnZ2t8DWXory8PGHatGmCqampoKWlJWhpaQmmpqbC1KlThdzcXLHjFREcHCxkZ2cX2f/s2TMhODhYhERF2dvbCw8ePJD/ubitVq1aIidVLjg4WHBwcBC2bNkiGBgYyP+d2LFjh9CyZUuR01FxWJCRKExMTCRVkN29e1cYMWKEUKlSJaFr167C5cuXxY5UBAsyUsbHx0eoXr26sHLlSiE2NlaIjY0VVq5cKVhZWQk+Pj5ixytCS0tLaZH74MEDfv+qSe3atYUjR44IgqD470RcXJxgbm4uZjQqgeYuTU6S8eTJE5XfI0hkpDwzMxNTpkyBo6Mjrl69ioiICPzxxx+iPw9SGZlMVmROkFTmCL1P0tPTMXjwYNSoUQM6OjrQ1tZW2KRm27Zt2LhxI0aPHo2GDRuiYcOGGD16NNatW4dt27aJHa8I4X/PBH1TbGysJJ/uERISonTpkOfPn0tquZbX3b17F46OjkX2FxYWSuaucSqKd1nSW6tcuTLu3buH6tWr4+OPPy7TreB//vknbGxsyidgMebPn4958+bBysoK27dvl9yCn28SBAFDhw6VPyPyxYsX8PHxka8xlJOTI2a898bQoUORmpqK6dOnw9raWvJFr56entIFQGvVqgVdXd3yD1SMVzcZyGQyODk5KXxdCwoKkJWVBR8fHxETKhccHAwfHx/5U0heefbsGYKDgzFjxgyRkhXPxcUFJ0+eLHKn8K5du9CkSRORUlFpOKmf3pqZmRmio6Ph7OwMLS0tpKenS2Yx1ZJoaWnBwMAAnp6eJfZ8SOUh2GVZcBUANmzY8I6TvN9MTExw8uRJNG7cWOwoZRISEoL4+Hhs2LBBXqzn5ORg+PDhqFOnDgIDA0VO+NKmTZsgCAK+/PJLhIWFKTxWTVdXF/b29nB3dxcxoXLF/Zt29OhR9O/fH//++69IyYr3+++/w9vbGwEBAQgJCUFwcDCuX7+OzZs3Y+/evejYsaPYEUkJ9pDRW/P09ET79u3lazT16tWr2N/Mjx49Wp7RSqTsIdhSxkKrfNja2kpmSL04bz6e6siRI6hZsyYaNWoE4OXwX25uLjp06CBGPKW8vb0BvOy5a9WqFSpVqiRyopJpao8e8PJ5oX/88QdCQkJgZGSEGTNmoGnTpvjjjz9YjEkYe8jorT1//hybNm1CYmIiFi5ciJEjRxbp3n/lhx9+KOd0RKo5dOgQFi5ciFWrVkn2WYBl7S0FpFHIP3nyBKampvI/l+RVO7Fpao8eaS4WZKRW7du3x6+//irJx4kQFefNRVSzs7ORn58PQ0PDIj05GRkZ5R1P42lra8vnmRa3IPOryf5SW0fv+PHjGtGj97rbt29DJpPJH/V05swZbNu2DS4uLhg1apTI6ag4HLIktTp27Jj8z69qfU0aFqSKKSwsTOwIb+3ff//F9evXAQB169aV1DzOo0ePyu+gfP3fCKl6vUevSZMmeP78ebELREulR+91AwcOxKhRozB48GCkpaXB09MTrq6u2Lp1K9LS0iR5IwKxh4zegc2bN2PBggVISEgAADg5OeGbb77B4MGDRU5G9P7Jzs7G119/jc2bN8ufYaitrY0hQ4bgxx9/LHb6gBjy8/MxZ84cfPnll/LeGynS5B494GWPb3R0NOrWrYslS5YgPDwcp06dwqFDh+Dj48PHU0kUe8hIrRYtWoTp06dj3Lhx8PDwAAD89ddf8PHxwYMHDzBhwgSRExKV7PUfxq97+PAhqlevLrkfwP7+/jh+/Dj++OMPhf/nfH19MXHiRKxYsULkhP9PR0cHCxYswJAhQ8SOUiJN69F7U15envyO2yNHjqB79+4AgHr16uHevXtiRqOSlPtStPRes7e3FzZt2lRk/8aNGwV7e3sREhGp5s0nIrxy9+5dQV9fX4REJatSpYpw7NixIvuPHj0qVK1atfwDlaJ79+7Cxo0bxY5RJnl5eUJwcLBw+/ZtsaOopHnz5sKUKVOEEydOCPr6+kJMTIwgCIIQFRUl2NjYiJyOisMeMlKre/fuoVWrVkX2t2rVir+ZkaS9eoi0TCbD2rVrYWxsLD9WUFCAEydOoF69emLFK9azZ89gaWlZZH/16tWVrjAvts6dO+Pbb7/F5cuX8eGHH8oXNn7lVW+OFGhKj96b5s2bh169emH+/PkYOnSofDmUPXv2oHnz5iKno+JwDhmplaurKwYOHIjvvvtOYf+sWbMQHh6Oy5cvi5SMqGS1atUCAKSkpKBmzZoKiwW/WuYgJCQELVq0ECuiUh06dECVKlWwefNm6OvrA3i5FI23tzcyMjJw5MgRkRMq0tIq/ol9UpyT1aNHD/Tu3Vu+jpqmKCgowJMnT1C5cmX5vuTkZBgaGhYZjidpYA8ZqVVwcDD69++PEydOyOeznDp1ChEREdi5c6fI6YiKl5SUBODl0i27d+9Gfn4+ZDIZqlatKnKyki1evBheXl5FFobV19fHwYMHRU5X1KsbDzSFJvXovbl8yytmZmZwcnLCpEmTuDCshLGHjNTu/Pnz+OGHHxAXFwcAcHZ2xsSJE/kMNZK8x48f47vvvsPOnTvx6NEjAC9/yH3++eeYNWuWZNfXe/bsGbZu3Yr4+HgAL/+fGzRoEAwMDERO9v+OHj2KcePGITo6ushSEZmZmWjVqhVWrlyJ1q1bi5RQOU3q0du0aZPS/Y8fP8b58+cRHh6OXbt2oVu3buWcjMqCBRmJYu7cufDx8ZHsDziqeDIyMuDu7o67d+9i0KBB8keBXbt2Ddu2bYOtrS1Onz6tMAREZde9e3e0b9++2DutlyxZgmPHjuHXX38t52QVx6JFi7Br1y6cPn1a7CikBAsyEoWpqSliYmLg4OAgdhQiAMD48eMRERGBI0eOFJkkn5aWhk8++QQdOnSQxOO/9uzZU+a2UhlSs7Ozw4EDB+SF7pvi4+PxySefIDU1tZyTKaepPXoluXHjBlq2bMmnTUgU55CRKPh7AEnNb7/9hlWrVim9Y9HKygrz58+Hj4+PJAqynj17lqmdlIbU0tPTS3z8kI6ODv79999yTFSysLAwjBw5UulK/GZmZhg9ejQWLVqkUQVZTk4OdHV1xY5BxSh+cJyIqAK5d+8e6tevX+xxV1dXpKWllWOi4hUWFpZpk0oxBgA2Nja4cuVKsccvXboEa2vrckxUstjYWHTq1KnY45988gnOnz9fjone3rp169C4cWOxY1Ax2ENGRASgatWqSE5OLvaRPklJSfLV26XgxYsXOHLkCLp27QoACAgIQE5Ojvy4jo4OQkJC5EthiK1Lly6YPn06OnXqVCTT8+fPERgYKP8sUqBpPXrAy6c2KJOZmYkLFy7gxo0bOHHiRDmnorJiQUZEBMDLywtTp07F4cOHiwzr5OTkyIsJqdi4cSP27dsnL2KWLl2K+vXry++sjI+Ph5WVVbE/pMvbtGnTsHv3bjg5OWHcuHGoW7cugJc5ly1bhoKCAkydOlXklP/vVY+eo6Oj0uNS69EDgIsXLyrdb2pqio4dO2L37t3y9fZIejipn0RhYmKC2NhYTuonybhz5w7c3Nygp6eHsWPHol69ehAEAXFxcVi+fDlycnJw7tw52Nraih0VANC6dWtMnjxZvoTBm/9PbdmyBcuWLUNUVJSYMRWkpKTgq6++wsGDB+XzSGUyGby8vLBs2TJJFQtff/01IiMjcfbsWaU9es2bN0f79u3lT3ggelssyEht8vPzsW3bNnh5eSmdGP26Ll26YN26dZL7DZMqtqSkJIwZMwaHDh1SKBg6duyIpUuXFttbIgZra2tERUXB3t4eAFCtWjWcPXtW/vrGjRto1qwZMjMzxQtZjEePHuHmzZsQBAF16tSR5FIi6enpaNq0KbS1tYvt0btw4UKp/9YRlRULMlIrQ0NDxMXFwc7OTuwoRP/Zo0ePkJCQAABwdHSU1NyxVwwMDBATEyMvFN4UHx+Pxo0b48WLF+Wc7P2hST16pPk4h4zUqnnz5oiJiWFBRhqtcuXKkn8Ic82aNXHlypViC7JLly4Ve4MClY2dnR3279+vET16pPnYQ0ZqtXPnTgQEBGDChAlKn/vWsGFDkZIRvV/8/Pxw5MgRnD9/XukcJzc3N3h6emLx4sUiJSQiVbAgI7VS9tw3mUwGQRAktUglkaZLT09H48aNoauri3HjxsHJyQkAcP36dSxduhT5+fm4ePEi5zgRaQgWZKRWKSkpJR7nUCaR+iQlJeGrr77C4cOHi9yEsHz5ct7FTKRBWJAREWm4jIwM3Lx5E4B0b0IgopKxIKN34tq1a0hNTUVubq7Cfqk86JiIiEhKeJclqdWtW7fQq1cvXL58WT53DHg5jAKAc8iIiIiU4MPFSa38/PxQq1Yt3L9/H4aGhrh69SpOnDgBNzc3REZGih2PiIhIkjhkSWpVtWpVHD16FA0bNoSZmRnOnDmDunXr4ujRo5g4cWKxz1ojIiKqyNhDRmpVUFAAExMTAC+Ls3/++QfAy7srr1+/LmY0IiIiyeIcMlIrV1dXxMbGolatWmjRogXmz58PXV1drF69mrfgExERFYNDlqRWBw8eRHZ2Nnr37o2bN2+ia9euuHHjBqpUqYLw8HB8/PHHYkckIiKSHBZk9M5lZGSgcuXK8jstiYiISBHnkNE7cfPmTRw8eBDPnz/nIpVERESlYEFGavXw4UN06NABTk5O6NKlC+7duwcAGD58OCZOnChyOiIiImliQUZqNWHCBFSqVAmpqakwNDSU7+/fvz8OHDggYjIiIiLp4l2WpFaHDh3CwYMHUbNmTYX9derUKfXB40RERBUVe8hIrbKzsxV6xl7JyMiAnp6eCImIiIikjwUZqcWrBWBbt26NzZs3y/fLZDIUFhZi/vz5aN++vVjxiIiIJI3LXpBaVK5cGcuWLUOjRo3w8ccfo2nTpjh69Ci6d++Oq1evIiMjA6dOnULt2rXFjkpERCQ5nENGajF79myMHj0anTp1wrVr17By5UqYmJggKysLvXv3xtixY2FtbS12TCIiIkliDxmpTVJSEoYPH45r165h9erV6N69u9iRiIiINAILMlK7pUuXYsKECXB2doaOjmIn7IULF0RKRUREJF0csiS1SklJwe7du1G5cmX06NGjSEFGRERERfGnJanNmjVrMHHiRHh6euLq1auoVq2a2JGIiIg0AgsyUotOnTrhzJkzWLp0KYYMGSJ2HCIiIo3CgozUoqCgAJcuXSqyQj8RERGVjpP6iYiIiETGlfqJiIiIRMaCjIiIiEhkLMiIiIiIRMaCjIiIiEhkLMiIiIiIRMaCjIiIiEhkLMiIiIiIRMaCjIiIiEhk/wdhP5C2PUEREwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#display heatmap of columns select numerical values thanks to seaborn\n",
    "sns.heatmap(df.select_dtypes(include = ['float64', 'int64']).corr(), annot = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c1d508",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "<img src=\"./image/heatmap.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2bb5b8f",
   "metadata": {},
   "source": [
    "## III) Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf39756e",
   "metadata": {},
   "source": [
    "You will remember that during our analysis we noticed that some columns were infested with undefined values.\\\n",
    "When a column contains too much undefined data, it is not usable.\n",
    "\n",
    "\n",
    "**Exerice :**\\\n",
    "Remove the columns from the dataset: ```Critic_Score```, ```Critic_Count```, ```User_Score```, ```User_Count```, ```Developer```, ```Rating```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1791d893",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before  (16719, 16)\n",
      "After  (16719, 10)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('video_games.csv')\n",
    "print('Before ', df.shape)\n",
    "#Remove columns: Critic_Score, Critic_Count, User_Score, User_Count, Developer, Rating.\n",
    "df = df.drop(['Critic_Score', 'Critic_Count', 'User_Score', 'User_Count', 'Developer', 'Rating'], axis=1)\n",
    "print('After ', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6e1ec4b",
   "metadata": {},
   "source": [
    "**Expected Result:**\\\n",
    "Before  (16719, 16)\\\n",
    "After  (16719, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e181fe73",
   "metadata": {},
   "source": [
    "When we have a column that contains few undefined values and to be able to work with it anyway it is frequent to replace the defined values by the mode.\\\n",
    "The mode is the value that appears most in the column.\\\n",
    "Be careful, this technique must be used sparingly or the results may be strongly biased.\n",
    "\n",
    "**Exercice :**\\\n",
    "Replace the undefined values in the ```Genre``` column with the mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "518be4a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling NaN values in Genre with the mode\n",
    "df['Genre'].fillna(df['Genre'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccef9fb",
   "metadata": {},
   "source": [
    "When a column that contains unique values also contains undefined values, we prefer to delete the rows that have undefined values.\\\n",
    "This method is used for proper names, or ids for example.\n",
    "\n",
    "**Exerice :**\\\n",
    "Delete the rows that have an undefined ```Name``` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d4168e32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before  (16719, 10)\n",
      "After  (16717, 10)\n"
     ]
    }
   ],
   "source": [
    "# Removing the 2 missing rows from Name\n",
    "\n",
    "print('Before ', df.shape)\n",
    "# code here ->\n",
    "df = df.dropna(axis=0, subset=['Name'])\n",
    "print('After ', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdcfba13",
   "metadata": {},
   "source": [
    "**Expected Result:**\\\n",
    "Before  (16719, 10)\\\n",
    "After  (16717, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310fd461",
   "metadata": {},
   "source": [
    "**Exercice :**\\\n",
    "Perform the same task for the undefined values in the column : ```Year_of_Release```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "e324bea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before  (16717, 10)\n",
      "After  (16448, 10)\n"
     ]
    }
   ],
   "source": [
    "# Droping 269 missing rows from Year_of_Release\n",
    "\n",
    "print('Before ', df.shape)\n",
    "# code here -> \n",
    "df = df.dropna(axis=0, subset=['Year_of_Release'])\n",
    "print('After ', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd130a7a",
   "metadata": {},
   "source": [
    "**Expected Result:**\\\n",
    "Before  (16717, 10)\\\n",
    "After  (16448, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597ace03",
   "metadata": {},
   "source": [
    "**Exercice :**\\\n",
    "Perform the same task for the undefined values in the column : ```Publisher```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "31b6c375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before  (16448, 10)\n",
      "After  (16416, 10)\n",
      "--------------------\n",
      "Name               0\n",
      "Platform           0\n",
      "Year_of_Release    0\n",
      "Genre              0\n",
      "Publisher          0\n",
      "NA_Sales           0\n",
      "EU_Sales           0\n",
      "JP_Sales           0\n",
      "Other_Sales        0\n",
      "Global_Sales       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Removing the 54 missing rows from Publisher\n",
    "\n",
    "print('Before ', df.shape)\n",
    "# code here -> \n",
    "df = df.dropna(axis=0, subset=['Publisher'])\n",
    "print('After ', df.shape)\n",
    "print('-'*20)\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accf36be",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "Before  (16448, 10)\\\n",
    "After  (16416, 10)\\\n",
    "--------------------\\\n",
    "Name       &nbsp;        0\\\n",
    "Platform    &nbsp;       0\\\n",
    "Year_of_Release &nbsp;   0\\\n",
    "Genre        &nbsp;      0\\\n",
    "Publisher   &nbsp;       0\\\n",
    "NA_Sales    &nbsp;       0\\\n",
    "EU_Sales     &nbsp;      0\\\n",
    "JP_Sales    &nbsp;       0\\\n",
    "Other_Sales  &nbsp;      0\\\n",
    "Global_Sales   &nbsp;    0\\\n",
    "dtype: int64"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "285a0d49",
   "metadata": {},
   "source": [
    "## IV) Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b907ff9",
   "metadata": {},
   "source": [
    "We now have clean data that can be analyzed.\\\n",
    "The previous steps are very important for our analysis to be relevant, that's the job of a data scientist.\n",
    "\n",
    "Now we will perform an analysis on the global sales of video games.\n",
    "\n",
    "**Exercice :**\\\n",
    "Get the 10 best-selling games with their ```Global_sales``` and their ```name```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "6fdc137d",
   "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>Name</th>\n",
       "      <th>Global_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>82.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>40.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>35.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>32.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tetris</td>\n",
       "      <td>30.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New Super Mario Bros.</td>\n",
       "      <td>29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wii Play</td>\n",
       "      <td>28.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>New Super Mario Bros. Wii</td>\n",
       "      <td>28.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Duck Hunt</td>\n",
       "      <td>28.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name  Global_Sales\n",
       "0                 Wii Sports         82.53\n",
       "1          Super Mario Bros.         40.24\n",
       "2             Mario Kart Wii         35.52\n",
       "3          Wii Sports Resort         32.77\n",
       "4   Pokemon Red/Pokemon Blue         31.37\n",
       "5                     Tetris         30.26\n",
       "6      New Super Mario Bros.         29.80\n",
       "7                   Wii Play         28.92\n",
       "8  New Super Mario Bros. Wii         28.32\n",
       "9                  Duck Hunt         28.31"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 Games by Global Sales\n",
    "df[['Name', 'Global_Sales']].sort_values(by='Global_Sales', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c21cd592",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "<img src=\"./image/games_per_gs.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00cdb5f8",
   "metadata": {},
   "source": [
    "We have a dataframe that contains the 10 best selling games in history.\\\n",
    "It's good but it's not very visual, in general to present data analysis work, a part of data visualization is done in order to render our results in graphical form.\n",
    "To realize data visualization, there are several interesting libraries and notably Seaborn a library based on the matplotlib library.\n",
    "\n",
    "**Exercice :**\\\n",
    "Display a graph to see the 10 best selling games in history."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f6a1841f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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11my55ZarVMvs2bMbHAAAAPB50bSxC2D5mjZtmokTJ+b444/PFVdckW233TZVVVX56le/mq222mqVxzvnnHOy5557JkmuueaabLjhhrnlllty6KGHZuzYsTniiCMycuTIJEmfPn1y6aWXpqqqKpdffnlatGiRJBkyZEhOO+20lZrvyCOPzKhRo/KXv/wlSTJ58uTccMMN5RVcS3zlK19pcD5+/Ph07tw5U6dOzRZbbFFuHzlyZA4++ODlznfxxRdn+PDh+fa3v50kOfXUU/PYY4/l4osvTnV19XLve/7559OmTZskyQcffJC2bdvmxhtvTEVFRYN+X/va1xqEaocffvgK53vjjTdSWVmZPfbYI+uuu2569OiRHXbYYbl1/KuxY8dmzJgxK90fAAAA1iZWSn3GfeUrX8nf/va33HbbbRk6dGhqa2uz7bbbZuLEias81k477VT+u2PHjunbt2+mTZuWJHn22WczceLE8uqsNm3aZK+99srixYvz2muvle8bOHDgSs/XuXPn7Lvvvpk4cWImTJiQfffdN+utt95S/V5++eUcfvjh2WSTTVJRUZGePXsmSd54440G/T5p7mnTpmXQoEEN2gYNGlR+xuXp27dvpkyZkilTpuRPf/pTvvWtb+WQQw7JU089tcL5P2m+Qw45JHPnzs0mm2yS448/PrfccksWLVq0wlo+btSoUZk1a1b5ePPNN1f6XgAAAPiss1JqLdCiRYvsueee2XPPPfODH/wg3/jGN3LOOedk+PDhWWedj3LF+vr6cv/V2YR7zpw5+eY3v7nMPY8+vjF569atV2ncY489tvwK4C9+8Ytl9tlvv/2y0UYb5aqrrkrXrl2zePHibLHFFkttpL6qc6+sZs2apXfv3uXzAQMG5NZbb80ll1yS//mf/1nt+bt3757p06fnvvvuy7333ptvf/vb+clPfpJJkyZl3XXX/cT7mzdvnubNm6/SnAAAALC2sFJqLbT55puXNyDv3LlzkqSurq58/eObnn/cY489Vv77vffey0svvZR+/fol+Whj8qlTp6Z3795LHc2aNVvtWocOHZoFCxZk4cKFy9wH6u9//3umT5+es88+O7vvvnv69euX9957b7Xm6tevXyZPntygbfLkydl8881XeawmTZo0+OXA1Z2vZcuW2W+//XLppZemtrY2jz76aJ5//vlVrgcAAAA+b6yU+gz7+9//nkMOOSTHHntsttpqq7Rt2zZPPfVULrroohxwwAFJPgo9dtxxx1xwwQXZeOON88477+Tss89e5njnnntuOnXqlA022CDf//73s9566+XAAw9Mkpx55pnZcccdM2LEiHzjG99I69atM3Xq1Nx7770ZN27caj9DkyZNyq+zNWnSZKnrHTp0SKdOnfLf//3f6dKlS954442cddZZqzXXGWeckUMPPTQDBgzIHnvskdtvvz0333xz7rvvvhXet2jRorz99ttJkn/+85+58cYbM3Xq1Jx55plrNN/EiRPz4Ycf5ktf+lJatWqV//mf/0nLli2z0UYbJfno9by33nor11577Wo9LwAAAKzNhFKfYW3atMmXvvSl/OxnP8uMGTOycOHCdO/ePccff3y+973vlfuNHz8+xx13XLbbbrv07ds3F110Ub785S8vNd4FF1yQk08+OS+//HK22Wab3H777eVVUFtttVUmTZqU73//+9l1111TX1+fXr165bDDDlvj5/jXDcM/bp111skNN9yQk046KVtssUX69u2bSy+9NIMHD17leQ488MD8/Oc/z8UXX5yTTz45G2+8cSZMmPCJY73wwgvp0qVLkqRVq1bp1atXLr/88nz9619fo/nat2+fCy64IKeeemo+/PDDbLnllrn99tvTqVOnJB+tbvvXfbMAAADgi6JU//HNiPhcqq2tTXV1dd577720b9++scthNc2ePTvt2rXL72/rmdatvXkLwOfD7kNmNHYJAMCnbMn311mzZq14oUqBNQEAAABAEqEUAAAAAI3AnlJfAIMHD463NAEAAIDPEiulAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAACicUAoAAACAwgmlAAAAAChc08YuAFg1g6ueTUVFRWOXAQAAAGvESikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACieUAgAAAKBwQikAAAAACte0sQsAVs3YsWPTvHnzxi4DgEYyevToxi4BAOBTYaUUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUhZo4cWLat2/f2GU0itdffz2lUilTpkxJktTW1qZUKmXmzJmNWhcAAAA0BqEUSZLhw4enVCrlhBNOWOraiSeemFKplOHDh6/xPIcddlheeumlNRpj9OjR2WabbRq0PfTQQ2nfvn1GjhyZ+vr6T3Xsf3X33XenVCrl7bffbtDepUuX9OzZs0HbkiDq/vvvT/fu3VNXV5ctttgiSbLzzjunrq4u7dq1W+16AQAAYG0llKKse/fuueGGGzJ37txy27x58/LrX/86PXr0WOPxFy5cmJYtW2b99ddf47E+7s4778xee+2VU089NZdccklKpdIqj1FfX59FixatVN9ddtklTZs2TW1tbblt2rRpmTt3bt577728/vrr5faampo0b948gwYNSpMmTVJZWZmmTZsmSZo1a5bKysrVqhcAAADWdkIpyrbddtt07949N998c7nt5ptvTo8ePTJgwIAGfe++++7ssssuad++fTp16pRhw4ZlxowZ5etLVgjdeOONqaqqSosWLXL99dcv8/W9yy+/PL169UqzZs3St2/fXHfddStd869//escfPDBueiii/LDH/6w3H7ddddl4MCBadu2bSorK/O1r30t77zzTvn6klfn7rrrrmy33XZp3rx5/ud//idjxozJs88+m1KplFKplIkTJy41Z5s2bbL99ts3CKVqa2uzyy67ZNCgQUu177jjjmnRooXX9wAAAOBjhFI0cOyxx2bChAnl8/Hjx+eYY45Zqt/777+fU089NU899VTuv//+rLPOOjnooIOyePHiBv3OOuusnHzyyZk2bVr22muvpca55ZZbcvLJJ+e0007Ln//853zzm9/MMccck5qamk+s9Re/+EWOOeaYjB8/PiNGjGhwbeHChTnvvPPy7LPP5tZbb83rr7++zNcPzzrrrFxwwQWZNm1a9txzz5x22mnp379/6urqUldXl8MOO2yZc1dXVzeosaamJoMHD05VVVWD9tra2lRXV3/iswAAAMAXTdPGLoDPliOPPDKjRo3KX/7ylyTJ5MmTc8MNNzRY/ZMkX/nKVxqcjx8/Pp07d87UqVPLeyYlyciRI3PwwQcvd76LL744w4cPz7e//e0kyamnnprHHnssF1988QrDnGnTpmXEiBH51a9+lSOOOGKp68cee2z570022SSXXnpptt9++8yZMydt2rQpXzv33HOz5557ls/btGmTpk2bprKycrlzJx+FUueff37q6urSpUuXTJo0KWeccUYWLVqUyy+/PEny6quv5o033ljtUGr+/PmZP39++Xz27NmrNQ4AAAB8FlkpRQOdO3fOvvvum4kTJ2bChAnZd999s9566y3V7+WXX87hhx+eTTbZJBUVFeUNvt94440G/QYOHLjC+aZNm5ZBgwY1aBs0aFCmTZu2wvs23HDDbLvttvnJT36Surq6pa7/6U9/yn777ZcePXqkbdu2qaqqWq36lmfnnXdOs2bNUltbm6lTp2bu3LnZdtttM3DgwLz77rt57bXXUltbm5YtW2bHHXdcrTnGjh2bdu3alY/u3buv1jgAAADwWSSUYinHHntsJk6cmGuuuabBiqOP22+//fKPf/wjV111VR5//PE8/vjjSZIFCxY06Ne6det/S41t27bNfffdl9atW6e6urpBMPX+++9nr732SkVFRa6//vo8+eSTueWWWz7V+lq1apUddtghNTU1qampyS677JImTZpk3XXXzc4771xuHzRoUJo1a7Zac4waNSqzZs0qH2+++eZqjQMAAACfRUIpljJ06NAsWLAgCxcuXOY+UH//+98zffr0nH322dl9993Tr1+/vPfee6s1V79+/TJ58uQGbZMnT87mm2/+ifd26NAh9913XyoqKjJ48OD87W9/S5K8+OKL+fvf/54LLrggu+66azbbbLMGm5yvSLNmzfLhhx+uVN/q6urU1tamtrY2gwcPLrfvtttuqa2tzaRJk9ZoP6nmzZunoqKiwQEAAACfF0IpltKkSZNMmzYtU6dOTZMmTZa63qFDh3Tq1Cn//d//nVdeeSUPPPBATj311NWa64wzzsjEiRNz+eWX5+WXX85//dd/5eabb87pp5++Uve3b98+9957bzp06FAOpnr06JFmzZrlsssuy6uvvprbbrst55133kqN17Nnz7z22muZMmVK/u///q/Bnk7/qrq6Oi+//HLuueee8uuBSVJVVZVbb701b775pk3OAQAAYDmEUizTilbmrLPOOrnhhhvypz/9KVtssUVOOeWU/OQnP1mteQ488MD8/Oc/z8UXX5z+/fvnyiuvzIQJExqsPPok7dq1yx//+Mest956qaqqyoIFCzJx4sT89re/zeabb54LLrggF1988UqN9ZWvfCVDhw5NdXV1OnfunN/85jfL7bvTTjulefPmqa+vz3bbbVdu/9KXvpSFCxemTZs22X777Vf6OQAAAOCLpFRfX1/f2EUAn2z27Nlp165dzjrrrDRv3ryxywGgkYwePbqxSwAAWKEl319nzZq1wq1orJQCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHCl+vr6+sYuAvhks2fPTrt27TJr1qxUVFQ0djkAAACwTCv7/dVKKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBCKQAAAAAKJ5QCAAAAoHBNG7sAYNW8dc4jmd28dWOXAfCFt+EFuzZ2CQAAazUrpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFDqC6K2tjalUikzZ85MkkycODHt27dv1JoAAACALy6h1FrmiiuuSNu2bbNo0aJy25w5c7Luuutm8ODBDfouCaJmzJiRnXfeOXV1dWnXrl2S5LDDDstLL720wrkmTZqUIUOGpGPHjmnVqlX69OmTo48+OgsWLPjUn+tf/WuItjpef/31lEql8tGxY8dUVVXloYce+vQKXU1CQQAAAL7ohFJrmerq6syZMydPPfVUue2hhx5KZWVlHn/88cybN6/cXlNTkx49eqRXr15p1qxZKisrUyqVkiQtW7bM+uuvv9x5pk6dmqFDh2bgwIF58MEH8/zzz+eyyy5Ls2bN8uGHH/77HjDJwoULP9Xx7rvvvtTV1eXBBx9M165dM2zYsPy///f/PtU5VsWn/XwAAACwNhJKrWX69u2bLl26pLa2ttxWW1ubAw44IBtvvHEee+yxBu3V1dXlv1fl9b0//vGPqayszEUXXZQtttgivXr1ytChQ3PVVVelZcuWDca49dZb06dPn7Ro0SJ77bVX3nzzzQZjXX755eVgrG/fvrnuuusaXC+VSrn88suz//77p3Xr1jn++OPLdXfo0CGlUinDhw9Pkvzud7/LlltumZYtW6ZTp07ZY4898v7776/wM+vUqVMqKyuzxRZb5Hvf+15mz56dxx9/vHz9z3/+c/bee++0adMmG2ywQY466qj83//9X/n6iuZcvHhxzj333Gy44YZp3rx5ttlmm9x9993le5es1rrxxhtTVVWVFi1a5Prrr88xxxyTWbNmlVdxjR49eoXPAAAAAJ83Qqm1UHV1dWpqasrnNTU1GTx4cKqqqsrtc+fOzeOPP14Od1ZVZWVleXXRinzwwQf58Y9/nGuvvTaTJ0/OzJkz89WvfrV8/ZZbbsnJJ5+c0047LX/+85/zzW9+M8ccc0yD+pNk9OjROeigg/L8889nzJgxuemmm5Ik06dPT11dXX7+85+nrq4uhx9+eI499thMmzYttbW1Ofjgg1NfX79SzzR37txce+21SZJmzZolSWbOnJkhQ4ZkwIABeeqpp3L33Xfn//2//5dDDz00ST5xzp///Of56U9/mosvvjjPPfdc9tprr+y///55+eWXG8x91lln5eSTT860adNSXV2dSy65JBUVFamrq0tdXV1OP/30peqdP39+Zs+e3eAAAACAz4umjV0Aq666ujojR47MokWLMnfu3DzzzDOpqqrKwoULc8UVVyRJHn300cyfP3+1Q6lDDjkk99xzT6qqqlJZWZkdd9wxu+++e77+9a+noqKi3G/hwoUZN25cvvSlLyVJrrnmmvTr1y9PPPFEdthhh1x88cUZPnx4vv3tbydJTj311Dz22GO5+OKLG9T2ta99Lcccc0z5/LXXXkuSrL/++uUVXTNmzMiiRYty8MEHZ6ONNkqSbLnllp/4LDvvvHPWWWedfPDBB6mvr892222X3XffPUkybty4DBgwIOeff365//jx49O9e/e89NJLmTNnzgrnvPjii3PmmWeWg7gLL7wwNTU1ueSSS/KLX/yi3G/kyJE5+OCDy+ft2rVLqVRKZWXlcuseO3ZsxowZ84nPBwAAAGsjK6XWQoMHD87777+fJ598Mg899FA23XTTdO7cOVVVVeV9pWpra7PJJpukR48eqzVHkyZNMmHChPz1r3/NRRddlG7duuX8889P//79U1dXV+7XtGnTbL/99uXzzTbbLO3bt8+0adOSJNOmTcugQYMajD1o0KDy9SUGDhz4iTVtvfXW2X333bPlllvmkEMOyVVXXZX33nvvE++78cYb88wzz+Smm25K7969M3HixKy77rpJkmeffTY1NTVp06ZN+dhss82SfBSCrWjO2bNn529/+9un9nz/atSoUZk1a1b5+NfXIgEAAGBtJpRaC/Xu3TsbbrhhampqUlNTk6qqqiRJ165d07179zzyyCOpqanJkCFD1niubt265aijjsq4cePywgsvZN68eeXVWJ+m1q1bf2KfJk2a5N57781dd92VzTffPJdddln69u1bXlW1PN27d0+fPn1y0EEH5fzzz89BBx2U+fPnJ/nolwv322+/TJkypcHx8ssvZ7fddlvtOVfn+f5V8+bNU1FR0eAAAACAzwuh1Fqquro6tbW1qa2tzeDBg8vtu+22W+6666488cQTq/3q3vJ06NAhXbp0abCx+KJFixr8EuD06dMzc+bM9OvXL0nSr1+/TJ48ucE4kydPzuabb77CuZbs+fSvv/RXKpUyaNCgjBkzJs8880yaNWuWW265ZaWf4T/+4z/StGnT/PKXv0ySbLvttnnhhRfSs2fP9O7du8GxJEha3pwVFRXp2rXraj/fv/tXDAEAAOCzzJ5Sa6nq6uqceOKJWbhwYXmlVJJUVVVlxIgRWbBgwRqFUldeeWWmTJmSgw46KL169cq8efNy7bXX5oUXXshll11W7rfuuuvmO9/5Ti699NI0bdo0I0aMyI477pgddtghSXLGGWfk0EMPzYABA7LHHnvk9ttvz80335z77rtvhfNvtNFGKZVKueOOO7LPPvukZcuWeeGFF3L//ffny1/+ctZff/08/vjjeffdd8sB2MoolUo56aSTMnr06Hzzm9/MiSeemKuuuiqHH354vvvd76Zjx4555ZVXcsMNN+Tqq6/OU089tcI5zzjjjJxzzjnp1atXttlmm0yYMCFTpkzJ9ddfv8I6evbsmTlz5uT+++/P1ltvnVatWqVVq1Yr/RwAAACwtrNSai1VXV2duXPnpnfv3tlggw3K7VVVVfnnP/+Zvn37pkuXLqs9/g477JA5c+bkhBNOSP/+/VNVVZXHHnsst956a4MQrFWrVjnzzDPzta99LYMGDUqbNm1y4403lq8feOCB+fnPf56LL744/fv3z5VXXpkJEyY0WN21LN26dcuYMWNy1llnZYMNNsiIESNSUVGRBx98MPvss0823XTTnH322fnpT3+avffee5We7eijjy5v0L5kpdOHH36YL3/5y9lyyy0zcuTItG/fPuuss84nznnSSSfl1FNPzWmnnZYtt9wyd999d2677bb06dNnhTXsvPPOOeGEE3LYYYelc+fOueiii1bpGQAAAGBtV6pf8tv2sIomTpyYkSNHZubMmY1dyhfC7Nmz065du0wdeVfaNl/1PaoA+HRteMGujV0CAMBn0pLvr7NmzVrh/shWSgEAAABQOKEUAAAAAIUTSrHahg8f7tU9AAAAYLUIpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAoXNPGLgBYNd3G7JyKiorGLgMAAADWiJVSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABSuaWMXAKyay4YfkhbrrtvYZQB8Lpx24x2NXQIAwBeWlVIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFG6tDqUmTpyY9u3bN3YZn3nDhw/PgQce+G+dY/DgwRk5cuS/dY7Pmp49e+aSSy5p7DIAAABgrdSoodTw4cNTKpVSKpXSrFmz9O7dO+eee24WLVrUmGU1miWfRalUSkVFRbbffvv8/ve//7fNd8wxx+Tss89eau527dpl0KBBeeCBB/5tc3+WTZw4scHn0aZNm2y33Xa5+eabG7s0AAAA+Nxo9JVSQ4cOTV1dXV5++eWcdtppGT16dH7yk580dlmNZsKECamrq8tTTz2VQYMG5T/+4z/y/PPPf+rzfPjhh7njjjuy//77LzX35MmTs95662XYsGF59dVXP/W51wYVFRWpq6tLXV1dnnnmmey111459NBDM3369MYuDQAAAD4XGj2Uat68eSorK7PRRhvlW9/6VvbYY4/cdtttSZL33nsvX//619OhQ4e0atUqe++9d15++eXljvXuu+9m4MCBOeiggzJ//vwsXrw4Y8eOzcYbb5yWLVtm6623zu9+97ty/9ra2pRKpdxzzz0ZMGBAWrZsmSFDhuSdd97JXXfdlX79+qWioiJf+9rX8sEHH5Tvmz9/fk466aSsv/76adGiRXbZZZc8+eSTS417//33Z+DAgWnVqlV23nnnlQo02rdvn8rKymy66aY577zzsmjRotTU1JSvv/nmmzn00EPTvn37dOzYMQcccEBef/318vUPP/wwp556atq3b59OnTrlu9/9burr65ea55FHHsm6666b7bfffqm5t9hii1x++eWZO3du7r333iTJpEmTssMOO6R58+bp0qVLzjrrrBWuaLvzzjvTrl27XH/99StV95JXDM8///xssMEGad++fXnV3BlnnJGOHTtmww03zIQJExrM8/zzz2fIkCFp2bJlOnXqlP/8z//MnDlzlhr34osvTpcuXdKpU6eceOKJWbhw4Qr/HUqlUiorK1NZWZk+ffrkRz/6UdZZZ50899xzy+z/+uuvp1QqZcqUKeW2mTNnplQqpba2ttz25z//OXvvvXfatGmTDTbYIEcddVT+7//+b4W1AAAAwOdRo4dS/6ply5ZZsGBBko8Chaeeeiq33XZbHn300dTX12efffZZZqDw5ptvZtddd80WW2yR3/3ud2nevHnGjh2ba6+9NldccUVeeOGFnHLKKTnyyCMzadKkBveOHj0648aNyyOPPFIOTy655JL8+te/zp133pk//vGPueyyy8r9v/vd7+amm27KNddck6effjq9e/fOXnvtlX/84x8Nxv3+97+fn/70p3nqqafStGnTHHvssSv9OSxatCi/+tWvkiTNmjVLkixcuDB77bVX2rZtm4ceeiiTJ09OmzZtMnTo0PJn9tOf/jQTJ07M+PHj8/DDD+cf//hHbrnllqXGv+2227LffvulVCotc/6WLVsmSRYsWJC33nor++yzT7bffvs8++yzufzyy/OrX/0qP/rRj5Z5769//escfvjhuf7663PEEUesVN1J8sADD+Rvf/tbHnzwwfzXf/1XzjnnnAwbNiwdOnTI448/nhNOOCHf/OY389e//jVJ8v7772evvfZKhw4d8uSTT+a3v/1t7rvvvowYMaJBPTU1NZkxY0ZqampyzTXXZOLEiZk4ceJK/1t8+OGHueaaa5Ik22677Urf969mzpyZIUOGZMCAAXnqqady99135//9v/+XQw89dJn958+fn9mzZzc4AAAA4POiaWMXsER9fX3uv//+3HPPPfnOd76Tl19+ObfddlsmT56cnXfeOUly/fXXp3v37rn11ltzyCGHlO+dPn169txzzxx00EG55JJLUiqVMn/+/Jx//vm57777stNOOyVJNtlkkzz88MO58sorU1VVVb7/Rz/6UQYNGpQkOe644zJq1KjMmDEjm2yySZLkP/7jP1JTU5Mzzzwz77//fi6//PJMnDgxe++9d5Lkqquuyr333ptf/epXOeOMM8rj/vjHPy7Pc9ZZZ2XffffNvHnz0qJFi+V+DocffniaNGmSuXPnZvHixenZs2c5tLjxxhuzePHiXH311eUwacKECWnfvn1qa2vz5S9/OZdccklGjRqVgw8+OElyxRVX5J577llqnt///vf52c9+tswaPvjgg5x99tlp0qRJqqqq8stf/jLdu3fPuHHjUiqVstlmm+Vvf/tbzjzzzPzwhz/MOuv8/9nmL37xi3z/+9/P7bffXn72lak7STp27JhLL70066yzTvr27ZuLLrooH3zwQb73ve8lSUaNGpULLrggDz/8cL761a/m17/+debNm5drr702rVu3TpKMGzcu++23Xy688MJssMEGSZIOHTpk3LhxadKkSTbbbLPsu+++uf/++3P88ccv999h1qxZadOmTZJk7ty5WXfddfPf//3f6dWr13Lv+STjxo3LgAEDcv7555fbxo8fn+7du+ell17Kpptu2qD/2LFjM2bMmNWeDwAAAD7LGj2UuuOOO9KmTZssXLgwixcvzte+9rWMHj06999/f5o2bZovfelL5b6dOnVK3759M23atHLb3Llzs+uuu+ZrX/tag19Ce+WVV/LBBx9kzz33bDDfggULMmDAgAZtW221VfnvDTbYIK1atSoHUkvannjiiSTJjBkzsnDhwnKIlSTrrrtudthhhwZ1/eu4Xbp0SZK888476dGjx3I/j5/97GfZY4898uqrr+aUU07JpZdemo4dOyZJnn322bzyyitp27Ztg3vmzZuXGTNmZNasWamrq2vwmTVt2jQDBw5s8ArftGnT8re//S277757g3E+Hoh17tw5v/rVr7LVVltl9OjR2WmnnRqsqho0aFDmzJmTv/71r+Xn+d3vfpd33nknkydPbvBa4CfVvUT//v0bBFwbbLBBtthii/J5kyZN0qlTp7zzzjvl59h6663LgdSSuhYvXpzp06eXQ6n+/funSZMm5T5dunT5xH262rZtm6effjrJRyHdfffdlxNOOCGdOnXKfvvtt8J7l+fZZ59NTU1NOez6uBkzZiwVSo0aNSqnnnpq+Xz27Nnp3r37as0NAAAAnzWNHkpVV1fn8ssvT7NmzdK1a9c0bbpqJTVv3jx77LFH7rjjjpxxxhnp1q1bkpT3FbrzzjvLbR+/5+PWXXfd8t+lUqnB+ZK2xYsXr1Jdyxo3ySeOU1lZmd69e6d3796ZMGFC9tlnn0ydOjXrr79+5syZk+222668T9PHde7ceaXruu2227LnnnsutWJrSSDWrl27VRpviQEDBuTpp5/O+PHjM3DgwPIzr2zdy/rcP41/i9UZY5111knv3r3L51tttVX++Mc/5sILL1xmKLUkTPt4+Pevr5nOmTOnvIrrXy0JLT+uefPmS/23CgAAAJ8Xjb6nVOvWrdO7d+/06NGjQSDVr1+/LFq0KI8//ni57e9//3umT5+ezTffvNy2zjrr5Lrrrst2222X6urq/O1vf0uSbL755mnevHneeOONcsiz5FiT1Sa9evVKs2bNMnny5HLbwoUL8+STTzao69Owww47ZLvttsuPf/zjJB/tZ/Tyyy9n/fXXX+qZ2rVrl3bt2qVLly4NPrNFixblT3/6U4Nxf//73+eAAw5Yar4lgdi/BlL9+vUr7+m1xOTJk9O2bdtsuOGG5bZevXqlpqYmv//97/Od73yn3P5Jda+ufv365dlnn83777/foK4lr/992pasIluWJZ9ZXV1due3jm54nH30OL7zwQnr27LnU5/Dx1V4AAADwRdDoodTy9OnTJwcccECOP/74PPzww3n22Wdz5JFHplu3bksFKk2aNMn111+frbfeOkOGDMnbb7+dtm3b5vTTT88pp5ySa665JjNmzMjTTz+dyy67rLxp9epo3bp1vvWtb+WMM87I3XffnalTp+b444/PBx98kOOOO25NH3spI0eOzJVXXpm33norRxxxRNZbb70ccMABeeihh/Laa6+ltrY2J510Unnz75NPPjkXXHBBbr311rz44ov59re/nZkzZ5bHe+edd/LUU09l2LBhK13Dt7/97bz55pv5zne+kxdffDG///3vc8455+TUU09t8Lpdkmy66aapqanJTTfdlJEjRybJStW9Oo444oi0aNEiRx99dP785z+npqYm3/nOd3LUUUeVX91bXfX19Xn77bfz9ttv57XXXst///d/55577llmmJd8tDH8jjvumAsuuCDTpk3LpEmTcvbZZzfoc+KJJ+Yf//hHDj/88Dz55JOZMWNG7rnnnhxzzDH58MMP16heAAAAWNt8ZkOp5KPNsLfbbrsMGzYsO+20U+rr6/OHP/xhqdexko/2TvrNb36T/v37Z8iQIXnnnXdy3nnn5Qc/+EHGjh2bfv36ZejQobnzzjuz8cYbr1FdF1xwQb7yla/kqKOOyrbbbptXXnkl99xzTzp06LBG4y7L0KFDs/HGG+fHP/5xWrVqlQcffDA9evTIwQcfnH79+uW4447LvHnzUlFRkSQ57bTTctRRR+Xoo4/OTjvtlLZt2+aggw4qj3f77bdnhx12yHrrrbfSNXTr1i1/+MMf8sQTT2TrrbfOCSeckOOOO26p0GWJvn375oEHHshvfvObnHbaaStV9+po1apV7rnnnvzjH//I9ttvn//4j//I7rvvnnHjxq32mEvMnj07Xbp0SZcuXdKvX7/89Kc/zbnnnpvvf//7y71n/PjxWbRoUbbbbruMHDlyqV8n7Nq1ayZPnpwPP/wwX/7yl7Pllltm5MiRad++/VLhHgAAAHzeleo//k4Wn3v7779/dtlll3z3u99t7FJYRbNnz067du3yo4O+nBbLCGYBWHWn3XhHY5cAAPC5s+T766xZs1a4GMXyjC+YXXbZJYcffnhjlwEAAAB8wTX6r+9RLCukAAAAgM8CK6UAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCrXYoNWPGjJx99tk5/PDD88477yRJ7rrrrrzwwgufWnEAAAAAfD6tVig1adKkbLnllnn88cdz8803Z86cOUmSZ599Nuecc86nWiAAAAAAnz+rFUqdddZZ+dGPfpR77703zZo1K7cPGTIkjz322KdWHAAAAACfT6sVSj3//PM56KCDlmpff/3183//939rXBQAAAAAn2+rFUq1b98+dXV1S7U/88wz6dat2xoXBQAAAMDn22qFUl/96ldz5pln5u23306pVMrixYszefLknH766fn617/+adcIAAAAwOfMaoVS559/fjbbbLN07949c+bMyeabb57ddtstO++8c84+++xPu0YAAAAAPmears5NzZo1y1VXXZUf/OAH+fOf/5w5c+ZkwIAB6dOnz6ddHwAAAACfQ6sVSi3Ro0eP9OjR49OqBQAAAIAviNUKperr6/O73/0uNTU1eeedd7J48eIG12+++eZPpTgAAAAAPp9WK5QaOXJkrrzyylRXV2eDDTZIqVT6tOsCAAAA4HNstUKp6667LjfffHP22WefT7seAAAAAL4AViuUateuXTbZZJNPuxZgJXxn4m9TUVHR2GUAAADAGllndW4aPXp0xowZk7lz537a9QAAAADwBbBaK6UOPfTQ/OY3v8n666+fnj17Zt11121w/emnn/5UigMAAADg82m1Qqmjjz46f/rTn3LkkUfa6BwAAACAVbZaodSdd96Ze+65J7vsssunXQ8AAAAAXwCrtadU9+7dbbQMAAAAwGpbrVDqpz/9ab773e/m9ddf/5TLAQAAAOCLYLVe3zvyyCPzwQcfpFevXmnVqtVSG53/4x//+FSKAwAAAODzabVCqUsuueRTLgMAAACAL5LV/vU9AAAAAFhdqxVKfdy8efOyYMGCBm02QQcAAABgRVZro/P3338/I0aMyPrrr5/WrVunQ4cODQ4AAAAAWJHVCqW++93v5oEHHsjll1+e5s2b5+qrr86YMWPStWvXXHvttZ92jQAAAAB8zpTq6+vrV/WmHj165Nprr83gwYNTUVGRp59+Or179851112X3/zmN/nDH/7w76gVvtBmz56ddu3a5SfH3JaWzVo3djkAn0knXjGksUsAAPjCW/L9ddasWSvc4mm1Vkr94x//yCabbJLko/2j/vGPfyRJdtlllzz44IOrMyQAAAAAXyCrFUptsskmee2115Ikm222Wf73f/83SXL77benffv2n1pxAAAAAHw+rVYodcwxx+TZZ59Nkpx11ln5xS9+kRYtWuSUU07JGWec8akWCAAAAMDnT9PVuemUU04p/73HHnvkxRdfzJ/+9Kf07t07W2211adWHAAAAACfT6sUSs2dOzf3339/hg0bliQZNWpU5s+fX77+2GOP5dxzz02LFi0+3SoBAAAA+FxZpVDqmmuuyZ133lkOpcaNG5f+/funZcuWSZIXX3wxXbp0abCSCgAAAAD+1SrtKXX99dfnP//zPxu0/frXv05NTU1qamryk5/8pLzpOQAAAAAszyqFUq+88kq23HLL8nmLFi2yzjr//xA77LBDpk6d+ulVBwAAAMDn0iq9vjdz5swGe0i9++67Da4vXry4wXUAAAAAWJZVWim14YYb5s9//vNyrz/33HPZcMMN17goAAAAAD7fVimU2mefffLDH/4w8+bNW+ra3LlzM2bMmOy7776fWnEAAAAAfD6t0ut73/ve9/K///u/6du3b0aMGJFNN900STJ9+vSMGzcuixYtyve+971/S6EAAAAAfH6sUii1wQYb5JFHHsm3vvWtnHXWWamvr0+SlEql7LnnnvnlL3+ZDTbY4N9SKAAAAACfH6sUSiXJxhtvnLvvvjv/+Mc/8sorryRJevfunY4dO37qxQEAAADw+bTKodQSHTt2zA477PBp1gIAAADAF8QqbXQOAAAAAJ8GoRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKwb9Zz549c8kllzR2GQAAAPCZIpTiC6lUKq3wGD169HLvff3111MqlTJlypSVmuvJJ5/Mf/7nf346hQMAAMDnRNPGLgAaQ11dXfnvG2+8MT/84Q8zffr0clubNm3WeI4FCxakWbNm6dy58xqPBQAAAJ83VkrxhVRZWVk+2rVrl1Kp1KDthhtuSL9+/dKiRYtsttlm+eUvf1m+d+ONN06SDBgwIKVSKYMHD06SDB8+PAceeGB+/OMfp2vXrunbt2+Shq/v1dfXZ/To0enRo0eaN2+erl275qSTTir02QEAAOCzwEop+BfXX399fvjDH2bcuHEZMGBAnnnmmRx//PFp3bp1jj766DzxxBPZYYcdct9996V///5p1qxZ+d77778/FRUVuffee5c59k033ZSf/exnueGGG9K/f/+8/fbbefbZZ5fZd/78+Zk/f375fPbs2Z/ugwIAAEAjEkrBvzjnnHPy05/+NAcffHCSj1ZGTZ06NVdeeWWOPvro8ut4nTp1SmVlZYN7W7dunauvvrpBUPVxb7zxRiorK7PHHntk3XXXTY8ePbLDDjsss+/YsWMzZsyYT/HJAAAA4LPD63vwMe+//35mzJiR4447Lm3atCkfP/rRjzJjxoxPvH/LLbdcbiCVJIccckjmzp2bTTbZJMcff3xuueWWLFq0aJl9R40alVmzZpWPN998c7WfCwAAAD5rrJSCj5kzZ06S5KqrrsqXvvSlBteaNGnyife3bt16hde7d++e6dOn57777su9996bb3/72/nJT36SSZMmZd11123Qt3nz5mnevPkqPgEAAACsHYRS8DEbbLBBunbtmldffTVHHHHEMvssWQn14YcfrtYcLVu2zH777Zf99tsvJ554YjbbbLM8//zz2XbbbVe7bgAAAFjbCKXgX4wZMyYnnXRS2rVrl6FDh2b+/Pl56qmn8t577+XUU0/N+uuvn5YtW+buu+/OhhtumBYtWqRdu3YrNfbEiRPz4Ycf5ktf+lJatWqV//mf/0nLli2z0UYb/ZufCgAAAD5b7CkF/+Ib3/hGrr766kyYMCFbbrllqqqqMnHixGy88cZJkqZNm+bSSy/NlVdema5du+aAAw5Y6bHbt2+fq666KoMGDcpWW22V++67L7fffns6der073ocAAAA+Ewq1dfX1zd2EcAnmz17dtq1a5efHHNbWjZb8d5VAF9UJ14xpLFLAAD4wlvy/XXWrFmpqKhYbj8rpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMI1bewCgFXzn5dUpaKiorHLAAAAgDVipRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAAAhWva2AUAq2b6dgPTpkmTxi4DoNH1e3FaY5cAAMAasFIKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFAKAAAAgMIJpQAAAAAonFCKtdrgwYMzcuTIxi4DAAAAWEVCqeUYPnx4SqVSLrjgggbtt956a0qlUqG1TJo0KUOGDEnHjh3TqlWr9OnTJ0cffXQWLFhQaB2rolQqpVQq5bHHHmvQPn/+/HTq1CmlUim1tbVrPM/NN9+c8847b43G6NmzZ7neJk2apGvXrjnuuOPy3nvvrXF9AAAAwLIJpVagRYsWufDCCxs1nJg6dWqGDh2agQMH5sEHH8zzzz+fyy67LM2aNcuHH37YaHUlSX19fRYtWrTc6927d8+ECRMatN1yyy1p06bNGs+9JJDr2LFj2rZtu8bjnXvuuamrq8sbb7yR66+/Pg8++GBOOumk5fb/8MMPs3jx4jWeFwAAAL6ohFIrsMcee6SysjJjx45dYb+HH344u+66a1q2bJnu3bvnpJNOyvvvv58kGTduXLbYYoty3yUrra644ooG85x99tnLHPuPf/xjKisrc9FFF2WLLbZIr169MnTo0Fx11VVp2bJlkmT06NHZZpttGtx3ySWXpGfPnuXz4cOH58ADD8yYMWPSuXPnVFRU5IQTTmiw2mrx4sUZO3ZsNt5447Rs2TJbb711fve735Wv19bWplQq5a677sp2222X5s2b5+GHH17u53L00UfnhhtuyNy5c8tt48ePz9FHH71U3zPPPDObbrppWrVqlU022SQ/+MEPsnDhwvL1Jc949dVXZ+ONN06LFi2SLP363nvvvZevf/3r6dChQ1q1apW99947L7/88nJrXKJt27aprKxMt27dUl1dnaOPPjpPP/10+frEiRPTvn373Hbbbdl8883TvHnzvPHGG58431/+8pfst99+6dChQ1q3bp3+/fvnD3/4wyfWAwAAAJ93QqkVaNKkSc4///xcdtll+etf/7rMPjNmzMjQoUPzla98Jc8991xuvPHGPPzwwxkxYkSSpKqqKlOnTs27776b5KNX8dZbb73yq2sLFy7Mo48+msGDBy9z/MrKytTV1eXBBx9c4+e5//77M23atNTW1uY3v/lNbr755owZM6Z8fezYsbn22mtzxRVX5IUXXsgpp5ySI488MpMmTWowzllnnZULLrgg06ZNy1ZbbbXc+bbbbrv07NkzN910U5LkjTfeyIMPPpijjjpqqb5t27bNxIkTM3Xq1Pz85z/PVVddlZ/97GcN+rzyyiu56aabcvPNN2fKlCnLnHP48OF56qmnctttt+XRRx9NfX199tlnnwYB1yd56623cvvtt+dLX/pSg/YPPvggF154Ya6++uq88MILWX/99T9xvhNPPDHz588vr3K78MILV3ql2Pz58zN79uwGBwAAAHxeCKU+wUEHHZRtttkm55xzzjKvjx07NkcccURGjhyZPn36ZOedd86ll16aa6+9NvPmzcsWW2yRjh07loOd2tranHbaaeXzJ554IgsXLszOO++8zPEPOeSQHH744amqqkqXLl1y0EEHZdy4casVUDRr1izjx49P//79s+++++bcc8/NpZdemsWLF2f+/Pk5//zzM378+Oy1117ZZJNNMnz48Bx55JG58sorG4xz7rnnZs8990yvXr3SsWPHFc557LHHZvz48Uk+Wm20zz77pHPnzkv1O/vss7PzzjunZ8+e2W+//XL66afnf//3fxv0WbBgQa699toMGDBgmWHYyy+/nNtuuy1XX311dt1112y99da5/vrr89Zbb+XWW29dYZ1nnnlm2rRpk5YtW2bDDTdMqVTKf/3XfzXos3Dhwvzyl7/MzjvvnL59++att976xPneeOONDBo0KFtuuWU22WSTDBs2LLvtttsKa1li7NixadeuXfno3r37St0HAAAAawOh1Eq48MILc80112TatGlLXXv22WczceLEtGnTpnzstddeWbx4cV577bWUSqXstttuqa2tzcyZMzN16tR8+9vfzvz58/Piiy9m0qRJ2X777dOqVatlzt2kSZNMmDAhf/3rX3PRRRelW7duOf/889O/f//U1dWt0nNsvfXWDebZaaedMmfOnLz55pt55ZVX8sEHH2TPPfds8CzXXnttZsyY0WCcgQMHrvScRx55ZB599NG8+uqrmThxYo499thl9rvxxhszaNCgVFZWpk2bNjn77LPzxhtvNOiz0UYbLTPQWmLatGlp2rRpgxVOnTp1St++fZf5b/dxZ5xxRqZMmZLnnnsu999/f5Jk3333bbBvV7NmzRqEYSsz30knnZQf/ehHGTRoUM4555w899xzK6zj40aNGpVZs2aVjzfffHOl7wUAAIDPOqHUSthtt92y1157ZdSoUUtdmzNnTr75zW9mypQp5ePZZ5/Nyy+/nF69eiX5aN+j2traPPTQQxkwYEAqKirKQdWkSZNSVVX1iTV069YtRx11VMaNG5cXXngh8+bNK+9Ltc4666S+vr5B/1V5XW3JcyTJnXfe2eBZpk6d2mBfqSRp3br1So/bqVOnDBs2LMcdd1zmzZuXvffee6k+jz76aI444ojss88+ueOOO/LMM8/k+9///lK/Lrgq866q9dZbL717906fPn0yZMiQXHLJJXnkkUdSU1NT7tOyZctV/uXFb3zjG3n11Vdz1FFH5fnnn8/AgQNz2WWXrdS9zZs3T0VFRYMDAAAAPi+aNnYBa4sLLrgg22yzTfr27dugfdttt83UqVPTu3fv5d5bVVWVkSNH5re//W1576jBgwfnvvvuy+TJk3PaaaetUi0dOnRIly5dypupd+7cOW+//Xbq6+vLocmy9lx69tlnM3fu3PIG6Y899ljatGmT7t27p2PHjuXNu1cmJFsVxx57bPbZZ5+ceeaZadKkyVLXH3nkkWy00Ub5/ve/X277y1/+ssrz9OvXL4sWLcrjjz9efh3y73//e6ZPn57NN998lcZaUufHN2lf3fm6d++eE044ISeccEJGjRqVq666Kt/5zndW9fEAAADgc0UotZK23HLLHHHEEbn00ksbtJ955pnZcccdM2LEiHzjG99I69atM3Xq1Nx7770ZN25ckmSrrbZKhw4d8utf/zp33HFHko9CqdNPPz2lUimDBg1a7rxXXnllpkyZkoMOOii9evXKvHnzcu211+aFF14or7gZPHhw3n333Vx00UX5j//4j9x999256667llpZs2DBghx33HE5++yz8/rrr+ecc87JiBEjss4666Rt27Y5/fTTc8opp2Tx4sXZZZddMmvWrEyePDkVFRXL/MW8lTV06NC8++67y13p06dPn7zxxhu54YYbsv322+fOO+/MLbfcssrz9OnTJwcccECOP/74XHnllWnbtm3OOuusdOvWLQcccMAK7/3nP/9ZDvbefPPNfPe7303nzp2Xu9fXys43cuTI7L333tl0003z3nvvpaamJv369SuPsdlmm2Xs2LE56KCDVvl5AQAAYG3m9b1VcO6552bx4sUN2rbaaqtMmjQpL730UnbdddcMGDAgP/zhD9O1a9dyn1KplF133TWlUim77LJL+b6KiooMHDhwha+l7bDDDpkzZ05OOOGE9O/fP1VVVXnsscdy6623llc09evXL7/85S/zi1/8IltvvXWeeOKJnH766UuNtfvuu6dPnz7Zbbfdcthhh2X//ffP6NGjy9fPO++8/OAHP8jYsWPTr1+/DB06NHfeeWc23njjNfnYUiqVst5666VZs2bLvL7//vvnlFNOyYgRI7LNNtvkkUceyQ9+8IPVmmvChAnZbrvtMmzYsOy0006pr6/PH/7wh6y77rorvO+HP/xhunTpkq5du2bYsGFp3bp1/vjHP6ZTp05rNN+HH36YE088sfx5brrppvnlL39Zvn/69OmZNWvWaj0rAAAArM1K9f+6GRGfS8OHD8/MmTM/8Vfo+OyaPXt22rVrlyd690mbZbwGCfBF0+/FFf+IBQAAjWPJ99dZs2atcH9kK6UAAAAAKJxQCgAAAIDC2ej8C2LixImNXQIAAABAmZVSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABROKAUAAABA4YRSAAAAABSuaWMXAKyavn96KhUVFY1dBgAAAKwRK6UAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKJxQCgAAAIDCCaUAAAAAKFzTxi4AWDU7/nrHNGnZpLHLAPi3ef7o5xu7BAAACmClFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFAAAAACFE0oBAAAAUDihFF8ItbW1KZVKmTlzZpJk4sSJad++/RqNOXjw4IwcOXKNawMAAIAvIqEUa5Urrrgibdu2zaJFi8ptc+bMybrrrpvBgwc36LskiJoxY0Z23nnn1NXVpV27dkmSww47LC+99NIK5yqVSuWjXbt2GTRoUB544IFP/ZkAAADgi0goxVqluro6c+bMyVNPPVVue+ihh1JZWZnHH3888+bNK7fX1NSkR48e6dWrV5o1a5bKysqUSqUkScuWLbP++ut/4nwTJkxIXV1dJk+enPXWWy/Dhg3Lq6+++uk/GAAAAHzBCKVYq/Tt2zddunRJbW1tua22tjYHHHBANt544zz22GMN2qurq8t/r87re+3bt09lZWW22GKLXH755Zk7d27uvffeZfa97rrrMnDgwLRt2zaVlZX52te+lnfeeSdJUl9fn969e+fiiy9ucM+UKVNSKpXyyiuvrMKnAAAAAGs/oRRrnerq6tTU1JTPa2pqMnjw4FRVVZXb586dm8cff7wcSn0aWrZsmSRZsGDBMq8vXLgw5513Xp599tnceuutef311zN8+PAkH70KeOyxx2bChAkN7pkwYUJ222239O7d+1OrEwAAANYGQinWOtXV1Zk8eXIWLVqUf/7zn3nmmWdSVVWV3XbbrbyC6tFHH838+fM/tVDqgw8+yNlnn50mTZqkqqpqmX2OPfbY7L333tlkk02y44475tJLL81dd92VOXPmJEmGDx+e6dOn54knnkjyUYj161//Oscee+wyx5s/f35mz57d4AAAAIDPC6EUa53Bgwfn/fffz5NPPpmHHnoom266aTp37pyqqqryvlK1tbXZZJNN0qNHjzWa6/DDD0+bNm3Stm3b3HTTTfnVr36Vrbbaapl9//SnP2W//fZLjx490rZt23J49cYbbyRJunbtmn333Tfjx49Pktx+++2ZP39+DjnkkGWON3bs2LRr1658dO/efY2eBQAAAD5LhFKsdXr37p0NN9wwNTU1qampKYc/Xbt2Tffu3fPII4+kpqYmQ4YMWeO5fvazn2XKlCl5++238/bbb+foo49eZr/3338/e+21VyoqKnL99dfnySefzC233JKk4et+3/jGN3LDDTdk7ty5mTBhQg477LC0atVqmWOOGjUqs2bNKh9vvvnmGj8PAAAAfFY0bewCYHVUV1entrY27733Xs4444xy+2677Za77rorTzzxRL71rW+t8TyVlZUrtd/Tiy++mL///e+54IILyiuaPv4LgUvss88+ad26dS6//PLcfffdefDBB5c7ZvPmzdO8efPVLx4AAAA+w6yUYq1UXV2dhx9+OFOmTGmwx1NVVVWuvPLKLFiw4FPd5PyT9OjRI82aNctll12WV199NbfddlvOO++8pfo1adIkw4cPz6hRo9KnT5/stNNOhdUIAAAAnyVCKdZK1dXVmTt3bnr37p0NNtig3F5VVZV//vOf6du3b7p06VJYPZ07d87EiRPz29/+NptvvnkuuOCCXHzxxcvse9xxx2XBggU55phjCqsPAAAAPmtK9fX19Y1dBHyRPPTQQ9l9993z5ptvNgjUPsns2bPTrl279Lu8X5q0bPJvrBCgcT1/9PONXQIAAGtgyffXWbNmpaKiYrn97CkFBZk/f37efffdjB49OocccsgqBVIAAADweeP1PSjIb37zm2y00UaZOXNmLrroosYuBwAAABqVUAoKMnz48Hz44Yf505/+lG7dujV2OQAAANCohFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhhFIAAAAAFE4oBQAAAEDhmjZ2AcCqeexrj6WioqKxywAAAIA1YqUUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQOKEUAAAAAIUTSgEAAABQuKaNXQCwisZumDQvNXYVAGtu9KzGrgAAgEZkpRQAAAAAhRNKAQAAAFA4oRQAAAAAhRNKAQAAAFA4oRQAAAA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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph top 10 games by global sales and annotation\n",
    "top_10_selling_games = df.nlargest(10, 'Global_Sales').sort_values(by='Global_Sales', ascending=True)[['Name', 'Global_Sales']]\n",
    "colors = sns.color_palette()\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.barh(top_10_selling_games['Name'], top_10_selling_games['Global_Sales'], color=colors)\n",
    "plt.xlabel('Global Sales in Millions')\n",
    "plt.ylabel('Game')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6eff54a",
   "metadata": {},
   "source": [
    "**Expected output:**\\\n",
    "<img src=\"./image/graph.png\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66464438",
   "metadata": {},
   "source": [
    "# Congratz !\n",
    "\n",
    "During this workshop, we were able to draw conclusions from the data, such as which games were the most sold.\\\n",
    "But all of this required a first understanding of the data, and then a cleaning of the data.\\\n",
    "Who knows what impertinent results we would have had if we had not cleaned the data?\n",
    "\n",
    "Thank you for attending this workshop.\\\n",
    "Good job, your job is done for now.\\\n",
    "You got way above our expectation, and you're entirely ready to do Data Analysis in any other mission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d6ac0b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}