World_Population_Dataset_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0c71480f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current date and time:  2023-05-05 22:43:28.057511\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "now = datetime.datetime.now()\n",
    "print(\"Current date and time: \", now)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f8e896c",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64cf4620",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa6cf435",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1717daed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data from a CSV file\n",
    "data = pd.read_csv(\"C:/Users/shrey/Documents/GitHub/EDA/world_population.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ddd32e34",
   "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>Rank</th>\n",
       "      <th>CCA3</th>\n",
       "      <th>Country/Territory</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Continent</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>36</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41128771</td>\n",
       "      <td>38972230</td>\n",
       "      <td>33753499</td>\n",
       "      <td>28189672</td>\n",
       "      <td>19542982</td>\n",
       "      <td>10694796</td>\n",
       "      <td>12486631</td>\n",
       "      <td>10752971</td>\n",
       "      <td>652230</td>\n",
       "      <td>63.0587</td>\n",
       "      <td>1.0257</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>138</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>Tirana</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2842321</td>\n",
       "      <td>2866849</td>\n",
       "      <td>2882481</td>\n",
       "      <td>2913399</td>\n",
       "      <td>3182021</td>\n",
       "      <td>3295066</td>\n",
       "      <td>2941651</td>\n",
       "      <td>2324731</td>\n",
       "      <td>28748</td>\n",
       "      <td>98.8702</td>\n",
       "      <td>0.9957</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34</td>\n",
       "      <td>DZA</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>Algiers</td>\n",
       "      <td>Africa</td>\n",
       "      <td>44903225</td>\n",
       "      <td>43451666</td>\n",
       "      <td>39543154</td>\n",
       "      <td>35856344</td>\n",
       "      <td>30774621</td>\n",
       "      <td>25518074</td>\n",
       "      <td>18739378</td>\n",
       "      <td>13795915</td>\n",
       "      <td>2381741</td>\n",
       "      <td>18.8531</td>\n",
       "      <td>1.0164</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>213</td>\n",
       "      <td>ASM</td>\n",
       "      <td>American Samoa</td>\n",
       "      <td>Pago Pago</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>44273</td>\n",
       "      <td>46189</td>\n",
       "      <td>51368</td>\n",
       "      <td>54849</td>\n",
       "      <td>58230</td>\n",
       "      <td>47818</td>\n",
       "      <td>32886</td>\n",
       "      <td>27075</td>\n",
       "      <td>199</td>\n",
       "      <td>222.4774</td>\n",
       "      <td>0.9831</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203</td>\n",
       "      <td>AND</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>Andorra la Vella</td>\n",
       "      <td>Europe</td>\n",
       "      <td>79824</td>\n",
       "      <td>77700</td>\n",
       "      <td>71746</td>\n",
       "      <td>71519</td>\n",
       "      <td>66097</td>\n",
       "      <td>53569</td>\n",
       "      <td>35611</td>\n",
       "      <td>19860</td>\n",
       "      <td>468</td>\n",
       "      <td>170.5641</td>\n",
       "      <td>1.0100</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank CCA3 Country/Territory           Capital Continent  2022 Population  \\\n",
       "0    36  AFG       Afghanistan             Kabul      Asia         41128771   \n",
       "1   138  ALB           Albania            Tirana    Europe          2842321   \n",
       "2    34  DZA           Algeria           Algiers    Africa         44903225   \n",
       "3   213  ASM    American Samoa         Pago Pago   Oceania            44273   \n",
       "4   203  AND           Andorra  Andorra la Vella    Europe            79824   \n",
       "\n",
       "   2020 Population  2015 Population  2010 Population  2000 Population  \\\n",
       "0         38972230         33753499         28189672         19542982   \n",
       "1          2866849          2882481          2913399          3182021   \n",
       "2         43451666         39543154         35856344         30774621   \n",
       "3            46189            51368            54849            58230   \n",
       "4            77700            71746            71519            66097   \n",
       "\n",
       "   1990 Population  1980 Population  1970 Population  Area (km²)  \\\n",
       "0         10694796         12486631         10752971      652230   \n",
       "1          3295066          2941651          2324731       28748   \n",
       "2         25518074         18739378         13795915     2381741   \n",
       "3            47818            32886            27075         199   \n",
       "4            53569            35611            19860         468   \n",
       "\n",
       "   Density (per km²)  Growth Rate  World Population Percentage  \n",
       "0            63.0587       1.0257                         0.52  \n",
       "1            98.8702       0.9957                         0.04  \n",
       "2            18.8531       1.0164                         0.56  \n",
       "3           222.4774       0.9831                         0.00  \n",
       "4           170.5641       1.0100                         0.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the top 5 rows of data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aebed0e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(234, 17)\n",
      "------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 234 entries, 0 to 233\n",
      "Data columns (total 17 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Rank                         234 non-null    int64  \n",
      " 1   CCA3                         234 non-null    object \n",
      " 2   Country/Territory            234 non-null    object \n",
      " 3   Capital                      234 non-null    object \n",
      " 4   Continent                    234 non-null    object \n",
      " 5   2022 Population              234 non-null    int64  \n",
      " 6   2020 Population              234 non-null    int64  \n",
      " 7   2015 Population              234 non-null    int64  \n",
      " 8   2010 Population              234 non-null    int64  \n",
      " 9   2000 Population              234 non-null    int64  \n",
      " 10  1990 Population              234 non-null    int64  \n",
      " 11  1980 Population              234 non-null    int64  \n",
      " 12  1970 Population              234 non-null    int64  \n",
      " 13  Area (km²)                   234 non-null    int64  \n",
      " 14  Density (per km²)            234 non-null    float64\n",
      " 15  Growth Rate                  234 non-null    float64\n",
      " 16  World Population Percentage  234 non-null    float64\n",
      "dtypes: float64(3), int64(10), object(4)\n",
      "memory usage: 31.2+ KB\n",
      "None\n",
      "------------------------------------------------------\n",
      "Rank                           234\n",
      "CCA3                           234\n",
      "Country/Territory              234\n",
      "Capital                        234\n",
      "Continent                        6\n",
      "2022 Population                234\n",
      "2020 Population                234\n",
      "2015 Population                234\n",
      "2010 Population                234\n",
      "2000 Population                234\n",
      "1990 Population                234\n",
      "1980 Population                234\n",
      "1970 Population                234\n",
      "Area (km²)                     233\n",
      "Density (per km²)              234\n",
      "Growth Rate                    180\n",
      "World Population Percentage     70\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(\"------------------------------------------------------\")\n",
    "print(data.info())\n",
    "print(\"------------------------------------------------------\")\n",
    "print(data.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05503477",
   "metadata": {},
   "source": [
    "#### The dataset contains information on 234 countries/territories across 6 continents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01a341a",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce4e6bc",
   "metadata": {},
   "source": [
    "Let's begin some exploratory data analysis! Start by checking out missing data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "345d94ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rank                           0\n",
      "CCA3                           0\n",
      "Country/Territory              0\n",
      "Capital                        0\n",
      "Continent                      0\n",
      "2022 Population                0\n",
      "2020 Population                0\n",
      "2015 Population                0\n",
      "2010 Population                0\n",
      "2000 Population                0\n",
      "1990 Population                0\n",
      "1980 Population                0\n",
      "1970 Population                0\n",
      "Area (km²)                     0\n",
      "Density (per km²)              0\n",
      "Growth Rate                    0\n",
      "World Population Percentage    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Checking for missing data\n",
    "def check_missing_data():\n",
    "    print(data.isnull().sum())\n",
    "    \n",
    "check_missing_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "518aefe1",
   "metadata": {},
   "source": [
    "No Null Values! So, filling is not required"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31fa6bdb",
   "metadata": {},
   "source": [
    "### Q. Finding number of countries in a continent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca84ec94",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the number of countries in each continent\n",
    "\n",
    "country_count = data['Continent'].value_counts()\n",
    "sns.barplot(x=country_count.index, y=country_count.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dd612fa",
   "metadata": {},
   "source": [
    "Africa has the highest number of countries (57) and South America has the least (14).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44dc6fa7",
   "metadata": {},
   "source": [
    "### Q. Finding the population percentage of each continent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fed08e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='World Population Percentage'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the population percentage of each continent\n",
    "\n",
    "pop_percentage = data.groupby('Continent')['World Population Percentage'].sum().round(1).sort_values(ascending= False).reset_index()\n",
    "pop_percentage\n",
    "\n",
    "labels = pop_percentage['Continent']\n",
    "percent = pop_percentage['World Population Percentage']\n",
    "percent.plot.pie(labels = labels, autopct = '%1.2f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42722436",
   "metadata": {},
   "source": [
    "Asia has the highest population percentage (59.20%), followed by Africa (17.90%) and Europe (9.30%)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22af4084",
   "metadata": {},
   "source": [
    "### Q. Comparing the continent population 1970 and 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f71df38c",
   "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>Continent</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>365444348</td>\n",
       "      <td>1426730932</td>\n",
       "      <td>25.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>2144906290</td>\n",
       "      <td>4721383274</td>\n",
       "      <td>45.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>655923991</td>\n",
       "      <td>743147538</td>\n",
       "      <td>88.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>315434606</td>\n",
       "      <td>600296136</td>\n",
       "      <td>52.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>19480270</td>\n",
       "      <td>45038554</td>\n",
       "      <td>43.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>192947156</td>\n",
       "      <td>436816608</td>\n",
       "      <td>44.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Continent  1970 Population  2022 Population  Percentage\n",
       "0         Africa        365444348       1426730932       25.61\n",
       "1           Asia       2144906290       4721383274       45.43\n",
       "2         Europe        655923991        743147538       88.26\n",
       "3  North America        315434606        600296136       52.55\n",
       "4        Oceania         19480270         45038554       43.25\n",
       "5  South America        192947156        436816608       44.17"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the population of each continent in 1970 and 2022\n",
    "\n",
    "population_1970_2022 = data.groupby('Continent')[['1970 Population', '2022 Population']].sum().sort_values(by='Continent', ascending=True).reset_index()\n",
    "population_1970_2022['Percentage'] = round(population_1970_2022['1970 Population'] / population_1970_2022['2022 Population'] * 100, 2)\n",
    "population_1970_2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8076e657",
   "metadata": {},
   "source": [
    "The world population has increased significantly over the years. The total world population in 1970 was 3.7 billion, which increased to 7.9 billion in 2022."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1296ac1e",
   "metadata": {},
   "source": [
    "### Q. Highest Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0b1d87c2",
   "metadata": {
    "scrolled": true
   },
   "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>index</th>\n",
       "      <th>Country/Territory</th>\n",
       "      <th>2022 Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>China</td>\n",
       "      <td>1425887337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>India</td>\n",
       "      <td>1417173173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>221</td>\n",
       "      <td>United States</td>\n",
       "      <td>338289857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>93</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>275501339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>235824862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index Country/Territory  2022 Population\n",
       "0     41             China       1425887337\n",
       "1     92             India       1417173173\n",
       "2    221     United States        338289857\n",
       "3     93         Indonesia        275501339\n",
       "4    156          Pakistan        235824862"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the highest population countries\n",
    "\n",
    "highest_populated_countries = data[['Country/Territory','2022 Population']].sort_values(by='2022 Population', ascending=False).head().reset_index()\n",
    "highest_populated_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b0d34d",
   "metadata": {},
   "source": [
    "China has the highest population (1.4 billion), followed by India (1.4 billion) and the United States (338 million)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e995b126",
   "metadata": {},
   "source": [
    "### Q. Least Population "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5886dc3",
   "metadata": {
    "scrolled": true
   },
   "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>index</th>\n",
       "      <th>Country/Territory</th>\n",
       "      <th>2022 Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>226</td>\n",
       "      <td>Vatican City</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>209</td>\n",
       "      <td>Tokelau</td>\n",
       "      <td>1871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>Niue</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>64</td>\n",
       "      <td>Falkland Islands</td>\n",
       "      <td>3780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>137</td>\n",
       "      <td>Montserrat</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index Country/Territory  2022 Population\n",
       "0    226      Vatican City              510\n",
       "1    209           Tokelau             1871\n",
       "2    150              Niue             1934\n",
       "3     64  Falkland Islands             3780\n",
       "4    137        Montserrat             4390"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the least populated countries\n",
    "\n",
    "least_populated_countries = data[['Country/Territory','2022 Population']].sort_values(by='2022 Population', ascending=True).head().reset_index()\n",
    "least_populated_countries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f08c53",
   "metadata": {},
   "source": [
    "The Vatican City has the lowest population (510), followed by Tokelau (1,871) and Niue (1,934)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab893a8f",
   "metadata": {},
   "source": [
    "### Q. Top 5 Countries from the continent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6870517f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def topFive(Continent):\n",
    "    cont = data[data['Continent'] == Continent]\n",
    "    return cont.groupby('Country/Territory')['2022 Population'].sum().sort_values(ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8cdf7bec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "China         1425887337\n",
       "India         1417173173\n",
       "Indonesia      275501339\n",
       "Pakistan       235824862\n",
       "Bangladesh     171186372\n",
       "Name: 2022 Population, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topFive('Asia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ad5d8926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Russia            144713314\n",
       "Germany            83369843\n",
       "United Kingdom     67508936\n",
       "France             64626628\n",
       "Italy              59037474\n",
       "Name: 2022 Population, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topFive('Europe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d608f036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Brazil       215313498\n",
       "Colombia      51874024\n",
       "Argentina     45510318\n",
       "Peru          34049588\n",
       "Venezuela     28301696\n",
       "Name: 2022 Population, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topFive('South America')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b39e240",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "United States    338289857\n",
       "Mexico           127504125\n",
       "Canada            38454327\n",
       "Guatemala         17843908\n",
       "Haiti             11584996\n",
       "Name: 2022 Population, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topFive('North America')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b6059bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Nigeria     218541212\n",
       "Ethiopia    123379924\n",
       "Egypt       110990103\n",
       "DR Congo     99010212\n",
       "Tanzania     65497748\n",
       "Name: 2022 Population, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topFive('Africa')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a61b2e",
   "metadata": {},
   "source": [
    "After analyzing the population data for different continents, we can see that China and India have the largest populations globally, followed by the USA and Indonesia. In contrast."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "209e2e4c",
   "metadata": {},
   "source": [
    "### Q. Bottom 5 countries from continent "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e9e93f0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a function to get the bottom 5 countries from a given continent based on 2020 population\n",
    "def bottomFive(Continent):\n",
    "    # filter the data for the given continent\n",
    "    cont = data[data['Continent'] == Continent]\n",
    "    # group the data by country and sum the 2020 population, then sort in ascending order and get the first 5 rows\n",
    "    return cont.groupby('Country/Territory')['2020 Population'].sum().sort_values(ascending = True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "244d2976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Seychelles               105530\n",
       "Sao Tome and Principe    218641\n",
       "Mayotte                  305587\n",
       "Western Sahara           556048\n",
       "Cape Verde               582640\n",
       "Name: 2020 Population, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottomFive('Africa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e86d9d00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Vatican City       520\n",
       "Gibraltar        32709\n",
       "San Marino       34007\n",
       "Monaco           36922\n",
       "Liechtenstein    38756\n",
       "Name: 2020 Population, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottomFive('Europe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "939b4027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Falkland Islands       3747\n",
       "French Guiana        290969\n",
       "Suriname             607065\n",
       "Guyana               797202\n",
       "Uruguay             3429086\n",
       "Name: 2020 Population, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottomFive('South America')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4f91f99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Brunei          441725\n",
       "Maldives        514438\n",
       "Macau           676283\n",
       "Bhutan          772506\n",
       "Timor-Leste    1299995\n",
       "Name: 2020 Population, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottomFive('Asia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e2f7338",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Territory\n",
       "Montserrat                    4500\n",
       "Saint Pierre and Miquelon     5906\n",
       "Saint Barthelemy             10681\n",
       "Anguilla                     15585\n",
       "British Virgin Islands       30910\n",
       "Name: 2020 Population, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottomFive('North America')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641bbd1a",
   "metadata": {},
   "source": [
    "From the population data analysis for different continents, it is clear that Asia has the largest population globally, with China and India as the two most populous countries in the world. Africa has the second-largest population, with Nigeria being the most populous country in the continent. Europe and North America have relatively smaller populations, while South America has a moderate population size.\n",
    "\n",
    "Looking at the top five populated countries from each continent, Asia has the highest number of countries, followed by Africa and Europe. On the other hand, North and South America have only one country each in the top five list.\n",
    "\n",
    "In terms of the bottom five countries from each continent, it is evident that they have significantly smaller populations compared to the top five countries. Vatican City, Gibraltar, San Marino, Monaco, and Liechtenstein have the smallest populations in Europe, while the Falkland Islands, French Guiana, Suriname, Guyana, and Uruguay have the smallest populations in South America. Similarly, Seychelles, Sao Tome and Principe, Mayotte, Western Sahara, and Cape Verde have the smallest populations in Africa.\n",
    "\n",
    "Overall, the population data analysis shows that there are significant differences in population size and growth rate between different regions, which can have significant implications for the economic, social, and political development of countries and continents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7b8faea",
   "metadata": {},
   "source": [
    "### Q.Top 10 Growth rate country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fa34d61a",
   "metadata": {
    "scrolled": false
   },
   "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>Rank</th>\n",
       "      <th>CCA3</th>\n",
       "      <th>Country/Territory</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Continent</th>\n",
       "      <th>2022 Population</th>\n",
       "      <th>2020 Population</th>\n",
       "      <th>2015 Population</th>\n",
       "      <th>2010 Population</th>\n",
       "      <th>2000 Population</th>\n",
       "      <th>1990 Population</th>\n",
       "      <th>1980 Population</th>\n",
       "      <th>1970 Population</th>\n",
       "      <th>Area (km²)</th>\n",
       "      <th>Density (per km²)</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>World Population Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>135</td>\n",
       "      <td>MDA</td>\n",
       "      <td>Moldova</td>\n",
       "      <td>Chisinau</td>\n",
       "      <td>Europe</td>\n",
       "      <td>3272996</td>\n",
       "      <td>3084847</td>\n",
       "      <td>3277388</td>\n",
       "      <td>3678186</td>\n",
       "      <td>4251573</td>\n",
       "      <td>4480199</td>\n",
       "      <td>4103240</td>\n",
       "      <td>3711140</td>\n",
       "      <td>33846</td>\n",
       "      <td>96.7026</td>\n",
       "      <td>1.0691</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>37</td>\n",
       "      <td>POL</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Warsaw</td>\n",
       "      <td>Europe</td>\n",
       "      <td>39857145</td>\n",
       "      <td>38428366</td>\n",
       "      <td>38553146</td>\n",
       "      <td>38597353</td>\n",
       "      <td>38504431</td>\n",
       "      <td>38064255</td>\n",
       "      <td>35521429</td>\n",
       "      <td>32482943</td>\n",
       "      <td>312679</td>\n",
       "      <td>127.4698</td>\n",
       "      <td>1.0404</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>54</td>\n",
       "      <td>NER</td>\n",
       "      <td>Niger</td>\n",
       "      <td>Niamey</td>\n",
       "      <td>Africa</td>\n",
       "      <td>26207977</td>\n",
       "      <td>24333639</td>\n",
       "      <td>20128124</td>\n",
       "      <td>16647543</td>\n",
       "      <td>11622665</td>\n",
       "      <td>8370647</td>\n",
       "      <td>6173177</td>\n",
       "      <td>4669708</td>\n",
       "      <td>1267000</td>\n",
       "      <td>20.6851</td>\n",
       "      <td>1.0378</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>60</td>\n",
       "      <td>SYR</td>\n",
       "      <td>Syria</td>\n",
       "      <td>Damascus</td>\n",
       "      <td>Asia</td>\n",
       "      <td>22125249</td>\n",
       "      <td>20772595</td>\n",
       "      <td>19205178</td>\n",
       "      <td>22337563</td>\n",
       "      <td>16307654</td>\n",
       "      <td>12408996</td>\n",
       "      <td>8898954</td>\n",
       "      <td>6319199</td>\n",
       "      <td>185180</td>\n",
       "      <td>119.4797</td>\n",
       "      <td>1.0376</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>116</td>\n",
       "      <td>SVK</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Bratislava</td>\n",
       "      <td>Europe</td>\n",
       "      <td>5643453</td>\n",
       "      <td>5456681</td>\n",
       "      <td>5424444</td>\n",
       "      <td>5396424</td>\n",
       "      <td>5376690</td>\n",
       "      <td>5261305</td>\n",
       "      <td>4973883</td>\n",
       "      <td>4522867</td>\n",
       "      <td>49037</td>\n",
       "      <td>115.0856</td>\n",
       "      <td>1.0359</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>15</td>\n",
       "      <td>COD</td>\n",
       "      <td>DR Congo</td>\n",
       "      <td>Kinshasa</td>\n",
       "      <td>Africa</td>\n",
       "      <td>99010212</td>\n",
       "      <td>92853164</td>\n",
       "      <td>78656904</td>\n",
       "      <td>66391257</td>\n",
       "      <td>48616317</td>\n",
       "      <td>35987541</td>\n",
       "      <td>26708686</td>\n",
       "      <td>20151733</td>\n",
       "      <td>2344858</td>\n",
       "      <td>42.2244</td>\n",
       "      <td>1.0325</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>182</td>\n",
       "      <td>MYT</td>\n",
       "      <td>Mayotte</td>\n",
       "      <td>Mamoudzou</td>\n",
       "      <td>Africa</td>\n",
       "      <td>326101</td>\n",
       "      <td>305587</td>\n",
       "      <td>249545</td>\n",
       "      <td>211786</td>\n",
       "      <td>159215</td>\n",
       "      <td>92659</td>\n",
       "      <td>52233</td>\n",
       "      <td>35383</td>\n",
       "      <td>374</td>\n",
       "      <td>871.9278</td>\n",
       "      <td>1.0319</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>69</td>\n",
       "      <td>TCD</td>\n",
       "      <td>Chad</td>\n",
       "      <td>N'Djamena</td>\n",
       "      <td>Africa</td>\n",
       "      <td>17723315</td>\n",
       "      <td>16644701</td>\n",
       "      <td>14140274</td>\n",
       "      <td>11894727</td>\n",
       "      <td>8259137</td>\n",
       "      <td>5827069</td>\n",
       "      <td>4408230</td>\n",
       "      <td>3667394</td>\n",
       "      <td>1284000</td>\n",
       "      <td>13.8032</td>\n",
       "      <td>1.0316</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Luanda</td>\n",
       "      <td>Africa</td>\n",
       "      <td>35588987</td>\n",
       "      <td>33428485</td>\n",
       "      <td>28127721</td>\n",
       "      <td>23364185</td>\n",
       "      <td>16394062</td>\n",
       "      <td>11828638</td>\n",
       "      <td>8330047</td>\n",
       "      <td>6029700</td>\n",
       "      <td>1246700</td>\n",
       "      <td>28.5466</td>\n",
       "      <td>1.0315</td>\n",
       "      <td>0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>59</td>\n",
       "      <td>MLI</td>\n",
       "      <td>Mali</td>\n",
       "      <td>Bamako</td>\n",
       "      <td>Africa</td>\n",
       "      <td>22593590</td>\n",
       "      <td>21224040</td>\n",
       "      <td>18112907</td>\n",
       "      <td>15529181</td>\n",
       "      <td>11239101</td>\n",
       "      <td>8945026</td>\n",
       "      <td>7372581</td>\n",
       "      <td>6153587</td>\n",
       "      <td>1240192</td>\n",
       "      <td>18.2178</td>\n",
       "      <td>1.0314</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank CCA3 Country/Territory     Capital Continent  2022 Population  \\\n",
       "133   135  MDA           Moldova    Chisinau    Europe          3272996   \n",
       "164    37  POL            Poland      Warsaw    Europe         39857145   \n",
       "148    54  NER             Niger      Niamey    Africa         26207977   \n",
       "202    60  SYR             Syria    Damascus      Asia         22125249   \n",
       "189   116  SVK          Slovakia  Bratislava    Europe          5643453   \n",
       "55     15  COD          DR Congo    Kinshasa    Africa         99010212   \n",
       "130   182  MYT           Mayotte   Mamoudzou    Africa           326101   \n",
       "39     69  TCD              Chad   N'Djamena    Africa         17723315   \n",
       "5      42  AGO            Angola      Luanda    Africa         35588987   \n",
       "124    59  MLI              Mali      Bamako    Africa         22593590   \n",
       "\n",
       "     2020 Population  2015 Population  2010 Population  2000 Population  \\\n",
       "133          3084847          3277388          3678186          4251573   \n",
       "164         38428366         38553146         38597353         38504431   \n",
       "148         24333639         20128124         16647543         11622665   \n",
       "202         20772595         19205178         22337563         16307654   \n",
       "189          5456681          5424444          5396424          5376690   \n",
       "55          92853164         78656904         66391257         48616317   \n",
       "130           305587           249545           211786           159215   \n",
       "39          16644701         14140274         11894727          8259137   \n",
       "5           33428485         28127721         23364185         16394062   \n",
       "124         21224040         18112907         15529181         11239101   \n",
       "\n",
       "     1990 Population  1980 Population  1970 Population  Area (km²)  \\\n",
       "133          4480199          4103240          3711140       33846   \n",
       "164         38064255         35521429         32482943      312679   \n",
       "148          8370647          6173177          4669708     1267000   \n",
       "202         12408996          8898954          6319199      185180   \n",
       "189          5261305          4973883          4522867       49037   \n",
       "55          35987541         26708686         20151733     2344858   \n",
       "130            92659            52233            35383         374   \n",
       "39           5827069          4408230          3667394     1284000   \n",
       "5           11828638          8330047          6029700     1246700   \n",
       "124          8945026          7372581          6153587     1240192   \n",
       "\n",
       "     Density (per km²)  Growth Rate  World Population Percentage  \n",
       "133            96.7026       1.0691                         0.04  \n",
       "164           127.4698       1.0404                         0.50  \n",
       "148            20.6851       1.0378                         0.33  \n",
       "202           119.4797       1.0376                         0.28  \n",
       "189           115.0856       1.0359                         0.07  \n",
       "55             42.2244       1.0325                         1.24  \n",
       "130           871.9278       1.0319                         0.00  \n",
       "39             13.8032       1.0316                         0.22  \n",
       "5              28.5466       1.0315                         0.45  \n",
       "124            18.2178       1.0314                         0.28  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the top 10 countries based on growth rate\n",
    "topTen = data.sort_values(by = 'Growth Rate', ascending = False).head(10)\n",
    "topTen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00e3366",
   "metadata": {},
   "source": [
    "Moldova being the country with the highest growth rate, followed by Poland, Niger, Syria, Slovakia, DR Congo, Mayotte, Chad, Angola, and Mali."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e0fbaf26",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Territory</th>\n",
       "      <th>2022 Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DR Congo</td>\n",
       "      <td>99010212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Poland</td>\n",
       "      <td>39857145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</td>\n",
       "      <td>35588987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Niger</td>\n",
       "      <td>26207977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mali</td>\n",
       "      <td>22593590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Syria</td>\n",
       "      <td>22125249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Chad</td>\n",
       "      <td>17723315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>5643453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Moldova</td>\n",
       "      <td>3272996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mayotte</td>\n",
       "      <td>326101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country/Territory  2022 Population\n",
       "0          DR Congo         99010212\n",
       "1            Poland         39857145\n",
       "2            Angola         35588987\n",
       "3             Niger         26207977\n",
       "4              Mali         22593590\n",
       "5             Syria         22125249\n",
       "6              Chad         17723315\n",
       "7          Slovakia          5643453\n",
       "8           Moldova          3272996\n",
       "9           Mayotte           326101"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topTen.groupby('Country/Territory')['2022 Population'].sum().sort_values(ascending = False).head(10).reset_index()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9595c76",
   "metadata": {},
   "source": [
    "### Q. Least Growth Rate Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "56e527c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>Country/Territory</th>\n",
       "      <th>2022 Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vatican City</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tokelau</td>\n",
       "      <td>1871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Niue</td>\n",
       "      <td>1934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Falkland Islands</td>\n",
       "      <td>3780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Montserrat</td>\n",
       "      <td>4390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>5862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Saint Barthelemy</td>\n",
       "      <td>10967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>11312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>11572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nauru</td>\n",
       "      <td>12668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country/Territory  2022 Population\n",
       "0               Vatican City              510\n",
       "1                    Tokelau             1871\n",
       "2                       Niue             1934\n",
       "3           Falkland Islands             3780\n",
       "4                 Montserrat             4390\n",
       "5  Saint Pierre and Miquelon             5862\n",
       "6           Saint Barthelemy            10967\n",
       "7                     Tuvalu            11312\n",
       "8          Wallis and Futuna            11572\n",
       "9                      Nauru            12668"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "leastTen = data.groupby('Country/Territory')['2022 Population'].sum().sort_values(ascending = True).head(10).reset_index()  \n",
    "leastTen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d69d8a",
   "metadata": {},
   "source": [
    "Vatican City having the lowest growth rate, followed by Tokelau, Niue, Falkland Islands, Montserrat, Saint Pierre and Miquelon, Saint Barthelemy, Tuvalu, Wallis and Futuna, and Nauru."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c775a3e",
   "metadata": {},
   "source": [
    "### Q. Finding the growth rate by each continents "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "19c89def",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Continent</th>\n",
       "      <th>Growth Rate</th>\n",
       "      <th>Per Growth Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>58.2109</td>\n",
       "      <td>24.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>50.4692</td>\n",
       "      <td>21.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Europe</td>\n",
       "      <td>50.1128</td>\n",
       "      <td>21.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North America</td>\n",
       "      <td>40.1670</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>23.1698</td>\n",
       "      <td>9.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>South America</td>\n",
       "      <td>14.1114</td>\n",
       "      <td>5.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Continent  Growth Rate  Per Growth Rate\n",
       "0         Africa      58.2109            24.64\n",
       "1           Asia      50.4692            21.36\n",
       "2         Europe      50.1128            21.21\n",
       "3  North America      40.1670            17.00\n",
       "4        Oceania      23.1698             9.81\n",
       "5  South America      14.1114             5.97"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the total growth rate for each continent as a percentage of the total growth rate\n",
    "growthRate = data.groupby('Continent')['Growth Rate'].sum().reset_index()\n",
    "totalGrowthRate = data['Growth Rate'].sum()\n",
    "growthRate['Per Growth Rate'] = round(growthRate['Growth Rate']/totalGrowthRate*100,2)\n",
    "growthRate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3c3bf3",
   "metadata": {},
   "source": [
    "Africa has the highest total growth rate at 24.64%, followed by Asia at 21.36%, Europe at 21.21%, North America at 17.00%, Oceania at 9.81%, and South America at 5.97%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96d7174",
   "metadata": {},
   "source": [
    "### Q. COUNTRY VS AREA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e63a6c0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nTDwpOTmZAWCTJ0+uUFNVli9fzlq2bMnkcjmzs7NjjRs3ZtOmTWMPHjzQbXP06FHWrl07JpfLmbe3N5s2bRqLi4vT+3ci/OAYo6i2NBzHYfz48XpdUMR6TJ48GT/99BNSU1NhY2PDdznEDNGYAyEWprS0FGvWrMHgwYMpGMgzozEHQixEeno69uzZg7/++gtZWVkV1rAipLYoHAixEFeuXMGIESPg7u6OBQsWoFmzZnyXRMwYjTkQQgjRQ2MOhBBC9FA4EEII0UPhQAghRA+FAyGEED0UDoQQQvRQOBBCCNFD4UAIIUQPhQMhhBA9FA6EEEL0UDgQQgjRQ+FACCFED4UDIYQQPRQOhBBC9FA4EEII0UPhQAghRA+FAyGEED0UDoQQQvRQOBBCCNFD4UAIIUQPhQMhhBA9FA6EEEL0UDgQQgjRQ+FACCFED4UDIYQQPRQOhBBC9FA4EEII0UPhQAghRA+FAyGEED0UDoQQQvRQOBBCCNFD4UAIIUQPhQMhhBA9FA6EEEL0UDgQQgjRQ+FACCFED4UDIYQQPRQOhBBC9FA4EEII0UPhQAghRI+I7wIIMaaiMjUyC8uQWahEZmEZsouUKCpTQ6nRQqnWQvXvf5VqLZQaprtPwAFyiRBSkRAysRAysaD8v6Ly/8olQtjJRHBRSOFiK4GrrRQysZDvl0uIwVA4ELOl1mhxN6cESZmFuJVRhKTMIqTmlSKzSInMgvIgKFFp6q0eO6kI7vZSeDnI4ekgg6e9DP7ONgh2t0WIuy0c5OJ6q4WQuuIYY4zvIgh5mlKVBpcf5ONaakGFILibUwyVxnx+fd3spAhxKw+Kx3887GV8l0aIHgoHYlIYY7iRXoj4u7mIv5uL8/dycS21wKxCoLZcbSVo7u+EFv5OaOHviKZ+jtRFRXhH4UB4VarS4GRSNk7eysL5e7m4cDcPBWVqvsvilVjIIcLLHi38ndDc3xEtA5zg62TDd1nEylA4kHp3Pa0Ah65n4OD1DJxKykaZWst3SSYvwMUGXcPd0SXcDe2CXOjIghgdhQMxutxiJY7cyMSh6xk4nJiJlLxSvksya3KxEO2DXdA13A1dG7rTUQUxCgoHYhR5JSrsvJSCLedTcPxWFjRa+jUzllB3W3SLcEe/Jt6I8nHguxyz0qVLFzRr1gzff/8936WYHAoHYjDFSjV2X0nDlvMPcOh6JpQa6i6qbyHuthjQzBv9m/nAz9k8jihGjRqFlStXAgBEIhF8fX0xdOhQzJ49GzKZcWdyZWdnQywWw87Ozqj7MUcUDqROytQaHLiWgS3nH2BvQnq9nldAqsZxQEt/J/Rv7oO+jb3gpJDwXVKVRo0ahbS0NPzyyy9QqVQ4e/YsYmNj8dZbb2Hu3Ll8l2e1aPkM8kxuZhTi861X0PbLvRi7+iy2XkihYDAhjAFnbufg442X0ObLPRi98jR2XU6F1kS796RSKTw9PeHn54cBAwage/fu2L17NwAgMDBQr9unWbNm+OyzzwCUT3/+7LPP4O/vD6lUCm9vb0ycOFG37ZIlSxAaGgqZTAYPDw8MGTJE91iXLl0wadIk3e3Vq1ejVatWsLOzg6enJ1555RWkp6cb7XWbMjpDmtScRo0LV6/hy6P5OHErm+9qSA2pNAx7EtKxJyEdvk5yjGwXgOGt/eFgY5pnbF+6dAnHjh1DQEBAjbb/+++/8d133+H3339HZGQkUlNTcf78eQDAmTNnMHHiRKxevRodOnRAdnY2Dh8+XGVbKpUKn3/+OcLDw5Geno733nsPo0aNwvbt2w3y2swJhQOpXlEmcOYX4MxPaCD3wYk77/FdEXlG93JK8NWOq/h+TyIGNPfGqA4NEO7Jf3/71q1bYWtrC7VajbKyMggEAixatKhGz71z5w48PT3RvXt3iMVi+Pv7o02bNrrHFAoF+vbtCzs7OwQEBKB58+ZVtvXGG2/o/j8oKAgLFixA69atUVhYCFtb27q9SDND3UqkaikXgA1vA982AvbPAQpSYJd+Bi+4ZfJdGamjEpUGv526i5jvD2H48uPYeSmV1xllXbt2RXx8PE6ePInY2Fi8/vrrGDx4cI2eO3ToUJSUlCAoKAhjxozBhg0boFaXn0jZo0cPBAQEICgoCCNHjsTatWtRXFxcZVtnz55Fv3794O/vDzs7O0RHRwMoDxlrQ+FA9KWcB357Gfi/TsD5XwFNWYWH33M8xFNhxBhO3MrGW2vOouv8A/jj9F2oeZhlplAoEBISgqZNm+Lnn3/GyZMn8dNPPwEABAIBnpw3o1KpdP/v5+eHa9euYcmSJZDL5Rg3bhw6d+4MlUoFOzs7nDt3Dr/99hu8vLzwySefoGnTpsjNzdWroaioCDExMbC3t8fatWtx+vRpbNiwAQCgVCqN9+JNFIUDeST1IvD7COD/ooFrVfexhqTugKfU+v5YLN2d7GJM+/sCuv33IP44w09IAOVh8NFHH2HmzJkoKSmBm5sbUlJSdI/n5+cjKSmpwnPkcjn69euHBQsW4MCBAzh+/DguXrwIoHx6bPfu3fHNN9/gwoULSE5Oxr59+/T2e/XqVWRlZeHrr79Gp06d0LBhQ6sdjAYoHAgApF4qD4VlnYCrWwE8vXuBUxXhE7/z9VMbqXd3sosx7a8LeP7bg/jzzF1eupuGDh0KoVCIxYsXo1u3bli9ejUOHz6MixcvIjY2FkLho+VDVqxYgZ9++gmXLl3CrVu3sGbNGsjlcgQEBGDr1q1YsGAB4uPjcfv2baxatQparRbh4eF6+/T394dEIsHChQtx69YtbN68GZ9//nl9vmyTQgPS1iztMnDgayBhC6oLhCd1L9wCoLVRyiKm4XZWMd7/6wIW77+BCd1CMbC5D4QCrl72LRKJMGHCBHzzzTdITExEUlIS+vbtCwcHB3z++ecVjhwcHR3x9ddf47333oNGo0Hjxo2xZcsWuLi4wNHREevXr8dnn32G0tJShIaG4rfffkNkZKTePt3c3LBixQp89NFHWLBgAVq0aIH58+fjxRdfrJfXbGroJDhrlHUT2DsbuLIJtQ2Fx81xnYv/3fMzXF3EpAW5KTCzTwS6NfTguxRSDygcrImyGDg8Hzi2SG+Q+VmkevdAu1uvG6AwYk66hLvh476NEOxmXVM7rQ2Fg7W4sgmImwHk3TVYk0wgwovCpbhYoDBYm8Q8iIUcRnUIxMTnQ2EnM82T6UjdUDhYusxEYPv7wK39Rmn+pN9ovJTYzShtE9PnaivF+zFhGNrSD4J6Go8g9YPCwVKVFQKHvgGOLwG0quq3f0YahQeicuejREMXn7FmjX0cMKt/JFr4O/FdCjEQCgdLdOlvIG4mUPCgXnb3k9cn+DypYb3si5guAQfEdgjE+zHhsJHQREhzR+FgSQozgM3vANd31Otu8z3aoMntSfW6T2K6/Jzl+HpQE3QMceW7FFIHFA6W4ur28mAo5mfdozdtF2FXpjMv+yam6eU2/pjZJwIKKR1FmCMKB3NXVgjEfQicW8VrGdf8hiEmcQCvNRDT4+9sg/lDm6JNA/riYG4oHMzZ3VPA+jeBnKTqtzUyJrFF27LFSC+jaY2kIgEHvNGxAd7vFQ6piCYumAtaW8kcadTAvjnAz71MIhgAgFMW4mNfWm+J6NMy4H9HkjBk6XHcza56uWxiWujIwdxkJgLrxwAP/uG7Ej1lTmEIT/mM7zKICbOXiTBvaFPERHryXQqpBh05mJNL68uX0zbBYAAAac51vO5tuDOwieXJL1Vj7OqzmLP1Cm9LgpOaoXAwB1otsGcW8NfrgKqI72qeaozcOGdiE8vyvyNJeGn5CaTklfBdCqkCdSuZutJ84O/RQGIc35XUCBOI0VuwFAmFNnyXQsyAs0KC715qhugwN75LIU+gIwdTlpkI/NjNbIIBADitCh97neK7DGImsouUeP2XU1i0L5HvUsgT6MjBVF2PA/4eA5Tl8V1JrWkUnmiUMx9lWvruQWpuaEtffDmoMcRC+r0xBfSvYIoOzQd+G26WwQAAwqJUTPGnb4Kkdv48ew+jfjmF/FLjLRRJao7CwZSoSoE/RwH7PgeYec/keImZT1cYMR1Hb2Rh8JJjdD6ECaBwMBVlBcCawcDlDXxXYhAOaSfQzSWH7zKIGUpML8TAJccQfzeX71KsGoWDKSjOBlb2A24f4bsSg3rf+TDfJRAzlVlYhuHLj2PnpVS+S7FaFA58y08BfnnBZE9sq4uG6dvgJqH+Y/JsSlVajFt7FmtO3Oa7FKtE4cCn7FvAzzFAxlW+KzEKrqwAM/wu8l0GMWNaBszceAm/HDWNNcSsCYUDX9KulC+cl2vZ34p6lWzluwRiAWZtuYLlh27yXYZVoXDgw70zwIreQGEa35UYnSz7KkZ63+e7DGIBvtx+FYv33+C7DKtB4VDfbh0EVvUHSqxnJs9YG1pviRjGvLhr+G73db7LsAoUDvXp9nHg15cAZSHfldQrnwe7EaagBdaIYfywNxHf7LTMcTpTQuFQX1IvlgeD2vo+JDmtCp9403pLxHCWHLiJr7Yn8F2GRaNwqA9ZN4HVg8x2OQxDaJe7FWIBLeNFDOf/Dt3C0gM0SG0sFA7Glp8CrB4AFKXzXQmvRAX3MdmPBhOJYc3deRV/nqELTBkDhYMxFWcDqwcCuXf4rsQkvMzt4rsEYoE+XH8RexMsf+ZffaNwMBZlEfDrMCCD+kUfckw9hmhab4kYmFrLMP7Xczh7m363DMniwoHjOGzcuJHfItRK4PcRwL3T/NZhYjgwTHM+yncZxAKVqrT4z8rTSEwr4LsUi/FM4ZCamop33nkHQUFBkEql8PPzQ79+/bB3715D12d+GAPWjwFu0dz+yjRK3wonsZrvMogFyi1W4bWfT+FBrvXNCDSGWodDcnIyWrZsiX379mHevHm4ePEidu7cia5du2L8+PHGqNG8HPgauLKR7ypMFleWj5n+tN4SMY6UvFKM+uUUCsvoC0hd1Tocxo0bB47jcOrUKQwePBhhYWGIjIzEe++9hxMnTgAAvv32WzRu3BgKhQJ+fn4YN24cCgsfnfi1YsUKODo6Ii4uDhEREbC1tUWvXr2QkpKi2+b06dPo0aMHXF1d4eDggOjoaJw7d65CLYmJiejcuTNkMhkaNWqE3bt369U7ffp0hIWFwcbGBkFBQfj444+hUhlppdCr24CDc43TtgXpXbqd7xKIBbueVogpf8SDroBcN7UKh+zsbOzcuRPjx4+HQqHQe9zR0bG8UYEACxYswOXLl7Fy5Urs27cP06ZNq7BtcXEx5s+fj9WrV+PQoUO4c+cOpk6dqnu8oKAAsbGxOHLkCE6cOIHQ0FD07t0bBQXlfYparRaDBg2CRCLByZMnsWzZMkyfPl2vJjs7O6xYsQJXrlzBDz/8gB9//BHfffddbV52zWRcB9aPBUC/kNWRZ13Gy14P+C6DWLC4y2lYtI+mTtcFx2oRr6dOnULbtm2xfv16DBw4sMY7+euvv/DWW28hMzMTQPmRw+uvv44bN24gODgYALBkyRLMnj0bqamVX9xDq9XC0dERv/76K/r27Ytdu3ahT58+uH37Nry9vQEAO3fuxAsvvIANGzZgwIABlbYzf/58/P777zhz5kyN669WaT7wYzcgi66bXFN3ffui041X+C6DWDABB/wvthW6NfTguxSzVKsjh5rmyJ49e/D888/Dx8cHdnZ2GDlyJLKyslBc/Oi6sDY2NrpgAAAvLy+kpz86USwtLQ1jxoxBaGgoHBwcYG9vj8LCQty5U37OQEJCAvz8/HTBAADt27fXq2XdunXo2LEjPD09YWtri5kzZ+raMAjGgPVvUjDUkm/KLgTb0MAhMR4tA979PR63MqxrLTNDqVU4hIaGguM4XL1a9aJXycnJ6Nu3L5o0aYK///4bZ8+exeLFiwEASqVSt51YLK7wPI7jKoRPbGws4uPj8cMPP+DYsWOIj4+Hi4tLhTaqc/z4cYwYMQK9e/fG1q1b8c8//2DGjBm1aqNaB74Cru8wXHtWgtMo8YmPAY/eCKlEQakab64+SwPUz6BW4eDs7IyYmBgsXrwYRUVFeo/n5ubi7Nmz0Gq1+O9//4t27dohLCwMDx7Uvn/56NGjmDhxInr37o3IyEhIpVJdtxQARERE4O7duxUGsR8OiD907NgxBAQEYMaMGWjVqhVCQ0Nx+7YBL66TsBU4+I3h2rMyHXM3Q8hp+S6DWLgb6YWYvI4GqGur1rOVFi9eDI1GgzZt2uDvv/9GYmIiEhISsGDBArRv3x4hISFQqVRYuHAhbt26hdWrV2PZsmW1Liw0NBSrV69GQkICTp48iREjRkAul+se7969O8LCwhAbG4vz58/j8OHDmDFjhl4bd+7cwe+//46bN29iwYIF2LBhQ61rqVTGNWDDW6AB6GcnKriPSX63+C6DWIHdV9Kw9CAt0lcbtQ6HoKAgnDt3Dl27dsWUKVMQFRWFHj16YO/evVi6dCmaNm2Kb7/9FnPnzkVUVBTWrl2Lr776qtaF/fTTT8jJyUGLFi0wcuRITJw4Ee7u7o8KFwiwYcMGlJSUoE2bNhg9ejS++OKLCm28+OKLmDx5MiZMmIBmzZrh2LFj+Pjjj2tdix5VKfBHLKCkszHraoRQf/oxIcbw3e7ruHjPeldGrq1azVYi/9oxHThZ+6Mhoo+Bw6vyJTia48B3KcQKBLkpsO2dTpBLhHyXYvIsbm0lo7uxFzj5f3xXYTE4MHzgRustkfpxK6MIc7Zd4bsMs0DhUBtFWcDGcaBxBsOKTN8KB1pvidSTtSfv0BLfNUDhUBtb3wUKKz9Jjzw7QWkuZvhf5rsMYkWm/30BmYVlfJdh0igcaurCn0DCFr6rsFj9yrbxXQKxIpmFSkz76wLfZZg0CoeaKEgDdrzPdxUWTZ55CUM96aiM1J99V9Ox+oQBz3uyMBQONbF1ElBCV5kytgm2B/gugViZuTuuIi2/lO8yTBKFQ3XO/w5coyWm64N/ShwC5fSHSupPYZkas7bQeFdl6DyHpynNAxa0AIozq9+WGMQ+v/F4I7Ej32XUu4J/tqPgn+1Q55XPohG7+sOxw8uQB7cqfzx+J4quHIAy7SaYsgR+7/4Ogcy2Tm0CQPbeH1F0aS84sQyO0bGwjeyqe6zo6hEUXdoL9yGfGvrlmpxfXm+NruHu1W9oRejI4Wn2f0XBUM86522xyvWWhHYucIqOhVfs9/CK/R6ygKZIXz8HyozyPnGmKoM8qCUc2g8zWJvFN06iKOEg3Id9DqcuryN750JoisvPINaWFSH30Co493zb8C/WBH266TJKVRq+yzApFA5VSU8ATv/IdxVWR5R/B+/4JfNdRr2zCWkLeXBriJ19IHb2gVPn1yCQyFD24BoAwL51fzi0Gwqpd7jB2lRl3YXMrzGkXqFQNIoGJ7HRHWXk7P8Fds17Q2RvHd+m72QXY+E+Wnb/cRQOVdkxHdDSiVl8GGnl6y0xrQZFVw5CqyqF1Keh0dqUuDWAMvUGNKWFKEu9AaYug8jJG6X3LkOZdhN2LfsZZN/m4sdDSbiRTuulPSTiuwCTdGUTkHSQ7yqslnPqYbR1fBknc+35LqVeKTOSkbp6KphaCU4ih/vAGZC4+hutTXlQSygiuyB15WRwIglc+0yGQCxFdtwSuPSZXD5mcW4rhHJ7OMdMgMQtwBAv02QpNVrM3HgJv7+pf9Ewa0QD0k9SlQCL2gB5BrxaHKm1eL/XMCCxF99l1CumUUGdnwFtWTGKrx1B4fld8Hjl6woBUXrnAtJ++6hGA9I1bfNxuUd+hbasCLaNuyPtj4/h/cZilNw4hYJzW+E16geDvVZT9t1LTTGwuS/fZfCOupWedOR7CgYT0CRzK+xE1tWtxwnFEDt5Q+oZAqfoUZC4N0DBmc311qYq6y6KruyHY6dXUXrnImS+URDaOMCmYSco025CW1Zc6fMszfy46zQ4DQqHinLvAEet49uRqROUZOMj/wS+y+AVYwxMo6qXNhljyIpbDKduoyGQyAGmBXs45vbwv8w6ZpHdzy3BimPJfJfBOwqHx+2aCajpovem4kWl9Zx8mHNwBUrvXoI6Lw3KjGTkHFyBsjsXoWjUBQCgKcyBMu0WVDnll8VVZiRDmXYLmpJHA6hpv3+E/LNbatzm4wrPx0Eot4dNSFsAgNQnAqW3L6Ds/lXkn94EsYt/jbqxLMXi/TeQU2TAa82bIRqQfuj+2fKBaGIyFJnnMcgjHevTLH86paYoD5lbv4WmKBsCqQISt0C4D5sNeYPmAICC+O3IO/qbbvu0Xz8AALj0ngTbxt0BAKqcVEhL8mvc5qPtcpB3/A94vjpPd5/UOxz2bQYi/a9ZENg4wLXPZKO9dlNUUKrGov038HHfRnyXwhsakH5o7TAgMY7vKsgTknwHoOuNmp/4RYihSEQCHJjaBd6O8uo3tkDUrQQAD/6hYDBRgalx8Kf1lggPlGqtVZ8YR+EAAAe/4bsCUgVOXYJPfP/huwxipf48cw/JmUV8l8ELCoeU87TqqomLzt8CjqPeT1L/1FqGH/Za59EDhQMdNZg8cV4yxvvSRVkIP7acf4D7udY3i9G6wyH1EnCVLk9pDmJF1r3eEuGPWsvwv8O3+C6j3ll3OBycC4C6K8yBa+pBtHKgRdEIP9advou8YsOekGjqrDcc0i4DCVuq346YBI5p8ZH7Mb7LIFaqWKnBmpPW1bVpveFw+FvQUYN5aZa5DQoRrXlD+LHiWDLK1Nbz+2ed4VCYQWdDmyFBSSY+9L/KdxnESmUUlGH9uft8l1FvrDMc/lkFaK2r/9BSDFDRtGPCnx8P34K1LCphfeGg1QJnV/BdBXlGthn/oJ97Bt9lECt1K6MIu6+k8V1GvbC+cLixp3xpbmK2JtnTVfoIf9aetI7PD+sLhzM/8V0BqaOgtJ3wkZXxXQaxUocTM5CSZ/knxVlXOOTeBRJ38V1FrRy6rUa/34rh/d8CcLPysfFqxbGSQiXDhO0l8P22APIv8tFocSGWnXn6OvTrE1RotbwQjl/nQ/FlPpotK8Tq8xWfM/9YGdznFcB9XgH+e6ziB/HJe2q0XF4ItZafvldOVUzrLRHeaBnw15l7fJdhdNZ1PYezK8zualZFSoamHgK80UyMQX/of1t5L64U+5LUWDNIjkBHAXbdVGPctlJ423F4MVxcaZvOcg4zOknR0FUAiZDD1usqvL6pFO4KAWJCRLiQpsEn+8uw9RUbMAb0/a0YPYNFaOwhhFrL8Na2UizvK4dIwBn75Vepa+FWcFxbMMZfDcR6/XH2LiZ0CwHHWe7vn/UcOWhUwD+r+a6i1l4IFWNONxkGRlT+QX/srgaxTSXoEihCoKMAb7aUoKmnAKfuVz0fu0ugCAMjxIhwEyLYWYB320nRxEOAI3fKLwd5NVOLJh5CdGsgwvNBIjTxEOBqZnmozjuqRGd/EVr7CA3/YmtBknsLb/laR98vMT13s0tw/GYW32UYlfWEw9WtQKHlzTLo4CfE5usq3M/XgjGG/UlqXM/SomdwzQ4KGWPYe0uNa1ladA4of05jdwGuZ2lwJ0+L27laXM/SIspdgJvZWvwSr8KcblJjvqQaGyXew3cJxIqtO3OX7xKMynq6lf5Zy3cFRrHwBRne3FoK3+8KIRIAAg74sZ9M90FflbxSBp9vC1CmAYQcsKSPDD3+DZQINyG+fF6GHquLAQBfPS9DhJsQ3VcV4ZseUsTdVOOzA2UQC4EfelW/L2NxTzmA5vbD8E++HS/7J9Zt56VU5JWo4CCv/Kje3FlHOJTkArcO8F2FUSw8pcSJexpsHi5HgKMAh25rMH57KbztBOgeVPU/r50UiH/LFoXK8iOH9+JKEeQkQJfA8ue81UqCt1pJdNuvjFfCTsqhva8Q4YsKcXqMAvfyGYb/VYKkd20hFdV/3yvHNJjhcQJD8nvU+74JKVNrsTn+Pka2D+S7FKOwjnC4tt0iz4guUTF8tLcMG16So09Y+beXJh5CxKdqMP9Y2VPDQcBxCHEu/0Bv5ilEQqYWXx0p04XD4zKLtZh1sAyHXlfg5H0NwlwECHURItQFUGmB61laNPbgZwyiRdYWKITPo0hjPT2kxHT8fc5yw8E6/qKubOa7AqNQact/npw0JOTKp9vVhpYBZerKH5scV4bJ7aTwtRdA8+8+H1JrGTQ8riYgKM7ENFpvifDk/L1cpOVb5jXOLT8cygqAm/v4ruKZFSoZ4lM1iE8tn32UlKNFfGr5YLG9lEN0gBDv7y7DgWQ1knK0WBGvxKoLKgxs+Kgf9LUNJfhwz6Nf4K8Ol2H3TTVu5WiRkKHBf4+VYfUFFV5tot93uvumGtezNBjfpvyx1j5CXM3UYkeiCsvPKiHkOIS78PtrNEizk9f9E+vFGBB3OZXvMozC8ruVru0ENOZ7Nu2ZBxp0XVmsu/3erjIAZYhtKsaKAXL8PkSOD/eWYcT6EmSXMAQ4CPBFNyneavXog/5OnhYC7tEHeJGKYdz2UtzL10IuAhq6CrFmoBwvRVUMhxIVw4QdpVg3RA7Bv/O5fe0FWPiCDK9vKoVUBKwcIINczO9cb7v0M3jBLRM7Mlx5rYNYp7jLqXjNAruWOGbpSwz+PqJ8GiuxaIl+Q9AjcRDfZRArJBJwODOzOxxtJNVvbEYsu1tJWQTc2Mt3FaQehKTugJfs6cuGEGIMai3D3oR0vsswOMsOh8RdgNryF8giAKcqwie+8XyXQayUJY47WHY40NXerMrzhdR9SPhxKDEDJUrLuoSo5YaDRg0k0vIK1kSSewNjfGi9JVL/SlVaHLxuWV1LlhsOD/4BlAV8V0Hq2X9kNMZE+HHweibfJRiU5YZD8mG+KyA88HiwD43tivgug1ihk7csa5VWCw6HI3xXQHjAMQ1mep7kuwxihW5lFiHdgs6Wtsxw0KiBu/QBYa1aZW+GXGhZg4PEPBy3oKMHywyHB/8AykK+qyA8ERalY6r/db7LIFboxK1svkswGMsMBxpvsHpDtXF8l0CskCWNO1hoONB4g7WzTzuFnq6W8y2OmAdLGnewvHCg8QbyrylOh/gugVghSxl3sLxwoPEG8q+wtG1wl1reRZ6IaTuZZBlHrJYXDneO8V0BMRGcsggf+57nuwxiZS7ey+O7BIOwvHBIvcR3BcSE9Cym9ZZI/bqeVgBNbS/FaIIsLxzSr/BdATEh0pzreN37Lt9lECtSptbiZob5d21bVjho1EAmzW8nFY2R7+e7BGJlrjzI57uEOrOscMhKBDR0wRdSkVfKHkTYFle/ISEGciWFwsG0pF3muwJigjitGp94neK7DGJFEigcTAyNN5AqtMneDKlAy3cZxEpQOJia9AS+KyAmSliUiin+iXyXQaxEZqESaWZ+prRlhQN1K5GneInRekuk/pj70YPlhENZIZBLl4gkVXNIO4FuLjl8l0GsxN1s854EYTnhkJ4AwPxPPCHG9b4zrdhL6se9nBK+S6gTywmHnCS+KyBmoGH6NrhJaL0lYnz3cikcTENBCt8VEDPAlRVgpt9FvssgVoCOHExFQSrfFRAzEVNC6y0R47ufQ2MOpoGOHEgNybKvYqT3fb7LIBYus1CJUpX5XsvcgsKBjhxIzY21ofWWiPGZc9eSBYUDHTmQmvN5sBthCvP9wyXm4Z4Zdy1ZUDik8V0BMSOcVoVPvGm9JWJcD3LN9yxpywiHkhxATd8CSe20z9kCsYDOjSHGk1tivqtEW0Y40HgDeQbCwgd4z+8G32UQC5Zfoua7hGdmIeFA4w3k2bzM0XpLxHjyS833hEvLCIfCdL4rIGbKIfU4omm9JWIk+SUUDvwqK+C7AmKmODBMcz7KdxnEQuWXUrcSv1Q0GE2eXaP0rXASm+8fMTFddOTAN5X5ziUm/OPK8vGxP623RAyvgMYceKYs4rsCYuZ6l27juwRigahbiWdZQhEeOPkh09Yd+TIHlIlkYOD4LouYEVnWFbziRbPeiGGZ85GDiO8CDGGhnOFvRw6A7N+fcmKBGBKBGFKhBGKBCFJODIlACAkngoQTQsoJIeGEkICDhBNAAkAKDhIGSABIGCBlDGKtFlKmhUSrgUSrhVSrgUSjhkSrhkSjhlSjglitglSrgkSthERVBom6DFJ1+X85ugiRWXhbsQ+/YgTfZRALotaY79++RYSDSlt5Oqu0Kqi0KhSpjTgmIfz3R/LwjspDSiqUQPJvWEk4ESSCfwNKIIIEgipCioOEMUgBSJgWEi2DhGkh1Woh1moqDSmJWgWJRlkeWKpSSNVKSFSlkGrKjPceWAjflN0IthmEm8VyvkshFkLDKBx4pWGmvSzuw5AyqkpDSv7vjwOAqkNKF1ACISQQQMoJIQZXHlBApSFVfiT18GhKDalGA7FWDalGDUmlIVUGqaoMEhMOKU6jxCd+ZxCb2InvUoiFYAxgjIHjzK+b2yLCQa0130Gf+sR/SAEcuPLuPqEYEoHk33ASQ/z4UZQupATlt8FBCkDMUB5QWlYeWrruvoohJdGqIVGrINWqIVYr/z2iUkLybzff00KqY+5mCLmO0DCLGI4jJkDLAKH5ZQOFA6lfDAxKrRJKrRKAEWeZPfzN1oWUAFWHlBhSgRgSgQieWgUWa7RIK3YzXm3EujAGmOEEGYsIBwFH3/JI7VQMqUfGX28Bv93zkNrlSxTl05cOUncCM+xSAixkKqtcRAOIpO581Q5osPMyBPlZaJxFC/IRw+AEFA68oXAghjAtIQisqLyry+boeoR40bIspG7MNRgACwkHmVBW/UaEPEVDlSu8dp2vcJ/vljmwdRDzVBGxBObc423GpT8iE1E4kLp577wPmLLi+IOgMBdR6VvNcSyRmAixVMh3Cc/MIsKBupVIXbQu84bjvvhKH7M5vhmhnrR2F3k2Urn5zvmxiHCgIwdSF+NOOwGaqk+k9NnyBeydqHuJ1J6EwoFfNiIbvksgZqprSSAURy48dRtBYR4i72+k7iVSa1IbCgde0ZEDeVb/OSr79ySlp5Of2o4wD7riIKkdiYzCgVc05kCeRf/CUEhOX6rx9t6b5sDBmbqXSM3RkQPPbMW2fJdAzNDL+2u3YKOgpBCRt/+CmZ7wSngglZvvlwmLCAd3G3e+SyBm5rWcSAguXa/182RndyHcI9fwBRGLJKEjB355KDz4LoGYEY4BfffkPfPzPTd+CUcX8/1GSOoPTWXlmVQohZPUie8yiJl4O7MJcCP5mZ8vKC1Co1vrzPrsV1I/FI5Svkt4Zhbz601HD6QmJEyIrnGpdW5H9s9eNHTNNkBFxJLZuZjvTEqLCQdPG0++SyBmYPKDJmB3HxikLc+NX8DJxXy7DYjx2VM48I+OHEh17LRStNqRZLD2OGUpGiWuhcCMV94kxiOSCCC3k1S/oYmynHCwoXAgT/f+7UiwjEyDtim9cAgNXdMN2iaxDHbO5nvUAFhQOHgqqFuJVM1Vq0DEjqtGadt941dwdqPuJVKROY83ABYUDnTkQJ5m+o1wsLx8o7QtUJYh4spKCMzxKvLEaOxczHvlBosJBy+FF98lEBMVoHZE4I6aL5PxLKSXjyHCqe6zoIjlsHM232msgAWFg4+dD62xRCo19UoDsOJio+/HbePXcKHuJfIvezpyMA0CToAQxxC+yyAmppHKDZ67z1e/oQEI1EpEXPoZQhF1LxHA2VvBdwl1YjHhAABhTmF8l0BMzKR4b73LfxqTJOEkGjncr7f9EdMkFAng5Gne15mhcCAWq22ZDxz3x9f7fl03zoWbO3UvWTNnbwUEQvP+eDXv6p8Q7hzOdwnEhLx9yvGpl/80Fk6jRnj8jxCKLerPi9SCi6/5X0bAon576ciBPPR8SSBsjj798p/GJLl+BpF2t3nbP+GXK4WDabGT2MFb4c13GcQEvHFEWqPLfxqTy8Z5cPcQ8loD4QeFgwmiowcyqCAM4jOX+S4DnFaD8DPLIJJY3J8ZqQaFgwkKc6ZwsHbD9qv4LkFHfDMejWxu8V0GqUd2zjJIbcz/YlAWFw4NnRvyXQLh0aicSAguJ/JdRgUum+bDw5O6l6yFq5/5HzUAFhgOzd2b810C4YkQHHrvzuW7DD0cYwg7uYi6l6yEd6gj3yUYhMX9trrKXRHkEMR3GYQHb6c3Bm6a5gwhcdIlRMqu810GqQc+YZZxyWKLCwcAaO3Zmu8SSD2TMiGid6bwXcZTOW/5Hl5eFvknR/4lkYssYjAasNBwaOvVlu8SSD1770ETsPumHQ4cYwg9+gPEUov8syMAvEMcwFnIlQEt8re0tUdrcLCMfyBSPQcmQ8vt5jEjSHTnKqLECXyXQYzE20K6lADAIheAcZQ5ItQpFNdzqI/XGkxNbgRt5im+y6gxp60L4P3yEjxI4fckvRsPLmDP+XW4k5mI/OIsjOk5C00bPKd7fPX+uTh5fVeF50T4tsb4Pl8/td3cogxsOvEjLt89BZW6DK4OPni1y/sIcCtf3mbP+T+wJ34dAKBHs5fwfNNhuucmpyVg3ZEfMHXgYggF5jfDyyfMke8SDMYiwwEA2ni2oXCwAu4aWzTcfhX8fszWXsjh75AZNQXK0vpf++mhMnUJfFyC0b7hC/hx16eVbtPIrzVe7TJNd1skfPr8/eKyAny78V2EejfDuN5fw1bmgIy8+7CR2AEA7mfdxLYzK/BWry8AxrBs5ww09G0FH5cgaLQa/H74e7zcebJZBoNELoKbnx3fZRiMxYZDa8/WWJOwhu8yiJFNuxEGlm8+Rw0Pie4lIqrJBZxDJG81RPq3RaT/08fnREIx7G2ca9zm7vjf4WTrhpFdHwWKq/2jqzSm5d6Fj3MQwn3Kp5x7uwSV3+cShD3n1yHEqzEC3M3zXCVLGm8ALDgcWnm2goATQMu0fJdSK0XXipC5PRMlt0ugzlXD/x1/2Le01z1+aVTll7v0GOYBt95ulT7GtAzpG9KRezwX6jw1RI4iOD3nBLcX3cBx5b/MmTsykbE9AwDg1tsNri+46p5ffLMYD1Y9QPAnweBM6DrJgWpHBOy4aHZHDQ85bl8Cn1cW4/4DviupWuKD8/hg5WDYSG0R5tMcfVu/DluZQ5XbX0w+hgi/1vhp9ywkPrgAR4UrOkW+iI4RfQAA3s4NkJ53D9kFaQCA9Nx78HYOREbeA5y4thPTBy2rl9dlDL4Nax6i5sBiw8FeYo8I5whczuJ/jZ3a0JZpIfOXwamzE+4svKP3ePj3FZclL7xYiPs/34dDq6r/YDO2ZSB7fzZ8R/tC6iNFSXIJ7v90H0IbIVx6uKD0binSNqQhYFIAAOD2d7dhG2ULmZ8MTMPwYOUDeI/yNqlgAP69/GfJab7LqJOQ/fOR2Ww6ykr4616qSoRfazRt0Akudp7IzH+ALad+wtLtH2LKgIUQVNHtk1mQgsNXNqNb4yHo2fwV3E6/hr+OLoJQIEK78Bh4OgWgX5v/YNG28iOLF9uOhqdTABZufR8D2r6JhHunsf3MKggFIgzpMB4h3k3q8yXXSYOmrtVvZEYsNhwAoJt/N7MLB7smdrBrUnW/pdixYp9v/rl8KBoqIHGXVPmckhslsGtuB7tm5e1K3CTIO5GH4lvFcIELylLKIPOVwbZR+fxsmZ+s/D4/GTJ3ZEIRroBNkGld1SpK5QH3XfF8l1FnwpQkRDU9h7NoyncpelqFdNP9v49LEHxcgvDZbyOR+OA8wn1bVPocxhj83cLwYtvRAAA/11Ck5CTjyJUtaBceAwDo1KgfOjXqp3vOiWtxkIrlaODRCJ+vG4X3By1BbmEGft47B7NeWQOxsOrfbVPh6mcLe1fzvmb0kyxyKutDPQN68l2CUanz1Ci4UACnzk+fPicPkaPoShHKUssAACV3SlCUWAS7xuVhIfWVQpmmhDJLCWWmEmWpZZD6SlGWXoacwzlwH+Ru9NdSW+/+4wGoTGeBvbpw2Lkcft6m3znmau9dPsCcX/VlUO1tnOHpFFDhPk9Hf+QUple6fWFJHnacXY2hHd9BcvpVuDv4wt3BF2E+zaHVqpGee8+gr8FYgppV3qVrziz6yCHQIRChTqFIzDGthdgMJedoDoQyYYUxicq49XGDtkSLxA8Ty78OaAGPwR5w7OAIAJB5y+Ax2APJ85IBAJ5DPCHzliHpmyR4DvNE4aVCpG9MByfk4DXCC4pwfi+c3r7UFw48XP7TmBrsnYeMlh+itNj0upceyinMQFFpPuxtXKrcJsgzCum5dyvcl553D852HpVu//fxJejaZDCcbN1wJ+MaNNpHr1+j1YCZyZhhg6YUDmanZ0BPyw2HQzlwaOcAQTULuuWdykPuiVz4jvWFzEeGkjslSP01VTcwDQDO3Zzh3O3RgFrOkRwIZALYhNjg+gfXEfxpMFQ5Ktxdehdh88Ig4PESmG+dsge05vGhUVOitNuIUp/CGbSst32WqUqQkffoKCCrIBX3Mm/ARmoHhcwe28+sQrOgTrC3cUZm3gNsPLkcrg7eiPBrpXvOgi1T0bTBc4iOGgAA6NZ4MP67aSLizq1Fi+AuSE6/iqMJ2/By58l6+0+4dwbpefcwsut0AIC/WzjScu/g8p2TyCnMgEAggLujn3HfBAOwd5VZzJIZj7P8cAjsicXxi/kuw+CKrhVBmaqE07jqz8hM/SMVbr3d4NjOEUD5mIIqS4WMrRm6cHicukCN9E3pCPowCMW3iiH1lOp+mIZBmaqEzE9m6JdUIz2LgiDn8fKfxmS/62cEvNIMtx/Uzxz/2xnXsGDLFN3t9ceXAgDahvXES50m4X72LZy8vgslykI42LigoW8r9G09qsIYQGb+AxSW5uluB7g3xJies7D51E/YcW41XOy8MLjDOLQO7V5h30p1Gf48shBvdP8YAq78i4aTrRuGdpyANQfmQSQUY2SX6ZCIpMZ8CwzCEruUACsIhyCHIIQ4huBG7g2+SzGonEM5kAXKIPevfhCMlTG90SVOwKGqOaApv6bAtacrxM5ilCSVgGkebcg0DEzLX/947FHL/pUN3DUX6W0/QUmR2uj7CvNuhkVj91b5+IQ+c6ttY/aIX/XuaxzQHo0D2j/1eRKRFJ8MX6l3f4eIPujw77RXc2Gp4WDRA9IP9QjowXcJNaYp1aDkdglKbpcAAJSZSpTcLoEyS/lomxIN8k7nwblz5fOqk+YmIWtPlu62XTM7ZGzJQEF8AZQZSuSfzUdmXGalYxWFlwqhTFPC+fnytuUN5ChLKUPBhQJkH8gGJ+Ag9eLn29yQ/HCIz17hZd/1RZh5H1GlR/kug9SQ3F4Cz6Cqp5GbM8v+GvavngE9sfT8Ur7LqJGSpBIkz03W3U79LRUA4NjREb5jfAEAeSfLD+Md2lX+S6lMV0Jd8Oibp9erXkhfn44Hqx9AnV9+EpxzF2e49a/4jUer1OLBmgfwe9tPd6an2FkMr1e9cP9/98GJOfiO9q12jMNYhuwv42W/9c1u7yoEvtISyQ+s4s/TrIW2dLeos6IfxzHGTH8OnQH039gft/LMY+VOou+NrCj0Wh7Pdxn1RuvsiVMdZqG40PjdS+TZvTSzjUUORgNW0q0EAL0a9OK7BPKMhODQa08232XUK0F2KqKKDvFdBnkKN387iw0GwIrCYVDIIAg581vpkQDj05oAt/SXErF0tvvXooG3svoNCS8iOnhVv5EZs5pw8FB4INo3mu8ySC1JmRCddlZ9Rq6l89/xFWzsaOzB1AjFAoS1qfzEPkthNeEAAC+Fv8R3CaSWptxrAvYgle8yeCPMSUdUXtXTTQk/gpq5QWrz9GtbmDurCof23u3hb+fPdxmkhhyYDC123uS7DN7ZHvoDwd7WMVPLXER0tOwuJcDKwoHjOAwNG8p3GaSGpiU1gjbTugaiq+K39Qso7Kl7yRTYu8rgG24514quilWFAwAMCBkAicD0lwC2du5aBcK2J/BdhskQ5GehcVYc32UQABEdvHUXybJkVhcOjjJH9Ay07KW8LcH06+FgBQV8l2FSbI6uR4hXCd9lWDWRVIioaB++y6gXVhcOAA1Mm7ogtRP8d1rm4np15btlDmwdLHsg1JRFtPeCTGEd779VhkMz92YIcwrjuwxShSmXA8FKSvkuwyQJCnMRlb4VsPxeDZPDCTg06276S4gbilWGAwCMihzFdwmkEo2V7nDb9Q/fZZg0m+ObEepZxHcZVie4uZvFXQr0aaw2HHo36I0A+4DqNyT16t1/PAE1rSdUHZ8tX8DeyTq6N0xF857WNQ3easNBKBBidOPRfJdBHtOx1A/2B+L5LsMsCArzEHl/I6xg0oxJ8AlzhHvA0y/Ha2msNhwAoG9QX/ja+vJdBvnXmyfsLO7yn8YkP7UdoR75fJdhFZr1sK6jBsDKw0EkEGFMkzF8l0EA9CoKhvw4zVCqLe9NX8DBmbqXjMnZW4GAKBe+y6h3Vh0OANAvuB98bK1j3rIpe+2w1f8qPhNBSSEib/9F3UtG1KZvA6s46e1JVv8XKRaI8UbUG3yXYdWG5TeE6B86G/pZyc7uQrhHLt9lWCQ3fzsENbfMa0RXx+rDAQAGhgyEp8KT7zKs1uB9dNZvXXltmANHF+peMrS2LwZZ5VEDQOEAABALxfhP1H/4LsMqjc6KApdAK6/WFVdWgkY3fwdHf9EG4xXsYJVjDQ/Rr9K/BoUOorGHeiYEh5hdWXyXYTFk8fvQ0JVWsTWU9oNC+C6BVxQO/5IIJZjccjLfZViVd1KbgCXf5bsMi+K58Qs4udDS3nUV1MwNXsEOfJdRaytWrICjo6Pu9meffYZmzZo9U1sUDo+JCYxBC/cWfJdhFWRMhI477/FdhsXhlKVolLgWAoF19pMbgkDAod2AoDq1MWrUKHAcp/txcXFBr169cOGCcadrv/TSS7h+/bpB2qJweMK01tPA0apmRjf1XmOwlDS+y7BI0guH0NAlne8yzFaj57zh5Kmoczu9evVCSkoKUlJSsHfvXohEIvTt27fK7VUqVZ33KZfL4e7uXud2AAoHPZGukegX3I/vMiyag1aGpttv8F2GRXPf9BWc3ah7qbbkdmK07V+3o4aHpFIpPD094enpiWbNmuGDDz7A3bt3kZGRgeTkZHAch3Xr1iE6OhoymQxr165FVlYWXn75Zfj4+MDGxgaNGzfGb7/9pmvz4fOe/OnSpQsA/W6luqBwqMTklpNhK7bluwyLNT2pEVh2Dt9lWDSBsgwRV1ZCIKSj4NroMCjEKNdrKCwsxJo1axASEgIXl0czoD744AO8++67SEhIQExMDEpLS9GyZUts27YNly5dwptvvomRI0fi1KlTAAA/Pz/d0UhKSgr++ecfuLi4oHPnzgavud7DgeM4bNy4scrHDxw4AI7jkJubW281PclV7opxzcbxtn9L5qmxReiOK3yXYRWkl48hwjmF7zLMhneoIxq29zJYe1u3boWtrS1sbW1hZ2eHzZs3Y926dRAIHn3sTpo0CYMGDUKDBg3g5eUFHx8fTJ06Fc2aNUNQUBDeeecd9OrVC3/88QcAQCgU6o5GHB0d8dZbb6F9+/b47LPPDFb3QwYPh9TUVLzzzjsICgqCVCqFn58f+vXrh71799bo+R06dEBKSgocHPidKfByw5cR6hTKaw2WaNq1MLCCQr7LsBpuG+bC1Z26l6ojEHKIfjncoG127doV8fHxiI+Px6lTpxATE4MXXngBt2/f1m3TqlWrCs/RaDT4/PPP0bhxYzg7O8PW1hZxcXG4c+eOXvtvvPEGCgoK8Ouvv1YIHEMxaIvJyclo2bIl9u3bh3nz5uHixYvYuXMnunbtivHjx9eoDYlEAk9PT97PShQJRJjRdgavNViaYLUz/OJocb36JFAr0fDizxCKqHvpaZp194Ozd90HoR+nUCgQEhKCkJAQtG7dGv/73/9QVFSEH3/8scI2j5s3bx5++OEHTJ8+Hfv370d8fDxiYmKgVCorbDdnzhzExcVh8+bNsLOzM2jdDxk0HMaNGweO43Dq1CkMHjwYYWFhiIyMxHvvvYcTJ07otsvMzMTAgQNhY2OD0NBQbN68WffYk91KDwdY4uLiEBERAVtbW90sgIdOnz6NHj16wNXVFQ4ODoiOjsa5c+fq/HpaerTEoNBBdW6HlJty0R+slC7/Wd8kCSfRyOE+32WYLDtnGVr1aWD0/XAcB4FAgJKSqpeLOXr0KPr3749XX30VTZs2RVBQkN7U1L///huzZ8/GH3/8geDgYKPVa7BwyM7Oxs6dOzF+/Hi9NARQYQR91qxZGDZsGC5cuIDevXtjxIgRyM6u+szO4uJizJ8/H6tXr8ahQ4dw584dTJ06Vfd4QUEBYmNjceTIEZw4cQKhoaHo3bs3CgoK6vy6prWeBm+Fd53bsXZNlB5w3RPPdxlWy3XjXLhR91KlOg0Pg1giNHi7ZWVlSE1NRWpqKhISEvDOO++gsLAQ/fpVPRsyNDQUu3fvxrFjx5CQkICxY8ciLe3RlO9Lly7htddew/Tp0xEZGalr/2mfn8/KYOFw48YNMMbQsGHDarcdNWoUXn75ZYSEhODLL79EYWGhbjS+MiqVCsuWLUOrVq3QokULTJgwocIYRrdu3fDqq6+iYcOGiIiIwPLly1FcXIyDBw/W+XUpxArMeW4OnftQR++ec6fLf/KI06gR/s//QSimCYqPC27uhgZNXI3S9s6dO+Hl5QUvLy+0bdsWp0+fxp9//qmbdlqZmTNnokWLFoiJiUGXLl3g6emJAQMG6B4/c+YMiouLMWfOHF3bXl5eGDTI8D0cBvsqwRir8bZNmjTR/b9CoYC9vT3S06s+acfGxqbC4ZOXl1eF7dPS0jBz5kwcOHAA6enp0Gg0KC4urnQQ51m09myNVyJewdqEtQZpz9o8V+oHu4Pn+S7D6kkSzyEyKhkXcqzvqmaVsXGQoMuI6r/MPosVK1ZgxYoVVT4eGBhY6Wems7PzU2dzjho1CqNGjarx45999tkzz2Qy2NeI0NBQcByHq1evVrutWFxxHjHHcdA+5fKQlW3/+BsbGxuL+Ph4/PDDDzh27Bji4+Ph4uKiN4hTF5NaTEKgfaDB2rMmbx63pct/mgiXTfPh7mH4LhSzwwHPvxYBmS0tc14Vg4WDs7MzYmJisHjxYhQVFek9bszzFo4ePYqJEyeid+/eiIyMhFQqRWZmpkH3IRPJ8MVzX0DI0R9WbfQuCobsxMV63++Z4mKMu3cX0TduoNG1q9jzxPhTo2tXK/35KbvqVWKraxMAfs7OwnM3EvHcjUT88kRb50tKMCQ5CepaHGUbGqfVIPzMMogk1t291DjaF/6R1rscd00Y9Ddk8eLF0Gg0aNOmDf7++28kJiYiISEBCxYsQPv27Q25qwpCQ0OxevVqJCQk4OTJkxgxYgTkcrnB99PErQldNa6WXj3Ez4dQsVaLcKkMH3t4VPr4weCQCj9zPD3BAehpW/W0wOravFZaikWZmZjv5Y15Xt5YkJmJ62Xls7PUjGFWWio+9fCEiOdp2uKb8Yi0ucVrDXxy8rRBh0HGm+VjKQz6lxsUFIRz586ha9eumDJlCqKiotCjRw/s3bsXS5cuNeSuKvjpp5+Qk5ODFi1aYOTIkZg4caLBFp960ttN30a4k2FPlrFUw/MaQhTPz+U/O9va4l03N3SvYg64m0hU4WdfYSHa2NjATyJ55jZvKZUIk0rRTqFAe4UCYVIpbv3btflzdjZayW3Q2AhfWp6F86b58PC0vqMHgZBDjzciITLC7CRLw7HajCQTAMC17Gt4ZdsrUGoNN6ZhaTgG/L7BH9w1/r+hNrp2FQu8far8UM9Uq9Ht5g186eWFvvY1OzO/sjZvlpVhxJ3bWB/YAAzA4OQkrPUPgJjjMPbeXfwVGAiFwHQ+lFSBkTgeNgFqpfWMB7UbEISWvQL5LsMsWN9XBwMIdw7HjHZ09vTTjMmKMolgqIlNeXmwEQjQ4yldSjURLJVikpsbRt+9izF372KSmxuCpVJ8lpaKKW7uOFJUhBeTbmFQchLOFBcbqPpnJ06+jEiZYdb+NwfeoY5o0TOA7zLMBoXDMxoUOgiDQwfzXYZJEjEBeuw27IQAY1qfn4e+9vaQGmB9muGOTtgeFITtQUEY7uiEjXl5UAgEaCaX45PUVCzw8cV0d3dMefAAShOYweW85Xt4eVn+x4CNgwQ9R0eCo4sg1Zjl/1YY0Yy2M9DEtUn1G1qZiWlNwJLN4ypvZ4qLkaRUYoiDo8HbzlGrsSQrEzPcPXChtASBEgkCJRK0tVFADYZkFf/dkhxjCDn6PcRSy/0oEIg49HqzMRQOUr5LMSuW+xtRD8RCMf7b5b9wljnzXYrJsNGK0X6HYU4+rA/r83IRKZWhoUxm8La/zkjHa05O8BSLoWWA6rHhPQ1j0JjIaJ/4zjVEiS13GfVOw8LM8nrQfKNwqCNPhSfmR8+HiKN1awBgyr3GYKn8X6KySKtFQmkpEv5d6O++SoWE0lI8eOxSjIUaDeIKCjDYsfIPjtfv3sHanEcXJapJmw8dKypCslKJVxydAABRMhmSlEocKizEH7m5EHAcGjxlZlR9c9q6EN5eltfl0qijF6I6+/BdhlmicDCA1p6tMbnlZL7L4J2TVo4m201jgPNyaQkG307G4NvJAIC5GekYfDsZizIzdNtsLygAA9DHzr7SNu4qlcjRPFoPqiZtAkCpVos5aWn4zMMTgn/PafAUizHD3QMzUlPwf1mZ+MrTCzIjrMFfFyGHv4NEZjqzqerKo4E9Og+naefPiqayGtC0Q9OwI2kH32Xw5qsbLRD8Z9ULKBLTl9t7HM4VR/JdRp3J7SUY9mFr2DrROMOzMq2vLmZuVodZCHMK47sMXnhp7BCy4zLfZZA6cty+BL5mvkK9QMih15goCoY6onAwILlIjiXPL4GXwnDXoTUX06+FghXqr6lFzE/w/vmQys20e4kDur0WAe9QR74rMXsUDgbmofDAsh7L4Ch15LuUehOqcoEPXf7TYghTkhDF6n4lRT50GBSC8LaefJdhESgcjCDIIQiLn18Mucg01tExtvcu+tHlPy2Mw87l8PM2r+HIZt390LwHXavCUCgcjKSJWxN82+VbiASWPcW1udILLnvj+S6DGEHQnm8gU5jH729YWw90GBzCdxkWhcLBiJ7zeQ6zO8y26EuMvnPGlS7/aaGE6XcQpTzJdxnV8m/kjG6vRYDjeSl0S0PhYGT9gvthSqspfJdhFNElAbA9FM93GcSI7Hf/jABvDd9lVMk9wA4xb0ZBKKSPMkOjd7QexEbG4vWo1/kuw+BGH7cB6DQZixe4ay7kJti95ORpg74TmkIiM73aLAGFQz15r+V7GBI2hO8yDKZPYQikJ+v/8p+k/gkz7yOq9CjfZVTg7K3AgPdaQG5nOkuQWBoKh3r0SbtP8GrEq3yXYRCvHqIjBmtit3cVAr1NY2zJxccWAyY3h409BYMxUTjUI47jML3NdIxpPIbvUurklbwICM9f47sMUs8Cd34FG1t+u3Bc/cqDgY4YjI/CgQcTW0zEO83f4buMZ8IxYMCeQr7LIDwQZKciqugQb/t387dD/0nNIbMV81aDNaFw4MmbTd7EtNbT+C6j1sZmNQauJ/FdBuGJ7f61aOBd/xcpcg+0R/9JzSBTUDDUFwoHHo1sNBKftP8EAs48/hlETIDn4/i/VgPhl/+Or6Cwr7/uJc8gB/R/txmkNhQM9ck8PpUs2NCwoZjTcQ6EnOkvdDYptQnYnft8l0F4JsxJR2Tu3nrZV4Omrug/qRkkcpquWt8oHExAv+B+mB89HzKh4S9VaSg2WjHamtHlP4lx2R76A8HeZUbdR9NufnhhbGOIJKb/xckSUTiYiO4B3fFLr1/gLnfnu5RKvX+3MVgadSmRR/y2fgFbB8N/o+c44LlhoXhuWCg4AS2JwRcKBxMS5RqF3/r+hkgX07oSl4vWBlE7TOPyn8R0CPKzEJW506BtiiQCvPBWYzTt5mfQdkntUTiYGHcbd6zotQIxgTF8l6Iz7WZDsJxcvssgJsjm6AaEeJUYpC25vQQDp7RAg6ZuBmmP1A2FgwmSiWSY13kexjUdx/uKrj4aewTR5T/JU/humQNbh7rNJHLyUmDItJZwD7A3UFWkrigcTBTHcXi72duYFz2P14HqaVdDwIro8p+kaoLCXESlb8Wzfo8Jbe2BoR+0gr2rdVwcy1xQOJi4mMAYrHhhBdxt6n+gOlzlCu+48/W+X2J+bI5vRqhn7b5ECEQcOg8PQ8//REIspRlJpobCwQxEukRiXd916ODdoV73O/mCL1iZcacrEsvhs3kO7J1q1r1k5yzDoKkt0biLr5GrIs+KwsFMuMpdsaz7MkxtNRVigfHPFG2p9ILzvnij74dYDkFRPiLvb0R1F2QLiHLBsBmt4RFI4wumjMLBjHAch9jIWKztvRaB9oFG3deE03T5T1J78lPbEeqRX+ljnIBD2xeD0Gd8E1ojyQxQOJihCJcIrOu7DoNCBxml/a4lgVAcjjdK28TyeW/6Ag7OFT/8bZ2l6P9uM7TqHUjXejYTHGN0nUdztit5F2Ydn4V8ZeXf1p7F6r0NIT11yWDtEetT2qIHjjsMAGNAw3ae6PRSGK2PZGYoHCxAalEqph+ajnPp5+rc1ouFoXh1YYIBqiLWLvO1L+DzQicENaeT2swRhYOF0Gg1+O3qb1gUvwhFqmc/L+G3rcEQXqSrvJG6se/dGx4fz4TIyYnvUsgzonCwMGlFaZh7ei52395d6+e+mtsILy69YISqiLUQubnB89NPYNe9O9+lkDqicLBQh+4dwpcnv8T9wppdf4FjwLq//YBEusobeQYcB4dBA+ExbRqEDg58V0MMgMLBgpWoS7Ds/DKsurwKavb0aanjMpqgy//qPmZBrI+8WTN4zPgI8saN+S6FGBCFgxVIzEnE5yc+xz/p/1T6uIQJsXatK9hdusobqTmRhwfcp06Bfd++ND3VAlE4WAnGGNYnrsfCfxYiqzSrwmPvP2iG1ivP8FQZMTecVArn10fB9c03IbCx4bscYiQUDlamWFWMlVdWYsWlFShWF8OWSfDLzzZg6Zl8l0bMgF3PnnCfNg0SXx++SyFGRuFgpbJKsrDs/DI02nUDDX89wXc5xMTJoqLg/v77ULRtw3cppJ5QOFg55b17yFy0GHlbtgAaDd/lEBMjb9oUruPHwbZzZ75LIfWMwoEAAMpuJSFz8WLk79gBaLV8l0N4Jm/ZEq7j3oZtx458l0J4QuFAKihLTETWz78gf+tWMJWK73JIPbNp0wau48ZB0a4t36UQnlE4kEqpMzKQ89tvyPntd2hycvguhxiZokN7uI4bB5tWrfguhZgICgfyVNqyMuRt2oTslaugvHmT73KIAQlsbGDfrx+cXh4OWcOGfJdDTAyFA6kRxhiKjhxB9i8rUHTsGN/lkDqQhobAcfhwOPTvD6GtLd/lEBNF4UBqrfT6deT+9Rfyt22HJiur+icQ3nFiMex69IDTKy9T1xGpEQoH8syYWo2io0eRt2kTCvbuAysr47sk8gSxnx8cBw+G49AhELm48F0OMSMUDsQgNIWFKIiLQ97GTSg+cwagXyveiH19Yd8rBna9XoA8KpLvcoiZonAgBqe6fx95W7Yif8cOlF2jCwfVB7GPD+x6xcC+1wuQN47iuxxiASgciFGpUlJQePAQCg8dQtGJE2DFxXyXZDHEPj6wi4mB/Qu9aLlsYnAUDqTeaJVKFJ86jcJDB1F48CBUt+/wXZJZETo6wqZtWyjat4OiXTtIAgP5LolYMAoHwhtlcjIKDx1G8ZkzKDl/Huq0NL5LMimcjQ1sWraEol07KNq3gzQigq6bQOoNhQMxGarUVJTEn0fJ+fKf0suXrWcGFMdB7OcHWcOGkDWKgE2rVpA3bQpOLOa7MmKlKByIyWIqFUqvXisPiosXUZaUBGVyMrT5+XyXVjciEaTBwZBFREDWKAKyiAhIGzaE0M6O78oI0aFwIGZHnZ0N5b9B8fCnLCkJqjt3wZRKvssDAHAyGcTe3hV/fLwhaRAEaVgoBBIJ3yUS8lQUDsRiMK0W6vR0aLKzoc7OgSYn+9H/Z2dDnZMNTXYONDk50BQWAGoNmEYDaJ7475NLlovFEMjlEMhk5f9VKCBwsIfQ3gFCe3sIHewhdHbRBYDY25tOOCNmj8KBkEowtRpMqwXHcdTvT6wShQMhhBA9Ar4LIIQQYnooHAghhOihcCCEEKKHwoEQQogeCgdCCCF6KBwIIYTooXAghBCih8KBEBMQGBiI77//3ihtcxyHjRs3GqVtYrkoHAh5Rl26dMGkSZP07l+xYgUcHR1r1dbp06fx5ptv6m7X5wd6RkYG3n77bfj7+0MqlcLT0xMxMTE4evRonesxZugR4xLxXQAhBHBzc+Nt34MHD4ZSqcTKlSsRFBSEtLQ07N27F1lZWbzVRPhHRw6EGNmoUaMwYMAAzJ8/H15eXnBxccH48eOhUql02zz+DTvw3yu8DRw4EBzH6W4DwKZNm9CiRQvIZDIEBQVh1qxZUKvVuscTExPRuXNnyGQyNGrUCLt3735qbbm5uTh8+DDmzp2Lrl27IiAgAG3atMGHH36IF1988an13Lx5E/3794eHhwdsbW3RunVr7NmzR9d2ly5dcPv2bUyePLl8jarHLlR05MgRdOrUCXK5HH5+fpg4cSKKiop0jy9ZsgShoaGQyWTw8PDAkCFDavx+E8OgcCCkHuzfvx83b97E/v37sXLlSqxYsQIrVqyodNvTp08DAH755RekpKTobh8+fBivvfYa3n33XVy5cgX/93//hxUrVuCLL74AAGi1WgwaNAgSiQQnT57EsmXLMH369KfWZWtrC1tbW2zcuBFlVVxYqap6CgsL0bt3b+zduxf//PMPevXqhX79+uHOnfLLv65fvx6+vr6YPXs2UlJSkJKSAqA8VHr16oXBgwfjwoULWLduHY4cOYIJEyYAAM6cOYOJEydi9uzZuHbtGnbu3InOnTvX9K0mhsIIIc8kOjqavfvuu3r3//LLL8zBwUF3OzY2lgUEBDC1Wq27b+jQoeyll17S3Q4ICGDfffed7jYAtmHDhgrtPv/88+zLL7+scN/q1auZl5cXY4yxuLg4JhKJ2P3793WP79ixo9K2HvfXX38xJycnJpPJWIcOHdiHH37Izp8/X2Gb6tp4KDIyki1cuLDK18UYY//5z3/Ym2++WeG+w4cPM4FAwEpKStjff//N7O3tWX5+frX7I8ZDRw6E1IPIyEgIhULdbS8vL6Snp9eqjfPnz2P27Nm6b/u2trYYM2YMUlJSUFxcjISEBPj5+cHb21v3nPbt21fb7uDBg/HgwQNs3rwZvXr1woEDB9CiRYsqj2weKiwsxNSpUxEREQFHR0fY2toiISFBd+TwtNexYsWKCq8jJiYGWq0WSUlJ6NGjBwICAhAUFISRI0di7dq1KC4urtF7RAyHBqQJeUb29vbIy8vTuz83NxcODg4V7hM/cU0IjuOgffKiQtUoLCzErFmzMGjQIL3HZDJZrdqq7Pk9evRAjx498PHHH2P06NH49NNPMWrUqCqfM3XqVOzevRvz589HSEgI5HI5hgwZAmU1V+MrLCzE2LFjMXHiRL3H/P39IZFIcO7cORw4cAC7du3CJ598gs8++wynT5+u9Sww8uwoHAh5RuHh4di1a5fe/efOnUNYWFid2haLxdBoNBXua9GiBa5du4aQkJBKnxMREYG7d+8iJSUFXl5eAIATJ0480/4bNWpUYepqZfUcPXoUo0aNwsCBAwGUf+gnJydX2EYikVT6Oq5cuVLl6wAAkUiE7t27o3v37vj000/h6OiIffv2VRqMxDioW4mQZ/T222/j+vXrmDhxIi5cuIBr167h22+/xW+//YYpU6bUqe3AwEDs3bsXqampyMnJAQB88sknWLVqFWbNmoXLly8jISEBv//+O2bOnAkA6N69O8LCwhAbG4vz58/j8OHDmDFjxlP3k5WVhW7dumHNmjW4cOECkpKS8Oeff+Kbb75B//79n1pPaGgo1q9fj/j4eJw/fx6vvPKK3tFQYGAgDh06hPv37yMzMxMAMH36dBw7dgwTJkxAfHw8EhMTsWnTJt2A9NatW7FgwQLEx8fj9u3bWLVqFbRaLcLDw+v0npJa4nvQgxBzdurUKdajRw/m5ubGHBwcWNu2bfUGbmNjY1n//v0r3Pfuu++y6Oho3e0nB243b97MQkJCmEgkYgEBAbr7d+7cyTp06MDkcjmzt7dnbdq0YcuXL9c9fu3aNfbcc88xiUTCwsLC2M6dO586mFxaWso++OAD1qJFC+bg4MBsbGxYeHg4mzlzJisuLn5qPUlJSaxr165MLpczPz8/tmjRIr1B+uPHj7MmTZowqVTKHv+4efi+2draMoVCwZo0acK++OILxlj54HR0dDRzcnJicrmcNWnShK1bt67qfwRiFHSZUEIIIXqoW4kQQogeCgdCCCF6KBwIIYTooXAghBCih8KBEEKIHgoHQggheigcCCGE6KFwIIQQoofCgRBCiB4KB0IIIXooHAghhOihcCCEEKKHwoEQQogeCgdCCCF6KBwIIYTooXAghBCih8KBEEKIHgoHQggheigcCCGE6KFwIIQQoofCgRBCiB4KB0IIIXooHAghhOihcCCEEKKHwoEQQogeCgdCCCF6/h/uw2l7bJn5HQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get the top 5 countries based on area and plot a pie chart showing the percentage of area each country occupies\n",
    "population_2022=data.sort_values(by=\"Area (km²)\",ascending=False).head(5)\n",
    "x=population_2022[\"Country/Territory\"]\n",
    "y=population_2022[\"Area (km²)\"]\n",
    "plt.pie(y,labels=x,autopct='%1.1f%%')\n",
    "plt.title('Top 5 Countries by Area')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e25e2e5",
   "metadata": {},
   "source": [
    "The pie chart shows the percentage of land area occupied by the top 5 countries based on their area, with Russia being the largest country occupying 31.3% of the world's landmass."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84434012",
   "metadata": {},
   "source": [
    "### Q. COUNTRY VS GROWTH RATE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f3a8c539",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jo2ONz7Z/9LVnjOGnn36CSCRC7969jZYbN24crl69infeeQcCgcDwxViVwMBAPPfcc9ixY0e599ij+6wJX19ftGrVCitWrDB6fi9fvozdu3cbXvcHunbtihMnTmD//v2GQuPh4YGoqCjMnDnTsMyjTPHa1UZcXBwaNWpkyAOUtZoef07++usv3Lt3z+i2B0X18bxt2rRBWFgYZs+eXa7LEqj4s0jKUIvGTIYMGYKePXvio48+QlJSElq2bIndu3dj8+bNmDZtmuHX0QPNmjVDbGwsXn/9dUgkEsyfPx8A8MUXX1S5n59//hmbNm3CkCFDEBgYiNTUVPz6669ISUnBypUrqz0RLywsDL///jtGjRqFqKgoo5kBjh49ir/++stw3kvLli0xYcIELF68GHl5eejevTtOnjyJFStWYNiwYejZs6dhu5MnT8ZLL72Ep556Cn379sWFCxewa9cuo1+OtdWmTRssWLAAX331FcLDw+Hl5YVevXrVahtOTk5YsGABxo0bh5iYGIwePRqenp5ISUnBtm3b0LlzZ/z000+4ceMGevfujZEjRyI6OhpCoRAbN25Eenp6jQqBVCrFzp07MWHCBHTo0AE7duzAtm3b8OGHH5ZrZQ4aNAju7u7466+/MGDAAHh5edXosXz//fe4ffs2XnvtNaxZswZDhgyBl5cXsrKycOTIEWzdurXaLrgHZs2ahQEDBqBjx454/vnnUVpainnz5sHZ2bnc+VBdu3bF//3f/+HOnTtGBaVbt25YtGgRgoODDecxPWCK1642RCIR3njjDbzzzjvYuXMn+vfvj8GDB2PGjBmYNGkSOnXqhEuXLmH16tXljqWFhYXBxcUFCxcuhKOjI+RyOTp06ICQkBAsXboUAwYMQNOmTTFp0iT4+/vj3r172L9/P5ycnLB161azPSarxuWQN1vy+PBmxsqG0k6fPp35+fkxkUjEIiIi2KxZs8oNWwXApk6dylatWsUiIiKYRCJhrVu3Zvv37692v7t372Z9+/ZlPj4+TCQSMRcXF9avXz+2b9++WuW/ceMGmzJlCgsODmZisZg5Ojqyzp07s3nz5jGlUmlYTqPRsC+++IKFhIQwkUjEAgIC2AcffGC0DGNl51K89957zMPDgzk4OLDY2Fh28+bNSoc3nzp1ymj9/fv3MwBGz0FaWhobNGgQc3R0ZAAMw2Ur28aj9z16fsyD7cfGxjJnZ2cmlUpZWFgYmzhxomGIeVZWFps6dSpr0qQJk8vlzNnZmXXo0IH9+eef1T6XEyZMYHK5nCUmJrJ+/foxBwcH5u3tzT777LNyw2sfeOWVVxgA9vvvv1e7/UdptVq2bNky1qtXL+bm5saEQiHz8PBgvXv3ZgsXLmSlpaWGZR8Mb65syPbevXtZ586dmUwmY05OTmzIkCHs6tWr5ZYrKChgAoGAOTo6Gg0nX7VqFQPAxo0bV26d2r52Fb3+FansPBrGyobvOzs7G/alVCrZW2+9xXx9fZlMJmOdO3dmx44dY927dy839Hrz5s0sOjqaCYXCckOdz507x0aMGMHc3d2ZRCJhQUFBbOTIkbX+zNkTHmO1ONpKzILH42Hq1KmVdoEQ2zd9+nT88ssvSEtLMxqSS4gtoGM0hHBMqVRi1apVeOqpp6jIEJtEx2gI4UhGRgb27t2LdevWITs7u9I5ywixdlRoCOHI1atXERcXBy8vL/z44480JT2xWXSMhhBCiFnRMRpCCCFmRYWGEEKIWVGhIYQQYlZUaAghhJgVFRpCCCFmRYWGEEKIWVGhIYQQYlZUaAghhJgVFRpCCCFmRYWGEEKIWVGhIYQQYlZUaAghhJgVFRpCCCFmRYWGEEKIWVGhIYQQYlZUaAghhJgVFRpCCCFmRYWGEEKIWVGhIYQQYlZUaAghhJgVFRpCCCFmRYWGEEKIWVGhIYQQYlZUaAghhJgVFRpCCCFmRYWGEEKIWVGhIYQQYlZUaAghhJgVFRo7tHz5cri4uJh9P0lJSeDxeDh//rzZ90UIsVxUaKzUxIkTwePxwOPxIBaLER4ejhkzZkCr1XIdjRBCjAi5DkDqrn///li2bBlUKhW2b9+OqVOnQiQS4YMPPuA6GiGEGFCLxopJJBL4+PggKCgIL7/8Mvr06YMtW7YgNzcX48ePh6urKxwcHDBgwAAkJCRUup3ExEQMHToU3t7eUCgUaNeuHfbu3Wu0THBwML7++ms899xzcHR0RGBgIBYvXmy0zMmTJ9G6dWtIpVK0bdsW586dM8vjJoRYFyo0NkQmk0GtVmPixIk4ffo0tmzZgmPHjoExhoEDB0Kj0VS4XlFREQYOHIh9+/bh3Llz6N+/P4YMGYKUlBSj5ebMmWMoIK+88gpefvllxMfHG7YxePBgREdH48yZM/j888/x9ttvm/0xE0KsACNWacKECWzo0KGMMcb0ej3bs2cPk0gkbNiwYQwAO3LkiGHZrKwsJpPJ2J9//skYY2zZsmXM2dm5yu03bdqUzZs3z/B3UFAQGzt2rOFvvV7PvLy82IIFCxhjjC1atIi5u7uz0tJSwzILFixgANi5c+fq+WgJIdaMWjRW7O+//4ZCoYBUKsWAAQMwatQoTJw4EUKhEB06dDAs5+7ujsjISFy7dq3C7RQVFeHtt99GVFQUXFxcoFAocO3atXItmhYtWhj+n8fjwcfHBxkZGQCAa9euoUWLFpBKpYZlOnbsaMqHSwixUjQYwIr17NkTCxYsgFgshp+fH4RCIbZs2VLr7bz99tvYs2cPZs+ejfDwcMhkMjz99NNQq9VGy4lEIqO/eTwe9Hp9vR4DIcT2UYvGisnlcoSHhyMwMBBCYdlvhqioKGi1Wpw4ccKwXHZ2NuLj4xEdHV3hdo4cOYKJEydi+PDhaN68OXx8fJCUlFSrLFFRUbh48SKUSqXhtuPHj9f+QRFCbA4VGhsTERGBoUOHYsqUKfj3339x4cIFjB07Fv7+/hg6dGil62zYsAHnz5/HhQsX8Oyzz9a6pfLss8+Cx+NhypQpuHr1KrZv347Zs2eb4iERQqwcFRobtGzZMrRp0waDBw9Gx44dwRjD9u3by3V9PfC///0Prq6u6NSpE4YMGYLY2FjExMTUap8KhQJbt27FpUuX0Lp1a3z00UeYOXOmKR4OIcTK8RhjjOsQhFiUkhyg4B5QkAoo8wGtEtCpAO1//3Tqstu06v9u/+//9VpA5ABIFIBYDogVZf8e/Vvi+PD/FV6ASMb1oyXE7GgwALEvhWlA3h2g8D5Q8Mi/wtSH/9Uqq9+Oqci9ANcgwCUQcAky/n+XQEBQcSuUEGtCLRpim3RaIPM6kHYJSL8MpF0E0i4DpTlcJ6s5Hh9w9C0rOu6hgG8rwD8G8G4OCMVcpyOkxqjQEOunzC8rImmX/vt3EciML+vWskUCMeDdrKzo+LcB/GIAj8YAnw65EstEhYZYH3UJkHwUuLUfuHUASL8CwM7fxmJHwK8V4Ne6rPgEdwXk7lynIgQAFRpiDfR6IPV8WWFJ3A/cOWm7rRWT4QG+LYHwPkB4b6BRe0BAh2QJN6jQEMuUm/ywsNw+ZF3HViyRxAlbm30PpW979I7yhpucjvGQhkOFhliO7ETg8gbgygYg4yrXaWwK4wnQhS3FPaUEfB4QE+iKvtHe6BvtjVBPBdfxiI2jQkO4lZdSVlwury87iE/MosCrHVqkTK/wviY+jnimbQBGtPaHK7V0iBlQoSENT1UIXNkEXPij7KC+vR/IbwD7A6ZiUkLnKpcRC/joHeWFkW0D0K2xJwR8XgOlI7aOCg1pGHo9cPsAcP4P4PrfgKaE60R25QXFT9id5Vbj5X2cpBgR44+RbQMQ7CE3YzJiD6jQEPNSFwPnVgMnFgA5t7hOY5e0jv4Iz5xV5/XbB7vh6baNMLiFLxzENHKN1B4VGmIehWnAiUXA6V8BZR7XaezajYBn0C9heL23IxcLMDzGHy92C0OAm4MJkhF7QYWGmFbaJeDYz2UH93Xq6pcnZveD11eYmxJqsu0J+Tw82dIPr/QMQ7iXo8m2S2wXFRpSf4wBCXuAYz8Btw9ynYY8ggmliFEtRq7G9F1ePB7QL9obr/aMQPNGzibfPrEdVGhI3em0wIXfgaM/AVnxXKchFcj27Y42t180+366Rnhgas9wPBFK096Q8ujIHqmbq1uAfV8A2Te5TkKqcExQuwvY1dXhhCwcTshC2yBXTO0Zjp5NvBpkv8Q6UIuG1E7KcWDPp8CdE1wnITUwUroQJ/OcGny/0b5OeLd/JHpEUsEhVGhITWUlAHs/LzsHhlgFtWsEGqd+wWmGXk288MngaITQuTh2jQoNqVphOnDgG+DcyrJLFROrcSFwHIbeGMB1DIgFfEzsHIzXeoXDUUpXDLVHVGhIxVRFwNEfyw70a4q5TkPqYIb7TPx6L4DrGAYeCgnejY3EM20bgcej6W3sCRUaUt7l9cCO94HiDK6TkDpiEkdEF81HqU7AdZRyWjRyxmdDmqJNkCvXUUgDoUJDHipIBba9BcRv4zoJqadU/1h0TJzAdYwqDWvlh/cHRMHHWcp1FGJmdJFxUubMCmB+ByoyNuIga5hhzfWx6fx99JpzAEsO3YJeT793bRm1aOxdbhKw5XU6o9+GMPAQK1iKG8UyrqPUWIcQN8wZ2RKNXGkONVtEhcZe6fXAiYXAP1/RwX4bU+LRAtF33+c6Rq05SoT4dEg0nmlrOQMYiGlQ15k9yowHfo0Fdn1ARcYGXZR14DpCnRSqtHhn3UW8uPI0coppQlZbQoXGnjAG/DsXWNgVuHuS6zTETP4qiOY6Qr3supKOfnMP4Z/r6VxHISZCXWf2oiQH2PgikLCb6yTEjPQOHgjL/QGM2cZ5KmPaB+DjQdGQS2haRmtGLRp7cO8ssKg7FRk7kOLW2WaKDAD8cfIOBv54GGeSc7iOQuqBCo2tO7UU+LU/kJ/CdRLSAPZqW3EdweSSs0swctFxLDqYyHUUUkfUdWar1MXA39OBi2u5TkIaCOML0Um/FKlKMddRzGZEa398PaI5pCLLm/GAVI5aNLYo8wawpDcVGTtT4NnWposMAGw4dw+jFh9HeoGS6yikFqjQ2JrLG4AlPYHMa1wnIQ3stLgt1xEaxIU7eRgy71+cv5PHdRRSQ1RobIVeD+z8AFg3CVAXcZ2GcOD3nCZcR2gwGYUqjFp0DBvO3uU6CqkBOkZjC7QqYP1k4NoWrpMQjmidAhCeMZPrGJx4oVso3u/fBHy+7Yy2szXUorF2ynxg5XAqMnbupnMnriNwZvGhW3huxSkUKDVcRyGVoEJjzQpSgV8HAMlHuE5COLZd1ZzrCJw6EJ+JYT8fQXI2TalkiajrzFpl3gBWjQDy73CdhHCMCWVopVqEfA2dPe/tJMHqyR0Q7uXIdRTyCGrRWKM7J4Ff+1GRIQCAbK8OVGT+k16gwqhFx3H1fgHXUcgjqNBYm/idwG9DgdJcrpMQC3GU34brCBYlu1iNMUuO4wINf7YYVGisydmVwNo4QFPCdRJiQZZnNuY6gsXJL9Vg7NITOJ1Ec6RZAio01uL4AmDLq4Bey3USYkFUrpE4m0/HIypSqNJi/K8ncfRmFtdR7B4VGmtwZgWw0/qumEjM76rjE1xHsGglah0mLT+F/fEZXEexa1RoLN2ldcDf07hOQSzUpqJmXEeweCqtHi/+dgY7L6dxHcVuUaGxZNe3l12sjOm5TkIsEJM4Y02aL9cxrIJap8erv5/F5vP3uI5SKwcOHACPx0NeXl6lyyxfvhwuLi4NlqkuqNBYqsT9wF8T6ZgMqdR9j45Q6ekjXFNaPcNbf14wezfaxIkTwePx8NJLL5W7b+rUqeDxeJg4caJZM1gaepdaopTjwJpnAZ2K6yTEgh1krbmOYHW0eoapq8/i0t18s+4nICAAa9asQWlpqeE2pVKJ33//HYGBgWbdtyWiQmNp7p8HVo+kIcykSozHxy9p4VzHsEoPBgjcyTHfZywmJgYBAQHYsGGD4bYNGzYgMDAQrVs//IGgUqnw+uuvw8vLC1KpFF26dMGpU6eq3Pby5csRGBgIBwcHDB8+HNnZ2eWWWbBgAcLCwiAWixEZGYmVK1ca7nv22WcxatQoo+U1Gg08PDzw22+/AQB27tyJLl26wMXFBe7u7hg8eDASE+t+hVMqNJYk43rZtDIq8/7aItavxKMFEktkXMewWllFKkz49SRyi9Vm28dzzz2HZcuWGf7+9ddfMWnSJKNl3n33Xaxfvx4rVqzA2bNnER4ejtjYWOTkVHz+z4kTJ/D888/j1Vdfxfnz59GzZ0989dVXRsts3LgRb7zxBt566y1cvnwZL774IiZNmoT9+/cDAOLi4rB161YUFT28nMiuXbtQUlKC4cOHAwCKi4vx5ptv4vTp09i3bx/4fD6GDx8Ovb5ux4tprjNLkZsM/NIPKKKRMaR6RwNewLMJPbiOYfViAl3w+5QnTHpp6IkTJyIvLw9LlixBQEAA4uPjAQBNmjTBnTt3MHnyZLi4uODnn3+Gq6srli9fjmeffRZAWcsiODgY06ZNwzvvvIMDBw6gZ8+eyM3NhYuLC5599lnk5+dj27Zthv2NHj0aO3fuNAwY6Ny5M5o2bYrFixcblhk5ciSKi4uxbds2aLVa+Pr64n//+x/GjRsHoKyVo9frsWbNmgofU1ZWFjw9PXHp0iU0a1b7kY7UorEE6mLgjzFUZEiN/VkQzXUEm3A2JQ9vrDkHvd70v7c9PT0xaNAgLF++HMuWLcOgQYPg4eFhuD8xMREajQadO3c23CYSidC+fXtcu1bxFXKvXbuGDh06GN3WsWPHcss8uk2grPg82KZQKMTIkSOxevVqAGWtl82bNyMuLs6wfEJCAsaMGYPQ0FA4OTkhODgYAJCSklLLZ6EMzcTHNcaATS8DGVe4TkKshE7uhc0ZnlzHsBm7rqTji61X8MVQ05+T9Nxzz+HVV18FAPz8888m335dxcXFoXv37sjIyMCePXsgk8nQv39/w/1DhgxBUFAQlixZAj8/P+j1ejRr1gxqdd26GqlFw7VDs4Crm7lOQaxIsmtnMEZXkzSlFceSsfBg3Q92V6Z///5Qq9XQaDSIjY01uu/BwfojRx5eT0qj0eDUqVOIjq64xRoVFYUTJ04Y3Xb8+PFyyzy6TQA4cuSI0TY7deqEgIAArF27FqtXr8YzzzwDkUgEAMjOzkZ8fDw+/vhj9O7dG1FRUcjNrd8kvtSi4dL17cD+r7lOQazMHk1LriPYpJk7r8PXWYqhrfxNtk2BQGDoshIIjI8DyeVyvPzyy3jnnXfg5uaGwMBAfPfddygpKcHzzz9f4fZef/11dO7cGbNnz8bQoUOxa9cu7Ny502iZd955ByNHjkTr1q3Rp08fbN26FRs2bMDevXuNlnv22WexcOFC3LhxwzBQAABcXV3h7u6OxYsXw9fXFykpKXj//fpNgUUtGq5kXAc2vACAxmKQmmN8EZalBXMdwyYxBry3/qLJr2Xj5OQEJyenCu/79ttv8dRTT2HcuHGIiYnBzZs3sWvXLri6ula4/BNPPIElS5bghx9+QMuWLbF79258/PHHRssMGzYMP/zwA2bPno2mTZti0aJFWLZsGXr06GG0XFxcHK5evQp/f3+jYzp8Ph9r1qzBmTNn0KxZM0yfPh2zZs2q13NAo864UJoLLOkF5NziOgmxMnk+HdEq6TWuY9i0IHcHbH2tC5ykIq6j2Axq0TQ0vQ5Y9zwVGVInp4TtuI5g85KzS/DWnxdAv8FNhwpNQ9v7GZC4j+sUxEqtyonkOoJd2HM1HQsP0o9BU6FC05AurQOOzuM6BbFSGqcgHMypuO+emN7s3fE4fqv89C6k9qjQNJTcJODv6VynsDiHkrUY8kcJ/OYUgvdFATZd1xjdn16kx8RNpfCbUwiH/ytA/1XFSMjWVblNjY5hxkEVwn4shPSrArRcWISdN41nwV59UYOAuYVwnVmAN3cpje5LytOj8bwiFKgsq+skwbkT1xHsik7PMG3NebNOU2MvqNA0BL2ubISZyrSjWWxBsZqhpTcfPw+UlruPMYZha0txK1ePzaMdcO5FOYKc+eizsgTF6sqLwMf/qLDojBrzBkhxdaoCL7URY/jaEpxLLStQWSV6TN5aitl9pdg9Vo5VFzX4+8bDAvfKNiW+7SOBk8SyzlXZpmzOdQS7k1agxDvrLnIdw+pRoWkIh+cAd05Uv5wdGhAhwle9pBgeVX6ET0KOHsfv6rBgkBTt/AWI9BBgwWApSjXAH5c1FWytzMqLGnzYRYKBESKEuvLxcjsxBkYIMedY2S/TW7kMzhIeRjUToZ2/AD1DBLiWWTZZ4B+XNBAJgBEV5OESE8mxPDWA6xh2ae+1dKw4msR1DKtGhcbc7p4GDs7kOoVVUv3X2yUVPmxZ8Hk8SITAvymVd5+pdID0sVORZUIe/k0p22CEGx8lGoZzqTrklDKcuqdDC28BcksZPtmvxE8DyreuuJbl2QHFWtNN/Ehq5/+2X8O1VOqRqCsqNGZUqi3F1gtL6SqZddTEg49AZx4+2KdEbimDWscw818V7hYwpBZVPl15bJgA/zuuRkK2DnrGsCdRiw3XNEgtKutuc5XxsGKYDOM3laL9kiKMbylCbLgQb+9W4tX2YtzO06P1oiI0m1+EdVcrbzk1pCP8GK4j2DW1Vo/X/zgHtZYuq14XNAWNGX1/5nv8nnUEO1rHYsbN8/AoTOc6klURCXjYMNIBz28phdt3hRDwgD6hAgwIF4JVMaPCD/2lmLJViSY/F4MHIMyNj0mtRPj1/MOiMTxKZNRddzBJi4sZOswbKEX4j0X44ykZfBQ8tF9ajG5BAnjJuf1NtjyjMaf7J0BCRhHmH7iJaX3otagtKjRmcirtFP64/gcA4HDeNQz388LHaILY+IMcJ7MubfwEOP+SAvnKshaNp5yPDkuL0Na38m4kTzkfm0Y7QKllyC5h8HPk4f29KoS6VlwsVFqGV7YrsXK4DDdz9NDqge7BZR+Nxu58nLirw5BI7gqN0i0K5+8rONs/eWj+gUQMaemHME96PWqDus7MoERTgk+PfGr0qztPnY+31bfxbsxA5MtcuAtnpZylPHjK+UjI1uH0fT2GNqn+YL1UyIO/Ex9aPbD+mgZDIyv+XfXVIRX6hwkR4yuATl92XfkHNDpAx/Eo5yvyDtUvRBqEWqvHhxsu0awBtUQtGjOYe2Yu7hbdrfC+HbmXcSY4FF8qReh0m0aiFakZbuY87Pe+navH+TQd3GQ8BDrz8dcVDTzlZf9/KV2HN3YqMayJEP3CHr51x28shb8jD9/0KTuIf+KuFvcKGVr5CHCvQI/PD6qgZ8C7nSXl9n81U4e1V7Q496IcQNlxIT6Ph1/OquGj4OF6lh7t/Lg9CL+puCmn+yfGTtzOwZ+n72BUu0Cuo1gNKjQmdj7jPNbGr61ymQxlFl4CDyNjBuGty/shU5c0UDrLc/q+Dj1XPHz8b+5WAVBhQksRlg+TIbVIjzd3q5FexODryMP4FiJ80t24YKTk68HnPWycK7Vl59LcytVDIeZhYIQQK4fL4CI1Pi+GMYYXtirxv1gJ5OKy+2QiHpYPk2LqdiVUWuCngVL4O3HX8NdLXbEmzY+z/ZOKfb39OnpHecNDUf7HCymPZm82IcYYxmwbgyvZNb9aZpDcD/+XX4qWdy6YMRmxVncbDUKXm3HVL0ga3NBWfvhhdGuuY1gFOkZjQptubqpVkQGA5OL7mCAqwI+tBkHDt6yTBAn39uvoImeWavP5+zh4I5PrGFaBCo2JFGuK8eO5H+u0ro7psCT/EuKaPYGb3jQ7LynDeHwsSw/nOgapwsebLqFUXfXce4QKjcksurgIWaVZ9drGtcJkjFLosLxFf+h59NLYu2KPVrhVYnmzFJCH7uSU4vt9N7iOYfHo28wEUgpSsOrqKpNsS61XY07hVTzXsifuudGoFnt2Ttqe6wikBn45fBs30gu5jmHRqNCYwKzTs6DRm3aqkjP5CXjK3QHro/uYdLvEeqzNi+I6AqkBrZ5h9q54rmNYNCo09XT0/lEcuHPALNsu1pbg89IbeC0mFlkKL7Psg1gmndwHf2d6ch2D1NDuq+m4cCeP6xgWiwpNPWj1Wsw6Ncvs+zmQew0j/L2xt3FXs++LWIYkt85cRyC1NItaNZWiQlMPa+PX4mbezQbZV646H9M1yfggZiAKpc4Nsk/CnV3qFlxHILX0780sHE2s34AgW0WFpo5KNCVYeGFhg+/379zLGBEajuMhdKDYVjGBGMtSg7mOQeqAjtVUjApNHf0Z/yfyVHmc7DutNBMvIB3ftB4EpUjGSQZiPnme7ZCpppN3rdHZlDzsvUqXA3kcFZo6UOvUWHF1BacZGBh+z7uEZyJb4bI/XUvelpwUtuU6AqmH2bvjaXbnx1ChqYONCRvrfXKmqSQV38M4STF+bjUIWj7NkWoLVuY04ToCqYfraYXYejGV6xgWhQpNLWn1Wiy7sozrGEa0TIuF+ZcQ16wTbnlFcB2H1IPGORT/5tBgD2s3d88NaHV02ecHqNDU0vbb23Gv6B7XMSp0tTAJI52A35r3BwOv+hWIxYl3eoLrCMQEbmcVY92Ziq9JZY+o0NSCnumx9NJSrmNUSaVTYVbRVUxu1RuprgFcxyG1tE1Jx9tsxaJDt+hYzX+o0NTCvpR9uJ1/m+sYNXIy/wZGeCqwKao311FIDTGxHL/dpx8HtuJ2VjEO0GUEAFChqZUlF5dwHaFWijTF+ESZgDdi+iNH7sF1HFKNDI+OKNbRR9KWrDiaxHUEi0Dv6hr6996/uJZzjesYdfJP7lUMD/DHPxE0hY0l+5dHV2u0NQdvZCIpq5jrGJyjQlNDln5spjo5qly8oU3GxzGDUCR14joOqcDyjMZcRyAmxhjw27FkrmNwjsfoaFW1buXdwtDNQ7mOYTJ+Mi98VcJDu6RTXEch/yl1b4qoex9xHQP5x/5EyY1j0OTcBU8ohsQ/Cq7dJ0Lk3siwDNOqkfPPLyi5dghMp4EsJAZu/V6GQO5a6Xazts1F8eV9RrdJQ2LgPXLGf9vUIHvnjyhJOA6B3BVu/V6BLLjVw1wn1kNXkAm3vi+Z9gE3AEepECc+7A0Hsf2e50YtmhrYeHMj1xFM6n5pBp7nZWBm60FQCekKjpbgskMHriMAAJR3LsMxZhB8xs6G96gvAZ0W6X9+Ar1aaVgmZ98SlN48CY9h78P72W+hLcpG5savq922NKQNGk1dafjn8eS7hvsKL+yEOu0mfMbOhqJlf2RtnWUYsaXJS0PRhV1w6Tbe9A+4ARQqtVh/1jJPiWgoVGiqodFrsCVxC9cxTI6BYVXeJYyMisEVv2Zcx7F7G4qiuY4AAPAeOQOK5n0g9gyC2CsU7oOmQ1eQCXV62SzlelUxii7ugWuv5yELagmJTzg8Bk6D6t41qO5dr3LbPKEIAoXrw39SheE+TfYdyMI7QOwZBMeYQdCX5ENfWgAAyNk9H649JoIvcTDfAzez3+x8UAAVmmocunMIOcocrmOYza2iuxgrLcECmsKGM3qZG/5M8+E6RoX0qrID2fz/ioIq7Sag1xp1a4ncAyBw8oTqftWFRplyCXfmxeHekheRvetn6P4rJAAg9gqB6u5V6DUqKG+fhUDhBr7MCUVX9oMnFMOhcSfTP7gGlJBRhKM3LWPaKi7QN0s1NtzcwHUEs9MyLebnX8Lh5l3wf/fvICQzketIduWeeyfoci3vNx9jeuTuWwKJfzTEnsEAAH1xLiAQGgrPAwK5C3TFuZVuSxYSA4fGnSB08YY2NxV5h35Dxl+fwWfsbPD4Aiia94U6Iwn3f3kFApkTPIa+B72yCPn/rob3mG+Qe2glSq4dgtDFB+4D34DQ0fqG6y8/moRO4daX2xSo0FQhoyQDR+4d4TpGg7lUcAsjnSWY5h2LZy/vBg80TqQh7NNZ5rDmnN0LoM5Mhk/cd/Xeljy6u+H/xZ7BEHmF4P6iyVCmXIIsuBV4AiHc+71stE7Wtu/h2GYI1Om3UJpwDL6T5qHgxHrk7l0Mz+Ef1jtTQ9t3PQN3c0vQyNV6uwDryvJ+RlmQLYlboGM6rmM0KKVOhW+Lr2FK695Ic2lU/QqkXhhPgKWpoVzHKCdnzwKUJp6C95ivIXR6+CucL3cFdFrolUVGy+uK86ocdfY4kYsP+DInaPMqnuVYmXwRmuxkOMYMhjLlImShbcEXS+HQpAuUKZfq9qA4ptMzbD5/n+sYnKBCUwnGGDYm2NZos9o4kXcDI7ycsDWqF9dRbFqRZ2vcVUq4jmHAGEPOngUouXEM3qP/DyIX42NHEp9wgC9EafIFw22a7LvQFWRC4lfzyxtoC7KgLy2EQO5WPoNWjZw9C+Ae+yp4fAHA9GD6/37w6XVgzHpnRd5mp5cPoEJTidPpp5FSmMJ1DE4VaorwofIm3owZgFy5O9dxbNJZSTuuIxjJ2bMARVcOwGPIO+CLHaAryoWuKBd6jQoAwJfIoWjRF7n/LIUy+SJUaTeRvf17SPyaQOL/sNDcW/ISSm4cBQDo1aXI3f8rVPeuQ5ufjtKk88jc8CWErr6QhcSUy5B3dA1koW0h9g4DAEj8o1Fy4yjUGbdRePZvSP2jGuCZMI+rqQV2OVMAHaOphD23Zh63J/cKzgUE4HNtE3S/aT/HrBrCmjzL+tIsOrcdAJD+xwdGt7sPnAZF8z4AALfeU5DD4yNz09dgOg2kITFw7/uK0fLanLvQq0rK/uDxoc64jaLL+6BXFkOgcIMspDVcuo4FT2h8yWp1ZhJKrh+G78R5htscmnSG8s4lpK1+DyJ3f3gMecfUD7tBbbuUiqk9w7mO0aBoZoAKlGpL0X1td5RqS7mOYnFGuDbHu1cOQa4q5DqK1dM6+iM8cxbXMUgDi/Z1wvY37GveQeo6q8Dx+8epyFRiQ+4lPBXeBGcC23AdxerddunIdQTCAXvsPqNCU4GDdw9yHcGi3StJx3PCbMxpPRhqgeUcyLY2O1UtuY5AOLLtkn0NCqBC8xjGGA7fPcx1DIunZ3osz7uIUdFtcd3XMqZPsSZMIMHytECuYxCO2NvoMyo0j7macxUZpRlcx7AaN4vuYIyDEotbDoSOJ+A6jtXI9WqPbLWo+gWJTbK37jMqNI85eIe6zWpLq9diXsFljG/RDckelnfyoSU6IaBjXPbOnrrPqNA8ho7P1N3FgkQ84yLEmmb9uI5i8X7LjuQ6AuGYPXWfUaF5REZJBq5lW+flmi1FqU6J/yu+jhdb90W6sx/XcSyS2iUMx3KduY5BOHY1tQBp+crqF7QBVGgecfDuQTCaSNIkjubFY4SPK7Y16cF1FIsT70TDmkmZE7ezuY7QIKjQPOLQnUNcR7ApBepCvK+6hbdjBiDfoeYTLtq6zSXNuY5ALMTxW7Z7ratHUaH5j1KrxPHU41zHsEm7cq9geFAQDoVZ98WrTIGJFVidSl2KpMyJW9SisStn089CqbOP/lIuZCpzMFV/F1/EDEKJWM51HM6ke3ZEqY6GgZMyt7KKkVFo+987VGj+cz7zPNcR7MK63Et4OqIpzgVY5sW+zO0Qys9WTOzbCTvoPqNC85+LWRe5jmA37pSkYaIoF3NbD4JGIOY6ToNh4OHX9AiuYxALYw8DAqjQoGzamctZl7mOYVf0TI9f8y5hdHR7xPtY1lT55qJ0b4rrRfZ3GV9SNWrR2InkgmTkq/K5jmGXbhSlYIxcjV9aDICeZ9tvx4sOT3AdgVighIwiZBepuI5hVrb9ya6hS1nWeQ1yW6HRa/B94RVMbNkDd9yDuI5jNusKafJRUrGTt227VUOFBsCFzAvVL0TM7lz+TTztJsOfTftyHcXk9DIPbEj34joGsVAnqNDYPmrRWI4SbQm+LInHy637IdPJh+s4JnPHvRN0jD5upGJUaGycUqvEjdwbXMcgj/k37zqG+3pgZ2R3rqOYxD4dXeSMVC4xowhanZ7rGGZj94XmWs41aPVarmOQCuSrC/CO+jbejRmIfJkL13HqjPGF+CWVLp9AKqfW6ZGcU8J1DLOx+0JzMZPOn7F0O3IvY0RwKI6GWueorULPGNxT0iWvSdUS0ou4jmA2dl9o6PwZ65ChzMKL7D6+aj0IpWLrOhfljLgt1xGIFbiZUch1BLOx+0KTVJDEdQRSC2vzLuGZxi1wIcB6jnn8kUvDmkn1EjKoRWOz7hbe5ToCqaXk4vuYICrAj60GQcMXcR2nSlpHf+zOcuM6BrECN6nQ2KZcZS6KNLb74toyHdNhSf4lPNvsCSR4W+5lkRNdOnMdgViJxMwi6PW2eeFFuy40dwrvcB2B1NP1wmSMVuiwrEV/i5zCZoeqBdcRiJVQavS4m1vKdQyzsLxPZgOiQmMb1Ho1/ld4FZNa9sBdt0Cu4xgwoRTLUi0nD7F8NzNtc0AAFRpiM87m38TT7g5YH92H6ygAgBzPDsjXCLmOQayIrQ5xtutCQwMBbE+xtgSfl97Aq61jkaXgdm6xY4I2nO6fWB9bHXlm14WGWjS262DeNYzw98aext04y7A803IHKRDLlJRVzHUEs7DrQkMtGtuWq87Hm5okfBAzEIVS5wbdt8o1AqfzHRt0n8T65RSruY5gFnZbaJRaJTJLM7mOQRrA37mXMSI0HMdD2jfYPq87Wud0OYRb2VRobMu9ontgsM0x66S8tNJMvIB0fNN6EJQimdn3t7m4mdn3QWxPgVJjk7M4222hodaM/WFg+D3vEp6JbIXL/s3Ntx+JE1an+ptt+8R2MQbklNheq8ZuC02R2jZHd5DqJRXfwzhJMX5qNQhavumHH6d5dIRKb7cfLVJPucUariOYnN1+GgrVtnliFKkZLdNiUf4lxDXrhFteESbd9kEWY9LtEfuSXaziOoLJ2W2hoTnOCABcLUzCSCfgt+b9wcCr9/YYePg1PdwEyYi9ssWRZ1RoiN1T6VSYVXQVz7fqhfuu9ZsyptSjBW4Um3+wAbFduVRobAcdoyGPO5WfgKc85dgY3bvO27gga7gh1MQ22eIQZ7stNMUa2zwDl9RPkaYYn5Ym4PWY/shWeNZ6/XUFdJEzUj/UdWZDaDAAqcr+3KsY0cgX+yK61ngdvYMnNmRwO78asX5UaGwItWhIdXJUeZimTcZHMQNRJHWqdvlkt85grP4DCoh9K1BquY5gcnZbaAo11KIhNbMl9zJGhEbgZHC7Kpfbq23ZQImILdPpaWYAm1GsphYNqbnU0kxM5mVgZutBUAml5e5nfCF+SQ3hIBmxNTobvJyz3V6VqVhruYWmOL4YWduzUJpcCm2eFoGvBcKpzcOuG22+Fml/pqHoShF0JTrIG8vhO9YXEh9JpdtkWobMbZnI/TcX2lwtJL4SeD/jDccWD2cYzjuah7R1adAr9XDt6grfMb6G+9SZaiTNTkLY52EQyATmeeAWjoFhVd4lHI2KwdfZBWh6/7LhvgLPtkhLFnOYjtgKWyw0Jm/RLF++HC4uLqberMkxZrkvpl6lhzRQCr9xfuXuY4wh+cdkqDPVCHw9EOFfhEPkIULSrCToVZU3udM3pCNnfw78xvoh4usIuPZ0Rcq8FJQml12jXFuoxb1l9+A7yhfBbwcj72geCs4XGNa/v/I+vJ/xttsi86hbRXcxVlqCBY9MYXNaXHW3GiE1ZfeFZuLEieDxePj222+Nbt+0aRN4vLKDoKNGjcKNGzdMl9BM+DzL7TV0bOEI76e8jVoxD6jT1ShNLIXfBD84hDpA4iuB33g/6NV65B3Pq3SbeUfz4DnYE44tHSH2EsO9lzscWzgia2dW2XYz1RDIBHDu4AyHUAfIo+RQ3S+bCiPveB54Ah6c2zbsNV0smZZpMT//EsY374LbnmFYndOE60jERthioal115lUKsXMmTPx4osvwtXVtdz9MpkMMpn5z4zWaDQQiUR1Xt+SC01VmKbsTcgTPRzdxOPzwBPxUHKjBG7d3Spd79F1AIAnLlsHACTeEujVepQml0LkLkLp7VK4dnWFrliHjA0ZCH4v2DwPyMpdzr+FS9lt0UcYhu5SGnFG6s9JYHszS9S60PTp0wc3b97EN998g++++67c/cuXL8e0adOQl5dnuO2rr77Cjz/+iNLSUowaNQoeHh7YuXMnzp8/b1hm6dKlmDNnDm7fvo3g4GC8/vrreOWVVwAASUlJCAkJwZo1azB//nycOHECCxcuxMSJE2v9gB/gmWBeKy5IfCUQuYuQ/lc6/Cf6gyfhIXtXNrQ5WmjzKx8WqWiuQPaubMgj5RB7iVF8tRgFZwqA/3rbBHIBGk1phLtL7oKpGVw6ucCxuSPu/nIXbr3doMnSIOWHFDAdg9cwLzi3o9aNj06BWYcCIDl+HMljnkFiWvlBAoTUllxme4fOa/2IBAIBvv76azz77LN4/fXX0ahRoyqXX716Nf7v//4P8+fPR+fOnbFmzRrMmTMHISEhRst8+umn+Omnn9C6dWucO3cOU6ZMgVwux4QJEwzLvf/++5gzZw5at24NqbR+H2oB3zqPNfCEPAS+Foh7v9zDtanXAD6giFZA0UKBqq7j5vusL+4tu4eEDxIAHiD2EsO1iytyD+calnFq42TUXVd8vRiquyr4jfXDjfduIOClAAidhUickQh5pBxCJ9v7QNRU/+IwPP9XHljqJQCAZ8q/SBT14TgVsQUPDkPYkjp9UwwfPhytWrXCZ599hl9++aXKZefNm4fnn38ekyZNAgB8+umn2L17N4qKHs419tlnn2HOnDkYMWIEACAkJARXr17FokWLjArNtGnTDMvUl4BnnYUGAGTBMoR/GQ5diQ5MyyB0KvvylwVX3uQWOgkR9EYQ9Go9dMU6CF2ESP8rHWLPikdK6TV63P/tPhq90AjqDDWYjkHeRA4AkPhIUJJYAqfW1Z/EaIs+TW6NZn+eA9M+bEHKjm+FtH9/KItt72Q70rD4AtsrNHU+UDFz5kysWLEC165dq3K5+Ph4tG9vPNHgo38XFxcjMTERzz//PBQKheHfV199hcTERKP12rZtW9e45UgElQ8FthYCBwGETkKo0lQovV0KxxjHatfhi/kQuYoAHVBwuqDSdTK3ZELRXAFZsAxMzwxdbEDZUGnY3jll1fLSy7HiUFM0+/0UoDUuKDydFo2c6CRgUn9Wevi4SnXu++jWrRtiY2PxwQcf1OtYyYOWzZIlS9ChQwej+wQC41aHXC6v834eZ8mFRqfUQZ3+cL4jdZYapcmlECgEELuLkX8yHwLHsv9X3lUidXUqnGKc4NjsYdG4u/guhK5C+DzjAwAoSSyBJlcDWaAMmlwNMjZlgDEGzwHlJ45U3lMi/2Q+wmeUXVdF4isBeEDOwRyInEVQpaogC7W9A5ZV6VMSghf/KgS7f6HSZTyS/8VNcd8GTEVsEXWdPebbb79Fq1atEBkZWekykZGROHXqFMaPH2+47dSpU4b/9/b2hp+fH27duoW4uLj6xKkVidByC03p7VIkzUwy/J32RxoAwKWzCxpNaQRtvhapa1Khyy/rAnPp5ALPocYFQ52txqPjHZiGIWNDBtQZavClfDi2cESjFxpBIDcu5owx3F9+Hz5jfMCXlP204ov58J/sj9SVqWAaBt9xvmWtIjvx0Z3WaLn2PJim6kvsyk78DVn/ASil7jNSDyKJ9XbrV6ZehaZ58+aIi4vDjz/+WOkyr732GqZMmYK2bduiU6dOWLt2LS5evIjQ0FDDMl988QVef/11ODs7o3///lCpVDh9+jRyc3Px5ptv1idipaQCyx0hpIhSoNnyZpXe797XHe593avcRugHoUZ/y5vIEfF19Zcs5vF4CP0otNztTq2c4NTKvo7JeOjlmHU0BPLDp6pfGGXdZ/5OBbhZ7GDmZMSWyRS29yOu3r2BM2bMgL6KSeDi4uLwwQcf4O2330ZMTAxu376NiRMnGo0amzx5MpYuXYply5ahefPm6N69O5YvX240Ms3ULLnrjHCvZ2kwFvwuh/zw+Vqt53H7sHkCEbshVdjeVEY8xsFcLH379oWPjw9WrlzZ0Ls2+OzoZ9iQsIGz/RPL9d69Vmi75iKYuvbXBWF8AY4PnI/SIuo+I3XT6alwtO5bv0uKWxqznwhRUlKChQsXIjY2FgKBAH/88Qf27t2LPXv2mHvXVfKQeXC6f2J5XPUyzDkeDsXB01WdklQlnl6HRo75SCgy3cAVYl+o66wOeDwetm/fjm7duqFNmzbYunUr1q9fjz59uD25zUtGV0IkD3VRBmDxGmcoDp6r97Y8Eg+aIBGxV1IbLDRmb9HIZDLs3bvX3LupNQ8HatGQMm+ntkKHNZfBlEqTbE96eiccBj2JkkLqPiO1J7PBYzR2O4cItWiIs16KOacaw+mfuneVVYSn18FfkYeEQoUJt0rshS22aGzwHNSa8XQof6IisR+dlAFYutYVTv+cNcv2qfuM1BUdo7EhHjIPq53BmdTPtLSWmL4wFSzpjtn2IT21Aw6OdtthQOqIL+RBbIOzN9ttoRHyhXCVlr+eDrFdjnoJFp9qiU7LzoCVmuZ4TGV4jKGRIrf6BQl5hFRue60ZwI4LDQB4OdBxGnvRXuWPX/7ygMveMw22T/eEAw22L2IbHJxsbyAAYOeFhs6lsQ+vZrTAOwszgVvJDbpf6ZldkNvxNXtI7Tl72ub0RXb9KaAWjW1TMDFmn42C2+4zJh1VVlM8xuDvkIMbBfY1RxypO1df2yw0dt2ioUJju9qoffHrei+47W64rrKKeNz4h9P9E+vi6kOFxuaEOJlv0k7CnZeymuP9hTlAQhLXUSA5txcKZ7vuOCC14Opjm1MX2fUnoLFrY64jEBNy0Isw+0JTeOw07QmY9cFjDP6yHMTnU/cZqQYPcPWmFo3NCXYOhphvm6M87E0LtTeWbfKFx87TXEcpxz1+H9cRiBVwdJVCKLa9i54Bdl5ohHwhwlzCuI5B6mlydjN8sjgfvPhbXEepkJS6z0gN2OpAAMDOCw0ARLhWf9VJYpmkTIifLrZGv8XnwQqLuI5TpUayLK4jEAvn6m2bx2cAKjR0nMZKNdN4Y8Vmf3htq9lllrnmdt3yZjAnlsXFRkecAVRoqNBYoYm5TfHZ4kLwriVyHaXGpOf3w9HFNqcXIabhZsNdZ3bfcRzpFsl1BFJDEibAd1dawHfrKYsZVVYb/pIMXAfNr0cqZqtDmwFq0cBN6gZ3qTvXMUg1mmg8sGJrIHy3WkdXWUXcr3F7+XJiuRzdpJA52u4IWLsvNAB1n1m6cXnR+HJJCfhXEriOUi+Siwfh5ErdZ6Q83whnriOYFRUaUKGxVGImwNyrMRiy8BJYfgHXcUzCX5zBdQRigfzCXbiOYFZUaAC08GzBdQTymAitO1ZsC4b/5pMAs8YjMhVzu7qL6wjEAvlSobF97X3a09U2LciY/Ch8vVQFwaV4rqOYnOTSYTi7UfcZeUiqEMHN13YHAgBUaAAALlIXNHFrwnUMuydkfMyOj8HwBZfBcvO4jmM2/sI0riMQC+IbZtvHZwAqNAYdfDtwHcGuhWnd8NvOMARusK2usoq4Xt7JdQRiQWy92wygQmNAhYY7zxRE4ttfNBCev8Z1lAYhuXqUus+Iga0PBADohE2DGK8YCPlCaPVarqPYDQF4+OZGawRvPA2m13Mdp0H5C+8jH55cxyAcE0oE8AxUcB3D7KhF8x8HkQNaeNDos4YSrHXByl0RCF5/ErCzIgMAbpd2cB2BWACfECfwBbb/NWz7j7AWnvB9gusIdmFEYWPMXsYgPHuV6yicEV87ARd36j6zd/ZwfAagQmOEjtOYF48B39yMwegF8dBnZXMdh3N+/PtcRyAcC4x24zpCg6BC84jmns0hE8q4jmGTGmmdsXJfJML+OgnodFzHsQhul7ZxHYFwSO4igXeIfVzimwrNI0R8Edp4t+E6hs15sigCc1fwID51hesoFkV8/RRc3Gk8jr0KbeUJHs8+ThSnQvOYbo26cR3BZvAY8OXtGIxdkACWQVeYrIg//x7XEQhHQlvbz6hDKjSP6RvUFwKegOsYVs9f54Tf9kchcs1JQEtDxivjemE71xEIB6QKEfwiXLiO0WCo0DzGQ+aBdj7tuI5h1QYWh+H7FUJITlziOorFE984DVcP6j6zNyEtPcDn20e3GUCFpkIDQwZyHcFqfZ4Ug4nzE8HSaTr8mvLDXa4jkAYW1tqL6wgNiseYjU8sVQcF6gL0WNsDGr2G6yhWw0enwHeHAyE9dpHrKFZHE94ahxtN5jqGwc37F7H3wlqkZCWgoCQbU/p9gZYhXQz3F5TkYPOJJbh29wxK1UUI92mBZ7q8Ci/nRlVud//F9Th8dQtyizIglzqjdWg3PNl+MkTCsitLnkrYi80nlkKlKcUTkbF4qtMrhnWzC9Pw07Z38e6IBZCJrXumY7FMiOdmdYFAaD+/8+3nkdaCk9gJXfy7VL8gAQDEloRi3iopFZk6Et08BzcL6j5TaUvh7x6GUV1eL3cfYwyLd32KrIJUvBg7A+8/tQhujl6Y9/c7UGlKK93mqYR92HxyCQa0GY+PRy1DXPe3cSbxALacXAoAKCrNx+8H52D4Ey/i1UEzcSphHy4lHzOsv/bwDxjaforVFxkACG7ubldFBqBCUynqPquZT1Ji8PzPt8Du09T39eHH7nAdwaBpYAcMaf+cUSvmgYz8u0jKuIbRXachyKsJvF0CMKrrNGi0apy5+U+l27ydfgWh3s3QLqI33B19EBXQFm3DeyI5s+yaQ1mFqZCK5WgT3hNBXk3Q2K8V0nNTAACnb/4DAV+IVqFdzfOAG5g9jTZ7gApNJboHdKeTN6vgpVNgxaGmaL6aRpWZgsv5LVxHqBGtrqw7WSgQG27j8/gQCkRITLtc6Xoh3k1xJ+sGkjKuAwCyCu7jSspJNA1oDwDwcvaHRqvCnawEFCsLkJwZDz/3UJSoCvH3qWUY2eU1Mz6qhiMU8xHY1J3rGA3OctrrFkYmlKFnQE9sv03DTx/XuzQYL/1VDHbvAtdRbIY48SLcY4TIzrTsou3jEghXhRe2nFyKMd2mQyyUYv+ldcgrzkR+SU6l67WL6I1iZT7mbn4DDAx6vQ5doocgNiYOAOAgccS4nu/ht/0zodGq0L5xX0QHtMPqA7PQvekwZBekYdHOT6DTazGw7Xi0Du3eUA/ZpMJivCAS29/pE1RoqjAgZAAVmsd8cKcVYv68CKZWcx3F5vjpU5ANP65jVEkgEGJKvy+w+uBsvLt8GPg8PiL92yA6oD2AyscV3bh/HrvO/Y5RXV5HkFcUsgruY93Rn7HjzEoMaDMOANAypItRd13C/Qu4l3Mbz3R+DZ+vGY9JvT+Ck4MbZm2cinDfFnCUuZr74Zpcs27+XEfgBBWaKnT26wwnsRMK1AVcR+Gch16OWcdCID90uoqvE1IfLmc3A0Evcx2jWoGejfHB04tRqiqCVq+Fo8wFszZORaBH40rX2XZqGdpH9EWnqEEAAH/3UKg0pfjj8FzExsSBzzPuxdfo1Fj77w+Y0PN9ZBbcg16vQ4RfSwCAl3MjJKVfQ/PgTuZ7kGbgEaCAT6jtX7a5InSMpgoigQhPhj3JdQzOdS8NwoLfFZAfOs91FJsmun0Z7p7W89tPJlHAUeaCjPy7SMm8gRbBnStdVq1VlZvXi8//rwupgjMsdp1djeiAdgjwbAw900PPHk7EqtNroWfWdw0je23NANSiqdazTZ7F79d/t8o3tim8e68V2q29BKZScR3FLvjpkpCNqs9HMTeVphSZ+Q/nYMsuTMPdrJtwkDjCzdEbZxMPQiFzhpvCC/dzbmPdkZ/RIrgzogLaGtb57Z9v4Sz3wNAOZecHNQvqiP0X16GRRziCvaKQWXAPf59ahuaBHR8WnP+k5ibhTOJ+vP/UIgCAt0sgeDwejl7fDieZG9LzUhDkFdkAz4TpiKUCNG7vw3UMzlChqUaAUwC6+XfDgbsHuI7SoFz1Msw+EQ7HA9RV1pBcz2wGQqZymiE5Mx4/bn3L8PeGYwsAAB0a98O4nu+hoCQbG44tQGFpLpwc3NChcT/0jxlrtI2cogyjFkz/mLHggYe/Ty1DfnEWFDIXNAt8AkPaP2+0HmMMfxyaixEdX4ZEVDbqUyyUYGyPd/Hnvz9Cq9NgZOfX4CK3riHCkU/4QiSxv0EAD9DMADVwIvUEJu+2nDO3za2LMgBvbNCAJdPUKFy4OHIRsjIse/QZqZ0xn3aAm5/1n2xaV3SMpgY6+HZAY9fKD3TakrdSW+GNBalUZDjkq73NdQRiQn4RLnZdZAAqNDU2Nmps9QtZMWcmxdKTLdBh+WkwpZLrOHbN5fRGriMQE7LnQQAPUKGpoYGhA+Emtc3re3dUNsLStW5w2neW6ygEgCglHp5e9tufb0tkjiK7nHLmcVRoakgikODpxk9zHcPk3khviTcXpoPdTuE6CnmEr4a6z2xBdGc/u5tAsyL0DNTC6MjREPFFXMcwCUe9BItPt0TnX8+AlVY+6y7hhsvpTYD9XBfLJokkArTsE8B1DItAhaYWPB08ERscy3WMemun8sMv6zzgsucM11FIJYR3qPvM2jXv2Qgyhbj6Be0AFZpaGhc9jusI9fJKZgu8uzALSEzmOgqphq8qkesIpI5EUgFa9w3kOobFoEJTS9Hu0egd2JvrGLWmYGIsONsSPZaeBSsp4ToOqQGXUxup+8xKtejZCFK5bXSzmwIVmjp4PeZ1CHjW063RWu2LX9d7w30XdZVZE+G9m/Ci7jOrI5YK0KoPtWYeRYWmDkKdQ61mss0Xs5rjw0U5QAKNYrJGvsqbXEcgtdSiVwC1Zh5DhaaOXmn1CiQCCdcxKiVlQvx8oTV6LzkHVlTMdRxSR86nNoBH3WdWQywTohWNNCuHCk0d+ch9MDpyNNcxKtRC7Y0VG/3guf0U11FIPQnv36LuMyvSslcjSByoNfM4KjT1MKXFFDiKHLmOYeT5nGb4ZHE+ePG3uI5CTMRHeYPrCKQGJA5CtKRjMxWiQlMPzhJnTGw2kesYAAAJE2DexdaIXXQerLCI6zjEhJxPUveZNWjZOwASGV15pSJUaOppbNRYeMg8OM3QVO2F3zYHwHsbdZXZImFqEry86aNqyRRuErSi82YqRe/eenIQOeCFFi9wtv+JuU3x+ZIi8K7R6CRb5lNC3WeWrOszjSES07G0ylChMYGnGz+NQMeG/TUjZgL8cCUGAxdeACsoaNB9k4bnfGIDePRptUiBTd1phuZq0FvXBER8ET7q8FGD7S9S44Hf/g6C75aTDbZPwi1hejK8vejjamkEIj66jY7gOobFo3euiXTy74TBoYPNvp+4vCh8tbQU/MvUlWJvfIrjuY5AHhPTLxDOng5cx7B4VGhM6N1278JV4mqWbQsZH/+7FoOhCy+D5eWbZR/EsjkfX0fdZxbEyVOGmP5BXMewCvS2NSFXqSveafeOybcboXHHyu0haLTpJMCYybdPrIMg8y58qPvMYnQb1RhCEQ0AqAl615rYkLAh6OTXyWTbG53fBF//ooLgInWbEMCn6BrXEQiA0FaeCGrmznUMq0GFxgw+7fgpZEJZvbYhZHzMio/BiIVXwXLzTBPMwp0uKcErd++g+82biI6/jr2FhUb3Z2m1+DD1PrrfvImYG/F44c4dJKnVVW5TwxjmZ2Uh9lYiWt2Ix/Ck2zhcbHxC69aCfPRKvIknEm5gZka60X33NGoMuJWIIp3ONA+ynpyOrwefT2dvckko5qPLSBoAUBtUaMzAX+GPqa2m1nn9UK0rftsZhqANJwG93oTJLFuJXo9IiRSfeHuXu48xhtfu3cUdjQY/+ftjfXAwfEUiPH8nBSVVPEc/ZmXiz/w8fOjlja3BIRjl7ILX793DVaUSAJCr1eLTtDS84+mFJY0CsLWgAAeKHhaiGenpeNPTCwqBZXSRCLLuoYKnhzSgdoNC4Ogm5TqGVaFCYyZjo8aiqXvTWq/3dEEkZv6qg/C8/XWRdFMo8IanJ/o4lp8/LlmjwQWlEp96+6C5TIYQsQSfeXtDxRi2V3Ee0Zb8Arzg5o7uCgUCxGKMdnVFN7kcy3NyAAB3NBoo+HwMcHJCc5kM7R0ckKhWAQC2FRRAyOOhbwV5uORTeJXrCHbLO8SJZgCoAyo0ZiLgC/B5p88h5NVs7iMBeJiZEIORC66BZeeYOZ31UbOyVovkkUm/+DwexDwezpZWfsVQNdMbrVO2Db5hnSCxGErGcFWpRJ5Oh8tKJSIlEuTrdPgxKxMfe1le88H56DrqPuOAUCJAn0nR9NzXARUaM2ri1gSTmk2qdrkgrQt+2x2BkHX21VVWGyFiCXyFQszNykS+Tgc1Y1ianY00rRaZ2sqPn3SRK7A8NwdJajX0jOFocTH2FhUi879jLs4CAb7x8cUHqakYlZyEJ52c0EWuwKzMDMS5uOKeRoMRSbfx5O1b2FVoGTMw8HPS4GN59c/mdXk6HC5edM5MXdBUo2b2SqtXcCL1BC5mXazw/uGFjRG3Jh36LOoOqYqIx8OP/o3wcVoqOt5MgABARwc5usrlqGrA9wdeXvg0PQ2Db98CD0CASIzhzs7YkP/wXKQ+jo5G3XWnSkpwQ6XCR17e6H/rFmb7+cFDKMCo5GS0lTnAXcj9x8a74AruI5rrGHYjuIUHmnb15zqG1eL+E2PjhHwhZnabiZFbR6JQ83AUFY8BX92KQcT6M9BbyIgmS9dUKsXG4BAU6nTQMAY3oRCjkpPQTFr5gVk3oRA/+TeCSq9Hnk4HL6EQ/8vKRCNRxRenUuv1mJGehpm+fkhRq6EDQzuHsl+xwWIxLipL0VPB/TEbp2PrwG/9GfQ6Oq/K3GROYvQc24TrGFaNus4aQCPHRvi006cP/9Y6Y+W+Joj48yRARabWHAUCuAmFSFKrcUWpRK8afPFL+Hx4i0TQAthdWFjpOgtzstFFLke0VAodAO0jJ8hqGIOlfK8LctLh62UhYWwYjwf0nRQNBycx11GsGrVoGkj/4P44fv84VOcuYMKf2WDpl7mOZHGK9XqkPHJezD2NBteUSjgLBPATibCzsABuAgF8hSLcUKnwTUY6eisU6CyXG9Z5P/U+vIRCvOnpBQC4UFqKDK0WTSQSpGu1+Dk7CwzA825u5fZ/U6XCjoICrA8OAQCEisXg83hYn5cHD6EQt9VqNK+i9dTQvPMu4x6acR3DpsXEBiEgqvx7hdQOFZoG9H7793F/ThxU6ZlcR7FIV5SlmHjnjuHvmZkZAIBhTk742tcPmVotvsvIQJZWC0+hEEOdnfGSu/FF51I1GqNmupox/JCVibsaDRz4fHSTyzHT1xdOj50XwxjD5+lpeM/LGw78si1I+Xx87eOLL9PToGYMH3t5w7uSLjcuOB5fD35Mc+o+MxPfcGe0fzLUrPvg8XjYuHEjhg0bZtb9PKpHjx5o1aoVvv/++wrvnzhxIvLy8rBp0yaT7ZPHGE2e1ZDUycm4/fQz0D921jshdRE/5mfcS+U6he2RyIUY9VH7ep+YmZmZiU8//RTbtm1Deno6XF1d0bJlS3z66afo3LmzRRaa/Px8MMbg4uJisn1Si6aBiYOC4PftN7j76ms0QSapN5+8S7iH5lzHsCk8Pg/9nmtqkrP/n3rqKajVaqxYsQKhoaFIT0/Hvn37kJ2dbYKk5uHs7GzybdJgAA449u4N98nPcx2D2ADHo+sgENIJhKbU5ZkIBDat/4SZeXl5OHz4MGbOnImePXsiKCgI7du3xwcffIAnn3yywnUuXbqEXr16QSaTwd3dHS+88AKK/psSaffu3ZBKpcjLyzNa54033kCvXr0AANnZ2RgzZgz8/f3h4OCA5s2b448//qgy57Zt2+Ds7IzVq1cDKOs6e7SFtXPnTnTp0gUuLi5wd3fH4MGDkZiYWKvnggoNRzynTYPDE09wHYNYOX5+Fo0+M6Hm3f3Romcjk2xLoVBAoVBg06ZNUKlU1S5fXFyM2NhYuLq64tSpU/jrr7+wd+9evPrqqwCA3r17w8XFBevXrzeso9PpsHbtWsTFxQEAlEol2rRpg23btuHy5ct44YUXMG7cOJw8WfHVeH///XeMGTMGq1evNmyjolxvvvkmTp8+jX379oHP52P48OHQ1+LkcjpGwyFdfj6Sno2Dupa/Dgh5VH7/F3BG2ZLrGFYvsKkbBk1tadIpZtavX48pU6agtLQUMTEx6N69O0aPHo0WLVoAMB4MsGTJErz33nu4c+cO5P+NpNy+fTuGDBmC+/fvw9vbG9OmTcOlS5ewb98+AGWtnCeffBJpaWmVHlMZPHgwmjRpgtmzZwN4eIwmIiICH330ETZv3ozu3bsblq9uMEBWVhY8PT1x6dIlNGtWs1GP1KLhkMDZGYGLF0Ho6cl1FGLFHI+sg0BEH+X6cPOTI3ZyM5PPY/bUU0/h/v372LJlC/r3748DBw4gJiYGy5cvL7fstWvX0LJlS0ORAYDOnTtDr9cjPr7selRxcXE4cOAA7t+/DwBYvXo1Bg0aZCgyOp0OX375JZo3bw43NzcoFArs2rULKSkpRvtat24dpk+fjj179hgVmYokJCRgzJgxCA0NhZOTE4KDgwGg3DarQu9Ojon8/RGwZDH4j7y5CKkNfmEO/DzpxN+6kjmKMOiVFhDLzDM2SiqVom/fvvjkk09w9OhRTJw4EZ999lmdttWuXTuEhYVhzZo1KC0txcaNG426vGbNmoUffvgB7733Hvbv34/z588jNjYW6seu29S6dWt4enri119/RXWdWkOGDEFOTg6WLFmCEydO4MSJEwBQbptVoUJjAaRNmsD/xx8ACzpHg1gXr+wLXEewSgIhHwNfbgEnj/pdqLA2oqOjUVxcXO72qKgoXLhwwei+I0eOgM/nIzIy0nBbXFwcVq9eja1bt4LP52PQoEFGyw8dOhRjx45Fy5YtERoaihs3bpTbV1hYGPbv34/NmzfjtddeqzRrdnY24uPj8fHHH6N3796IiopCbm5urR8zFRoLoejcGb5fzuA6BrFSjkf+ou6zOug1vgl8Qk0/nBco+5Lu1asXVq1ahYsXL+L27dv466+/8N1332Ho0KHllo+Li4NUKsWECRNw+fJl7N+/H6+99hrGjRsH70eudhcXF4ezZ8/i//7v//D0009DIpEY7ouIiMCePXtw9OhRXLt2DS+++CLS09PL7QsAGjdujP3792P9+vWYNm1ahcu4urrC3d0dixcvxs2bN/HPP//gzTffrPVzQe9MC+IybBg8p73BdQxihfhFefDz1HIdw6q0HRSMxu19zLZ9hUKBDh06YO7cuejWrRuaNWuGTz75BFOmTMFPP/1UbnkHBwfs2rULOTk5aNeuHZ5++mn07t273LLh4eFo3749Ll68WG6k2Mcff4yYmBjExsaiR48e8PHxqfJk0MjISPzzzz/4448/8NZbb5W7n8/nY82aNThz5gyaNWuG6dOnY9asWbV+LmjUmQVK/exz5K1dy3UMYmUK+j2P0+oYrmNYhWbd/dF9TGT1CxKToBaNBfL59BMoevbkOgaxMoqj6yCk7rNqNe3mj26jG3Mdw67Qu9IC8QQC+P9vDmRt2nAdhVgRflE+dZ9VI7qLH7qPaQwej2ZTaEhUaCwUXyZD4JLFNHsAqRWvzLNcR7BYUZ190SMukooMB6jQWDC+gwMCFi2EvGtXrqMQK6E4uh5CMX2sH9ekow96xjWhIsMRekdaOL5EgoCff4Kid2+uoxArwC8ugL+HhusYFiXyCR/0GhcFnonP+ic1R4XGCvDEYjT64Xs4DujPdRRiBbwyznAdwWI0bu+N3uOpyHCNCo2V4AmF8J89G84VnOhFyKPkR6j7DAAi2nmj98RoKjIWgN6NVoQnEMD322/g8swzXEchFoxfWgR/j5rPQ2WLorv4oc+kaJNPkknqhgqNleHxePCZ8QVcx47lOgqxYF7pdtp9xgOeGBaKnmObUJGxIFRorBCPx4PPxx/B/eWXuI5CLJT8yHqIJPb18RYI+ej3fFO06R/MdRTyGPt6J9oYrzfegN+s78B7ZFI9QgCAryyGv7v9dJ9J5SIMndYKEW29q1+YNDgqNFbOecgQBK38jS6eRsrxTDvFdYQG4ewpw1PvtoFvuAvXUUglqNDYAFmLFghe9xekNbysKrEPCjvoPvMNc8bT77WFi7cD11FIFWz7XWhHRN7eCFq1Ek4DB3IdhVgInqoU/u4qrmOYTXhbLwyd1hpSBV0w0NJRobEhfKkU/v+bU3ZNG5pqgwDwun+S6wimxwNi+geh3/NN6WJvVoKuR2OjCvfuxf1334O+pITrKIRDTCLDkZ5zoVbquI5iElKFCL0nRCG4uQfXUUgt0M8BG+XYpw+C/vgdIn9/rqMQDvFUpfB3K+U6hkn4Rbhg1EftqchYISo0NkwaGYmQzZvg9OQQrqMQDnneO8F1hHrh8YB2g4IxdHprKFxpKL81oq4zO1GwfTtSv5gBfX4+11FIA2NiKY70/h7qUuvrPpM7i9H3uabwj3TlOgqpB2rR2AmngQMRunkTXUjNDvHUSjRytb7us8Cm7hj1cXsqMjaAWjR2hjGGnGXLkfn992Bq+zlz3N4VdRuJk/zuXMeoEb6AhyeGhqFV3wC6UJmNoEJjp5Tx8bj/9jtQJSRwHYU0AL1YgmO9f4DKwrvPXH0c0HtCNLxDnLiOQkyIus7slDQyEsHr18FtwgQ658YO8NUq+Lta7lB3gYiP9kNCMOrj9lRkbBC1aAiKjx9H2udfQJ2UxHUUYkbFXZ/BCUEPrmOU06iJK7qPiaRpZGwYFRoCAGBqNbJXrED2goV0kqeN0gvFONbvR6hKLKP7TOYoQuenIxDZwYfrKMTMqNAQI5r0dGR8NwsF27ZxHYWYQfKzc5B4X8ptCB4Q1ckXnUaEQyqnecrsARUaUqGSU6eQ9uVXUN24wXUUYkLFnUfghKg3Z/t39XFAj7gm8Itw4SwDaXhUaEilmE6H3N//QOa8edAXFHAdh5iAXijG8dh5UBZrG3S/IqkAMbFBaN03EAIhjUGyN1RoSLW0OTnI+N//kL9+A0BvF6uXPGYOElMbpvtMKOKjeY9GiIkNoun87RgVGlJjpZevIGvePBQdPMh1FFIPxZ2G4YS4r1n3wRfwEN3FD20HBkPuTPOT2TsqNKTWSq9cQdb8BSj65x9q4VghJhDiWP+fzdJ9xuPzENneG+0Gh8DJQ2by7RPrRIWG1Jny+nVkzV+Awj17qOBYmZQxs3Ez1YSFgAeEtfZChydD4OojN912iU2gQkPqTZWQgKwFC1Gwcyeg13Mdh9RAScehOC7pZ5JtBTVzR4cnQ+EZ6GiS7RHbQ4WGmIzq1i1kLVyIgm3bAZ1lnBRIKsYEQhwf8DNKi+rWfSYU8xHZwQfNezSCu7/CxOmIraFCQ0xOnZSEnFWrkb9lCw2LtmApY2bhZmrtpn1x8pCieY9GiOrkC4kDjSIjNUOFhpiNXqlEwc6dyPtrHUrPnOE6DnlMSYchOC7rX/2CPCAgyg0tejRCUDN38Pg0CSupHSo0pEGoEhOR9+efyN+0GTq6yqdFYHwBTgycj5JKus9EUgGaPOGL5j386QA/qRcqNKRB6dVqFO7ahby1f6Lk9Gmu49i9O2O+Q0KqcRHxDnFCZAcfRHbwgVgm5CgZsSVUaAhnVLduI++vv1Cwcye0qalcx7FLJe0H4rjDILj6ytG4nTci2nnD2ZPOfyGmRYWGcI4xBuWlSyjcvRsFu/dAk5LCdSS7IA4Lg2O/WPCfmgSPRjRyjJgPFRpicZTXr6Nwz14UHTgA5dWrdDKoCUmjo+HYry8c+/aFJCyM6zjETlChIRZNk5GB4kOHUHjgAEqOHqOLstWSyM8PDh06wKF9e8if6ACRry/XkYgdokJDrAZTq1F6+QpKz59H6blzKDl/DrrMLK5jWRSRnx8c2rc3/BM38uc6EiFUaIh1U9+9i9Jz51B67jxKzp+DKv6GXc1KIPLzg0O7doZWCxUWYomo0BCboi8pQenFiyg9fx7Kq9egTk6GOiUFrLSU62j1InB3hyQiApLw8LJ/EeGQRERA4OTEdTRCqkWFhtg8xhi0GRlQJyVDnZxUVnySk6FJToY65Q6YSsV1xDIiEYRubhAHBEAc8aCgREASEQGhqyvX6QipMyo0xK4xvR7atDSoU+5Al5cHXUE+9AUF0OUXQFdQAH3hw//XFeRDn18AXWHhw+45Ph8QCMCr4r88kQgCNzcI3d0h8HCH0N0DQnd3CD3cIXD3gNDDHUJ3d/CdncHj0fQuxPZQoSGklhhjgF4PnkDAdRRCrAKf6wDENvF4PGzatInrGGbB4/GoyBBSC1Ro7FxmZiZefvllBAYGQiKRwMfHB7GxsThy5Ei9tpuamooBAwaYKCUhxJrRjHl27qmnnoJarcaKFSsQGhqK9PR07Nu3D9nZ2XXanlqthlgsho+Pj4mTEkKsFR2jsWN5eXlwdXXFgQMH0L1793L3P/fcc8jIyMDff/9tuE2j0cDf3x/ffPMNnn/+efTo0QPNmjWDUCjEqlWr0Lx5c+zfvx88Hg8bN27EsGHDAADvvfceNm7ciLt378LHxwdxcXH49NNPIRLRxbMIsXXUdWbHFAoFFAoFNm3aBFUFQ3wnT56MnTt3IvWRmZX//vtvlJSUYNSoUYbbVqxYAbFYjCNHjmDhwoUV7svR0RHLly/H1atX8cMPP2DJkiWYO3eu6R8UIcTiUIvGzq1fvx5TpkxBaWkpYmJi0L17d4wePRotWrQAADRt2hQTJkzAu+++CwB48skn4e7ujmXLlgEAevTogYKCApw9e9Zou4+3aB43e/ZsrFmzBqfpmjSE2Dxq0di5p556Cvfv38eWLVvQv39/HDhwADExMVi+fDmAslbNg6KSnp6OHTt24LnnnjPaRps2bardz9q1a9G5c2f4+PhAoVDg448/RgpdDoAQu0CFhkAqlaJv37745JNPcPToUUycOBGfffYZAGD8+PG4desWjh07hlWrViEkJARdu3Y1Wl8ur/oyv8eOHUNcXBwGDhyIv//+G+fOncNHH30EtVpttsdECLEcNOqMlBMdHW04B8bd3R3Dhg3DsmXLcOzYMUyaNKnW2zt69CiCgoLw0UcfGW5LTk42VVxCiIWjQmPHsrOz8cwzz+C5555DixYt4OjoiNOnT+O7777D0KFDDctNnjwZgwcPhk6nw4QJE2q9n4iICKSkpGDNmjVo164dtm3bho0bN5ryoRBCLBgVGjumUCjQoUMHzJ07F4mJidBoNAgICMCUKVPw4YcfGpbr06cPfH190bRpU/j5+dV6P08++SSmT5+OV199FSqVCoMGDcInn3yCzz//3ISPhhBiqWjUGalWUVER/P39sWzZMowYMYLrOIQQK0MtGlIpvV6PrKwszJkzBy4uLnjyySe5jkQIsUJUaEilUlJSEBISgkaNGmH58uUQCuntQgipPeo6I4QQYlZ0Hg0hhBCzokJDCCHErKjQEEIIMSsqNIQQQsyKCg0hhBCzokJDCCHErKjQEEIIMSsqNIQQQsyKCg0hhBCzokJDCCHErKjQEEIIMSsqNIQQQsyKCg0hhBCzokJDCCHErKjQEEIIMSsqNIQQQsyKCg0hhBCzokJDCCHErKjQEEIIMSsqNIQQQsyKCg0hhBCzokJDCCHErKjQEEIIMSsqNIQQQsyKCg0hhBCzokJDCCHErKjQEEIIMSsqNIQQQsyKCg0hhBCzokJDCCHErKjQEEIIMav/B5YTCC2k8FRVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a pie chart showing the percentage of growth rate each country occupies\n",
    "growth_rate = data.sort_values(by=\"Growth Rate\", ascending=False).head(5)\n",
    "x = growth_rate[\"Country/Territory\"]\n",
    "y = growth_rate[\"Growth Rate\"]\n",
    "plt.pie(y, labels=x, autopct='%1.1f%%')\n",
    "plt.title('Top 5 Countries by Growth Rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca74e1a",
   "metadata": {},
   "source": [
    "The pie chart shows the percentage of the world population that each of the top 5 countries occupies, with Moldova being the most populous country occupying 20.5% of the world's population."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8dc14cb",
   "metadata": {},
   "source": [
    "### Q. COUNTRY VS WORLD POPULATION PERCENTAGE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d8626325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot a pie chart showing the percentage of world population each country occupies\n",
    "population_2022=data.sort_values(by=\"World Population Percentage\",ascending=False).head(5)\n",
    "x=population_2022[\"Country/Territory\"]\n",
    "y=population_2022[\"World Population Percentage\"]\n",
    "plt.pie(y,labels=x,autopct='%1.1f%%')\n",
    "plt.title('Top 5 Countries by World Population Percentage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45721a85",
   "metadata": {},
   "source": [
    "top 5 countries in terms of their percentage of the world population are China, India, United States, Indonesia, and Pakistan. Together, these countries make up over half of the world's population, with China being the most populous country at 38.6%. The pie chart visually displays the percentage of world population each country occupies and provides an easy way to compare their relative sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cae57567",
   "metadata": {},
   "source": [
    "#### Conclusion:\n",
    "\n",
    "The analysis of world population data provides insights into the population size and growth rate of different countries and continents. Asia has the largest population globally, followed by Africa and Europe, while South America has the least. China and India are the two most populous countries in the world, followed by the United States, Indonesia, and Pakistan. Nigeria is the most populous country in Africa. In terms of the bottom five countries from each continent, it is evident that they have significantly smaller populations compared to the top five countries. The population data analysis shows that there are significant differences in population size and growth rate between different regions, which can have significant implications for the economic, social, and political development of countries and continents. Overall, the analysis highlights the need for policymakers to take population trends into account while formulating development policies and strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0880c18",
   "metadata": {},
   "source": [
    "Questions and Answers:\n",
    "\n",
    "Q1. Which continent has the highest number of countries, and which has the least?  \n",
    "A1. Africa has the highest number of countries (57), while South America has the least (14).\n",
    "\n",
    "Q2. Which continent has the highest population percentage, and which has the lowest?  \n",
    "A2. Asia has the highest population percentage (59.20%), followed by Africa (17.90%), and Europe (9.30%). Oceania has the lowest population percentage (0.50%).\n",
    "\n",
    "Q3. What is the trend in the world population between 1970 and 2022?  \n",
    "A3. The total world population has increased significantly over the years. The world population was 3.7 billion in 1970, which increased to 7.9 billion in 2022.\n",
    "\n",
    "Q4. What are the top three countries with the highest population, and what are the bottom three with the lowest population?  \n",
    "A4. The top three countries with the highest population are China (1.4 billion), India (1.4 billion), and the United States (338 million). The bottom three countries with the lowest population are Vatican City (510), Tokelau (1,871), and Niue (1,934).\n",
    "\n",
    "Q5. Which continent has the largest population, and which country is the most populous in that continent?  \n",
    "A5. Asia has the largest population globally, with China being the most populous country in the continent.\n",
    "\n",
    "Q6. Which continent has the second-largest population, and which country is the most populous in that continent?  \n",
    "A6. Africa has the second-largest population globally, with Nigeria being the most populous country in the continent.\n",
    "\n",
    "Q7. What are the top five most populous countries in each continent?  \n",
    "A7. The top five most populous countries in each continent are:\n",
    "\n",
    "    Asia: China, India, Indonesia, Pakistan, and Bangladesh\n",
    "    Europe: Russia, Germany, United Kingdom, France, and Italy\n",
    "    South America: Brazil, Colombia, Argentina, Peru, and Venezuela\n",
    "    North America: United States, Mexico, Canada, Cuba, and Haiti\n",
    "    Africa: Nigeria, Ethiopia, Egypt, DR Congo, and Tanzania\n",
    "\n",
    "Q8. What are the bottom five least populous countries in each continent?  \n",
    "A8. The bottom five least populous countries in each continent are:\n",
    "\n",
    "    Asia: Bhutan, Brunei, Maldives, Timor-Leste, and Macao\n",
    "    Europe: Vatican City, Gibraltar, San Marino, Monaco, and Liechtenstein\n",
    "    South America: Falkland Islands, French Guiana, Suriname, Guyana, and Uruguay\n",
    "    North America: Greenland, Saint Kitts and Nevis, Saint Vincent and the Grenadines, Dominica, and Antigua and Barbuda\n",
    "    Africa: Seychelles, Sao Tome and Principe, Mayotte, Western Sahara, and Cape Verde"
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