Country_Vaccinations.csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "25a013c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5477188c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8e925d2e",
   "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>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://covid19.who.int/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://covid19.who.int/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://covid19.who.int/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://covid19.who.int/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://covid19.who.int/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "0  Afghanistan      AFG  2021-02-22                 0.0                0.0   \n",
       "1  Afghanistan      AFG  2021-02-23                 NaN                NaN   \n",
       "2  Afghanistan      AFG  2021-02-24                 NaN                NaN   \n",
       "3  Afghanistan      AFG  2021-02-25                 NaN                NaN   \n",
       "4  Afghanistan      AFG  2021-02-26                 NaN                NaN   \n",
       "\n",
       "   people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "0                      NaN                     NaN                 NaN   \n",
       "1                      NaN                     NaN              1367.0   \n",
       "2                      NaN                     NaN              1367.0   \n",
       "3                      NaN                     NaN              1367.0   \n",
       "4                      NaN                     NaN              1367.0   \n",
       "\n",
       "   total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "0                             0.0                            0.0   \n",
       "1                             NaN                            NaN   \n",
       "2                             NaN                            NaN   \n",
       "3                             NaN                            NaN   \n",
       "4                             NaN                            NaN   \n",
       "\n",
       "   people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                                  NaN                             NaN   \n",
       "1                                  NaN                            34.0   \n",
       "2                                  NaN                            34.0   \n",
       "3                                  NaN                            34.0   \n",
       "4                                  NaN                            34.0   \n",
       "\n",
       "                                            vaccines  \\\n",
       "0  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "1  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "2  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "3  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "4  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "\n",
       "                 source_name            source_website  \n",
       "0  World Health Organization  https://covid19.who.int/  \n",
       "1  World Health Organization  https://covid19.who.int/  \n",
       "2  World Health Organization  https://covid19.who.int/  \n",
       "3  World Health Organization  https://covid19.who.int/  \n",
       "4  World Health Organization  https://covid19.who.int/  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Country_vaccinations.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca219b4",
   "metadata": {},
   "source": [
    "This shows that Afganistan  is not working on its vaccination program seriously and is lacking behind the world since most of its population is still not vaccinated."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344ba47b",
   "metadata": {},
   "source": [
    "Also the number of people of afganistan who get vaccinated everyday is quite low for its population as well as in terms of people in its surrounding countries where population is much more as compared to Afgansitan such as India ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a9496e",
   "metadata": {},
   "source": [
    "One of the reasons why the vaccination is slow in Afganistan is maybe due to lack of its own vaccines which is produced by the country itself since most of the vaccines used here come from other countries such as America,France and India."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6ab3fefd",
   "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>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86507</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2022-03-25</td>\n",
       "      <td>8691642.0</td>\n",
       "      <td>4814582.0</td>\n",
       "      <td>3473523.0</td>\n",
       "      <td>139213.0</td>\n",
       "      <td>69579.0</td>\n",
       "      <td>57.59</td>\n",
       "      <td>31.90</td>\n",
       "      <td>23.02</td>\n",
       "      <td>4610.0</td>\n",
       "      <td>Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.arcgis.com/home/webmap/viewer.html...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86508</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2022-03-26</td>\n",
       "      <td>8791728.0</td>\n",
       "      <td>4886242.0</td>\n",
       "      <td>3487962.0</td>\n",
       "      <td>100086.0</td>\n",
       "      <td>83429.0</td>\n",
       "      <td>58.25</td>\n",
       "      <td>32.38</td>\n",
       "      <td>23.11</td>\n",
       "      <td>5528.0</td>\n",
       "      <td>Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.arcgis.com/home/webmap/viewer.html...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86509</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2022-03-27</td>\n",
       "      <td>8845039.0</td>\n",
       "      <td>4918147.0</td>\n",
       "      <td>3493763.0</td>\n",
       "      <td>53311.0</td>\n",
       "      <td>90629.0</td>\n",
       "      <td>58.61</td>\n",
       "      <td>32.59</td>\n",
       "      <td>23.15</td>\n",
       "      <td>6005.0</td>\n",
       "      <td>Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.arcgis.com/home/webmap/viewer.html...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86510</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2022-03-28</td>\n",
       "      <td>8934360.0</td>\n",
       "      <td>4975433.0</td>\n",
       "      <td>3501493.0</td>\n",
       "      <td>89321.0</td>\n",
       "      <td>100614.0</td>\n",
       "      <td>59.20</td>\n",
       "      <td>32.97</td>\n",
       "      <td>23.20</td>\n",
       "      <td>6667.0</td>\n",
       "      <td>Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.arcgis.com/home/webmap/viewer.html...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86511</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2022-03-29</td>\n",
       "      <td>9039729.0</td>\n",
       "      <td>5053114.0</td>\n",
       "      <td>3510256.0</td>\n",
       "      <td>105369.0</td>\n",
       "      <td>103751.0</td>\n",
       "      <td>59.90</td>\n",
       "      <td>33.48</td>\n",
       "      <td>23.26</td>\n",
       "      <td>6874.0</td>\n",
       "      <td>Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.arcgis.com/home/webmap/viewer.html...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "86507  Zimbabwe      ZWE  2022-03-25           8691642.0          4814582.0   \n",
       "86508  Zimbabwe      ZWE  2022-03-26           8791728.0          4886242.0   \n",
       "86509  Zimbabwe      ZWE  2022-03-27           8845039.0          4918147.0   \n",
       "86510  Zimbabwe      ZWE  2022-03-28           8934360.0          4975433.0   \n",
       "86511  Zimbabwe      ZWE  2022-03-29           9039729.0          5053114.0   \n",
       "\n",
       "       people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "86507                3473523.0                139213.0             69579.0   \n",
       "86508                3487962.0                100086.0             83429.0   \n",
       "86509                3493763.0                 53311.0             90629.0   \n",
       "86510                3501493.0                 89321.0            100614.0   \n",
       "86511                3510256.0                105369.0            103751.0   \n",
       "\n",
       "       total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "86507                           57.59                          31.90   \n",
       "86508                           58.25                          32.38   \n",
       "86509                           58.61                          32.59   \n",
       "86510                           59.20                          32.97   \n",
       "86511                           59.90                          33.48   \n",
       "\n",
       "       people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "86507                                23.02                          4610.0   \n",
       "86508                                23.11                          5528.0   \n",
       "86509                                23.15                          6005.0   \n",
       "86510                                23.20                          6667.0   \n",
       "86511                                23.26                          6874.0   \n",
       "\n",
       "                                                vaccines         source_name  \\\n",
       "86507  Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...  Ministry of Health   \n",
       "86508  Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...  Ministry of Health   \n",
       "86509  Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...  Ministry of Health   \n",
       "86510  Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...  Ministry of Health   \n",
       "86511  Oxford/AstraZeneca, Sinopharm/Beijing, Sinovac...  Ministry of Health   \n",
       "\n",
       "                                          source_website  \n",
       "86507  https://www.arcgis.com/home/webmap/viewer.html...  \n",
       "86508  https://www.arcgis.com/home/webmap/viewer.html...  \n",
       "86509  https://www.arcgis.com/home/webmap/viewer.html...  \n",
       "86510  https://www.arcgis.com/home/webmap/viewer.html...  \n",
       "86511  https://www.arcgis.com/home/webmap/viewer.html...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b27de915",
   "metadata": {},
   "source": [
    "Zimbabwe is currently doing great in terms of vaccination and is increasing the number of people who get vaccinated both daily and in total as well"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f001d7dc",
   "metadata": {},
   "source": [
    "There is a steady increase in the vaccination maybe due to its good relationships with rich countries like America , France and India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b2f7400",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [2]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Chaging the Nan value\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mfillna(\u001b[38;5;241m0\u001b[39m,inplace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "#Chaging the Nan value\n",
    "df.fillna(0,inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9da856e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1297680"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56a973bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86512, 15)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "700825e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9f2128dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "818d2c65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=86512, step=1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9bf73ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'iso_code', 'date', 'total_vaccinations',\n",
       "       'people_vaccinated', 'people_fully_vaccinated',\n",
       "       'daily_vaccinations_raw', 'daily_vaccinations',\n",
       "       'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred',\n",
       "       'people_fully_vaccinated_per_hundred', 'daily_vaccinations_per_million',\n",
       "       'vaccines', 'source_name', 'source_website'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f6a1ed3",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba7a65d1",
   "metadata": {},
   "source": [
    "Defines all the columns used to store the values in the following dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2b1adcf",
   "metadata": {},
   "source": [
    "Can be used to know what all data is represented in the table without going through all of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae9232a8",
   "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>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79603</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>TUR</td>\n",
       "      <td>2021-05-14</td>\n",
       "      <td>25561998.0</td>\n",
       "      <td>14814316.0</td>\n",
       "      <td>10747682.0</td>\n",
       "      <td>159721.0</td>\n",
       "      <td>124422.0</td>\n",
       "      <td>30.06</td>\n",
       "      <td>17.42</td>\n",
       "      <td>12.64</td>\n",
       "      <td>1463.0</td>\n",
       "      <td>Pfizer/BioNTech, Sinovac, Turkovac</td>\n",
       "      <td>COVID-19 Vaccine Information Platform</td>\n",
       "      <td>https://covid19asi.saglik.gov.tr/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "79603  Turkey      TUR  2021-05-14          25561998.0         14814316.0   \n",
       "\n",
       "       people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "79603               10747682.0                159721.0            124422.0   \n",
       "\n",
       "       total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "79603                           30.06                          17.42   \n",
       "\n",
       "       people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "79603                                12.64                          1463.0   \n",
       "\n",
       "                                 vaccines  \\\n",
       "79603  Pfizer/BioNTech, Sinovac, Turkovac   \n",
       "\n",
       "                                 source_name  \\\n",
       "79603  COVID-19 Vaccine Information Platform   \n",
       "\n",
       "                          source_website  \n",
       "79603  https://covid19asi.saglik.gov.tr/  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3e6cf6",
   "metadata": {},
   "source": [
    "Gives us a sample of the table along with a sample value stored in a row ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "740b5f36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 86512 entries, 0 to 86511\n",
      "Data columns (total 15 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   country                              86512 non-null  object \n",
      " 1   iso_code                             86512 non-null  object \n",
      " 2   date                                 86512 non-null  object \n",
      " 3   total_vaccinations                   43607 non-null  float64\n",
      " 4   people_vaccinated                    41294 non-null  float64\n",
      " 5   people_fully_vaccinated              38802 non-null  float64\n",
      " 6   daily_vaccinations_raw               35362 non-null  float64\n",
      " 7   daily_vaccinations                   86213 non-null  float64\n",
      " 8   total_vaccinations_per_hundred       43607 non-null  float64\n",
      " 9   people_vaccinated_per_hundred        41294 non-null  float64\n",
      " 10  people_fully_vaccinated_per_hundred  38802 non-null  float64\n",
      " 11  daily_vaccinations_per_million       86213 non-null  float64\n",
      " 12  vaccines                             86512 non-null  object \n",
      " 13  source_name                          86512 non-null  object \n",
      " 14  source_website                       86512 non-null  object \n",
      "dtypes: float64(9), object(6)\n",
      "memory usage: 9.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811198fe",
   "metadata": {},
   "source": [
    "Tells us about the DataType of all the columns along with the values that can be stored in the follwoing columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7c2ebb1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                                 object\n",
       "iso_code                                object\n",
       "date                                    object\n",
       "total_vaccinations                     float64\n",
       "people_vaccinated                      float64\n",
       "people_fully_vaccinated                float64\n",
       "daily_vaccinations_raw                 float64\n",
       "daily_vaccinations                     float64\n",
       "total_vaccinations_per_hundred         float64\n",
       "people_vaccinated_per_hundred          float64\n",
       "people_fully_vaccinated_per_hundred    float64\n",
       "daily_vaccinations_per_million         float64\n",
       "vaccines                                object\n",
       "source_name                             object\n",
       "source_website                          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fea3c5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93faf33a",
   "metadata": {},
   "source": [
    "Tells us about the case where a duplicate values exists in the dataset and also finds a sum of all of such values in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "716cd136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                                    0\n",
       "iso_code                                   0\n",
       "date                                       0\n",
       "total_vaccinations                     42905\n",
       "people_vaccinated                      45218\n",
       "people_fully_vaccinated                47710\n",
       "daily_vaccinations_raw                 51150\n",
       "daily_vaccinations                       299\n",
       "total_vaccinations_per_hundred         42905\n",
       "people_vaccinated_per_hundred          45218\n",
       "people_fully_vaccinated_per_hundred    47710\n",
       "daily_vaccinations_per_million           299\n",
       "vaccines                                   0\n",
       "source_name                                0\n",
       "source_website                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c0134b",
   "metadata": {},
   "source": [
    "tells us whether a certain dataset contains null values inside the columns of the dataset we are dealing with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7d623f56",
   "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>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20826</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>DNK</td>\n",
       "      <td>2020-12-08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson&amp;Johnson, Moderna, Pfizer/BioNTech</td>\n",
       "      <td>Statens Serum Institute</td>\n",
       "      <td>https://covid19.ssi.dk/overvagningsdata/downlo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43117</th>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson&amp;Johnson, Moderna, Novavax, Pfizer/BioN...</td>\n",
       "      <td>National Health Service</td>\n",
       "      <td>https://data.gov.lv/dati/eng/dataset/covid19-v...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55543</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>NZL</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.health.govt.nz/our-work/diseases-a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75399</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>2020-12-21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson&amp;Johnson, Moderna, Pfizer/BioNTech</td>\n",
       "      <td>Federal Office of Public Health</td>\n",
       "      <td>https://opendata.swiss/en/dataset/covid-19-sch...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           country iso_code        date  total_vaccinations  \\\n",
       "20826      Denmark      DNK  2020-12-08                 1.0   \n",
       "43117       Latvia      LVA  2020-12-04                 1.0   \n",
       "55543  New Zealand      NZL  2021-02-14                 1.0   \n",
       "75399  Switzerland      CHE  2020-12-21                 1.0   \n",
       "\n",
       "       people_vaccinated  people_fully_vaccinated  daily_vaccinations_raw  \\\n",
       "20826                1.0                      NaN                     NaN   \n",
       "43117                1.0                      NaN                     NaN   \n",
       "55543                1.0                      NaN                     NaN   \n",
       "75399                1.0                      1.0                     NaN   \n",
       "\n",
       "       daily_vaccinations  total_vaccinations_per_hundred  \\\n",
       "20826                 NaN                             0.0   \n",
       "43117                 NaN                             0.0   \n",
       "55543                 NaN                             0.0   \n",
       "75399                 NaN                             0.0   \n",
       "\n",
       "       people_vaccinated_per_hundred  people_fully_vaccinated_per_hundred  \\\n",
       "20826                            0.0                                  NaN   \n",
       "43117                            0.0                                  NaN   \n",
       "55543                            0.0                                  NaN   \n",
       "75399                            0.0                                  0.0   \n",
       "\n",
       "       daily_vaccinations_per_million  \\\n",
       "20826                             NaN   \n",
       "43117                             NaN   \n",
       "55543                             NaN   \n",
       "75399                             NaN   \n",
       "\n",
       "                                                vaccines  \\\n",
       "20826          Johnson&Johnson, Moderna, Pfizer/BioNTech   \n",
       "43117  Johnson&Johnson, Moderna, Novavax, Pfizer/BioN...   \n",
       "55543                Oxford/AstraZeneca, Pfizer/BioNTech   \n",
       "75399          Johnson&Johnson, Moderna, Pfizer/BioNTech   \n",
       "\n",
       "                           source_name  \\\n",
       "20826          Statens Serum Institute   \n",
       "43117          National Health Service   \n",
       "55543               Ministry of Health   \n",
       "75399  Federal Office of Public Health   \n",
       "\n",
       "                                          source_website  \n",
       "20826  https://covid19.ssi.dk/overvagningsdata/downlo...  \n",
       "43117  https://data.gov.lv/dati/eng/dataset/covid19-v...  \n",
       "55543  https://www.health.govt.nz/our-work/diseases-a...  \n",
       "75399  https://opendata.swiss/en/dataset/covid-19-sch...  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['total_vaccinations']==1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d5da171e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                                  223\n",
       "iso_code                                 223\n",
       "date                                     483\n",
       "total_vaccinations                     42828\n",
       "people_vaccinated                      40194\n",
       "people_fully_vaccinated                37425\n",
       "daily_vaccinations_raw                 27692\n",
       "daily_vaccinations                     40516\n",
       "total_vaccinations_per_hundred         17881\n",
       "people_vaccinated_per_hundred           9078\n",
       "people_fully_vaccinated_per_hundred     8772\n",
       "daily_vaccinations_per_million         12405\n",
       "vaccines                                  84\n",
       "source_name                               81\n",
       "source_website                           119\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "551e3811",
   "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>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.360700e+04</td>\n",
       "      <td>4.129400e+04</td>\n",
       "      <td>3.880200e+04</td>\n",
       "      <td>3.536200e+04</td>\n",
       "      <td>8.621300e+04</td>\n",
       "      <td>43607.000000</td>\n",
       "      <td>41294.000000</td>\n",
       "      <td>38802.000000</td>\n",
       "      <td>86213.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.592964e+07</td>\n",
       "      <td>1.770508e+07</td>\n",
       "      <td>1.413830e+07</td>\n",
       "      <td>2.705996e+05</td>\n",
       "      <td>1.313055e+05</td>\n",
       "      <td>80.188543</td>\n",
       "      <td>40.927317</td>\n",
       "      <td>35.523243</td>\n",
       "      <td>3257.049157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.246004e+08</td>\n",
       "      <td>7.078731e+07</td>\n",
       "      <td>5.713920e+07</td>\n",
       "      <td>1.212427e+06</td>\n",
       "      <td>7.682388e+05</td>\n",
       "      <td>67.913577</td>\n",
       "      <td>29.290759</td>\n",
       "      <td>28.376252</td>\n",
       "      <td>3934.312440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.264100e+05</td>\n",
       "      <td>3.494642e+05</td>\n",
       "      <td>2.439622e+05</td>\n",
       "      <td>4.668000e+03</td>\n",
       "      <td>9.000000e+02</td>\n",
       "      <td>16.050000</td>\n",
       "      <td>11.370000</td>\n",
       "      <td>7.020000</td>\n",
       "      <td>636.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.590096e+06</td>\n",
       "      <td>2.187310e+06</td>\n",
       "      <td>1.722140e+06</td>\n",
       "      <td>2.530900e+04</td>\n",
       "      <td>7.343000e+03</td>\n",
       "      <td>67.520000</td>\n",
       "      <td>41.435000</td>\n",
       "      <td>31.750000</td>\n",
       "      <td>2050.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.701230e+07</td>\n",
       "      <td>9.152520e+06</td>\n",
       "      <td>7.559870e+06</td>\n",
       "      <td>1.234925e+05</td>\n",
       "      <td>4.409800e+04</td>\n",
       "      <td>132.735000</td>\n",
       "      <td>67.910000</td>\n",
       "      <td>62.080000</td>\n",
       "      <td>4682.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.263129e+09</td>\n",
       "      <td>1.275541e+09</td>\n",
       "      <td>1.240777e+09</td>\n",
       "      <td>2.474100e+07</td>\n",
       "      <td>2.242429e+07</td>\n",
       "      <td>345.370000</td>\n",
       "      <td>124.760000</td>\n",
       "      <td>122.370000</td>\n",
       "      <td>117497.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       total_vaccinations  people_vaccinated  people_fully_vaccinated  \\\n",
       "count        4.360700e+04       4.129400e+04             3.880200e+04   \n",
       "mean         4.592964e+07       1.770508e+07             1.413830e+07   \n",
       "std          2.246004e+08       7.078731e+07             5.713920e+07   \n",
       "min          0.000000e+00       0.000000e+00             1.000000e+00   \n",
       "25%          5.264100e+05       3.494642e+05             2.439622e+05   \n",
       "50%          3.590096e+06       2.187310e+06             1.722140e+06   \n",
       "75%          1.701230e+07       9.152520e+06             7.559870e+06   \n",
       "max          3.263129e+09       1.275541e+09             1.240777e+09   \n",
       "\n",
       "       daily_vaccinations_raw  daily_vaccinations  \\\n",
       "count            3.536200e+04        8.621300e+04   \n",
       "mean             2.705996e+05        1.313055e+05   \n",
       "std              1.212427e+06        7.682388e+05   \n",
       "min              0.000000e+00        0.000000e+00   \n",
       "25%              4.668000e+03        9.000000e+02   \n",
       "50%              2.530900e+04        7.343000e+03   \n",
       "75%              1.234925e+05        4.409800e+04   \n",
       "max              2.474100e+07        2.242429e+07   \n",
       "\n",
       "       total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "count                    43607.000000                   41294.000000   \n",
       "mean                        80.188543                      40.927317   \n",
       "std                         67.913577                      29.290759   \n",
       "min                          0.000000                       0.000000   \n",
       "25%                         16.050000                      11.370000   \n",
       "50%                         67.520000                      41.435000   \n",
       "75%                        132.735000                      67.910000   \n",
       "max                        345.370000                     124.760000   \n",
       "\n",
       "       people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \n",
       "count                         38802.000000                    86213.000000  \n",
       "mean                             35.523243                     3257.049157  \n",
       "std                              28.376252                     3934.312440  \n",
       "min                               0.000000                        0.000000  \n",
       "25%                               7.020000                      636.000000  \n",
       "50%                              31.750000                     2050.000000  \n",
       "75%                              62.080000                     4682.000000  \n",
       "max                             122.370000                   117497.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f6539f10",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'sns'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [45]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Plot the unique values\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpeople_vaccinated\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msns\u001b[49m\u001b[38;5;241m.\u001b[39mcountplot()\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'sns'"
     ]
    }
   ],
   "source": [
    "#Plot the unique values\n",
    "\n",
    "data.people_vaccinated.sns.countplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9d739937",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'duplicate'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [46]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Find the duplicates\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mduplicate\u001b[49m()\u001b[38;5;241m.\u001b[39msum()\n",
      "File \u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:5575\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5568\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5569\u001b[0m     name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m   5570\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m   5571\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m   5572\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m   5573\u001b[0m ):\n\u001b[0;32m   5574\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5575\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getattribute__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'duplicate'"
     ]
    }
   ],
   "source": [
    "#Find the duplicates\n",
    "data.duplicate().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "d9cf85fe",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'module' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [53]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mpd\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdaily_vaccinations\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mboxplot()\n",
      "\u001b[1;31mTypeError\u001b[0m: 'module' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "pd[['daily_vaccinations']].boxplot()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0c16a5e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.corr() # Finding out the correlation between the features\n",
    "sns.set(rc={'figure.figsize': (20,20)}) # Plotting the heatmap of correlation between features\n",
    "sns.heatmap(corr, annot=True, cmap='Reds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d0d3a5e3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABIMAAARsCAYAAAANGVagAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABrC0lEQVR4nOz9f5TedX0n/D+v+ZUhP0hu44QkJv5qO4O71aXn1MZbxqt314KyjVpj4UC3aBUtsp3K1XuT3TJRgqGMu0vOdlAq5VhdKLWyTBnR5hzcsLb3jlNx1ptzrOBK0tbaJoTAGAwmEyZzZeb6/uEy3zsduCZeQ/nMeD0e/3xyfa7Aef55nWfe79erVKvVagEAAACgKbQUHQAAAACAF48yCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJtBUd4FknTpzI5Zdfnj/4gz/Ipk2bnvPvfPvb387v/M7vzH5+6qmnsnr16uzdu/fFigkAAACwpC2KMuiv/uqv8uEPfzjf/e536/6917zmNfnCF76QJHnmmWdy6aWX5oYbbvinDwgAAADwY2JRXBO75557smvXrqxbt2723X333Zd3vvOdecc73pH+/v6cOnXqjP/m9ttvz+tf//r87M/+7IsdFwAAAGDJWhRl0E033XRGqfPXf/3Xueeee3L33XfnC1/4QtauXZtPf/rTs98fP34899xzT/r6+oqICwAAALBkLYprYv/Y2NhY/v7v/z6XXXZZkqRareaf/bN/Nvv9F7/4xfziL/5i1q5dW1REAAAAgCVpUZZB09PTueSSS/LhD384STIxMZHp6enZ7//7f//vufrqq4uKBwAAALBkLYprYv/Yli1b8sADD+To0aOp1Wq54YYbcueddyZJarVavvWtb+VnfuZnCk4JAAAAsPQsypNB559/fvr6+vKe97wnMzMzec1rXpPf+I3fSPLDdfLt7e1ZtmxZwSkBAAAAlp5SrVarFR0CAAAAgBfHorwmBgAAAMA/DWUQAAAAQBNRBgEAAAA0kUUxQPr735/IzIzRRQDA4rZ27cocPXqi6BgAAHW1tJTyf/wfK573+0VRBs3M1JRBAMCS4DcLALDUuSYGAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAA8xgeHkq5vCWtra0pl7dkeHio6EgAAA1rKzoAAMBiNjw8lIGBGzM4eGu2br04e/fuS6XSlyTZtu3SgtMBAPzoSrVarVZ0iKNHT2RmpvAYAABzlMtbMjBwc3p7y+nqWpXx8eMZHR1Jf/+OjIyMFR0PAGCOlpZS1q5d+fzfv4hZAACWnAMH9ufxxx8745rY448/lgMH9hcdDQCgIU4GAQDUccEF5+f06en8wR98evaa2Ac/eFXa2lrzjW88WnQ8AIA5nAwCAFigUqn+ZwCApUQZBABQx5EjR3L99Temv39HOjs709+/I9dff2OOHDlSdDQAgIYogwAA6uju7smGDRszMjKW6enpjIyMZcOGjenu7ik6GgBAQ5RBAAB1VCrbU6n0ZXR0JNVqNaOjI6lU+lKpbC86GgBAQwyQBgCYx/DwUAYH9+TAgf3p7u5JpbI927ZdWnQsAIDnNN8AaWUQAMBZ6upalfHx40XHAACoyzYxAAAAAGYpgwAAAACaiDIIAAAAoIkogwAAAACaiDIIAGAew8NDKZe3pLW1NeXylgwPDxUdCQCgYW1FBwAAWMyGh4cyMHBjBgdvzdatF2fv3n2pVPqSxHp5AGBJsloeAKCOcnlLBgZuTm9veXa1/OjoSPr7d2RkZKzoeAAAc8y3Wl4ZBABQx/r1a3Lw4Hja29tny6BqtZrNm7ty5MixouMBAMwxXxnkmhgAQB3d3T15//vfky9/+YFMTZ1KR8eyvPnNF6W7u6foaAAADTFAGgCgjvXrN+T++/fmV3/113Ls2LH86q/+Wu6/f2/Wr99QdDQAgIa4JgYAUMemTV15+9t/OY888s0cOLA/3d09+emffl2++MX7cujQeNHxAADmmO+amJNBAAB1TE2dSrn882e8K5d/PlNTpwpKBACwME4GAQDUsXHjS7Jq1ap8+tN3za6Wv+qqK3P8+PEcPvxU0fEAAOZwMggAYAFWrlyZp5/+QR5++JupVqt5+OFv5umnf5CVK5//BxYAwGLmZBAAQB3r16/Je97z3nz2s388u03sX//rX8udd/4Xq+UBgEXJySAAgAXo7u7J2972zhw6NJ5arZZDh8bztre902p5AGDJUgYBANRRqWxPpdKX0dGRVKvVjI6OpFLpS6WyvehoAAANcU0MAGAew8NDGRzcM7tavlLZnm3bLi06FgDAc5rvmpgyCADgLHV1rcr4+PGiYwAA1GVmEAAAAACzlEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAwj+HhoZTLW9La2ppyeUuGh4eKjgQA0LC2ogMAACxmw8NDGRi4MYODt2br1ouzd+++VCp9SZJt2y4tOB0AwI+uVKvVakWHOHr0RGZmCo8BADBHubwlAwM3p7e3nK6uVRkfP57R0ZH09+/IyMhY0fEAAOZoaSll7dqVz/u9MggAoI7169fk4MHxtLe3z5ZB1Wo1mzd35ciRY0XHAwCYY74yyMwgAIA6urt7Mjb24BnvxsYeTHd3T0GJAAAWRhkEAFBHpbI9lUpfRkdHUq1WMzo6kkqlL5XK9qKjAQA0xDUxAIB5DA8PZXBwTw4c2J/u7p5UKtsNjwYAFi0zgwAAXiDPzgwCAFjMzAwCAAAAYJYyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAOYxPDyUcnlLWltbUy5vyfDwUNGRAAAa1lZ0AACAxWx4eCgDAzdmcPDWbN16cfbu3ZdKpS9JrJcHAJYkq+UBAOool7dkYODm9PaWZ1fLj46OpL9/R0ZGxoqOBwAwx3yr5ZVBAAB1rF+/JgcPjqe9vX22DKpWq9m8uStHjhwrOh4AwBzzlUFmBgEA1NHd3ZOxsQfPeDc29mC6u3sKSgQAsDDKIACAOiqV7alU+jI6OpJqtZrR0ZFUKn2pVLYXHQ0AoCEGSAMA1LFt26X5+tfHcvnl78rU1Kl0dCzLlVe+x/BoAGDJcjIIAKCO4eGhPPDAvtx9972ZmprK3Xffmwce2Ge9PACwZBkgDQBQh21iAMBSY5sYAMAC2CYGACw1tokBACyAbWIAwI8bZRAAQB22iQEAP25cEwMAmMfw8FAGB/fkwIH96e7uSaWy3TYxAGDRMjMIAOAF8uzMIACAxczMIAAAAABmKYMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCABgHsPDQymXt6S1tTXl8pYMDw8VHQkAoGFtRQcAAFjMhoeHMjBwYwYHb83WrRdn7959qVT6kiTbtl1acDoAgB9dqVar1YoOcfToiczMFB4DAGCOcnlLBgZuTm9vOV1dqzI+fjyjoyPp79+RkZGxouMBAMzR0lLK2rUrn/d7ZRAAQB3r16/JwYPjaW9vny2DqtVqNm/uypEjx4qOBwAwx3xlkJlBAAB1dHf3ZGzswTPejY09mO7unoISAQAsjDIIAKCOSmV7KpW+jI6OpFqtZnR0JJVKXyqV7UVHAwBoiGtiAADzGB4eyuDgnhw4sD/d3T2pVLYbHg0ALFpmBgEAvECenRkEALCYmRkEAAAAwCxlEAAAAEATUQYBAAAANBFlEAAAAEATUQYBAAAANBFlEADAPIaHh1Iub0lra2vK5S0ZHh4qOhIAQMPaig4AALCYDQ8PZWDgxgwO3pqtWy/O3r37Uqn0JUm2bbu04HQAAD+6Uq1WqxUd4ujRE5mZKTwGAMAc5fKWDAzcnN7ecrq6VmV8/HhGR0fS378jIyNjRccDAJijpaWUtWtXPu/3yiAAgDrWr1+TgwfH097ePlsGVavVbN7clSNHjhUdDwBgjvnKIDODAADq6O7uydjYg2e8Gxt7MN3dPQUlAgBYGGUQAEAdlcr2VCp9GR0dSbVazejoSCqVvlQq24uOBgDQENfEAADmMTw8lMHBPTlwYH+6u3tSqWw3PBoAWLTMDAIAeIE8OzMIAGAxMzMIAAAAgFnKIACAeQwPD6Vc3pLW1taUy1syPDxUdCQAgIa1FR0AAGAxGx4eysDAjRkcvDVbt16cvXv3pVLpSxJzgwCAJcnMIACAOsrlLRkYuDm9veXZmUGjoyPp79+RkZGxouMBAMxhgDQAwAKsX78mBw+Op729fbYMqlar2by5K0eOHCs6HgDAHPOVQa6JAQDU0d3dkz17/kPuv3/v7Gr5Sy7Zmu7unqKjAQA0xABpAIA6LrzwTfn4x38vV1xxZY4fP54rrrgyH//47+XCC99UdDQAgIa4JgYAUEe5vCWXXLJ1zsmg++/fa2YQALAomRkEALAAZgYBAEvNfGWQa2IAAHV0d/dkbOzBM96NjT1oZhAAsGQpgwAA6qhUtqdS6cvo6Eiq1WpGR0dSqfSlUtledDQAgIa4JgYAMI/h4aEMDu6ZnRlUqWzPtm2XFh0LAOA5mRkEAPACeXZmEADAYvaCzAy69dZb80u/9Ev5pV/6pfyn//Sf5nz/7W9/O9u2bctb3vKW7Ny5M6dPn248MQAAAAD/ZOYtg7761a9mdHQ0n//853PfffflW9/6Vh544IEz/s6OHTty/fXX57/9t/+WWq2We+65558sMAAAAACNm7cM6urqyu/8zu+ko6Mj7e3t+Ymf+IkcPnx49vvHHnssk5OTueCCC5Ik27Zty5e+9KV/ssAAAAAANK5tvr/wUz/1U7N//u53v5v7778/n/vc52bfPfnkk+nq6pr93NXVlSeeeOIFjgkAAADAC2HeMuhZf/3Xf52rr746/+7f/bu88pWvnH0/MzOTUqk0+7lWq53x+WzUG2oEALCYdHWtKjoCAMCCnFUZ9NBDD+VDH/pQ+vv780u/9EtnfLd+/fqMj4/Pfv7e976XdevW/UghbBMDABYzq+UBgKVkwdvEHn/88fzmb/5m9uzZM6cISpKXvexlWbZsWR566KEkyRe+8IWUy+UFRAYAWDyGh4cyMHBjBgZuzuTkZAYGbs7AwI0ZHh4qOhoAQENKtVqt7pGc3/3d3829996bl7/85bPvLr/88vz5n/95PvShD+W1r31tHn300Xz4wx/OiRMn8s//+T/Pxz72sXR0dJx1CCeDAIDFqlzekksu2Zr77987ezLo2c8jI2NFxwMAmGO+k0HzlkEvBmUQALBYnXfe6mza9PLccsvvZ+vWi7N3775ce+1v5tChf8gTTzxddDwAgDkWfE0MAKCZtbd3ZMuWN6S/f0c6OzvT378jW7a8Ie3tZ38KGgBgMVEGAQDUMTV1Kp///L254oorc/z48VxxxZX5/OfvzdTUqaKjAQA0RBkEAFBHR8eyvPOd78rnPndXVq1alc997q68853vSkfHsqKjAQA0RBkEAFBHtTqVsbGvnbFNbGzsa6lWp4qOBgDQkLaiAwAALGY9PefnVa/6iVx++bsyNXUqHR3L8uY3X5QVK5YXHQ0AoCFOBgEA1HHhhW/Kvn1fys6duzIxMZGdO3dl374v5cIL31R0NACAhlgtDwBQR7m8JZdcsjX33783Bw7sT3d3z+znkZGxouMBAMwx32p5ZRAAQB3r16/JwYPjaW9vT1fXqoyPH0+1Ws3mzV05cuRY0fEAAOaYrwxyTQwAoI7u7p6MjT14xruxsQfT3d1TUCIAgIVRBgEA1FGpbE+l0pfR0ZFUq9WMjo6kUulLpbK96GgAAA1xTQwAYB7Dw0MZHNwzOzOoUtmebdsuLToWAMBzMjMIAOAF8uzMIACAxczMIAAAAABmKYMAAAAAmogyCAAAAKCJKIMAAOYxPDyUcnlLWltbUy5vyfDwUNGRAAAa1lZ0AACAxWx4eCgDAzdmcPDWbN16cfbu3ZdKpS9JbBQDAJYk28QAAOool7dkYODm9PaWZ7eJjY6OpL9/R0ZGxoqOBwAwh9XyAAALsH79mhw8OJ729vbZMqharWbz5q4cOXKs6HgAAHNYLQ8AsADd3T0ZG3vwjHdjYw+mu7unoEQAAAujDAIAqKNS2Z5KpS+joyOpVqsZHR1JpdKXSmV70dEAABrimhgAwDyGh4cyOLgnBw7sT3d3TyqV7YZHAwCLlplBAAAvkGdnBgEALGZmBgEALNDw8FDK5S1pbW1Nubwlw8NDRUcCAGhYW9EBAAAWs+HhoQwM3JjBwVuzdevF2bt3XyqVviRxVQwAWJJcEwMAqKNc3pKBgZvT21uevSY2OjqS/v4dGRkZKzoeAMAcZgYBACzA+vVrcvDgeNrb22fLoGq1ms2bu3LkyLGi4wEAzGFmEADAAnR392Rs7MEz3o2NPZju7p6CEgEALIwyCACgjkpleyqVvoyOjqRarWZ0dCSVSl8qle1FRwMAaIgB0gAAdWzbdmm+/vWxXH75uzI1dSodHcty5ZXvMTwaAFiynAwCAKhjeHgoDzywL3fffW+mpqZy99335oEH9lkvDwAsWQZIAwDUYZsYALDU2CYGALAAtokBAEuNbWIAAAtgmxgA8ONGGQQAUIdtYgDAjxvXxAAA5jE8PJTBwT05cGB/urt7Uqlst00MAFi0zAwCAHiBPDszCABgMTMzCAAAAIBZyiAAAACAJqIMAgCYx/DwUMrlLWltbU25vCXDw0NFRwIAaFhb0QEAABaz4eGhDAzcmMHBW7N168XZu3dfKpW+JDFEGgBYkgyQBgCoo1zekoGBm9PbW54dID06OpL+/h0ZGRkrOh4AwBy2iQEALMD69Wty8OB42tvbZ8ugarWazZu7cuTIsaLjAQDMMV8Z5JoYAEAd3d092bPnP+T++/fmwIH96e7uySWXbE13d0/R0QAAGmKANABAHRde+KZ8/OO/lyuuuDLHjx/PFVdcmY9//Pdy4YVvKjoaAEBDXBMDAKijXN6SSy7ZOudk0P337zUzCABYlMwMAgBYADODAIClZr4yyDUxAIA6urt7Mjb24BnvxsYeNDMIAFiylEEAAHVUKttTqfRldHQk1Wo1o6MjqVT6UqlsLzoaAEBDXBMDAJjH8PBQBgf3zM4MqlS2Z9u2S4uOBQDwnFwTAwAAAGBWW9EBAAAWs+HhoQwM3JjBwVuzdevF2bt3XyqVviRxOggAWJJcEwMAqKNc3pKBgZvT21ue3SY2OjqS/v4dVssDAIuS1fIAAAtgtTwAsNSYGQQAsABWywMAP26UQQAAdVgtDwD8uHFNDABgHlbLAwBLiZlBAAAvkGdnBgEALGZmBgEAAAAwSxkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAA8xgeHkq5vCWtra0pl7dkeHio6EgAAA1rKzoAAMBiNjw8lIGBGzM4eGu2br04e/fuS6XSlyTZtu3SgtMBAPzoSrVarVZ0iKNHT2RmpvAYAABzlMtbMjBwc3p7y+nqWpXx8eMZHR1Jf/+OjIyMFR0PAGCOlpZS1q5d+bzfK4MAAOpYv35NDh4cT3t7+2wZVK1Ws3lzV44cOVZ0PACAOeYrg8wMAgCoo7u7J2NjD57xbmzswXR39xSUCABgYZRBAAB1VCrbU6n0ZXR0JNVqNaOjI6lU+lKpbC86GgBAQ1wTAwCYx/DwUAYH9+TAgf3p7u5JpbLd8GgAYNEyMwgA4AXy7MwgAIDFzMwgAIAFGh4eSrm8Ja2trSmXt2R4eKjoSAAADWsrOgAAwGI2PDyUgYEbMzh4a7ZuvTh79+5LpdKXJK6KAQBLkmtiAAB1lMtbcsklW3P//XtnZwY9+3lkZKzoeAAAc8x3TczJIACAOvbvfzQnT57M4ODv/39OBv1mDh78h6KjAQA0xMwgAIA62ts7ctVVV6e3t5z29vb09pZz1VVXp729o+hoAAANUQYBANRRrU7lD//w9oyOjqRarWZ0dCR/+Ie3p1qdKjoaAEBDlEEAAHX09JyfX/mVy9LfvyOdnZ3p79+RX/mVy9LTc37R0QAAGqIMAgCoo1LZnnvvHcrAwM2ZnJzMwMDNuffeoVQq24uOBgDQENvEAADmMTw8lMHBPbPbxCqV7dbKAwCL1nzbxJRBAABnqatrVcbHjxcdAwCgrvnKINfEAAAAAJqIMggAAACgiSiDAAAAAJqIMggAYB7XXbc9mzZ1pVQqZdOmrlx3nU1iAMDSpQwCAKjjuuu25447PpOdO3dlYmIiO3fuyh13fEYhBAAsWbaJAQDUsWlTV3bu3JVrrumb3SZ222235qabPppDh8aLjgcAMIfV8gAAC7Bu3bn57nePZPny5bNl0MmTJ/PKV67Pk0/+oOh4AABzWC0PALAAHR3Lcuednznj3Z13fiYdHcsKSgQAsDDKIACAOq688j3Zvfv63HbbrTl58mRuu+3W7N59fa688j1FRwMAaIhrYgAA87juuu256647MzV1Kh0dy3Llle/Jxz62p+hYAADPycwgAIAXyLMzgwAAFjMzgwAAFmh4eCjl8pa0tramXN6S4eGhoiMBADSsregAAACL2fDwUAYGbszg4K3ZuvXi7N27L5VKX5Jk27ZLC04HAPCjc00MAKCOcnlLBgZuTm9vefaa2OjoSPr7d2RkZKzoeAAAc5gZBACwAOvXr8nBg+Npb2+fLYOq1Wo2b+7KkSPHio4HADCHmUEAAAvQ3d2TsbEHz3g3NvZgurt7CkoEALAwyiAAgDoqle2pVPoyOjqSarWa0dGRVCp9qVS2Fx0NAKAhBkgDANSxbdul+frXx3L55e/K1NSpdHQsy5VXvsfwaABgyXIyCACgjuHhodx333DOO++8tLS05Lzzzst99w1bLw8ALFnKIACAOnbv/kja2lpzyy2fzOTkZG655ZNpa2vN7t0fKToaAEBDlEEAAHUcPnw4n/jE7entLae9vT29veV84hO35/Dhw0VHAwBoiDIIAAAAoIkogwAA6ti4cWP6+q4+Y5tYX9/V2bhxY9HRAAAaogwCAKjj+utvzDPPnMxll70zHR0dueyyd+aZZ07m+utvLDoaAEBDlEEAAPPo6FiWDRs2pKWlJRs2bEhHx7KiIwEANEwZBABQx+DgnnzqU3fkoYceyfT0dB566JF86lN3ZHBwT9HRAAAaogwCAKjjwIH9efzxwymXt6S1tTXl8pY8/vjhHDiwv+hoAAANKdVqtVrRIY4ePZGZmcJjAADMccEF52d6ejq33fbpbN16cfbu3Zdrrrkqra2t+cY3Hi06HgDAHC0tpaxdu/L5v38RswAALEn/+J/Oiv+nNACAximDAADqOHLkSHbt2p3+/h3p7OxMf/+O7Nq1O0eOHCk6GgBAQ5RBAAB1dHf3ZMOGl2VkZCzT09MZGRnLhg0vS3d3T9HRAAAaogwCAKijUtmeSqUvo6MjqVarGR0dSaXSl0ple9HRAAAaYoA0AMA8Lrvsl/M//sdfpFarpVQq5ed//hdyzz33FR0LAOA5GSANALAA1123PV/5ykhuuOGmTExM5IYbbspXvjKS665zMggAWJqcDAIAqGPTpq68/e2/nEce+WYOHNif7u6e/PRPvy5f/OJ9OXRovOh4AABzOBkEALAAU1On8rWvPZiBgZszOTmZgYGb87WvPZipqVNFRwMAaEhb0QEAABazUqmUlStX5PLL35WpqVPp6FiWV7/61SmVSkVHAwBoiJNBAAB11Gq1PProt/MLv/AvMz4+nl/4hX+ZRx/9dhbBTXsAgIY4GQQAUEepVEpPz/n5i7/483R1daWjY1nOP/812b//0aKjAQA0xMkgAIA6arVannzyyZx33nlpaWnJeeedlyeffNLJIABgyXIyCACgjtbWtkxMnMgPfvCDzMzM5PHHH09ra0taW/2MAgCWJieDAADqWLZsWU6dOpVf+7V359ixY/m1X3t3Tp06lWXLlhUdDQCgIcogAIA6Tp6cyFve8q/yJ3/yx1mzZk3+5E/+OG95y7/KyZMTRUcDAGiIMggAYB5XXfUbOXRoPLVaLYcOjeeqq36j6EgAAA1TBgEA1LFx48b81m9dndHRkVSr1YyOjuS3fuvqbNy4sehoAAANUQYBANRx/fU35vTp6Vx77b9JZ2dnrr323+T06elcf/2NRUcDAGiINRgAAHVs23ZpkmRwcE+SZMWKFdm5c9fsewCApaZUq9VqRYc4evREZmYKjwEAUFdX16qMjx8vOgYAQF0tLaWsXbvy+b9/EbMAAAAAUDBlEADAPIaHh1Iub0lra2vK5S0ZHh4qOhIAQMPMDAIAqGN4eCg7d/77LF++PEkyMTGRnTv/fZKYGwQALElOBgEA1LF790fS1taaW275ZCYnJ3PLLZ9MW1trdu/+SNHRAAAaogwCAKjj8OHDufzyf53+/h3p7OxMf/+OXH75v87hw4eLjgYA0BBlEADAPP7kT/44AwM3Z3JyMgMDN+dP/uSPi44EANAwZRAAQB1tbW05fbp6xrvTp6tpazN6EQBYmkq1Wq1WdIijR09kZqbwGAAAc5x33uq85CVrs3z58hw6dDCbNm3OyZMn89RTR/PEE08XHQ8AYI6WllLWrl35/N+/iFkAAJacnp7z8+53vzcrVqxIqVTKihUr8u53vzc9PecXHQ0AoCHKIACAOiqV7bn33qEzZgbde+9QKpXtRUcDAGiIa2IAAPMYHh7K4OCeHDiwP93dPalUtmfbtkuLjgUA8JzmuyamDAIAOEtdXasyPn686BgAAHWZGQQAAADALGUQAAAAQBNRBgEAAAA0EWUQAMA8hoeHUi5vSWtra8rlLRkeHio6EgBAw9qKDgAAsJgNDw9lYODGDA7emq1bL87evftSqfQliY1iAMCSZJsYAEAd5fKWDAzcnN7e8uw2sdHRkfT378jIyFjR8QAA5rBNDABgAQ4c2J8/+7P7smlTV0qlUjZt6sqf/dl9OXBgf9HRAAAaogwCAKjj3HPPzR/90X/Jzp27MjExkZ07d+WP/ui/5Nxzzy06GgBAQ5RBAAB1nDhxIqtWnZvXvvZ1aW9vz2tf+7qsWnVuTpw4UXQ0AICGKIMAAOo4ffp0du++Kf39O9LZ2Zn+/h3ZvfumnD59uuhoAAANUQYBANTR0bEs3//+sYyMjGV6ejojI2P5/vePpaNjWdHRAAAaYrU8AEAdV175ntxww4fz+78/mO9973t56UtfmvHx7+V973t/0dEAABriZBAAQB2vf/2WrFy5Ik899f3MzMzkqae+n5UrV+T1r99SdDQAgIYogwAA6hgc3JM77/xcDh8+mlqtlsOHj+bOOz+XwcE9RUcDAGhIqVar1YoOcfToiczMFB4DAGCO9evX5ODB8bS3t6era1XGx4+nWq1m8+auHDlyrOh4AABztLSUsnbtyuf//kXMAgCw5HR392Rs7MEz3o2NPZju7p6CEgEALIwyCACgjkpleyqVvoyOjqRarWZ0dCSVSl8qle1FRwMAaIhrYgAA8xgeHsrg4J4cOLA/3d09qVS2Z9u2S4uOBQDwnFwTAwAAAGBWW9EBAAAWs+HhoQwM3JjBwVuzdevF2bt3XyqVviRxOggAWJJcEwMAqKNc3pKBgZvT21ue3SY2OjqS/v4dGRkZKzoeAMAcrokBACzAgQP78/jjj6Vc3pLW1taUy1vy+OOP5cCB/UVHAwBoiJNBAAB1XHDB+Tl9ejp/8Aefnr0m9sEPXpW2ttZ84xuPFh0PAGAOJ4MAABaoVKr/GQBgKVEGAQDUceTIkVx//Y3p79+Rzs7O9PfvyPXX35gjR44UHQ0AoCHKIACAOrq7e7Jhw8aMjIxleno6IyNj2bBhY7q7e4qOBgDQEKvlAQDqqFS25zd+49dzzjnLc+jQwWzatDnPPHMyv/u7/7HoaAAADVEGAQDMY3LyVI4dezq1Wi2PP/54Ojs7i44EANAw18QAAOrYvfsjWbFiee655/OZmprKPfd8PitWLM/u3R8pOhoAQEOUQQAAdRw+fDif+MTt6e0tp729Pb295XziE7fn8OHDRUcDAGiIMggAAACgiZgZBABQx8aNG/P+9787q1evmR0g/fTTx7Jx48aiowEANMTJIACAOi65ZGtOnDiRycnJJMnk5GROnDiRSy7ZWnAyAIDGKIMAAOr4y7/8Sq699t9m7dq1KZVKWbt2ba699t/mL//yK0VHAwBoSKlWq9WKDnH06InMzBQeAwBgjvXr1+TgwfG0t7enq2tVxsePp1qtZvPmrhw5cqzoeAAAc7S0lLJ27crn//5FzAIAsOR0d/dkbOzBM96NjT2Y7u6eghIBACyMMggAoI5KZXsqlb6Mjo6kWq1mdHQklUpfKpXtRUcDAGiIbWIAAHVs23Zpvv71sVx++bsyNXUqHR3LcuWV78m2bZcWHQ0AoCFOBgEA1DE8PJQHHtiXu+++N1NTU7n77nvzwAP7Mjw8VHQ0AICGGCANAFBHubwll1yyNfffvzcHDuxPd3fP7OeRkbGi4wEAzDHfAGnXxAAA6ti//9GcPHkyg4O/n61bL87evftSqfxmDh78h6KjAQA0xDUxAIA62ts7ctVVV6e3t5z29vb09pZz1VVXp729o+hoAAANUQYBANRRrU7lD//w9jO2if3hH96eanWq6GgAAA1RBgEA1NHTc35+5VcuS3//jnR2dqa/f0d+5VcuS0/P+UVHAwBoiDIIAKCOSmV77r13KAMDN2dycjIDAzfn3nuHUqlsLzoaAEBDbBMDAJjH8PBQBgf3zG4Tq1S2Z9u2S4uOBQDwnObbJqYMAgA4S11dqzI+frzoGAAAdc1XBrkmBgAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAAAATUQZBAAAANBElEEAAPMYHh5Kubwlra2tKZe3ZHh4qOhIAAANays6AADAYjY8PJSBgRszOHhrtm69OHv37kul0pck2bbt0oLTAQD86Eq1Wq1WdIijR09kZqbwGAAAc5TLWzIwcHN6e8vp6lqV8fHjGR0dSX//joyMjBUdDwBgjpaWUtauXfn837+IWQAAlpwDB/bn8ccfO+Oa2OOPP5YDB/YXHQ0AoCFOBgEA1HHBBefn9Onp/MEffHr2mtgHP3hV2tpa841vPFp0PACAOZwMAgBYoFKp/mcAgKVEGQQAUMeRI0dy/fU3pr9/Rzo7O9PfvyPXX39jjhw5UnQ0AICGKIMAAOro7u7J3/zNX5/x7m/+5q/T3d1TUCIAgIU5qzLoxIkT2bp1aw4dOjTnu1tvvTW/8Au/kHe84x15xzvekc9+9rMveEgAgKJceOGb8vGP/16uuOLKHD9+PFdccWU+/vHfy4UXvqnoaAAADWmb7y/81V/9VT784Q/nu9/97nN+/8gjj+Q//+f/nJ/5mZ95obMBABTuL//yK7n44rfmpps+ml27+tPRsSwXX/zW/OVffqXoaAAADZn3ZNA999yTXbt2Zd26dc/5/SOPPJLbb789b3vb27J79+6cOnXqBQ8JAFCU/fsfzbe+9XDuvvveTE1N5e677823vvVw9u+3SQwAWJrmLYNuuumm/OzP/uxzfjcxMZHXvOY12bFjRz7/+c/nBz/4QT75yU++4CEBAIrS3t6Rq666Or295bS3t6e3t5yrrro67e0dRUcDAGhIqVar1c7mL/7Lf/kv80d/9EfZtGnT8/6d//W//lf6+/tz3333vVD5AAAK1dLSkle84hX5zGc+k97e3oyOjuZ973tf/v7v/z4zMzNFxwMA+JHNOzOonsOHD+erX/1qfuVXfiVJUqvV0tb2o/8vjx49kZmZs+qkAABeVD095+dVr/qJvPWtl2Rq6lQ6OpblzW++KJ2d52R8/HjR8QAA5mhpKWXt2pXP//1C/uednZ25+eabc/DgwdRqtXz2s5/NRRddtJD/JQDAonLhhW/Kvn1fys6duzIxMZGdO3dl374v2SYGACxZDZVBH/jAB/Lwww/nJS95SXbv3p1rrrkmb33rW1Or1fLe9773hc4IAFCYv/zLr+RDH/rtfO5zd2XVqlX53Ofuyoc+9Nu2iQEAS9ZZ3+n68z//89k/f+pTn5r981ve8pa85S1veWFTAQAsEgcO7M9v/dZvn/HuJ3/yp3LgwP6CEgEALMxZD5D+p2RmEACwWF1wwfmZnp7Obbd9Olu3Xpy9e/flmmuuSmtra77xDevlAYDF5590ZhAAQDP4x/90Vvw/pQEANE4ZBABQx5EjR7Jr1+709+9IZ2dn+vt3ZNeu3Tly5EjR0QAAGqIMAgCoo7u7Jxs2vCwjI2OZnp7OyMhYNmx4Wbq7e4qOBgDQEGUQAEAdlcr2VCp9GR0dSbVazejoSCqVvlQq24uOBgDQEAOkAQDmMTw8lMHBPTlwYH+6u3tSqWzPtm2XFh0LAOA5GSANAAAAwKy2ogMAACxmw8NDGRi4MYODt86ulq9U+pLE6SAAYElyTQwAoI5yeUsGBm5Ob285XV2rMj5+PKOjI+nv35GRkbGi4wEAzDHfNTFlEABAHevXr8nBg+Npb2+fLYOq1Wo2b+7KkSPHio4HADCHmUEAAAvQ3d2TsbEHz3g3Nvag1fIAwJKlDAIAqMNqeQDgx41rYgAA87BaHgBYSswMAgB4gTw7MwgAYDEzMwgAAACAWcogAAAAgCaiDAIAAABoIsogAAAAgCaiDAIAAABoIsogAAAAgCaiDAIAAABoIsogAAAAgCaiDAIAAABoIsogAAAAgCaiDAIAAABoIsogAIB5XHfd9mza1JVSqZRNm7py3XXbi44EANAwZRAAQB3XXbc9d9zxmezcuSsTExPZuXNX7rjjMwohAGDJKtVqtVrRIY4ePZGZmcJjAADMsWlTV3bu3JVrrulLV9eqjI8fz2233ZqbbvpoDh0aLzoeAMAcLS2lrF278nm/VwYBANSxbt25+e53j2T58uWzZdDJkyfzyleuz5NP/qDoeAAAc8xXBrkmBgBQR0fHstx552fOeHfnnZ9JR8eyghIBACyMMggAoI4rr3xPdu++PrfddmtOnjyZ2267Nbt3X58rr3xP0dEAABrimhgAwDyuu2577rrrzkxNnUpHx7JceeV78rGP7Sk6FgDAc3JNDABggV7/+i159atfnZaWlrz61a/O61+/pehIAAANays6AADAYjY8PJSBgRszOHhrtm69OHv37kul0pck2bbt0oLTAQD86FwTAwCoo1zekoGBm9PbW57dJjY6OpL+/h0ZGRkrOh4AwBxWywMALMD69Wty8OB42tvbZ8ugarWazZu7cuTIsaLjAQDMYWYQAMACdHf3ZGzswTPejY09mO7unoISAQAsjDIIAKCOSmV7KpW+jI6OpFqtZnR0JJVKXyqV7UVHAwBoiGtiAADzuOyyX87/+B9/kVqtllKplJ//+V/IPffcV3QsAIDn5JoYAMACXHfd9nzlKyO54YabMjExkRtuuClf+cpIrrvOySAAYGlyMggAoI5Nm7qyc+euXHNN3+wA6dtuuzU33fTRHDo0XnQ8AIA5bBMDAFiAdevOzXe/eyTLly+fLYNOnjyZV75yfZ588gdFxwMAmGO+MqjtRcwCALDkdHQsy/bt1+aRR76ZAwf2p7u7Jz/9069LR8eyoqMBADTEzCAAgDre+MYL86d/+l/zhje8MU899VTe8IY35k//9L/mjW+8sOhoAAANcU0MAKCOcnlLXvWqn8iXv/xApqZOpaNjWd785ovyd3/3txkZGSs6HgDAHK6JAQAswIED+/PlL4+mvb19dmZQtVrN5s1dRUcDAGiIa2IAAHV0d/dkbOzBM96NjT2Y7u6eghIBACyMMggAoI5KZXsqlb6Mjo6kWq1mdHQklUpfKpXtRUcDAGiImUEAAPMYHh7K4OCe2W1ilcr2bNt2adGxAACe03wzg5RBAABn6dmZQQAAi9l8ZZBrYgAAAABNRBkEAAAA0ESUQQAAAABNRBkEADCP4eGhlMtb0tramnJ5S4aHh4qOBADQsLaiAwAALGbDw0PZufPfZ/ny5UmSiYmJ7Nz575PERjEAYElyMggAoI7duz+StrbW3HLLJzM5OZlbbvlk2tpas3v3R4qOBgDQEGUQAEAdhw8fzic+cXt6e8tpb29Pb285n/jE7Tl8+HDR0QAAGqIMAgAAAGgiyiAAgDo2btyYvr6rMzo6kmq1mtHRkfT1XZ2NGzcWHQ0AoCHKIACAOq6//sbMzEzn2mv/TZYtW5Zrr/03mZmZzvXX31h0NACAhtgmBgBQx7MbwwYH96RUKmXFihXZuXOXTWIAwJLlZBAAwDy+/vWxfOc738nMzEy+853v5OtfHys6EgBAw5RBAAB1XHfd9txxx2eyc+euTExMZOfOXbnjjs/kuuu2Fx0NAKAhpVqtVis6xNGjJzIzU3gMAIA5Nm3qytvf/st55JFv5sCB/enu7slP//Tr8sUv3pdDh8aLjgcAMEdLSylr1658/u9fxCwAAEvO1NSpfO1rD2Zg4OZMTk5mYODmfO1rD2Zq6lTR0QAAGqIMAgCoo1Qq5Rd/8aL09pbT3t6e3t5yfvEXL0qpVCo6GgBAQ5RBAAB11Gq13HXXnbnttltz8uTJ3HbbrbnrrjuzCG7aAwA0xMwgAIA6yuUtWbasM9/85jdSq9VSKpXyutddkFOnJjMyYqsYALD4mBkEALAAF174pjzyyMO54YabMjExkRtuuCmPPPJwLrzwTUVHAwBoiJNBAAB1lMtbcsklW3P//Xtnt4k9+9nJIABgMZrvZJAyCACgjvXr1+TgwfG0t7enq2tVxsePp1qtZvPmrhw5cqzoeAAAc7gmBgCwAN3dPRkbe/CMd2NjD6a7u6egRAAAC6MMAgCoo1LZnkqlL6OjI6lWqxkdHUml0pdKZXvR0QAAGuKaGADAPIaHhzI4uGd2ZlClsj3btl1adCwAgOfkmhgAAAAAs9qKDgAAsJgNDw9lYODGDA7emq1bL87evftSqfQlidNBAMCS5JoYAEAd5fKWDAzcnN7e8uw2sdHRkfT377BaHgBYlFwTAwBYgAMH9ufxxx9Lubwlra2tKZe35PHHH8uBA/uLjgYA0BAngwAA6rjggvNz4sSJrF69JocOHcymTZvz9NPHsnLlynzjG48WHQ8AYA4ngwAAFuDkyZM5cWIi73//B3PixIn//ZzIyZMni44GANAQZRAAQB3Hjh1LX9+1+dzn7sqqVavyuc/dlb6+a3Ps2LGiowEANEQZBAAwj97eckZGxjI9PZ2RkbH09paLjgQA0DCr5QEA6ti4cWP+9b++NNVqdfZde3t7Nm7cWGAqAIDGORkEAFDH6tVrUq1Ws2LFiiTJihUrUq1Ws3r1mmKDAQA0SBkEAFDHo49+O+Xy/5XNm1+elpaWbN788pTL/1ceffTbRUcDAGiIa2IAAHXUarV8+tN3ZfXq1enqWpXx8eN5+umn81M/tbnoaAAADVEGAQDUUSqVctVVV+bJJ5/IgQP7093dk3XrzkupVCo6GgBAQ1wTAwCo4/zzX5ORkf8nr3jFK/PEE0/kFa94ZUZG/p+cf/5rio4GANAQJ4MAAOqo1Wr5F//iZ7Jv35fS1dWVUqmUf/EvfianTk0WHQ0AoCHKIACAOg4c2J+DB8fT3t4+OzOoWq1m8+auoqMBADREGQQAUEd3d0/27PkPuf/+vbMzgy65ZGu6u3uKjgYA0BAzgwAA6rjwwjfl4x//vVxxxZU5fvx4rrjiynz847+XCy98U9HRAAAaUqrVarWiQxw9eiIzM4XHAACYo1zekmXLOvPNb34jtVotpVIpr3vdBTl1ajIjI2NFxwMAmKOlpZS1a1c+//cvYhYAgCVn//5H8/DD38wNN9yUiYmJ3HDDTXn44W9m//5Hi44GANAQZRAAwDze/e5fzzXX9GX58uW55pq+vPvdv150JACAhimDAADqqNVq+fKXH8jo6Eiq1WpGR0fy5S8/kEVw0x4AoCG2iQEA1NHRsSwbNmzM5Ze/K1NTp9LRsSwXXPAzeeKJJ4qOBgDQECeDAADqeOMbL8z//J9fy/Ll56RUKmX58nPyP//n1/LGN15YdDQAgIYogwAA6jhw4NGcc845OXFiIrVaLSdOTOScc87JgQMGSAMAS5MyCACgjsOHD+cDH7gmP/mTP5mWlpb85E/+ZD7wgWty+PDhoqMBADREGQQAMI+77/5sBgZuzuTkZAYGbs7dd3+26EgAAA1TBgEA1NHW1papqakz3k1NTaWtzR4OAGBpKtUWwV7Uo0dPZGam8BgAAHOcd97qvOQlL8ny5Sty6NDBbNq0OSdPTuSpp57KE088XXQ8AIA5WlpKWbt25fN//yJmAQBYcnp6zs/P/dz/mSeeeCK1Wi1PPPFEfu7n/s/09JxfdDQAgIYogwAA6rjwwjdl374vZefOXZmYmMjOnbuyb9+XcuGFbyo6GgBAQ1wTAwCoo1zekksu2Zr779+bAwf2p7u7Z/bzyMhY0fEAAOaY75qYMggAoI7169fk4MHxtLe3p6trVcbHj6darWbz5q4cOXKs6HgAAHPMVwZZgwEAUEd3d0/27PkPc04GdXf3FB0NAKAhyiAAgDouvPBNGRzck1KpJTMzMzlw4ED279+T973vA0VHAwBoiAHSAAB13HvvUJJk7dq1ZzyffQ8AsNQogwAA6jh27Pv5yEd251vf+pvUarV861t/k498ZHeOHft+0dEAABqiDAIAmMf557+m7mcAgKVEGQQAUEdra1t+8zc/kNHRkVSr1YyOjuQ3f/MDaW01ehEAWJqUQQAAdfz6r78vx449nQ9+8H3p7OzMBz/4w8+//uvvKzoaAEBD/JMWAEAdH/vYniTJXXfdmZmZmRw79nTe9773z74HAFhqSrVarVZ0iKNHT2RmpvAYAAB1dXWtyvj48aJjAADU1dJSytq1K5//+xcxCwDAkjQ8PJRyeUtaW1tTLm/J8LC18gDA0uWaGABAHcPDQxkYuDGDg7dm69aLs3fvvlQqfUmSbdsuLTgdAMCPzjUxAIA6yuUtGRi4Ob295dlrYqOjI+nv35GRkbGi4wEAzDHfNTFlEABAHevXr8nBg+Npb2+fLYOq1Wo2b+7KkSPHio4HADCHmUEAAAvQ3d2TsbEHz3g3NvZgurt7CkoEALAwZgYBANRRqWzPb/zGr+ecc5bn0KGD2bRpc5555mR+93f/Y9HRAAAa4mQQAMA8nr1UXyqVzvgMALAUKYMAAOoYHNyTT33qjjz00COZnp7OQw89kk996o4MDu4pOhoAQEOUQQAAdRw4sD9/9mf3ZdOmrpRKpWza1JU/+7P7cuDA/qKjAQA0RBkEAFDHueeuzp13fiY7d+7KxMREdu7clTvv/EzOPXd10dEAABqiDAIAqOP48eNZvXp1Xvva16W9vT2vfe3rsnr16hw/frzoaAAADVEGAQDUMT19Om9/+y/n8svflY6Ojlx++bvy9rf/cqanTxcdDQCgIcogAIA62tra8oUv3Je77743U1NTufvue/OFL9yXtra2oqMBADTErxgAgDpWrlyZp58+lg9+8H353ve+l5e+9KV5+uljWb3azCAAYGlyMggAoI6nn346K1aszFNPfT8zMzN56qnvZ8WKlXn66aeLjgYA0BBlEABAHe3tHdmx47ocPnw0tVothw8fzY4d16W9vaPoaAAADVEGAQDUUa1O5Q//8PaMjo6kWq1mdHQkf/iHt6danSo6GgBAQ8wMAgCoo6fn/LzqVT+Ryy9/V6amTqWjY1ne/OaLsmLF8qKjAQA0xMkgAIA6LrzwTdm370vZuXNXJiYmsnPnruzb96VceOGbio4GANCQUq1WqxUd4ujRE5mZKTwGAMAc5fKWLFvWmW9+8xup1WoplUp53esuyKlTkxkZGSs6HgDAHC0tpaxdu/J5v3dNDACgjkcf/XZaWlry0pd25XvfG89LX9qVhx/+q8zMzBQdDQCgIa6JAQDMY/ny5bn99s/k1KlTuf32z2T5cvOCAIClSxkEADCPzs7ldT8DACwlyiAAgHm86U3l9PfvSGdnZ/r7d+RNbyoXHQkAoGHKIACAOtasWZMvfOHzueKKK3P8+PFcccWV+cIXPp81a9YUHQ0AoCG2iQEA1DE8PJRK5TczOTk5+66zszODg7+fbdsuLTAZAMBzm2+bmJNBAADzWLFiZTZvfnlaWlqyefPLs2LF8/+4AgBY7JRBAAB1DA7uyac+dUceeuiRTE9P56GHHsmnPnVHBgf3FB0NAKAhyiAAgDoOHNifxx8/nHJ5S1pbW1Mub8njjx/OgQP7i44GANAQM4MAAOq44ILzMz09ndtu+3S2br04e/fuyzXXXJXW1tZ84xuPFh0PAGAOM4MAABboH//TWfH/lAYA0Li2ogMAACxmR44cSW9vOe9619tSq9VSKpXypjf9fEZHR4qOBgDQECeDAADqOPfc1RkdHckNN9yUiYmJ3HDDTRkdHcm5564uOhoAQEPMDAIAqGPDhpekra0109MzOX26mra29rS2tuT06ek8/vhTRccDAJhjvplBrokBANQxPX06MzPTaWn54YHqWm0mU1Onswj+PQ0AoCGuiQEAzGPZsmUZGvpCpqamMjT0hSxbtqzoSAAADVMGAQDMY3JyMg8//M1Uq9U8/PA3Mzk5WXQkAICGmRkEAFDHunXn5tWv/on83d99Z3ab2Kte9ep85zt/myef/EHR8QAA5phvZpCTQQAAdSxfvjzf+c7fzm4PO/fc1fnOd/42y5cvLzgZAEBjlEEAAHV0dPxwPtCJE8fPeD77HgBgqVEGAQDUcezY9/OhD/3f+amf6k5LS0t+6qe686EP/d85duz7RUcDAGiIMggAYB69veWMjIxleno6IyNj6e0tFx0JAKBhyiAAgDo2btyY3/qtqzM6OpJqtZrR0ZH81m9dnY0bNxYdDQCgIcogAIA6rr/+xkxMnMxll70zHR0dueyyd2Zi4mSuv/7GoqMBADREGQQAMI/OzmXZsGFDSqVSNmzYkM5Ow6MBgKVLGQQAUMfg4J5ceeV7s2LFipRKpaxYsSJXXvneDA7uKToaAEBD2ooOAACwmO3f/2gmJk7mllt+P1u3Xpy9e/fl2mt/M4cO/UPR0QAAGuJkEABAHe3tHXn/+69Ob2857e3t6e0t5/3vvzrt7R1FRwMAaIgyCACgjmp1Kp/+9O1nbBP79KdvT7U6VXQ0AICGKIMAAOro6Tk/73rXZenv35HOzs709+/Iu951WXp6zi86GgBAQ5RBAAB1VCrbc++9QxkYuDmTk5MZGLg59947lEple9HRAAAaUqrVarWiQxw9eiIzM4XHAAB4TsPDQxkc3JMDB/anu7snlcr2bNt2adGxAACeU0tLKWvXrnz+71/ELAAAS9LXvz6W73znO5mZmcl3vvOdfP3rY0VHAgBomDIIAKCO667bnjvu+Ex27tyViYmJ7Ny5K3fc8Zlcd51rYgDA0uSaGABAHZs2dWXnzl255pq+dHWtyvj48dx226256aaP5tCh8aLjAQDM4ZoYAMACTE2dyne+89fZtKkrpVIpmzZ15Tvf+etMTZ0qOhoAQEOUQQAAdbS0tOSuu+4845rYXXfdmZYWP6MAgKXJrxgAgHn840v1xV+yBwBonDIIAKCOmZmZvPvd78lNN300K1asyE03fTTvfvd7MjMzU3Q0AICGKIMAAOro6FiWiYmTefWrX52Wlpa8+tWvzsTEyXR0LCs6GgBAQ5RBAAB1vPGNF+ZP//S/5g1veGOeeuqpvOENb8yf/ul/zRvfeGHR0QAAGmK1PABAHeXylrzqVT+RL3/5gUxNnUpHx7K8+c0X5e/+7m8zMjJWdDwAgDmslgcAWIADB/Zn69a3nXFNbOvWt+XAgf1FRwMAaIiTQQAAdVxwwfk5fXo6f/AHn87WrRdn7959+eAHr0pbW2u+8Y1Hi44HADCHk0EAAAtUKtX/DACwlCiDAADqOHLkSK6//sb09+9IZ2dn+vt35Prrb8yRI0eKjgYA0BBlEABAHd3dPdmwYWNGRsYyPT2dkZGxbNiwMd3dPUVHAwBoiDIIAKCOSmV7KpW+jI6OpFqtZnR0JJVKXyqV7UVHAwBoiAHSAADzGB4eyuDgnhw4sD/d3T2pVLZn27ZLi44FAPCc5hsgrQwCADhLXV2rMj5+vOgYAAB12SYGALBAw8NDKZe3pLW1NeXylgwPDxUdCQCgYW1FBwAAWMyGh4eyc+e/z/Lly5MkExMT2bnz3yeJq2IAwJLkZBAAQB27d38kbW2tueWWT2ZycjK33PLJtLW1ZvfujxQdDQCgIcogAIA6Dh8+nE984vb09pbT3t6e3t5yPvGJ23P48OGiowEANEQZBAAAANBEzAwCAKhj48aNef/7353Vq9fksccO5WUv25Snnz6WjRs3Fh0NAKAhTgYBANRxySVbc/z48Tz22GOZmZnJY489luPHj+eSS7YWHQ0AoCHKIACAOu6/f286OzvT0vLDn00tLS3p7OzM/ffvLTgZAEBjlEEAAHUcPnw4K1euyj33fD5TU1O5557PZ+XKVQZIAwBLljIIAGAe11zzW2dsE7vmmt8qOhIAQMNKtVqtVnSIo0dPZGam8BgAAHOsW3duzj13dVavXp1Dhw5m06bNefrpp/ODHzydJ5/8QdHxAADmaGkpZe3alc/7vW1iAAB1rFmzJseOHcvExInUarUcPvxYpqens2bNmqKjAQA0xDUxAIC6SkmS6enpM57PvgcAWGqUQQAAdRw79v0f6T0AwGKnDAIAmEepVMpHPzqQiYmJfPSjAymVnAoCAJYuZRAAwDyWL1+e1772dWlvb89rX/u6LF++vOhIAAANs00MAKCOdevOTWfnOenq6spjjx3Ky162KePj45mcfMY2MQBgUZpvm5iTQQAAdbS0tGRy8plMTk5mZmYmk5OTmZx8Ji0tfkYBAEuTXzEAAHW8973vT6lUytGjR5MkR48eTalUynvf+/6CkwEANKat6AAAAIvZxz62J0ly1113ZmpqOm1tbbnyyvfMvgcAWGqcDAIAmMfrX78lr371q9PS0pJXv/rVef3rtxQdCQCgYU4GAQDUMTw8lIGBGzM4eGu2br04e/fuS6XSlyTZtu3SgtMBAPzobBMDAKijXN6SgYGb09tbTlfXqoyPH8/o6Ej6+3dkZGSs6HgAAHPMt01MGQQAUMf69Wty8OB42tvbZ8ugarWazZu7cuTIsaLjAQDMMV8Z5JoYAEAd3d09ef/735Mvf/mBTE2dSkfHsrz5zRelu7un6GgAAA05qwHSJ06cyNatW3Po0KE5333729/Otm3b8pa3vCU7d+7M6dOnX/CQAABFWb9+Q+6/f29+9Vd/LceOHcuv/uqv5f7792b9+g1FRwMAaMi8ZdBf/dVf5Yorrsh3v/vd5/x+x44duf766/Pf/tt/S61Wyz333PNCZwQAKMxXv/qX+bmfe0P+5E/+OGvWrMmf/Mkf5+d+7g356lf/suhoAAANmbcMuueee7Jr166sW7duznePPfZYJicnc8EFFyRJtm3bli996UsveEgAgKJMTZ3K448fzt1335upqancffe9efzxw5maOlV0NACAhsxbBt1000352Z/92ef87sknn0xXV9fs566urjzxxBMvXDoAgIKVSqW8+c0Xpbe3nPb29vT2lvPmN1+UUqlUdDQAgIYsaID0zMzMGT+EarVaQz+M6k24BgAoUq1Wyx//8Z157Wv/WT74wQ/mrrs+lT/+4ztTq9XS1bWq6HgAAD+yBZVB69evz/j4+Ozn733ve895nWw+VssDAIvV+ee/Jq961U/kuuv682//7b9NR8eyXHTRW/N3f/e3GR8/XnQ8AIA55lstf1bbxJ7Py172sixbtiwPPfRQkuQLX/hCyuXyQv6XAACLSqWyPV//+tdy3nnnpVQq5bzzzsvXv/61VCrbi44GANCQhsqgD3zgA3n44YeTJHv27MnHPvaxvPWtb83Jkyfz7ne/+wUNCABQtMnJU3n88cdTq9Xy+OOPZ3LS8GgAYOkq1Wq1wu9nuSYGACxWF1xwfqanp3PbbZ/O1q0XZ+/efbnmmqvS2tqab3zj0aLjAQDM8U96TQwA4Mfd4cOH84lP3H7GNrFPfOL2HD58uOhoAAANUQYBAAAANBFlEABAHRs3bkxf39UZHR1JtVrN6OhI+vquzsaNG4uOBgDQEGUQAEAd119/Y2ZmpnPttf8my5Yty7XX/pvMzEzn+utvLDoaAEBD2ooOAACwmG3bdmmSZHBwT0qlUlasWJGdO3fNvgcAWGpsEwMAOEtdXasyPn686BgAAHXZJgYAsEDXXbc9mzZ1pVQqZdOmrlx33faiIwEANEwZBABQx3XXbc8dd3wmO3fuysTERHbu3JU77viMQggAWLJcEwMAqGPTpq68/e2/nEce+WYOHNif7u6e/PRPvy5f/OJ9OXRovOh4AABzuCYGALAAU1On8rWvPZiBgZszOTmZgYGb87WvPZipqVNFRwMAaIgyCACgjlKplF/8xYvS21tOe3t7envL+cVfvCilUqnoaAAADVEGAQDUUavVctddd+a2227NyZMnc9ttt+auu+7MIrhpDwDQEDODAADqKJe35FWv+ol8+csPZGrqVDo6luXNb74of/d3f5uRkbGi4wEAzGFmEADAAlQq2/Otbz2Su+++N1NTU7n77nvzrW89kkrFNjEAYGlyMggAYB7Dw0MZHNwzu02sUtmebdsuLToWAMBzmu9kkDIIAOAsdXWtyvj48aJjAADU5ZoYAAAAALOUQQAAAABNRBkEADCP667bnk2bulIqlbJpU1euu87waABg6VIGAQDUcd1123PHHZ/Jzp27MjExkZ07d+WOOz6jEAIAliwDpAEA6ti0qStvf/sv55FHvjm7Teynf/p1+eIX78uhQ+NFxwMAmGO+AdJtL2IWAIAlZ2rqVP7iL/57li9fkVqtlomJifzFX/z3TE2dKjoaAEBDXBMDAJjH5OSp3HLLJ3Pq1A+fk5OKIABg6XJNDACgjnXrzk2SdHWty/j4k7PPJHnyyR8UGQ0A4DnNd03MySAAgHl0dp6T73//+0mS73//++nsPKfgRAAAjVMGAQDU0dralnPO6cw993w+U1NTueeez+ecczrT2mr0IgCwNPkVAwBQx8zMdKrV07nssnfm9Olq2tra09nZmZmZ6aKjAQA0xMkgAIA6NmzYkOnp02e8m54+nQ0bNhSUCABgYZRBAAB1nDx5MlNTU/nIRz6aiYmJfOQjH83U1FROnjxZdDQAgIYogwAA6jh27Fj6+q7N5z53V1atWpXPfe6u9PVdm2PHjhUdDQCgIcogAIB5XHhhOSMjY5mens7IyFguvLBcdCQAgIYpgwAA6ti4cWP6+q7O6OhIqtVqRkdH0td3dTZu3Fh0NACAhiiDAADquP76G/PMMydz2WXvTEdHRy677J155pmTuf76G4uOBgDQEGUQAMA8OjqWZcOGDWlpacmGDRvS0bGs6EgAAA1TBgEA1DE4uCef+tQdeeihRzI9PZ2HHnokn/rUHRkc3FN0NACAhpRqtVqt6BBHj57IzEzhMQAA5li/fk0OHhxPe3t7urpWZXz8eKrVajZv7sqRI8eKjgcAMEdLSylr1658/u9fxCwAAEtOd3dPxsYePOPd2NiD6e7uKSgRAMDCtBUdAABgMatUtuc3fuPXc845y3Po0MFs2rQ5zzxzMr/7u/+x6GgAAA1RBgEAzGNy8lSOHXs6tVotjz/+eDo7O4uOBADQMDODAADquOCC83PixImsXr0mjz12KC972aY8/fSxrFy5Mt/4xqNFxwMAmMPMIACABTh8+HA6O8/JLbd8MpOTk7nllk+ms/OcHD58uOhoAAANUQYBAMzjgx/sS29vOe3t7entLeeDH+wrOhIAQMNcEwMAqGPdunOzatW5WbPm/39N7NixYzl+/Ad58skfFB0PAGCO+a6JGSANAFDHmjVrZsufJDl48B9m3wMALEWuiQEA1HHy5DM/0nsAgMVOGQQAUMfU1Kkkybp169LS0pJ169ad8R4AYKlRBgEAzOOKK67MI4/8Taanp/PII3+TK664suhIAAANM0AaAKCOdevOTVtbe5Lk9OnqGX82QBoAWIwMkAYAWKDTp6vP+WcAgKXINTEAAACAJqIMAgCYR6lUqvsZAGApUQYBAMyjs/OcDA/vzdTUVIaH96az85yiIwEANMwAaQCAOtatOzdJ0tW1Lt/73nhe+tKujI8/mSQGSAMAi5IB0gAAL4BnC6BnnwAAS5VrYgAAdZRKz/1z6fneAwAsdn7FAADUUavN/EjvAQAWO2UQAMBZWL16zRlPAIClShkEADCPlpaWTExMJEkmJibS0uInFACwdBkgDQAwj5mZmf/Pn6fP+AwAsNT4Zy0AgLOwdu1Lz3gCACxVyiAAgLNgtTwA8ONCGQQAAADQRJRBAABnYcWKFWc8AQCWKmUQAMBZmJycPOMJALBUKYMAAOaxfv3G2Q1iMzMzWb9+Y8GJAAAapwwCAJjHkSOHs3z58iTJ8uXLc+TI4YITAQA0ThkEAHAWnnnmmTOeAABLlTIIAGAe7e3tZ1wTa29vLzgRAEDjlEEAAPNoa2vL5s0vT0tLSzZvfnna2tqKjgQA0DBlEABAHS0tLTl16lTe//4P5vjx43n/+z+YU6dOpaXFzygAYGkq1Wq1WtEhjh49kZmZwmMAAMxx3nmr09bWlmq1Ovuuvb09p0+fzhNPPF1gMgCA59bSUsratSuf//sXMQsAwJKzevXqnD59Ol1d61IqldLVtS6nT5/O6tWri44GANAQZRAAQB0nTpzIOeeck87OzpRKpXR2duacc87JiRMnio4GANAQZRAAQB2nT5/O9PR0Dh78h8zMzOTgwX/I9PR0Tp8+XXQ0AICGKIMAAObxw4HRrUmSlpbWnDp1quBEAACNUwYBAJyFiy66OOPj47nooouLjgIAsCC2iQEA1LFu3blZvXpNnn762Oy7Zz8/+eQPigsGAPA8bBMDAFigp58+llLphz+bSqWWM4ohAIClRhkEAHAWVqxYnpaWlqxYsbzoKAAAC9JWdAAAgKXg2VXyVsoDAEudk0EAAPN49orY830GAFhK/JIBAJhHrTaTt7zlkoyPj+ctb7kktdpM0ZEAABpmmxgAQB3r1p37vN/ZJgYALEa2iQEAAAAwSxkEADCPUqmUj350IBMTE/noRwdSKpWKjgQA0DDXxAAA6nj2mlhra2ump6dnn4lrYgDA4uSaGADAArW1tc0WQNPT02lrays4EQBA45RBAAB1lEqlnD59Oi0tP/zZ1NLSktOnT7sqBgAsWcogAIA6nr1RPzMzc8ZzEdy0BwBoiDIIAGAeHR3Lsnnzy9PS0pLNm1+ejo5lRUcCAGiYMggAYB7nnHNObrnlk5mcnMwtt3wy55xzTtGRAAAaZpsYAEAdz24Tey62iQEAi5FtYgAAC/DsoOj29vYzngZIAwBLlTIIAKCOWq2WUqmUarWaJKlWqymVSgZIAwBLljIIAGAey5cvT1vbD08EtbW1Z/ny5QUnAgBonDIIAGAek5OT2bBhQ1paWrJhw4ZMTk4WHQkAoGHKIACAeUxPT+e889bn0KFDOe+89Zmeni46EgBAw9qKDgAAsNh1dp6T//f//Z/ZuHHj7OfJyWcKTgUA0BgngwAA5jE5+UxaWn74s6mlpUURBAAsacogAICzMDMzc8YTAGCpUgYBAAAANBFlEAAAAEATUQYBAJyFzs7OlEqldHZ2Fh0FAGBBbBMDADgLk5OTZzwBAJYqJ4MAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAM5CqdRyxhMAYKnyawYA4Cx0db00LS0t6ep6adFRAAAWpK3oAAAAS8GTTz55xhMAYKlyMggAAACgiSiDAAAAAJqIMggAAACgiSiDAADOQqlUOuMJALBUKYMAAM5CrVY74wkAsFQpgwAAAACaiDIIAOAsdHWtS6lUSlfXuqKjAAAsiDIIAOAsHD36vdRqtRw9+r2iowAALIgyCADgLMzMzJzxBABYqpRBAAB1PN/2MFvFAIClShkEAFDH820Ps1UMAFiqlEEAAAAATUQZBAAAANBElEEAAGehpaXljCcAwFLl1wwAwFmwTQwA+HGhDAIAAABoIsogAAAAgCaiDAIAAABoIsogAAAAgCaiDAIAOAtr1qxJS0tL1qxZU3QUAIAFaSs6AADAYlcqlXLs2LEkybFjx1IqlVKr1YoNBQDQICeDAADmUavVsnr1miTJ6tVrFEEAwJKmDAIAqKNUKv3v5w///L8/zr4HAFhqlEEAAHXUarWcf/5rcuzYsdRqtRw7diznn/8ap4MAgCVLGQQAUEdbW1v+4R/+Pps3vzwtLS3ZvPnl+Yd/+Pu0tRm9CAAsTX7FAADU0dHRkZMnT6a9/QeZmZnJD37wg5w8eTLLly8vOhoAQEOcDAIAqOOHRVB7nn76WJLk6aePpb29PSdPniw2GABAg5RBAADzaGtrO+OamCtiAMBSpgwCAJjHM888k1OnJpMkp05N5plnnik4EQBA4/yzFgDAWXjyySfPeAIALFVOBgEAAAA0EWUQAMBZKJVazngCACxVfs0AAJyFUql0xhMAYKlSBgEAnIWZmekzngAAS5UyCADgLKxcuTItLS1ZuXJl0VEAABbENjEAgLNw4sSJM54AAEuVk0EAAAAATUQZBAAAANBElEEAAGfBNjEA4MeFMggAYB6lUimtrT8ctdja2qYQAgCWNGUQAMA8arVaVq5ckVKplJUrV6RWqxUdCQCgYbaJAQCchWPHjp3xBABYqpwMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAgLPQ3t5+xhMAYKlSBgEAnIXp6ekzngAAS5UyCADgLJRKpTOeAABLlTIIAOAsOBkEAPy4UAYBAAAANBFlEAAAAEATUQYBAMyjs7MzbW0/3CLW1taezs7OghMBADROGQQAcBY2bNiQUqmUDRs2FB0FAGBB2ooOAACwmJVKpUxOTubgwX9IktmnrWIAwFLlZBAAQB21Wu1Heg8AsNgpgwAAAACaiDIIAOAsrFmzJqVSKWvWrCk6CgDAgpgZBABwFo4dO3bGEwBgqXIyCADgLLS0tJzxBABYqvyaAQA4CzMzM2c8AQCWKmUQAAAAQBNRBgEAnIVSqeWMJwDAUuXXDADAWajVZs54AgAsVcogAAAAgCaiDAIAAABoIsogAICzYLU8APDjwq8ZAICzYLU8APDjQhkEAHAWVq5cmZaWlqxcubLoKAAAC9JWdAAAgKXgxIkTZzwBAJYqJ4MAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAM5K6R89AQCWJmUQAMBZ6OxcdsYTAGCpUgYBAJyFycnJM54AAEuVMggAAACgiSiDAAAAAJqIMggAAACgiSiDAADm8YpXvDLnn/+atLS05PzzX5NXvOKVRUcCAGhYW9EBAAAWu4MHD6alpSUzMzP5m7/5m8zMzBQdCQCgYWd1MujP/uzP8q/+1b/KxRdfnM9+9rNzvr/11lvzC7/wC3nHO96Rd7zjHc/5dwAAlqqZmemcPl1Nkpw+Xc3MzHTBiQAAGjfvyaAnnngiv/d7v5fh4eF0dHTk8ssvz5YtW/KTP/mTs3/nkUceyX/+z/85P/MzP/NPGhYAAACAhZn3ZNBXv/rVvOENb8iaNWuyfPnyvOUtb8mXvvSlM/7OI488kttvvz1ve9vbsnv37pw6deqfLDAAAAAAjZv3ZNCTTz6Zrq6u2c/r1q3LN7/5zdnPExMTec1rXpMdO3bkFa94RX7nd34nn/zkJ/Pbv/3bZx1i7dqVP2JsAIDidXWtKjoCAMCPbN4yaGZmJqVSafZzrVY74/OKFSvyqU99avbz+973vvT39/9IZdDRoycyM1M7678PAPBia29vT7VanX0myfj48YJTAQDM1dJSqnvwZt5rYuvXr8/4+Pjs5/Hx8axbt2728+HDh/Onf/qns59rtVra2iwpAwB+vDxbAD37BABYquYtg974xjfmwQcfzFNPPZVnnnkm+/btS7lcnv2+s7MzN998cw4ePJharZbPfvazueiii/5JQwMAAADQmHnLoPPOOy+//du/nXe/+9355V/+5WzdujWve93r8oEPfCAPP/xwXvKSl2T37t255ppr8ta3vjW1Wi3vfe97X4zsAAAAAPyISrVarfBhPWYGAQCL1bp15z7vd08++YMXMQkAwNlZ8MwgAAAAAH58KIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCAAAAKCJKIMAAAAAmogyCADgLKxYseKMJwDAUqUMAgCYR6lUysTERJJkYmIipVKp4EQAAI1TBgEAzKNWq9X9DACwlCiDAAAAAJqIMggAAACgiSiDAADOQqnUcsYTAGCp8msGAOAs1GozZzwBAJYqZRAAAABAE1EGAQAAADQRZRAAAABAE1EGAQAAADQRZRAAAABAE1EGAQAAADQRZRAAAABAE1EGAQAAADQRZRAAAABAE1EGAQAAADQRZRAAAABAE1EGAQCcldI/egIALE3KIACAs1L7R08AgKVJGQQAAADQRJRBAAAAAE1EGQQAAADQRJRBAAAAAE1EGQQAcBZWrFiRUqmUFStWFB0FAGBB2ooOAACwFExMTJzxBABYqpwMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgAAAGgiyiAAAACAJqIMAgA4Cy0tLWc8AQCWKr9mAADOwszMzBlPAIClShkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQAAAABNRBkEAAAA0ESUQQDA/6+9ew/Rqt73OP4ZNSM8RbabsYhuSGR0oSCzIuymTeqM2uSFLhqVSlQ76H6DbhRRRlFDVBYbgihLtqgz5Y1CSLIpu5CSwW531VKnrM7xVjbznD82DGdOY2NljfV7vf5bs5611nf5/PPwZv2WAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABRGDAAAAAAoiBgEAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICCiEEAAAAABenT0wMAAJx7bn3+/e9/9fQYv9ixxw7q6RG6NHDgYfnnP5t6egwAYBdVValUKt19qKmpKY899lh+/PHHXHTRRbngggs67V+1alVuvfXWbNq0Kccff3zuvPPO9Omz453p6683pr292zEAAP5wNTV7bXff+vX//QdOAgCwY3r1qsrf/vZf29/f3QnWrVuXhx56KM8++2zmzJmT559/Ph9++GGnz1x//fW57bbbsnDhwlQqlbzwwgu/fXIAAAAAdrpuH9957bXXcuKJJ2bvvfdOktTW1mbBggW58sorkyRr1qzJ1q1bc+yxxyZJGhoa8sgjj+T888//3YYGAHbM3/8+NZs2berpMf7U6urq0tzc3OXfL7nE753fol+/fmlsfLKnxwCA4nQbg9avX5/q6uqO7Zqamrz33nvb3V9dXZ1169bt5DEBgF9j06ZN+cc/nu3pMf4yqqv3TGvr//T0GH8ZYhoA9IxuY1B7e3uqqqo6tiuVSqft7vbviJ9bxwYA/HoNDQ157bVXenoM6NK4ceNSXb1nT48BAMXpNgbtt99+Wb58ecd2a2trampqOu1vbW3t2P7qq6867d8RXiANAL+PurpxPT3CX4ong3Y+/54AsPP95hdIn3zyyVm2bFk2bNiQLVu2ZNGiRRk6dGjH/gMOOCC777573nrrrSTJ3LlzO+0HAAAAYNfRbQwaMGBArr766kyePDljx45NXV1djjnmmEydOjUrVqxIkjzwwAO59957c/bZZ2fz5s2ZPHny7z44AAAAAL9cVaVS6fH1WZaJAQB/BpaJAQB/Br95mRgAAAAAfx1iEAAAAEBBxCAAAACAgohBAAAAAAURgwAAAAAKIgYBAAAAFEQMAgAAACiIGAQAAABQEDEIAAAAoCBiEAAAAEBBxCAAAACAgohBAAAAAAURgwAAAAAKIgYBAAAAFEQMAgAAACiIGAQAAABQEDEIAAAAoCBiEAAAAEBBxCAAAACAgohBAAAAAAURgwAAAAAKIgYBAAAAFEQMAgAAACiIGAQAAABQEDEIAAAAoCBiEAAAAEBBxCAAAACAgohBAAAAAAURgwAAAAAKIgYBAAAAFEQMAgAAACiIGAQAAABQEDEIAAAAoCBiEAAAAEBBxCAAAACAgohBAAAAAAURgwAAAAAKIgYBAAAAFEQMAgAAACiIGAQAAABQEDEIAAAAoCBiEAAAAEBBxCAAAACAgvTp6QGSpFevqp4eAQBgh/jdAgDs6rr7vVJVqVQqf9AsAAAAAPQwy8QAAAAACiIGAQAAABREDAIAAAAoiBgEAAAAUBAxCAAAAKAgYhAAAABAQcQgAAAAgIKIQQAAAAAFEYMAAAAACiIGAQC7jJtuuimzZ8/e7v4xY8YkSRobG9PY2PhHjbVDbr311qxYseJXHXvzzTdnzZo1SZKpU6dm3bp1O3M0AIBOxCAA4E9j7ty5PT3Cdt1zzz05+uijf9WxLS0tqVQqSZInn3wyAwYM2JmjAQB0IgYBAD2mUqnk3nvvTW1tbSZNmpTPPvssSfLQQw9lwoQJHX//6quvkiSHH354p+NnzZqVa6+9tmO7sbExM2bM2O71zjnnnKxcuTJJ0tbWlqFDh+brr7/O/PnzM2HChIwePTpnn3123n777STJqlWrMn78+NTX1+fCCy/M2rVrU6lUMn369NTW1mbkyJF5+umnkySTJk1KS0tLWlpacskll+Tyyy9PbW1trrrqqvzwww/bva8ZM2Zk/fr1mTZtWr755pucccYZWb16ddrb23P33Xdn1KhRqaur67iv7Z1/48aNmTZtWhoaGtLQ0JCXX355Z3xFAMBfkBgEAPSYhQsX5v33309zc3MefvjhfPbZZ2lra8tHH32UmTNnZuHChdl///0zb968Lo8fOXJkli1blo0bNyZJmpubO5aSdWXMmDF58cUXkySvv/56Bg0alP79+2fmzJl5/PHHM2/evEyZMqUjvFx33XW5/PLL09TU1BF+FixYkLfffjtNTU2ZNWtWZs+endbW1k7Xeeedd3Lbbbdl/vz5+eKLL7J06dJ8+umnXd7XtGnTUlNTkxkzZqR///4d53juuefy5ZdfZt68eZk1a1YWLVqUJUuWbPf8ixcvzgEHHJDZs2fnnnvuyfLly3/19wIA/LX16ekBAIByvfHGGznrrLOy2267ZZ999snQoUPTu3fv3HjjjZk1a1Y+/vjjvPvuuznooIO6PL5fv3459dRTs3jx4hx44IE58MADf3aJ1ahRozJx4sTccMMNaW5uzujRo9OrV688+uijeeWVV/Lxxx/njTfeSK9evbJhw4a0trbm9NNPT5Kcf/75SZK77rorI0aMSN++fdO3b98ul64ddthh2W+//ZIkAwcOzHfffZeDDz54h+8r+c8TQOecc0569+6dPfbYI/X19Vm2bFnOOOOMLs9/3HHH5cEHH8y6dety2mmn5YorrtixLwEAKI4ngwCAHlNVVdXxrpwk6dOnT7799ttceumlaW9vT21tbYYNG9bpM//fueeem+bm5jQ1NaWhoeFnr1ddXZ1DDz00LS0tWbZsWc4888xs2rQp48aNy+rVqzN48OBMmjQpSbLbbrulqqqq49jvv/8+n3/+efr06dPp76tXr87mzZs7XWf33Xf/yT2uXLnyF91Xe3t7p+1KpZK2trbtnv+QQw7J/PnzU19fn+XLl2fcuHE/OQcAQCIGAQA96KSTTsr8+fPzww8/5Lvvvsurr76aqqqqnHDCCTnvvPNyyCGHZMmSJR0RpCvHH3981q5dm5aWlgwbNqzba44ZMyb33XdfhgwZkj322COffPJJqqqqctlll2XIkCFZvHhx2trasueee2bAgAFZunRpkv+8vPrhhx/O4MGDs2jRomzbti1btmzJlClTduh//3rzzTe3e1+9e/f+yT2eeOKJmTNnTtra2rJly5Y0NTVlyJAh2z3/M888k8bGxowYMSK33357NmzY0LF8DgDg/7JMDADoMcOGDcuKFStSV1eXfffdNwMHDszWrVvzwQcfpL6+Pkly1FFHZfXq1T97nuHDh+fbb79N3759u73m8OHDc/vtt+e6665LkgwaNChHHHFERowYkaqqqpxyyil56623kiTTp0/PHXfckenTp6d///65//77U1NTk5UrV6ahoSHt7e2ZPHlyDj300G6vO3LkyFx55ZVd3tdpp52WadOm5amnnur4/MSJE/PJJ59kzJgx2bZtW+rr6zN8+PC0tLR0ef6xY8fmmmuuSX19fXr37p3rr78+e+21V7dzAQDlqar83PPJAAC7sEqlkm3btuXiiy/OLbfckiOPPLKnRwIA2OV5MggA+NNqbW3NqFGjMn78+I4Q9NJLL+WJJ57o8vNdvewZAKA0ngwCAAAAKIgXSAMAAAAURAwCAAAAKIgYBAAAAFAQMQgAAACgIGIQAAAAQEHEIAAAAICC/C9aUv4bDCUVVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['daily_vaccinations']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c20ea97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f48d6cfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "# plot a histogram showing the distribution of the target values\n",
    "sns.distplot(data.daily_vaccinations, bins=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "23285d25",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "# plot a histogram showing the distribution of the target values\n",
    "sns.distplot(data.total_vaccinations, bins=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1468e682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['total_vaccinations']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d3d536c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['people_fully_vaccinated']].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d23062fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "# plot a histogram showing the distribution of the target values\n",
    "sns.distplot(data.people_fully_vaccinated, bins=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a7b9f168",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'daily_vaccinations_per_million' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [63]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mpie(\u001b[43mdaily_vaccinations_per_million\u001b[49m)\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'daily_vaccinations_per_million' is not defined"
     ]
    }
   ],
   "source": [
    "plt.pie()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6c6c3667",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(rc={'figure.figsize':(7,5)})\n",
    "# plot a histogram showing the distribution of the target values\n",
    "sns.distplot(data.daily_vaccinations_raw, bins=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a1d9c07",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcountry\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124miso_code\u001b[39m\u001b[38;5;124m'\u001b[39m])\u001b[38;5;241m.\u001b[39mmean()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
     ]
    }
   ],
   "source": [
    "data.groupby(['country', 'iso_code']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62f9ee32",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef0674f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2269b6b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ff51176",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7437a698",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}