Deaths_of_covid19_data_analysis_notebook.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    },
    "colab": {
      "name": "Deaths of covid19 data analysis notebook.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "IE-mx5kGjvnW"
      ],
      "toc_visible": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cj_Hn32NjvmI",
        "colab_type": "text"
      },
      "source": [
        "# Welcome to Covid19 Data Analysis Notebook\n",
        "------------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KjBIE7hgjvmJ",
        "colab_type": "text"
      },
      "source": [
        "### Let's Import the modules "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xA6BGaKljvmK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "75ddcb69-9a9e-4f33-a014-0c5a8af0aed4"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt \n",
        "print('Modules are imported.')"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Modules are imported.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osD2o4yejvmP",
        "colab_type": "text"
      },
      "source": [
        "## Task 2 "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUUI26cQjvmP",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.1: importing covid19 dataset\n",
        "importing \"Covid19_Confirmed_dataset.csv\" from \"./Dataset\" folder. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0PlAuOzTjvmQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "00c381a3-54e4-4c1c-fe7d-bf16866772d6"
      },
      "source": [
        "corona_dataset_csv= pd.read_csv(\"sample_data/time_series_covid19_deaths_global.csv\")\n",
        "corona_dataset_csv.head(10)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/22/20</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "      <th>6/19/20</th>\n",
              "      <th>6/20/20</th>\n",
              "      <th>6/21/20</th>\n",
              "      <th>6/22/20</th>\n",
              "      <th>6/23/20</th>\n",
              "      <th>6/24/20</th>\n",
              "      <th>6/25/20</th>\n",
              "      <th>6/26/20</th>\n",
              "      <th>6/27/20</th>\n",
              "      <th>6/28/20</th>\n",
              "      <th>6/29/20</th>\n",
              "      <th>6/30/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>33.0000</td>\n",
              "      <td>65.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>205</td>\n",
              "      <td>216</td>\n",
              "      <td>218</td>\n",
              "      <td>219</td>\n",
              "      <td>220</td>\n",
              "      <td>227</td>\n",
              "      <td>235</td>\n",
              "      <td>246</td>\n",
              "      <td>249</td>\n",
              "      <td>257</td>\n",
              "      <td>265</td>\n",
              "      <td>270</td>\n",
              "      <td>294</td>\n",
              "      <td>300</td>\n",
              "      <td>309</td>\n",
              "      <td>327</td>\n",
              "      <td>357</td>\n",
              "      <td>369</td>\n",
              "      <td>384</td>\n",
              "      <td>405</td>\n",
              "      <td>426</td>\n",
              "      <td>446</td>\n",
              "      <td>451</td>\n",
              "      <td>471</td>\n",
              "      <td>478</td>\n",
              "      <td>491</td>\n",
              "      <td>504</td>\n",
              "      <td>546</td>\n",
              "      <td>548</td>\n",
              "      <td>569</td>\n",
              "      <td>581</td>\n",
              "      <td>598</td>\n",
              "      <td>618</td>\n",
              "      <td>639</td>\n",
              "      <td>675</td>\n",
              "      <td>683</td>\n",
              "      <td>703</td>\n",
              "      <td>721</td>\n",
              "      <td>733</td>\n",
              "      <td>746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>41.1533</td>\n",
              "      <td>20.1683</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>37</td>\n",
              "      <td>38</td>\n",
              "      <td>39</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>44</td>\n",
              "      <td>44</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>49</td>\n",
              "      <td>51</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>58</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>28.0339</td>\n",
              "      <td>1.6596</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>582</td>\n",
              "      <td>592</td>\n",
              "      <td>600</td>\n",
              "      <td>609</td>\n",
              "      <td>617</td>\n",
              "      <td>623</td>\n",
              "      <td>630</td>\n",
              "      <td>638</td>\n",
              "      <td>646</td>\n",
              "      <td>653</td>\n",
              "      <td>661</td>\n",
              "      <td>667</td>\n",
              "      <td>673</td>\n",
              "      <td>681</td>\n",
              "      <td>690</td>\n",
              "      <td>698</td>\n",
              "      <td>707</td>\n",
              "      <td>715</td>\n",
              "      <td>724</td>\n",
              "      <td>732</td>\n",
              "      <td>741</td>\n",
              "      <td>751</td>\n",
              "      <td>760</td>\n",
              "      <td>767</td>\n",
              "      <td>777</td>\n",
              "      <td>788</td>\n",
              "      <td>799</td>\n",
              "      <td>811</td>\n",
              "      <td>825</td>\n",
              "      <td>837</td>\n",
              "      <td>845</td>\n",
              "      <td>852</td>\n",
              "      <td>861</td>\n",
              "      <td>869</td>\n",
              "      <td>878</td>\n",
              "      <td>885</td>\n",
              "      <td>892</td>\n",
              "      <td>897</td>\n",
              "      <td>905</td>\n",
              "      <td>912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>42.5063</td>\n",
              "      <td>1.5218</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>-11.2027</td>\n",
              "      <td>17.8739</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Antigua and Barbuda</td>\n",
              "      <td>17.0608</td>\n",
              "      <td>-61.7964</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>-38.4161</td>\n",
              "      <td>-63.6167</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>433</td>\n",
              "      <td>445</td>\n",
              "      <td>452</td>\n",
              "      <td>467</td>\n",
              "      <td>484</td>\n",
              "      <td>500</td>\n",
              "      <td>508</td>\n",
              "      <td>520</td>\n",
              "      <td>528</td>\n",
              "      <td>539</td>\n",
              "      <td>556</td>\n",
              "      <td>569</td>\n",
              "      <td>583</td>\n",
              "      <td>608</td>\n",
              "      <td>632</td>\n",
              "      <td>648</td>\n",
              "      <td>664</td>\n",
              "      <td>693</td>\n",
              "      <td>717</td>\n",
              "      <td>735</td>\n",
              "      <td>765</td>\n",
              "      <td>785</td>\n",
              "      <td>815</td>\n",
              "      <td>833</td>\n",
              "      <td>854</td>\n",
              "      <td>878</td>\n",
              "      <td>913</td>\n",
              "      <td>948</td>\n",
              "      <td>979</td>\n",
              "      <td>992</td>\n",
              "      <td>1011</td>\n",
              "      <td>1043</td>\n",
              "      <td>1078</td>\n",
              "      <td>1116</td>\n",
              "      <td>1150</td>\n",
              "      <td>1184</td>\n",
              "      <td>1207</td>\n",
              "      <td>1232</td>\n",
              "      <td>1280</td>\n",
              "      <td>1307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Armenia</td>\n",
              "      <td>40.0691</td>\n",
              "      <td>45.0382</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>74</td>\n",
              "      <td>77</td>\n",
              "      <td>81</td>\n",
              "      <td>87</td>\n",
              "      <td>91</td>\n",
              "      <td>98</td>\n",
              "      <td>113</td>\n",
              "      <td>120</td>\n",
              "      <td>127</td>\n",
              "      <td>131</td>\n",
              "      <td>139</td>\n",
              "      <td>158</td>\n",
              "      <td>170</td>\n",
              "      <td>176</td>\n",
              "      <td>183</td>\n",
              "      <td>190</td>\n",
              "      <td>200</td>\n",
              "      <td>211</td>\n",
              "      <td>217</td>\n",
              "      <td>227</td>\n",
              "      <td>245</td>\n",
              "      <td>258</td>\n",
              "      <td>264</td>\n",
              "      <td>269</td>\n",
              "      <td>285</td>\n",
              "      <td>293</td>\n",
              "      <td>302</td>\n",
              "      <td>309</td>\n",
              "      <td>319</td>\n",
              "      <td>332</td>\n",
              "      <td>350</td>\n",
              "      <td>360</td>\n",
              "      <td>372</td>\n",
              "      <td>386</td>\n",
              "      <td>397</td>\n",
              "      <td>410</td>\n",
              "      <td>421</td>\n",
              "      <td>426</td>\n",
              "      <td>433</td>\n",
              "      <td>443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Australian Capital Territory</td>\n",
              "      <td>Australia</td>\n",
              "      <td>-35.4735</td>\n",
              "      <td>149.0124</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>New South Wales</td>\n",
              "      <td>Australia</td>\n",
              "      <td>-33.8688</td>\n",
              "      <td>151.2093</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>48</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 165 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Province/State       Country/Region  ...  6/29/20  6/30/20\n",
              "0                           NaN          Afghanistan  ...      733      746\n",
              "1                           NaN              Albania  ...       58       62\n",
              "2                           NaN              Algeria  ...      905      912\n",
              "3                           NaN              Andorra  ...       52       52\n",
              "4                           NaN               Angola  ...       11       13\n",
              "5                           NaN  Antigua and Barbuda  ...        3        3\n",
              "6                           NaN            Argentina  ...     1280     1307\n",
              "7                           NaN              Armenia  ...      433      443\n",
              "8  Australian Capital Territory            Australia  ...        3        3\n",
              "9               New South Wales            Australia  ...       49       49\n",
              "\n",
              "[10 rows x 165 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yKBcAOsjvmT",
        "colab_type": "text"
      },
      "source": [
        "#### Let's check the shape of the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bkO5au3NjvmU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "682387f3-ccc8-4597-8d2e-343fc66f1af8"
      },
      "source": [
        "corona_dataset_csv.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(266, 165)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NR_J5rKWjvmZ",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.2: Delete the useless columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkL0U4Lgjvma",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrdYJWKWjvmc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "233e4d34-8f7b-4071-f70a-8d6f939fc7ac"
      },
      "source": [
        "\n",
        "corona_dataset_csv.head()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/22/20</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "      <th>6/19/20</th>\n",
              "      <th>6/20/20</th>\n",
              "      <th>6/21/20</th>\n",
              "      <th>6/22/20</th>\n",
              "      <th>6/23/20</th>\n",
              "      <th>6/24/20</th>\n",
              "      <th>6/25/20</th>\n",
              "      <th>6/26/20</th>\n",
              "      <th>6/27/20</th>\n",
              "      <th>6/28/20</th>\n",
              "      <th>6/29/20</th>\n",
              "      <th>6/30/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>205</td>\n",
              "      <td>216</td>\n",
              "      <td>218</td>\n",
              "      <td>219</td>\n",
              "      <td>220</td>\n",
              "      <td>227</td>\n",
              "      <td>235</td>\n",
              "      <td>246</td>\n",
              "      <td>249</td>\n",
              "      <td>257</td>\n",
              "      <td>265</td>\n",
              "      <td>270</td>\n",
              "      <td>294</td>\n",
              "      <td>300</td>\n",
              "      <td>309</td>\n",
              "      <td>327</td>\n",
              "      <td>357</td>\n",
              "      <td>369</td>\n",
              "      <td>384</td>\n",
              "      <td>405</td>\n",
              "      <td>426</td>\n",
              "      <td>446</td>\n",
              "      <td>451</td>\n",
              "      <td>471</td>\n",
              "      <td>478</td>\n",
              "      <td>491</td>\n",
              "      <td>504</td>\n",
              "      <td>546</td>\n",
              "      <td>548</td>\n",
              "      <td>569</td>\n",
              "      <td>581</td>\n",
              "      <td>598</td>\n",
              "      <td>618</td>\n",
              "      <td>639</td>\n",
              "      <td>675</td>\n",
              "      <td>683</td>\n",
              "      <td>703</td>\n",
              "      <td>721</td>\n",
              "      <td>733</td>\n",
              "      <td>746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Albania</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>37</td>\n",
              "      <td>38</td>\n",
              "      <td>39</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>44</td>\n",
              "      <td>44</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>49</td>\n",
              "      <td>51</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>58</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>582</td>\n",
              "      <td>592</td>\n",
              "      <td>600</td>\n",
              "      <td>609</td>\n",
              "      <td>617</td>\n",
              "      <td>623</td>\n",
              "      <td>630</td>\n",
              "      <td>638</td>\n",
              "      <td>646</td>\n",
              "      <td>653</td>\n",
              "      <td>661</td>\n",
              "      <td>667</td>\n",
              "      <td>673</td>\n",
              "      <td>681</td>\n",
              "      <td>690</td>\n",
              "      <td>698</td>\n",
              "      <td>707</td>\n",
              "      <td>715</td>\n",
              "      <td>724</td>\n",
              "      <td>732</td>\n",
              "      <td>741</td>\n",
              "      <td>751</td>\n",
              "      <td>760</td>\n",
              "      <td>767</td>\n",
              "      <td>777</td>\n",
              "      <td>788</td>\n",
              "      <td>799</td>\n",
              "      <td>811</td>\n",
              "      <td>825</td>\n",
              "      <td>837</td>\n",
              "      <td>845</td>\n",
              "      <td>852</td>\n",
              "      <td>861</td>\n",
              "      <td>869</td>\n",
              "      <td>878</td>\n",
              "      <td>885</td>\n",
              "      <td>892</td>\n",
              "      <td>897</td>\n",
              "      <td>905</td>\n",
              "      <td>912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Angola</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 163 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region  1/22/20  ...  6/28/20  6/29/20  6/30/20\n",
              "0            NaN    Afghanistan        0  ...      721      733      746\n",
              "1            NaN        Albania        0  ...       55       58       62\n",
              "2            NaN        Algeria        0  ...      897      905      912\n",
              "3            NaN        Andorra        0  ...       52       52       52\n",
              "4            NaN         Angola        0  ...       11       11       13\n",
              "\n",
              "[5 rows x 163 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vFtgrHxpl3GY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "countires= list(corona_dataset_csv['Country/Region'])"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3R97YZYjvmf",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.3: Aggregating the rows by the country"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFdcMXQkjvmg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated=corona_dataset_csv.groupby(\"Country/Region\").sum()"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3qZakqwjvmj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "23bcb483-7c04-451b-b4b0-0682df8e6e19"
      },
      "source": [
        "corona_dataset_aggregated.head()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/22/20</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "      <th>6/19/20</th>\n",
              "      <th>6/20/20</th>\n",
              "      <th>6/21/20</th>\n",
              "      <th>6/22/20</th>\n",
              "      <th>6/23/20</th>\n",
              "      <th>6/24/20</th>\n",
              "      <th>6/25/20</th>\n",
              "      <th>6/26/20</th>\n",
              "      <th>6/27/20</th>\n",
              "      <th>6/28/20</th>\n",
              "      <th>6/29/20</th>\n",
              "      <th>6/30/20</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>205</td>\n",
              "      <td>216</td>\n",
              "      <td>218</td>\n",
              "      <td>219</td>\n",
              "      <td>220</td>\n",
              "      <td>227</td>\n",
              "      <td>235</td>\n",
              "      <td>246</td>\n",
              "      <td>249</td>\n",
              "      <td>257</td>\n",
              "      <td>265</td>\n",
              "      <td>270</td>\n",
              "      <td>294</td>\n",
              "      <td>300</td>\n",
              "      <td>309</td>\n",
              "      <td>327</td>\n",
              "      <td>357</td>\n",
              "      <td>369</td>\n",
              "      <td>384</td>\n",
              "      <td>405</td>\n",
              "      <td>426</td>\n",
              "      <td>446</td>\n",
              "      <td>451</td>\n",
              "      <td>471</td>\n",
              "      <td>478</td>\n",
              "      <td>491</td>\n",
              "      <td>504</td>\n",
              "      <td>546</td>\n",
              "      <td>548</td>\n",
              "      <td>569</td>\n",
              "      <td>581</td>\n",
              "      <td>598</td>\n",
              "      <td>618</td>\n",
              "      <td>639</td>\n",
              "      <td>675</td>\n",
              "      <td>683</td>\n",
              "      <td>703</td>\n",
              "      <td>721</td>\n",
              "      <td>733</td>\n",
              "      <td>746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>37</td>\n",
              "      <td>38</td>\n",
              "      <td>39</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>44</td>\n",
              "      <td>44</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>49</td>\n",
              "      <td>51</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>58</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>582</td>\n",
              "      <td>592</td>\n",
              "      <td>600</td>\n",
              "      <td>609</td>\n",
              "      <td>617</td>\n",
              "      <td>623</td>\n",
              "      <td>630</td>\n",
              "      <td>638</td>\n",
              "      <td>646</td>\n",
              "      <td>653</td>\n",
              "      <td>661</td>\n",
              "      <td>667</td>\n",
              "      <td>673</td>\n",
              "      <td>681</td>\n",
              "      <td>690</td>\n",
              "      <td>698</td>\n",
              "      <td>707</td>\n",
              "      <td>715</td>\n",
              "      <td>724</td>\n",
              "      <td>732</td>\n",
              "      <td>741</td>\n",
              "      <td>751</td>\n",
              "      <td>760</td>\n",
              "      <td>767</td>\n",
              "      <td>777</td>\n",
              "      <td>788</td>\n",
              "      <td>799</td>\n",
              "      <td>811</td>\n",
              "      <td>825</td>\n",
              "      <td>837</td>\n",
              "      <td>845</td>\n",
              "      <td>852</td>\n",
              "      <td>861</td>\n",
              "      <td>869</td>\n",
              "      <td>878</td>\n",
              "      <td>885</td>\n",
              "      <td>892</td>\n",
              "      <td>897</td>\n",
              "      <td>905</td>\n",
              "      <td>912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 161 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                1/22/20  1/23/20  1/24/20  ...  6/28/20  6/29/20  6/30/20\n",
              "Country/Region                             ...                           \n",
              "Afghanistan           0        0        0  ...      721      733      746\n",
              "Albania               0        0        0  ...       55       58       62\n",
              "Algeria               0        0        0  ...      897      905      912\n",
              "Andorra               0        0        0  ...       52       52       52\n",
              "Angola                0        0        0  ...       11       11       13\n",
              "\n",
              "[5 rows x 161 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyS-FnT1l1Yt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated.to_csv(\"Aggregated_Deaths.csv\")"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TkVkKdkljvmm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b17fa5f0-99e8-41c1-83f9-c212522b701a"
      },
      "source": [
        "corona_dataset_aggregated.shape"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188, 161)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmTzvmiqjvmq",
        "colab_type": "text"
      },
      "source": [
        "### Task 2.4: Visualizing data related to a country for example China\n",
        "visualization always helps for better understanding of our data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iuj6gxb3jvmq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "32480c13-8cd7-4963-b74c-04c55f49f5d9"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Italy\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Spain\"].plot()\n",
        "corona_dataset_aggregated.loc['India'].plot()\n",
        "plt.legend()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Cases\")\n"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Wg9LY_tpBKV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "2a201b53-012b-42f5-b80d-53091b150826"
      },
      "source": [
        "\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Italy\"].plot()\n",
        "corona_dataset_aggregated.loc[\"Spain\"].plot()\n",
        "corona_dataset_aggregated.loc['India'].plot()\n",
        "corona_dataset_aggregated.loc['Brazil'].plot()\n",
        "plt.legend()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Cases\")"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QldLIptIrJcD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "countries=list(corona_dataset_aggregated.index)"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1rRf6j4riRf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c5cb2135-aa93-420c-9646-fbe43910eb0f"
      },
      "source": [
        "countries"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Afghanistan',\n",
              " 'Albania',\n",
              " 'Algeria',\n",
              " 'Andorra',\n",
              " 'Angola',\n",
              " 'Antigua and Barbuda',\n",
              " 'Argentina',\n",
              " 'Armenia',\n",
              " 'Australia',\n",
              " 'Austria',\n",
              " 'Azerbaijan',\n",
              " 'Bahamas',\n",
              " 'Bahrain',\n",
              " 'Bangladesh',\n",
              " 'Barbados',\n",
              " 'Belarus',\n",
              " 'Belgium',\n",
              " 'Belize',\n",
              " 'Benin',\n",
              " 'Bhutan',\n",
              " 'Bolivia',\n",
              " 'Bosnia and Herzegovina',\n",
              " 'Botswana',\n",
              " 'Brazil',\n",
              " 'Brunei',\n",
              " 'Bulgaria',\n",
              " 'Burkina Faso',\n",
              " 'Burma',\n",
              " 'Burundi',\n",
              " 'Cabo Verde',\n",
              " 'Cambodia',\n",
              " 'Cameroon',\n",
              " 'Canada',\n",
              " 'Central African Republic',\n",
              " 'Chad',\n",
              " 'Chile',\n",
              " 'China',\n",
              " 'Colombia',\n",
              " 'Comoros',\n",
              " 'Congo (Brazzaville)',\n",
              " 'Congo (Kinshasa)',\n",
              " 'Costa Rica',\n",
              " \"Cote d'Ivoire\",\n",
              " 'Croatia',\n",
              " 'Cuba',\n",
              " 'Cyprus',\n",
              " 'Czechia',\n",
              " 'Denmark',\n",
              " 'Diamond Princess',\n",
              " 'Djibouti',\n",
              " 'Dominica',\n",
              " 'Dominican Republic',\n",
              " 'Ecuador',\n",
              " 'Egypt',\n",
              " 'El Salvador',\n",
              " 'Equatorial Guinea',\n",
              " 'Eritrea',\n",
              " 'Estonia',\n",
              " 'Eswatini',\n",
              " 'Ethiopia',\n",
              " 'Fiji',\n",
              " 'Finland',\n",
              " 'France',\n",
              " 'Gabon',\n",
              " 'Gambia',\n",
              " 'Georgia',\n",
              " 'Germany',\n",
              " 'Ghana',\n",
              " 'Greece',\n",
              " 'Grenada',\n",
              " 'Guatemala',\n",
              " 'Guinea',\n",
              " 'Guinea-Bissau',\n",
              " 'Guyana',\n",
              " 'Haiti',\n",
              " 'Holy See',\n",
              " 'Honduras',\n",
              " 'Hungary',\n",
              " 'Iceland',\n",
              " 'India',\n",
              " 'Indonesia',\n",
              " 'Iran',\n",
              " 'Iraq',\n",
              " 'Ireland',\n",
              " 'Israel',\n",
              " 'Italy',\n",
              " 'Jamaica',\n",
              " 'Japan',\n",
              " 'Jordan',\n",
              " 'Kazakhstan',\n",
              " 'Kenya',\n",
              " 'Korea, South',\n",
              " 'Kosovo',\n",
              " 'Kuwait',\n",
              " 'Kyrgyzstan',\n",
              " 'Laos',\n",
              " 'Latvia',\n",
              " 'Lebanon',\n",
              " 'Lesotho',\n",
              " 'Liberia',\n",
              " 'Libya',\n",
              " 'Liechtenstein',\n",
              " 'Lithuania',\n",
              " 'Luxembourg',\n",
              " 'MS Zaandam',\n",
              " 'Madagascar',\n",
              " 'Malawi',\n",
              " 'Malaysia',\n",
              " 'Maldives',\n",
              " 'Mali',\n",
              " 'Malta',\n",
              " 'Mauritania',\n",
              " 'Mauritius',\n",
              " 'Mexico',\n",
              " 'Moldova',\n",
              " 'Monaco',\n",
              " 'Mongolia',\n",
              " 'Montenegro',\n",
              " 'Morocco',\n",
              " 'Mozambique',\n",
              " 'Namibia',\n",
              " 'Nepal',\n",
              " 'Netherlands',\n",
              " 'New Zealand',\n",
              " 'Nicaragua',\n",
              " 'Niger',\n",
              " 'Nigeria',\n",
              " 'North Macedonia',\n",
              " 'Norway',\n",
              " 'Oman',\n",
              " 'Pakistan',\n",
              " 'Panama',\n",
              " 'Papua New Guinea',\n",
              " 'Paraguay',\n",
              " 'Peru',\n",
              " 'Philippines',\n",
              " 'Poland',\n",
              " 'Portugal',\n",
              " 'Qatar',\n",
              " 'Romania',\n",
              " 'Russia',\n",
              " 'Rwanda',\n",
              " 'Saint Kitts and Nevis',\n",
              " 'Saint Lucia',\n",
              " 'Saint Vincent and the Grenadines',\n",
              " 'San Marino',\n",
              " 'Sao Tome and Principe',\n",
              " 'Saudi Arabia',\n",
              " 'Senegal',\n",
              " 'Serbia',\n",
              " 'Seychelles',\n",
              " 'Sierra Leone',\n",
              " 'Singapore',\n",
              " 'Slovakia',\n",
              " 'Slovenia',\n",
              " 'Somalia',\n",
              " 'South Africa',\n",
              " 'South Sudan',\n",
              " 'Spain',\n",
              " 'Sri Lanka',\n",
              " 'Sudan',\n",
              " 'Suriname',\n",
              " 'Sweden',\n",
              " 'Switzerland',\n",
              " 'Syria',\n",
              " 'Taiwan*',\n",
              " 'Tajikistan',\n",
              " 'Tanzania',\n",
              " 'Thailand',\n",
              " 'Timor-Leste',\n",
              " 'Togo',\n",
              " 'Trinidad and Tobago',\n",
              " 'Tunisia',\n",
              " 'Turkey',\n",
              " 'US',\n",
              " 'Uganda',\n",
              " 'Ukraine',\n",
              " 'United Arab Emirates',\n",
              " 'United Kingdom',\n",
              " 'Uruguay',\n",
              " 'Uzbekistan',\n",
              " 'Venezuela',\n",
              " 'Vietnam',\n",
              " 'West Bank and Gaza',\n",
              " 'Western Sahara',\n",
              " 'Yemen',\n",
              " 'Zambia',\n",
              " 'Zimbabwe']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppzM4PDJrqlR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "64eae2b2-0292-4c7a-aac4-049656e941b1"
      },
      "source": [
        "print(countries)\n",
        "lis=['Afghanistan', 'Albania']\n",
        "corona_dataset_aggregated.loc[\"India\"].plot()\n",
        "plt.legend()\n",
        "plt.xlabel(\"Date\")\n",
        "plt.ylabel(\"Number of Cases\")"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola', 'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi', 'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada', 'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia', 'Comoros', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark', 'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France', 'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece', 'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana', 'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania', 'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico', 'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway', 'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania', 'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia', 'Saint Vincent and the Grenadines', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia', 'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka', 'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*', 'Tajikistan', 'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda', 'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay', 'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Western Sahara', 'Yemen', 'Zambia', 'Zimbabwe']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEGCAYAAACtqQjWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXxU9b3/8deHsO/7GiQoi4ILSwRcq9ZdC9pahbpXpZte+7O9ra1epdf2Xlurbb21VrQqthW0WhV3qRV3hLAvggQIEAhr2JGEJJ/fH+dEx3GSTMhMJjN5Px+PeeTM93zPOZ+ZOTOfnO/3e84xd0dERCQRmqQ6ABERyRxKKiIikjBKKiIikjBKKiIikjBKKiIikjBNUx1Afevatavn5OSkOgwRkbQyd+7cbe7eraZ6jS6p5OTkkJeXl+owRETSipmtjaeemr9ERCRhlFRERCRhlFRERCRhGl2fSiwHDx6ksLCQAwcOpDqUpGvZsiXZ2dk0a9Ys1aGISAZKWlIxs0eBC4Et7n50WPYUMDis0hHY6e7DzCwH+BhYEc6b5e7fDZcZCTwOtAJeAW52dzezzsBTQA5QAFzq7jsOJdbCwkLatWtHTk4OZnYoq0gL7s727dspLCykf//+qQ5HRDJQMpu/HgfOjSxw98vcfZi7DwOeBf4ZMXtV5bzKhBJ6ELgBGBg+Ktd5K/Cmuw8E3gyfH5IDBw7QpUuXjE4oAGZGly5dGsURmYikRtKSiru/AxTHmmfBr/elwNTq1mFmvYD27j7Lg8spPwFcFM4eB0wJp6dElB+STE8olRrL6xSR1EhVR/0pwGZ3XxlR1t/M5pvZ22Z2SljWByiMqFMYlgH0cPeicHoT0KOqjZnZRDPLM7O8rVu3JugliIikh3Xb93PP68vZsjv5rRSpSioT+OJRShFwmLsPB24BnjSz9vGuLDyKqfLGMO4+2d1z3T23W7caTwhNibZt29aq/syZM7nwwgsBmD59OnfffXcywhKRDPDS4o088NYqDlYk//5Z9T76y8yaAl8HRlaWuXsJUBJOzzWzVcAgYAOQHbF4dlgGsNnMerl7UdhMtqU+4m+Ixo4dy9ixY1Mdhog0UK8sLmJY34706dgq6dtKxZHKmcByd/+sWcvMuplZVjh9OEGH/OqweWu3mY0J+2GuAl4IF5sOXB1OXx1RntZmzpzJaaedxiWXXMKRRx7J5ZdfTuXdOV977TWOPPJIRowYwT//+fkYh8cff5wbb7wRgBdffJHRo0czfPhwzjzzTDZv3pyS1yEiDcO67ftZsmE35x/Ts162l8whxVOB04CuZlYI3OnufwHG8+UO+lOB/zazg0AF8F13r+zk/z6fDyl+NXwA3A08bWbXAWsJOv7r7BcvLmXZxt2JWNVnhvRuz51fGxp3/fnz57N06VJ69+7NSSedxPvvv09ubi433HAD//73vxkwYACXXXZZzGVPPvlkZs2ahZnxyCOP8Jvf/IZ77703US9FRNLMK0uCrufzju5VL9tLWlJx9wlVlF8To+xZgiHGsernAUfHKN8OfLVuUTZMo0aNIjs7aPUbNmwYBQUFtG3blv79+zNw4EAArrjiCiZPnvylZQsLC7nssssoKiqitLRU56OINHKvLi7i2OwO9O3cul62pzPqo9TmiCJZWrRo8dl0VlYWZWVlcS970003ccsttzB27FhmzpzJpEmTkhChiKSD9cX7WVi4i1vPO7Letqlrf6WJI488koKCAlatWgXA1KmxT/HZtWsXffoEo66nTJkSs46INA7/mFuIGVxwTP00fYGSStpo2bIlkydP5oILLmDEiBF07949Zr1JkybxzW9+k5EjR9K1a9d6jlJEGoqy8gqemrOOUwd2q7emLwCrHFnUWOTm5nr0Tbo+/vhjjjrqqBRFVP8a2+sVaYzeWLqJiX+dy0NXjuScoXUf+WVmc909t6Z6OlIREclAU2evo3u7Fnz1yNitGsmipCIikmHWF+9n5idbuez4vjTNqt+feSWVUGNpBmwsr1OkMZvyQQFNzJgw6rB637aSCkEn+Pbt2zP+B7fyfiotW7ZMdSgikiR7S8p4as56zj+mF73r4bIs0XSeCpCdnU1hYSGN4QrGlXd+FJHM9PSc9ewpKeO6k1Nz4rOSCtCsWTOdeS4iaa+8wnn8gwJG9uvEsL4dUxKDmr9ERDLEO59sZV3xfq49KSdlMSipiIhkiL/NWkvXti04e0j9XJE4FiUVEZEMULhjP/9esYXxx/eledPU/bQrqYiIZIBps9cDMH5U35TGoaQiIpLmSsrKmTZnPWcM7k52p/q7zlcsSioiImnu5UVFbNtbwpUn9Et1KEoqIiLpzN159P01DOjelq8M6pbqcJRURETSWd7aHSzZsJtrTszBzFIdjpKKiEg6e/S9NXRo1Yyvj+iT6lCAJCYVM3vUzLaY2ZKIsklmtsHMFoSP8yPm/czM8s1shZmdE1F+bliWb2a3RpT3N7OPwvKnzKx5sl6LiEhDtHn3Ad5Ytpnxx/eldfOGcYGUZB6pPA6cG6P8d+4+LHy8AmBmQ4DxwNBwmT+ZWZaZZQEPAOcBQ4AJYV2AX4frGgDsAK5L4msREWlwnpqznvIK51uj6/9qxFVJWlJx93eA4jirjwOmuXuJu68B8oFR4SPf3Ve7eykwDRhnQcPhGcAz4fJTgIsS+gJERBqwsvIKps5exykDu9KvS5tUh/OZVPSp3Ghmi8LmsU5hWR9gfUSdwrCsqvIuwE53L4sqFxFpFGau2ErRrgNc3oCOUqD+k8qDwBHAMKAIuLc+NmpmE80sz8zyGsPl7UUk8/39o7XB7YKP6pHqUL6gXpOKu29293J3rwAeJmjeAtgARF5bIDssq6p8O9DRzJpGlVe13cnunuvuud26pX4ct4hIXVTeLnj88X1pVs+3C65JvUZjZr0inl4MVI4Mmw6MN7MWZtYfGAjMBuYAA8ORXs0JOvOne3CLxreAS8LlrwZeqI/XICKSatPmrMOAy1Jwu+CaJG0MmplNBU4DuppZIXAncJqZDQMcKAC+A+DuS83saWAZUAb8wN3Lw/XcCLwOZAGPuvvScBM/BaaZ2S+B+cBfkvVaREQaioPlFTw1p5AzjuxOnxTcLrgmSUsq7j4hRnGVP/zu/ivgVzHKXwFeiVG+ms+bz0REGoUZyzazbW8Jl49O/XW+YmlYjXEiIlKtqbPX0adjK05tANf5ikVJRUQkTWzZfYD387dx8fA+ZDVJ/XW+YlFSERFJE9MXbqTC4aLhDfe0PCUVEZE08dz8DRyb3YEB3dumOpQqKamIiKSBTzbvYenG3Vw0rOEepYCSiohIWnhqznqymhhfO653qkOplpKKiEgDV7TrU/42ay3jjutNt3YtUh1OtZRUREQauN/PWIk7/L+zBqU6lBopqYiINGD5W/bwj7nruXzMYfTt3DrV4dRISUVEpAH7y3sFNG/ahBtPH5DqUOKipCIi0kAdOFjOSws3cv7RvejStmH3pVRSUhERaaBeX7qJPSVlXDIyO9WhxE1JRUSkgXpmbiF9OrZizOFdUh1K3JRUREQaoI07P+W9/G18Y2Q2TRrodb5iUVIREWmAHnl3DQZcMiJ9mr5ASUVEpMFZt30/f51VwKW5fTmsS8MfRhxJSUVEpIG5540VNG3SJC1OdoympCIi0oAs2bCLFxdu5IZT+tOjfctUh1NrSioiIg3Iw++upm2Lplx/6uGpDuWQKKmIiDQQG3d+ysuLihh/fF/at2yW6nAOSdKSipk9amZbzGxJRNk9ZrbczBaZ2XNm1jEszzGzT81sQfj4c8QyI81ssZnlm9n9ZmZheWczm2FmK8O/nZL1WkRE6sOUDwqocOeak3JSHcohS+aRyuPAuVFlM4Cj3f1Y4BPgZxHzVrn7sPDx3YjyB4EbgIHho3KdtwJvuvtA4M3wuYhIWtpbUsaTs9dx3jG9yO6UXiO+IiUtqbj7O0BxVNkb7l4WPp0FVDsA28x6Ae3dfZa7O/AEcFE4exwwJZyeElEuIpJ2nviwgD0Hyph4Snr2pVRKZZ/Kt4FXI573N7P5Zva2mZ0SlvUBCiPqFIZlAD3cvSic3gT0qGpDZjbRzPLMLG/r1q0JCl9EJDH2lpTx8DurOX1wN47r2zHV4dRJSpKKmd0GlAF/D4uKgMPcfThwC/CkmbWPd33hUYxXM3+yu+e6e263bt3qELmISOI98WEBO/Yf5OYz0++8lGhN63uDZnYNcCHw1TAZ4O4lQEk4PdfMVgGDgA18sYksOywD2Gxmvdy9KGwm21JPL0FEJGEij1KGpflRCsRxpGJmvzGz9mbWzMzeNLOtZnbFoWzMzM4FfgKMdff9EeXdzCwrnD6coEN+ddi8tdvMxoSjvq4CXggXmw5cHU5fHVEuIpI2MukoBeJr/jrb3XcTHF0UAAOA/6xpITObCnwIDDazQjO7Dvgj0A6YETV0+FRgkZktAJ4BvuvulZ383wceAfKBVXzeD3M3cJaZrQTODJ+LiKSNTDtKgfiavyrrXAD8w913haeKVMvdJ8Qo/ksVdZ8Fnq1iXh5wdIzy7cBXawxERKSByrSjFIgvqbxkZsuBT4HvmVk34EBywxIRyWzuzqPvreErgzLnKAXiaP5y91uBE4Fcdz8I7Cc4R0RERA7R/tJytu0t5YQj0ueujvGIp6O+NUG/xoNhUW8gN5lBiYhkuu17SwHo3KZ5iiNJrHg66h8DSgmOViAY0vvLpEUkItIIbN9XAkDXto0vqRzh7r8BDgKEQ4HT54bJIiIN0OdHKi1SHElixZNUSs2sFeEZ62Z2BOGJiiIicmiK9wVJpUuGNX/FM/rrTuA1oK+Z/R04CbgmmUGJiGS67ZVJJcOav2pMKu4+w8zmAWMImr1udvdtSY9MRCSDbd9bQstmTWjdvN6vlpVU8Yz+Ogk44O4vAx2Bn5tZv6RHJiKSwYr3ldIlw/pTIL4+lQeB/WZ2HMEVhFcR3NdEREQO0bZ9pRnX9AXxJZWy8GrC44AH3P0Bgut3iYjIISreV5JxnfQQX1LZY2Y/A64AXjazJkCz5IYlIpLZiveWZtxwYogvqVxGMIT4OnffRHBPk3uSGpWISAZzd7btK824Ex8hvtFfm4D7Ip6vQ30qIiKHbF9pOaVlFRl3iRaIb/TXGDObY2Z7zazUzMrNbFd9BCcikomKM/S6XxBf89cfgQnASqAVcD3wp2QGJSKSybZ9dt2vxtmngrvnA1nuXu7ujwHnJjcsEZHMlclHKvGcyrnfzJoDC8zsN0ARcSYjERH5ssorFDfW81SuDOvdCOwD+gLfSGZQIiKZ7LPrfjWmIcVm1s3Mhrj7Wnc/4O673f0XBPeZj6uj3sweNbMtZrYkoqyzmc0ws5Xh305huZnZ/WaWb2aLzGxExDJXh/VXmtnVEeUjzWxxuMz9ZqZL8otIg1e8t5RWzbJo1Twr1aEkXHVHKv8HdI1R3hn4Q5zrf5wv97/cCrzp7gOBN8PnAOcBA8PHRMI7TZpZZ4IrJY8GRgF3ViaisM4NEcupr0dEGrztGXqJFqg+qQxw93eiC939XeDYeFYeLl8cVTwOmBJOTwEuiih/wgOzgI5m1gs4B5jh7sXuvgOYAZwbzmvv7rPCy8g8EbEuEZEGa/u+0oy8RAtUn1Squ75XXS7T0sPdi8LpTUCPcLoPsD6iXmFYVl15YYzyLzGziWaWZ2Z5W7durUPoIiJ1t31vCV0ycDgxVJ9U8s3s/OhCMzsPWJ2IjYdHGJ6IddWwncnunuvuud26dUv25kREqrS/tIx12/fTo33LVIeSFNUNKf4hwQUkLwXmhmW5wAnAhXXY5mYz6+XuRWET1pawfAPByLJK2WHZBuC0qPKZYXl2jPoiIg3Ws3ML2VNSxiUjYzaspL0qj1TcfSVwDPA2kBM+3gaOdfdP6rDN6UDlCK6rgRciyq8KR4GNAXaFzWSvA2ebWaewg/5s4PVw3u7wMjIGXBWxLhGRBqeiwnn0/QKO69uREYd1qnmBNFTtyY/uXgI8dqgrN7OpBEcZXc2skGAU193A02Z2HbAWuDSs/gpwPpAP7AeuDWMoNrO7gDlhvf9298rO/+8TjDBrBbwaPkREGqS3VmxhzbZ93D9hOJl6BkRSb47s7hOqmPXVGHUd+EEV63kUeDRGeR5wdF1iFBGpD+7O5HdW06tDS847umeqw0kaXW5FRKQevJe/jY/WFDPx1MNplpW5P73VnVH/Zvj31/UXjohI5nF37nl9BX06tuJbow9LdThJVV3zVy8zOxEYa2bTgC80ALr7vKRGJiKSIV5fuolFhbu455JjadE08y7NEqm6pHIH8F8EQ3Xvi5rnwBnJCkpEJFOUVzi/feMTBnRvy9dHZNe8QJqrMqm4+zPAM2b2X+5+Vz3GJCKSMf45r5D8LXt58PIRZDXJzBFfkeK5R/1dZjYWODUsmunuLyU3LBGR9FdSVs7v/7WSY7M7cG4Gj/iKFM896v8XuBlYFj5uNrP/SXZgIiLpbtrs9WzY+Sn/ec7gjD0vJVo856lcAAxz9woAM5sCzAd+nszARETSWXmF85f31jCyXydOHhDrLiKZKd7B0h0jpjskIxARkUwyc8UW1hXv59qTchrNUQrEd6Tyv8B8M3uLYFjxqXx+Yy0REYnh8Q8K6Nm+JecMbRx9KZXi6aifamYzgePDop+6+6akRiUiksbyt+zh3ZXb+M9zBmf02fOxxHXtr/CKwNOTHIuISEaY8sFamjdtwvjj+9ZcOcM0rhQqIpJkuw8c5Nl5hYw9rnfG3t2xOkoqIiIJ9I+8QvaXlnPNiTmpDiUlqk0qZpZlZsvrKxgRkXRWUeE88WEBuf06cXSfxjlQttqk4u7lwAozy+zLaoqIJMBbK7awdvt+rm6kRykQX0d9J2Cpmc0G9lUWuvvYpEUlIpJm3J0/zVxFn46tGs0lWWKJJ6n8V9KjEBFJc7PXFDN37Q7+e9zQRjeMOFI856m8bWb9gIHu/i8zaw1k9g0BRERq6YGZq+jatjmX5ja+YcSR4rmg5A3AM8BDYVEf4PlkBiUikk7mr9vBO59s5bqTD6dls8b9P3c8x2g/AE4CdgO4+0qg+6Fu0MwGm9mCiMduM/uhmU0ysw0R5edHLPMzM8s3sxVmdk5E+blhWb6Z6dIxIlLvysoruP35JXRv14IrxmhMUzx9KiXuXlp5QTQza0pw58dD4u4rgGHhurKADcBzwLXA79z9t5H1zWwIMB4YCvQG/mVmg8LZDwBnAYXAHDOb7u7LDjU2EZHa+uustSzduJs/fms47Vo2S3U4KRfPkcrbZvZzoJWZnQX8A3gxQdv/KrDK3ddWU2ccMM3dS9x9DZAPjAof+e6+2t1LgWlhXRGRerFp1wHufeMTTh3UjQuO6ZXqcBqEeJLKrcBWYDHwHeAV4PYEbX88MDXi+Y1mtsjMHjWzTmFZH2B9RJ3CsKyq8i8xs4lmlmdmeVu3bk1Q6CLS2N310jJKyyu4a9zQRnV5++rUmFTCm3NNAe4CfgFMcfdDbv6qZGbNgbEERz4ADwJHEDSNFQH31nUbldx9srvnuntut27dErVaEWnEZq7YwsuLi7jp9AH069Im1eE0GDX2qZjZBcCfgVUE91Ppb2bfcfdX67jt84B57r4ZoPJvuM2HgZfCpxuAyDF62WEZ1ZSLiCTNgYPl3PHCUg7v1oaJXzk81eE0KPE0f90LnO7up7n7V4DTgd8lYNsTiGj6MrPIBsmLgSXh9HRgvJm1MLP+wEBgNjAHGGhm/cOjnvHo8vwiUg/+/PYq1hXv55fjjqZF08Y9hDhaPKO/9rh7fsTz1cCeumzUzNoQjNr6TkTxb8xsGMHIsoLKee6+1MyeBpYBZcAPwmuSYWY3Aq8TnIz5qLsvrUtcIiI1WV+8nwdnruKCY3txYiO693y8qkwqZvb1cDLPzF4Bnib4wf8mwVHCIXP3fUCXqLIrq6n/K+BXMcpfIRg4ICJSL+56aRlNzLj9gqNSHUqDVN2RytcipjcDXwmntwKtkhaRiEgDNXPFFt5YtpmfnDuYXh30MxhLlUnF3a+tz0BERBqykrJyfvHiMg7v2obrTu6f6nAarHhGf/UHbgJyIuvr0vci0pj85b01rNm2jynfHqXO+WrE01H/PPAXgrPoK5IbjohIw7Nx56f835v5nDO0B18ZpHPdqhNPUjng7vcnPRIRkQbqV698TIU7t18wJNWhNHjxJJU/mNmdwBtASWWhu89LWlQiIg3EB/nbeHlREbecNYi+nVunOpwGL56kcgxwJXAGnzd/efhcRCRjlZSVc8f0pRzWuTUTT9WZ8/GIJ6l8Ezg8vBKwiEijMfnt1eRv2ctj1x7f6G++Fa94LtOyBOiY7EBERBqS1Vv38n9v5XPhsb04ffAh35ew0YnnSKUjsNzM5vDFPhUNKRaRjOTu3P78Elo0bcIdX1PnfG3Ek1TuTHoUIiINyD/nbeCDVdv51cVH071dy1SHk1ZqTCru/nZ9BCIi0hAU7yvlly8vY2S/Tkw4Xvecr614zqjfw+f3pG8ONAP2uXv7ZAYmIlLf3J07XljCngNl/M/Fx9Ckie7mWFvxHKm0q5y24H6Z44AxyQxKRCQV/vbROl5aVMR/njOYwT3b1byAfEk8o78+44HngXOSFI+ISEosKtzJXS8u47TB3fjeV45IdThpK57mr69HPG0C5AIHkhaRiEg927z7ABOfmEvXts2579Jhavaqg3hGf0XeV6WM4K6M45ISjYhIPfu0tJwbnshj94GDPPPdE+ncpnmqQ0pr8fSp6L4qIpKx7nhhCYs37OKhK0YypLfGH9VVdbcTvqOa5dzd70pCPCIi9eaZuYX8Y24h/3HGAM4e2jPV4WSE6jrq98V4AFwH/LSuGzazAjNbbGYLzCwvLOtsZjPMbGX4t1NYbmZ2v5nlm9kiMxsRsZ6rw/orzezqusYlIo3DJ5v38F/PL2HM4Z25+cxBqQ4nY1R3O+F7K6fNrB1wM3AtMA24t6rlaul0d98W8fxW4E13v9vMbg2f/xQ4DxgYPkYDDwKjzawzwRn/uQTn0sw1s+nuviNB8YlIBtqxr5Trp+TRtmVT/jB+OFnqmE+YaocUh0cOvwQWESSgEe7+U3ffkqR4xgFTwukpwEUR5U+EQ5pnAR3NrBfB0OYZ7l4cJpIZwLlJik1EMkBZeQU3Tp3Hpl0HeOjKkfRor8uwJFKVScXM7gHmAHuAY9x9UoKPABx4w8zmmtnEsKyHuxeF05uAHuF0H2B9xLKFYVlV5V9gZhPNLM/M8rZu3ZrAlyAi6eax9wt4Pz+4rteIwzqlOpyMU93orx8RXJX4duC24GR6AIygo76uwyROdvcNZtYdmGFmyyNnurubmVexbK24+2RgMkBubm5C1iki6adwx37um/EJZx7VnUtGZqc6nIxUXZ9Krc62ry133xD+3WJmzwGjgM1m1svdi8Lmrcpmtg1A34jFs8OyDcBpUeUzkxm3iKQnd2fS9KUATBo7lIh/lCWBkpo4qmJmbcLOf8ysDXA2wc3ApgOVI7iuBl4Ip6cDV4WjwMYAu8JmsteBs82sUzhS7OywTETkCx54K59/fbyFW84aRHYn3Ws+WeI5oz4ZegDPhf8pNAWedPfXwhuBPW1m1wFrgUvD+q8A5wP5wH6CUWi4e7GZ3UXQ9wPw3+5eXH8vQ0TSwZMfreO3b3zCxcP7cN3J/VMdTkYz98bVxZCbm+t5eXmpDkNE6slrS4r4/t/nceqgbjx8VS7NslLSQJP2zGyuu+fWVE/vrohkrA9Xbec/pi7guL4d+dPlI5RQ6oHeYRHJSB+u2s71U+bQr0trHrvmeFo3T1Vrf+OipCIiGeet5Vu45rHZ9OrYir9dP5qOrXXl4fqi1C0iGeXlRUX88Kn5DO7Zjie+PVqXsq9nOlIRkYzxdN56bpo6j+OyO/LkDWOUUFJARyoikhEef38Nk15cxikDu/LQlSPVh5IietdFJK25O394cyW//9dKzhnag/snDKdF06xUh9VoKamISNoqKSvn1mcX89z8DXxjRDa//sYxNNWw4ZRSUhGRtLRzfykTn5jL7IJifnz2IH5w+gBdz6sBUFIRkbSzbvt+rnl8NoXFn3L/hOGMPa53qkOSkJKKiKSVBet3cv2UORwsd/52/WhG9e+c6pAkgpKKiKSNlxZt5Mf/WEi3di146tpRHNGtbapDkihKKiLS4JWVV/Dr15bz8LtrGNmvEw9dOZKubVukOiyJQUlFRBq0/aVl3PTkfN5cvoWrTujH7RcMoXlTjfBqqJRURKTBKtr1KROfmMvSjbu466KjuXJMv1SHJDVQUhGRBumDVdu46cn5lJRV8PBVuXz1qB6pDknioKQiIg3Os3ML+cmzi+jftQ1/vmIkA7qrQz5dKKmISIPh7jz87mr+55XlnDSgCw9dmUvbFvqZSif6tESkQdi2t4SfPrOIN5dv4fxjevK7y4bpGl5pqN6HUJhZXzN7y8yWmdlSM7s5LJ9kZhvMbEH4OD9imZ+ZWb6ZrTCzcyLKzw3L8s3s1vp+LSJSdxUVztTZ6zjzvrd5N38bd35tCH+cMEIJJU2l4kilDPiRu88zs3bAXDObEc77nbv/NrKymQ0BxgNDgd7Av8xsUDj7AeAsoBCYY2bT3X1ZvbwKEamzxYW7uP2FJSxcv5NROZ355cVHM6hHu1SHJXVQ70nF3YuAonB6j5l9DPSpZpFxwDR3LwHWmFk+MCqcl+/uqwHMbFpYV0lFpIErK6/gnjdWMPmd1XRp04LfXXYcFw3rowtCZoCU9qmYWQ4wHPgIOAm40cyuAvIIjmZ2ECScWRGLFfJ5ElofVT46ySGLSB3t3F/KTVPn8+7KbUwY1ZdbzzuKDq2apTosSZCUnZZqZm2BZ4Efuvtu4EHgCGAYwZHMvQnc1kQzyzOzvK1btyZqtSJSC+7O9IUbOfO+d5i1eju//sYx/O/Xj1VCyTApOVIxs2YECeXv7v5PAHffHDH/YeCl8OkGoG/E4tlhGdWUf4G7TwYmA+Tm5noCXoKI1ML64v3c/vwS3v5kK8dmd+Dxa4/n6D4dUh2WJEG9JxULGk3/Anzs7vdFlPcK+1sALgaWhNPTgdjCuEoAABAlSURBVCfN7D6CjvqBwGzAgIFm1p8gmYwHvlU/r0JE4nGwvIJH3l3DH978hCwz7vzaEK46IYesJuo7yVSpOFI5CbgSWGxmC8KynwMTzGwY4EAB8B0Ad19qZk8TdMCXAT9w93IAM7sReB3IAh5196X1+UJEpGrz1u3g5/9czPJNezhnaA8mjR1Krw6tUh2WJJm5N67WoNzcXM/Ly0t1GCIZa/eBg9zz2gr+9tFaerZvyS/GDuXsoT1THZbUkZnNdffcmurpjHoRSQh359Ulm5g0fSnb9pZwzYk5/OjswbrMSiOjT1tE6mzDzk+54/klvLl8C0N7t+eRq3M5NrtjqsOSFFBSEZFDtufAQR56ezWPvLeaJmbcfsFRXHNiDk2zdBOtxkpJRUQOyZyCYm56cj6bdh9g3LDe/OTcI+nTUR3xjZ2SiojU2uPvr+Gulz+mb6dWPP+DkxjWV01dElBSEZFaefz9NUx6cRlnDenBvZceR/uWOiNePqekIiJx2ba3hCc+XMv9b67k7CE9eODyETRT34lEUVIRkWrt3F/Kb99YwVNz1nOw3D+7gZYSisSipCIiMRXvK2XanHU8/M5qdh8oY8KovlxzYg4Duut+J1I1JRUR+YLFhbuY8mEB0xdupLSsgpMHdOW2C47iqF7tUx2apAElFRGhtKyCV5cUMeWDAuat20nr5llcmpvNVSfk6E6MUitKKiKN2Kqte3lh/gamzlnP1j0l5HRpzR0XDuEbI7N1nxM5JEoqIo2Iu7Nm2z5eXbKJlxYV8XHRbszgtEHduPrEHE4d2I0muiy91IGSikiGq6hw5q3bwfSFG/n38i0U7vgUgBGHdeSOC4dw/jG96NmhZYqjlEyhpCKSYUrLKli8YRdz1xYzp2AHc9fuoHhfKS2bNeGUgd2YeOrhnHFkd7I7tU51qJKBlFRE0tyu/QeZt24HcwqKyVu7g4Xrd1JSVgFATpfWnD64OycP7MJZQ3rqMvSSdNrDRNKIu1O441PyKo9CCnawYvMeAJo2MYb2bs8VY/qR268TI3M60b2dmrWkfimpiDRQBw6Ws3LzXpZv2s2KTXtYsXkPyzftYeueEgDatmjKiH6duODYXuTmdGJY3460bq6vtKSW9kCRBqCsvIIlG3cza/V2FqzbyYrNe1i7fR8V4d2+mzdtwsDubTllYFeG9e1Ibr/ODO7ZjiyN1JIGRklFpJ65O59s3svsgmIKi/ezcste5qwpZk9JGQD9urTmyJ7t+NpxvTmyZzsG92xHv86tdeMrSQtpn1TM7FzgD0AW8Ii7353ikESAIHmsL/6UhYU72bTrAFv3lrBqy16WbtzNpt0HAGie1YTDurTmwuN6c8IRXRhzeGf1g0haS+ukYmZZwAPAWUAhMMfMprv7stRGJunE3SmvcMoqnIPlFZRXOAfLnbKKCsrKg/Ky8orPyyqc/SXlbNp9gE27PmXjrgPsOVCGu/NpaTnF+0sp3lfK9r2l7A2PPiBIIP27tuH4/p056YgunDywK707tNLJhpJR0jqpAKOAfHdfDWBm04BxQMKTym3PLeajNcVfKHP3mHVjlsauGrO4Nuutoioeo3asulUtH3Od9RhXbdcbq3ZVdSvcKSt3DkYkjbro3KY5HVo1w4AWzbLo0qY5fTu1pnOb5gzo3pbhh3Wkb+fWtGvRFDMlEMls6Z5U+gDrI54XAqOjK5nZRGAiwGGHHXZIG+rdsRWDY11Yr4rfiFjFVf2gxK6bnPVWHe+XZ8RabVU/ibHrVhFXrLpV/tbGF1fsmlXH1TTLaJbVhKZNjKaf/TWaNWlCVhOjWdYXy5s2aRKUNWlCVpbRqlkWPdu3pGeHlrRsllVV8CKNTronlbi4+2RgMkBubu4h/Vv6g9MHJDQmEZFMlO7DSTYAfSOeZ4dlIiKSAumeVOYAA82sv5k1B8YD01Mck4hIo5XWzV/uXmZmNwKvEwwpftTdl6Y4LBGRRiutkwqAu78CvJLqOEREJP2bv0REpAFRUhERkYRRUhERkYRRUhERkYSxqi69kanMbCuw9hAX7wpsS2A4iaK4akdx1Y7iqp1Mjaufu3erqVKjSyp1YWZ57p6b6jiiKa7aUVy1o7hqp7HHpeYvERFJGCUVERFJGCWV2pmc6gCqoLhqR3HVjuKqnUYdl/pUREQkYXSkIiIiCaOkIiIiiePuGf8AHgW2AEuiyscADxPc434usDj8e0Y4vzXwMrAcWArcHbV8L+ANYBjwYVhnEXBZRJ3+wEdAPvAU0DxiXl/gLYLbHy8Fbo4RW5ewzl7gj1Hb/xXBnS/3xnjNdYotok5LYDawMFzHLyLmjQduAy4P170Y+AA4LmodfwZOAu4J38tFwHNAx4g6PwvjWAGcU8XneMixVPdex7MvhHVGhuX5wP2EzcfhvCxgPvBSjJiODD+DEuDH8eybiYgpxvoKwroLgLza7GthvVvD9/eW8H1cBLxJcP5CZZ2rgZXh4+o6xFLda07afg90BJ4h2E8/Bk6oxfexuv2jrvtXTXGNCt/LBQTfj4sjlj2X4HuVD9waFXON3+Hqlo/52cbzo5zuD+BUYARfTiq/AL4BDAd6h2VHAxvC6dbA6eF0c+Bd4LyI5a8FfgQMAgaGZb2BIsIfTOBpYHw4/Wfge1FfgBHhdDvgE2BIVGxtgJOB78bYiceE64j15apTbBHrMaBtON2M4Ms4Jnw+JfwinAh0CsvOAz6KWscCgh/ds4GmYdmvgV+H00PCL0ILgi/8KiArkbFU917Hsy+Ez2eH77kBr0btC7cAT/LFpFIZU3fgeIIfw+ikEnPfTERMMdZXAHStZjtV7mthvbeAbsDpQOuw7HvAU+F0Z2B1+LdTON3pEGOp7jUnbb8PP7PrI77zHeN9j2rYP+q6f9UUV2s+/271IvhHpSnB924VcHi43EK+uN/X9L2pdvmYn211MzPpAeTw5aTyHtAhqsyAYqBFjHX8Abgh4vlTsd7g8I0fGK5rW8SHfQLwejUxvgCcFSs24JronThiXqwvV0JjC+u0BuYBo8PlFxL1nzHBj0nkF+Uo4OkY67oY+Hs4/TPgZxHzXif8TyyRsVT1XsezL4Rf1OUR8yYAD4XT2QT/sZ9BmFRixQRMIiqpVLVv1jWmKl5zAbF/yGvc14D2wPsxlh1eWR69feAhYEJdYqnuO5no/R7oAKyJ3o/ieY9q+izquH/FHVdY1h/YTJBUvvC9JuK7Fmsfjf7eVLd8VY9G26diZl2Bg+6+K2rWN4B57l4SVb8j8DWCHw/MLAsY7O7LouqNIsjoqwgOlXe6e1k4uxDoU0U8OQRf0I+qiS3e15bo2LLMbAHBfz8z3P2jMNaFHu5pEa4j+C+r0nnAazFW++2Ien0ImjMqJSuWynXkhMt8FD6PZ1/oE8YVK8bfAz8BKiLmVxVTXBIQUywOvGFmc81sYg3biXYm4b4fJfI9jvtzrGUsMb+T0RKw3/cHtgKPmdl8M3vEzNrE+R5V+Vkk4LOMKy4zG21mSwmasL4bvsbqPpN4vje1+UyBDLhJVx2cTdD2+hkzG0rQLHN2VHlTYCpwv7uvDotHE/4oRdTrBfyVoC25wsziCsTM2gLPAj90991m9q3o2GopYbEBuHs5MCxMrM+Z2dEE7axf+ME2s9MJdsiTI4rPIWiSiKx3G1AG/D3uIBITy5fe67A47n0hmpldCGxx97lmdlrErC/FVEuHHFM1Tnb3DWbWHZhhZssJjrLi2dfOBR6LiucKIBf4SrJiqeVrrut+35SgKfImd//IzP5A0I/0cXRctVTXzzKuuMJ/sIaa2VHAFDOraf+L+3tTG432SIWo/6DNLJug8/gqd18VVXcysNLdf1/N8u0JOvVvc/dZYfF2oGOYlCD40myIXLGZNSP4kfu7u/8z1roT8NoOKbZo7r6ToF39XKK+KGZ2LPAIMM7dt4dlrQnafjdG1LsGuBC4POI/pA0EHemVEh5LWB7rvYb49oUNYVzRMZ4EjDWzAmAacIaZ/S06pkNQl5hicvcN4d8t4bpGRW+nGqMI2vwr4zmToIN3bMQRRNyfYzyx1PCdjKWu+30hUBj+OEPQMT4ier1VqO6zqOtnWau43P1jgoEER1P9ZxLP96bW380q28Uy7UFEuzVRbYkEIysWAl+PsdwvCX6ImkSVfwC0C6ebEzQN/DDG8v/gi52C34+YZ8ATwO+jymK1c15DnH0qiYgtok43Pu8UbEUwWGEc8F5EncMIRoacGLXsBUSMmCNIAMuAblH1hvLFjvrVxO6or0ssX3qvD2FfiO5IPT9q/mnASwRt4O/FWH4ScfSpJDKmiHptIvaJNuE+cl48+1r4+UyLeD6coClpYNRynQna/juFjzVA50OJpbrXnMz9PtynBkd8XvfE8x5V9Vkk6rOsKS6C701lP1E/YCPBVYmbEnyf+vN5R/tQovZRqv7exFy+qs/E3RtHUiFouioCDhJk/Z8Cj0fMvx3Yx+dD8hYQjNjJJmj7/Tii/HqCH7d/Ryx/RbjuyOWHhfMOD3eW/HBnbhGx3Mnh+hdFLHdHZGxhvQKCDr29YfyVI8R+Ez6vCP9OSlRsEcsfSzBUdhGwJIzvEmBSRJ1HgB0R688Ly/8InBZRL5+gfbay3p8j5t1G8EO1gipGMNUxlljv9fkEzTc17gvhvNxwu6vC1xb9Q3MaQVKJjqln+PnsBnaG0+2r2DevS2RMEes4nOAHoXI49m3R26lqXwN+DFwTUedfBB3BlbFMj5j37fBzzgeuPdRYanjNSdvvCYYi54X7yfMEo/bi/T5+6bNI1GdZU1zAleF7uYBgAMtFEfPOJxjtuIrgqA3i/N5UtXx1j0Z5mRYzux3Id/dph7j8FUC2u9+d2MgadmwR23gEeMQ/b1Koqt48YLS7H0x1LNUsX6f3O11iqst2zGwGQVNNUapjqWb5pOz3CYgrKZ9lAuKq0z5a7bobY1IREZHkaMwd9SIikmBKKiIikjBKKiIikjBKKiIikjBKKiJJZGblZrbAzJaa2UIz+5GZVfu9M7Oc8KoKImlHSUUkuT5192HuPpTgsufnAXfWsEwOoKQiaUlDikWSyMz2unvbiOeHA3MIznbuR3Bdqjbh7Bvd/QMzm0Vwdec1BJcmvx+4m+DkyhbAA+7+UL29CJFaUFIRSaLopBKW7QQGA3uACnc/YGYDganunhtemPLH7n5hWH8iwVnXvzSzFsD7wDfdfU29vhiRODTmqxSLpFoz4I9mNgwoJ7ixVCxnA8ea2SXh8w4E9wZRUpEGR0lFpB6FzV/lBPeDuZPgGlrHEfRvHqhqMYLLnr9eL0GK1IE66kXqiZl1I7gq7h89aHfuABS5ewXBBQGzwqp7CG55XOl14Hvhpfsxs0Fm1gaRBkhHKiLJ1Sq8U2UzghuT/RW4L5z3J+BZM7uK4L4Y+8LyRUC5mS0EHie4jXUOMM+Cu0xtBS6qrxcgUhvqqBcRkYRR85eIiCSMkoqIiCSMkoqIiCSMkoqIiCSMkoqIiCSMkoqIiCSMkoqIiCTM/wd77oRZ6HXurwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwYufLIRrqhx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "3c931104-eb07-4cae-c348-772297524910"
      },
      "source": [
        "for c in countries:\n",
        "  corona_dataset_aggregated.loc[c].plot(figsize=(20,10))\n",
        "  plt.xlabel(\"Date\")\n",
        "  plt.ylabel(\"Number of Deaths\")\n",
        "  "
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wWxbBXo9rqUx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52pUSpc5rqRo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4j-AUb7Pjvmt",
        "colab_type": "text"
      },
      "source": [
        "### Task3: Calculating a good measure \n",
        "we need to find a good measure reperestend as a number, describing the spread of the virus in a country. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tLz7sF6Ljvmu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "1defacd9-7e56-4404-a97c-273a5a542ebe"
      },
      "source": [
        "corona_dataset_aggregated.loc['India'].plot()\n",
        "plt.legend()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f61c293b358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bgnQCppwjvmw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "fefcd981-346c-4dd0-a52a-578fffb4c96f"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"India\"][:75].plot()\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"][:75].plot()\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f61c287a0b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUzI90kKnS_D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "80df19a6-d03c-4b40-f806-fc0fc29ca1ab"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"India\"][-75:].plot()\n",
        "\n",
        "corona_dataset_aggregated.loc[\"China\"][-75:].plot()\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f61c27b72b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHDJt-Rcjvmz",
        "colab_type": "text"
      },
      "source": [
        "### task 3.1: caculating the first derivative of the curve"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lYyrQBtwjvmz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "2e68fcd5-3195-4490-e8a4-69ccc107f259"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().plot()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c295b748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAD4CAYAAAAKA1qZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5QcZZ3/8fe3e66ZyUwyyeSekACBQLgTA4rrItfAKsHbCquCistx1f3psv4QDvtbL7u4uu5ZBXR1EVDcZQVEXVgBIQZcQCQQknDJBTIJ5DKQzOQ6mclcu5/fH/X0TE1Pz7W7MpXM53XOnKmueqr6213V/e3nUlXmnENERMamxGgHICIio0dJQERkDFMSEBEZw5QERETGMCUBEZExrGi0AxjI5MmT3dy5c0c7DBGRw8qLL764yzlXO5SysU4Cc+fOZeXKlaMdhojIYcXMtgy1rJqDRETGMCUBEZExTElARGQMUxIQERnDlARERMYwJQERkTFMSUBEZAxTEhCJUHN7F/+9un60wxDpl5KASISWrdvBl+5bQ/2+1tEORSQnJQGRCHV0pQHo9P9F4kZJQCRCaZf5rzv4STwpCYhEKOWzgJKAxJWSgEiEMvfwTisHSEwpCYhEKFMTSCkLSEwpCYhESH0CEndKAiIRynz5pzU4SGJKSUAkQt1JQDUBiSklAZEIpXwNIKUkIDGlJCASoUwNwCkJSEwNmgTM7C4zazCzV0PzvmNmG8zsZTP7tZlNCC270czqzOw1M7s4NH+Jn1dnZjcU/qWIxE+6e3TQKAci0o+h1AR+CizJmrcMOMk5dwrwOnAjgJmdCFwBLPTr/JuZJc0sCfwAuAQ4EbjSlxU5oml0kMTdoEnAOfcUsCdr3uPOuS7/8Dlglp9eCtzrnGt3zr0B1AGL/V+dc26zc64DuNeXFTmipbpHBykJSDwVok/g08CjfnomsC20bLuf19/8PszsWjNbaWYrGxsbCxCeyOjRGcMSd3klATO7CegC7ilMOOCcu905t8g5t6i2trZQmxUZFd1nDKs5SGKqaKQrmtkngfcB57ueoQ/1wOxQsVl+HgPMFzliqU9A4m5ENQEzWwJcD1zmnDsYWvQQcIWZlZrZPGA+8DzwAjDfzOaZWQlB5/FD+YUuEn9p9QlIzA1aEzCznwPnApPNbDvwVYLRQKXAMjMDeM4591nn3Fozux9YR9BM9HnnXMpv5wvAY0ASuMs5tzaC1yMSK+m0+gQk3gZNAs65K3PMvnOA8jcDN+eY/wjwyLCiEznMqTlI4k5nDItESM1BEndKAiIRSmuIqMSckoBIhDREVOJOSUAkQpkagC4gJ3GlJCASobRuLykxpyQgEiH1CUjcKQmIREgXkJO4UxIQiZDTeQISc0oCIhHS6CCJOyUBkQipT0DiTklAJEI6Y1jiTklAJEJpf29h9QlIXCkJiEQo0xeg8wQkrpQERCKUOVNYFQGJKyUBkQhpdJDEnZKASIR0PwGJOyUBkQhpdJDEnZKASIR0noDEnZKASIRSuoqoxJySgEiEdD8BiTslAZEIpTU6SGJOSUAkQuoTkLgbNAmY2V1m1mBmr4bm1ZjZMjPb6P9P9PPNzG41szoze9nMzgitc7Uvv9HMro7m5YjEi4aIStwNpSbwU2BJ1rwbgOXOufnAcv8Y4BJgvv+7FvghBEkD+CpwFrAY+GomcYgcyTREVOJu0CTgnHsK2JM1eylwt5++G7g8NP9nLvAcMMHMpgMXA8ucc3ucc3uBZfRNLCJHHDUHSdyNtE9gqnPubT+9A5jqp2cC20Lltvt5/c3vw8yuNbOVZraysbFxhOGJxEMqnfmvLCDxlHfHsAvGvhXsCHfO3e6cW+ScW1RbW1uozYqMip4LyCkJSDyNNAns9M08+P8Nfn49MDtUbpaf1998kSOaLiAncTfSJPAQkBnhczXwYGj+VX6U0NnAft9s9BhwkZlN9B3CF/l5Ikc09QlI3BUNVsDMfg6cC0w2s+0Eo3y+BdxvZtcAW4A/98UfAS4F6oCDwKcAnHN7zOwfgBd8uW8457I7m0WOON1DRJUFJKYGTQLOuSv7WXR+jrIO+Hw/27kLuGtY0Ykc5npqAkoCEk86Y1gkQj0XkBvlQET6oSQgEiGnC8hJzCkJiERIo4Mk7pQERCKk0UESd0oCIhHStYMk7pQERCKkq4hK3CkJiERIt5eUuFMSEImQ+gQk7pQERCKU6QtQc5DElZKASITUJyBxpyQgEqHM+QHqE5C4UhIQiVDP/QRGORCRfigJiERIo4Mk7pQERCKkPgGJOyUBkYiEzxJWDpC4UhIQiUj4178uICdxpSQgEpFwN4CagySulAREIhL+4tcF5CSulAREItIrCSgHSEwpCYhEJDwsVENEJa6UBEQioj4BORzklQTM7G/MbK2ZvWpmPzezMjObZ2YrzKzOzO4zsxJfttQ/rvPL5xbiBYjEVbgfQElA4mrEScDMZgL/B1jknDsJSAJXAN8GvuucOxbYC1zjV7kG2Ovnf9eXEzliqU9ADgf5NgcVAeVmVgSMA94GzgMe8MvvBi7300v9Y/zy883M8nx+kdhKaXSQHAZGnAScc/XAvwBbCb789wMvAvucc12+2HZgpp+eCWzz63b58pOyt2tm15rZSjNb2djYONLwREadU5+AHAbyaQ6aSPDrfh4wA6gAluQbkHPudufcIufcotra2nw3JzJqeo0OUhKQmMqnOegC4A3nXKNzrhP4FXAOMME3DwHMAur9dD0wG8AvrwZ25/H8IrHW+2SxUQxEZAD5JIGtwNlmNs637Z8PrAOeBD7sy1wNPOinH/KP8cufcE4/j+TIlfniTyZMzUESW/n0Cawg6OBdBbzit3U78BXgOjOrI2jzv9Ovcicwyc+/Drghj7hFYi/zxV+kJCAxVjR4kf45574KfDVr9mZgcY6ybcBH8nk+kcNJph+gOJkgpeYgiSmdMSwSkUxrZ1HSUMunxJWSgEhEMr/+ixIJjQ6S2FISEIlIurs5yHSymMSWkoBIRDLnCRQlTZeNkNhSEhCJSKYFqDiR0OggiS0lAZGIpFy4JqAkIPGkJCASkZ7zBBI6Y1hiS0lAJCKZzuBi1QQkxpQERCKS6QwuSmqIqMSXkoBIRDK//pMJwzl0wpjEkpKASETCzUGgu4tJPCkJiESkuzkokfCPlQUkfpQERCKScr1rAilVBSSGlAREIhIeIgq9bzcpEhdKAiIRSYcuGwG6xaTEk5KASEQyrT/FSfUJSHwpCYhEpPsCcgk/Okh9AhJDSgIiEem5qUymJjCa0YjkpiQgEhGNDpLDgZKASESyzxPQGcMSR0oCIhHJPmNYo4MkjpQERCKSdr2HiKo1SOIoryRgZhPM7AEz22Bm683snWZWY2bLzGyj/z/RlzUzu9XM6szsZTM7ozAvQSSeekYH+Y5hZQGJoXxrArcAv3XOLQBOBdYDNwDLnXPzgeX+McAlwHz/dy3wwzyfWyTWum8v2V0TUBKQ+BlxEjCzauA9wJ0AzrkO59w+YClwty92N3C5n14K/MwFngMmmNn0EUcuEnOprCGiGh0kcZRPTWAe0Aj8xMxWm9kdZlYBTHXOve3L7ACm+umZwLbQ+tv9vF7M7FozW2lmKxsbG/MIT2R09Vw7SH0CEl/5JIEi4Azgh86504EWepp+AHDBmLhhHfrOududc4ucc4tqa2vzCE9kdPWMDtJlIyS+8kkC24HtzrkV/vEDBElhZ6aZx/9v8Mvrgdmh9Wf5eSJHpJ7bS6pPQOJrxEnAObcD2GZmx/tZ5wPrgIeAq/28q4EH/fRDwFV+lNDZwP5Qs5HIESfTB1DcPTpoNKMRya0oz/X/GrjHzEqAzcCnCBLL/WZ2DbAF+HNf9hHgUqAOOOjLihyx+p4noJqAxE9eScA5twZYlGPR+TnKOuDz+TyfyOEk3ecCckoCEj86Y1gkIt33E0joAnISX0oCIhHJ/PJPaoioxJiSgEhENERUDgdKAiIR6TNEVFUBiSElAZGIZPoAMs1BupS0xJGSgEhEnHOYhW8qM8oBieSgJCASkZRzJMzwFQGNDpJYUhIQiUjaQdKMREIni0l8KQmIRCSdDpqDEqYkIPGlJCASkbRzJBNGMpMEdO0giSElAZGIpNJBLcAyfQKqCUgMKQmIRCTtHAnrGSLqlAQkhpQERCKSdo5Ewrr7BFJqDpIYUhIQiUjaOZJm+KtGqGNYYklJQCQiqTSYGabRQRJjSgIiEXHOkUzQMzpISUBiSElAJCKpdOaMYfUJSHwpCYhEJO2CIaIJ9QlIjCkJiEQkGB0UOmNY1w6SGFISEIlIz+gg3VlM4ktJQCQimT6BzBnDag6SOMo7CZhZ0sxWm9lv/ON5ZrbCzOrM7D4zK/HzS/3jOr98br7PLRJnzkEifO0gJQGJoULUBL4IrA89/jbwXefcscBe4Bo//xpgr5//XV9O5IgV1ATUJyDxllcSMLNZwJ8Bd/jHBpwHPOCL3A1c7qeX+sf45edb5iwakSNQOnNTme7bS45yQCI55FsT+B5wPZAZAT0J2Oec6/KPtwMz/fRMYBuAX77flxc5IqWz7iymC8hJHI04CZjZ+4AG59yLBYwHM7vWzFaa2crGxsZCblrkkEo7eg0R1e0lJY7yqQmcA1xmZm8C9xI0A90CTDCzIl9mFlDvp+uB2QB+eTWwO3ujzrnbnXOLnHOLamtr8whPZHRpiKgcDkacBJxzNzrnZjnn5gJXAE845z4GPAl82Be7GnjQTz/kH+OXP+FUP5YjWCrt/AXkgscaHSRxFMV5Al8BrjOzOoI2/zv9/DuBSX7+dcANETy3SGw4R9btJZUEJH6KBi8yOOfc74Hf++nNwOIcZdqAjxTi+UQOB9lDRHV7SYkjnTEsEpHsIaKqCEgcKQmIRCSTBAASpuYgiSclAZGIpF3PTeaTCVPHsMSSkoBIRILRQcG0malPQGJJSUAkIsHtJX1NwAzlAIkjJQGRiKSy+gR0xrDEkZKASETS6Z7hoQn1CUhMKQmIRCQYHRRMJ8w0OkhiSUlAJCLpcJ9AwnSegMSSkoBIRDK3lwTfJ6DmIIkhJQGRiGRuLwlBc5CulyhxpCQgEpFUVp+ARgdJHCkJiEQkcz8BUJ+AxJeSgEhE0ungTGEAM91PQOJJSUAkIsHooGA6mdAQUYknJQGRiPQeHaTmIIknJYFRsmrrXrbtOTjaYUiE0r1GB2mIqMSTksAo+ev/Ws2/Lnt9tMOQCGWfMawhohJHSgKjZN/BDrbsbhntMCRC2aODNERU4khJYBSk046WjhTb97aOdigSoXTahUYHqU9A4klJYBQc7EwB0HCgnTY/LUeetEO3l5TYUxIYBS3tXd3Tb+1TbeBI1WeIqPoEJIZGnATMbLaZPWlm68xsrZl90c+vMbNlZrbR/5/o55uZ3WpmdWb2spmdUagXcbhpDiUBNQkducJDRIPbS45yQCI55FMT6AL+1jl3InA28HkzOxG4AVjunJsPLPePAS4B5vu/a4Ef5vHch7WWAiWB5vYu9rR0FCIkiUD4AnJJQ6ODJJZGnAScc28751b56QPAemAmsBS42xe7G7jcTy8FfuYCzwETzGz6iCM/jPWuCYz8XIGbH17Pp37yfCFCkgjoAnJyOChIn4CZzQVOB1YAU51zb/tFO4CpfnomsC202nY/L3tb15rZSjNb2djYWIjwYqelvaczuD6PPoH6fa3U72srREgSgfAQUd1eUuIq7yRgZpXAL4EvOeeawstcUP8d1pHvnLvdObfIObeotrY23/Bi6WBHUBOYWlWaV3NQU2snTW2dhQpLCsg5h3M9F5ALRgeNclAiOeSVBMysmCAB3OOc+5WfvTPTzOP/N/j59cDs0Oqz/LwxJ9McdPy0qryagw60ddLRldYw0xjKtPz0vr2kagISP/mMDjLgTmC9c+5fQ4seAq7201cDD4bmX+VHCZ0N7A81G40pmY7hBdPGs7OpnfaukX2JN7UF2znQ1jVISTnUMu3/vfoElAQkhvKpCZwDfAI4z8zW+L9LgW8BF5rZRuAC/xjgEWAzUAf8GPhcHs99WGv2fQLzp1QC8NYI2/UP+KYgNQnFT+ZXf/j2kuoXljgqGumKzrlnAOtn8fk5yjvg8yN9viNJS3sXFSVJ5tSMA4IRQvMmVwxrG+1dKdo6g0bmplYlgbjpTgI6Y1hiTmcMj4KW9i4qSouY1Z0Eht85HG4CUnNQ/HT3CZj6BCTelARGQXN7F5WlRUyrKqO0KMH9K7exu7l9WNsIf/FnmoNuXb6R+1/Y1t8qcghl+gR8DgjOGFZNQGJISWAUZGoCyYTxzx8+hXVvNfH+257hjV19Ly0dPrEsLNwE1NQalLnvhW3ct1JJIA4yZwd3jw4yQxUBiSMlgVHQ0p5iXEkSgKWnzeSBz76LvQc7uePpzb3KPfV6I6d9/XFe2ravzzayawLOOXY1t1PX0KzLE8RAz+igzMliurOYxJOSwCjINAdlnDyrmvMWTOGxtTt7NRn8z0tv0ZV23PbExj7bCI8IOtDWSUtHivauNPtbO3U9oRjI7Mbeo4OUBCR+lARGQUtH0BwUdvFJ09jV3M7qrXuB4JfkExsaKCtO8Lv1Dax/u4kNO5p45JXg1Irs5qA9zT1f/Jsadcey0dYzOgj/X81BEk9KAqMg0ycQ9t7jaylJJvjtqzsAWLNtL7tbOrjpz06ksrSIL967mstu+wOfu2cVze1d3c1BkytLaGrrZFdLT8fypsbmQ/diJKdMEtDtJSXulAQi1tze1WfkT9AclOw1b3xZMe+eP5nfrt2Bc45l6xooShhLT5vBJ955FK/vbKZ2fCkADU1tNLV1kjCYVl1GU2snu8M1gQYlgdGW3SdghpqDJJZGfLLYWPL0xkaOrq1k5oTyIa/TmUrzH3/cwq1PbKS5rYuPn30UXzx/PuPLimjrTPepCQAsWTiNJzY08IuV21m2bgdnHV1DVVkxf3PBcZx7XC1dacfH7ljBjqY2DrR1Mb6smKqyYg60dbHH1wSqy4tVE4gBl9UnkDTTyWISS6oJDKKtM8U1P13JP/5m3ZDXae9Kcc3dK/nGb9Zx0oxqPrJoNj/745t86b41tHQEl4yozJEELl44jaMnV3D9L19mU2MLF5wQXIW7pCjBWUdPYmpVGQANTe00tXYyvqyIqrLioDnI1wTeMXei+gRiINe1g5QDJI5UExjEq/X76Uil+f1rjRzs6GJcycBv2YG2Tq67/yWeer2Rb37gZK5cPBszwznH79bv7L6MdK6aQPW4YpZd96f87+sNPL1xFx88fVav5dOqgySw0zcHVZUVU1VeFHQMt3RQUZJk4Yxqlm9ooK0zRVlxss9zyKGRzjpPIJHQBeQknpQEBvHilmC0Tmtnit+/1silJ+e+GdpTrzdy469e6b5JzDeWLuQvzprTvXzOpHHsau6goSlotsmVBCD40jhvwVTOWzC1z7LK0iIqSpLsbGqnqa2LqvKemsDu5nZqKks4ZkolzsGbu1tYMK0qr9cuI5f51R++n4DO35A4UnPQIFZt3cusieVMqijpHp6ZbePOA3z+nlWMK0ly/ZLjuffas7nqnXN7lZk9MbhO0IYdwX13sjuGh2pqVVlQE2jtDPoEyos52JFiZ1M7kypKOaY2uBDdZjUJjapcQ0Q1OkjiSDWBATjnWLV1H+ccM4nykiIeXFNPW2eKVNrx8+e38qtV9UyqLGFzYwulxUnu/vRiZvTTeZy5Yuj6tw8AUDFIs1J/plSVstN3DFeVFTO+LNjOlt0tnDC9iqMnB5en3rizGU4e0VNIAeQaIqocIHGkmsAAtu9tpfFAO2ccNZFLT57GwY4U77/tGRbf/Dv+8eH1lBYn2N/aSVlxgh9fdWa/CQDCSSCoCfTXHDSYqVVl7DwQ9AlkOoYB3trfxqTKEspLkpwyq5p7Vmxhr84cHjU9F5ALDRFVFpAYUk1gAKv82btnzJnIgmnjec9xtXR0pTj76ElcfvpMzjxq4pC3NWFcMeNLi7qTQK7RQUMxraqMnfvb6UynqSoPmoMyaiqC8wi++YGT+cC//YG/e/BVvn/l6d1fRHLoZJr/wxeQ03kCEkdjOgkcaOtky+6DnDSzOufy1Vv3Ma4kyYJp4ylKJvjZpxeP+LnMjFk14/KuCUypKqMjFdxMpqqsiKqynu1MriwB4KSZ1XzpguP4zmOvsWThNN5/6oxe23DOKTFErM8QUY0Okpga081BN/zyFT74b8/2uT1jXUMzP3iyjt+8/BanzKqmKFmYt2lOTU9z0UhrAlOrSrungz6BnprAJJ8EAD77p8dw0swqvvXoBto6U7R2pLj54XUs+d5TnPK1x3ngxe1AcB5EprNaCke3l5TDxZitCazeupeH/WifFZv3cOGJU1m+fif/9OgG6vxlF06bPYEvX3R8wZ4z0y+QMCgrHlliyZwwBgRDRMt7dmGmOQiCZogbLzmBj92xgv/44xZWb9vLo6/u4N3HTqa8JMmXf/ESr9bv5/G1O3hrfxtXLp7DjZcuYFNDM60dKRbNraGkaEz/RsiLbi8ph4sxmQScc/zToxuYXFlCS3uKP9Tt4oITpvC1/1lLwoyvX7aQixZOZXr10C8TMRSzfRKoKCkacXPM1PE9SSAzRDRjUkVJr7LnHDuZ9xxXyz89up60g5suPYG/fM/RtHWm+Nw9q/jps29y8sxqzj9hKv+5Ygs/f35raNtFvOe4Ws6aV8PxU8czqbKE0qJgWOv06rKC1Y6OVLq9pBwuxmQS+PXqep5/Yw//sHQhv1vfwDN1u1i9bR/b9rTyLx85lQ+fOWvwjYxAdxIYYVMQBENEM6rKiqksKcIs6IgMNwdl3LBkAZfV7eJDZ8zkM38yD4Cy4iT//okzeaV+P6fNmkAiYVxy8jSeen0Xp8yqpjiZ4PG1O3h64y4efrnvuRFTq0r56KLZfOKdc6kdX4pzjm17WpldU66+Bi+7T8B8c5D6YyRuxkQSSKUda7btZXJlKS+8uZfrH3iJxXNruGLxHNo609z8yHrueHozJUUJLlrY90zdQpnTnQRGfjmHsuIkE8YVs+9gMEQ0kTDGlxbR1NZFTUXfJHDijCqevfE8aitLe335FCcTnDGnZ3TTu46ZzLuOmdz9+MITp+KcY/veVrbsPsjulnY6utJ0phyPr9vBbU/Wcdcf3uTjZx/FHzfv5qVt+/jImbP45gdPprhAtYRcl9w+XGT3CWRqBM713HdYJA4O+SfMzJYAtwBJ4A7n3LeifL4NO5r4yi9f6XWLxnOOncSPr1pEcTLBOccGX3yPvLKDixdO7R53H4WZE8oxG3mncMbU8WXsO9jZ3RQ0vqwY5+hursk2JdSENBxmxuyacd01mIy/OGsOmxqb+dajG/jR/25idk05HzpjFr94cTtv72/j0++eS21lGb9ctZ1X6/dz8qxqxpcW8fi6nWzdc5CJ40qYMaGMY2orWTizmsVza5g/pbL7C7MzlebW5Rv5wZN1XHfhcXzhvPkjin80pYMBXL36BCC4xWQCZQGJj0OaBMwsCfwAuBDYDrxgZg8554Z+ic4h2tvSwS3LN/Kfz22hqryYb37gZIoSxsGOLq5YPKf74moLpo1nUkUJu1s6uOzUmYUOo5ey4iRTx5fl/et2SlUpr+080H22cFV5McXJQ/vFckxtJT++ahFv72+ltrKUomSCs+bV8P8efJVn6nYBUJJMcMKMKv5rxVY6UmnecVQNH33HbPYf7GT73lYeX7eTe1/YBgTnP1x44lQSBive2MOGHQeYO2kc//L468yYUM7WPQd56KW3OG7KeOZPrWTbnoPsau6gelwxU8eXccyUCkqLktQ1NNPWmWJSRQlH11Zy+pwJPPV6Iz999k3au9LUVJRwVM04jq6toKw4iZlRXV7MpIoSJlaUUDu+lOlVZSQSRnN7F7sOBNd6Ki1OMHFcCc3tXWxubKG6vJj5Uyoxg4YD7VSXF/e6YF/PBeSCx5kEp34BiZtDXRNYDNQ55zYDmNm9wFKgoEngjV0tLP3+MzS3d/HRd8zh/158fM6mEgg+nH8yfzK/W9/AeQumFDKMnJaePqNPB+5wTasqo7w42d3sUju+lM6u6GowAwl3nv/5O2bz/lNnsGbbPrbvPcj5J0ylpqKEjq40rZ0pqst7x5jpS3jujd0sX7+TX7y4jaJEgmOmVHLbladz4YlTufLHz3Hd/S8BcNa8Gta+vZ/frt3BzAnlTKkq5a19rTyxv4HWzuAS3cVJo7QoSXN7V6/nOmlmFcdOqWJXczvPbtrNr1bX9/uayouTjC8rouFAe79lILh3Q2cqzcGOFGYw3Z/D0dTa1T2yqucCcsH/S295unt6KNLO0dTWxf7WTipKkkwcV9J9AtpockBrR4o9LR0UJYyJFSWUjtHRZP29F9n7rqq8mOa24K6AVeVFVJcXD3gsLJhexW1Xnh55/HYor2xoZh8GljjnPuMffwI4yzn3hVCZa4FrAebMmXPmli1bhv08zjlufng9H1k0m+OnjR+0/K7mdnY1tx82V91cvXUvr9Tv775IXf2+VtJp16fZ5nDTlUqTTFivvovGA+3c9sRGLjt1Bovm1gBBc1G43yGddry1v5WOrjSza8ZRnEzQ1pli/dtNrN66j2OnVPIn8yf32m5bZ4rOVJp0Gva1drCnJfjb0dTGpoYW9rd2cnRtBdOryzCD1o40ew92UFoUJKldB9pZtXUvZcVJ5k6qYO/BDrbsPkhZcZKqsiKa27tIO/j7951IeUmSuoYD3LK8jlSmnWgYqsqKqS4vpqWji70HO2NzNdKy4iQ140pIOceelg46U8N/bUeK/t6L8L5rau1ifFkRlb4Pr6m1E0f/+3LupAquX7JgRPGY2YvOuUVDKhu3JBC2aNEit3LlykMWn4jIkWA4SeBQ19/qgdmhx7P8PBERGQWHOgm8AMw3s3lmVgJcATx0iGMQERHvkHYMO+e6zOwLwGMEQ0Tvcs6tPZQxiIhIj0N+noBz7hHgkUP9vCIi0tfYHNMlIiKAkoCIyJimJCAiMoYpCYiIjGGH9GSx4TKzRmD4pwzDZGBXgcMpBMU1fHGNTXENT1zjgvjGlk9cRznnaodSMC1r14MAAAiDSURBVNZJYKTMbOVQz5Y7lBTX8MU1NsU1PHGNC+Ib26GKS81BIiJjmJKAiMgYdqQmgdtHO4B+KK7hi2tsimt44hoXxDe2QxLXEdknICIiQ3Ok1gRERGQIlARERMYy51xs/oC7gAbg1az5ZwM/Jrg38YvAK/7/eX75OOBhYAOwFvhW1vrTgceB04A/+jIvAx8NlZkHrADqgPuAktCy2cCTBLfBXAt8MUdsk3yZZuD7Wc9/M7ANaM7xmvOKLVSmDHgeeMlv4+uhZVcANwEf89t+BXgWODVrGz8CzgG+49/Ll4FfAxNCZW70cbwGXNzPfhxxLAO910M5FnyZM/38OuBWfLNnaHkSWA38JkdcC/x+aAe+PJTjs1BxZW3vTV92DbByOMebL3eDf4+v8+/ly8BygvHjmTJXAxv939V5xDLQa47s2AcmAA8QHKvrgXcO4zPZ774o0DE2WGyL/fu5huBz8oHQuksIPl91wA1Z2x30szzQ+jn372AFDuUf8B7gDPomga8DHwJOB2b4eScB9X56HPBeP10CPA1cElr/U8DfAscB8/28GcDb+C844H7gCj/9I+Cvsg7WM/z0eOB14MSs2CqAdwOfzXHAne23keuDkFdsoe0YUOmniwk+OGf7x3f7g/ZdwEQ/7xJgRdY21hB8QV4EFPl53wa+7adP9AdsKcGHcxOQLGQsA73XQzkW/OPn/XtuwKPhY8Evvw74L3ongUxcU4B3EHx5ZSeBnMdnoeLK2t6bwOQBnqff482XexKoBd4LjPPz/gq4z0/XAJv9/4l+euIIYxnoNUd27Pt99pnQ537CUN+jgfZFgY6xwWIbR89nbDrBj4sigs/fJuBov95L9D7+B/v8DLh+zv070MLR+APm0jcJPANUZ80zYA9QmmMbtwB/GXp8X643wr9B8/22doV2yjuBxwaI8UHgwlyxAZ/MPuBCy3J9EAoamy8zDlgFnOXXf4m+v1QmZh3UJwD359jWB4B7/PSNwI2hZY/hf+EUMpb+3uuhHAv+A7UhtOxK4N9Dj2cR/CI+D58EcsUFfI2sJNDf8VmIuHJs701yf/EOerwBVcAfcqx7emZ+jvfl34Er84lloM9loY99oBp4I/tYGsp7NIRjJN9jbMix+XnzgJ0ESaDX55vQZy7XcZr9+Rlo/f7+Yt8nYGaTgU7n3P6sRR8CVjnn2rPKTwDeT/BBx8ySwPHOuXVZ5RYTZMpNBNXGfc65Lr94OzCzn3jmEnyYVgwQ21BfW6FjS5rZGoJfFcuccyt8rC85f0SEXEPwCybjEuC3OTb76VC5mQRV+4yoYslsY65fZ4V/PJRjYaaPq78YvwdcD4Tvit5fXENSoLiyOeBxM3vRzK4d5HmyXYA//rOE3+ch78thxpLzc5mtAMf+PKAR+ImZrTazO8ysYojvUb/7okD7ckixmdlZZraWoEnns/51DrRfhvL5Gc5+BUbhpjIjcBFBu2E3M1tI0ExxUdb8IuDnwK3Ouc1+9ln4L5FQuenAfxC0g6bNbEiBmFkl8EvgS865JjP7i+zYhqlgsQE451LAaT4R/trMTiJoH+z1BWtm7yU4cN4dmn0xQfU8XO4moAu4Z8hBFCaWPu+1nz3kYyEXM3sf0OCce9HMzg0t6hPXMOUVVz/e7ZyrN7MpwDIz20BQixnK8bYE+ElWPB8HFgF/GlUsw3zN+R77RQRNc3/tnFthZrcQ9IOsz45rmAqxL4cUm/9htNDMTgDuNrPBjsEhf36GI/Y1AbJ+oZrZLILOyqucc5uyyt4ObHTOfW+A9asIOpFvcs4952fvBib4JALBAV4f3rCZFRN8Kd3jnPtVrm0X4LWNKLZszrl9BG3CS8g6qM3sFOAOYKlzbrefN46gzfKtULlPAu8DPhb65VFP0HGbUfBY/Pxc7zUM7Vio93HlivEc4DIzexO4FzjPzP4zO64RyDeuPpxz9f5/g9/W4uznGcBigjbrTDwXEHQmXhb6hT7kfTmUWAb5XOaS77G/Hdjuv0gh6IQ9I3u7/RhoXxRiXw4rNufceoLO65MYeL8M5fMz7M9ov+1Eo/VHqM2VrDYwgh73l4AP5ljvHwm+OBJZ858FxvvpEoJq8pdyrP8LendAfS60zICfAd/Lmperfe6TDLFPoBCxhcrU0tP5VE7QOb4UeCZUZg7BiIF3Za37Z4RGVBF8Ya8DarPKLaR3x/BmcncM5xNLn/d6BMdCdqfdpTnKnAv8hqD99pkcy7/GEPoECh2XL1cROi4q/HFyyVCON7+P7g09Pp2gaWV+1no1BO3WE/3fG0DNSGIZ6DVHeez74+r40P76zlDeo/72RSH35WCxEXx+Mn0dRwFvEVw1tIjgczWPno7dhWQdp/T/+cm5fn/7xTkXryRA0JTzNtBJkE2/Avw0tPzvgBZ6hlatIRjNMYug3XJ9aP5nCL6Mngit/3G/7fD6p/llR/sdW+cPvNLQeu/22385tN7fh2Pz5d4k6Dxq9vFnRhD9s3+c9v+/VqjYQuufQjDs8WXgVR/fh4GvhcrcAewNbX+ln/994NxQuTqCdsVMuR+Flt1E8KXyGv2Mbskzllzv9aUETRmDHgt+2SL/vJv8a8vVQXcuQRLIjmua30dNwD4/XdXP8XlNoeMK7e+X6Blie1P28/R3vAFfBj4ZKvM7gk7HTCwPhZZ92u/rOuBTI41lkNcc2bFPMLR0pT9W/ptgVNdQP5N99kUh9+VgsQGf8O/nGoKBE5eHll1KMCpuE0HNCIb4+elv/QG/d0frC38of34nXJHH+h9nCONkj7TYsg6Us4dQbhVQHIdYonq/D7e4Rvo8wDJgehxiGWD9SI79AsQV2b4sQGx5HacD/enaQSIiY9jh0DEsIiIRURIQERnDlARERMYwJQERkTFMSUBEZAxTEhARGcP+P0HABBr/ufhcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5dY0CSj5jvm2",
        "colab_type": "text"
      },
      "source": [
        "### task 3.2: find maxmimum infection rate for China"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xiMac2cyjvm2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c9e3f1f1-e1f4-416c-be45-e81d027074b7"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"China\"].diff().max()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1290.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xpXWFB0jvm5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "6b93ef81-6189-4902-b70d-b4edaf1da089"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Italy\"].diff().plot()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c2762668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nykv1Gltjvm8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "fc4f5562-0683-4118-fbe6-64ccb50c26fe"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Italy\"].diff().max()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "919.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgoxP6AYjvnB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "204bcf41-3705-469c-c55e-e3b470e26ce8"
      },
      "source": [
        "corona_dataset_aggregated.loc[\"Spain\"].diff().max()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1179.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZ7Y9GKijvnF",
        "colab_type": "text"
      },
      "source": [
        "### Task 3.3: find maximum infection rate for all of the countries. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5a4XttnIjvnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6e64bf2a-b377-4fcf-8f21-ec6aea4ac437"
      },
      "source": [
        "countires = list(corona_dataset_aggregated.index)\n",
        "max_infection_rates = []\n",
        "\n",
        "for c in countires:\n",
        "    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
        "len(max_infection_rates)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "188"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgKUA332jvnH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_dataset_aggregated['max_infection_rates']=max_infection_rates"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-EUpfjqjvnJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "0ec5b0ab-3950-431c-cad6-5931434124ce"
      },
      "source": [
        "corona_dataset_aggregated.head()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>...</th>\n",
              "      <th>5/23/20</th>\n",
              "      <th>5/24/20</th>\n",
              "      <th>5/25/20</th>\n",
              "      <th>5/26/20</th>\n",
              "      <th>5/27/20</th>\n",
              "      <th>5/28/20</th>\n",
              "      <th>5/29/20</th>\n",
              "      <th>5/30/20</th>\n",
              "      <th>5/31/20</th>\n",
              "      <th>6/1/20</th>\n",
              "      <th>6/2/20</th>\n",
              "      <th>6/3/20</th>\n",
              "      <th>6/4/20</th>\n",
              "      <th>6/5/20</th>\n",
              "      <th>6/6/20</th>\n",
              "      <th>6/7/20</th>\n",
              "      <th>6/8/20</th>\n",
              "      <th>6/9/20</th>\n",
              "      <th>6/10/20</th>\n",
              "      <th>6/11/20</th>\n",
              "      <th>6/12/20</th>\n",
              "      <th>6/13/20</th>\n",
              "      <th>6/14/20</th>\n",
              "      <th>6/15/20</th>\n",
              "      <th>6/16/20</th>\n",
              "      <th>6/17/20</th>\n",
              "      <th>6/18/20</th>\n",
              "      <th>6/19/20</th>\n",
              "      <th>6/20/20</th>\n",
              "      <th>6/21/20</th>\n",
              "      <th>6/22/20</th>\n",
              "      <th>6/23/20</th>\n",
              "      <th>6/24/20</th>\n",
              "      <th>6/25/20</th>\n",
              "      <th>6/26/20</th>\n",
              "      <th>6/27/20</th>\n",
              "      <th>6/28/20</th>\n",
              "      <th>6/29/20</th>\n",
              "      <th>6/30/20</th>\n",
              "      <th>max_infection_rates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>216</td>\n",
              "      <td>218</td>\n",
              "      <td>219</td>\n",
              "      <td>220</td>\n",
              "      <td>227</td>\n",
              "      <td>235</td>\n",
              "      <td>246</td>\n",
              "      <td>249</td>\n",
              "      <td>257</td>\n",
              "      <td>265</td>\n",
              "      <td>270</td>\n",
              "      <td>294</td>\n",
              "      <td>300</td>\n",
              "      <td>309</td>\n",
              "      <td>327</td>\n",
              "      <td>357</td>\n",
              "      <td>369</td>\n",
              "      <td>384</td>\n",
              "      <td>405</td>\n",
              "      <td>426</td>\n",
              "      <td>446</td>\n",
              "      <td>451</td>\n",
              "      <td>471</td>\n",
              "      <td>478</td>\n",
              "      <td>491</td>\n",
              "      <td>504</td>\n",
              "      <td>546</td>\n",
              "      <td>548</td>\n",
              "      <td>569</td>\n",
              "      <td>581</td>\n",
              "      <td>598</td>\n",
              "      <td>618</td>\n",
              "      <td>639</td>\n",
              "      <td>675</td>\n",
              "      <td>683</td>\n",
              "      <td>703</td>\n",
              "      <td>721</td>\n",
              "      <td>733</td>\n",
              "      <td>746</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>33</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>34</td>\n",
              "      <td>35</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>36</td>\n",
              "      <td>37</td>\n",
              "      <td>38</td>\n",
              "      <td>39</td>\n",
              "      <td>42</td>\n",
              "      <td>43</td>\n",
              "      <td>44</td>\n",
              "      <td>44</td>\n",
              "      <td>45</td>\n",
              "      <td>47</td>\n",
              "      <td>49</td>\n",
              "      <td>51</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>58</td>\n",
              "      <td>62</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>592</td>\n",
              "      <td>600</td>\n",
              "      <td>609</td>\n",
              "      <td>617</td>\n",
              "      <td>623</td>\n",
              "      <td>630</td>\n",
              "      <td>638</td>\n",
              "      <td>646</td>\n",
              "      <td>653</td>\n",
              "      <td>661</td>\n",
              "      <td>667</td>\n",
              "      <td>673</td>\n",
              "      <td>681</td>\n",
              "      <td>690</td>\n",
              "      <td>698</td>\n",
              "      <td>707</td>\n",
              "      <td>715</td>\n",
              "      <td>724</td>\n",
              "      <td>732</td>\n",
              "      <td>741</td>\n",
              "      <td>751</td>\n",
              "      <td>760</td>\n",
              "      <td>767</td>\n",
              "      <td>777</td>\n",
              "      <td>788</td>\n",
              "      <td>799</td>\n",
              "      <td>811</td>\n",
              "      <td>825</td>\n",
              "      <td>837</td>\n",
              "      <td>845</td>\n",
              "      <td>852</td>\n",
              "      <td>861</td>\n",
              "      <td>869</td>\n",
              "      <td>878</td>\n",
              "      <td>885</td>\n",
              "      <td>892</td>\n",
              "      <td>897</td>\n",
              "      <td>905</td>\n",
              "      <td>912</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>52</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 162 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                1/22/20  1/23/20  ...  6/30/20  max_infection_rates\n",
              "Country/Region                    ...                              \n",
              "Afghanistan           0        0  ...      746                 42.0\n",
              "Albania               0        0  ...       62                  4.0\n",
              "Algeria               0        0  ...      912                 30.0\n",
              "Andorra               0        0  ...       52                  4.0\n",
              "Angola                0        0  ...       13                  2.0\n",
              "\n",
              "[5 rows x 162 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvxTroTYjvnM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FiANPtNWjvnO",
        "colab_type": "text"
      },
      "source": [
        "### Task 3.4: create a new dataframe with only needed column "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMAGNsjrjvnP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "corona_data=pd.DataFrame(corona_dataset_aggregated[\"max_infection_rates\"])"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WtLSH22jvnU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "1cd3bcac-4b9a-4727-ce8b-a6e5cf25483a"
      },
      "source": [
        "corona_data.head()"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>max_infection_rates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max_infection_rates\n",
              "Country/Region                     \n",
              "Afghanistan                    42.0\n",
              "Albania                         4.0\n",
              "Algeria                        30.0\n",
              "Andorra                         4.0\n",
              "Angola                          2.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IE-mx5kGjvnW",
        "colab_type": "text"
      },
      "source": [
        "### Task4: \n",
        "- Importing the WorldHappinessReport.csv dataset\n",
        "- selecting needed columns for our analysis \n",
        "- join the datasets \n",
        "- calculate the correlations as the result of our analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHBu7hJdjvnX",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.1 : importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O-k2jixLjvnX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "happiness_report_csv=pd.read_csv(\"/content/sample_data/worldwide_happiness_report.csv\")"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "295eZWmzjvna",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "7604b2c0-0242-46bd-c672-d9501dcb2add"
      },
      "source": [
        "happiness_report_csv.head()"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Overall rank</th>\n",
              "      <th>Country or region</th>\n",
              "      <th>Score</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <th>Generosity</th>\n",
              "      <th>Perceptions of corruption</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Finland</td>\n",
              "      <td>7.769</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "      <td>0.153</td>\n",
              "      <td>0.393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>7.600</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "      <td>0.252</td>\n",
              "      <td>0.410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Norway</td>\n",
              "      <td>7.554</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "      <td>0.271</td>\n",
              "      <td>0.341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>7.494</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "      <td>0.354</td>\n",
              "      <td>0.118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>7.488</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "      <td>0.322</td>\n",
              "      <td>0.298</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Overall rank Country or region  ...  Generosity  Perceptions of corruption\n",
              "0             1           Finland  ...       0.153                      0.393\n",
              "1             2           Denmark  ...       0.252                      0.410\n",
              "2             3            Norway  ...       0.271                      0.341\n",
              "3             4           Iceland  ...       0.354                      0.118\n",
              "4             5       Netherlands  ...       0.322                      0.298\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLXe9B29jvnd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "239b6368-f64d-4c53-e11f-1beb9cd14a1d"
      },
      "source": [
        "happiness_report_csv.shape"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inqAI5Yejvng",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.2: let's drop the useless columns "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCk70Gz_jvng",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "b3ebec6c-a896-464a-9046-3b38359aff12"
      },
      "source": [
        "happiness_report_csv.columns"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Overall rank', 'Country or region', 'Score', 'GDP per capita',\n",
              "       'Social support', 'Healthy life expectancy',\n",
              "       'Freedom to make life choices', 'Generosity',\n",
              "       'Perceptions of corruption'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmSWzYCujvnj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "useless_cols=['Overall rank','Score','Generosity','Perceptions of corruption']"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qy_uyassjvnm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "8023d2e8-28ad-4f3b-9dd3-b74134ebf6d8"
      },
      "source": [
        "happiness_report_csv.drop(useless_cols,axis=1,inplace=True)\n",
        "happiness_report_csv.head()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "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 or region</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Finland</td>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Norway</td>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Country or region  ...  Freedom to make life choices\n",
              "0           Finland  ...                         0.596\n",
              "1           Denmark  ...                         0.592\n",
              "2            Norway  ...                         0.603\n",
              "3           Iceland  ...                         0.591\n",
              "4       Netherlands  ...                         0.557\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvxT8R4Hjvnp",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.3: changing the indices of the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWFQ_hFLjvnq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "happiness_report_csv.set_index(\"Country or region\",inplace=True)"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6j5Il2vjvns",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "69f9dcfd-3afa-49a9-d94a-7ec338ffb45c"
      },
      "source": [
        "happiness_report_csv.head()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country or region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Finland</th>\n",
              "      <td>1.340</td>\n",
              "      <td>1.587</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Denmark</th>\n",
              "      <td>1.383</td>\n",
              "      <td>1.573</td>\n",
              "      <td>0.996</td>\n",
              "      <td>0.592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Norway</th>\n",
              "      <td>1.488</td>\n",
              "      <td>1.582</td>\n",
              "      <td>1.028</td>\n",
              "      <td>0.603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Iceland</th>\n",
              "      <td>1.380</td>\n",
              "      <td>1.624</td>\n",
              "      <td>1.026</td>\n",
              "      <td>0.591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>1.396</td>\n",
              "      <td>1.522</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.557</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   GDP per capita  ...  Freedom to make life choices\n",
              "Country or region                  ...                              \n",
              "Finland                     1.340  ...                         0.596\n",
              "Denmark                     1.383  ...                         0.592\n",
              "Norway                      1.488  ...                         0.603\n",
              "Iceland                     1.380  ...                         0.591\n",
              "Netherlands                 1.396  ...                         0.557\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9heAl1hjvnu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UfbHbuuXjvnv",
        "colab_type": "text"
      },
      "source": [
        "### Task4.4: now let's join two dataset we have prepared  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsOOBioKjvnw",
        "colab_type": "text"
      },
      "source": [
        "#### Corona Dataset :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FG-s6dWFjvnw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "2299dcfa-0468-4554-e996-8ad65905a0c4"
      },
      "source": [
        "corona_data.head()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>max_infection_rates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                max_infection_rates\n",
              "Country/Region                     \n",
              "Afghanistan                    42.0\n",
              "Albania                         4.0\n",
              "Algeria                        30.0\n",
              "Andorra                         4.0\n",
              "Angola                          2.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pzFXHUaUjvny",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "272950d3-35d1-41e5-c6e3-e882b9c63dde"
      },
      "source": [
        "corona_data.shape"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(188, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WrG3fvmtjvn1",
        "colab_type": "text"
      },
      "source": [
        "#### wolrd happiness report Dataset :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdB3f6fPjvn2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c0436cff-08a4-4b07-dbf9-5c2a72e5926d"
      },
      "source": [
        "happiness_report_csv.shape"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(156, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvfdUBpVjvn4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "6c4adf70-3946-48b8-93c8-ddae1b2d8590"
      },
      "source": [
        "data=corona_data.join(happiness_report_csv, how=\"inner\")\n",
        "data.head()"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>max_infection_rates</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>42.0</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.848</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>30.0</td>\n",
              "      <td>1.002</td>\n",
              "      <td>1.160</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>48.0</td>\n",
              "      <td>1.092</td>\n",
              "      <td>1.432</td>\n",
              "      <td>0.881</td>\n",
              "      <td>0.471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.850</td>\n",
              "      <td>1.055</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             max_infection_rates  ...  Freedom to make life choices\n",
              "Afghanistan                 42.0  ...                         0.000\n",
              "Albania                      4.0  ...                         0.383\n",
              "Algeria                     30.0  ...                         0.086\n",
              "Argentina                   48.0  ...                         0.471\n",
              "Armenia                     19.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgyULhQ0jvn8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "106c6512-b540-45bd-d864-f6e1da0789e5"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0K5elCljvn_",
        "colab_type": "text"
      },
      "source": [
        "### Task 4.5: correlation matrix "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1V5Z_OljvoA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "19b49c15-d0fd-4212-8de2-475fe340b56d"
      },
      "source": [
        "data.corr()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>max_infection_rates</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>max_infection_rates</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.198641</td>\n",
              "      <td>0.121379</td>\n",
              "      <td>0.226578</td>\n",
              "      <td>0.089609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GDP per capita</th>\n",
              "      <td>0.198641</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.757521</td>\n",
              "      <td>0.859431</td>\n",
              "      <td>0.394799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Social support</th>\n",
              "      <td>0.121379</td>\n",
              "      <td>0.757521</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.751632</td>\n",
              "      <td>0.456317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <td>0.226578</td>\n",
              "      <td>0.859431</td>\n",
              "      <td>0.751632</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.423146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freedom to make life choices</th>\n",
              "      <td>0.089609</td>\n",
              "      <td>0.394799</td>\n",
              "      <td>0.456317</td>\n",
              "      <td>0.423146</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              max_infection_rates  ...  Freedom to make life choices\n",
              "max_infection_rates                      1.000000  ...                      0.089609\n",
              "GDP per capita                           0.198641  ...                      0.394799\n",
              "Social support                           0.121379  ...                      0.456317\n",
              "Healthy life expectancy                  0.226578  ...                      0.423146\n",
              "Freedom to make life choices             0.089609  ...                      1.000000\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aWrE3G4GjvoF",
        "colab_type": "text"
      },
      "source": [
        "### Task 5: Visualization of the results\n",
        "our Analysis is not finished unless we visualize the results in terms figures and graphs so that everyone can understand what you get out of our analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fYE9jiHWjvoG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "outputId": "2d9c8dd9-292a-4bb0-9b3a-f8a7c83e8c18"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>max_infection_rates</th>\n",
              "      <th>GDP per capita</th>\n",
              "      <th>Social support</th>\n",
              "      <th>Healthy life expectancy</th>\n",
              "      <th>Freedom to make life choices</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>42.0</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.517</td>\n",
              "      <td>0.361</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.848</td>\n",
              "      <td>0.874</td>\n",
              "      <td>0.383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>30.0</td>\n",
              "      <td>1.002</td>\n",
              "      <td>1.160</td>\n",
              "      <td>0.785</td>\n",
              "      <td>0.086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>48.0</td>\n",
              "      <td>1.092</td>\n",
              "      <td>1.432</td>\n",
              "      <td>0.881</td>\n",
              "      <td>0.471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>19.0</td>\n",
              "      <td>0.850</td>\n",
              "      <td>1.055</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.283</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             max_infection_rates  ...  Freedom to make life choices\n",
              "Afghanistan                 42.0  ...                         0.000\n",
              "Albania                      4.0  ...                         0.383\n",
              "Algeria                     30.0  ...                         0.086\n",
              "Argentina                   48.0  ...                         0.471\n",
              "Armenia                     19.0  ...                         0.283\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RanXKtfVjvoL",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.1: Plotting GDP vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ulhedW7qjvoL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "394c0f5b-2222-4d31-f4b0-567cc5ada60a"
      },
      "source": [
        "x=data[\"GDP per capita\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c26bd7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvbEjjM7jvoQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "4a4991e5-8928-436b-bcf9-54deeb482eb3"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c269d278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ELkSBE9jvoS",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.2: Plotting Social support vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r3Zw-6xEjvoT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "3f588bb0-8279-4b72-9822-4128aaa4deaf"
      },
      "source": [
        "x=data[\"Social support\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c29d4f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K_U-FH-jvoX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "aeff4eba-8638-497d-ef7f-88bdc6f9bfd6"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c2963eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Er8MnBJpjvoa",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ql2T97rTjvob",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "30cd793a-c185-40dc-ed53-e6b8bd2dfe8b"
      },
      "source": [
        "x=data[\"Healthy life expectancy\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c2b62e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hKfRxTSjvod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "a7781c9b-c333-4f78-b998-be1fc311aa18"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c2b24128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1p9R1Oxjvof",
        "colab_type": "text"
      },
      "source": [
        "### Task 5.4: Plotting Freedom to make life choices vs maximum Infection rate"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mbgxtWupjvog",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8ebdf326-81ec-4a5c-e767-341eeb013467"
      },
      "source": [
        "x=data[\"Freedom to make life choices\"]\n",
        "y=data[\"max_infection_rates\"]\n",
        "sns.scatterplot(x,np.log(y+1))"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c2ab55f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67_H9y_9jvoi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "963e4fba-8f27-40f9-8cfe-67ef7ad38729"
      },
      "source": [
        "sns.regplot(x,np.log(y+1))"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f61c2ac3b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq83hlUpoP6F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgo4SiDDoQxx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ylj8F2NoQr_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OG1cTuvMsZmV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 120,
      "outputs": []
    }
  ]
}