_COVID19 Data Analysis Using Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to Covid19 Data Analysis Notebook\n",
    "------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's Import the modules "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modules are imported.\n"
     ]
    }
   ],
   "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.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.1: importing covid19 dataset\n",
    "importing \"Covid19_Confirmed_dataset.csv\" from \"./Dataset\" folder. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <th>4/29/20</th>\n",
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       "      <th>0</th>\n",
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       "      <td>65.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Albania</td>\n",
       "      <td>41.1533</td>\n",
       "      <td>20.1683</td>\n",
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       "      <td>0</td>\n",
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       "      <td>726</td>\n",
       "      <td>736</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</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",
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       "      <td>25</td>\n",
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       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
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       "<p>5 rows × 104 columns</p>\n",
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       "  Province/State Country/Region      Lat     Long  1/22/20  1/23/20  1/24/20  \\\n",
       "0            NaN    Afghanistan  33.0000  65.0000        0        0        0   \n",
       "1            NaN        Albania  41.1533  20.1683        0        0        0   \n",
       "2            NaN        Algeria  28.0339   1.6596        0        0        0   \n",
       "3            NaN        Andorra  42.5063   1.5218        0        0        0   \n",
       "4            NaN         Angola -11.2027  17.8739        0        0        0   \n",
       "\n",
       "   1/25/20  1/26/20  1/27/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
       "0        0        0        0  ...     1092     1176     1279     1351   \n",
       "1        0        0        0  ...      609      634      663      678   \n",
       "2        0        0        0  ...     2811     2910     3007     3127   \n",
       "3        0        0        0  ...      717      723      723      731   \n",
       "4        0        0        0  ...       24       25       25       25   \n",
       "\n",
       "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0     1463     1531     1703     1828     1939     2171  \n",
       "1      712      726      736      750      766      773  \n",
       "2     3256     3382     3517     3649     3848     4006  \n",
       "3      738      738      743      743      743      745  \n",
       "4       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv=pd.read_csv(\"covid19_Confirmed_dataset.csv\")\n",
    "corona_dataset_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's check the shape of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 104)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.2: Delete the useless columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_csv.drop([\"Lat\",\"Long\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
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       "      <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",
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       "<p>5 rows × 102 columns</p>\n",
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      ],
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       "  Province/State Country/Region  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  \\\n",
       "0            NaN    Afghanistan        0        0        0        0        0   \n",
       "1            NaN        Albania        0        0        0        0        0   \n",
       "2            NaN        Algeria        0        0        0        0        0   \n",
       "3            NaN        Andorra        0        0        0        0        0   \n",
       "4            NaN         Angola        0        0        0        0        0   \n",
       "\n",
       "   1/27/20  1/28/20  1/29/20  ...  4/21/20  4/22/20  4/23/20  4/24/20  \\\n",
       "0        0        0        0  ...     1092     1176     1279     1351   \n",
       "1        0        0        0  ...      609      634      663      678   \n",
       "2        0        0        0  ...     2811     2910     3007     3127   \n",
       "3        0        0        0  ...      717      723      723      731   \n",
       "4        0        0        0  ...       24       25       25       25   \n",
       "\n",
       "   4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "0     1463     1531     1703     1828     1939     2171  \n",
       "1      712      726      736      750      766      773  \n",
       "2     3256     3382     3517     3649     3848     4006  \n",
       "3      738      738      743      743      743      745  \n",
       "4       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_csv.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2.3: Aggregating the rows by the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_aggregated=corona_dataset_csv.groupby(\"Country/Region\").sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1092</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</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>...</td>\n",
       "      <td>609</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</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>...</td>\n",
       "      <td>2811</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</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>...</td>\n",
       "      <td>717</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</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>...</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/21/20  4/22/20  4/23/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1092     1176     1279   \n",
       "Albania               0        0        0  ...      609      634      663   \n",
       "Algeria               0        0        0  ...     2811     2910     3007   \n",
       "Andorra               0        0        0  ...      717      723      723   \n",
       "Angola                0        0        0  ...       24       25       25   \n",
       "\n",
       "                4/24/20  4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \n",
       "Country/Region                                                                 \n",
       "Afghanistan        1351     1463     1531     1703     1828     1939     2171  \n",
       "Albania             678      712      726      736      750      766      773  \n",
       "Algeria            3127     3256     3382     3517     3649     3848     4006  \n",
       "Andorra             731      738      738      743      743      743      745  \n",
       "Angola               25       25       26       27       27       27       27  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 100)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x245b5efaa00>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"].plot()\n",
    "corona_dataset_aggregated.loc[\"Italy\"].plot()\n",
    "corona_dataset_aggregated.loc[\"Spain\"].plot()\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_dataset_aggregated.loc['China'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc[\"China\"][:3].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.1: caculating the first derivative of the curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6rIVgTOBNpoVwFtAMfENEnheRr4lIETBPVY8AuJ/V7vgFQKPv/IOubIHbHlxucizlcgjhQQGhKBahI25JZWOCbjIBIQJcANyuqucDnbjuoWFkywvoCOVDX0Bko4hsE5Ftzc3N461v4A3XQiiJWZeRMWZyAeEgcFBVt7jn38MLEMdcNxDuZ5Pv+IW+82uBw668Nkv5EKp6h6quVtXVVVVVk6h6MPXnEAZ1GeVHbJSRMWbiAUFVjwKNIrLMFV0GvAxsBja4sg3AQ257M7BeRGIishgvebzVdSu1i8haN7roOt85Jof6RhmFLYdgjBkqMsnz/wS4V0TygNeAj+EFmQdE5HrgAPAhAFXdKSIP4AWNJHCTqmY6rm8E7gIKgEfcw+RYtjuVwVoIxhjPpAKCqm4HVmfZddkwx28CNmUp3wasnExdzOiSbr6iaLZRRhYQjAk8u1M5QFLpYXIIsQg9iTQJm+DOmECzgBAgyRFyCACd1kowJtAsIARIaoQcAmDTVxgTcBYQAmSk+xDAJrgzJugsIATISHcqg3UZGRN0FhACZNi5jDJdRhYQjAk0CwgBMtwoo74uI8shGBNoFhACJOGmroiGsyeVLYdgTLBZQAiQ4XIIxdZCMMZgASFQhsshFOVZC8EYYwFhSjW2dvFPP3q5ry9/qg2XQwiFhKK8sAUEYwLOAsIU+snOo3ztV/toaOqYlt+fmf46Ehr6thfn24ynxgSdBYQp1NwRB2BfS+e0/P6+FkJ46JpENsGdMcYCwhRqae8FYP/x6QkIw+UQAIrzo3YfgjEBZwFhCrW4FsL+aWshZB9lBFAcC9PRY8toGhNkFhCmUMs0dxklUiO0EGIROuOpIeXGmOCwgDCFMgHh9eNd0/L7U2klHBK8lUoHKo5FLYdgTMBZQJgi6bRyvKOXaFg4eqqH7t6p/zaedAEhm5L8CO3WZWRMoE06IIhIWESeF5EfuedzReRREdnrfs7xHXuziDSIyG4RucJXvkpEdrh9t0q2r7CzXFt3gmRaObe2HJiexHIqnc7aXQT9o4xUp+ceCWPM9MtFC+HPgF2+558BHlPVeuAx9xwRWQ6sB1YA64DbRCTszrkd2AjUu8e6HNRrRsl0F61e5MXH6Ugsj9RCKIpFSCt0JyyPYExQTSogiEgt8NvA13zFVwF3u+27gat95fepalxV9wENwBoRqQFKVfVp9b6e3uM75w0jcw/CKhcQ9vlaCE3tPfzObU/x2K5jp7UOqbQO30KwCe6MCbzJthBuAT4N+Fdnn6eqRwDcz2pXvgBo9B130JUtcNuDy4cQkY0isk1EtjU3N0+y6lOrpcO7B6GusojK4hivt/Qnlh/b1cTzB07y8XufY8trx09bHbwWQva33KbANsZMOCCIyPuBJlV9dqynZCnTEcqHFqreoaqrVXV1VVXVGH/tzNDS7rUQKotjLK4sHNBCeKqhhcriGLVzCvjDu7ex83DbaalDKjVCC8GW0TQm8CbTQrgYuFJE9gP3AZeKyLeAY64bCPezyR1/EFjoO78WOOzKa7OUv6G0dMQJh4TygiiLKor6cgjptPLrV4/zzvpKvnn9hZTkR9jw9a00tuZ+aGoinSaSZdoK8HUZWQvBmMCacEBQ1ZtVtVZV6/CSxY+r6rXAZmCDO2wD8JDb3gysF5GYiCzGSx5vdd1K7SKy1o0uus53zhtGS0eciqI8QiFhcWURTe1xOuNJXjnaTmtnLxctrWR+eQH3XH8hHfEkt/28Ied1GDGHELNlNI0JutNxH8IXgMtFZC9wuXuOqu4EHgBeBn4M3KSqmSEtN+IlphuAV4FHTkO9plVLRy+VxTEA6iqKAG/o6a9fbQHg4qUVACytLubKt8znoe2Hc35fwEijjDIBodMCgjGBlZOAoKo/V9X3u+3jqnqZqta7n62+4zap6hJVXaaqj/jKt6nqSrfvE/oGHAzf0hGnssQFhMpCwLtj+amGFs6qKqKmrKDv2N+7cBFdvSl+uL2/50xV+ewPd/C9Zw8yUV4OIftbbqOMjDF2p/IUaWmPU1mcB/S3EPYe62DLvlYuXlI54Nhza8tYuaCUe595ve9GsQefO8S3njnAfz/52oTrMJYWQrvlEIwJLAsIU0BVaenopcp1GRXFIlSXxHho+yG6elN93UUZIsI1axbxytF2njtwkuMdcf7p4ZfJC4fYfaydQye7J1SP1AhJ5VgkRDQs1kIwJsAsIEyBUz1JelPpvhwCeK2E11o6EYG1Z1UMOefK8+ZTHIvw7S0H2PTwLjriSW5Zfx4AT7zSNOT4sRiphSAi3vQV1kIwJrAsIEyBzLQVlSV5fWWZPMLK+WWUF+YNOac4FuHq8+fz0PZDPPj8IW541xLeu/IMFs4t4Oe7JxYQRhplBF7LxZLKxgSXBYQp4L8pLaOu0ssjXLR0aOsg45o1i0imlcWVRdz07qWICO9eVs1TDcfpmcCcQ8kRksrgBSEbdmpMcFlAmAKZaSv8AWFpVTEA71g6/B3Xy+eX8vkPLOer11xAftSbB/Ddy6rpTqTYsq9v8BbJVHpM3+yTI+QQwJsC27qMjAkuCwhToK/LyBcQLjtnHnd97K1DEsqDfezixSyfX9r3/G1LKohFQn15hN5kmg/f8Qwf+MqvSKTSw70M0L9AznAyU2AbY4LJAsIUaOmIExKYW9SfKwiHhEuWVWddvWwk+dEwFy2p6Msj/PMju3j29RO81tzJQ9tHnvEjOUoOoTjfVk0zJsgsIEyBlo5e5hbljfjtfDzefXY1+4938dUnGvjGU/v56EV1LK8p5bYnGkilh7+nb/QWQtgCgjEBZgFhCrR0xAd0F03Wu5d5M4r/y092c/6Z5fzN+87hTy5dymstnTy848iw53kthJGTypZDMCa4LCCMU0c8OWpf/WC5DggL5xZSX13MnMIoX73mAvIiIa5YcQb11cV89fEG0sO0EkZvIUTpTqRIjvP6jDFvDBYQxul9X/4lX3l8fDORegFh6L0Gk3H7tav47g0XMb/cmwMpFBI+celSdh9r59FhVl5LjrCmMvTPZ9QZt2U0jQkiCwjjcKonwYHWLnYdOTWu81rae3PaQgBvVtSl1cUDyn77zTXUVRTyH4/vJdv8gMmUjjzstG8K7NzOsmqMmR0sIIxDZtGaI209Yz6nM56kO5Hqm+n0dIqEQ3z8kqW8dOgUv9gzdInRkZbQBO9OZbAWgjFBZQFhHBpbvUnlDo9jcrls9yCcTlefv4D5Zfl85fGGIa2E0aau6J8C21oIxgSRBYRxyLQQjnf2jnnqiP6AkNscwnDyIiFuuGQJ214/MeBuZvDuaB7txjSwKbCNCSoLCOPQeKJ/neOxdhs1tw+dtuJ0+9+rF1JZHBuS/B6thVBii+QYE2gTDggislBEnhCRXSKyU0T+zJXPFZFHRWSv+znHd87NItIgIrtF5Apf+SoR2eH23SrjvX13ijS2dpGp2Vi7jTIthKopyCFk5EfDbHznYn7V0ML2xpN95cm0Eh4hqZxpIXT0eENrG5o6JjSJnjFmdppMCyEJ/IWqngOsBW4SkeXAZ4DHVLUeeMw9x+1bD6wA1gG3iUjYvdbtwEag3j3WTaJep03jiW7OOcObV2jwIjVPvNLE5heGTh3R0NQxZNqKqfB7Fy6ivDA6oJUwlumvAf71p7tZ8bmf8J5//wVX3PIkLx1qO+31NcZMvwkHBFU9oqrPue12YBewALgKuNsddjdwtdu+CrhPVeOqug9oANaISA1QqqpPu7WU7/GdM2OoKo2tXby1zmvwHDk5sMvolp/t4VP3b6ehqb2v7NDJbr6z9QAfeMt8ouGp7Z0rikX4/bWL+NmuY5zs6kVVRx1lVBKLcNnZ1ZxTU8rHLq7jH69aQTyR5n/d/mu+5VvO0xjzxhTJxYuISB1wPrAFmKeqR8ALGiJS7Q5bADzjO+2gK0u47cHl2X7PRryWBGeeeWYuqj5mze1x4sk0S6qLqSqJDegyUlUamjpIppW/++FOvv1HFyIi/NtPdqPAX12xbErrmvGW2nIA9rV08uYFZQBER2ghhELCnR9964Cy9725hk898AKf/eFLPLmnmb+/ckXfzXDGmDeWSX9tFZFi4PvAJ1V1pDu2sv0l0hHKhxaq3qGqq1V1dVXV8OsInA6ZhPLCOYXML8vncFt/QDjS1kNnb4pza8t4+rXjbH7hMC8dauPB5w9x/dsXUzuncErrmpFZhOf1410k3XQWI+UQsqkojvGNj76Vm997Nk/ubeayf/sFt//8VXqTNr2FMW80kwoIIhLFCwb3quqDrviY6wbC/cys93gQWOg7vRY47Mprs5TPKAfckNOFcwuZX14woIXQ0NQBwGfWnc2bF5Sx6eFd/P3mncwtyuPGS5ZMS30BFs4tICReCyEzC+pIOYThhELCH79rCY/++bt4R30lX/zxK/z5A9tzXFtjzHSbzCgjAe4Edqnqv/t2bQY2uO0NwEO+8vUiEhORxXjJ462ue6ldRNa617zOd86MkbkprXZOgQsIPX196pmAUD+vhH+8eiXNHXG2vX6CT76nntL86LTVORYJM7+8gP3HO/tbCCPkEEazcG4hd1y3mg+uquWXe5otp2DMG8xkWggXA78PXCoi293jfcAXgMtFZC9wuXuOqu4EHgBeBn4M3KSqmTGNNwJfw0s0vwo8Mol6nRaNrV1Ul8TIj4apKcunO5HiZJd3R29DcwflhVEqi/M4b2E5N7xrCW+tm8NH1kxtniObxZVF7D/eNakWwmBvqS3jVE+SY6fik34tY8zMMeGksqr+iuz9/wCXDXPOJmBTlvJtwMqJ1mUqNJ7oYuFcLxewwCVVD7d1M6coj4amDpZWFfetfvbX685GVce9GtrpsKiikP/3whGSaa/PPxeL9LxpXgkArxw9xRll+ZN+PWPMzGB3Ko9RY2s3Z7qAkBllc9gNPW1o6hgy8+hMCAYAdRVFtHUnaHF3TOeihbDsDC8g7DnWPsqRnqb2Hv78/u2c6rE5koyZySwgjEEileZIWzcL53iBoKbc+1Z8+GQ3rZ29tHb2DgkIM0VdhTfSqKHZy3PkooVQXpjHvNIYrxwdW0B4+MUj/OD5Qzzz6vFJ/25jzOljAWEMDp/sJq1Q61oIlUUx8sIhDrd19yWUZ2xAcENPM/XM1Q1yy84oHXMLYaubZG+vq4MxZmaygDAGfUNO3f0EoZBQU57P4ZM9Mz4gZIaevtqUuxYCwLJ5xew91tGXrB6OqvYHhDEGEGPM9LCAMAaZIadnVvTfYFZTls+Rk93sbWqnIBpmftnMvHs3M/Q0E7hykUMAL7EcT6Z5/XjniMe92tzJ8c5eQgJ7jlkLwZiZzALCGDSe6CIaFs4o7R9Rk7k5raGpgyXVRYRy9If2dFhcWcS+Fu8Pd65aCGe7Sf52j5JH2LLPyxtcevY8Xm0evUVhjJk+FhDGoLG1i/nlBQP+mC4oL+DoqR72HGunvrpkGms3urqKInpT3rDTkdZUHo+l1cWIwO5RuoG27muluiTG5curiSfTfYsMGWNmHgsIY9DY2tWXP8ioKSsgrXDsVHzG5g8yFvm6uiZzp7JfQV6YuoqiEVsIqsqW11pZs3gu9fPGN1TVGDP1LCCMQeOJbhbOHZgjmF/e3320pGpmB4TFbqQR5C6HAPCmecUjthAaW7s5eqqHCxfPpd4FTRtpZMzMZQFhBA1N7fzBXb+htbO3r888Y4FvCuiZ3kKo8wWEXOUQwBt6ur+lc9hV1TL5gzWLKyjJjzK/LN9GGhkzg+VkPYQ3ms54ki/++BXu3XKAwmiYm997NtdcOHBeohoXEKJhGdAlMxMtnFNISCCtuW0hLJtXQlq9exxWuvUW/Lbua6W8MNrXOlg6r8RaCMbMYBYQBmloaueGbz3Ha80dXLt2EX92WT0VxUPXQy6ORSjNjzCvNH/KV0Mbr7xIiAVzCmhs7SaSw7pmprDYfbQ9e0DY38qaurl9I7DeVF3MN187TiqtOW2pALT3JHj9eFfWehhjxmZm/yWbYptfOMyVX3mKE529fPP6C/k/V63MGgwyls8vZdWiOVNYw4nLTGGRyxZCXUUheZFQ1kTx0bYeXj/exZrFc/vK6ucVE0+mOXgi+0ijzS8c5tcNLeOaVltVefjFI1z2b7/g/f/xK379asv4L8QYA1gLAfD+qPz7o3v4j8cbWLVoDl+95oIxzeJ518fWEJohk9iNpq6iiF/ubcnpN/NIOMTSqmK27GvlG0/t48WDbTQ0dXCqJ0FrpzeZ3oWLK/qO7x9p1MGiiqIBr3X/bw7w19/f4R1XXcx1F9WxbF4Jze1xmtt7WDi3kEuWVQ+of0NTO5se3sUTu5tZXlNKLBribx7cwY8/+U7yo+GcXacxQRH4gNCbTPOZ77/Ig88f4sOrF/JPv7NyzF1As+mPTiaxnMsWAnitpO89e5DtjSepLolxdk0pZ1UVUZofZVFFISsX9Cfjl/aNNGrn8uXz+sqfff0Ef/fDnbx9aSVXnTefu5/ez9/98KWh11BRyB+8fTHVJfl885n9PNVwnMK8MJ/97XP46EV1PPNaK9feuYVbH9vLp9edndPrNMHQ0hFn+4GTvMf3+QySQAeEtu4EH7/3WZ5qOM6nLn8Tf3Lp0hkzbXWuXXp2NU81tOR8fedPX7GMdSvOYOWCslFbVaX5UWrK8tnrm8Li2KkebvzWs5xRls9Xrjmf8sI8Priqlh2H2mjrTlBVEqOiKMbWfa389y9f43MP7QRgflk+f3XFMta/dWFft97b6yv54Kpa7njyNd5/7nyWzy/NWg9jsulJpPjoN7by0qFT3PLh87j6/AXTXaUpJ7N1GcTVq1frtm3bJnz+S4fa+Pi9z3H4ZDdf+N1z+eCq2tFPMpN23de30toZ50d/8g7auhNc9/Wt7D3Wzg8+fnFfknokzx84wcnuBO9YWpk1QX6is5fLv/QLasoK+I+PnD9gyK0xI/mbH+zg21sOUFdRSEtHL//zp+8YMH/ZG4WIPKuqq7PtmzEtBBFZB3wZCANfU9UvnI7fo6rc/5tGPrd5JxVFedz/x2tZtWju6CeanKivLubeLcd5tbmDP7pnG42tXXzlmgvGFAwAzj9z5CT+nKI8/vGqldz07ee45F9/zvKaUn5rxTzKC7y1rVPqdQscbevhaFsP8WSKlHqfi6XVxVx+zjze+aYqimIz5r9GVoNX5FNVXjzYxrbXT7C8xhvskBcJDXs8eP8OLzSepK07QTQcIi8SIj8apjgWoSQ/ggAtHb20dMRJptOcU1PK0qrinI5UyyVVpbG1m11HT1FWEGXZvBLmFOUN2A/ZF6968LmDfHvLAW541xKuXXsm7/3yL/nT+57nuze8bcaPIsylGdFCEJEwsAdvDeaDwG+Aj6jqy8OdM9EWwlefaOBffrKbd9RXcsuHzxtxFJHJvUzyuDAvTCwS4j+vXcWFZ1WMfuI4HTrZzSM7jvA/O47w3IGTA/ZFw8K80nzmleZTmBdGRPr+oLZ1J8iLhDinppT5ZfnUlBVQXRqjrCBKWUGUkvwIhXlhCqIR8iJCPJmmN5kmrVBZnEd1ST4Fef25JVWlO5HieEcvJ7sSiHjDgKPhEKpKKq0kUkooBJFQiLxwiHgyxfHOXk509tLWnaA7kaKrN0VLR5y9xzrYc6yd1s5eFlUUUl9dQllBlCf3NnOkrafv9xblhVmzeC69qTQHT3Rz+GQ3+ZEwVaUxKotjHGnr7pvFdzxikRBnuTvzE6k0qbSSVkUVFKUgGqYwz/s3SqWVnmSaeCJFTVk+b15QxooFZX1riRxt66GtO0EyraRSiog3JUphXpjeZJpXmztpaOrg2KkeSguilBdEKS/03ofSgiglsQhdvSlO9SQ40ZVg99F22roHrspXWRwjPxqivSdJRzxJSX6Es88o4ZyaUhaUFxASIa3Kv/10D+fWlnHvH15IJBziRy8e5hPffp4/fudZfOzixeRHvWCZFw71DaPuiCdpOtVDc3ucwrwIFcV5VBTnEYuEB4yUm0g3dDyZors3RUl+NOdDtEdqIcyUgPA24O9V9Qr3/GYAVf3n4c6ZaEBobO1i8wuHueFdS3L+D21Gt73xJFd/9Snqq4u5c8Nbp6RJ3hFPkkh6k/uJeLmMbLPTJlJptu0/wc92HWPPsXYOnezmyMkeuoe5E3s4hXlhBO9GwFRa+yYWnKyCaJj6ecXUV5dQWZLH/pZO9jZ10Nwe521nVXDFijNYu6SCnYfa+MWeZrbua6U4P0LtnELml+cTT6TdqK04lSV5nLewnLfUljOvNJ9EKk1vKk13b4qOuPfHM61QWZRHZYn3penlw6fYcaiN/S2dhEJCNCyEQyFCAiEXVHsSaboSKbriScIhIT8aJhoO0djaxd6mdvyT3YZDQnEsQjQsREIhUqr09KboSqQIh4SzKotYWl1MTVk+HfEkJ7sSnOjqpa07yanuBO09CYpiEUrzvSCxpLqYlQtKWV5TyqmeJHuOtrP7WDuptFKSH6E4FuFEV4JXjp5i99F2unr739cF5QX84KaLqC7pz4P91Xdf4LvPHhzyPkRCQigk9CbH976GQ0IkJOSFQ0QjIWLuEQ5J/2clme77EpCR+TKSTKX7AuznPrCcD7/1zBF+2/BmQ0D4ILBOVf/QPf994EJV/cSg4zYCGwHOPPPMVa+//vqU19VMjqry8z3NrF40h5L86HRXZ1SqSldvirbuBG3dCdp7knQnUnT3JulNKXnhELGo16XQ0h6nqT1Oa2cvgreQUkiEsoIoFUV5lBVGESCRUnpTKQQhEvb+SKhCbypNIqXEIiEqivKYU5RHaUGUorww+VGvRTWbBz1096bYdfQUqt5cYFXFsWG7n7J1ceVSOq109CZJp9UFjOiALjbwRiD+bNcx2roT9CRSdCdSJJLee5dMKXOKvKVkK4tjdPemaOno5XhHnERaEbwvH17LyZNKp0mmtO/9702miSe9snBICLsgmwlwBXlh2nuSnOjq5VR3gkg4RH40RCwS5n1vrpnwPVCzIYeQ7Z0fEqlU9Q7gDvBaCKe7Uib3RIR3L6ue7mqMmYhQFItQFIswv3xmLoI0WxTkhblglBxQxukOfKGQ94d3JHmREO97c81prcdMM1OyJQeBhb7ntcDhaaqLMcYE0kwJCL8B6kVksYjkAeuBzdNcJ2OMCZQZ0WWkqkkR+QTwE7xhp19X1Z3TXC1jjAmUGREQAFT1f4D/me56GGNMUM2ULiNjjDHTzAKCMcYYwAKCMcYYxwKCMcYYYIbcqTwRItIMjOdW5UogiMtp2XUHT1Cv3a57bBapalW2HbM2IIyXiGwb7nbtNzK77uAJ6rXbdU+edRkZY4wBLCAYY4xxghQQ7pjuCkwTu+7gCeq123VPUmByCMYYY0YWpBaCMcaYEVhAMMYYA8yCgCAiXxeRJhF5aVD520Tkv0XkchF5VkR2uJ+Xuv2FIvKwiLwiIjtF5AuDzq8RkZ+KyHki8rQ75kUR+bDvmMUiskVE9orI/W5q7ikhIgtF5AkR2eXq9mdZrr3CHdMhIl8ZdP4mEWkUkY4srz2jr91Xh3wR2SoiL7g6/oNv34jvv++4m0Xk90TkUyLysrvOx0Rkke+YDe4694rIhqm8Rl8dJnyts/2z7n5/WESeF5Ef+coC8TmfUVR1Rj+AdwIXAC8NKv8H4HeB84H5rmwlcMhtFwLvdtt5wC+B9/rO/xjwF8CbgHpXNh84ApS75w8A6932fwI3TuF11wAXuO0SYA+wfNC1FwFvB24AvjLo/LXuNTqyvPaMvnZfPQUodttRYAuwdizvv+81ngCqgHcDha7sRuB+tz0XeM39nOO258yma53tn3X3Oz8FfBv4ka8sEJ/zmfSY9gqM8cNSx9CA8CugbFCZAMeBWJbX+DLwR77n9+P+wA467gWg3r1WCxBx5W8DfjKN/wYPAZdnu3bgo4P/o/j2ZfuPMquu3dWhEHgOb63tMb3/QCnwVJbXOj9TDnwE+C/fvv8CPjLbrnXQvln1WcdbIfEx4FIGBoTAfc6n+zHju4yyEZFKIKGqbYN2/S7wvKrGBx1fDnwA70OHiISBZar68qDj1uB9w3oVqABOqmrS7T4ILMjxpYyJiNTh/RHbMsK1j/W1Ztu1h0VkO9AEPKqqI/0bDH7/34N7zwe5HnjEbS8AGn37Zuu1Zl6jnNn3Wb8F+DSQ9tUvUJ/zmWLGLJAzTr8F/NRfICIrgC+6ff7yCPAd4FZVfc0VX4jXJPcfVwN8E9igqmmRrKt8T/kYXREpBr4PfFJVT4nINQy69nGaNdcOoKop4Dz3h+4HIrISOJexvf/rgG8MOu5aYDXwrkxRtl+bk8qP0ySvdVZ+1kXk/UCTqj4rIpf4dg35Pz5OM/q6Z6pZ2UIA3gv8OPNERGqBHwDXqeqrg469A9irqreMcH4p8DDwWVV9xhW3AOXuPxl4zdrDubyI0YhIFC8Y3KuqD7riAXWfgFlx7YOp6kng53h/5Mf6/q8BtvqOew/wt8CVvm/WB4GFvnNm67XC7PysXwxcKSL7gfuAS0XkW4PrPQEz/bpnpunusxrLA18OAe8b3Qv031RX7p7/bpbz/gnvD2poUPmvgVK3nYfXvP5klvO/y8CE08en8JoFuAe4ZVBZ37X7yj/KGPtWZ8O1++pQRX/yrwAvWfqBsbz/wArgPt/z8/G6CeoHHTcX2IeXUJ7jtufOpmud7Z91Xx0uAX4UtM/5THpMewXG8CH5Dt6ogATet7m/Bu7y7f8s0Als9z2q8aK9Art85X/o/uM97jv/Wvfa/vPPc/vOwvuG2eA+OEMSeKfxut/u6v+ir16f81+7O24/0Ap0uH+fzEik/+uep93Pv58t1+6r37nA8+7f4CV3/avH+P7/JfBR33E/A475jtns2/cH7jobgI9N0+d8Mtc6qz/rvvpdghcQBlz3G/1zPpMes27qChH5LNCgqvdN8PxrgVpV/cKoB88wQb72jLH+G4jIo3jdKkempma5F9T3O6jXPRPMuoBgjDHm9JitSWVjjDE5ZgHBGGMMYAHBGGOMYwHBGGMMYAHBGGOMYwHBGGMMAP8fwKT2seossL8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc['China'].diff().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### task 3.2: find maxmimum infection rate for China"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15136.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc['China'].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6557.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc['Italy'].diff().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9630.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.loc['Spain'].diff().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.3: find maximum infection rate for all of the countries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "countries=list(corona_dataset_aggregated.index)\n",
    "max_infection_rates=[]\n",
    "for c in countries:\n",
    "    max_infection_rates.append(corona_dataset_aggregated.loc[c].diff().max())\n",
    "corona_dataset_aggregated[\"max_infection_rate\"]=max_infection_rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</th>\n",
       "      <th>4/22/20</th>\n",
       "      <th>4/23/20</th>\n",
       "      <th>4/24/20</th>\n",
       "      <th>4/25/20</th>\n",
       "      <th>4/26/20</th>\n",
       "      <th>4/27/20</th>\n",
       "      <th>4/28/20</th>\n",
       "      <th>4/29/20</th>\n",
       "      <th>4/30/20</th>\n",
       "      <th>max_infection_rate</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",
       "    </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>...</td>\n",
       "      <td>1176</td>\n",
       "      <td>1279</td>\n",
       "      <td>1351</td>\n",
       "      <td>1463</td>\n",
       "      <td>1531</td>\n",
       "      <td>1703</td>\n",
       "      <td>1828</td>\n",
       "      <td>1939</td>\n",
       "      <td>2171</td>\n",
       "      <td>232.0</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>...</td>\n",
       "      <td>634</td>\n",
       "      <td>663</td>\n",
       "      <td>678</td>\n",
       "      <td>712</td>\n",
       "      <td>726</td>\n",
       "      <td>736</td>\n",
       "      <td>750</td>\n",
       "      <td>766</td>\n",
       "      <td>773</td>\n",
       "      <td>34.0</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>...</td>\n",
       "      <td>2910</td>\n",
       "      <td>3007</td>\n",
       "      <td>3127</td>\n",
       "      <td>3256</td>\n",
       "      <td>3382</td>\n",
       "      <td>3517</td>\n",
       "      <td>3649</td>\n",
       "      <td>3848</td>\n",
       "      <td>4006</td>\n",
       "      <td>199.0</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>...</td>\n",
       "      <td>723</td>\n",
       "      <td>723</td>\n",
       "      <td>731</td>\n",
       "      <td>738</td>\n",
       "      <td>738</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>743</td>\n",
       "      <td>745</td>\n",
       "      <td>43.0</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>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  1/28/20  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan           0        0        0        0        0        0        0   \n",
       "Albania               0        0        0        0        0        0        0   \n",
       "Algeria               0        0        0        0        0        0        0   \n",
       "Andorra               0        0        0        0        0        0        0   \n",
       "Angola                0        0        0        0        0        0        0   \n",
       "\n",
       "                1/29/20  1/30/20  1/31/20  ...  4/22/20  4/23/20  4/24/20  \\\n",
       "Country/Region                             ...                              \n",
       "Afghanistan           0        0        0  ...     1176     1279     1351   \n",
       "Albania               0        0        0  ...      634      663      678   \n",
       "Algeria               0        0        0  ...     2910     3007     3127   \n",
       "Andorra               0        0        0  ...      723      723      731   \n",
       "Angola                0        0        0  ...       25       25       25   \n",
       "\n",
       "                4/25/20  4/26/20  4/27/20  4/28/20  4/29/20  4/30/20  \\\n",
       "Country/Region                                                         \n",
       "Afghanistan        1463     1531     1703     1828     1939     2171   \n",
       "Albania             712      726      736      750      766      773   \n",
       "Algeria            3256     3382     3517     3649     3848     4006   \n",
       "Andorra             738      738      743      743      743      745   \n",
       "Angola               25       26       27       27       27       27   \n",
       "\n",
       "                max_infection_rate  \n",
       "Country/Region                      \n",
       "Afghanistan                  232.0  \n",
       "Albania                       34.0  \n",
       "Algeria                      199.0  \n",
       "Andorra                       43.0  \n",
       "Angola                         5.0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_dataset_aggregated.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3.4: create a new dataframe with only needed column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "corona_data=pd.DataFrame(corona_dataset_aggregated[\"max_infection_rate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</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>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  232.0\n",
       "Albania                       34.0\n",
       "Algeria                      199.0\n",
       "Andorra                       43.0\n",
       "Angola                         5.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "source": [
    "### Task 4.1 : importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "happiness_report_csv=pd.read_csv(\"worldwide_happiness_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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  Score  GDP per capita  Social support  \\\n",
       "0             1           Finland  7.769           1.340           1.587   \n",
       "1             2           Denmark  7.600           1.383           1.573   \n",
       "2             3            Norway  7.554           1.488           1.582   \n",
       "3             4           Iceland  7.494           1.380           1.624   \n",
       "4             5       Netherlands  7.488           1.396           1.522   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  Generosity  \\\n",
       "0                    0.986                         0.596       0.153   \n",
       "1                    0.996                         0.592       0.252   \n",
       "2                    1.028                         0.603       0.271   \n",
       "3                    1.026                         0.591       0.354   \n",
       "4                    0.999                         0.557       0.322   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                      0.393  \n",
       "1                      0.410  \n",
       "2                      0.341  \n",
       "3                      0.118  \n",
       "4                      0.298  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.2: let's drop the useless columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "useless_cols=[\"Overall rank\",\"Score\",\"Generosity\",\"Perceptions of corruption\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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  GDP per capita  Social support  Healthy life expectancy  \\\n",
       "0           Finland           1.340           1.587                    0.986   \n",
       "1           Denmark           1.383           1.573                    0.996   \n",
       "2            Norway           1.488           1.582                    1.028   \n",
       "3           Iceland           1.380           1.624                    1.026   \n",
       "4       Netherlands           1.396           1.522                    0.999   \n",
       "\n",
       "   Freedom to make life choices  \n",
       "0                         0.596  \n",
       "1                         0.592  \n",
       "2                         0.603  \n",
       "3                         0.591  \n",
       "4                         0.557  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.drop(useless_cols,axis=1,inplace=True)\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.3: changing the indices of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  \n",
       "Country or region                                \n",
       "Finland                                   0.596  \n",
       "Denmark                                   0.592  \n",
       "Norway                                    0.603  \n",
       "Iceland                                   0.591  \n",
       "Netherlands                               0.557  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.set_index(\"Country or region\",inplace=True)\n",
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task4.4: now let's join two dataset we have prepared  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Corona Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</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>232.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                max_infection_rate\n",
       "Country/Region                    \n",
       "Afghanistan                  232.0\n",
       "Albania                       34.0\n",
       "Algeria                      199.0\n",
       "Andorra                       43.0\n",
       "Angola                         5.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(187, 1)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### wolrd happiness report Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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  Social support  Healthy life expectancy  \\\n",
       "Country or region                                                            \n",
       "Finland                     1.340           1.587                    0.986   \n",
       "Denmark                     1.383           1.573                    0.996   \n",
       "Norway                      1.488           1.582                    1.028   \n",
       "Iceland                     1.380           1.624                    1.026   \n",
       "Netherlands                 1.396           1.522                    0.999   \n",
       "\n",
       "                   Freedom to make life choices  \n",
       "Country or region                                \n",
       "Finland                                   0.596  \n",
       "Denmark                                   0.592  \n",
       "Norway                                    0.603  \n",
       "Iceland                                   0.591  \n",
       "Netherlands                               0.557  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 4)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "happiness_report_csv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</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>232.0</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>34.0</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>199.0</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>291.0</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>134.0</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_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               232.0           0.350           0.517   \n",
       "Albania                    34.0           0.947           0.848   \n",
       "Algeria                   199.0           1.002           1.160   \n",
       "Argentina                 291.0           1.092           1.432   \n",
       "Armenia                   134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=corona_data.join(happiness_report_csv,how=\"inner\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4.5: correlation matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</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_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250118</td>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.078196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP per capita</th>\n",
       "      <td>0.250118</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.394603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Social support</th>\n",
       "      <td>0.191958</td>\n",
       "      <td>0.759468</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>0.456246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <td>0.289263</td>\n",
       "      <td>0.863062</td>\n",
       "      <td>0.765286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.427892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <td>0.078196</td>\n",
       "      <td>0.394603</td>\n",
       "      <td>0.456246</td>\n",
       "      <td>0.427892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              max_infection_rate  GDP per capita  \\\n",
       "max_infection_rate                      1.000000        0.250118   \n",
       "GDP per capita                          0.250118        1.000000   \n",
       "Social support                          0.191958        0.759468   \n",
       "Healthy life expectancy                 0.289263        0.863062   \n",
       "Freedom to make life choices            0.078196        0.394603   \n",
       "\n",
       "                              Social support  Healthy life expectancy  \\\n",
       "max_infection_rate                  0.191958                 0.289263   \n",
       "GDP per capita                      0.759468                 0.863062   \n",
       "Social support                      1.000000                 0.765286   \n",
       "Healthy life expectancy             0.765286                 1.000000   \n",
       "Freedom to make life choices        0.456246                 0.427892   \n",
       "\n",
       "                              Freedom to make life choices  \n",
       "max_infection_rate                                0.078196  \n",
       "GDP per capita                                    0.394603  \n",
       "Social support                                    0.456246  \n",
       "Healthy life expectancy                           0.427892  \n",
       "Freedom to make life choices                      1.000000  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max_infection_rate</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>232.0</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>34.0</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>199.0</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>291.0</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>134.0</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_rate  GDP per capita  Social support  \\\n",
       "Afghanistan               232.0           0.350           0.517   \n",
       "Albania                    34.0           0.947           0.848   \n",
       "Algeria                   199.0           1.002           1.160   \n",
       "Argentina                 291.0           1.092           1.432   \n",
       "Armenia                   134.0           0.850           1.055   \n",
       "\n",
       "             Healthy life expectancy  Freedom to make life choices  \n",
       "Afghanistan                    0.361                         0.000  \n",
       "Albania                        0.874                         0.383  \n",
       "Algeria                        0.785                         0.086  \n",
       "Argentina                      0.881                         0.471  \n",
       "Armenia                        0.815                         0.283  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.1: Plotting GDP vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GDP per capita', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[\"GDP per capita\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='GDP per capita', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.2: Plotting Social support vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Social support', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[\"Social support\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Social support', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D7XfT0xaZNRlMVRmZSvGRN1zZMNPbynHgybTLZNKbWzyTCl7mef+hC9xx91OEHSEecZhOuYzPpAEICQxNplC8+caOA5kibQR97VFecEkfn/mVnftUdedi1/N9IhCRLwGXAg8BuVemQGBHgNd7cBb4PyJyDbAPeJeqTiz+MMNoTqxBbD6LxdirnR/xu7gXOvCQwIPHhviNLz7A1lUdvO81z5s1a+DYhQnWdrfx5us3sn1T75Lse/6WPt7FVr76wHHOjE6xujPulZHOpBmZSVMoIZRzAo7A9Zv72L6pl52betm8IkFnPMJnfFwvSGhoJ3C53x28j+tuB96hqveJyB3A+5gjYicitwG3AQwMDFTgsoZRH6xBbD611uDJEaT+P+fAM65yemQGEQiJcOTCJB+46zH+y7Eh7tx/kpADiWiIZ0en+X+/+yTvesXWJSV1VZX1vW3s2raKfUeHePDYcP5EUEjuLLW+J04q4/LRN5Y3GSCII3gUWAOcLutKszkBnFDV+7Lf34nnCGahqruB3eCFhipwXcOoC63eIFaMIJPBKkmQ01nOgR8+N4EIOCK4qqQzLqB87seH6WuPEg8vvbpnZCrFg8eG2Ht0iP1HhzkzOj3vPmFHUBQUQo6g2dtEhLXd5f8tBXEEK4HHReR+YCZ3o6reHPSiqnpGRI6LyHNV9WfAK4HHgz6PYTQL9dr9Njr1CAEVO52lMy77jw1xw+13zwoV5Rz4TDpDyBFcBVchEnKIhR2mUpmyq3uSaZdHT46wN5vgfXpwfF4Nflc8zI5NvWwf6CUkwpfuO0oqnWF4KoWrnkPoaAuTdpU3X7+x7PcpiCP4UNlXKc47gC9nK4YOAW+t8PMbRsNQr91vs1KJCquFQkC58tTciWBsOsXJ4WnCBfd7/zce5Y9Tz+PN12/k9u8c9E4C2cC8ovQmYgxPepU5z5ydIBZ26GuP0h4NM51yWdPVNs8eV5VDZyfyC/8jJ0fmicVFQsLV67u9xX9TL5f1d+AUJNP72qN89YHjZNxxktmqofU97bz5+o1LCkUFrRpaDVyf/fZ+Va2ZJq5VDRn1YjmWfTbya6pUhdWtu++dF46bTKaJOMJkys0//9OD46QyLut72uiIR3Cztf59iRh//qZruP/QBXb/6BBHzk8QDgkr26OkMi7nJ1K0R0MXex4UetsjhEOhfI7g2dFp9h/1wj0PHhtmeCo1y0YBLu3vYOemXnZs6uXKdV3E5nQrL4WOWJjV3W0VrRr6r8DHgT149n9KRN6jqncuyVLDaGCaYfB4UBr9NVWqwmqhBH2uPPUzP3yG40OTuKqs7ooRj4SysX+IhS+Gd56/pY/nb+nj/kMX8lU8k0mXvkSEvvYY4zPpvP7/xEyGX9i+lp8eOs9f7nmaE0PzQ0Sru2Ls2NRLdzzCIydGODsxw8HTY1y7oSeQE8jZc3p0irVdbUs6FQQJDf0RcH3uFCAiq4Dv4SV6DWNZshzLPhv9NVWqwmpugl7VK7/s74xzaX8Hf5KtsPn9rx3g/MTMrMcWC+/kHALArX99L11xr3PZEe9koQqTKZcv3Xds1uPaYyGu2+jt+Hdu6mVdT5wHDg/l+wQKtYTehb9qo8I+g3IeP5cgjsCZEwo6Tx0lKozGopFDDUthOZZ91vo1Bf3bqFSF1W0vvYT33/UYaTdFLOQldtOu8os7NsySdHjz9Ru54+6n8onf6ZS7YPJVVTlyfpKQCMcvTDGTcZkbXQ87whXruvL1/MMTKf5x3wm++sAxfvizs7z5+o1L1hLy83gRIeT40wIN4gi+IyL/Bnwl+/2bgG8FeLyxTGn0UMNSWI5ln7V8TeX8bSylwqpQzmHzqg5+d9dlfPWB4xw9fzG5+tUHjgPkF8y5zVtr5oRZzo3PsP/YMPuODrH/6BDnJ+bLN0QcIRp2+K87NnLL9RvyC/T9hy7wl3uenrdzn0qmWdUZm/UcfquNwNMi6ooXLN8CbdEQg2PTrOqMEQ07REPOrK7txQgymOY9IvILwEu8y7JbVf/Z7+ON5UujhxqWwnIs+1zqawqywy/nbyNIhZWqMp1yiyp4wsXF/o67n6IjK9dQLIxSGPaZSmY4cGKYT//gafYdHeLI+fknpbAjbFqRIJVWplJp1vckisboF9q5pzKe3XO1hIpVG80l5Ajre9q4MJEkEQ3hiLf7n0ym2bSinc54pORzzHs9Qe6sql8Hvh74KsayZjmGT3LUsuyzVuG1pbymoGMc9x8bIuO6xMIhVnXG6IxHfP1tLNZf4Cl4ZphIeuMaM+7ilY+lwigZV/nZmTH2HfPKOh8/NUp6znNGww6SfWxXWxjX9Wwo1UE8b+eOt/OPhh3SrpYMR4Ud777RsNe3EAs7hEMO73zFVj5w12PMpDO0RUJLHlxU0hGIyI9V9QYRGWP2zAEBVFW7yrqysWxYjuGTQmoxF6DW4bVyX1PQMY65bty0q5wanmZdj7ejDfq3kUy7TKUyTCWDj2ucuxh7CV44dG6cD3zjMR48PsTEzGxhOEdg25oudm7qZfumHu747pOcHJ1mfCbFTDpDbyJK2JGSMf2FVEQ39bXncwW5cNQvv2CAlz13FbFwKL/4LxTjr/QGpaQjUNUbsl87y7qCsexZjuGTajN39z80MdMU4TW/p7+cw1jdGefUyBSiAMqZkWn6u+Il/zaSaZepZIbpdIbpVOld/2Ks7WpjcGzaqxpKZphMZvI7/h8/fS5/v429bfkE7zUbe+iIeb+L+w9d4OjQFKGcU8sog2PT9HfGSsb0F0pE/9LzN/Kybf3cdNUaYuEQsbCD4zOxm6OSG5RA6qOq+iulbjNaD+uaDUax3f+R8xNs6JkdH27E8Jrf01/OYUjUW9zOjc+QzCgKRRvDSsX6gzKTyvDIyRH2HR1icGyaUyPzdXvaoyFesGUFOwZ62L6pl9Vd8aLPlQstKV4sXgRcF85NJLl8Tfeidjx/Sx/vkq38w97jnBmZZkNvgttuvIRXXb5mSa+v0gTJEVxR+I2IhIEdlTXHaFZaYaxiKfzG+IuFVyKOw7NjM3S1RfP3a8TwWjljHLvaInS1RZhMejX8ufck42o+zj+VzHjaOWXiqvL04Hh+MMsjJ0dIZWY/n+BJOKzsiHHLjg3cfO26WfINC3F6dIqVHVHOjiVxUbyHKOkMRUtMczpEsXCIWMThTddv5M3PL62eXM8SbD85gj8E/ifQJiKjuZuBJFllUMNodYLE+IuFV1Z3xTgxPN3w4TW/p78Xbenj03ueIeMqsbBDZzxMNBzi11+ymeHJZNkDWwo5MzKdVeocYv+xIUanZ8s0C/Cc1Z3s2OTt+K9c1000HLz1KRfn7++KcWEiSSrj4oiwuS/BCy5dkU/ixiOhfDI3KPUuwfaTI/go8FER+aiq/mHVLTKMJiRImWSx8Eo45PCc/g56EtGGD6+VOv3tOTjInftP0tceYXgimXVqLm95wQCX9ndwoUgdvh/GplM8eHw4v+s/NTw/3LO2O86OrG7PdRt76GoLXko5l1ycP+wIG3sTzKQzZFx4303b2Lwi4btWfzHqXYIdJDR0v4h0q+oIeMPngV2q+i/VMMwwmokgJbQLhVfe/7ptDbnwB8F1lb/a8wwi0BWP0hnz3pOpVIb9x0Z4y4v8P1cq4/L4qdG8WueTz44xN2fcEQtz3UBPfvFf31O6Dt8vIkIs7PCfrlxDTyLC539yhJPDU1Vx0vUuwQ7iCD5Y2ECmqsMi8kHgXypulWE0GUFKaBstub6U2HQuyTudylX4uBy9MJHX4cnhp2s2J9+QW/gfPjHMdGq+TPMV67rZsclb/Lf2d/qWUShFyBHikRDxbGw/Fr7YmXvTVWu56aq1FblOIbn3/uzYDIOj0zgiZFSJhhy62sJsXtFR8WsWI5DW0BIfbxjLlqAltI2SXA8am55JZ5hJu8ykXJIZl2TanVfTv1DtfLGu2bNjM+zPNnLtPzZcNGy0ZVU7Owa8Hf9VG7pnPW+5hB2HWMQhEnLycgzl5A+WQuF73xUPcXY8BSgRB5IZl8GxJLdeX/6MgSAEWcj3isifA5/Gayx7B97QecNoeRptl++XUrHpZNr1dvrZRi4/9fyLibhNJtMcOD6Sj/MfvTA/9LGyI5oP9Wwf6KWvPVrkKsGIhBzaoqF8QjdSRkK30hS+92dGpgkLZIC0C4mol2D/6aELvLMGtgRxBO/AGy7/tez3/w78ccUtMowmpVF2+UEojE2rKqpe+OXI+QmOnJsoq6SzUMTt9MgkXfEom1Ym+PL9R3n8G2PznEkiGuLajT1sH/CauTb2tS05ARt2HOJRh7ZIiLZIqKxKnmqSk99ws2GgqVSGaMjxnIGrbFnVgao2Xo5AVSeA94lIh6qOV9EmwzBqQMZV1nV7XbfxSCg/A3cqlWF1Z7wsJ6CqHB+a4uTIFIlYiPGZDM+OjfPU2YtLhiNw+dqu/K5/25rOJS/UIsJDR4f4+/uPcWpkioG+9oY9keXlN/BKXNMZzwGnMy6hkBemgtr2kQTpLH4x8DmgAxgQkWuAt6nq71TLOMMwKkPGVS++n3KZSXux/bTr8sbr1nPH3U+RcbWkFv9CDE0m2X90iH1Hh/npofOMzBnHCDDQl8gu/D1cs6GH9tjFpafcSVux7G6/LRLi3mfO8YnvP0UkJPQmog0thZ4LCa3pjnslsAIhBzIu4CprumJLFpELSpDQ0CeAVwN3AajqARG5sSpWGYZRNq6rJDPegp9b/BeSbCilxV+M6ax8w94jQ+w7NsShsxPz7hNyJB+L/52XXcrPXbG66HMFmbSVi/PvPXyBz//kCCeGp/JVTrt/dLgptJqgQH5DhHU9XsI843rS1pf1dzCenaK21BNNriIpsmrzVaXuG1SG+vic2N3SWgMNw1gyybTL9x4/w9/8+Agnhyd9LeaFFGrxFyPjXpRv2Ht0iMdOzZdviGUrb0IOdLdF8kNRplIZvv3omQUdQU7HJ+MqJ4amvK5dR9h9zzO8ZOtK4pEQbVFv1x9yhD0HB/mTbx+cV+U0mUyzZo5WUCNqNcHsUuPOeITO+EX5ja/c9sKKXKOwIgl106XuH8QRHM+Gh1REosA7gSfKNdQwjODMDfFMpzLc+8z5/K66swLzawFOj0zlF/6Hjg0Xl29Y08nObJx/cjrN//3Nx1FVZtIuvYkoHbFwyf6B06Oequfg2AwOQkgE11WOXJjkmcHxeTvihaqccjLVzSCFXlhqnM64PDs6Q8p1iWQdXbG5DkH7POa+T6UI4gh+C7gDWA+cwKsaenuAxxuGEYDcouot+pls/H5+iKfY4JULEzN85JuP0xEP+4q7j0558g37s4v/6SJqnet64vl6/msL5BvuP3SBT//wGXKxgpxMM8QJObLg1K2w47ChJ8Gjp4Y9J5BLGKsSgqJhnYU6cKMhIZXRhtdqgoulxrd/5yBHzk8SCQkbetpIuTovr1GuBlGx92kx/IjO3a6q7wVerqq/7PuZDcMIRDIX009fTOj6GcAyd/DK+Eya4akUrnqLd7ETQjLt8tipbD3/sWGePDPG3Ct1xcNcN9Cb7+Jd2z1/Qb//0AU+8s3HmUplCDuCq+A4IArnJmZY0R7LJ54jIU+YLR7xvkZCDu94xWX8xhf3EhJQvOoZVVjTHSsa1lmog3vr6i7eduOWpunj2LWtn8/ec4jNKxKzXsvcvEa5GkTF3qfF8HOv14rIHwN/CPyjr2c1DKMkhc1aC+32/TC3k3do0uvOjYUdBMmPMvw//3GEIxcmsvINI8yk58s3XLW+26vn39zLZf0di8o05xK906kMIefi+EIBMgohhfe++rm88vLVC9by79rWz3P6Ozh8boKM69XUr+qMEXKE/s758wEW6+Butj4OP/pC5WoQFb5PfvDjCL4DnAPaszLUgvc7t1GVRsNTT433QlJZOYbcTn86tTT9/ULmdvIm0y6Cl7QdmUoxmcwwmUyT0Wl+Njg267GXrerIyzRfvb6bWAD5hlxIKhp2SGcUxxHE8apf1ve2sbozzn/evqHk87z3pm358Efh4v6iLX3cuvveeb+7ZuzgLoYffapyx8AWvk+IU3Kd9yND/R7gPSLyDVV9Q6n7G0ajsFSN93KdiOsq0wUJXU+2uDKLfjFyJaBfvu8Yx4cmEBFUlTOjM/Pu298ZuyjTPNBDb6J8+YZcSKqvPcqzo9OoC+LATMYl48JvvexSX89TbHF/0ZY+7tx/sujvLkf13lH/LGWjMS9pPDZDKuOdinJJ46WMgc2dkORtRx4pdV8JMgS6nuzcuVP37t1bbzOMJuLW3ffO2035LdMrdCKFH8BiYxbTGZfp7IzdmXSGZHppYxb9ks64PHF6LF/dc/DM6DyZZkfI1/P/5g1beO3Va5Ys3xANe9INv/13+zk3Pk17LMLoVIpz4zPMpF0S0RCffPN1S9qlL/S7i4Yczo7PMD6TJuMqIUfoiIX5s1uuqfmpIMjfyGLP8bFvP8FTZ8eJOA6ru2KEQ86s58k5m3JPQCKyT1V3LnafIJ3FbwRuB/oh3x1toSGjYVmKxvtiSboXXbYiX80znVr6fF2/qCrHLkyy76g3nOXAiWEmk7NbeUKOcPnaTlZ3xTl6bpLR6SRruxOB+grmEs1O32qLeKJtOdnn39l1aX632hkPE85W7gRZCOeSW/TuP3KBeNhhZUcsX53UFglx8IwXnQ6J909dGJ5M8bFvP1FzR1CJYTL5pLGrCyaNa5H7CFI++qfAz6uq9Q4YTUG58VW46ERyQmyuKmFHOHxunJNDi+vqV5ILE8m8TPO+o0OcG58v07xpRVa+YaCXazZ2+64UWQgRb4fbFg3RHp2d5J0bCrll+3p+euhCReL1ew4O8p47DzA2nc7OM84wM+y9111tEU/9VCHigJN1RiKgrnL4fO0bxyo1TKbeQ2kgmCN4ttJOQERCwF7gpKq+vpLPbRhB46v5uv20y+rOOGfHp4mHLyZPp1IZ1nS1la2N44epVIaHTwyzP7vrP3RuvnxDX3uU7QM97NzUy/ZNvazsiC35uiJCIhqiPRYmEQnlF9pCiuVc7tx/ckkngEJu/85BhiZThBwhGhKSGSXtKmdGp/OnjXAFxkJWiqVsNKrxPEsh6DyCr+FNJMtnoVT1n5Zw/XfhdSdbeMmoOItVmKhqfrBKsbr9X9yxgTvufgrV2Zr6123s9q2N44eMqzz57Fh+1//YqdF58g3xiMM1Gy6OY6zUnNyQI9ldf5i2BRb/Qqo9V/fQuQkcwStZFSHKRc2knPbO7d85yFOD44iqdxpQcBW2rmpf8vWD4nejUSqh/KItfXx6zzOkXZdYyKE7ESESCtW0GS6II+gCJoH/VHCbAmU5AhHZALwO+F/A75fzHIZRil3b+nnZc1ddFGFLuZwcnirZrLWQGFuxLt6pVIavPnDclyNQVU4NT7Mvu/A/eGyY8ZnZtd6OwLY1nWzPLvyXr+2q2CCVSMjJ7/zjASd91TqEEXKEKIKIzEru58JH6YxL2HHoTUR4703bqmLDYvgpZS1Vubbn4CB37j9JbyLC2HSambTLhYkUb981UNOcR5B5BG+t8LX/AvgDoHOhO4jIbcBtAAMDAxW+vLEcKQzv5Dp1Uxn11aE7l2JibJ/4/pOzunih9DzekakUDx7zKnv2Hx3mzOh8+YYNvW3sGPBCPddt7KEjvrQ4fyGRkENHLEx7LLykcYzVDmFcsiLB02cnEHf2bv+ylReff9e2fj5+yzUN00ewUCL3k997ks/9+DCj02kcgRXtURLR8LxTVO6U1d0WZ1V2JZxMpms2mSyHH4mJP1DVPxWRT1GkdFdVA9srIq8HBlV1n4jsWuh+qrob2A1e+WjQ6xjLm1x4p1CPp9xF3y9+5vEm0y6PnLw4jvHpwfF5H5zutgjbB7xwz/ZNvfOUM5dK2HFIxEJZ4belz/iF4DmXoLzvNc/j3XcemFUa2hOL8L7XPG/W/Rq9g/iT33uSO+5+mlykzVU4m03yr+lum3WKaoREMfg7EeQSxJUs4n8JcLOIvBaIA10i8neq+pYKXsNYRuRUN5Ppi3H9tOst+tVM3s6l2DzeVMblpVtX8tX7j7Hv2DCPnByZ10sQCQlXr+/Ox/kvLSHfUA4hx4vfd8TCtEUrs/gXUu2u3l3b+vmzBtrtl8vnfnwYRzxnnHEzZAe/cX4iyZrutlmnqEZIFIO/zuJ/zX79wmL3E5FPqeo7/FxUVf8QT7uI7Ing3eYEjBxz5Rhy07SKEWSwSSXI5Q6++NOjnBiexBEhlXH51A+ennffrf0d+YX/ynVdgeQb/JLb+bdHl7b4++2QrdVuvJGO/0G7hyeSGXIRuLDj5PtMXGXe5LFqn7L8UrlApLfLN4xApPKhnYwX5km5gTR4lpq89cv4TJqHjg1n1TqHOFGkl2B1lyffsHNTL9dt7KU74V8GOAiRkOOVeUZDFQn7LFWKI+i1FlpUa2lHEHuD2tQe9f4GHSHbfOeQzLiIMG/yWKNoJ1XSEZSFqu4B9tTZDKMGJNNuQclmJvCiX4y5EsxQOnnrh1TG5YnTo/k4/8EzY/PkG9pjIa7b2Jtf/Nf1xCtS1lmMeCREIhoiEV1awrcY1S4LzVFqUa2VHUHwY9Nc5/bKbau46+EzpF0XR7zTTcgR3vWKy3jnq54z7xqNkPOouyMwlh+5yp3cDj+ZcUmll77oF8NP8tYPqsqR85PsOzrE/mNDPHR8mOnU7HBU2BGuWNfF9uzC/5zVnXm5hUqTa/DK1flX6zpQu4RlqUW1URKnhZSyqZhzOzk8xc1Xr+H7B88ykczQHg3x32+4pKgTaBQq6Qgap+XPqBnFkri10t6B4snbtKv5YSiLcW58hv3ZcM/+o0Ocn5gv37BlZTvbs4NZrl7fEygOHzSJnRvOnsjO6K3W6WIutUpYllpUGyVxWkgpmxZybmdGkzz8oVfXxeZyCCI6F1fV6Tm3rVTVc9lv76ioZXM4eGaMW3ff25RVBMuFXDw/6SOJWysWavwqtuBOJTMcODGcrecf4kgRfZoVHdH8OMYdm3rpay9PptlvEjsW8fR82qIhYuHKJ5P9UG7CMmgStdSiWsyO0akUEUe44fa76zJPotR704inmHLwLUMtIo8Av6mq92a//wXgo6pak/NOz8A23fGuzyxZ3dAoTaH8QmH1TjVCO9Uk4yo/OzOWT/A+fmqU9JxAf1skxDUbu/ND2Af6KiPf8PtfOzAvZDWVyrCiPcZf/vJ1+QavSnUML5WgUsflSDD7eUyhHR2xMGfHZ+hui5Qt8+z3dS/mzBZ7b5YidV4r/MhQB3EEVwF/i5fYXQesAP67qp5Yop2+6BnYpi99z+ca7k1udorF8/3Oyl0K1aj9V1VODk/l9fkfOj7MxMxsmWZPvqErP4f38rVdRUcoLpVb//peuuJhhJxKpiCijE+n+fH7Xlnx60Ftp7GVuwAGcTjVXmSXMk8g9zqeGhxjbDpNbyLCyo5YVZzVUqnoPAJVfURE/hfwJWAMuLFWTqCQZjx2NQoZVy/u8jNe1U4t4/k5Kln7PzKZuijTfGyIZ4tM5drY25ZP8F6zsYeOWPVrJNZ2tXFhcob2aBjHERwRJpNpNvZVRxyt1qWX5YZEglTIVDvsUm6VUuF7vaYrTiQ0w4WJFOmMy9bVXfNKYhthVGopguQI/ga4FLgaeA7wryLyl6r66WoZV4x6J4+ahVwSt3CXX49FvxhLqf2fSWV45OQI+495sf6nB8fn3aenLeIJtg14s3hXV1i+YTFyNf5vf/mlfOSbT5DMuLQ5oXmNRJWm1qWXtUjsVvsa5Tqaue/1yo44iWh43kmlEfsiFiLI1uhRvFCQAodF5IXAn1fHrOJU+8PUrLiuzhJZq3XlTlCC1P67qjw9OJ6v53/k5Mg8meZo2MnLN+zc1Mslq9orLt+wGGHHoT02W9Hz565YQyTk1KxRqNZJy1p0xFb7GuU6Gr/vdSP2RSxEkNDQJ+Z8PwL8RsUtWoCMq/O68lqNXDw/ld/ha0NU7gSlVO3/mZHpfGXP/mNDjE7PlmkWYOvqQvmG7oo3WZXCj65PLRuFal16WYuO2Gpfo1xH4/e9bqaKoiDJ4q3AR4HL8YTiAFDVmmzPW214faqgaqfRQjtLpTBHEI84TM5kmEhleO7qTo4PTXJqeL5M89rueH7hv3ZjD91tweQbKpGcdrJNXh3xcE3r/P1QiUHqrUg5g+H9vteNUlFU6aqhHwMfBD4B/Dzw1uzjP7hUQ/2wnB1BYVPWdFZOOTNXz2AZkUy7fH3fCe46cIoLE0lSRV5rZzzMdRsvTuVa1xOsU7iQuY4n13T2rlcsnJzOOY4zo1Os70nwmy+9hFdfuaahFv+5lLOoGeXh571uFOdcaUewT1V3iMgjqnpV9rYfqepLK2BrSZaLI0hnZpdqzqSaL7QTFFXl8LkJ9mW7eB8+Psx0EZnmK9Z158s6t/ZXTr5hsZr+P3/TNfPuv+/IEH/x/aeIhoVEJMR0Ngzn9wPcLJUiRvVpBOdc0fJRYFpEHOApEfld4CRgf92LMFdkLZl2G3anX+m6/rNjMxfLOo8OMTSZmnefS1e1sz3bxXvVhu5ZC3Ul8ZOcjoYd2qNhErEQf/TPjxKPOAVJPsd3kq+elSLmgPxRy/epEQTl/BDEEfwekADeCXwEeDnwq1WwqemYOwg92WSduJWo659Mpjlw/OJUrqMX5ifEVnZEL8o0D5Qv3xCUhZLTa7vb6E1E541wXEqSr16VIs1UqlhP7H0qThBHoHjNZJuA3Kfkr/H6ClqGuU1ZueqdanfiVpNy6vrTGZeDZ8byu/7HT4/NO+0koiGu3diT3fX3VEy+ISiFwnRtkRDJbNL9Xa/cSm8RZ7SUCpxKVYoE3bU2U6liPbH3qThBHMGXgfcAjwDLO6jNfL2d3P8bNbSzFPyETlSV4xem2Jdd+A8cH2YiOV++4fK1XfkE77Y1nVWRbwjKiy9bSSIW4sv3HePU8FTJWO1S6tcrUcZZzq61mUoV64m9T8UJ4gjOqupdVbOkjrjuxSHouXh+s+/yg7BQ6GRFe4zvP/Es+456Sd6z4/PlGwb6EtmFv4drNvTQXgP5Bj+EHKE9Fs4Pbx9YkeDma9f7euxS6tcr0QRVzq612n0EyyX/0IhS141AkE/tB0Xkc8D3gfyKoKr/VHGrqkgutJNL3jZ6F24tyIVOJpJpVJWx6QxTqQzHh6Z47PTorPv2JiL5BO+OTb2s6ozVyer5VHJ4e7lJvko0QZWza61mF+5yiqs3yozgRiOII3grsA0vP5BbORVoWEeQKiKlvNxLNYOQcZWnBsd4+uw4bZEQh89NzBsaHg87XL2hO7/wX7KyvSJx/kpVKYkI7dFQfoZvI9T5L7VSpCMa4umz42RcJRpyWNkRIxySRXet1ezCrXVcvZqnj0aZEdxoBHEE1+T6BxqRwl3+co7nL5VTWZnmfUeHePD4MGNz5Bscgees7swv/Jev7aq4fEMlqpTasot/R1bdc7mw5+Ag5yeSpDOKI95m5uTwFD2JCO9/3eWLPrZapYq1jKsv9fThx4k0aklnPcNvQRzBvSJyuao+XjVrfFAv/fxmZXQqxYPHh/OL/+mR+fIN63ri+alc1w300BkPJt8QlHLVR6Nhh85YhPZYqCGS0NXgs/ccoqstQnsszNmxGZIZl7AjrOqI1W3xqmVcfSmnj2YOYdXb9iCO4Abg10TkMF6OQABV1ZqUj6Zd5cTQZEslccshmXZ57FSunn+YJ58dmxfu6YqHuS4f5+9hbXf58g3lEER9NBJy8tO8ai0sVw9yu28RyTtkVWVkan5DXq2oZVy9GXs4KkG9bQ/iCG6qmhU+yCV5jdm4qhw+O5Ev63z4xAgzReQbrlrfzfaBXnZu7uWy/o6ayjTPpZT6aDFZ51ah3lUtC4UnahVXb4QejnpQb9uDyFAfraYhhn/Ojs2wNxfnP1ZcvuGyVR1sz+r2XLW+u6EW1MIGr0IRuLe+ZDNru9uWXPHTzNSzqqVUeKIWO9N693DUi3rb3hhF38aiTMykeaggzn98aH4Ipb8zxs5NvWzf1Mv2gR56ErWRbwhCYaVQe1bGeWLG+2P/7Zdt4eXPW12xazVr3Xs9q1rqHZ6A+vdw1It62+5bfbTeXHXtdv3Gd++ptxk1IZ1xeeL0WH4O7xOnR5lbANUeDXHtQI+3+A/0sqG3rSFKJxeisFKoLRrKV3V95A1XVnyRaRT532bjhtvvzucncuTyEz967yvqaJl/GkHts1yqZXul1UeNKqGqHL0wyf6jQ+w9OsSB4yNMpWbLN4Qc4Yp1XewY6GX7ph62remqmExzLfiHvceJhb3Er4gQC4eqtttshJ1tI1PstARehdmZkWliYa93oast0jShlRyNWhrqh3rabo6gTlyYSLIvO4px79Ehzo8n591n04pEXq3zmg09TRc7L6z4GRyfmbfbrFYyrN6Jt0amWB7g3XceQID2WIipZIZkxuXUyBQz6QzRcKgpQivG0jBHUCOmUhkeOXFRpvnQuYl59+lrj7I9F+7Z1MvKjsaRb/BLJOTQHgvTHgsRC190XLVMhtU78dbIFDstnRyeAoWtqzuJhUOcHZthOp1hMpnhY2+8uml32IZ/zBFUiYyrPPnsWH7X/9ipUVKZ2YH+eMTh6g09+V3/5hX1kWleKnMF3opRy2RYvRNvjUyx01LGvdib0xmP0BmP5HMDCzmBZk3GG8WpiyMQkY3AF4E1eLpFu1X1jnrYUilUlVPD0/l6/gePDTM+M1++4blrZss3RJq0Q9YRIRELeQJvPga517IaxvRkFqbYaSnkCOjs399iJ6h6d8EaladeJ4I08D9Udb+IdAL7ROS79ZavCMrIVIoHjw3lZZrPjM6Xb9jQ25ZX67xuYw8d8eY9hIl4VTgd8TDtZQi81TIZ1sxJw2pS7LTUEQsj4PsEtVgyPvdzOyk0F3VZlVT1NHA6+/8xEXkCWA80tCNIpl0ePTmSb+Z6enB8nnxDd1uE7QNeuGf7pl7WdMXrYmsliecX/3BTVSoZ8yl2WsqJ2fk9QS2UjH9qcMxOCk1K3benIrIZuA64r8jPbgNuA1i3YWNtDcOTb3hmcDxbzz/MIydH5slcRMMOV63vZkd28b+0zvINlSKaLfXsiIWXrcBbq7LQacnvYr1QMj6Zdulus7LdZqSujkBEOoCvA7+nqqNzf66qu4Hd4DWU1cKmM6PT7M/u+PcfG54n9iXA1tUd+XDPVeu7G1IMrRy9/0jI4aFjQ/zdfcc4OTxlR3ujKAsl43MNfIVY2W5zUDdHICIRPCfw5XpOORufTvPg8WFv8T82xIki8g2ru2L5yp7rNvbSnaiuTPNSCaL3H3IkX+t/7zPn+fi/P2lHe2NRFkrGf/aeQ1a226TUq2pIgL8BnlDVP6/ltVMZl8dPj3o7/qNDHDwzNl++IRbiuo0XZZrX9zS2fMNcSun95yp+OmORWU1q1pFr+GWh8JKV7TYn9ToRvAT4FeAREXkoe9v/VNVvVfpCqsqR85P5Rq4DJ4aZTs2O84ez8g3bs7v+56zubOqk6EJ6/8+OTdPfFV+w4sc6co2lYGW7zUu9qoZ+jBdurwrnxmfYf2w4v+s/PzFfvmHLyva8TPPV65tPvmExCvX+RYSQI0yn0mxe0U5HbOFfuXXkNibN1Lw196Sw5+Agt+6+tylsb2XqXjVUCaaSGQ6cuCjTfOT8/B3sio5ofhzjjk299LU3nkxzpXjLCwf4xPeeIu26JKJhplIZ0i4lj+jWkdt4NHPzVjPb3mo0pSPIuMrBM6PsPzrM3qNDPH56dN6g+rZIiGs2dud1ezb1Nad8g18KNX62rOpgZUcs8BHdjvaNRzPnbZrZ9lajaRxBMu3yLw+eZN+xIR46PszEzGyZZkfgeWu78rv+563tXPb174tp/JTbWWsduY1FM+dtmtn2VqNpHMGR8xN88u6nZ9020JfId/Fes7Fn0fj3ciHkeDusjlh4WeU1jOI0c96mmW1vNZpq5expi7A9G+PfMdBD/zKQb/BDUIE3Y/nQzHmbZra91WiaUZXPueIa/dYPfrws5Bv80hYN5WUebPFvXWz8orEU/IyqbBpH0Cozi2MRb/Fvj4aWfY7DMIzqYzOLm4RYJER7NER7LNy08wkMw2hezBHUiZy6py3+hmHUG3MENWSheb6GUSuaqUt5qbTSa10qthWtMiFH6IxHWNfTxsa+BH3tUXMCRl3IdfoOjk3P6vTdc3Cw3qZVnFZ6rZXAHEEVEPGkndd0xxnoS7CqM7bgUHfDqBWFnb4i3tdISPIjJpcTrfRaK4GFhipIIuqFfdqjYZwmVi81liet1OnbSq+1EpgjWCK5cs+OmM3zNRqbVur0baXXWgksNFQGkZBDbyLKxr4E63va6G6LmBMwGp633biFVEaZTKZR9b4u107fVnqtlcBOBD5ZTODNMJqBVlKXbaXXWgmss3gRFhrpaBiGPxqhhLMRbKgnfjqLLTQ0BxFv57+6K86mFQn6O+PmBAyjDBqhhLMRbGgGzBFkiUdCrOyMsakvwequOO0m9GYYS6IRSjgbwYZmoKVzBJGQQ2fci/ubwJthVJZGKOFsBBuagZZzBGHHoT0WoiMetg5fw6gijVDC2Qg2NAMtsQ12ROiIh1nb3cbAigQrOmLmBAyjyjRCCWcj2NAMLNsTgYjQFglZp69h1IlGKOFsBBuagWVXPmqdvoZhGBdpmcE0uaSvafsbhmEEp2kdgWn7G4ZhVIamcgRhx6Ejbou/YRhGJWkaRxANOQyssJIvwzCMStM0AXVr8jUMw6gOTeMIDMMwjOpQt9CQiNwE3AGEgM+p6sfqZYth5Gh1pcrlhv0+/VGXE4GIhIBPA68BLgduFZHL62GLYeQwpcrlhf0+/VOv0NDzgadV9ZCqJoGvAm+oky2GAZhS5XLDfp/+qZcjWA8cL/j+RPa2WYjIbSKyV0T2nj17tmbGGa3J8aFJ2uZMnzOlyubFfp/+qZcjKFYDNE/rQlV3q+pOVd25atWqGphltDIbexNMpTKzbjOlyubFfp/+qZcjOAFsLPh+A3CqTrYYBmBKlcsN+336p16O4AFgq4hcIiJR4M3AXXWyxTCArFLlzVfQ3xlnZCpFf2ecD998hVWZNCn2+/RPXcpHVTUtIr8L/Bte+ejfqupj9bDFMArZta3fFoplhP0+/VG3PgJV/RbwrXpd3zAMw/CwzmLDMIwWxxyBYRhGi2OOwDAMo8UxR2AYhtHiNM3MYhEZA35WbztKsBI4V28jStAMNkJz2Gk2VgazsXIUs3OTqi7akds0g2mAn5UawFxvRGSv2VgZmsFOs7EymI2Vo1w7LTRkGIbR4pgjMAzDaHGayRHsrrcBPjAbK0cz2Gk2VgazsXKUZWfTJIsNwzCM6tBMJwLDMAyjCpgjMAzDaHEayhGIyE0i8jMReVpE3lfk5yIin8z+/GER2d6gdv5y1r6HReQnInJNo9lYcL/rRSQjIrfU0r7stUvaKCK7ROQhEXlMRH7YaDaKSLeI/KuIHMja+NY62Pi3IjIoIo8u8PO6f2582Fj3z0zWjkXtLLhfPT83JW0M/LlR1Yb4hydH/QywBYgCB4DL59zntcC38SacvRC4r0HtfDHQm/3/a2ptpx8bC+53N54K7C2NZiPQAzwODGS/729AG/8ncHv2/6uAC0C0xnbeCGwHHl3g543wuSllY10/M37tLPi7qMvnxud7Gfhz00gnAj8D7d8AfFE97gV6RGRto9mpqj9R1aHst/fiTWBrKBuzvAP4OjBYS+Oy+LHxl4B/UtVjAKpaazv92KhAp4gI0IHnCNK1NFJV78ledyHq/rkpZWMDfGZydpR6L6G+nxs/Ngb+3DSSI/Az0N7X0PsqE9SG38DbjdWSkjaKyHrgvwCfqaFdhfh5H58D9IrIHhHZJyK/WjPrPPzY+JfA8/BGrT4CvEtV3dqY55tG+NwEoR6fGV80wOfGD4E/N40kMeFnoL2vofdVxrcNIvJyvD/qG6pqUZFLF7ltro1/AbxXVTPeZrbm+LExDOwAXgm0AT8VkXtV9clqG5fFj42vBh4CXgFcCnxXRH6kqqNVti0IjfC58UUdPzN++Qvq+7nxQ+DPTSM5Aj8D7Rth6L0vG0TkauBzwGtU9XyNbMvhx8adwFezf8wrgdeKSFpV/6UmFvr/fZ9T1QlgQkTuAa4BauUI/Nj4VuBj6gVjnxaRw8A24P7amOiLRvjclKTOnxm/1Ptz44fgn5t6JGQWSHCEgUPAJVxMzF0x5z6vY3bS6/4GtXMAeBp4caO+l3Pu/3lqnyz28z4+D/h+9r4J4FHgygaz8X8DH8r+fzVwElhZh9/5ZhZOHtb9c+PDxrp+ZvzaOed+Nf/c+HwvA39uGuZEoAsMtBeR38r+/DN4WfrX4v3BTOLtxhrRzg8AK4C/yu4c0lpD5UKfNtYVPzaq6hMi8h3gYcAFPqeqi5b11dpG4CPA50XkEbyF9r2qWlO5YhH5CrALWCkiJ4APApECG+v+ufFhY10/MwHsrDulbCznc2MSE4ZhGC1OI1UNGYZhGHXAHIFhGEaLY47AMAyjxTFHYBiG0eKYIzAMw2hxzBEYTYuI/FFWXfHhrNLiC8p4jp0i8skS99klIv9f+ZZWFxHpEZHfqbcdRvPSMH0EhhEEEXkR8Hpgu6rOiMhKvKavQKjqXmBvpe2rFSISwlOb/B3gr+prjdGs2InAaFbW4rXRzwCo6jlVPQUgIq8UkQdF5JGsdnsse/v1Wa37AyJyv4h0Fu72ReT52Z8/mP363MUMEJErss/zUPZUslVENhfqxIvIu0XkQ9n/7xGRv8g+96Mi8vzs7R8SkS+JyN0i8pSI/Gb2dhGRj2fv+4iIvCl7+y4R+YGI/D2e0N3HgEuzdny8ou+y0RLYicBoVv4d+ICIPAl8D/iaqv5QROJ4rf+vVNUnReSLwG+LyF8BXwPepKoPiEgXMDXnOQ8CN2Y7il8F/AnwC4vY8FvAHar6ZRGJ4nUfry5hd7uqvlhEbgT+Frgye/vVePIP7cCDIvJN4EXAtXg6MSuBB7K6MeBJZF+pqodFZHP2/9eWuLZhFMUcgdGUqOq4iOwAXgq8HPiaeBPEHgQO60WlxS8Ab8fTXjmtqg9kHz8KMEdBshv4gohsxVPnjJQw46fAH4nIBjz996d8KFJ+JXv9e0SkS0R6srd/Q1WngCkR+QHeQn8D8BVVzQDPijdp6npgFE8v6HCpixmGHyw0ZDQtqppR1T2q+kHgd/F27wutxEJp6eWPAD9Q1SuBnwfiJa7/98DNeCeLfxORV+ANpSn8XM19jrk26CK3L+ZVJhazzTCCYI7AaEpE5LnZnXuOa4GjeOGdzSJyWfb2XwF+mL19nYhcn318p4jMPRF346mHAvw3HzZsAQ6p6ieBu/DCO88C/SKyIpubeP2ch+Xi/DcAI6o6kr39DSISF5EVeIJiDwD3AG8SkZCIrMIbUVhM3noM6Cxlr2EshIWGjGalA/hUNrSSxlPWvE1Vp8UbIP+P2YX+AeAzqprMJls/JSJteLv4V815zj/FCw39Pt5M2lK8CXiLiKSAM8CHVTUlIh8G7gMO4zmgQoZE5CdAF/DrBbffD3wTT475I6p6SkT+GS9PcADvhPAHqnpGRLYVPqGqnheR/8gmqb+tqu/xYbth5DH1UcOoESKyB3h3tmS18PYPAeOq+mf1sMswLDRkGIbR4tiJwDAMo8WxE4FhGEaLY47AMAyjxTFHYBiG0eKYIzAMw2hxzBEYhmG0OP8/WvIdRc6M0poAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.3: Plotting Healthy life expectancy vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Healthy life expectancy', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[\"Healthy life expectancy\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Healthy life expectancy', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/Mp33uE/g8tU9jsEfGWC4v356U9Vq4J7J539fzjaDhoAUZV5qEZ4pliuftmwOjcVYb5u63Q1U89xqcZ0yBn82aZEoEP9bzD6fOuqEc3YdGOP0dCLv8Y6Qn2vW97PtogGuXd9Pd8H7VCt8IoSDPiIBP5Ggn3CgtHxb2cbfGLNhrsdF5L8YY75bwTgVpSWoVXimWK78qekEQZ+vbt2ydu4/zYceeirbT/bsdIIPPfQUf/W2Kyo+XjWvU9pyYvjV9PAnYykePTjOrgNn2XtogmgyfyJZ0R3JhnNes6aHoL/2oscijupnW9DvGP0KUkCrubpwD6DGX1EKqFX7wlK58mt687NEatnX4J5v72cimsLvEwJ+H8bARDTFPd/eX/G5LfY6pS2baMpiNpEmlqyOh39kPJoN5zx3Ir+6VoBXrexi28ZBtm4cYP1AOyLCY6Pj3PXQM5ycirGyCtW+hQT9PtpCftpDjtEvt6K3kGoa/8asWiiKx6mVAmWxXPmQ30eyIFulloJno2dn8QnZHHQRMOLEwCtlIdcpZdlEExYzyXRVQjqWbXg2I5Y2Osaxifwc/kjAx2vX9bFt4wCvGxmgvyOU93imEUvAJ3mNWO5k04IngKDfl/Xq24N+AlW6o6im8a+syaSitAiZ8IxlG85MJ0haNn4RNgx2LHrfhbnymdBJs1TO5lKu5EPaspmtosGfSaTZe8gRS3v04DjT8fzuVwOdIba51bVXDfcSniO0ktuIBci+B5VUAQd8PiIhXzacUy1jf8FxarLXMhCRXuB+4HKcieO3VDVUWYrcfsMIH3roKSaiKXzi3CKnbcOZmQQ795+uaiy+3pWzGwbaefnMLGIbx+s3YBu4aLDyO425JB8y1bbT8VRVQjonJ2NOOuaBMZ48NolVoJZ20VAn2zY61bWbhjrLDq2cnIpVXAUc9DtefSToIxzwE5pjkbaaVNP4H6rw+fcC3zbGvE1EQoD222txlmrB0vbNQwx0hJhOpLFsQ8jvY7AzTMDt9lTtc6xn5eyH3/IqPvjQU8y45+b3Cb3hIB9+y6sq3lexieud29Zx6epuDo9F86ptK8U2hv0np9l14Cy7R8c5WBCWCvqFq9b2Ofo5I/0MudW1j42O8/tfeXre+P1jo+M8+PhRxmeTTESTDHaE6XQLtgqrgEMBx6uPuD+ez/MHEJFtOHo+2UnDGPMPFR9UpBt4ChgxZQ5A8/yXNrmZHrleXzVaB3qB6+95mN62YJ4HaYxhMpbih3fd2MCROSxm4s28tlp3GvGUxUwiTTRhkbYXnqkTS1nsy4qljTERzRdL620L8rqRfrZtHGTLuj7aCpqy58bvcyt6MyqexZ5n2Tanppy0z+XdYfw+H5Zt+PCbN/OmS5c3xNiLyOLy/EXki8BG4Ekgc99lgIqNP05twBngcyJyBbAPuNMYU/kqkdJ0FDM0tcqI8QqN7jw1F4tNsSx2p1HpZBJ3s3Rm5zH4GQ+7lCd+ZjqRFUt74sjEBWJp6wbas8VWr1rZPachLjd+n/88P9IjnJ1OcHYmydVr+3jvT2305Ge4krDPFuDScj31Mo57NfB+Y8yjInIv8GEKhOJE5DbgNoC1a9dW4bBKoyllaGYTKVb25Atk1TJFsd54WcK42hNvuZNJOXo6uRTLpPmb77/IL58bZiqeYvfoGC+emsl7jd8nvGbN+eraVb3libBB+fH7V6Zi9LQF8ft8+ATCwTB97SEmYykevH1r2cerN5UY/2eBFcDJKhz3GHDMGPOo+/9DOMY/D2PMDmAHOGGfKhxXaTClDE3KMsRSlic942rgZQnjaqeizjWZvOHiZczE00wnUhUXX2U87HDAl03vnIlbfOoHL+c9rzMc4HUb+tm60amu7YwsbGmzlIrnyp42OsIBIm42zobBTk5PxwkFzt9FNMNnt5KrMgg8LyKPAdlaZmPMzZUe1BjziogcFZFLjDH/CbwJeL7S/SjNRylDEwr4SFnGk55xtfCqhHG5IalyQzmF77ExziL3obFZjoxHyQQP5gvh5DIRTTJ6dgbLGKJJi8L4w6reiJudM8jlq7qrkh6Zq+LZHvJnBdrufNMmlufILXv5rm4uKjH+f1LlY78f+LKb6TMKvKvK+1c8SClDs2moKxv795pnvFQoZbzLMV6VrAtk3uNIwI9lDLYxxJIWy7sieYa/MIRzz3f209cWZDZlsaIrwo2bh5hOpNl1YIwXTk5dUEjkpEb6WNndxmfecdWCK12LEQr4+C+XLaevI8jnfnyI4+diJT+TXr6rm4tKtH3+Q0SWA9e4mx4zxpxe6IGNMU/irCMoLcRchsarnvFC8Fra6nzG+27g4//2Ai+ddmLmIwUFaOWsCxhjiKdsbrlmmI9/ez/JtF1S975wMdU2hsloiul4is5wgGdPTvHU8cm8MYQCPnxAe8hPT3uQtGVI24Z3blu/aMMfCviyYZzcjJyfvnwlP335ynlf34yf3UqyfX4F+CtgJ06dyqdE5EPGmIdqNDZlCdKsXlIlNEJnf77JphzjHU3ZrOlry07KuWMuFa47Oj6bLbyKpSws2/DqNT3ceePcuveZxVTLNswm05yZTmADtg3nYucrbIN+4WdevZJtGwe4Yk0vTx45N6+efjlk9HEyBj8368drE3etqCTs8/8C12S8fRFZBnwPZ7FWUcqmGb2kSqh32mo5k818i7rzjTk3XGeMwTYwm0gx2BnhTIGcMTiNT0oZ5eMTMfwiHBmPXdDoBCAc8NEZDtAR8pFI29z5pk1l7XcuypVMaKUGOZUYf19BmGcMqE8dslIVWsWjaTS1EnIrRTmTzXyLuvON+d3Xr+dPvvk8aStJKHBhKGeuxVvLNrxwciqrjnl4PP865AZshrrC9LjjiKUsVvYsLGPG74aUIq7yZbnyyuVcy6XyParE+H9bRL4DPOD+/3bgW9UfklILWsmjaTT1LugqNNxTsRRnZxIcGoty64493H7DyLyLusXGPJtMs7w7wrGJKCPLOvndN15UNORSbPH2E99/kZuOL+fsbII9o+NMxvKra/vag1y0rJOzM0miyRQd4SATUWdiMZiy+uPmIuJU4ebKJlTjWkL+JLiUvkeVLPh+SER+CXg9zmS9wxjzf2o2sibGi57BUq+g9RL1Tv3LNdxTsRQnJp0ipLBfzhunmy/j7psvK7nWcvsNI/zRN57FNinCfh9Rd8xvu3pNNh+/VMjlwcePkrYsxmfT2Ypa28CXHzuS97yRZR1sHXHE0i5Z0ZWVgc6QuXsoJ54vIoQCPtqDftpCTseqamT7zDdxL6XvUUXVD8aYrwJfrdFYlgReXOyD+ociWpl6L2rnTjZnZ5z4uyAMdUfyjNMDt113wRhSlk00abF5ZTfve+NF7HhklAOu6NnwPNWwtjG8eGqa/a9MES9RsJXRvt86MsCKnkjR52SYL56fEURrC/mJBPz4aqCRM9/EvZS+R/MafxH5kTHmehGZJl+zXwBjjOmu2eiaEC8u9oG3tWWWIvVc1M6dbA6NRQn7HcPfFXGMVKFxiqcsokmLaLKgxaFxPhMruiPZFM3CRiSJlMUTR5zetXtGxxibTZYcV3vQx1+97TULPq9MRk5h+mUtmW/iXkrfo3mNvzHmevd3V+2H0/x4cbEPmrMK0YvhM6+SmWxu3bHnAuMUTaZZ2dPG6ek4saR1gXZ9hlJCZl/cc5gzMwl2j46x7/DEBRk6QvFOTlJhf6fcRdpqdqyqlLkm7mb8HpWi7KvrqnrOu63VGe5rJ1bQXajWi31tBYtbxSab7ZuHuPvmyxjqijAZSzHUFfG0XHLmjub0dDzvjmbn/gXXFbYEt98wQsoyzCZSWJbNVDxJLGXzi1etZiaeLmn4wcm9jwR9GGNIpC3GZpOcnk7w3Mkp/v/vvsiuA2Mk0jY+gSvW9PDenxrhC++6hvaQHx/ns3YE17DI3OZFRGgL+envCLG6r411Ax0MdUfojgQbZvjno9m+R3NRScz/stx/RCQAvLa6w2l+GrnYl6HUZFOs5d+tO/Z40rNeSgtr9cK2DVs29PN7N23i87sP88rk+YVTgN/7p6dK6ugk0zYdoQAnJ+PZTJtcOkJ+rlnviKW9bkM/3Tl3t6GAj0TKQnyS7eZlbHNBRyoRR5StrcqLtPVmqdSplBPz/wPgD4E2EZnKbAaSuIqbynkaudhXyWTj9ZS1pbSwVksy7Q1nE2lX8MzwmuFe/nq4N/ucUk3F35PYQMoYdh04y95DE0QL2iP6XQXNW69Zy69cs6Zkrvy6/g6On5tlJmGRsmyCfh+dbQFW93bUZZG21izV8GM5Mf+/AP5CRP7CGPMHdRhT09Ooxb5KJhuve9ZLaWGt2hhX2TJj8Odrb5gby0+mbWJJi+lEmo9964W85wkw3NdGyjIkLYs1ve3ceu3aeStqM+qXg50B2oJ+EpaNZRvuuPGipn+/vO4kLYZKwj6PiUiPMWYSsg3Ytxtjvl6LgSnls5DJxuue9VJaWKsGWYOfdNobltvP1rINh8dnMcbwylT8gs5WkYCP167vY9vIAK8bGaC/I1TRuESEn9q8jK5IgH/Yc5gTc6hfNiNed5IWQyXG/6O5RV3GmHMi8lHg61UflVJzvO5Zt4IA3HykLZtoynJE08rw8DPMJNLsPTTOrgNjPHpwnOl4Ou9xv0+IBHwMdUX4X++4inCF1bBf2n2Yr+w7SjRp0RHy89tvGOGOmy7mZ69YVdF+wPshFa87SYuhIm2fRb5e8RDN4FkvlYW1cslIIkeTTjinnNaGGV6ZjLPrwFl2HxjjyWOTF2T1BHziSCG3BTAGLON8Bsox/LnSCZ/90Shf2HMYnziKm/G0zb0PO5207rjp4orOtxlCKl53khZDJcZ7r4j8NfAZnLTe9+M0XleaEPWsvUEibRFP2sRSjiRyuS2ybWPYf3LaMfij4xx0q3IzBP3CVcO9bN04wHUjAxw+G61ICjngcwqs2t0iq8xC7ed3OYY/4HN8QZ9A2ra5/0cHKzb+zRBSaQYnaaFUYvzfj9Ng/Z/c//8d+EjVR6TUjVbzrL2AbTu9iqNuKCdtl+/dx1IW+w5NsHvUqa6diOaLpfW0BbluxEnH3LKuL89bXd4dmXfhNhz0Z7VySgmjzSYtCjI48YmzvVLKEaRr9OdzKTtJlQi7zQIfFpFOY8xMDcekKEuKeCZun7JIpO2yvXuAszMJdh8Yy1bXFi7Yrhtoz4qlvWpld0USCD5xQkGOhx8o67UdIcf7zX2qbZztGcqN45clSEfjQ0BL1UmqpJPXNuB+oBNYKyJXALcbY36nVoNTlGbEynr36TnlFIphjOHl0zOO9v3oGC+eutDP6gwH2H7xMt5+7TCr5xFfKyTo99Ee8rP30Dif+/Ehjp2LVbTQ+p7rN3Dvwy+Ttp1KX9s4P++5fgNQWRy/XEG6pWh4vUAlYZ9PAD8NfBPAGPOUiNxQk1EpSpOREUuLpSwSqcpCIMm0zU+OTrD7wDi7D4xxZia/M1Yk6MRZ2kN+etuCpCzDviMTXH/RYFnGPxz00xHy0xEOEPT72Ln/NH/+rf0LWmjNxPXv/9FBZt1sn/dcvyG7vZI4fqWCdBm8liHktfGUS6WSzkcLyrErD/QpikdYzJc2bbmLtDm9ayvhXDTJnlEnHXPv4XHiqfzY/6reSDac8w+7DjMeTWY1nAI+J/7/4ONHS8bxw0E/naEAHeELBdIWu9B6x00Xl1zczY3jT8dTnJlOkEhbHJuIsXP/6aITQClBumJZNV7LEPLaeCqhEuN/1A39GBEJAXcAL8zzGkXxJJ/83ot8ZucBLNsQDvhIW/acX9pMGmYmnJMsoV9fCmMMh8ai2fj98yem8jQvfQKXruxm68YBtm4cYF1/e1b35i+/8590R/K/qpGgj1emYgXb/HSUMPi5VCN3vdTEmYnjW7bhxLk4Is7agghzXt9ys2q8liF03yOjJNMWYzNpkpZNyO+jKxJoinBVJcb/vcC9wGrgGE62z/tqMShFqSU795/mMzsPYBtDwCekLcPYbJKBjlDelzaV691XUGSVIW3ZPH18kt0Hxth1YIyTk/G8xyNBH9fmiKX1thevrl3Z3cbYbCJPvTWeslnR00Z7KEBbyAnrlKuEudjc9bkmzowRPz0VBwwYR9h5eVeEgF9KGsVys2q8VnT14qkppuJpfAh+Of9ZSltT87+4wZQj7HaPMeYu4I3GmHfUYUxKlWnWmGStuO+RUdK2I0AmOF4ptpNqeGR8lrMzCWIVFlllmI6neOygE8557NA4s4n8yOiyzrDT2WrjAFcO916gfFmMjHZOLGVltXMMcOeNF83bHasYi8ldn2/ifOC265xJ4Ev7MDj1BoOdEbrbghhj5jTS5WTVeK3oKpN9lamDEHHSeZNWZY5CIyjH8/8ZEfkI8AfAP9d4PEqVaeaYZK04OhEl7PfhfD8zX1JDIm1Y1hlhqqDZ+Hwcn4ixa3SM3QfO8vSxSQrD/5cs72Lrxn62jgxw0VBnxTLGr980SHvYz5f2HOHk5OK1cxaTu15q4pyOp7OGffvmIa5e21cTI+21oqtQwJe9K8zIWWMoa1JvNOUY/28DZ4EOV9I507hH2zgukHp64l6LkTYayzas7ImQTFtMzKZAXG/NGHw+yWrfz7ePF05OOemYB8Y4PJ7vzYYCPq5e28s2t7p2sDNc0RhFJCuD3B7yE/T7GO5v5+YrV1e0n7lYaO567sSZmcNEnGynyViK6+95mOG+draO9PPQE8erbqS9VnS1aaiLQ2MzTMXOx/y7O4KsH+hsyHgqQcotOBGRbxhj3lrj8ZRky5YtZu/evY06fNXI9cRzvxS16gZ0/T0P09sWzHqbmSrKeNrm2vX9LRECyhRZRd00zIy+fdqymI6nSVoGv0/4tWvX8uvb1hfdRzSZZu+hiaxY2mTB3UFfe5Dr3Oycq9f1XdBdbT78PmeC7gg7cgrl3h3M5UjUwsm4dcceDo3NMDaTQtyJM2XZWDas6A4z2BnOfqbfdvVqdo+Oe8JI18rhqvf3eSGIyD5jzJYLtldSbdhIlorxL9VjdagrwgO3XVfT4+VWUQZ9wsreNs99UBeLbRsSaZtE2qmmjZdIw3xsdHxerZtTU/Fsds6TR89dUF07MtjB1o2Owb9kRRe+CsM5fp/QEQ7QGQ6UlFOYi7kMD1ATo5Q5ZsqymIymSFg2tg1tQacrV27Gy4bBzpp8phc65loZ6MzE4oVJrhiljH8lFb6/CNwDDOGEfDTsswDqna2w1Kso05ZN3DXy8ZRVdgrmtSP9Fxh72xhePDWdzc45cCZfLC3gE64Y7s3m3y9ksdUnQnvYT2c4kOcALIS5QnpATcJ92zcP8bZj57j/RwdJWIaOUIBEOk3KNvjAkxkvtQ59Nqv8QyWfvr8Eft4Yo7n9i2Ah2QqLuWVdaBWlV8kWV6UsEil7QRk5uSRSFk8cOceuA45Y2thsMu/x7kiAazf0s23jINes76MjXLnBXmhIZz7mciQM1MTJ2Ln/NA89cZxlXWHWul706Nk0PgwBd5HTaxkvjUoP9XqWXSWf5FPVNvwi4gf2AseNMT9XzX17lUqzFaqRrVNpFaWXMMbRyfne86f43I8PcWKyeAPyShifTeaJpSUK7hbW9LVl0zEvX9VTkVhahozB7ww7efi1YD5HohbvdTEv2ofTH8CrGS+NSA9thiy7SvX8/wmnc1dWfMQY87VFHP9OnCrhlgkdVZqtUM1bVq+lyRXDGJON1cdSFvGUzaMHxoo2IL+TTWVNAMYYRs/OZrNz9r8ynfe4T+Dy1T3ZcM5w/8KMQiaG3xGqncHPZb73sxbvdTEvOhL0Z5vP2Ma5nr1tQTYNdS3qWNVisZ/7hXjwzZBlV4nx7waiwH/N2WaABRl/EVkD/Czw58DvLWQfzUolMcJq3rJ6LU0O8mUT4iUkj3MbkAPZL/Bc2jbJtM1Tx85l4/enp/PF0tpD/mx17bUb+ukpuMblUg8PvxTzvZ+1eK+LedGhgDCbdNZEgq7S52Q8zdYF3plVm8V87hfqwXutErkYlej5v6vKx/4b4H8AJd0DEbkNuA1g7dq1VT58c1DtW9ZGL07ZtiGedjz6Usa+kJNTsbK0bSZjKR49OM6uA2fZe2iCaEGDkeXdYbZtHGTrSD9XDPcSLFMOoZDcRdtqxvAXwlzvZy3e660j/Xxm5wHStk3Y76OnPUg0adPbFiBlmbxsn92j49yxwONUO16+0GuxUA/ea5XIxShH3uF/GGP+UkQ+BVzwLTXGVPz+isjPAaeNMftEZHup5xljdgA7wEn1rPQ4S4FmCNXMRSaMk8mzT1bYzATm0LbpbuPIeDQbznnuRH51rQCbV3ZlwzkbBjsWbKhFJCuL3B5qrMFvFJnF3r72INPxNIm0zfhsiqAf1vS1512T+aQc5juOV+LlC/Xgm+F7W47nn1nkrWaS/euBm0XkZ4AI0C0iXzLG/FoVj7Ek8GKoZj4yaZeZmP1ia0lytW3CAWEyls62QXzn5x7Pe2444GPLur5s79r+juJiaeXSFnI8/I5QIKvf0mxUy4vOeME9bRGWuffr0WSaM9MJYimral6ul+LlC/Xgm+F7W7UiLxH5lDHm/Qt43Xbgg/Nl+yyVIi8vsljjkDH2mVBOpeqX8zGTSPPgo0f4t+de4Vw0dcHt50BHKFtsddVwL+EFFEzlEvT76AwH6IwEFhwa8gqLLXDK/WycmU6wojuMiHBmOkHSsgn6hIDfR297qGpFVIVV6eDcSUzGUvzwrhsr3t9iaIYK3vlYdJFXGby+ivtS6sRCbrHjbo59PL0wqeNyeGUy7oZzzvLUsUnSBVW6Fw11sm3EScfctLyz4uraTIXvySkndfRXXzfMm161nI4FVtt6lcV40YWfjbPTCY5NOOssAb8Pv09I2QbL2FWVcvBSvLwZPPiFUk3jvyCMMTuBnQ0eRstSjnFIps/r2tfCswcnR3z/yWl2jzrZOQfP5lfXBv3CVcO9TrOTkQGGuiuvrs2Q0fYJ+oW+tiCT8SSf/sEBhroiS+JLnctisk4KPxsreiIcGoueL+93yyP62oPsHh2vmpRDrePlld7pNjpJolY03PgrjaWYcQgHfBwZn+X0VJx4yiZtL66KthSxlMUThyey1bUT0XyxtJ62INeNOOmYW9b1LVoOAZzUzH/ed4xI0Edn2DnvYMDvuRzsarEYL7rws9EVCeJzi7gsYwj5fQx2RuiKBKqawlhLb9tLi8mNpprGvzlXw1qc4b52Tk3FiAQD2MZgjLOIt6wzwkwiXfXjnZlOsGfUqa594si5C7R41g20Z7NzXrWye0HVtYVkUjO7wkHaQn5OTcc9n4NdLRbjRRfN6ff7QMgr4Iom0wsOyZTywmvlbXtpMbnRVCLsFjHGxAu2DRpjzrr/3lvVkRWw/5Vpbt2xZ8nE2xpJIm0RTzox+1+4chWf+P5LpKwUkaDP9fRNWbr25WCM4eXTM9lwzounZvIe9/uEV6/uycoprO5tq8pxM5r4nZEAHQWpmbWMKXtNz2UhXnTmHF48NcVMwqK/I8hAhyPV3BUJYKAqIZlGeOHNUHxVLyrx/B8Xkd82xuwBEJFfAv4CuBjAGPP56g/vPAGftPQt2mLI9KKNJ530y1yJ4y0b+rnzxk3zyhtXQjJt8+TRc1n9nMLq2o6wn9dtcLz7a9f30xmp3g1oOOikZnaGAyXvGmoVU/ZqSKESLzr3HFb2tHF2JsH4bIqUZdg01MUf/eylQHVCMh//txeyzd6dEFKY4Bx9fquBlxaTG00l37pfBT4rIjuBVcAAUNe8q1a5RVus92jZJm+Bdj7ly2LyxpVyLppkz+g4u0fH2Htoglgqv7p2VW8kG8559eqespuNl0OlqZnFvOGtI/3c98goH/nGswv22JdCSKHwHJZ1RegIBwi6E+lirk8uO/ef5qUzM/jlfOPzE5MxVvVEauqFN0PxVb2oRN7hGRH5c+CLwDRwgzHmWM1GVoKlfou2EO8xU0UbTWakjq2iz6smxhgOj0ez2jnPn5jKy78X4LJV3U52zsYB1vW3V7UqNuj3OSJqYT/hQOWpmbnecLU89mYNKRTL5c8lbdkcGoux3jZVu6O575FRgj6f0w9WzvcCPjWd4KrhvkWfUymWcupmpVQS8/97YCPwGpxQz7+IyKeNMZ+p1eCKsdRv0cr1HjNx+4wgWi3SLwtJWzZPH5/MhnNOnMtbAiIS9HHN+n62bRzgdRv66W1fXHVtIRkPvyMcqKpccLU89mYMKRTL5T9+Lg4I3e5Edmo6QdDnq+odzdGJKMu7w5ycTGDjSkFjSFvU3AtfqqmblVJJ2OdZ4D3GKQk+KCLXAX9dm2EVJ5pML/lbtFLe49HxWSZjKRKpC+P2tWQmnubRg04459GDY8wm8u8qlnWGs9W1Vw73Vl3D3e8TOsMBuiLBmunDV8tjb8aQQrFc/mMTMU5Nx+mKBLLnsKY3v65isXc0mYlyVW8kWy3sF2Hjsg41zHWikrDPJwr+nwTeXfURlcCyDUNdkSV/i5b5UrQF/dhug4xoMs1gZ4SxmcT8O6gCx8/FsuGcZ45PXjDRXLK8i60b+9k6MsBFQ51VFzkTEdpDmVaHtRdRq5bH3owhhWK5/Kt7Da9MJZiMpVjT107I7yNZsG602DuazEQZ9AsbBjuyk8xdb9684H0qlVFJ2GcTTnbPpThibAAYY+ri1lyyossTzaBrRaY94S3XDHPPt/eTTNs1Sb0shmUbXjg55cgpjI5xeCzfowsFfFy9ttcN5wywrCtcYk+LI5JNzSydqVMLKvHY51uMb7aQQrGJL+D3cfXavuz3LRMaquYdTTNOlEuNsoXdRORHwEeBTwA/D7zLff1Haze889RL2K1eedqWbbLKl7FkfkZORnemWqmXxYglLR4/PM7uA2PsGR1nMpZfXdvXHmSrq51z9bq+PDnlatLmyiTX2+AXknnf5zJES0Hkq5Byz6mc66N4k1LCbpUY/33GmNeKyDPGmFe7235ojHlDlcdalHoY/2p8uUtNHsm07SzSusqXi208vhBOT8XZPepo3//k6DlSBQ22RwY7sto5m1d2VSyWVi5Bt9lHZzhQ1ZTPWlOsB3I0mWaoK9LUd6Vq2Jc21VD1jIuID3hJRH4XOA4sqU/IYrM+MpNHwAfdkQAnJ2P84def4QM3bmLLhvq3tLON4aVTM9n4/ctn8qtrAz7hiuHebP79ip4Ij42Oc/8PD3J4fJZk2iboF9YPdC767iPg82W7XzWramazpnLOR61DVV6relYcKjH+HwDagTuAjwFvBH6jBmNqGAv9cqctm0Ta5tM/eBkwBPx+0pZTtWjZhn987GjdjH8iZfHEkXNZD39sNpn3eHckwLUbnHTMa9b30xE+/xHIqF2mLYvpWAoEEmk4NjFbUcP0DCJCR46mTrPTjKmcjcarVc9KZcbf4BR4rQMyFvLvcPL+lwTlfrmTadvtQ+vo2mdCOEcnok6v2ZxoSrFes9VmfDbJHlc7Z9/hCRIFYmlr+tqy2jmXr+opGVvPNEo/F7UQn+ATwbYNs0mLwVBgzobpufzkyAQPPn6UE+dirO3vWDKeXjOmcjaapVD1vFSpxPh/GfgQ8AxQ/4B1HSj15f6t169nMpbKdqwqlWM/V6/ZamKM4eDZ2Wx2zgsnp/Me9wlcnhFLGxlguL88zzTTKD1l2dmWhSKONtB8k1imAGvf4XHu/f7LjlZ+e2hJeXqaoVI5SzVUthSoxPifMcZ8s2Yj8QDbNw/xp8bwv//D+XKv6Ilwy5ZhNg51lpVjn9trttppminL5qmj59g96mTovDKVX13bHvJnq2uv3dBPT8EXrhwyk1fQ7yNtu1WXxjHsxSYxv0/ocEXUMnH8v//RoSXt6XkllbNZ4ugaKvMulRj/j4rI/cD3gawlNMZ8reqjqiOZlMt4yiKetlm/rJOP/9KrF7Sva0f6uZPqKWROxlI8dnCcXQfGePzQONFkfnXtiu6Im53TzxXDvYvuN5uZvDrDfiZmU1ji3OF0hAJ5k1h7KEBXpHgBlnp6taeZ4ugaKvMulRj/dwGbceL9mbCPAZrK+Gfi9YkapVwuViHziCuWtnt0jGePT5IbYRJg88qubHbOhsGOqla/5k5eadvJ9gn5hTV9Hbx2XS8PPXGMT/3gpTnj+F709LzuJVc6vmaKo2uozLtUkuefze9vBAvJ88+oXWaajc8Vr28Ulm149sQku152DH6mQXaGcMDHlnV9bN04wHUjA/R3VFcsbS4yHbCePHyOP/vWC2XVP3itEMpr48mM6b5HRnnp9DSziTSJtE3I72N5d5iA3zfv+K6/52F624J5E78xhslYih/eVVeVdaUJqEae/x4RudQY83wVx1VVLNvkFVIl0jblTm71ZDaR5vFDE+w6cJbHDo4zFc9vlzjQGXKqa0cGuHptL+E65sUX64D1uV3lx/G95ul5zUvOTEYpy2IymiJtGwyQtg0nJxOs6o3M29DEi3dXSvNRifG/HvhNETmIE/MXwBhjGpbqmbJsN17fuKrZcnllMu5k5xw4y1PHJkkX3IFctKwzm465aXlnzaprSzFXB6xK4/heWRQF761BZCajsZm0k1Hlfg5sY/D7hDPTCTYMdsw5Po2jK9WgEuP/5pqNokwyefUZ7z5te9fY28aw/+R0tthq9Oxs3uNBv3DVcG9WTmGoO1JiT7WjXH38Sj1NL8XYveYlZyajpGXj90k2o8oYJ6026Qr8zTU+r91dKc1JJZLOh2s5kPlIpG1OnKttsdRiiaUsnjg8kV2wnYjmi6X1tAW5bqSfrRsH2LKuL88g1Yti6ZnzUanqpZcyUbzmJWcmo5CbTusXyTbisd3/yxmfl+6ulOak7AXfRvPqK6823/juI40exgWcnUlkq2ufOHKOZEF17bqB9mx2zqtWdjdEuVJE6Ag5cfy24ML08csV/8oVP5uOpzgznSCetugIBfjkLVctWiBvIXhJuCw35n92OgnirFUFfIJl4OKhTu5682Y17ErVWLSqZ6PxivE3xvDy6Rl2uwb/xVP5Ymk+gdes6c12t1rdW93q3koIB/2OemYokK3YrTWZTJSZRJoT5+JOb1YMlg2retvKyrLxYoZONcnN9smk025a3q2hG6UmVCPbp2VJpm2ePHouG845PZ1f7dsZdsTSto4McO2GProilVfXVot6tD3MpdBD7ww5xvrMdAIRXH0gCAdk3iyWDAvJ0PHSOsN8aMhG8QJq/EtwLprkUbe6du+hCWKp/OraVb2RrHbOq1f3NFSXvt5tDzMUi+9PxVIYIJ62CPgcw29jGOyMlJ1lU2mGjtfWGRSlGVDj72KM4bBbXbvrwBjPn5jKFedEgEtXdWfTMdf1t9fNyJYi6PfRHQnSGWlMF6xiHjrg9HxN20STFuGAMNgZobstSDSZLivLptIMHa/l8itKM9DSxj9t2TxzfDKrjnniXL5YWiToY8u6fre6tp++9vpV15bCJ062Tlek8U1Rcj30qViKszMJkpaNT4T3bd/IQ08cz8bto8l02Vk2lWboeC2XX1GagYYYfxEZBv4BWIGjE7TDGHNvPY49HU/x2MEJdo+O8ejBMWYT+eGcZZ3h7GLtlcO9dYmbz0exqlsvkPHQ05bhxGQMH4Lg3CU99MRx3nb1anaPjlecZVNpHrvXcvkVpRlolOefBn7fGPOEiHQB+0Tku7WSjjh+LpYN5zx97ByF8j4XL3eqa7dtHGTjsuqKpS2GRod15iPjoZ+ejiPg1nwLK3oi+H3C7tHxBfe2rWRRNPdOIW3ZnJpKkLJtgj5h5/7TGvpRlCI0xPgbY04CJ92/p0XkBWA1UBXjb9mGF05OZcM5h8fyb/9DAR9Xr+1lmyuWNtgZrsZhq0JGTK07Emx4WGc+Mh767V/ah20MYb+PZV1huiJBjDF1C7tkxnHPt/dzaCxK0C+s6W0jZRtd+FWUEjQ85i8i64GrgEeLPHYbcBvAqjVzN0SJJS0eP+w0OtkzOs5kLL+6tq896IilbRzg6nV9ed22vEAjcvKrwfbNQ1y9tq/hYZftm4e475FR1g+0541DF34VpTgNNf4i0gl8FfiAMWaq8HFjzA5gBzhFXoWPn56Ku52tzvKTo+dIWflP2TDY4YZzBrhkRVfdxdLmIyO10BUJEA40bjJabI68V8IuuvCrKOXTMOMvIkEcw//lcruB2cbw0qmZbPz+5TP51bUBn3DFmh62bhxk28YBVvTUXyytHNpCfroiQU8s3lYjR94rYRdd+FWU8mlUto8Afw+8YIz563Jec2oqztt37GFsJpm3vTviVNdu2zjINev76Ag3PJJVlKDf54R1woGGFoQVUq0ceS+EXbwm4qYoXqZRlvL1wK8Dz4jIk+62PzTGfKvUCyZjKdpdw7+mry1bbHX5qh5PZsKAt3LyS1HNUEmjwy4qdawo5dOobJ8f4SQGlk1b0M97f2qErSMDDPd7+zY+klm8DQcaHtaZj8WGSnLXC6ZiKSzbZrDzfLitcF+11uBR3RxFKQ/vxB/mYbi/nV/ZMuxZwx/w+ehtDzHc386q3ja6IkHPG35wQiUpyxBNpjHGVFSJm1kvOD0dp7ctSHvIz+npJGdn4kX3Vfj8zPrCzv2na32aiqIU4M0AeZOQEVTrigQa0pilGiwmVFK4XrCsy/H4ZxMWAV/qgn2pBo+ieIfmtFgNJhL0Z7theXW9oRIWGiopFuMf7AwzGUvxw7tuLOv5moqpKI1BjX+ZZKQWOsJ+T2XrNJJK1ws0FVNRvINasTnw+4TutiCretsY7m+npz2ohj+HStcLFrO+oChKdVHPvwheKsLyMpWuF2gqpqJ4B+3h6xLwuUVYkQBB9e4VRVkiaA/fIogIHSFHJ79Zs3UURVEWQktavFDAR1fYuzr5iqIotaZljH8zSC20IrWu+K31/hWlWVnywe1I0M+yrjDrBtpZ1hVWw+8hal3xqxXFilKaJWn8m1VqodXIrfh1qqUDBP3CfY+MNsX+FaWZWTJhn8zibVckSFtIvftmoNYVv1pRrCilaXrjHw766QwH6Ao3V/tDpfYVv1pRrCilacqwj98n9LQFWdPXzureNnragmr4m5BaV/xqRbGilKapPP+MmFq7Vt4uCWpd8asVxYpSmqap8N2yZYvZu3dvo4eh1BhNzWxu9P3zHqUqfJsy7KMsTTQ1s7nR96+5UOOveAZNzWxu9P1rLtT4K57h6ESUtoIiPE3NbB70/Wsu1PgrnmG4r51YysrbpqmZzYO+f82FGn/FM2hqZnOj719zocZf8QzbNw9x982XMdQVYTKWYqgrwt03X6bZIk2Cvn/NhaZ6KoqiLGE01VNRFEXJosZfURSlBVHjryiK0oKo8VcURWlB1PgriqK0IGr8FUVRWpCGSTqLyJuBewE/cL8x5uONGovibRqtFNno4ytKLWiI5y8ifuAzwFuAS4FbReTSRoxF8TaNVops9PEVpVY0KuxzLfCyMWbUGJMEHgTe2qCxKB6m0UqRjT6+otSKRhn/1cDRnP+PudvyEJHbRGSviOw9c+ZM3QaneIdGK0U2+viKUisaZfyL9WC8QGfCGLPDGLPFGLNl2bJldRiW4jUarRTZ6OMrSq1olPE/Bgzn/L8GONGgsSgeptFKkY0+vqLUikYZ/8eBTSKyQURCwC3ANxs0FsXDNFopstHHV5Ra0ZBUT2NMWkR+F/gOTqrnZ40xzzViLIr32b55qKHGttHHV5Ra0LA8f2PMt4BvNer4iqIorYxW+CqKorQgavwVRVFaEDX+iqIoLYgaf0VRlBakaXr4isgZ4HCDDj8InG3Qsb1Aq58/6DVo9fOH5r0G64wxF1TJNo3xbyQisrdYA+RWodXPH/QatPr5w9K7Bhr2URRFaUHU+CuKorQgavzLY0ejB9BgWv38Qa9Bq58/LLFroDF/RVGUFkQ9f0VRlBZEjb+iKEoLosbfRUTeLCL/KSIvi8iHizz+DhF52v3ZJSJXNGKctWS+a5DzvGtExBKRt9VzfLWmnPMXke0i8qSIPCci/1HvMdaaMr4HPSLyLyLylHsN3tWIcdYKEfmsiJwWkWdLPC4i8kn3+jwtIlfXe4xVwxjT8j84stIHgBEgBDwFXFrwnG1An/v3W4BHGz3uel+DnOc9jKPI+rZGj7vOn4Fe4Hlgrfv/UKPH3YBr8IfAPe7fy4BxINTosVfxGtwAXA08W+LxnwH+Dacb4XXNbAfU83eYt6G8MWaXMWbC/XcPTvexpcS818Dl/cBXgdP1HFwdKOf8fxX4mjHmCIAxphWvgQG6RESAThzjn67vMGuHMeYRnHMqxVuBfzAOe4BeEVlZn9FVFzX+DmU1lM/h3Tiz/1Ji3msgIquBXwD+to7jqhflfAYuBvpEZKeI7BOR36jb6OpDOdfg08CrcNquPgPcaYyx6zM8T1CprfAsDWvm4jHKaigPICJvxDH+19d0RPWnnGvwN8BdxhjLcfyWFOWcfwB4LfAmoA3YLSJ7jDEv1npwdaKca/DTwJPAjcBG4Lsi8kNjzFSNx+YVyrYVXkeNv0NZDeVF5DXA/cBbjDFjdRpbvSjnGmwBHnQN/yDwMyKSNsZ8vS4jrC3lnP8x4KwxZhaYFZFHgCuApWL8y7kG7wI+bpwA+MsichDYDDxWnyE2nLJsRTOgYR+HeRvKi8ha4GvAry8hTy+Xea+BMWaDMWa9MWY98BDwO0vE8EMZ5w98A3iDiAREpB14HfBCncdZS8q5Bkdw7nwQkeXAJcBoXUfZWL4J/Iab9XMdMGmMOdnoQS0E9fwp3VBeRN7rPv63wB8DA8D/cj3ftFlCCn9lXoMlSznnb4x5QUS+DTwN2MD9xpiiKYHNSJmfgY8BnxeRZ3BCIHcZY5pR5rgoIvIAsB0YFJFjwEeBIGTP/1s4GT8vA1GcO6GmROUdFEVRWhAN+yiKorQgavwVRVFaEDX+iqIoLYgaf0VRlBZEjb+iKEoLosZfqRkiMlPw/ztF5NML3Nd2EfnXnL+35Tz2+YUqjIrI+oyCo4hsEZFPun+HReR7roLn2xey73ohIh9w6w4UpWw0z19pRrYDM8Cuau7UGLMX2Ov+exUQNMZcWc1j1IgPAF/CyTtXlLJQz19pCCKyTES+KiKPuz+vd7df6/ZL+In7+5KC160H3gv8d9crf4P70A3u80czdwEi8kUReWvOa78sIjfPMabtIvKvIjKEY0yvdI+xUUReKyL/4Qq6faeYkuMc5/SNjAiciNwuIl92/94pIn/jjvtZEbnW3d4hjq784+51eKu73S8i/1NEnnG15N8vIncAq4AfiMgP3Of9bxHZK47e/p/mjO+QiPypiDzh7mOzu71TRD6Xs99fEpF3i8gncl772yLy1+W8t0qT0GhNaf1Zuj+AhSMClvk5Anzafewfgevdv9cCL7h/dwMB9++bgK+6f28H/tX9+0+AD+Yc5/PAP+M4M5fiyBID/BTwdffvHuBgZt85r12Pq91ecIzcv4M4dxnL3P/fjlP9Wni+pc5pOU5F6BtwdID63e07gb9z/74hZxz/H/Br7t+97ms6gP8HR047c30y+zkEDOaMI7Pd7x7jNTnPe7/79+/gVCgD3AP8Tc7r+9zjHcC5+8E9/1c3+jOlP9X70bCPUktiJidsIiLvxBGHA8ewXyrn1UG7RaQLx0h/QUQ24aglBss81teNIy38vKs5gzHmP0TkM64n/4s4E8lCtOcvAS7HUbAEx6gW03Mpek7GmFMi8sfAD4BfMMbk6sU/4I71ERHpFpFe4L8CN4vIB93nRHAmk5uAv82cQ8F+cvkVEbkNJ6y7EmdCfNp97Gvu73041yQz7lsyLzZu3woReRj4ORF5AWcSeKbE8ZQmRI2/0ih8wFZjTCx3o4h8CviBMeYX3BDPzjL3l8jdTc7fXwTegWPcfmuBYxXgOWPM1nmeV/ScXF4NjOGEaHIp1Fcx7vF+yRjzn3mDcGaVOfVYRGQD8EHgGmPMhIh8HmfyyJC5Thbnv/+l9ns/Tueu/cDn5jqu0nxozF9pFP8O/G7mHxG50v2zBzju/v3OEq+dBrrKPM7ncRZEMcY8V9kQs/wnsExEtgKISFBELivyvKLn5Mby34KziPxB10BneLv7nOtxFCIncYTV3u8ae0Tkqpz9v1dEAu72fnd77vXoBmaBSfcO6C1lnF/huPsAjDGP4sgX/yruHYqydFDjrzSKO4At7gLj8ziLuAB/CfyFiPwYJ7xSjH8BfqFgwbcoxphTOLLLC/ZcjdPS8G3APSLyFM76xbYiT73gnEQkDPwd8FvGmBPA7wOfzRh2YEJEduF0R3u3u+1jOOGup8VJQ/2Yu/1+nHWTp91x/Kq7fQfwbyLyA2PMU8BPgOeAzwI/LuMU/wynQ9mz7n7fmPPYV4Afm/MtTJUlgqp6KksacfLfnwGudr1qzyAiO3EWrvfO99xGIU5txSeMMd9v9FiU6qKev7JkEZGbcOLVn/Ka4fc6ItIrIi/iLNqr4V+CqOevKIrSgqjnryiK0oKo8VcURWlB1PgriqK0IGr8FUVRWhA1/oqiKC3I/wXUpktbk2YT0wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5.4: Plotting Freedom to make life choices vs maximum Infection rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Freedom to make life choices', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=data[\"Freedom to make life choices\"]\n",
    "y=data[\"max_infection_rate\"]\n",
    "sns.scatterplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sshar\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Freedom to make life choices', ylabel='max_infection_rate'>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x,np.log(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
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