final_analysis_MASTER.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Final-analysis:-Impact-of-Covid-19-pandemic-on-the-air-quality\" data-toc-modified-id=\"Final-analysis:-Impact-of-Covid-19-pandemic-on-the-air-quality-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Final analysis: Impact of Covid-19 pandemic on the air quality</a></span><ul class=\"toc-item\"><li><span><a href=\"#Import-the-air-quality-and-covid-data-files\" data-toc-modified-id=\"Import-the-air-quality-and-covid-data-files-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Import the air quality and covid data files</a></span></li><li><span><a href=\"#How-has-change-in-Covid-19-active-cases-correlated-with-change-in-Australia's-air-quality-in-2020H1?\" data-toc-modified-id=\"How-has-change-in-Covid-19-active-cases-correlated-with-change-in-Australia's-air-quality-in-2020H1?-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>How has change in Covid-19 active cases correlated with change in Australia's air quality in 2020H1?</a></span></li><li><span><a href=\"#How-has-change-in-Covid-19-active-cases-correlated-with-change-in-India's-air-quality-in-2020H1?\" data-toc-modified-id=\"How-has-change-in-Covid-19-active-cases-correlated-with-change-in-India's-air-quality-in-2020H1?-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>How has change in Covid-19 active cases correlated with change in India's air quality in 2020H1?</a></span></li><li><span><a href=\"#How-has-change-in-Covid-19-active-cases-correlated-with-change-in-the-world's-air-quality-in-2020H1?\" data-toc-modified-id=\"How-has-change-in-Covid-19-active-cases-correlated-with-change-in-the-world's-air-quality-in-2020H1?-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>How has change in Covid-19 active cases correlated with change in the world's air quality in 2020H1?</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final analysis: Impact of Covid-19 pandemic on the air quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the air quality and covid data files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country_code</th>\n",
       "      <th>City</th>\n",
       "      <th>Specie</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>Country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>IR</td>\n",
       "      <td>Isfahan</td>\n",
       "      <td>pm25</td>\n",
       "      <td>129</td>\n",
       "      <td>54.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>10921.40</td>\n",
       "      <td>Iran, Islamic Republic of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-05-07</td>\n",
       "      <td>IR</td>\n",
       "      <td>Isfahan</td>\n",
       "      <td>pm25</td>\n",
       "      <td>168</td>\n",
       "      <td>17.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>14014.00</td>\n",
       "      <td>Iran, Islamic Republic of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>IR</td>\n",
       "      <td>Isfahan</td>\n",
       "      <td>pm25</td>\n",
       "      <td>127</td>\n",
       "      <td>17.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>3558.56</td>\n",
       "      <td>Iran, Islamic Republic of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>IR</td>\n",
       "      <td>Isfahan</td>\n",
       "      <td>pm25</td>\n",
       "      <td>113</td>\n",
       "      <td>26.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>11209.80</td>\n",
       "      <td>Iran, Islamic Republic of</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>IR</td>\n",
       "      <td>Isfahan</td>\n",
       "      <td>pm25</td>\n",
       "      <td>132</td>\n",
       "      <td>22.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>3209.67</td>\n",
       "      <td>Iran, Islamic Republic of</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country_code     City Specie  count   min    max  median  \\\n",
       "0  2020-02-24           IR  Isfahan   pm25    129  54.0  194.0   126.0   \n",
       "1  2020-05-07           IR  Isfahan   pm25    168  17.0  168.0    91.0   \n",
       "2  2020-05-28           IR  Isfahan   pm25    127  17.0  115.0    72.0   \n",
       "3  2020-02-20           IR  Isfahan   pm25    113  26.0  181.0    76.0   \n",
       "4  2020-02-23           IR  Isfahan   pm25    132  22.0  132.0    76.0   \n",
       "\n",
       "   variance               Country_name  \n",
       "0  10921.40  Iran, Islamic Republic of  \n",
       "1  14014.00  Iran, Islamic Republic of  \n",
       "2   3558.56  Iran, Islamic Republic of  \n",
       "3  11209.80  Iran, Islamic Republic of  \n",
       "4   3209.67  Iran, Islamic Republic of  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the air data for the 88 countries\n",
    "world_air = pd.read_csv(\"output_csv/clean_world_air.csv\", parse_dates=True)\n",
    "world_air.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1464925 entries, 0 to 1464924\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count    Dtype  \n",
      "---  ------        --------------    -----  \n",
      " 0   Date          1464925 non-null  object \n",
      " 1   Country_code  1464925 non-null  object \n",
      " 2   City          1464925 non-null  object \n",
      " 3   Specie        1464925 non-null  object \n",
      " 4   count         1464925 non-null  int64  \n",
      " 5   min           1464925 non-null  float64\n",
      " 6   max           1464925 non-null  float64\n",
      " 7   median        1464925 non-null  float64\n",
      " 8   variance      1464925 non-null  float64\n",
      " 9   Country_name  1464925 non-null  object \n",
      "dtypes: float64(4), int64(1), object(5)\n",
      "memory usage: 111.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Check the data types and any missing values\n",
    "world_air.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the data type of the Date column into datetime\n",
    "world_air['Date'] = world_air['Date'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1464925 entries, 0 to 1464924\n",
      "Data columns (total 10 columns):\n",
      " #   Column        Non-Null Count    Dtype         \n",
      "---  ------        --------------    -----         \n",
      " 0   Date          1464925 non-null  datetime64[ns]\n",
      " 1   Country_code  1464925 non-null  object        \n",
      " 2   City          1464925 non-null  object        \n",
      " 3   Specie        1464925 non-null  object        \n",
      " 4   count         1464925 non-null  int64         \n",
      " 5   min           1464925 non-null  float64       \n",
      " 6   max           1464925 non-null  float64       \n",
      " 7   median        1464925 non-null  float64       \n",
      " 8   variance      1464925 non-null  float64       \n",
      " 9   Country_name  1464925 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(4), int64(1), object(4)\n",
      "memory usage: 111.8+ MB\n"
     ]
    }
   ],
   "source": [
    "world_air.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Total active cases</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2005</td>\n",
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      "text/plain": [
       "         Date  Total active cases\n",
       "0  2020-01-22                 510\n",
       "1  2020-01-23                 606\n",
       "2  2020-01-24                 879\n",
       "3  2020-01-25                1350\n",
       "4  2020-01-26                2005"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the covid data total active cases for the 88 countries \n",
    "active_world_covid = pd.read_csv(\"output_csv/active_world_covid.csv\", parse_dates=True)\n",
    "active_world_covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162 entries, 0 to 161\n",
      "Data columns (total 2 columns):\n",
      " #   Column              Non-Null Count  Dtype \n",
      "---  ------              --------------  ----- \n",
      " 0   Date                162 non-null    object\n",
      " 1   Total active cases  162 non-null    int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 2.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check the data types and any missing values\n",
    "active_world_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the data type of the Date column into datetime\n",
    "active_world_covid['Date'] = active_world_covid['Date'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162 entries, 0 to 161\n",
      "Data columns (total 2 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   Date                162 non-null    datetime64[ns]\n",
      " 1   Total active cases  162 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(1)\n",
      "memory usage: 2.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Double check the data types\n",
    "active_world_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "      <th>Active cases</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
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      "text/plain": [
       "         Date    Country  Active cases\n",
       "0  2020-01-22  Australia             0\n",
       "1  2020-01-23  Australia             0\n",
       "2  2020-01-24  Australia             0\n",
       "3  2020-01-25  Australia             0\n",
       "4  2020-01-26  Australia             4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the covid active case data for Australia\n",
    "active_au_covid = pd.read_csv(\"output_csv/active_au_covid.csv\", parse_dates=True)\n",
    "active_au_covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162 entries, 0 to 161\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Date          162 non-null    object\n",
      " 1   Country       162 non-null    object\n",
      " 2   Active cases  162 non-null    int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 3.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check data types of the covid active case data for Australia\n",
    "active_au_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the data type of the Date column into datetime\n",
    "active_au_covid['Date'] = active_au_covid['Date'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162 entries, 0 to 161\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Date          162 non-null    datetime64[ns]\n",
      " 1   Country       162 non-null    object        \n",
      " 2   Active cases  162 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 3.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Double check the data types for the covid active case data for Australia\n",
    "active_au_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "         Date Country  Active cases\n",
       "0  2020-01-22   India             0\n",
       "1  2020-01-23   India             0\n",
       "2  2020-01-24   India             0\n",
       "3  2020-01-25   India             0\n",
       "4  2020-01-26   India             0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the covid active case data for India\n",
    "active_india_covid = pd.read_csv(\"output_csv/active_india_covid.csv\", parse_dates=True)\n",
    "active_india_covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162 entries, 0 to 161\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Date          162 non-null    object\n",
      " 1   Country       162 non-null    object\n",
      " 2   Active cases  162 non-null    int64 \n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 3.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check data types of the covid active case data for India\n",
    "active_india_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change the data type of the Date column into datetime\n",
    "active_india_covid['Date'] = active_india_covid['Date'].astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 162 entries, 0 to 161\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   Date          162 non-null    datetime64[ns]\n",
      " 1   Country       162 non-null    object        \n",
      " 2   Active cases  162 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 3.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Double check the data types for the covid active case data for India\n",
    "active_india_covid.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to do the following steps:\n",
    "    ## Transpose an air dataframe from long to wide: for each specie, turn the country_code into columns\n",
    "    ## For the world air data, get the median of specie values of the 88 countries to represent the world\n",
    "    ## Merge the covid data with the air data on Date.\n",
    "    \n",
    "def world_air_covid_each_specie(airdf, specie, covid_df):\n",
    "    air_median_specie_transposed = airdf[airdf[\"Specie\"] == specie].pivot_table(\n",
    "        index=\"Date\", columns=\"Country_code\", values=\"median\", aggfunc=np.median)\n",
    "\n",
    "    air_median_specie_transposed[f\"Median {specie}\"] = air_median_specie_transposed.median(axis=1)\n",
    "\n",
    "    final_air_median_specie_transposed = air_median_specie_transposed.iloc[:, -1].to_frame().reset_index()\n",
    "\n",
    "    air_covid_median_specie = pd.merge(\n",
    "        covid_df, final_air_median_specie_transposed, how=\"inner\", on=\"Date\")\n",
    "    return air_covid_median_specie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In the above function, the **air_median_specie_transposed** uses **aggfunc=np.median** to get the median value of the distribution of each specie's median values measured in cities under each country in the dataset. The **Median {specie}** column added to this dataframe then takes the median value of the distribution of each specie in all countries on the same date. The median aggregate functions are used to minimise the impact of outliers in the air quality dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to do the following steps:\n",
    "    ## Perform a linear regression on number of covid active cases vs median value of an air pollutant\n",
    "    ## Create equation of line to calculate predicted median value of an air pollutant for future Covid active cases\n",
    "    ## Generate a scatter plot of the number of covid active cases vs median value of an air pollutant\n",
    "    ## Plot the linear model on top of the scatter plot\n",
    "\n",
    "def linear_regression_plot(df, x, y, df_name, title):\n",
    "    (slope, intercept, rvalue, pvalue, stderr) = linregress(df[x], df[y])\n",
    "    regress_values = slope * df[x] + intercept\n",
    "\n",
    "    line_eq = f\"y = {round(slope, 2)}x + {round(intercept, 2)}\"\n",
    "\n",
    "    print(f\"The correlation coefficient is: {rvalue}\")\n",
    "    print(f\"The r-squared is: {rvalue**2}\")\n",
    "\n",
    "    plt.scatter(df[x], df[y], facecolors=\"dodgerblue\", alpha=0.5)\n",
    "    plt.plot(df[x], regress_values, \"b--\", linewidth=1)\n",
    "    plt.annotate(line_eq, (df[x].quantile(0.75), df[y].quantile(0.75)), fontsize=15, color=\"blue\")\n",
    "    \n",
    "    plt.xlabel(f\"Number of {x}\")\n",
    "    plt.ylabel(y)\n",
    "    plt.title(title)\n",
    "    plt.savefig(f\"images/{df_name}_linear.png\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How has change in Covid-19 active cases correlated with change in Australia's air quality in 2020H1?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "\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>Date</th>\n",
       "      <th>Country_code</th>\n",
       "      <th>City</th>\n",
       "      <th>Specie</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>Country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>490904</th>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>AU</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>co</td>\n",
       "      <td>56</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.12</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490905</th>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>AU</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>co</td>\n",
       "      <td>9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9.07</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490906</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>AU</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>co</td>\n",
       "      <td>45</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>17.91</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490907</th>\n",
       "      <td>2020-05-22</td>\n",
       "      <td>AU</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>co</td>\n",
       "      <td>14</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.19</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490908</th>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>AU</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>co</td>\n",
       "      <td>33</td>\n",
       "      <td>1.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.3</td>\n",
       "      <td>16.16</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country_code    City Specie  count  min  max  median  \\\n",
       "490904 2020-02-04           AU  Sydney     co     56  1.2  4.5     3.4   \n",
       "490905 2020-03-29           AU  Sydney     co      9  1.2  3.4     2.3   \n",
       "490906 2020-06-26           AU  Sydney     co     45  1.2  6.7     2.3   \n",
       "490907 2020-05-22           AU  Sydney     co     14  1.2  2.3     1.2   \n",
       "490908 2020-06-04           AU  Sydney     co     33  1.2  5.6     2.3   \n",
       "\n",
       "        variance Country_name  \n",
       "490904     10.12    Australia  \n",
       "490905      9.07    Australia  \n",
       "490906     17.91    Australia  \n",
       "490907      2.19    Australia  \n",
       "490908     16.16    Australia  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the air data for Australia\n",
    "au_air_df = world_air[world_air[\"Country_code\"]==\"AU\"].copy()\n",
    "au_air_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median co</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Active cases  Median co\n",
       "0 2020-01-22  Australia             0       2.15\n",
       "1 2020-01-23  Australia             0       2.15\n",
       "2 2020-01-24  Australia             0       3.15\n",
       "3 2020-01-25  Australia             0       2.10\n",
       "4 2020-01-26  Australia             4       2.30"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median CO data and covid active cases for Australia based on shared date\n",
    "au_air_covid_co = world_air_covid_each_specie(airdf=au_air_df, specie=\"co\", covid_df=active_au_covid)\n",
    "au_air_covid_co.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.27682230150022347\n",
      "The r-squared is: 0.07663058660788062\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median CO values\n",
    "linear_regression_plot(au_air_covid_co, \"Active cases\", \"Median co\", \"au_air_covid_co\",\n",
    "                       \"Number of active cases vs Median CO level in Australia 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a weak negative correlation between number of active covid cases and median CO values in Australia in 2020H1.\n",
    "* The r-squared value tells us that only 7.7% of the variability in the median CO values in Australia in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median pm25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Active cases  Median pm25\n",
       "0 2020-01-22  Australia             0         25.0\n",
       "1 2020-01-23  Australia             0         24.0\n",
       "2 2020-01-24  Australia             0         25.0\n",
       "3 2020-01-25  Australia             0         20.5\n",
       "4 2020-01-26  Australia             4         19.5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median pm2.5 data and covid active cases for Australia based on shared date\n",
    "\n",
    "au_air_covid_pm25 = world_air_covid_each_specie(airdf=au_air_df, specie=\"pm25\", covid_df=active_au_covid)\n",
    "au_air_covid_pm25.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.12186207920586578\n",
      "The r-squared is: 0.014850366348376704\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median PM2.5 values\n",
    "\n",
    "linear_regression_plot(au_air_covid_pm25, \"Active cases\", \"Median pm25\", \"au_air_covid_pm25\",\n",
    "                       \"Number of active cases vs Median PM2.5 level in Australia 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a very weak negative correlation between number of active covid cases and median PM2.5 values in Australia in 2020H1.\n",
    "* The r-squared value tells us that only 1.5% of the variability in the median PM2.5 values in Australia in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median pm10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>14.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Active cases  Median pm10\n",
       "0 2020-01-22  Australia             0         20.0\n",
       "1 2020-01-23  Australia             0         20.0\n",
       "2 2020-01-24  Australia             0         19.0\n",
       "3 2020-01-25  Australia             0         18.0\n",
       "4 2020-01-26  Australia             4         14.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median PM10 data and covid active cases for Australia based on shared date\n",
    "\n",
    "au_air_covid_pm10 = world_air_covid_each_specie(airdf=au_air_df, specie=\"pm10\", covid_df=active_au_covid)\n",
    "au_air_covid_pm10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.2800507595892614\n",
      "The r-squared is: 0.07842842794652229\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median PM10 values\n",
    "\n",
    "linear_regression_plot(au_air_covid_pm10, \"Active cases\", \"Median pm10\", \"au_air_covid_pm10\",\n",
    "                       \"Number of active cases vs Median PM10 level in Australia 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a weak negative correlation between number of active covid cases and median PM10 values in Australia in 2020H1.\n",
    "* The r-squared value tells us that only 7.8% of the variability in the median PM10 values in Australia in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median no2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Active cases  Median no2\n",
       "0 2020-01-22  Australia             0         3.8\n",
       "1 2020-01-23  Australia             0         4.7\n",
       "2 2020-01-24  Australia             0         3.4\n",
       "3 2020-01-25  Australia             0         2.1\n",
       "4 2020-01-26  Australia             4         3.3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median NO2 data and covid active cases for Australia based on shared date\n",
    "\n",
    "au_air_covid_no2 = world_air_covid_each_specie(airdf=au_air_df, specie=\"no2\", covid_df=active_au_covid)\n",
    "au_air_covid_no2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: 0.016429701804397717\n",
      "The r-squared is: 0.0002699351013814296\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median NO2 values\n",
    "\n",
    "linear_regression_plot(au_air_covid_no2, \"Active cases\", \"Median no2\", \"au_air_covid_no2\",\n",
    "                       \"Number of active cases vs Median NO2 level in Australia 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is almost no correlation between number of active covid cases and median NO2 values in Australia in 2020H1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median o3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>11.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>7.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>10.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Active cases  Median o3\n",
       "0 2020-01-22  Australia             0      11.40\n",
       "1 2020-01-23  Australia             0       9.00\n",
       "2 2020-01-24  Australia             0       7.55\n",
       "3 2020-01-25  Australia             0      11.20\n",
       "4 2020-01-26  Australia             4      10.40"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median O3 data and covid active cases for Australia based on shared date\n",
    "\n",
    "au_air_covid_o3 = world_air_covid_each_specie(airdf=au_air_df, specie=\"o3\", covid_df=active_au_covid)\n",
    "au_air_covid_o3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: 0.010602775504139674\n",
      "The r-squared is: 0.00011241884839118432\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median O3 values\n",
    "\n",
    "linear_regression_plot(au_air_covid_o3, \"Active cases\", \"Median o3\", \"au_air_covid_o3\",\n",
    "                       \"Number of active cases vs Median O3 level in Australia 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is almost no correlation between number of active covid cases and median O3 values in Australia in 2020H1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median so2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>Australia</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>Australia</td>\n",
       "      <td>4</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Country  Active cases  Median so2\n",
       "0 2020-01-22  Australia             0         1.5\n",
       "1 2020-01-23  Australia             0         1.5\n",
       "2 2020-01-24  Australia             0         1.6\n",
       "3 2020-01-25  Australia             0         1.1\n",
       "4 2020-01-26  Australia             4         1.4"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median SO2 data and covid active cases for Australia based on shared date\n",
    "\n",
    "au_air_covid_so2 = world_air_covid_each_specie(airdf=au_air_df, specie=\"so2\", covid_df=active_au_covid)\n",
    "au_air_covid_so2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.384509094238364\n",
      "The r-squared is: 0.14784724355200707\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median SO2 values\n",
    "\n",
    "linear_regression_plot(au_air_covid_so2, \"Active cases\", \"Median so2\", \"au_air_covid_so2\",\n",
    "                       \"Number of active cases vs Median SO2 level in Australia 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a weak negative correlation between number of active covid cases and median SO2 values in Australia in 2020H1.\n",
    "* The r-squared value tells us that 14.8% of the variability in the median SO2 values in Australia in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How has change in Covid-19 active cases correlated with change in India's air quality in 2020H1?"
   ]
  },
  {
   "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>Date</th>\n",
       "      <th>Country_code</th>\n",
       "      <th>City</th>\n",
       "      <th>Specie</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>variance</th>\n",
       "      <th>Country_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1220811</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>IN</td>\n",
       "      <td>Thrissur</td>\n",
       "      <td>co</td>\n",
       "      <td>24</td>\n",
       "      <td>2.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>58.36</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220812</th>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>IN</td>\n",
       "      <td>Thrissur</td>\n",
       "      <td>co</td>\n",
       "      <td>72</td>\n",
       "      <td>2.3</td>\n",
       "      <td>13.6</td>\n",
       "      <td>8.1</td>\n",
       "      <td>131.11</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220813</th>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>IN</td>\n",
       "      <td>Thrissur</td>\n",
       "      <td>co</td>\n",
       "      <td>67</td>\n",
       "      <td>1.4</td>\n",
       "      <td>12.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>126.02</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220814</th>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>IN</td>\n",
       "      <td>Thrissur</td>\n",
       "      <td>co</td>\n",
       "      <td>72</td>\n",
       "      <td>0.7</td>\n",
       "      <td>26.8</td>\n",
       "      <td>14.1</td>\n",
       "      <td>532.05</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220815</th>\n",
       "      <td>2020-06-13</td>\n",
       "      <td>IN</td>\n",
       "      <td>Thrissur</td>\n",
       "      <td>co</td>\n",
       "      <td>72</td>\n",
       "      <td>0.3</td>\n",
       "      <td>30.3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>671.30</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date Country_code      City Specie  count  min   max  median  \\\n",
       "1220811 2020-02-24           IN  Thrissur     co     24  2.1  11.2     5.3   \n",
       "1220812 2020-04-07           IN  Thrissur     co     72  2.3  13.6     8.1   \n",
       "1220813 2020-04-24           IN  Thrissur     co     67  1.4  12.2     9.2   \n",
       "1220814 2020-06-05           IN  Thrissur     co     72  0.7  26.8    14.1   \n",
       "1220815 2020-06-13           IN  Thrissur     co     72  0.3  30.3    15.0   \n",
       "\n",
       "         variance Country_name  \n",
       "1220811     58.36        India  \n",
       "1220812    131.11        India  \n",
       "1220813    126.02        India  \n",
       "1220814    532.05        India  \n",
       "1220815    671.30        India  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the air data for India\n",
    "in_air_df = world_air[world_air[\"Country_code\"]==\"IN\"].copy()\n",
    "in_air_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median pm25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>159.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country  Active cases  Median pm25\n",
       "0 2020-01-22   India             0        153.0\n",
       "1 2020-01-23   India             0        153.0\n",
       "2 2020-01-24   India             0        154.0\n",
       "3 2020-01-25   India             0        158.0\n",
       "4 2020-01-26   India             0        159.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median PM2.5 data and covid active cases for India based on shared date\n",
    "\n",
    "in_air_covid_pm25 = world_air_covid_each_specie(airdf=in_air_df, specie=\"pm25\", covid_df=active_india_covid)\n",
    "in_air_covid_pm25.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.5590992737953021\n",
      "The r-squared is: 0.3125919979584342\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median PM2.5 values\n",
    "\n",
    "linear_regression_plot(in_air_covid_pm25, \"Active cases\", \"Median pm25\", \"in_air_covid_pm25\",\n",
    "                       \"Number of active cases vs Median PM2.5 level in India 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median PM2.5 values in India in 2020H1.\n",
    "* The r-squared value tells us that 31% of the variability in the median PM2.5 values in India in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median no2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>12.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>17.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>17.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country  Active cases  Median no2\n",
       "0 2020-01-22   India             0       12.85\n",
       "1 2020-01-23   India             0       12.60\n",
       "2 2020-01-24   India             0       15.50\n",
       "3 2020-01-25   India             0       17.45\n",
       "4 2020-01-26   India             0       17.75"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median NO2 data and covid active case data for India based on shared date\n",
    "\n",
    "in_air_covid_no2 = world_air_covid_each_specie(airdf=in_air_df, specie=\"no2\", covid_df=active_india_covid)\n",
    "in_air_covid_no2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.5011354595928155\n",
      "The r-squared is: 0.2511367488613024\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median NO2 values\n",
    "\n",
    "linear_regression_plot(in_air_covid_no2, \"Active cases\", \"Median no2\", \"in_air_covid_no2\",\n",
    "                       \"Number of active cases vs Median NO2 level in India 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median NO2 values in India in 2020H1.\n",
    "* The r-squared value tells us that 25% of the variability in the median NO2 values in India in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median pm10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>88.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>88.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country  Active cases  Median pm10\n",
       "0 2020-01-22   India             0         77.0\n",
       "1 2020-01-23   India             0         73.5\n",
       "2 2020-01-24   India             0         79.5\n",
       "3 2020-01-25   India             0         88.5\n",
       "4 2020-01-26   India             0         88.5"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median PM10 data and covid active case data for India based on shared date\n",
    "\n",
    "in_air_covid_pm10 = world_air_covid_each_specie(airdf=in_air_df, specie=\"pm10\", covid_df=active_india_covid)\n",
    "in_air_covid_pm10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.6328098339844632\n",
      "The r-squared is: 0.4004482859874439\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median PM10 values\n",
    "\n",
    "linear_regression_plot(in_air_covid_pm10, \"Active cases\", \"Median pm10\", \"in_air_covid_pm10\",\n",
    "                       \"Number of active cases vs Median PM10 level in India 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median PM10 values in India in 2020H1.\n",
    "* The r-squared value tells us that 40% of the variability in the median PM10 values in India in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median o3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>9.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>10.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>9.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>7.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country  Active cases  Median o3\n",
       "0 2020-01-22   India             0       9.20\n",
       "1 2020-01-23   India             0      10.35\n",
       "2 2020-01-24   India             0       9.90\n",
       "3 2020-01-25   India             0       7.90\n",
       "4 2020-01-26   India             0       7.30"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median O3 data and covid active case data for India based on shared date\n",
    "\n",
    "in_air_covid_o3 = world_air_covid_each_specie(airdf=in_air_df, specie=\"o3\", covid_df=active_india_covid)\n",
    "in_air_covid_o3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.32558148446257373\n",
      "The r-squared is: 0.10600330302485314\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median O3 values\n",
    "\n",
    "linear_regression_plot(in_air_covid_o3, \"Active cases\", \"Median o3\", \"in_air_covid_o3\",\n",
    "                       \"Number of active cases vs Median O3 level in India 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a weak negative correlation between number of active covid cases and median O3 values in India in 2020H1.\n",
    "* The r-squared value tells us that only 10% of the variability in the median O3 values in India in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median so2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>4.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>4.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country  Active cases  Median so2\n",
       "0 2020-01-22   India             0        4.55\n",
       "1 2020-01-23   India             0        4.45\n",
       "2 2020-01-24   India             0        4.40\n",
       "3 2020-01-25   India             0        4.95\n",
       "4 2020-01-26   India             0        4.75"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median SO2 data and covid active case data for India based on shared date\n",
    "\n",
    "in_air_covid_so2 = world_air_covid_each_specie(airdf=in_air_df, specie=\"so2\", covid_df=active_india_covid)\n",
    "in_air_covid_so2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.6216286293193106\n",
      "The r-squared is: 0.3864221527894049\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median SO2 values\n",
    "\n",
    "linear_regression_plot(in_air_covid_so2, \"Active cases\", \"Median so2\", \"in_air_covid_so2\",\n",
    "                       \"Number of active cases vs Median SO2 level in India 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median SO2 values in India in 2020H1.\n",
    "* The r-squared value tells us that 39% of the variability in the median SO2 values in India in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Active cases</th>\n",
       "      <th>Median co</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>8.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>7.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>8.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country  Active cases  Median co\n",
       "0 2020-01-22   India             0       8.55\n",
       "1 2020-01-23   India             0       7.50\n",
       "2 2020-01-24   India             0       6.85\n",
       "3 2020-01-25   India             0       8.95\n",
       "4 2020-01-26   India             0       9.10"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median CO data and covid active case data for India based on shared date\n",
    "\n",
    "in_air_covid_co = world_air_covid_each_specie(airdf=in_air_df, specie=\"co\", covid_df=active_india_covid)\n",
    "in_air_covid_co.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.46327863178099465\n",
      "The r-squared is: 0.21462709066487043\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median CO values\n",
    "\n",
    "linear_regression_plot(in_air_covid_co, \"Active cases\", \"Median co\", \"in_air_covid_co\",\n",
    "                       \"Number of active cases vs Median CO level in India 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a weak negative correlation between number of active covid cases and median CO values in India in 2020H1.\n",
    "* The r-squared value tells us that 21% of the variability in the median CO values in India in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Comparing the correlations between the number of Covid-19 active cases and the median values of the air pollutant between Australia and India, it seems that Covid-19 has had somewhat larger impact on the air quality of India than Australia. While most of the correlations between the number of covid active cases and median values of the air pollutants in Australia are weak or non-existent, most of those in India are moderate. This could be explained by the fact that in normal situations, the air quality of India is generally much worse than that of Australia. Hence, the impact of lockdowns from Covid-19 would lead to more change in air quality in India."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How has change in Covid-19 active cases correlated with change in the world's air quality in 2020H1?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The world here means the 88 countries as explained in the data exploration notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Date</th>\n",
       "      <th>Total active cases</th>\n",
       "      <th>Median pm25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>510</td>\n",
       "      <td>62.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>606</td>\n",
       "      <td>68.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>879</td>\n",
       "      <td>66.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1350</td>\n",
       "      <td>66.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2005</td>\n",
       "      <td>65.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Total active cases  Median pm25\n",
       "0 2020-01-22                 510        62.75\n",
       "1 2020-01-23                 606        68.00\n",
       "2 2020-01-24                 879        66.25\n",
       "3 2020-01-25                1350        66.00\n",
       "4 2020-01-26                2005        65.25"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median PM2.5 data and covid active case data for the world based on shared date\n",
    "\n",
    "world_air_covid_median_pm25 = world_air_covid_each_specie(world_air, \"pm25\", active_world_covid)\n",
    "world_air_covid_median_pm25.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.7629165783871087\n",
      "The r-squared is: 0.5820417055778934\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median PM2.5 values\n",
    "\n",
    "linear_regression_plot(world_air_covid_median_pm25, \"Total active cases\", \"Median pm25\", \"world_air_covid_median_pm25\",\n",
    "                       \"World total active cases vs Median PM2.5 level in 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a strong negative correlation between number of active covid cases and median PM2.5 values in the 88 countries in 2020H1.\n",
    "* The r-squared value tells us that 58% of the variability in the median PM2.5 values in the 88 countries in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Date</th>\n",
       "      <th>Total active cases</th>\n",
       "      <th>Median pm10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>510</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>606</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>879</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1350</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2005</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Total active cases  Median pm10\n",
       "0 2020-01-22                 510         28.0\n",
       "1 2020-01-23                 606         26.0\n",
       "2 2020-01-24                 879         29.0\n",
       "3 2020-01-25                1350         28.0\n",
       "4 2020-01-26                2005         24.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median PM10 data and covid active case data for the world based on shared date\n",
    "\n",
    "world_air_covid_median_pm10 = world_air_covid_each_specie(world_air, \"pm10\", active_world_covid)\n",
    "world_air_covid_median_pm10.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.5067582713167661\n",
      "The r-squared is: 0.2568039455479571\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median PM10 values\n",
    "\n",
    "linear_regression_plot(world_air_covid_median_pm10, \"Total active cases\", \"Median pm10\", \"world_air_covid_median_pm10\",\n",
    "                       \"World total active cases vs Median PM10 level in 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median PM10 values in the 88 countries in 2020H1.\n",
    "* The r-squared value tells us that 25.7% of the variability in the median PM10 values in the 88 countries in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Date</th>\n",
       "      <th>Total active cases</th>\n",
       "      <th>Median co</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>510</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>606</td>\n",
       "      <td>4.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>879</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1350</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2005</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Total active cases  Median co\n",
       "0 2020-01-22                 510       3.85\n",
       "1 2020-01-23                 606       4.10\n",
       "2 2020-01-24                 879       4.50\n",
       "3 2020-01-25                1350       3.70\n",
       "4 2020-01-26                2005       3.20"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median CO data and covid active case data for the world based on shared date\n",
    "\n",
    "world_air_covid_median_co = world_air_covid_each_specie(world_air, \"co\", active_world_covid)\n",
    "world_air_covid_median_co.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.5012135612584958\n",
      "The r-squared is: 0.2512150339894239\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median CO values\n",
    "\n",
    "linear_regression_plot(world_air_covid_median_co, \"Total active cases\", \"Median co\", \"world_air_covid_median_co\",\n",
    "                       \"World total active cases vs Median CO level in 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median CO values in the 88 countries in 2020H1.\n",
    "* The r-squared value tells us that 25% of the variability in the median CO values in the 88 countries in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Date</th>\n",
       "      <th>Total active cases</th>\n",
       "      <th>Median no2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>510</td>\n",
       "      <td>12.850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>606</td>\n",
       "      <td>12.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>879</td>\n",
       "      <td>10.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1350</td>\n",
       "      <td>9.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2005</td>\n",
       "      <td>8.700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Total active cases  Median no2\n",
       "0 2020-01-22                 510      12.850\n",
       "1 2020-01-23                 606      12.700\n",
       "2 2020-01-24                 879      10.875\n",
       "3 2020-01-25                1350       9.800\n",
       "4 2020-01-26                2005       8.700"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median NO2 data and covid active case data for the world based on shared date\n",
    "\n",
    "world_air_covid_median_no2 = world_air_covid_each_specie(world_air, \"no2\", active_world_covid)\n",
    "world_air_covid_median_no2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.5844896343454943\n",
      "The r-squared is: 0.3416281326573296\n"
     ]
    },
    {
     "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": [
    "# Perform a linear regression model for the Number of Active cases vs Median NO2 values\n",
    "\n",
    "linear_regression_plot(world_air_covid_median_no2, \"Total active cases\", \"Median no2\", \"world_air_covid_median_no2\",\n",
    "                       \"World total active cases vs Median NO2 level in 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median NO2 values in the 88 countries in 2020H1.\n",
    "* The r-squared value tells us that 34% of the variability in the median NO2 values in the 88 countries in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Date</th>\n",
       "      <th>Total active cases</th>\n",
       "      <th>Median so2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>510</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>606</td>\n",
       "      <td>2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>879</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1350</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2005</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Total active cases  Median so2\n",
       "0 2020-01-22                 510        2.00\n",
       "1 2020-01-23                 606        2.10\n",
       "2 2020-01-24                 879        2.15\n",
       "3 2020-01-25                1350        2.15\n",
       "4 2020-01-26                2005        2.00"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median SO2 data and covid active case data for the world based on shared date\n",
    "\n",
    "world_air_covid_median_so2 = world_air_covid_each_specie(world_air, \"so2\", active_world_covid)\n",
    "world_air_covid_median_so2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: -0.5795984066016522\n",
      "The r-squared is: 0.3359343129351741\n"
     ]
    },
    {
     "data": {
      "image/png": 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/kiwfAp+o6ipglYg8D2wDzMmlXI7jOE5TcjmbSICbgBpVvSJFtgeA3UWkTES6ATthYwuO4zhOO5LLnsGuwAnAGyLyekg7DxgOoKrXqWqNiDwO/A/YANyoqm/mUCbHcRwnCTlTBqr6AiAZ5LsMuCxXcjiO4zjNU3TLUTiO4zgb48rAcRzHcWXgOI7juDJwHMdxKNJVS2tqLczlonoLajNpjK9i6jhOcVN0PYOaWpgy0wLaDKqyzykzLd1xHKdYKTpl8PhcC2jTswuUiH32rLR0x3GcYqXolMGieqiubJpWXWnpjuM4xUrRKYMhVVDX0DStrsHSHcdxipWiUwaTxtg4wYo1sEHtc0WDpTuO4xQrRacMxvWHyRNsnGBxvX1OnuCziRzHKW6KcmrpuP7+8Hccx4lSdD0Dx3EcZ2NcGTiO4ziuDBzHcRxXBo7jOA6uDBzHcRxcGTiO4zi4MnAcx3FwZeA4juNQpE5nj8yBW2fBx6tgQHc4aRs4ZGy+pXIcx8kfRacMHpkDv30RqiqgfzdYudZ+gysEx3GKl6IzE906yxRBj0ooKbHPqgpLdxzHKVaKThl8vAqqypumVZVbuuM4TrGSM2UgIsNE5BkRqRGRt0Tkh2ny7iAi60Xka7mSJ8aA7lC/rmla/TpLdxzHKVZyOWbQCPxYVWeKSDUwQ0SeVNW3o5lEpBT4HfBEDmX5gn1Hwu9fNOEkbFVl8IP926N2x3GcwiRnPQNVXayqM8P3OqAGGJIk6w+A+4CluZIlRk0t/Gs2bIjJiH3/fAN8uDLXtTuO4xQu7TJmICIjgO2AVxLShwBHAte1hxyPz4X3V0J5KVRX2Na93BrBB5Adxylmcq4MRKQKe/M/U1UT37+vAs5R1fXNlDFZRKaLyPTa2tpWy7KoHtauhzKJlB22FQ2pjnIcx+n85NTPQETKMUVwh6renyTLROBuEQHoBxwsIo2q+q9oJlWdAkwBmDhxorZWniFVUFEKjRugIlZ22HpWtrZUx3Gcjk8uZxMJcBNQo6pXJMujqiNVdYSqjgD+AZyeqAiyyaQxsGkPUwZrG6FxvX2KmBey4zhOsZJLM9GuwAnAPiLyetgOFpFTReTUHNabknH94YoDYefB5nDWqNC7C/zoy/C9ifmQyHEcpzDImZlIVV/AzPGZ5j85V7JEGdcf7si5N4PjOE7Houg8kB3HcZyNcWXgOI7juDJwHMdxXBk4juM4FGE8A7BlKR6fa05oQ6psyum4/vmWynEcJ38UXc+gphamzDSP40FV9jllpqU7juMUK0WnDB6fa97GPbtAidhnz0pLdxzHKVaKThksqofqhKUnqist3XEcp1gpujGDIVWwYBksWQ11DaYIBnaDkb3zLZnjOE7+KLqewdi+MPNjWNlgy1evbLDfY/vmWzLHcZz8UXTKYM6nMGEg9KiAVevsc8JAS3ccxylWis5MtKgeNu3V1Cy0QX3MwHGc4qboegZDqmysIEpdg6U7juMUK0XXM5g0Bn4/FT5dYlHPKkqhb1f46S75lsxxHCd/FF3PAMK62hrfMl5n23Ecp5NSdD2Dx+fC8J7wpQHxtBVrLN2XpHAcp1gpup6BO505juNsTNH0DGKL07240HoCUgI9K2CbATCgygeQHccpboqiZxBbnO71xbYw3doNsHYd1K+F5xfCW0ttYNlxHKdYKQplEFuc7t3l0LUMenWB8jJTCl3K4PNGHy9wHKe4KQplEBsnqF8L5SU2nbRnpS1HMbIX1K3Nt4SO4zj5pSiUQczRrKoC1m2wtPUKlaVQvw4GdM+vfI7jOPmmKJTBpDE2VrBZL3M0W70W1jVaD6F+LZy0Tb4ldBzHyS9plYGI9BCR0UnSt86dSNlnXH+YPAG2HQRb9oNu5dClHDbpDufuCoeMzbeEjuM4+SXl1FIRORq4ClgqIuXAyao6Ley+BZiQe/Gyx7j+PkjsOI6TinR+BucB26vqYhHZEfibiJynqveTwQoOIjIMuA0YCGwApqjqHxPyHA+cE37WA6ep6qxWnEdG1NTC32bBE/NgeQMg0KcSvjMBvjcxV7U6juMUPumUQamqLgZQ1VdFZG/gYREZiq3q0xyNwI9VdaaIVAMzRORJVX07kmcBsKeqLhORg4ApwE6tO5X01NTaAnWvfQTL15o2U4XP1sAVL1seVwiO4xQr6cYM6qLjBUEx7A0cDoxvrmBVXayqM8P3OqAGGJKQZ6qqLgs/XwaGtkz8zHl8Lnz6OdQ32kmXldgWUwq35qw/4jiOU/ikUwankWAOUtWVwCTgWy2pRERGANsBr6TJ9m3gsRTHTxaR6SIyvba2tiVVf8GieptJtD5hlVLFfq9oSHGg4zhOEZDSTBS13YvIAGCH8PNVVb0j0wpEpAq4DzgzKJNkefbGlMFuKWSZgpmQmDhxYiYmqo0YUmWhLUvFIpt9UTemEHpWpjrScRyn89Osn0GYVfQq8HXgaOAVEflaJoWHWUj3AXeEgedkebYGbgQOV9WcRSKeNMaC2FSV2Wh24wbbFBBxXwPHcYqbTFYtPR/YQVWXAohIf+A/wD/SHSQiAtwE1KjqFSnyDAfuB05Q1TktEbyljOtv0cx8NpHjOM7GZKIMSmKKIPApmXku7wqcALwhIq+HtPOA4QCqeh3wS6AvcK3pDhpVNWeP5XH94ZL9bHMcx3HiZKIMHheRJ4C7wu9jgEebO0hVX6AZfwRV/Q7wnQxkaBOPzLHZQgtXmGmooszGCLYbCCds7c5ojhGLebGo3saYJo3xe8MpHpp9w1fVs4Hrga2BbTDnsXPSH1U4PDIHfvsifLwKVq2zWUNL621Nopc+hMum2kPAKW5iMS9WNMCgKvucMtPvDad4yGQAuTvwgKr+CLgOWB8GhjsEt86Kr1a6XqG8FMpKYdnn0KMSPlltb4NOcROLedGzC5SIffas9HvDKR4ysf0/D1SKyBBs4PgUbG2iDsHHq6CqHBrW25RSwaaXrt1gq5auXe/xjx2Pje04mSgDUdXVwFHAn1T1SGDL3IqVPQZ0t5gFlaX2xqdYD6GixBRBRanHP3biMS+i1DX4veEUDxkpAxHZGTgeeCSkZTLwXBCctE08wlmpwLr10LgeeneFlQ3Qr5vHP3biMS9WrLEe5Io19tvvjeygCpdcAsOGQdeusMce8PrrzR8H8MAD8KUvQZcusOWWcM89uZW1JUybBqecAmPGQLdusPnmcOGFsGZNZsffcANstpmd2/bbw1NPbZznySdh112hZ08YMACOPBLeeSe75wGZKYMfAucC/1TVt0RkFPBM9kXJDYeMtZgFA7pbmMuelbBJlY0j7DwUzt7FZ4w48ZgXPSthcb19Tp7g90a2uPRS+PWv4Zxz4KGHoKoK9tsPlixJf9wLL8BXvwp77w2PPQaHHALHHgv//nf7yN0c99wD8+bZeT36KHz/+3DFFXD88c0fe/fdcOqpcOKJdm7jx8Ohh8Kbb8bzzJhh5zxkCPz973DttTB/Puy/P6xMup5DG1DVDrVtv/326jhOx+Hzz1V79FC98MJ4Wn29ar9+quefn/7YAw5Q3XvvpmkHHaS6667Zl/Pmm1U33bRlxyxdunHa9dergup776U/duxY1VNOif9ev151q61Ujz8+nnbOOaoDBqiuWxdPmzXLyn/00ZbJCkzXNM/Wogh76TjFwiOPQEkJLFjQNH3BAkt/8MH2l2nqVHuLPfroeFr37nDYYfZGnIqGBnjmmabHAXzjG/DSS7Bihf0+9FDYYgv4/PN4nj/8wUwvb72VvfNIRv8kPcfttrPPpUs33hdj/nyYM6fpuZWUwNe/3rRN1q0z81NZxDDfq5d9aqtWaUtNh7H9t4VYUJvXPob5n8KaSCP2rIAdh8L4fu5k1NlJ5VRWaM5mbZFn0iQYPBhuvRWO+X68nDfuhj594eCDUx+7fn3zD5iSEttaIv+yaVBaarbxKOPGpbf/z5tnD8Mtttj4uA0b7GG6ww5md99qKzj3XLjqKqipgZ//3Gz345tdbD/7TJ1qbbT55qnzzJ5tn2X94cqX4m1VPQQ++wxqa03RfPOb8Oc/w+9+B5MnQ309/OhH1ib77ptduTt9zyAW1OblRfDeZ00VAcCKtfDi+7BguTsZdWZSOZU9MqewnM3a6vxWWgonnww33gzXz7DjB3aHVx6BTfeEd5elPnbffaG8PP32rWYWr08m/1M10K27yRald29YvRrWrk1e1rIga+xNOHpcdP+gQXDNNXD11TYAe9JJ9nb+k5+kl3XDBmhsjG8bNlh6NG39+vRlJLJkCfzmN3DCCdCjR+p8Mdnve69pW72wtOn+7baDhx+2MZc+fWD4cOvtPPEEVGZ5peVmewZhYbrvAiOi+VW1RTEN8kUsqE1VJazekDzP6vWwpB627G/5vXfQ+Yg6lUH889ZZdt0T0/N1H6SSsyXyfOtbNnNnWQ1sujO8Ox1WfAw7HZK+nOuvh7q69GX365d6nyo8Mhuqy2x1YDaY/JVl0JikxxHrhUgzQXQT9yc77thj4f77bbC1pARmzdpY+SRy0UXWe0ikPOJSu+mm8N576cuJsXatmX2qquDKKzM7JvFadwt1x87trbfguOPgqKPsc9UqUwwHH2w9kHQKp6VkYiZ6APgv5nDWQj2Zf2JBbaor0uerW+tORp2ZRfX29hWlutKcEncaunF6vu6DVHK2RJ5Ro2Do1vDWv2HbneHVR2D4ljB6i/TljBmTmZkoFc89B2fvHf89ejv4/rX2Zr9mlb1lRx/Qy5ebPbw8xXoGsR7A8uVN02O/E3sMxx4L//gHfOUrG5ukkjF5so03xHj4YZgypem4SqZv36o2K+itt+DFF+OypyK2vzShVyTB1yV2br/4hZ3LTTfF8+y+OwwdCjfeaCajbJGJMuimHWgtokRiQW2WNTPvt7rCnYw6M0NCNzz2FgZ2vQd0t8/E9HzdB6nkbKk8exwJ914Kh5wGbzwLX/lB8+Xsu6890NNx0klwyy3J922/PZx1kzl5VlVAZTdLrx4MugHmzm1qR589e+PxgCijR5uimD0b9tyz6XElJTB2bDxt5Uo46ywzqzz4oJlRDjww/bkMHmxbjDffhIoKmNiKdZPPOsv8IZ58Mv05xYjleX8u9IvI8ME86NYjPjA9e7ZNq43Su7f1WObNa7mc6chkzOBhEUkz7FTYTBpj3saL6+yGTEZlCQysciejzkwqp7KTtiksZ7NsOb/95FtQUga3/Nxs4aP3aL6c6683J6p02wUXpD6+uhq+exh0HwE9RkK/4SZ/j82gqtrmycdYvdr8DQ46KHV5lZX2IIweBzbovPPO5oQV48wzrefx9NNmTvnOd+KzjXLNb38Lf/oT3H477JY0VuPGjBoFI0bD60/Hr/Xy1fDmM7DP/vF8m24Kr73W9NhPPzXT1YgR2ToDI5OewQ+B80SkAVhHLIa8ahatVbljXH8Y29e8jafdA/U1ULEpVA6D7jtDZRcY0gNG9sr/LBInd8ScyqKzXI4Zb+mjeidPLzQ5W8K2w+CrR8Odf4Ut9oYBfZu/v9PNfsmUVPIPPtecznr3trfiK64wJfWDH8SPve02G++YN88egmBmkr32sof9EUeYY9ejj8Ljj8ePe/hhuPlmm5LZq5c9mLfaCn74w9S9mGxx551w3nk2aD9kCLz8cnzf6NHxN/yLLrKtsTG+/5Jf22yhV4dD1Wbw3rNQtwR+e0E8z6mn2nmffLKZwVatsplFFRWZOba1iHROCIW4tcbp7Mf/Vr18quoBt6ruf7nqxNNURx1gv485X7X/aNXJk1VvuEG1trbFxTtOQfLkk+ac9OST+ZZEdcMG1YsvVh0yRLVLF9XddlOdObNpnptvNnkXLGia/s9/qo4fr1pRobr55qp33RXf9+mnqoMGqX73u02PefhhK+vBBzOXsTVOZyedZPUk226+OZ7vV7+ytESmTFEdPdrObbvtVP/zn43z3HOP6sSJqtXVqv37m9Pda6+1TE7V5p3ORDPwXBCR3sBmwBeWTFV9Pst6KSMmTpyo06dPb9ExV74Ery+GVxZDQ6N1bQQoLYEuAtWfwl4V8EGNzVVubITTToMdd7R5zHvtZdPXnI5JofkRtJSWyh/Lf+eVMO95+O8sGD+g/eRtDaQudbsAACAASURBVB39GnUERGSGpokkmUk8g+9gy1g/AVwYPi/IloDtQbdyeOkjWBe6aOuBRkKMgxLQYbB4Szj3D+bQMnq0dekGDID77rP1Udats2lrP/+5DRQ1t6aKUxh09KA1LZW/phYufQBe/g+88QhMPBJufL2wz7ejX6POQqZjBjsAL6vq3iKyBaYUOgxPLYCuZbBO4PPGpvu6lkH/bvEgN+P623S3ffaxLca6dWa/mzYNrrvO3OEvvRR+9jOb67vjjjYLIXG6m5NfsjFvP5+0VP7H58Jz18CiGthqd9j/OFi1vrDPt6Nfo85CJspgjaquERFEpFJVZ4tIFoaa2o+PV0FZifUQdI35HYCN4CsW06CuIf0c7PJyW0vlsMOapu+0k80r/vWv7fdzz8Hf/mYj/jvuaFPdunbNyWk5GZCNefv5pKXyL6qHH/zFYnd8kb+ssM+3o1+jzkImyuBDEekF/At4UkSWAR/lVqzsMqC7xTRYrzZOIBtsJkOpWNCbtgS5OfJI2yDusNOnj3kH3n67za2urYXp020e84472nopZZm0vNNmsjVvP1+0VP62nm8+bPcd/Rp1FpodM1DVI1V1uapeAPwCuAk4IteCZZOTtrEH/+eNUCbmb7ABi4fcvTx7QW5iLuSHHAJ/+YspgNpa61WsW2djD8cea6akt9+Gjz6CO+6Ad9/N/gqEjtHRg9a0VP62nG++bPcd/Rp1FlLOJhKRHqq6UkT6JNuvqp/lVLIUtGY2EdiCZNdOgw/qALVuaHmpjRlsNxBO2Lr97JMrV9q4xDvvwK9+ZeMQK1fanOjDDjNnnB12aOod6bSejj5TpbWziVp6vle+tPEb+oo1Zs8/a+e2n0c6Ovo16gg0N5sonTJ4WFUPFZEFmGk9ulyUquqo7IqaGa1VBoXOxx+bI4mqOaJMm2a/f/Yzc8yZNs1mOfVJqpodp+385EnrEUTHGzaoRX67fP/Uxzkdg+aUQUrLtaoeGj5H5kIwpykDIvPAH33UlML778eX0L3oInj2WRg40AJgXHIJLFxoq0h265YXkZ1Ohtvui5uUykBEJqQ7UFVnptsvIsOA24CBmIl+iqr+MSGPAH8EDgZWAyc3V25rqKmFi5+zmAbRmaUVpbDjYDhuK3hxoQW/gfY3G6XqIkfXHnnoIVMMs2fDa/OtS3/Lb2D2UzByNOy1m41T1NXZ7KXYSpCxst+qtbGRHl3igXyg/brmiec4tq8tINjc77c+gZVroEcljO+/sYxtMS80d2yuTBfprklz5Udlqgjek0tX2RLsq9ZBlzLYbgCcsE3LZZ00xsYIwMyodQ2mHI7JYoAYNwe1jvZot3RmoljQ+y7ARGAWZiraGnhFVdMuySQig4BBqjpTRKqBGcARqvp2JM/BwA8wZbAT8EdV3SlduS01E9XUwo+egLc/TZ2nexn07gp9wxTQurW2VtHZu+T+Ro0N2vWsbPoHTBWMPTH/sjqYNxu2K4efnmbrllx0EWyzDYzeCrrvB2VdYc4yKCm1WVRb9LNzFGB4z8zqzeY5vr8MZn4MEwbCpr3g/eUwcwlMGACb9o7vH93bFhgsKbGe0uZ9bAnkmIwtbbuWtHtbys6k3vXr4Z3PbNJB7JqUlqQvPyrTmkZ4dZF9KnY9S4FNqmD9BmvXn7bi/s3lQydXbdrZyVa7tdoDWVX3VtW9gfeBCao6UVW3B7YD5jZXsaoujr3lq2odUAMMSch2OHBbWDrjZaBXUCJZ4/G58P7K9HlWN8K69dCl3LYelXEntFwTdbgpEfvsWZm67sT8fXvA5ltD+ba2/5xzYPFiMyMtr4A+1fDaC/Ds6fDa7+C9+2z20ieroXZV5vVm8xyXrLYljpfUh9/14ffqpvvf/RS6hutRWWbpURlb2nbpZEo8ti1lZ1LvktV2Tj0q7RyX1DdfflSm+cstYFNjmH3TrRwqy613UFVpAZ1aI+u4/jZYfPn+9pnNh3Su2rSz017tlskS1luo6huxH6r6JrBtSyoRkRGYEnklYdcQ4IPI7w/ZWGEgIpNFZLqITK+tbdk8t1hwm3TBlBRojCxvXRF8D9rD6WVRvWn7KM05FTWXv0cPW09p88Ohby/osTUceBWMPRTKK6BuNaxYAv8+HW46G568GRa9mztHn0SZ6xqgqtzeZsE+q8otPbq/fp1dCzB/kLqGpjK2tO3SyZR4bFvKzqTeugY7JwhOjxkEV4rKFDt+/QZbVqVUbGtojPvOFJrTVq7atLPTXu2WietTjYjcCNyOPTe/ib3lZ4SIVAH3AWeqauI7erJn9EZ2K1WdAkwBMxNlWjdYV7ei1P4wKWXEPJRjZOqElo0udS6dimJ5qythTSkMmgB9traHxeeNsPtFMKTOFuj75APoPhTuPQMWbWvOcXvsYQFL2kqizNWVwVYeos9VV8DKtfaWHN1fVW7XorIMGtbHH6Kxc23LgOeQKliwzN7QY0pmYDcY2bvtZWfSFtXB1NOlLB6Jr7nyozLFji8tgfISc6iEeFu11okyl/gAdetor3bLpGdwCvAWtkbRmcDbIa1ZRKQcUwR3qOr9SbJ8CAyL/B5Klr2bJ42BTZuJvNCtzHwO1qyzLRMntGw56OTSqSiWd2A3e2Nc2QCfr7NAPv26wfBNYeSucMj3YcQulvfPN8CkSbam/EMPWTnf/z58+9sW/CQx0EZrznFgN/MIH1gVfleF392a7t+sr8m7ssHkH9it6bm2xVlpbF8bl1jZEHc8nPmxpbe17EzaItk1aa78qEyjekF9gzlR9uwCq9dBwzo7l/oGG/8qNKctdy5rHe3VbpkuYd0VGK6q72RcsM0UuhX4TFXPTJHnEOD/iA8gX62qO6YrtzV+BrmYTZRNB51cOhVlYzbR66/b8hrTpplH9cMPww03wBtvxJf53myz9PFxC2020ZUvwYLlZquvW2tv5gOrbOJA7PoV22yi9sBnE7WObLRbq53OIgV8BbgMqFDVkSKyLXCRqn6lmeN2A/4LvIFNLQU4DxgOoKrXBYVxDTAJm1p6iqqmfdIXitNZsTvozJpl8V5j4RBnzrRg4A89FFcQQ4fGl+goNIr9+jnFR6udziL8CtgReBZAVV8PA8JpUdUXSD9uS4i+8/0MZMga2dCwNbWwcDm8tti642P6QP/uxWX/3GYb26L07Ws+Dn/9qwUHuuceiw8xZUp8ie++fVtfZzbfKt1+XZx4zyQ1mYwZNKpqO4WWzi3ZsPPHyhjY3QadV66F6R/ZYGSx2z+32MLWWnr4YQv+s8ceNp++vt78H0aNgmuvNZ+Ba66B55+3fZmQ7UXU3H5dfHgQnfRkogzeFJHjgFIR2UxE/gRMzbFcOSEb83VjZYzsA9sPMlv2ejWbrTvPxBGxMYTBg00RPP00LFsGp5wCa9eaJ/XZZ9syHGedZcc89hjMmGH7E8n2XOtY4PaelWYa6lnp16+z434O6cnETPQD4HygAbgLC3v561wKlSuyEUQjWkb/7rbFbM3+IElPSUk80M8119jn2rWwfLl9f+YZUwjz59uMpvvug//9z5bW+HAlDE6YFdbWudbj+vs1KyY8iE56mlUGqroaUwbn516c3JINO3Eh2JoLwe6ZLRkqKmCTTez7739vW309fBBcEZ94wtZcWrwUBo6F066Ez+th/TqQnm7jL4R7oaNQCP/dQialmUhEHky3taeQ2SIbduJ825oLwe6ZaxmqqmzgGcyUNH8+PDUdJhwNqxXmTIcrvgWXHwUPXmDR5NasgU8+yU79HYVCuBc6Evn+7xY66Raqq8WWirgLW0aiycwgVX0u59Iloa1TS7M1myhfb2P5DECSbxmi7T64O2zbDZbPh332sUBBBxxg8R523BHOPBN23hlWr+68S3wXwr3Q0SjmnlRbppYOBPYHjgWOAx4B7lLVt7IrYvuSDTtxPm3NhWD3zJcMSds9rJK10042QD1njvk99OhhYxFDhsDIkaYgvv51OOggWyU0nYNcR6EQ7oWOho8TpSZdcJv1wOPA4yJSiSmFZ0XkIlX9U3sJmG1qauFvs/ITuyAfaxnlgmzIkIs3tJISm966xRbxtGXL4M03TUE0BvfzrbYyU9SOO8Lee8NXv9q2evNFtu+FbF2TTMsp5rf0QiTt+5GIVIrIUdgidd8HrgaSrTHUIaiphd9PtWUpysMCXy99CJdNzb2dNV9rGeWCtsrQnrbuigqYMAG+9z2LLw3w6qvwhz+Y38P8+Zb2rW+ZYvjpT+Ef/7DeQ6GTzXshW9ck03J8vKPwSDdmcCuwFfAYcHdYujrvtGXM4MqX4Jn37XuX0CdqaDQnqL1H5NbOms+1jHJBW2QoRFv3p5/C9OnWg5gzB269Fa67zhzoYstr7LVX4Y0/ZOteyNY1ybScQrwHOjttGTM4AVgFjAXOkPgiM4KtJNHMWqCFRyy2QXVFPK0irJOfaztrNu27hWD3bIsMhWjr7tsXDjzQthhHHWVOcdOmwRVX2PIbs2bBlVeacogtsdG9e/7kzta9kK1rkmk5hXgPFDvpxgw6wRBbU4ZU2UqYDevjPYNMYxdko+582/oLhY7SFgMGmEI46qh4WkUFfOUrpiD++U84/3zYdVebvRRTEFtvDZWVqcstRLJ1TTItp6PcA8VEJh7InYZJY+B/Sy3mbiyETizecS5s7onLDS9ZZUu25irYeEchn4HX22pW6d8fvvlN22KsXGnTWKdNs0X5jjgCLrzQ1mmKzWTafHOY81n+zXtRcnF/Znpt2+MecFpGRvEMCols+Bm0x2yiZEGs319hXeO1GwrjYZBP8hF4fb+R8J8FuQ/IHvtL/fGPNlg9bRoMGwPbnAkL/wvdukDfMUAv+N72+bkHcnl/+myiwqTN8QwKjUKJZ9AcPkCWP1K1/du1sGX//FyTy/8L9evhrceg5iVY+DZUdIPz74GdxQawd9ghvjRHrvH7s/jIRjwDpxX4AFn+SNX2H6+CnYZunN4e12TJGpNplyNtU4X6FVb3wmUWOW76dOjZ03oTDQ22zMb225sDXbbx+9NJpOiVQbIwgtkw46QaIKsQeysr9K5xS0MzJuvyQ37MAKnafkAIQJRu0DKbpotoWQuXw9p1tvQ52BLfG7rAkEo4en84+mjzbZg3z8YlnnvOBqdnzYJNN4Wbb7aB6TfegMrB8PQHHcN5MdemIDc1ZY9ON2OoJUQdX8rEnNFe+tC+5yJ4yvsrbJCu0B1tYu2yYBl8sNIC+CxcbjGDM3Ug+v1Uc+bLx7mmcsY6aZv0TlrZdIRKLGtgFcz8GBZ8ltpBrKTEYkmLmE/D1Km2pMadd8LYsbBoEZz4LZg4Bi4/BT6ZbmX87iF4c0l22iibEyly7VjmjmvZpaiVQTTYxfzlUFVpwWrmL8tN8JRBVTC8Z+EH14i1y5LVUFlmbdK13AL4JJM3WdCQTz+HT1bn51xTBa45ZGz6gDbZDH6SWNbI3jBhoL0MtCSYTnk5bLst9O4NY8bA926An/4LvvojGDQKqsrg4Qtg4mjYc0+4/no77rPP4gPZLWmjbL5V5zqYjAeryS5FbSaK2k3rGqAqOKPVhUhb2Q6e8pMnoW+CB2sh2mlj7RJtk4pSa5dMHYjWrueL6bsx2vNcUzljpXPSyqYdPVlZm/aCijK4fP+Wl9ek3J7Qb+t42rn3wILFcHh1fAG+o482E1PMc/qnP4V160y5xMi182KuxyV83CO7FLUyiNpNqythTaO5V8c8lLO9+FpHcbSJyRlrky5lcc/tTB2IKkrZSBm0x7m2xYaczeuTq2udqtzRg2D/yCyg//wHPvrIprUuXWppP/gBPPpo3DnujDOgSxczS+WCTNugtdeso/yfOgpFbSaK2k1H9YL6BhssHdU7N4uvFcIic5kQk3NgN1u7aWUDfL7O7N7J5E12Xn27Qr9u7XuubbUhZ/P65Opat6TcwYPh8MPhu9+133/5Czz1lK3SWltr3tTXX2+BhE480UKRxhRHNshE1rZcs47yf+ooFL2fQbZmE2U6b7ujzH7oiLOJsjF3PlezibJ5/tkst7ER3nrLehDTppk5aflymDw53oPYf38YNiw3srb1mnWU/1Mh4E5n7cRPnrQ3m5JIl3uD2uBcW2zETub4NcgOa9bAzJlxBXHMMRZFbtIkWw58hx1sPabWKogofs3aj7w5nYnIX4FDgaWqulWS/T2xOAnDgxyXq+rNuZIn1xSS/bJY35ba4xo017bt1fa5rKdLF9hlF9tirFtnfg/TpsG999oy37/8pY1D9OoV70UMHNiyc1i4HF5bbGbFMX2gf/e2X7Nivf/bSs56BiKyB1AP3JZCGZwH9FTVc0SkP/AOMFBV16Yrt1B7BqnWw8n2dL2OIkc+yPW5N1d+e7V9IV3jRx6Bl14yJVFWZr+nTLGprTvsYEt89+yZ+hzWr4d3PgtOeBtgi35QWtL6cymktik0musZ5GwAWVWfBz5LlwWoFguUUBXyNuZKnlzTHvO2M6GY517n+ho017bt1faFdI0POQQuvhieeMIUAcCIETYQfcEF5kTX2Gge1VdfbYrj88/j5zCyD2w/yHxZ1qv5srTlmhVS23Q08jm19BrgQeAjoBo4RlWTBhsUkcnAZIDhw4e3m4AtpRCCzhT73OtcXoPm2ra92r7Qr/EBB9gG9rZfUgKlpTZQfcstMHs2fOMaGNQLXp4Ow7eEHUeAlJoSb8v1K/S2KWTyqQwOBF4H9gFGA0+KyH9VdWViRlWdAkwBMxO1q5QdjEIau+hsNNe27dX2raknX3b0mBPcbrvZBtYzuHYGzHsX5r0Gz94By2vhyPNg4p5w111mYho9uuU+EH7/t558+hmcAtyvxlxgAbBFHuXpFPjc69zRXNu2V9u3tJ5CW8Ona1c4eHNbcO/Qc+Cnd8NZ90Lf8bBLf/j732HvvaFfP4sJAfD887B4cfNl+/3fevKpDBYC+wKIyABgc2B+HuXpFBTK2EVnpLm2ba+2b2k9hWhHTzyHAX3h/3aDXbaA+++HDz4ws9KRR1r+q66CrbaCoUPNFwLg3XfNJyJduX7/Z04uZxPdBewF9AM+Bn4FlAOo6nUiMhi4BRiErQJxqare3ly5hTqbyHEKlc4yl18VFiywmUoTJ8Lpp8Ntt5mn9a67wl//avu6dbPeh9OUvPkZqOqxzez/CDggV/U7jmN0Fju6CIwaZRvAtdfaDKWaGgsEJAI33WSzmMaONb+HSy6B6mobwC4r6pXYmqeomyeTQbVcBb9pq1yFWLbTOnJ9TVoSfD4f93tbKCuDL33JNjAT0hln2Iqt06ZBVZUtznfCCbDNNqYgTjvNprxC7hbp64gU7XIUmTinRPOsaYRXF5lzxE5DbCXPjuZQ5A45hUd7Oqpl8uLT3vd7e7FiBcyYYQriiCNMCey0k5mbdtwRjjrKQox2ZjwGcgqig2oQ/3x8bvJgJ29/aMFvBAt+s/OwjfO3l1yFWLbTOtrrmmTif5GP+7296NkT9tnHthizZ8fXX1q82Hwixo+HzTe3qa377AM7Z7jAYWegaJVBJs4puQ5+01q5CrFsp3UU0jXJx/2eTwYMgEMPtQ1sgPrRR+HVV01BPP+8KYOTTrK1mXbYwXoT0TWbOhNFqwwyGVTLdvCbbMlViGU7raMQrskXy5UvhTmfwlb92+9+LyREYORI2445Jp7+s5/Byy+bgnjlFVMGV18Nb7wRX6DvS1+yQWrouONyRRvcJhPnlGwGv8mmXIVYttM68n1Nog5p2wywB/7LH0Lvyva53zsC48bBKafY7KW777a0ffe1AekXX4Tjjzev6qefhhMmw1mXwfy5MKBb/h38WkLRDiCDzybqSG8tnZl8XpPE4DK1q+DNpbBuA2w3gIKfTVRIvPsu/OgqmP8WLH4Hjv8V9BkMT98DY74EF58AQ4bkTz4PbuM4Tko6i0NaoRBtT1VY+QlM/RfMfQPqFsCFF9rU1ssus57FDjtAnz7tI5vPJmoFLQ35WAx4j6Jzkuug9R2BTP7vmZ5/tD1FoGd/2OUEOKgSzvyyLefd0GAxqH/7W4sod8op0L07/Pe/FrP69NPNi7q98Z5BArkKutGRcf+EzktL/W062/XP5P8OmZ9/S9tq/Xqoq7NYEN/8pqV17WpjEN/+tsnz5S/bvsrKtp1r3oLbdFRic62XrIbKMgu60bXcgm7ke3GvfFGIC5052SGThd068/XP5P/ekvNv6UJ5paUWNvT4482spArLlsFDD1m86RtvhO98xzyp+/SBDz/MXVu4mSiB2Fzr6DzrilKba93Z5llnSiHNhXeyT3MOaZ35+mf6f2/J+bc1wFJlZdz34fTT7bOuDqZOhf457Il5zyCBIeHGqK6EhvWWtna9zbXu7POsUxFrkyjF2hbFSGe+/pn83wvh/Kur4cAD224qSocrgwRi874HdoOGRhtQ+nwdDKwq3nnW+Z4L7+SXznz9M/m/d+bzj+IDyEnw2UQb05lnkzjN05mvfzZnExUy7mfgOI7juJ+B4+SLzvA2mQ1a2w6duf0K8dx8zMBxckChBaHPF61th87cfoV6bq4MHCcHdOa5+S2hte3QmduvUM/NlYHj5IBF9TZdMUpnmZvfElrbDp25/Qr13FwZOE4OKIS56YVAa9uhM7dfoZ6bKwPHyQHFMje9OVrbDp25/TI9t5paW2L8J0/aZ67HFFwZOE4OaOkaNZ2V1rZDZ26/TM4tH4PMPrXUcXJEW9eo6Sy0th06c/s1d27RQWaIfz4+N3dt4j0Dx3GcAiMfg8w56xmIyF+BQ4GlqrpVijx7AVcB5cAnqrpnruRxnFySbyeittafb/lzJUdbymvvZWmisi5cDmsbYWTv+P5cDzLnsmdwCzAp1U4R6QVcC3xFVccDX8+hLI6TM/LtRNTW+vMtf67kaEt5sWMXLIMPVsLKtfaAXrA8N22TKOvA7jBzidXfXgPoOVMGqvo88FmaLMcB96vqwpB/aa5kcZxckm8norbWn2/5cyVHW8pr7yBXibKO7AMTBlh97TWAns8B5LFAuYg8C1QDf1TV25JlFJHJwGSA4cOHt5uAjpMJ+Q7+0tb68y1/ruRoS3ntHeQqmayb9oaKcrh8/+zWlYp8DiCXAdsDhwAHAr8QkbHJMqrqFFWdqKoT++cy1I/jtIJ8OxG1tf58y58rOdpSXnsHuSqEa5BPZfAh8LiqrlLVT4DngW3yKI/jtIp8O0i1tf58y58rOdpSXnsHuSqEa5BPZfAAsLuIlIlIN2AnoCaP8jhOq8i3g1Rb68+3/LmSoy3lxY4d2RuG9YAeFTC8F4zslZu2KYRrkLPgNiJyF7AX0A/4GPgVNoUUVb0u5DkbOAXYANyoqlc1V64Ht3Ecx2k5eQtuo6rHZpDnMuCyXMnQWSiUOeCOUwj4/yE3uAdygVMoc8AdpxDw/0PucGVQ4BTKHHDHKQT8/5A7XBkUOIUaCMNx8oH/H3KHK4MCpxDmHztOoeD/h9zhyqDAKYT5x45TKPj/IXe4MihwCmH+seMUCv5/yB0e3KYD0JmDfDhOS/H/Q27wnoHjOI7jPQPHcZyWEHV6qxBAYO2Gju8A5z0Dx3GcDIk6vZUJvLwIXvrQvnd0BzhXBo7jOBkSdXqbvxyqKi3wzfxlHd8BzpWB4zhOhkSd3uoaoLI0HvQGOrYDnCsDx3GcDIk6vcUC38SC3kDHdoBzZeA4jpMhUae3Ub2gvsEC34zq3fEd4Hw2keM4TobEnN4enwv16+DLQ/hiNlHPSjhmfMedTeTKwHEcpwV0Vqc3NxM5juM4rgwcx3EcVwaO4zgOrgwcx3EcXBk4juM4uDJwHMdxcGXgOI7jAKKq+ZahRYhILfB+Kw/vB3ySRXE6Kt4OcbwtDG8HozO3w6aqmtJDosMpg7YgItNVdWK+5cg33g5xvC0MbwejmNvBzUSO4ziOKwPHcRyn+JTBlHwLUCB4O8TxtjC8HYyibYeiGjNwHMdxklNsPQPHcRwnCa4MHMdxnOJRBiIySUTeEZG5IvKzfMvTEkTkryKyVETejKT1EZEnReTd8Nk7pIuIXB3O838iMiFyzEkh/7siclIkfXsReSMcc7WISGvryGEbDBORZ0SkRkTeEpEfFmM7hHq7iMirIjIrtMWFIX2kiLwS5LxHRCpCemX4PTfsHxEp69yQ/o6IHBhJT/p/aU0dOW6LUhF5TUQeLtY2yBqq2uk3oBSYB4wCKoBZwJb5lqsF8u8BTADejKT9HvhZ+P4z4Hfh+8HAY4AAXwZeCel9gPnhs3f43jvsexXYORzzGHBQa+rIcRsMAiaE79XAHGDLYmuHUK8AVeF7OfBKqP9e4Bsh/TrgtPD9dOC68P0bwD3h+5bhv1AJjAz/kdJ0/5eW1tEObfEj4E7g4dbI1xnaIGttmW8B2umG2Rl4IvL7XODcfMvVwnMYQVNl8A4wKHwfBLwTvl8PHJuYDzgWuD6Sfn1IGwTMjqR/ka+ldbRzezwA7O/tQDdgJrAT5jlblnjPA08AO4fvZSGfJP4PYvlS/V/CMS2qI8fnPhR4CtgHeLg18nX0NsjmVixmoiHAB5HfH4a0jswAVV0MED43CempzjVd+odJ0ltTR7sQut/bYW/ERdkOwTzyOrAUeBJ7i12uqo1JZPlCzrB/BdCXlrdR31bUkUuuAn4KbAi/WyNfR2+DrFEsykCSpHXWObWpzrWl6a2pI+eISBVwH3Cmqq5MlzVJWqdpB1Vdr6rbYm/HOwLj0siSrbZId77t2hYiciiwVFVnRJPTyNDp2iDbFIsy+BAYFvk9FPgoT7Jki49FZBBA+Fwa0lOda7r0oUnSW1NHThGRckwR3KGq97dSxg7fDlFUdTnwLDZm0EtEypLI8oWcYX9P4DNa3kaftKKOXLEr8BUReQ+4GzMVXdUK+TpyG2SVYlEG04DNwiyACmxw58E8y9RWHgRiM2FOwmzosfQTw0yXLwMrgmnjCeAAEekdZsMcgNk6FwN1INRDGwAABn9JREFUIvLlMHvmxISyWlJHzgiy3QTUqOoVkV1F1Q4AItJfRHqF712B/YAa4BngaynkjMn/NeBpNcP2g8A3wiyYkcBm2CB60v9LOKaldeQEVT1XVYeq6ogg39Oqenwr5OuwbZB18j1o0V4bNvNjDmZbPT/f8rRQ9ruAxcA67O3j25gt8ing3fDZJ+QV4M/hPN8AJkbK+RYwN2ynRNInAm+GY64h7pne4jpy2Aa7YV3u/wGvh+3gYmuHUO/WwGuhLd4EfhnSR2EPsrnA34HKkN4l/J4b9o+KlHV+kP8dwuypdP+X1tTRDu2xF/HZREXZBtnYfDkKx3Ecp2jMRI7jOE4aXBk4juM4rgwcx3EcVwaO4zgOrgwcx3EcXBk4LUREVET+EPn9ExG5IEtl3yIiX2s+Z5vr+brY6qfPRNK+JCKvh+0zEVkQvv8nTTmjROQbGdQ3Jiwd0Va5jxKRLSK/fyMie7e1XMcBVwZOy2kAjhKRfvkWJIqIlLYg+7eB01X1iwepqr6hqtuqLfHwIHB2+L1fmnJGYc5I7cVRwBfKQFXPV9Vn0uR3nIxxZeC0lEYsTuxZiTsS3+xFpD587iUiz4nIvSIyR0QuFZHjxdbkf0NERkeK2U9E/hvyHRqOLxWRy0RkmljcgO9Fyn1GRO7EnL4S5Tk2lP+miPwupP0Sc2C7TkQuy+SERaRERK4I5bwROcdLgb1DD+IMERkdZH9NRGaIyE7NlNtDRJ4WkZnhvA6N7DslpM0SkZtFZHfMCerKUN8IEbldRI4QkcNCG8SO3U9E/hm+HyQiL4U67hGR7knkGBvkmBXyjUglm4hUi8hjIe+bsbYQkR3CNZ4R9g8I6WeJyNsh/+2ZtLeTJ/Lt9eZbx9qAeqAH8B629spPgAvCvluAr0Xzhs+9gOXY8s+VwCLgwrDvh8BVkeMfx15SNsO8rbsAk4GfhzyVwHRs7fm9gFXAyCRyDgYWAv2x5YSfBo4I+54ljbdwkvM4JshVCgzEVqbcBFsG4l+RfN2ALuH7FsRjKIwBXk9STzlQHb5vArwbvm8DzCbu6Rz7vD12DtHf2Hr7HwBdQ/oNWI9lE+A5oFtIPx84L4kcM4DDwvcu4TxSyXYM8JfIsT3DNZkK9AtpxwNTwvfFQEX43ivf969vqbfYYkuOkzGqulJEbgPOAD7P8LBpGtbtEZF5wL9D+htA1O59r6puAN4VkfnYQ/UAYOvIG3lPTFmsBV5V1QVJ6tsBeFZVa0Odd2BBgv6VobxRdgPuVNX1wBIReQFbumJtQr5K4BoR2QbrQY0mPQL8TkR2w5ZhHhbMb/tggVE+A4h9pkJV14rIk8AhIvIAcCBwJhbvYUtgqljQtgrghSYC2PpM/VT1oVDWmpBekUK2/wGXisilwEOq+qKIbAuMB/4T6iklvhz4W8DtQa7WtL3TTrgycFrLVVhQlZsjaY0E06PYU6Eisq8h8n1D5PcGmt6HieujxJYN/oGqPhHdISJ7YT2DZCRbTri1ZFrWj7E39G9ib9b1zeQ/EVNsE1S1UURiPSGh5Usf34ONhawGXlLVVeEaPK6qJzRzbLK6ksqmqjUiMhEzWV0mFm7yMeB/qrp7knIOBPYEDgd+LiJbBaXqFBg+ZuC0ivC2ei/2AIrxHrB9+H449kBsKV8PNvrR2ADtO9hKo6eJLWEds3FvZPtO4BVgTxHpJza4fCxmMmkNz2MrW5YGW/iumKmqDgvBGaMnsFjNJnISzSuRntia/I0isj/xICn/CfX1AYvBHNIT64vyFBbt7NuYYgAz3ewpIqNCOd1FZLPoQaq6DPhERA4LebqISLdUsonIEMz89zfgCiwc69vAEBHZMeSpEJHxod2HqurTwNmYya5bM23i5AnvGTht4Q/A/0V+3wA8ICKvYg+nVG/t6XgHe2gPAE5V1TUiciMW9nNmeNutxWzlKVHVxSJyLrbcsACPquoD6Y5Jwz+weAGzsLfoH6nqUhFZDpSKyCxsee1rgH+IyLHYA70hVYGBvwEPich0rJf1bpD9fyLye+B5EWnEbPrfxlavvV5EfkzC+YeH9mPAcZjNHlX9WES+DXwRtB04L1ZPhONDub/BTF9fTSUbNp5xqYhsCHlPVdWGYMK7WkSqsefKH7DVO+8MaSVY7Oi6ZtrEyRO+aqnjOI7jZiLHcRzHlYHjOI6DKwPHcRwHVwaO4zgOrgwcx3EcXBk4juM4uDJwHMdxgP8HJcRkACc6YmcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a linear regression model for the Number of Active cases vs Median SO2 values\n",
    "\n",
    "linear_regression_plot(world_air_covid_median_so2, \"Total active cases\", \"Median so2\", \"world_air_covid_median_so2\",\n",
    "                       \"World total active cases vs Median SO2 level in 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a moderate negative correlation between number of active covid cases and median SO2 values in the 88 countries in 2020H1.\n",
    "* The r-squared value tells us that 33.6% of the variability in the median SO2 values in the 88 countries in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Date</th>\n",
       "      <th>Total active cases</th>\n",
       "      <th>Median o3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>510</td>\n",
       "      <td>11.900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>606</td>\n",
       "      <td>10.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>879</td>\n",
       "      <td>12.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1350</td>\n",
       "      <td>12.725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2005</td>\n",
       "      <td>12.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Total active cases  Median o3\n",
       "0 2020-01-22                 510     11.900\n",
       "1 2020-01-23                 606     10.400\n",
       "2 2020-01-24                 879     12.250\n",
       "3 2020-01-25                1350     12.725\n",
       "4 2020-01-26                2005     12.900"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a merged data frame for median O3 data and covid active case data for the world based on shared date\n",
    "\n",
    "world_air_covid_median_o3 = world_air_covid_each_specie(world_air, \"o3\", active_world_covid)\n",
    "world_air_covid_median_o3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The correlation coefficient is: 0.4355713621454461\n",
      "The r-squared is: 0.18972241152123936\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform a linear regression model for the Number of Active cases vs Median O3 values\n",
    "\n",
    "linear_regression_plot(world_air_covid_median_o3, \"Total active cases\", \"Median o3\", \"world_air_covid_median_o3\",\n",
    "                       \"World total active cases vs Median O3 level in 2020H1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There is a weak positive correlation between number of active covid cases and median O3 values in the 88 countries in 2020H1.\n",
    "* The r-squared value tells us that 19% of the variability in the median O3 values in the 88 countries in 2020H1 could be explained by the linear regression model (i.e. the change in the number of active cases in 2020H1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> In summary, most of the correlations between the number of active covid cases and median values of the air pollutants in the 88 countries in 2020H1 explained by the linear regression models are moderately strong. Out of the 6 air pollutants, only the median O3 is positively correlated with the number of total active cases. This could be explained by the fact that ground level ozone is mostly impacted by the presence of sunlight. Most of the 88 countries are in the Northern hemisphere during the first months into the pandemic. This means that most the studied countries are in summer, which explains for the higher O3 level."
   ]
  },
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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