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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#IMPACT-OF-COVID-19-PANDEMIC-ON-AIR-QUALITY\" data-toc-modified-id=\"IMPACT-OF-COVID-19-PANDEMIC-ON-AIR-QUALITY-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>IMPACT OF COVID-19 PANDEMIC ON AIR QUALITY</a></span><ul class=\"toc-item\"><li><span><a href=\"#Air-quality-Data-Exploration-and-Cleanup\" data-toc-modified-id=\"Air-quality-Data-Exploration-and-Cleanup-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Air quality Data Exploration and Cleanup</a></span><ul class=\"toc-item\"><li><span><a href=\"#Import-datasets-and-overview-of-the-air-quality-data\" data-toc-modified-id=\"Import-datasets-and-overview-of-the-air-quality-data-1.1.1\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>Import datasets and overview of the air quality data</a></span></li><li><span><a href=\"#Slice-and-dice-the-data-to-clean-up\" data-toc-modified-id=\"Slice-and-dice-the-data-to-clean-up-1.1.2\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>Slice and dice the data to clean up</a></span></li><li><span><a href=\"#Explore-the-data-through-graphs\" data-toc-modified-id=\"Explore-the-data-through-graphs-1.1.3\"><span class=\"toc-item-num\">1.1.3&nbsp;&nbsp;</span>Explore the data through graphs</a></span></li></ul></li><li><span><a href=\"#COVID-19-DATA-EXPLORATION-AND-CLEAN-UP\" data-toc-modified-id=\"COVID-19-DATA-EXPLORATION-AND-CLEAN-UP-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>COVID-19 DATA EXPLORATION AND CLEAN UP</a></span></li><li><span><a href=\"#COVID-19-in-AUSTRALIA-DATA-EXPLORATION-AND-CLEAN-UP\" data-toc-modified-id=\"COVID-19-in-AUSTRALIA-DATA-EXPLORATION-AND-CLEAN-UP-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>COVID-19 in AUSTRALIA DATA EXPLORATION AND CLEAN UP</a></span></li></ul></li></ul></div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IMPACT OF COVID-19 PANDEMIC ON AIR QUALITY"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Air quality Data Exploration and Cleanup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'config'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-6fa2f52104a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# Import API key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mconfig\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'config'"
+     ]
+    }
+   ],
+   "source": [
+    "# Dependencies and Setup\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import requests\n",
+    "import json\n",
+    "from pprint import pprint\n",
+    "\n",
+    "# Import API key\n",
+    "from config import api_key"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Source: https://aqicn.org/data-platform/covid19/\n",
+    "With COVID-19 spreading all over the world, the World Air Quality Index project team saw a surge in requests for global data. As a result, the WAQI project now provides a new dedicated data-set which is updated 3 times a day, and that covers approximately 380 major cities in the world - January 2020 until now. \n",
+    "\n",
+    "The data for each major cities is based on the average (median) of several stations. The data set provides min, max, median and standard deviation for each of the common air pollutants (PM2.5,PM10, Ozone ...) as well as meteorological data (Wind, Temperature, ...). All air pollutant data points are converted to the US EPA standard (i.e. no raw concentrations). All dates are UTC based. The count column is the number of samples used for calculating the median and standard deviation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import datasets and overview of the air quality data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identift periods of interest \n",
+    "periods = [\"2020\", \"2019Q1\", \"2019Q2\", \"2019Q3\", \"2019Q4\"]\n",
+    "\n",
+    "df_list = list()\n",
+    "\n",
+    "for period in periods:\n",
+    "    path = f\"historical_data/waqi-covid19-airqualitydata-{period}.csv\"\n",
+    "    df = pd.read_csv(path)\n",
+    "    df_list.append(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31/05/2020</td>\n",
+       "      <td>IR</td>\n",
+       "      <td>Isfahan</td>\n",
+       "      <td>temperature</td>\n",
+       "      <td>120</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>27.5</td>\n",
+       "      <td>331.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13/06/2020</td>\n",
+       "      <td>IR</td>\n",
+       "      <td>Isfahan</td>\n",
+       "      <td>temperature</td>\n",
+       "      <td>144</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>36.5</td>\n",
+       "      <td>27.5</td>\n",
+       "      <td>488.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3/07/2020</td>\n",
+       "      <td>IR</td>\n",
+       "      <td>Isfahan</td>\n",
+       "      <td>temperature</td>\n",
+       "      <td>67</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>128.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>28/03/2020</td>\n",
+       "      <td>IR</td>\n",
+       "      <td>Isfahan</td>\n",
+       "      <td>temperature</td>\n",
+       "      <td>240</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>136.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23/04/2020</td>\n",
+       "      <td>IR</td>\n",
+       "      <td>Isfahan</td>\n",
+       "      <td>temperature</td>\n",
+       "      <td>168</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>400.79</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date Country     City       Specie  count   min   max  median  \\\n",
+       "0  31/05/2020      IR  Isfahan  temperature    120  17.5  35.0    27.5   \n",
+       "1  13/06/2020      IR  Isfahan  temperature    144  16.0  36.5    27.5   \n",
+       "2   3/07/2020      IR  Isfahan  temperature     67  19.0  33.0    24.0   \n",
+       "3  28/03/2020      IR  Isfahan  temperature    240   3.0  14.0     9.5   \n",
+       "4  23/04/2020      IR  Isfahan  temperature    168   6.0  25.5    16.0   \n",
+       "\n",
+       "   variance  \n",
+       "0    331.51  \n",
+       "1    488.74  \n",
+       "2    128.08  \n",
+       "3    136.68  \n",
+       "4    400.79  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create dataframe that includes the series created before \n",
+    "airdf_2019_2020 = pd.concat(df_list, ignore_index=True)\n",
+    "airdf_2019_2020.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Slice and dice the data to clean up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['temperature', 'wind-speed', 'wind-gust', 'dew', 'pm25',\n",
+       "       'humidity', 'wind speed', 'pressure', 'wind gust', 'co', 'so2',\n",
+       "       'precipitation', 'no2', 'pm10', 'o3', 'aqi', 'pol', 'uvi', 'wd',\n",
+       "       'neph', 'mepaqi', 'pm1'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Display an overview of the Specie column\n",
+    "airdf_2019_2020[\"Specie\"].unique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "temperature      310236\n",
+       "humidity         310145\n",
+       "pressure         308517\n",
+       "pm25             270688\n",
+       "no2              266793\n",
+       "pm10             264859\n",
+       "wind-speed       263292\n",
+       "o3               250438\n",
+       "so2              226576\n",
+       "dew              226147\n",
+       "co               203776\n",
+       "wind-gust        172805\n",
+       "wind speed        47002\n",
+       "wind gust         29576\n",
+       "precipitation     26825\n",
+       "wd                25720\n",
+       "aqi                8267\n",
+       "uvi                5632\n",
+       "pol                3790\n",
+       "pm1                1380\n",
+       "mepaqi              564\n",
+       "neph                440\n",
+       "Name: Specie, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Identify the number of data points availabe per specie \n",
+    "airdf_2019_2020[\"Specie\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> We understand that \"Air movements influence the fate of air pollutants. So any study of air pollution should include a study of the local weather patterns (meteorology). If the air is calm and pollutants cannot disperse, then the concentration of these pollutants will build up. On the other hand, when strong, turbulent winds blow, pollutants disperse quickly, resulting in lower pollutant concentrations.\" https://www.qld.gov.au/environment/pollution/monitoring/air/air-monitoring/meteorology-influence/meteorology-factors#:~:text=Meteorological%20factors-,Meteorological%20factors,these%20pollutants%20will%20build%20up.\n",
+    "Hence the Meteorology parameters like temperature, humidity, pressure, wind speed, to name a few, should have some sorts of correlations with the air quality.\n",
+    "(http://www.bom.gov.au/vic/observations/melbourne.shtml)\n",
+    "\n",
+    "> However, due to the scope of our project, we'll only focus on air pollutant parameters to assess their changes before COVID-19 and 6 months into the pandemic. We're not trying to explain the causes of air quality change. Hence, we'll remove data related to the following meteorology-related species: **temperature, humidity, pressure, wind-speed, dew, wind-gust, wind speed, wind gust, precipitation, wd (wind direction), uvi**.\n",
+    "https://aqicn.org/publishingdata/\n",
+    "\n",
+    "> We'll also remove species with the least number of available data points  including **pol, pm1, mepaqi, neph**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identify all removed elements and update the dataframe \n",
+    "removed_elements = [\"temperature\", \"humidity\", \"pressure\", \"wind-speed\", \"dew\", \"wind-gust\",\n",
+    "                     \"wind speed\", \"wind gust\", \"precipitation\", \"wd\", \"uvi\", \"pol\", \"pm1\", \"mepaqi\", \"neph\"]\n",
+    "\n",
+    "clean_airdf = airdf_2019_2020[~airdf_2019_2020[\"Specie\"].isin(removed_elements)].reset_index(drop=True).copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>24/02/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7/05/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>28/05/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20/02/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23/02/2020</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date Country     City Specie  count   min    max  median  variance\n",
+       "0  24/02/2020      IR  Isfahan   pm25    129  54.0  194.0   126.0  10921.40\n",
+       "1   7/05/2020      IR  Isfahan   pm25    168  17.0  168.0    91.0  14014.00\n",
+       "2  28/05/2020      IR  Isfahan   pm25    127  17.0  115.0    72.0   3558.56\n",
+       "3  20/02/2020      IR  Isfahan   pm25    113  26.0  181.0    76.0  11209.80\n",
+       "4  23/02/2020      IR  Isfahan   pm25    132  22.0  132.0    76.0   3209.67"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clean_airdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pm25    270688\n",
+       "no2     266793\n",
+       "pm10    264859\n",
+       "o3      250438\n",
+       "so2     226576\n",
+       "co      203776\n",
+       "aqi       8267\n",
+       "Name: Specie, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Double check data points available for each air pollutant \n",
+    "clean_airdf[\"Specie\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More about AQI:\n",
+    "https://www.airnow.gov/aqi/aqi-basics/\n",
+    "https://www.airnow.gov/sites/default/files/2020-05/aqi-technical-assistance-document-sept2018.pdf\n",
+    "\"Five major pollutants:\n",
+    "EPA establishes an AQI for five major air pollutants regulated by the Clean Air Act. Each of these pollutants has a national air quality standard set by EPA to protect public health:\n",
+    "\n",
+    "* Ground-level ozone **o3** (ppm - parts per million)\n",
+    "* Particulate Matter - including PM2.5 **pm25** and PM10 **pm10** (μg/m3)\n",
+    "* Carbon Monoxide **co** (ppm)\n",
+    "* Sulfur Dioxide **so2** (ppb - parts per billion)\n",
+    "* Nitrogen Dioxide **no2** (ppb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "https://en.wikipedia.org/wiki/Air_pollution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"https://waqi.info/\n",
+    "The Air Quality Index is based on measurement of particulate matter (PM2.5 and PM10), Ozone (O3), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2) and Carbon Monoxide (CO) emissions. Most of the stations on the map are monitoring both PM2.5 and PM10 data, but there are few exceptions where only PM10 is available.\n",
+    "\n",
+    "All measurements are based on hourly readings: For instance, an AQI reported at 8AM means that the measurement was done from 7AM to 8AM.\n",
+    "More details https://aqicn.org/faq/\n",
+    "\n",
+    "\n",
+    "https://www.weatherbit.io/api/airquality-history#:~:text=Air%20Quality%20API%20(Historical),an%20air%20quality%20index%20score.\n",
+    "\n",
+    "aqi: Air Quality Index [US - EPA standard 0 - +500]\n",
+    "o3: Concentration of surface O3 (µg/m³)\n",
+    "so2: Concentration of surface SO2 (µg/m³)\n",
+    "no2: Concentration of surface NO2 (µg/m³)\n",
+    "co: Concentration of carbon monoxide (µg/m³)\n",
+    "pm25: Concentration of particulate matter < 2.5 microns (µg/m³)\n",
+    "pm10: Concentration of particulate matter < 10 microns (µg/m³)\n",
+    "\n",
+    "Some good info on air pollution impacts https://ourworldindata.org/air-pollution\n",
+    "https://www.who.int/health-topics/air-pollution#tab=tab_1\n",
+    "https://www.epa.vic.gov.au/for-community/airwatch\n",
+    "https://www.kaggle.com/frtgnn/clean-air-india-s-air-quality/data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1491397 entries, 0 to 1491396\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column    Non-Null Count    Dtype  \n",
+      "---  ------    --------------    -----  \n",
+      " 0   Date      1491397 non-null  object \n",
+      " 1   Country   1491397 non-null  object \n",
+      " 2   City      1491397 non-null  object \n",
+      " 3   Specie    1491397 non-null  object \n",
+      " 4   count     1491397 non-null  int64  \n",
+      " 5   min       1491397 non-null  float64\n",
+      " 6   max       1491397 non-null  float64\n",
+      " 7   median    1491397 non-null  float64\n",
+      " 8   variance  1491397 non-null  float64\n",
+      "dtypes: float64(4), int64(1), object(4)\n",
+      "memory usage: 102.4+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the Dtype of each column \n",
+    "clean_airdf.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that the Date column is of a generic object type. To perform some time related analysis on this data, we need to convert it to a datetime format. We will use the \"to_datetime()\" function to convert the Date column into a datetime object. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the Date column into a datetime object \n",
+    "clean_airdf[\"Date\"] = pd.to_datetime(clean_airdf[\"Date\"], format=\"%d/%m/%Y\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Country</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>498891</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>109</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>871.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498916</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>o3</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>56.5</td>\n",
+       "      <td>27.3</td>\n",
+       "      <td>2349.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498918</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>so2</td>\n",
+       "      <td>109</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>27.9</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>240.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498981</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>5643.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499035</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>109</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>49.4</td>\n",
+       "      <td>27.9</td>\n",
+       "      <td>1492.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625785</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Québec</td>\n",
+       "      <td>co</td>\n",
+       "      <td>101</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625812</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Québec</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>190</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>3196.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625930</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Vancouver</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>184</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2840.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>625991</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Vancouver</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>182</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>173.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>626148</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Vancouver</td>\n",
+       "      <td>o3</td>\n",
+       "      <td>182</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>101.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Date Country       City Specie  count   min    max  median  \\\n",
+       "498891 2018-12-31      AE  Abu Dhabi   pm10    109  30.0   62.0    56.0   \n",
+       "498916 2018-12-31      AE  Abu Dhabi     o3     81   0.5   56.5    27.3   \n",
+       "498918 2018-12-31      AE  Abu Dhabi    so2    109   1.6   27.9     4.6   \n",
+       "498981 2018-12-31      AE  Abu Dhabi   pm25     24  72.0  152.0   112.0   \n",
+       "499035 2018-12-31      AE  Abu Dhabi    no2    109   2.8   49.4    27.9   \n",
+       "...           ...     ...        ...    ...    ...   ...    ...     ...   \n",
+       "625785 2018-12-31      CA     Québec     co    101   0.1    0.4     0.2   \n",
+       "625812 2018-12-31      CA     Québec   pm25    190   7.0  101.0    58.0   \n",
+       "625930 2018-12-31      CA  Vancouver   pm25    184   1.0   86.0    18.0   \n",
+       "625991 2018-12-31      CA  Vancouver    no2    182   0.5   17.1     8.9   \n",
+       "626148 2018-12-31      CA  Vancouver     o3    182   0.3   12.1     2.7   \n",
+       "\n",
+       "        variance  \n",
+       "498891    871.33  \n",
+       "498916   2349.96  \n",
+       "498918    240.10  \n",
+       "498981   5643.19  \n",
+       "499035   1492.45  \n",
+       "...          ...  \n",
+       "625785      0.03  \n",
+       "625812   3196.19  \n",
+       "625930   2840.10  \n",
+       "625991    173.66  \n",
+       "626148    101.83  \n",
+       "\n",
+       "[200 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sort Country column alphabetically and Date column chronologically \n",
+    "clean_airdf = clean_airdf.sort_values(by = ['Date', 'Country'])\n",
+    "clean_airdf.head(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1491397 entries, 498891 to 366586\n",
+      "Data columns (total 9 columns):\n",
+      " #   Column    Non-Null Count    Dtype         \n",
+      "---  ------    --------------    -----         \n",
+      " 0   Date      1491397 non-null  datetime64[ns]\n",
+      " 1   Country   1491397 non-null  object        \n",
+      " 2   City      1491397 non-null  object        \n",
+      " 3   Specie    1491397 non-null  object        \n",
+      " 4   count     1491397 non-null  int64         \n",
+      " 5   min       1491397 non-null  float64       \n",
+      " 6   max       1491397 non-null  float64       \n",
+      " 7   median    1491397 non-null  float64       \n",
+      " 8   variance  1491397 non-null  float64       \n",
+      "dtypes: datetime64[ns](1), float64(4), int64(1), object(3)\n",
+      "memory usage: 113.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "clean_airdf.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('2018-12-31 00:00:00')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the earliest date the air quality dataset covers\n",
+    "clean_airdf[\"Date\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Timestamp('2020-07-03 00:00:00')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find the latest date the air quality dataset covers:\n",
+    "clean_airdf[\"Date\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['AE', 'AT', 'AU', 'BA', 'BD', 'BE', 'BG', 'BH', 'BO', 'BR', 'CA',\n",
+       "       'CH', 'CL', 'CN', 'CO', 'CW', 'CY', 'CZ', 'DE', 'DK', 'EC', 'EE',\n",
+       "       'ES', 'ET', 'FI', 'FR', 'GB', 'HK', 'HR', 'HU', 'ID', 'IE', 'IL',\n",
+       "       'IN', 'IR', 'IS', 'IT', 'JP', 'KR', 'KW', 'KZ', 'LK', 'LT', 'MK',\n",
+       "       'MN', 'MO', 'MX', 'MY', 'NL', 'NO', 'NP', 'NZ', 'PE', 'PH', 'PL',\n",
+       "       'PR', 'PT', 'RE', 'RO', 'RS', 'RU', 'SE', 'SG', 'SK', 'SV', 'TH',\n",
+       "       'TR', 'TW', 'UG', 'US', 'UZ', 'VN', 'XK', 'ZA', 'AR', 'KG', 'JO',\n",
+       "       'UA', 'IQ', 'SA', 'LA', 'DZ', 'PK', 'MM', 'TJ', 'TM', 'GT', 'GR',\n",
+       "       'AF', 'GE', 'CR', 'ML', 'CI', 'GN', 'GH'], dtype=object)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clean_airdf[\"Country\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 95 countries in the dataframe, including Australia (AU)...."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 615 cities in our dataframe. Let's see what cities in Australia covered in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sydney        3364\n",
+       "Brisbane      3348\n",
+       "Melbourne     3284\n",
+       "Wollongong    3253\n",
+       "Darwin        3208\n",
+       "Adelaide      3177\n",
+       "Perth         3082\n",
+       "Newcastle     2871\n",
+       "Launceston    1126\n",
+       "Hobart        1126\n",
+       "Canberra      1093\n",
+       "Name: City, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Identify the data points available for Australian cities \n",
+    "clean_airdf.loc[clean_airdf[\"Country\"]==\"AU\", \"City\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explore the data through graphs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['pm10', 'o3', 'so2', 'pm25', 'no2', 'co', 'aqi'], dtype=object)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Identifying the unique (common) air pollutants in the dataset \n",
+    "air_pollutants = clean_airdf[\"Specie\"].unique()\n",
+    "air_pollutants"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "LOOKING AT THE MEDIAN LEVELS OF CO2, AN AIR POLLUTANT THAT DUE TO ITS NATURE WOULD BE THE MOST IMPACTED BY COVID RESTRICTIONS SUCH AS LESS TRAVEL. WE WILL COMPARE A MAJOR AUSTRALIAN CITY WITH A MAJOR INDIAN CITY TO SHOW THAT AUSTRALIA'S AIR QUALITY AS IT STANDS IS QUITE GOOD. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Country</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>498891</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>109</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>871.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498916</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>o3</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>56.5</td>\n",
+       "      <td>27.3</td>\n",
+       "      <td>2349.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498918</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>so2</td>\n",
+       "      <td>109</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>27.9</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>240.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498981</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>5643.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499035</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>109</td>\n",
+       "      <td>2.8</td>\n",
+       "      <td>49.4</td>\n",
+       "      <td>27.9</td>\n",
+       "      <td>1492.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366299</th>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>ZA</td>\n",
+       "      <td>Worcester</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>10</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2711.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366413</th>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>ZA</td>\n",
+       "      <td>Worcester</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>5.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366487</th>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>ZA</td>\n",
+       "      <td>Worcester</td>\n",
+       "      <td>so2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>90.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366548</th>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>ZA</td>\n",
+       "      <td>Worcester</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>904.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>366586</th>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>ZA</td>\n",
+       "      <td>Worcester</td>\n",
+       "      <td>co</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>17.34</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1491397 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Date Country       City Specie  count   min    max  median  \\\n",
+       "498891 2018-12-31      AE  Abu Dhabi   pm10    109  30.0   62.0    56.0   \n",
+       "498916 2018-12-31      AE  Abu Dhabi     o3     81   0.5   56.5    27.3   \n",
+       "498918 2018-12-31      AE  Abu Dhabi    so2    109   1.6   27.9     4.6   \n",
+       "498981 2018-12-31      AE  Abu Dhabi   pm25     24  72.0  152.0   112.0   \n",
+       "499035 2018-12-31      AE  Abu Dhabi    no2    109   2.8   49.4    27.9   \n",
+       "...           ...     ...        ...    ...    ...   ...    ...     ...   \n",
+       "366299 2020-07-03      ZA  Worcester   pm25     10  17.0   63.0    21.0   \n",
+       "366413 2020-07-03      ZA  Worcester    no2     10   3.7    6.0     4.6   \n",
+       "366487 2020-07-03      ZA  Worcester    so2      7   0.6    8.7     8.7   \n",
+       "366548 2020-07-03      ZA  Worcester   pm10     10  10.0   35.0    10.0   \n",
+       "366586 2020-07-03      ZA  Worcester     co      9   0.1    3.9     0.2   \n",
+       "\n",
+       "        variance  \n",
+       "498891    871.33  \n",
+       "498916   2349.96  \n",
+       "498918    240.10  \n",
+       "498981   5643.19  \n",
+       "499035   1492.45  \n",
+       "...          ...  \n",
+       "366299   2711.67  \n",
+       "366413      5.69  \n",
+       "366487     90.47  \n",
+       "366548    904.00  \n",
+       "366586     17.34  \n",
+       "\n",
+       "[1491397 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clean_airdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CO DATA ON NEW DELHI IN INDA "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Country</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>526318</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>640</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>105.3</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>3773.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526323</th>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>668</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>116.8</td>\n",
+       "      <td>17.6</td>\n",
+       "      <td>3940.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526324</th>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>681</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>138.9</td>\n",
+       "      <td>19.9</td>\n",
+       "      <td>4782.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526362</th>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>663</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>133.8</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>2615.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>526386</th>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>645</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1728.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428264</th>\n",
+       "      <td>2020-06-29</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>804</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>84.5</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>415.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428213</th>\n",
+       "      <td>2020-06-30</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>732</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>84.2</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>486.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428172</th>\n",
+       "      <td>2020-07-01</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>892</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>41.6</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>207.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428310</th>\n",
+       "      <td>2020-07-02</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>908</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>38.1</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>196.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428204</th>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Delhi</td>\n",
+       "      <td>co</td>\n",
+       "      <td>470</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>33.4</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>216.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>564 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Date Country   City Specie  count  min    max  median  variance\n",
+       "526318 2018-12-31      IN  Delhi     co    640  0.1  105.3    21.5   3773.06\n",
+       "526323 2019-01-01      IN  Delhi     co    668  0.1  116.8    17.6   3940.94\n",
+       "526324 2019-01-02      IN  Delhi     co    681  0.1  138.9    19.9   4782.22\n",
+       "526362 2019-01-03      IN  Delhi     co    663  0.1  133.8    18.5   2615.45\n",
+       "526386 2019-01-04      IN  Delhi     co    645  0.2   91.0    15.0   1728.86\n",
+       "...           ...     ...    ...    ...    ...  ...    ...     ...       ...\n",
+       "428264 2020-06-29      IN  Delhi     co    804  0.1   84.5     6.2    415.50\n",
+       "428213 2020-06-30      IN  Delhi     co    732  0.1   84.2     6.9    486.74\n",
+       "428172 2020-07-01      IN  Delhi     co    892  0.1   41.6     7.4    207.65\n",
+       "428310 2020-07-02      IN  Delhi     co    908  0.1   38.1     7.6    196.80\n",
+       "428204 2020-07-03      IN  Delhi     co    470  0.1   33.4     7.8    216.10\n",
+       "\n",
+       "[564 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter dataframe to just India \n",
+    "india_df = clean_airdf.loc[clean_airdf[\"Country\"] == \"IN\"] \n",
+    "\n",
+    "# Filter dataframe to just Delhi  \n",
+    "delhi_df = india_df.loc[india_df[\"City\"] == \"Delhi\"]\n",
+    "\n",
+    "# Filter dataframe to just co air pollutant \n",
+    "delhi_co_df = delhi_df.loc[delhi_df[\"Specie\"] == \"co\"]\n",
+    "delhi_co_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "delhi_co_df.set_index('Date')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Filter dates for 2019 Q1 - Q2\n",
+    "start_date = '2019-01-01'\n",
+    "end_date = '2019-06-30'\n",
+    "d_mid_2019 = (delhi_co_df['Date'] >= start_date) & (delhi_co_df['Date'] <= end_date)\n",
+    "delhi_mid_2019 = delhi_co_df.loc[d_mid_2019]\n",
+    "delhi_mid_2019\n",
+    "\n",
+    "\n",
+    "# Filter dates for 2020 Q1 - Q2 \n",
+    "start_date = '2020-01-01'\n",
+    "end_date = '2020-06-30'\n",
+    "d_mid_2020 = (delhi_co_df['Date'] >= start_date) & (delhi_co_df['Date'] <= end_date)\n",
+    "delhi_mid_2020 = delhi_co_df.loc[d_mid_2020]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CO DATA ON MELBOURNE "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Country</th>\n",
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+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>655955</th>\n",
+       "      <td>2018-12-31</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>36</td>\n",
+       "      <td>1.2</td>\n",
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+       "    <tr>\n",
+       "      <th>655956</th>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
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+       "      <td>2.3</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655963</th>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>18</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>3.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655975</th>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>48</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>13.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>655942</th>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>54</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>13.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175518</th>\n",
+       "      <td>2020-06-29</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>115</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>111.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175618</th>\n",
+       "      <td>2020-06-30</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>107</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>46.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175566</th>\n",
+       "      <td>2020-07-01</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>115</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>99.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175597</th>\n",
+       "      <td>2020-07-02</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>17.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>175544</th>\n",
+       "      <td>2020-07-03</td>\n",
+       "      <td>AU</td>\n",
+       "      <td>Melbourne</td>\n",
+       "      <td>co</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>2.71</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>544 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Date Country       City Specie  count  min   max  median  \\\n",
+       "655955 2018-12-31      AU  Melbourne     co     36  1.2   3.4     2.3   \n",
+       "655956 2019-01-01      AU  Melbourne     co     18  1.2   2.3     1.2   \n",
+       "655963 2019-01-02      AU  Melbourne     co     18  1.2   2.3     1.2   \n",
+       "655975 2019-01-03      AU  Melbourne     co     48  1.2   4.5     2.3   \n",
+       "655942 2019-01-04      AU  Melbourne     co     54  1.2   4.5     2.3   \n",
+       "...           ...     ...        ...    ...    ...  ...   ...     ...   \n",
+       "175518 2020-06-29      AU  Melbourne     co    115  1.8  15.6     6.2   \n",
+       "175618 2020-06-30      AU  Melbourne     co    107  1.7  11.2     3.2   \n",
+       "175566 2020-07-01      AU  Melbourne     co    115  0.5   9.8     8.5   \n",
+       "175597 2020-07-02      AU  Melbourne     co    100  1.0   7.6     2.0   \n",
+       "175544 2020-07-03      AU  Melbourne     co     80  0.8   4.4     1.9   \n",
+       "\n",
+       "        variance  \n",
+       "655955      5.10  \n",
+       "655956      3.20  \n",
+       "655963      3.20  \n",
+       "655975     13.47  \n",
+       "655942     13.09  \n",
+       "...          ...  \n",
+       "175518    111.74  \n",
+       "175618     46.83  \n",
+       "175566     99.23  \n",
+       "175597     17.19  \n",
+       "175544      2.71  \n",
+       "\n",
+       "[544 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter dataframe to just Australia \n",
+    "au_df = clean_airdf.loc[clean_airdf[\"Country\"] == \"AU\"] \n",
+    "\n",
+    "# Filter dataframe to just Melbourne \n",
+    "mel_df = au_df.loc[au_df[\"City\"] == \"Melbourne\"]\n",
+    "\n",
+    "# Filter dataframe to just co air pollutant \n",
+    "mel_co_df = mel_df.loc[mel_df[\"Specie\"] == \"co\"]\n",
+    "mel_co_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Filter dates for 2019 Q1 - Q2\n",
+    "start_date = '2019-01-01'\n",
+    "end_date = '2019-06-30'\n",
+    "m_mid_2019 = (mel_co_df['Date'] >= start_date) & (mel_co_df['Date'] <= end_date)\n",
+    "mel_mid_2019 = mel_co_df.loc[m_mid_2019]\n",
+    "\n",
+    "# Filter dates for 2020 Q1 - Q2\n",
+    "start_date = '2020-01-01'\n",
+    "end_date = '2020-06-30'\n",
+    "m_mid_2020 = (mel_co_df['Date'] >= start_date) & (mel_co_df['Date'] <= end_date)\n",
+    "mel_mid_2020 = mel_co_df.loc[m_mid_2020]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.4"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mel_mid_2020['median'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "\n",
+    "# Plot\n",
+    "ax.plot(mel_mid_2019['Date'],\n",
+    "        mel_mid_2019['median'],\n",
+    "        color='purple', linewidth = 1)\n",
+    "\n",
+    "# Set titles and labels \n",
+    "ax.set(xlabel=\"Date\",\n",
+    "       ylabel=\"Median Levels of CO2\",\n",
+    "       title=\"Melbourne: Median Levels of CO2 in First Half of 2019\")\n",
+    "\n",
+    "\n",
+    "# Format tick marks \n",
+    "ax.set_ylim(0,6)\n",
+    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "\n",
+    "# Plot\n",
+    "ax.plot(mel_mid_2020['Date'],\n",
+    "        mel_mid_2020['median'],\n",
+    "        color='purple', linewidth = 1)\n",
+    "\n",
+    "# Set titles and labels \n",
+    "ax.set(xlabel=\"Date\",\n",
+    "       ylabel=\"Median Levels of CO2\",\n",
+    "       title=\"Melbourne: Median Levels of CO2 in First Half of 2019\")\n",
+    "\n",
+    "\n",
+    "# Format tick marks \n",
+    "ax.set_ylim(0,6)\n",
+    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set titles and labels \n",
+    "ax.set(xlabel=\"Date\",\n",
+    "       ylabel=\"Median Levels of CO2\",\n",
+    "       title=\"Melbourne: Median Levels of CO2 in Q1 2019\")\n",
+    "\n",
+    "\n",
+    "# Format tick marks \n",
+    "ax.set_ylim(1,4)\n",
+    "plt.setp(ax.get_xticklabels(), rotation=45)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Segment Q1 and Q2 dates for 2019 (MELB)\n",
+    "mel_Q1_2019_Dates = mel_Q1_2019['Date']\n",
+    "mel_Q2_2019_Dates = mel_Q2_2019['Date']\n",
+    "\n",
+    "# Segment co median levels for Q1 and Q2 (2019, melb)\n",
+    "mel_Q1_2019_co = mel_Q1_2019['median']\n",
+    "mel_Q2_2019_co = mel_Q2_2019['median']\n",
+    "\n",
+    "\n",
+    "# Segment Q1 and Q2 dates for 2020 (MELB)\n",
+    "mel_Q1_2020_Dates = mel_Q1_2020['Date']\n",
+    "mel_Q2_2020_Dates = mel_Q2_2020['Date']\n",
+    "\n",
+    "co_levels_2019Q1 = pd.DataFrame({\n",
+    "    \"Time\": mel_Q1_2019_Dates,\n",
+    "    \"Median CO levels\": mel_Q1_2019_co\n",
+    "})\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def specie_median_distribution(df, country):\n",
+    "\n",
+    "    country_air_df = df[df[\"Country\"] == country]\n",
+    "\n",
+    "    fig, ax = plt.subplots(\n",
+    "        figsize=(10, 2*len(unique_species)),\n",
+    "        ncols=1,\n",
+    "        nrows=len(unique_species)\n",
+    "        )\n",
+    "\n",
+    "    for index, specie in enumerate(unique_species):\n",
+    "        red_square=dict(markerfacecolor='r', marker='s', alpha=0.4)\n",
+    "\n",
+    "        country_air_df[country_air_df[\"Specie\"] == specie].boxplot(\n",
+    "            column=\"median\",\n",
+    "            flierprops=red_square,\n",
+    "            ax=ax[index],\n",
+    "            vert=False)\n",
+    "        ax[index].set_title=f\"Distribution of median {specie} values in {country} (2019-2020H1)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "specie_median_distribution(clean_airdf, \"AU\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## COVID-19 DATA EXPLORATION AND CLEAN UP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Covid-19 is sourced from here https://covid19api.com/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_url = \"https://api.covid19api.com/countries\"\n",
+    "country_covid_data = requests.get(country_url).json()\n",
+    "pprint(country_covid_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_covid_df = pd.DataFrame(country_covid_data)\n",
+    "country_covid_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge countries available on the air quality data and the covid data\n",
+    "country_covid_air_df = pd.merge(\n",
+    "    country_airdata_df, country_covid_df, how=\"left\", left_on=\"country_code\", right_on=\"ISO2\")\n",
+    "country_covid_air_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the country in the country_airdata_df but not country_covid_df\n",
+    "country_to_remove = country_covid_air_df[country_covid_air_df[\"ISO2\"].isna()][\"country_code\"].tolist()\n",
+    "country_to_remove"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_airdf = clean_airdf[~clean_airdf[\"Country\"].isin(country_to_remove)].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_airdf[\"Country\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_airdf.to_csv(\"air_data.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "country_covid_air_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del country_covid_air_df[\"ISO2\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Return a dataframe covering all countries in both the air quality data and covid-19 data.\n",
+    "country_covid_air_df = country_covid_air_df[~country_covid_air_df[\"country_code\"].isin(\n",
+    "    country_to_remove)].reset_index()\n",
+    "country_covid_air_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Explore one covid API - By Country Total All Status\n",
+    "covid_url_example = \"https://api.covid19api.com/total/country/australia\"\n",
+    "covid_data_example = requests.get(covid_url_example).json()\n",
+    "pprint(covid_data_example)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above api example covers covid-19 data until 4th July 2020. It also shows the number of confirmed, active, recovered, and death cases for each chosen country over the course of the current pandemic. Hence, we'll use this api to loop through our country_covid_air_df as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slug_list = country_covid_air_df[\"Slug\"].tolist()\n",
+    "len(slug_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_covid_url = \"https://api.covid19api.com/total/country/\"\n",
+    "    \n",
+    "country_list = list()\n",
+    "date_list = list()\n",
+    "active_list = list()\n",
+    "confirmed_list = list()\n",
+    "recovered_list = list()\n",
+    "deaths_list = list()\n",
+    "\n",
+    "print(\"Beginning Data Retrieval\")\n",
+    "print(\"-----------------------------------\")\n",
+    "\n",
+    "counter = 0\n",
+    "set_counter = 1\n",
+    "\n",
+    "for slug in slug_list:\n",
+    "    \n",
+    "    try:\n",
+    "        response = requests.get(base_covid_url + slug).json()\n",
+    "    \n",
+    "        for element in response:\n",
+    "            country_list.append(element['Country'])\n",
+    "            date_list.append(element['Date'])\n",
+    "            active_list.append(element['Active'])\n",
+    "            confirmed_list.append(element['Confirmed'])\n",
+    "            recovered_list.append(element['Recovered'])\n",
+    "            deaths_list.append(element['Deaths'])\n",
+    "\n",
+    "        counter += 1\n",
+    "        print(f\"Processing Record {counter} of Set {set_counter} | {slug}\")\n",
+    "\n",
+    "        if counter == 50:\n",
+    "            set_counter += 1\n",
+    "            counter = 0\n",
+    "\n",
+    "    except KeyError:\n",
+    "        print(\"Country not found. Skipping...\")\n",
+    "\n",
+    "print(\"-----------------------------------\")\n",
+    "print(\"Data Retrieval Complete\")\n",
+    "print(\"-----------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covid_df = pd.DataFrame({\n",
+    "    \"Country\": country_list,\n",
+    "    \"Date\": date_list,\n",
+    "    \"Active cases\": active_list,\n",
+    "    \"Confirmed cases\": confirmed_list,\n",
+    "    \"Recovered cases\": recovered_list,\n",
+    "    \"Deaths\": deaths_list\n",
+    "})\n",
+    "covid_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covid_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the Date column to datetime format\n",
+    "covid_df['Date'] = covid_df['Date'].astype('datetime64[ns]')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the earliest date the covid dataset covers:\n",
+    "covid_df[\"Date\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the latrest date the covid dataset covers:\n",
+    "covid_df[\"Date\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "covid_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covid_df.to_csv(\"covid_data.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## COVID-19 in AUSTRALIA DATA EXPLORATION AND CLEAN UP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "au_covid_url = \"https://interactive.guim.co.uk/docsdata/1q5gdePANXci8enuiS4oHUJxcxC13d6bjMRSicakychE.json\"\n",
+    "au_covid_data = requests.get(au_covid_url).json()\n",
+    "pprint(au_covid_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "au_covid_data.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "au_covid_data['sheets'].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "covid_by_state = au_covid_data['sheets']['updates']\n",
+    "covid_by_state[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_list = list()\n",
+    "date_list = list()\n",
+    "cumulative_case_count = list()\n",
+    "cumulative_recovered_count = list()\n",
+    "\n",
+    "for element in covid_by_state:\n",
+    "    state_list.append(element[\"State\"])\n",
+    "    date_list.append(element[\"Date\"])\n",
+    "    cumulative_case_count.append(element[\"Cumulative case count\"])\n",
+    "    cumulative_recovered_count.append(element[\"Recovered (cumulative)\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid = pd.DataFrame({\n",
+    "    \"State\": state_list,\n",
+    "    \"Date\": date_list,\n",
+    "    \"Cumulative case count\": cumulative_case_count,\n",
+    "    \"Cumulative recovered count\": cumulative_recovered_count\n",
+    "})\n",
+    "clean_au_covid.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid[\"Cumulative case count\"] = pd.to_numeric(\n",
+    "    clean_au_covid[\"Cumulative case count\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid[\"Cumulative recovered count\"] = pd.to_numeric(\n",
+    "    clean_au_covid[\"Cumulative recovered count\"].str.replace(\",\", \"\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the Date column to datetime format\n",
+    "clean_au_covid['Date'] = pd.to_datetime(\n",
+    "    clean_au_covid[\"Date\"], format=\"%d/%m/%Y\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid.fillna(0, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid[[\"Cumulative case count\", \"Cumulative recovered count\"]] = clean_au_covid[[\n",
+    "    \"Cumulative case count\", \"Cumulative recovered count\"]].astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clean_au_covid.tail(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the earliest date the covid dataset covers:\n",
+    "clean_au_covid[\"Date\"].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the latrest date the covid dataset covers:\n",
+    "clean_au_covid[\"Date\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_au_covid =  clean_au_covid[clean_au_covid[\"Date\"] <= \"2020-07-05\"].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_au_covid.to_csv(\"au_covid_data.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_au_covid[\"Date\"].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import time\n",
+    "# from scipy.stats import linregress\n",
+    "\n",
+    "# Incorporated citipy to determine city based on latitude and longitude\n",
+    "# from citipy import citipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# url = \"https://api.openaq.org/v1/measurements\"\n",
+    "\n",
+    "# data = requests.get(url).json()\n",
+    "# pprint(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# len(data[\"results\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# data[\"results\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data source: https://aqicn.org/api/\n",
+    "# base_url = \"https://api.waqi.info/feed/\"\n",
+    "# city = \"melbourne\"\n",
+    "# url = f\"{base_url}{city}/?token={api_key}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# url_2 = \"https://api.covid19api.com/country/south-africa/status/confirmed/live\"\n",
+    "# covid_data_2 = requests.get(url_2).json()\n",
+    "# pprint(covid_data_2[-1])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": true,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data_exploration_cleanup_TRACEY.ipynb b/data_exploration_cleanup_TRACEY.ipynb
index a3dfa5d..ad0df08 100644
--- a/data_exploration_cleanup_TRACEY.ipynb
+++ b/data_exploration_cleanup_TRACEY.ipynb
@@ -26,9 +26,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'config'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-6fa2f52104a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;31m# Import API key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mconfig\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'config'"
+     ]
+    }
+   ],
    "source": [
     "# Dependencies and Setup\n",
     "import matplotlib.pyplot as plt\n",
@@ -61,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +89,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -202,7 +221,7 @@
        "4    400.79  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -300,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -422,7 +441,7 @@
        "4  23/02/2020      IR  Isfahan   pm25    132  22.0  132.0    76.0   3209.67"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -511,8 +530,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -541,6 +562,126 @@
     "clean_airdf.info()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>24/02/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7/05/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>28/05/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>20/02/2020</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>23/02/2020</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date Country     City Specie  count   min    max  median  variance\n",
+       "0  24/02/2020      IR  Isfahan   pm25    129  54.0  194.0   126.0  10921.40\n",
+       "1   7/05/2020      IR  Isfahan   pm25    168  17.0  168.0    91.0  14014.00\n",
+       "2  28/05/2020      IR  Isfahan   pm25    127  17.0  115.0    72.0   3558.56\n",
+       "3  20/02/2020      IR  Isfahan   pm25    113  26.0  181.0    76.0  11209.80\n",
+       "4  23/02/2020      IR  Isfahan   pm25    132  22.0  132.0    76.0   3209.67"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clean_airdf.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -550,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -559,7 +700,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>148114</th>\n",
+       "      <td>2020-01-09</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>o3</td>\n",
+       "      <td>113</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>1541.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>968271</th>\n",
+       "      <td>2019-07-21</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>70</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>382.0</td>\n",
+       "      <td>184.0</td>\n",
+       "      <td>55872.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>968279</th>\n",
+       "      <td>2019-08-28</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>115</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>2383.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>968278</th>\n",
+       "      <td>2019-08-24</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Abu Dhabi</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>120</td>\n",
+       "      <td>84.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>1626.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>968704</th>\n",
+       "      <td>2019-08-17</td>\n",
+       "      <td>AE</td>\n",
+       "      <td>Dubai</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>167.0</td>\n",
+       "      <td>1863.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Date Country       City Specie  count    min    max  median  \\\n",
+       "148114 2020-01-09      AE  Abu Dhabi     o3    113    0.5   45.9    13.5   \n",
+       "968271 2019-07-21      AE  Abu Dhabi   pm10     70  117.0  382.0   184.0   \n",
+       "968279 2019-08-28      AE  Abu Dhabi   pm10    115   57.0  110.0    65.0   \n",
+       "968278 2019-08-24      AE  Abu Dhabi   pm10    120   84.0  140.0   108.0   \n",
+       "968704 2019-08-17      AE      Dubai   pm25     21  139.0  198.0   167.0   \n",
+       "\n",
+       "        variance  \n",
+       "148114   1541.68  \n",
+       "968271  55872.60  \n",
+       "968279   2383.01  \n",
+       "968278   1626.86  \n",
+       "968704   1863.48  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sort Country column and City alphabetically \n",
+    "clean_airdf = clean_airdf.sort_values(by = 'Country')\n",
+    "clean_airdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +837,7 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 1491397 entries, 0 to 1491396\n",
+      "Int64Index: 1491397 entries, 148114 to 1407141\n",
       "Data columns (total 9 columns):\n",
       " #   Column    Non-Null Count    Dtype         \n",
       "---  ------    --------------    -----         \n",
@@ -581,7 +851,7 @@
       " 7   median    1491397 non-null  float64       \n",
       " 8   variance  1491397 non-null  float64       \n",
       "dtypes: datetime64[ns](1), float64(4), int64(1), object(3)\n",
-      "memory usage: 102.4+ MB\n"
+      "memory usage: 113.8+ MB\n"
      ]
     }
    ],
@@ -591,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -600,7 +870,7 @@
        "Timestamp('2018-12-31 00:00:00')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -612,7 +882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -621,7 +891,7 @@
        "Timestamp('2020-07-03 00:00:00')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -633,24 +903,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(['IR', 'TJ', 'BR', 'CN', 'DK', 'ES', 'ML', 'SK', 'XK', 'CL', 'DE',\n",
-       "       'KW', 'MM', 'PH', 'PK', 'PL', 'RU', 'SE', 'SG', 'AE', 'BA', 'CZ',\n",
-       "       'ID', 'IS', 'MO', 'RO', 'AR', 'AU', 'EC', 'GH', 'HK', 'PE', 'UA',\n",
-       "       'EE', 'FR', 'JP', 'MN', 'FI', 'IE', 'IL', 'KZ', 'LA', 'UZ', 'BD',\n",
-       "       'BE', 'GR', 'KR', 'LK', 'MK', 'MX', 'TR', 'AF', 'AT', 'GT', 'BO',\n",
-       "       'CR', 'JO', 'PR', 'SA', 'SV', 'CA', 'IT', 'NO', 'RE', 'TM', 'ZA',\n",
-       "       'BH', 'LT', 'TH', 'BG', 'CH', 'HU', 'MY', 'NL', 'NZ', 'UG', 'VN',\n",
-       "       'ET', 'GE', 'GN', 'IQ', 'RS', 'TW', 'CI', 'CO', 'CY', 'DZ', 'HR',\n",
-       "       'IN', 'KG', 'CW', 'GB', 'NP', 'PT', 'US'], dtype=object)"
+       "array(['AE', 'AF', 'AR', 'AT', 'AU', 'BA', 'BD', 'BE', 'BG', 'BH', 'BO',\n",
+       "       'BR', 'CA', 'CH', 'CI', 'CL', 'CN', 'CO', 'CR', 'CW', 'CY', 'CZ',\n",
+       "       'DE', 'DK', 'DZ', 'EC', 'EE', 'ES', 'ET', 'FI', 'FR', 'GB', 'GE',\n",
+       "       'GH', 'GN', 'GR', 'GT', 'HK', 'HR', 'HU', 'ID', 'IE', 'IL', 'IN',\n",
+       "       'IQ', 'IR', 'IS', 'IT', 'JO', 'JP', 'KG', 'KR', 'KW', 'KZ', 'LA',\n",
+       "       'LK', 'LT', 'MK', 'ML', 'MM', 'MN', 'MO', 'MX', 'MY', 'NL', 'NO',\n",
+       "       'NP', 'NZ', 'PE', 'PH', 'PK', 'PL', 'PR', 'PT', 'RE', 'RO', 'RS',\n",
+       "       'RU', 'SA', 'SE', 'SG', 'SK', 'SV', 'TH', 'TJ', 'TM', 'TR', 'TW',\n",
+       "       'UA', 'UG', 'US', 'UZ', 'VN', 'XK', 'ZA'], dtype=object)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -661,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -685,29 +955,29 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>country_code</th>\n",
+       "      <th>Country Code</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>IR</td>\n",
+       "      <td>AE</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>TJ</td>\n",
+       "      <td>AF</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>BR</td>\n",
+       "      <td>AR</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>CN</td>\n",
+       "      <td>AT</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>DK</td>\n",
+       "      <td>AU</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -715,23 +985,23 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>90</th>\n",
-       "      <td>CW</td>\n",
+       "      <td>US</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>91</th>\n",
-       "      <td>GB</td>\n",
+       "      <td>UZ</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>92</th>\n",
-       "      <td>NP</td>\n",
+       "      <td>VN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>93</th>\n",
-       "      <td>PT</td>\n",
+       "      <td>XK</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>94</th>\n",
-       "      <td>US</td>\n",
+       "      <td>ZA</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -739,30 +1009,30 @@
        "</div>"
       ],
       "text/plain": [
-       "   country_code\n",
-       "0            IR\n",
-       "1            TJ\n",
-       "2            BR\n",
-       "3            CN\n",
-       "4            DK\n",
+       "   Country Code\n",
+       "0            AE\n",
+       "1            AF\n",
+       "2            AR\n",
+       "3            AT\n",
+       "4            AU\n",
        "..          ...\n",
-       "90           CW\n",
-       "91           GB\n",
-       "92           NP\n",
-       "93           PT\n",
-       "94           US\n",
+       "90           US\n",
+       "91           UZ\n",
+       "92           VN\n",
+       "93           XK\n",
+       "94           ZA\n",
        "\n",
        "[95 rows x 1 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Display an overview of the Country column\n",
-    "country_airdata_df = pd.DataFrame(clean_airdf[\"Country\"].unique(), columns=[\"country_code\"])\n",
+    "country_airdata_df = pd.DataFrame(clean_airdf[\"Country\"].unique(), columns=[\"Country Code\"])\n",
     "country_airdata_df"
    ]
   },
@@ -775,131 +1045,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(['Isfahan', 'Arāk', 'Karaj', 'Qom', 'Orūmīyeh', 'Yazd', 'Īlām',\n",
-       "       'Kerman', 'Khorramshahr', 'Tabriz', 'Bandar Abbas', 'Sanandaj',\n",
-       "       'Kermanshah', 'Khorramabad', 'Shiraz', 'Zanjān', 'Mashhad',\n",
-       "       'Tehran', 'Dushanbe', 'São José dos Campos', 'Vitória',\n",
-       "       'São Paulo', 'Beijing', 'Jieyang', 'Kunming', 'Hangzhou',\n",
-       "       'Chongqing', 'Qingdao', 'Haikou', 'Ürümqi', 'Qiqihar', 'Guiyang',\n",
-       "       'Shenzhen', 'Yunfu', 'Xuchang', 'Yinchuan', 'Shenyang', 'Lhasa',\n",
-       "       'Shanghai', 'Changchun', 'Foshan', 'Nanning', 'Fushun', 'Hefei',\n",
-       "       'Chengdu', 'Hohhot', 'Qinhuangdao', 'Shijiazhuang', 'Shantou',\n",
-       "       'Zhengzhou', 'Nanjing', 'Xining', 'Xi’an', 'Zhuzhou', 'Wuhan',\n",
-       "       'Tianjin', 'Changzhou', 'Nanchang', 'Shiyan', 'Xinxiang', 'Suzhou',\n",
-       "       'Harbin', 'Lanzhou', 'Jinan', 'Changsha', 'Hegang', 'Anyang',\n",
-       "       'Wuxi', 'Taiyuan', 'Guangzhou', 'Fuzhou', 'Ningbo', 'Xiamen',\n",
-       "       'Dongguan', 'Copenhagen', 'Las Palmas de Gran Canaria',\n",
-       "       'Salamanca', 'Barcelona', 'Donostia / San Sebastián',\n",
-       "       'Gasteiz / Vitoria', 'Córdoba', 'Valladolid', 'Santander', 'Palma',\n",
-       "       'Málaga', 'Zaragoza', 'Sevilla', 'Bilbao', 'Pamplona',\n",
-       "       'Castelló de la Plana', 'Huelva', 'Granada', 'Madrid', 'Valencia',\n",
-       "       'Burgos', 'Murcia', 'Santa Cruz de Tenerife', 'Oviedo', 'Bamako',\n",
-       "       'Košice', 'Bratislava', 'Pristina', 'Rancagua', 'Osorno',\n",
-       "       'Los Ángeles', 'Chillán', 'Calama', 'Puerto Montt', 'Valparaíso',\n",
-       "       'Quilpué', 'Santiago', 'Talca', 'Concepción', 'Temuco',\n",
-       "       'Stuttgart', 'Münster', 'Köln', 'Kassel', 'Karlsruhe', 'Mainz',\n",
-       "       'Dresden', 'Munich', 'Berlin', 'Düsseldorf', 'Freiburg',\n",
-       "       'Wiesbaden', 'Hannover', 'Augsburg', 'Darmstadt', 'Potsdam',\n",
-       "       'Hamburg', 'Ḩawallī', 'Yangon', 'Baguio', 'Manila', 'Butuan',\n",
-       "       'Karachi', 'Peshawar', 'Islamabad', 'Lahore', 'Łódź', 'Tarnów',\n",
-       "       'Gdańsk', 'Katowice', 'Poznań', 'Rybnik', 'Szczecin', 'Kielce',\n",
-       "       'Warsaw', 'Bydgoszcz', 'Zabrze', 'Wrocław', 'Płock', 'Kraków',\n",
-       "       'Chelyabinsk', 'Nizhniy Novgorod', 'Saint Petersburg',\n",
-       "       'Krasnoyarsk', 'Tomsk', 'Novosibirsk', 'Moscow', 'Stockholm',\n",
-       "       'Göteborg', 'Malmö', 'Uppsala', 'Singapore', 'Abu Dhabi', 'Dubai',\n",
-       "       'Sarajevo', 'Zenica', 'Tuzla', 'Prague', 'Ostrava', 'Brno',\n",
-       "       'Pilsen', 'Olomouc', 'Jakarta', 'Reykjavík', 'Macau', 'Ploieşti',\n",
-       "       'Craiova', 'Bacău', 'Râmnicu Vâlcea', 'Sibiu', 'Timişoara',\n",
-       "       'Piteşti', 'Arad', 'Cluj-Napoca', 'Bucharest', 'Baia Mare',\n",
-       "       'Oradea', 'Galaţi', 'Braşov', 'Iaşi', 'Constanţa', 'Brăila',\n",
-       "       'Buenos Aires', 'Sydney', 'Newcastle', 'Launceston', 'Hobart',\n",
-       "       'Darwin', 'Melbourne', 'Canberra', 'Adelaide', 'Brisbane', 'Perth',\n",
-       "       'Wollongong', 'Quito', 'Accra', 'Hong Kong', 'Lima', 'Odessa',\n",
-       "       'Ternopil', 'Kyiv', 'Kamianske', 'Dnipro', 'Zaporizhia', 'Lviv',\n",
-       "       'Kryvyi Rih', 'Ivano-Frankivsk', 'Tallinn', 'Tours', 'Marseille',\n",
-       "       'Nîmes', 'Besançon', 'Nantes', 'Rennes', 'Toulouse', 'Nancy',\n",
-       "       'Clermont-Ferrand', 'Paris', 'Lille', 'Orléans', 'Montpellier',\n",
-       "       'Lyon', 'Amiens', 'Dijon', 'Rouen', 'Grenoble', 'Toulon',\n",
-       "       'Limoges', 'Caen', 'Perpignan', 'Bordeaux', 'Nice',\n",
-       "       'Saint-Étienne', 'Metz', 'Strasbourg', 'Fukuoka', 'Kanazawa',\n",
-       "       'Kumamoto', 'Akita', 'Kochi', 'Gifu-shi', 'Kyoto', 'Hiroshima',\n",
-       "       'Shizuoka', 'Chiba', 'Sapporo', 'Yokohama', 'Tokyo', 'Saitama',\n",
-       "       'Naha', 'Nagasaki', 'Toyama', 'Niigata', 'Nara-shi', 'Okayama',\n",
-       "       'Sendai', 'Osaka', 'Miyazaki', 'Matsuyama', 'Wakayama',\n",
-       "       'Takamatsu', 'Nagoya', 'Ōita', 'Kobe', 'Nagano', 'Utsunomiya',\n",
-       "       'Kagoshima', 'Ulan Bator', 'Vantaa', 'Turku', 'Oulu', 'Helsinki',\n",
-       "       'Tampere', 'Dublin', 'Tel Aviv', 'Haifa', 'Jerusalem', 'Ashdod',\n",
-       "       'Netanya', 'Ashkelon', 'Petaẖ Tiqwa', 'Nur-Sultan', 'Almaty',\n",
-       "       'Vientiane', 'Tashkent', 'Dhaka', 'Liège', 'Brussels', 'Charleroi',\n",
-       "       'Gent', 'Namur', 'Antwerpen', 'Thessaloníki', 'Athens', 'Jeonju',\n",
-       "       'Seoul', 'Busan', 'Sejong', 'Ulsan', 'Yeosu', 'Gwangju',\n",
-       "       'Seongnam-si', 'Daejeon', 'Pohang', 'Changwon', 'Suwon',\n",
-       "       'Cheongju-si', 'Chuncheon', 'Daegu', 'Jeju City', 'Incheon',\n",
-       "       'Suncheon', 'Mokpo', 'Andong', 'Colombo', 'Skopje',\n",
-       "       'Ecatepec de Morelos', 'Guadalajara', 'Cuernavaca', 'Puebla',\n",
-       "       'Mexico City', 'Morelia', 'Mérida', 'San Luis Potosí', 'Monterrey',\n",
-       "       'Pachuca de Soto', 'Tepic', 'Toluca', 'Aguascalientes', 'Oaxaca',\n",
-       "       'Eskişehir', 'İzmit', 'Bursa', 'Ankara', 'Adana', 'Kayseri',\n",
-       "       'Kütahya', 'Balıkesir', 'Adapazarı', 'Trabzon', 'Antakya',\n",
-       "       'Samsun', 'İzmir', 'Konya', 'Sivas', 'Istanbul', 'Denizli',\n",
-       "       'Erzurum', 'Kabul', 'Salzburg', 'Linz', 'Innsbruck', 'Vienna',\n",
-       "       'Graz', 'Guatemala City', 'Cochabamba', 'San José', 'Amman',\n",
-       "       'Irbid', 'Zarqa', 'San Juan', 'Riyadh', 'Jeddah', 'Abha', 'Dammam',\n",
-       "       \"Ha'il\", 'Buraydah', 'Mecca', 'San Salvador', 'Hamilton',\n",
-       "       'Calgary', 'Winnipeg', 'Halifax', 'Kitchener', 'Edmonton',\n",
-       "       'Surrey', 'Mississauga', 'Québec', 'Vancouver', 'Victoria',\n",
-       "       'Montréal', 'Toronto', 'Ottawa', 'London', 'Bologna', 'Livorno',\n",
-       "       'Trieste', 'Modena', 'Prato', 'Florence', 'Naples', 'Rome',\n",
-       "       'Turin', 'Milan', 'Brescia', 'Parma', 'Trondheim', 'Oslo',\n",
-       "       'Stavanger', 'Bergen', 'Saint-Denis', 'Ashgabat', 'Middelburg',\n",
-       "       'Pretoria', 'East London', 'Johannesburg', 'Bloemfontein',\n",
-       "       'Cape Town', 'Vereeniging', 'Durban', 'Klerksdorp', 'Richards Bay',\n",
-       "       'Port Elizabeth', 'Worcester', 'Manama', 'Kaunas', 'Chon Buri',\n",
-       "       'Chiang Mai', 'Nakhon Pathom', 'Rayong', 'Lampang', 'Bangkok',\n",
-       "       'Samut Prakan', 'Sofia', 'Burgas', 'Plovdiv', 'Zürich', 'Debrecen',\n",
-       "       'Győr', 'Szeged', 'Pécs', 'Kecskemét', 'Miskolc', 'Budapest',\n",
-       "       'Kuantan', 'Miri', 'George Town', 'Klang', 'Malacca', 'Taiping',\n",
-       "       'Alor Setar', 'Kota Bharu', 'Kuching', 'Seremban', 'Ipoh',\n",
-       "       'Johor Bahru', 'Kuala Lumpur', 'Utrecht', 'Nijmegen', 'Haarlem',\n",
-       "       'Eindhoven', 'Rotterdam', 'Amsterdam', 'Dordrecht', 'Breda',\n",
-       "       'Groningen', 'Maastricht', 'The Hague', 'Auckland', 'Christchurch',\n",
-       "       'Wellington', 'Dunedin', 'Kampala', 'Ho Chi Minh City', 'Huế',\n",
-       "       'Hanoi', 'Haiphong', 'Hạ Long', 'Addis Ababa', 'Tbilisi',\n",
-       "       'Conakry', 'Baghdad', 'Novi Sad', 'Niš', 'Belgrade',\n",
-       "       'Taitung City', 'Taichung', 'Taoyuan City', 'Taipei', 'Hsinchu',\n",
-       "       'Keelung', 'Tainan', 'Douliu', 'Kaohsiung', 'Abidjan', 'Bogotá',\n",
-       "       'Medellín', 'Nicosia', 'Limassol', 'Algiers', 'Zagreb', 'Rijeka',\n",
-       "       'Split', 'Thrissur', 'New Delhi', 'Hyderabad', 'Delhi',\n",
-       "       'Chandigarh', 'Bhopal', 'Nagpur', 'Lucknow', 'Ghāziābād', 'Hāpur',\n",
-       "       'Gandhinagar', 'Chennai', 'Nashik', 'Mysore', 'Visakhapatnam',\n",
-       "       'Mumbai', 'Jaipur', 'Patna', 'Muzaffarnagar', 'Thiruvananthapuram',\n",
-       "       'Bengaluru', 'Shillong', 'Kolkata', 'Bishkek', 'Willemstad',\n",
-       "       'Edinburgh', 'Norwich', 'Liverpool', 'Belfast', 'Coventry',\n",
-       "       'Leeds', 'Cardiff', 'Bristol', 'Birmingham', 'Sheffield',\n",
-       "       'Newport', 'Leicester', 'Manchester', 'Reading', 'Plymouth',\n",
-       "       'Glasgow', 'Preston', 'Swansea', 'Southend-on-Sea', 'Kathmandu',\n",
-       "       'Pokhara', 'Biratnagar', 'Lisbon', 'Funchal', 'Oklahoma City',\n",
-       "       'Raleigh', 'Memphis', 'Jackson', 'Boston', 'Richmond', 'Portland',\n",
-       "       'Boise', 'Austin', 'Honolulu', 'Fresno', 'Milwaukee', 'Columbia',\n",
-       "       'Hartford', 'Washington D.C.', 'Chicago', 'Houston',\n",
-       "       'Indianapolis', 'Atlanta', 'Charlotte', 'Sacramento', 'Oakland',\n",
-       "       'Providence', 'Staten Island', 'Brooklyn', 'Springfield',\n",
-       "       'The Bronx', 'San Jose', 'Los Angeles', 'Detroit', 'Little Rock',\n",
-       "       'Baltimore', 'Phoenix', 'Omaha', 'El Paso', 'Dallas', 'Seattle',\n",
-       "       'Manhattan', 'Miami', 'Jacksonville', 'Las Vegas', 'San Antonio',\n",
-       "       'Philadelphia', 'San Diego', 'Columbus', 'Saint Paul', 'Denver',\n",
-       "       'Salt Lake City', 'Albuquerque', 'San Francisco', 'Salem',\n",
-       "       'Madison', 'Nashville', 'Tucson', 'Queens', 'Tallahassee',\n",
-       "       'Fort Worth', 'Chihuahua', 'Zamboanga'], dtype=object)"
+       "array(['Abu Dhabi', 'Dubai', 'Kabul', 'Buenos Aires', 'Innsbruck',\n",
+       "       'Salzburg', 'Vienna', 'Linz', 'Graz', 'Sydney', 'Brisbane',\n",
+       "       'Perth', 'Wollongong', 'Canberra', 'Darwin', 'Melbourne',\n",
+       "       'Launceston', 'Hobart', 'Adelaide', 'Newcastle', 'Tuzla',\n",
+       "       'Sarajevo', 'Zenica', 'Dhaka', 'Namur', 'Charleroi', 'Brussels',\n",
+       "       'Gent', 'Liège', 'Antwerpen', 'Plovdiv', 'Burgas', 'Sofia',\n",
+       "       'Manama', 'Cochabamba', 'São José dos Campos', 'São Paulo',\n",
+       "       'Vitória', 'Québec', 'Calgary', 'Hamilton', 'Winnipeg', 'London',\n",
+       "       'Edmonton', 'Kitchener', 'Victoria', 'Halifax', 'Toronto',\n",
+       "       'Vancouver', 'Surrey', 'Ottawa', 'Mississauga', 'Montréal',\n",
+       "       'Zürich', 'Abidjan', 'Valparaíso', 'Concepción', 'Temuco',\n",
+       "       'Rancagua', 'Quilpué', 'Santiago', 'Los Ángeles', 'Talca',\n",
+       "       'Osorno', 'Calama', 'Chillán', 'Puerto Montt', 'Suzhou', 'Jieyang',\n",
+       "       'Zhengzhou', 'Anyang', 'Guangzhou', 'Jinan', 'Ürümqi', 'Harbin',\n",
+       "       'Chongqing', 'Fushun', 'Tianjin', 'Fuzhou', 'Shijiazhuang',\n",
+       "       'Xiamen', 'Shiyan', 'Taiyuan', 'Zhuzhou', 'Hangzhou', 'Hefei',\n",
+       "       'Dongguan', 'Haikou', 'Guiyang', 'Foshan', 'Hohhot', 'Hegang',\n",
+       "       'Wuhan', 'Wuxi', 'Nanning', 'Ningbo', 'Yinchuan', 'Beijing',\n",
+       "       'Xining', 'Qinhuangdao', 'Nanjing', 'Qiqihar', 'Yunfu', 'Lhasa',\n",
+       "       'Nanchang', 'Xi’an', 'Xuchang', 'Xinxiang', 'Chengdu', 'Shanghai',\n",
+       "       'Qingdao', 'Changzhou', 'Changsha', 'Kunming', 'Shantou',\n",
+       "       'Shenyang', 'Shenzhen', 'Changchun', 'Lanzhou', 'Bogotá',\n",
+       "       'Medellín', 'San José', 'Willemstad', 'Nicosia', 'Limassol',\n",
+       "       'Ostrava', 'Prague', 'Olomouc', 'Pilsen', 'Brno', 'Karlsruhe',\n",
+       "       'Potsdam', 'Hamburg', 'Stuttgart', 'Munich', 'Köln', 'Hannover',\n",
+       "       'Mainz', 'Münster', 'Kassel', 'Freiburg', 'Augsburg', 'Darmstadt',\n",
+       "       'Wiesbaden', 'Berlin', 'Düsseldorf', 'Dresden', 'Copenhagen',\n",
+       "       'Algiers', 'Quito', 'Tallinn', 'Bilbao', 'Madrid', 'Santander',\n",
+       "       'Donostia / San Sebastián', 'Salamanca', 'Santa Cruz de Tenerife',\n",
+       "       'Córdoba', 'Castelló de la Plana', 'Sevilla', 'Burgos',\n",
+       "       'Valladolid', 'Valencia', 'Huelva', 'Zaragoza', 'Málaga', 'Palma',\n",
+       "       'Las Palmas de Gran Canaria', 'Barcelona', 'Gasteiz / Vitoria',\n",
+       "       'Oviedo', 'Pamplona', 'Granada', 'Murcia', 'Addis Ababa', 'Oulu',\n",
+       "       'Turku', 'Tampere', 'Vantaa', 'Helsinki', 'Strasbourg', 'Lille',\n",
+       "       'Caen', 'Perpignan', 'Limoges', 'Saint-Étienne', 'Amiens',\n",
+       "       'Orléans', 'Paris', 'Clermont-Ferrand', 'Metz', 'Nantes',\n",
+       "       'Bordeaux', 'Lyon', 'Nancy', 'Besançon', 'Montpellier', 'Rouen',\n",
+       "       'Rennes', 'Nîmes', 'Nice', 'Marseille', 'Grenoble', 'Toulouse',\n",
+       "       'Tours', 'Toulon', 'Dijon', 'Belfast', 'Cardiff', 'Newport',\n",
+       "       'Leeds', 'Swansea', 'Glasgow', 'Liverpool', 'Bristol', 'Norwich',\n",
+       "       'Reading', 'Leicester', 'Sheffield', 'Manchester',\n",
+       "       'Southend-on-Sea', 'Preston', 'Edinburgh', 'Coventry',\n",
+       "       'Birmingham', 'Plymouth', 'Tbilisi', 'Accra', 'Conakry',\n",
+       "       'Thessaloníki', 'Athens', 'Guatemala City', 'Hong Kong', 'Split',\n",
+       "       'Rijeka', 'Zagreb', 'Győr', 'Kecskemét', 'Miskolc', 'Budapest',\n",
+       "       'Szeged', 'Pécs', 'Debrecen', 'Jakarta', 'Dublin', 'Netanya',\n",
+       "       'Tel Aviv', 'Ashdod', 'Ashkelon', 'Haifa', 'Jerusalem',\n",
+       "       'Petaẖ Tiqwa', 'Ghāziābād', 'Delhi', 'Jaipur', 'Gandhinagar',\n",
+       "       'Thrissur', 'Bhopal', 'Bengaluru', 'Shillong', 'Patna', 'Lucknow',\n",
+       "       'Hyderabad', 'Chandigarh', 'Nagpur', 'Mumbai', 'Muzaffarnagar',\n",
+       "       'Mysore', 'Thiruvananthapuram', 'New Delhi', 'Hāpur', 'Kolkata',\n",
+       "       'Visakhapatnam', 'Nashik', 'Chennai', 'Baghdad', 'Shiraz',\n",
+       "       'Zanjān', 'Qom', 'Tehran', 'Khorramshahr', 'Tabriz',\n",
+       "       'Bandar Abbas', 'Isfahan', 'Sanandaj', 'Yazd', 'Khorramabad',\n",
+       "       'Īlām', 'Kermanshah', 'Kerman', 'Mashhad', 'Arāk', 'Karaj',\n",
+       "       'Orūmīyeh', 'Reykjavík', 'Prato', 'Naples', 'Florence', 'Parma',\n",
+       "       'Livorno', 'Rome', 'Bologna', 'Milan', 'Turin', 'Trieste',\n",
+       "       'Brescia', 'Modena', 'Irbid', 'Amman', 'Zarqa', 'Kanazawa',\n",
+       "       'Kagoshima', 'Wakayama', 'Akita', 'Kyoto', 'Yokohama', 'Kumamoto',\n",
+       "       'Utsunomiya', 'Chiba', 'Niigata', 'Osaka', 'Kobe', 'Fukuoka',\n",
+       "       'Okayama', 'Shizuoka', 'Kochi', 'Gifu-shi', 'Takamatsu', 'Sendai',\n",
+       "       'Nagoya', 'Nagasaki', 'Hiroshima', 'Naha', 'Matsuyama', 'Tokyo',\n",
+       "       'Nagano', 'Toyama', 'Saitama', 'Miyazaki', 'Ōita', 'Nara-shi',\n",
+       "       'Sapporo', 'Bishkek', 'Suwon', 'Jeonju', 'Daegu', 'Andong',\n",
+       "       'Suncheon', 'Jeju City', 'Pohang', 'Incheon', 'Seongnam-si',\n",
+       "       'Ulsan', 'Seoul', 'Yeosu', 'Gwangju', 'Changwon', 'Cheongju-si',\n",
+       "       'Sejong', 'Mokpo', 'Busan', 'Chuncheon', 'Daejeon', 'Ḩawallī',\n",
+       "       'Nur-Sultan', 'Almaty', 'Vientiane', 'Colombo', 'Kaunas', 'Skopje',\n",
+       "       'Bamako', 'Yangon', 'Ulan Bator', 'Macau', 'Oaxaca', 'Mexico City',\n",
+       "       'Toluca', 'Monterrey', 'Tepic', 'Mérida', 'Ecatepec de Morelos',\n",
+       "       'San Luis Potosí', 'Cuernavaca', 'Chihuahua', 'Morelia',\n",
+       "       'Pachuca de Soto', 'Guadalajara', 'Puebla', 'Aguascalientes',\n",
+       "       'Taiping', 'Johor Bahru', 'Malacca', 'Ipoh', 'Miri', 'Alor Setar',\n",
+       "       'Klang', 'Kota Bharu', 'Kuantan', 'Kuala Lumpur', 'Kuching',\n",
+       "       'Seremban', 'George Town', 'Utrecht', 'Amsterdam', 'Dordrecht',\n",
+       "       'Eindhoven', 'Haarlem', 'Groningen', 'The Hague', 'Rotterdam',\n",
+       "       'Maastricht', 'Nijmegen', 'Breda', 'Stavanger', 'Bergen',\n",
+       "       'Trondheim', 'Oslo', 'Kathmandu', 'Pokhara', 'Biratnagar',\n",
+       "       'Christchurch', 'Dunedin', 'Wellington', 'Auckland', 'Lima',\n",
+       "       'Baguio', 'Butuan', 'Manila', 'Zamboanga', 'Lahore', 'Peshawar',\n",
+       "       'Islamabad', 'Karachi', 'Katowice', 'Płock', 'Kraków', 'Gdańsk',\n",
+       "       'Zabrze', 'Łódź', 'Warsaw', 'Tarnów', 'Bydgoszcz', 'Poznań',\n",
+       "       'Wrocław', 'Kielce', 'Szczecin', 'Rybnik', 'San Juan', 'Funchal',\n",
+       "       'Lisbon', 'Saint-Denis', 'Iaşi', 'Sibiu', 'Râmnicu Vâlcea',\n",
+       "       'Constanţa', 'Cluj-Napoca', 'Bacău', 'Craiova', 'Piteşti', 'Arad',\n",
+       "       'Braşov', 'Ploieşti', 'Brăila', 'Bucharest', 'Oradea', 'Baia Mare',\n",
+       "       'Timişoara', 'Galaţi', 'Niš', 'Belgrade', 'Novi Sad', 'Moscow',\n",
+       "       'Saint Petersburg', 'Nizhniy Novgorod', 'Chelyabinsk',\n",
+       "       'Krasnoyarsk', 'Novosibirsk', 'Tomsk', 'Abha', 'Buraydah',\n",
+       "       'Jeddah', \"Ha'il\", 'Dammam', 'Mecca', 'Riyadh', 'Stockholm',\n",
+       "       'Malmö', 'Göteborg', 'Uppsala', 'Singapore', 'Košice',\n",
+       "       'Bratislava', 'San Salvador', 'Samut Prakan', 'Nakhon Pathom',\n",
+       "       'Lampang', 'Chon Buri', 'Chiang Mai', 'Rayong', 'Bangkok',\n",
+       "       'Dushanbe', 'Ashgabat', 'Konya', 'Adapazarı', 'Bursa', 'İzmir',\n",
+       "       'Istanbul', 'Adana', 'Eskişehir', 'Erzurum', 'Kayseri', 'İzmit',\n",
+       "       'Kütahya', 'Trabzon', 'Balıkesir', 'Samsun', 'Sivas', 'Denizli',\n",
+       "       'Antakya', 'Ankara', 'Douliu', 'Taichung', 'Tainan', 'Hsinchu',\n",
+       "       'Keelung', 'Taipei', 'Kaohsiung', 'Taoyuan City', 'Taitung City',\n",
+       "       'Dnipro', 'Ivano-Frankivsk', 'Odessa', 'Zaporizhia', 'Lviv',\n",
+       "       'Kamianske', 'Kryvyi Rih', 'Ternopil', 'Kyiv', 'Kampala',\n",
+       "       'Oakland', 'Raleigh', 'Jacksonville', 'Queens', 'Baltimore',\n",
+       "       'Boise', 'Hartford', 'Houston', 'Honolulu', 'Providence',\n",
+       "       'Staten Island', 'Sacramento', 'Tallahassee', 'Richmond',\n",
+       "       'Portland', 'Brooklyn', 'Boston', 'Springfield', 'Nashville',\n",
+       "       'Oklahoma City', 'Albuquerque', 'Phoenix', 'Philadelphia',\n",
+       "       'Indianapolis', 'Jackson', 'Omaha', 'Austin', 'Atlanta',\n",
+       "       'Columbus', 'Dallas', 'Manhattan', 'Las Vegas', 'El Paso',\n",
+       "       'Little Rock', 'The Bronx', 'Los Angeles', 'Columbia',\n",
+       "       'San Antonio', 'San Diego', 'San Francisco', 'Fresno',\n",
+       "       'Fort Worth', 'Salem', 'Miami', 'Chicago', 'Salt Lake City',\n",
+       "       'Tucson', 'Denver', 'Detroit', 'Madison', 'Milwaukee', 'San Jose',\n",
+       "       'Seattle', 'Memphis', 'Charlotte', 'Saint Paul', 'Washington D.C.',\n",
+       "       'Tashkent', 'Hạ Long', 'Haiphong', 'Huế', 'Hanoi',\n",
+       "       'Ho Chi Minh City', 'Pristina', 'Worcester', 'Klerksdorp',\n",
+       "       'Middelburg', 'East London', 'Port Elizabeth', 'Vereeniging',\n",
+       "       'Johannesburg', 'Durban', 'Cape Town', 'Bloemfontein',\n",
+       "       'Richards Bay', 'Pretoria'], dtype=object)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -931,7 +1202,7 @@
        "Name: City, Length: 615, dtype: int64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -963,8 +1234,8 @@
        "Adelaide      3177\n",
        "Perth         3082\n",
        "Newcastle     2871\n",
-       "Hobart        1126\n",
        "Launceston    1126\n",
+       "Hobart        1126\n",
        "Canberra      1093\n",
        "Name: City, dtype: int64"
       ]
@@ -987,28 +1258,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
+   "execution_count": 28,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
       "text/plain": [
        "array(['pm25', 'co', 'so2', 'no2', 'pm10', 'o3', 'aqi'], dtype=object)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Identifying the unique (common) air pollutants in the dataset \n",
     "unique_species = clean_airdf[\"Specie\"].unique()\n",
     "unique_species"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1035,12 +1309,662 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1008 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "specie_median_distribution(clean_airdf, \"AU\")"
+   ]
+  },
+  {
+   "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>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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1490741</th>\n",
+       "      <td>2019-12-01</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Rijeka</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>10734.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1490742</th>\n",
+       "      <td>2019-12-12</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Rijeka</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>48</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>962.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1490743</th>\n",
+       "      <td>2019-10-18</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Rijeka</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>43</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2226.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1490744</th>\n",
+       "      <td>2019-10-21</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Rijeka</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>33</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>11243.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1490745</th>\n",
+       "      <td>2019-11-13</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>Rijeka</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>4354.95</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>270688 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Date Country     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",
+       "1490741 2019-12-01      HR   Rijeka   pm25     44   1.0  172.0    29.0   \n",
+       "1490742 2019-12-12      HR   Rijeka   pm25     48   9.0   53.0    34.0   \n",
+       "1490743 2019-10-18      HR   Rijeka   pm25     43   9.0   74.0    30.0   \n",
+       "1490744 2019-10-21      HR   Rijeka   pm25     33   7.0  170.0    49.0   \n",
+       "1490745 2019-11-13      HR   Rijeka   pm25     15   1.0   66.0    13.0   \n",
+       "\n",
+       "         variance  \n",
+       "0        10921.40  \n",
+       "1        14014.00  \n",
+       "2         3558.56  \n",
+       "3        11209.80  \n",
+       "4         3209.67  \n",
+       "...           ...  \n",
+       "1490741  10734.50  \n",
+       "1490742    962.27  \n",
+       "1490743   2226.45  \n",
+       "1490744  11243.50  \n",
+       "1490745   4354.95  \n",
+       "\n",
+       "[270688 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clean_airdf[clean_airdf[\"Specie\"]==\"pm25\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>293324</th>\n",
+       "      <td>2020-01-10</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>22</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1554580.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293334</th>\n",
+       "      <td>2020-01-09</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293351</th>\n",
+       "      <td>2020-03-24</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>13</td>\n",
+       "      <td>814.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293384</th>\n",
+       "      <td>2020-03-14</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>14</td>\n",
+       "      <td>785.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>916.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293397</th>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1433760.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293407</th>\n",
+       "      <td>2020-03-23</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1379350.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299451</th>\n",
+       "      <td>2020-02-11</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>41</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1261770.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299522</th>\n",
+       "      <td>2020-02-09</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>40</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1188380.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299540</th>\n",
+       "      <td>2020-02-21</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>41</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1363900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299543</th>\n",
+       "      <td>2020-02-12</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1318070.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431159</th>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Nagpur</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>22</td>\n",
+       "      <td>394.0</td>\n",
+       "      <td>834.0</td>\n",
+       "      <td>834.0</td>\n",
+       "      <td>211968.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645613</th>\n",
+       "      <td>2019-02-18</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>23</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>861.0</td>\n",
+       "      <td>824.0</td>\n",
+       "      <td>1500550.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645638</th>\n",
+       "      <td>2019-02-19</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>18</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>899.0</td>\n",
+       "      <td>813.0</td>\n",
+       "      <td>1476270.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645892</th>\n",
+       "      <td>2019-02-26</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Oaxaca</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645912</th>\n",
+       "      <td>2019-02-25</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Oaxaca</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>2</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773253</th>\n",
+       "      <td>2019-05-15</td>\n",
+       "      <td>ZA</td>\n",
+       "      <td>Middelburg</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>14</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>773272</th>\n",
+       "      <td>2019-05-16</td>\n",
+       "      <td>ZA</td>\n",
+       "      <td>Middelburg</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>6</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>948320</th>\n",
+       "      <td>2019-05-20</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>682.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>824.0</td>\n",
+       "      <td>18956.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>948361</th>\n",
+       "      <td>2019-06-11</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>948369</th>\n",
+       "      <td>2019-06-12</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>24</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>1286230.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>948390</th>\n",
+       "      <td>2019-05-21</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>22</td>\n",
+       "      <td>131.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>825.0</td>\n",
+       "      <td>1009390.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297247</th>\n",
+       "      <td>2019-11-06</td>\n",
+       "      <td>GR</td>\n",
+       "      <td>Thessaloníki</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>8</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297573</th>\n",
+       "      <td>2019-11-06</td>\n",
+       "      <td>GR</td>\n",
+       "      <td>Athens</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>12</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1343496</th>\n",
+       "      <td>2019-11-21</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>18</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>991400.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1343509</th>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>815.0</td>\n",
+       "      <td>1433760.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Date Country             City Specie  count    min    max  \\\n",
+       "293324  2020-01-10      MX  San Luis Potosí   pm25     22    3.0  815.0   \n",
+       "293334  2020-01-09      MX  San Luis Potosí   pm25     24  815.0  815.0   \n",
+       "293351  2020-03-24      MX  San Luis Potosí   pm25     13  814.0  815.0   \n",
+       "293384  2020-03-14      MX  San Luis Potosí   pm25     14  785.0  815.0   \n",
+       "293397  2020-01-02      MX  San Luis Potosí   pm25     21    3.0  815.0   \n",
+       "293407  2020-03-23      MX  San Luis Potosí   pm25     24   31.0  815.0   \n",
+       "299451  2020-02-11      MX   Aguascalientes   pm25     41   61.0  815.0   \n",
+       "299522  2020-02-09      MX   Aguascalientes   pm25     40   82.0  815.0   \n",
+       "299540  2020-02-21      MX   Aguascalientes   pm25     41   60.0  815.0   \n",
+       "299543  2020-02-12      MX   Aguascalientes   pm25     46    2.0  815.0   \n",
+       "431159  2020-01-12      IN           Nagpur   pm25     22  394.0  834.0   \n",
+       "645613  2019-02-18      MX   Aguascalientes   pm25     23    4.0  861.0   \n",
+       "645638  2019-02-19      MX   Aguascalientes   pm25     18    8.0  899.0   \n",
+       "645892  2019-02-26      MX           Oaxaca   pm25      2  825.0  825.0   \n",
+       "645912  2019-02-25      MX           Oaxaca   pm25      2  825.0  825.0   \n",
+       "773253  2019-05-15      ZA       Middelburg   pm25     14  999.0  999.0   \n",
+       "773272  2019-05-16      ZA       Middelburg   pm25      6  999.0  999.0   \n",
+       "948320  2019-05-20      MX   Aguascalientes   pm25     24  682.0  825.0   \n",
+       "948361  2019-06-11      MX   Aguascalientes   pm25     24  825.0  825.0   \n",
+       "948369  2019-06-12      MX   Aguascalientes   pm25     24   63.0  825.0   \n",
+       "948390  2019-05-21      MX   Aguascalientes   pm25     22  131.0  825.0   \n",
+       "1297247 2019-11-06      GR     Thessaloníki   pm25      8  999.0  999.0   \n",
+       "1297573 2019-11-06      GR           Athens   pm25     12  999.0  999.0   \n",
+       "1343496 2019-11-21      MX  San Luis Potosí   pm25     18   65.0  815.0   \n",
+       "1343509 2020-01-02      MX  San Luis Potosí   pm25     21    3.0  815.0   \n",
+       "\n",
+       "         median    variance  \n",
+       "293324    815.0  1554580.00  \n",
+       "293334    815.0        0.00  \n",
+       "293351    815.0        1.41  \n",
+       "293384    815.0      916.04  \n",
+       "293397    815.0  1433760.00  \n",
+       "293407    815.0  1379350.00  \n",
+       "299451    815.0  1261770.00  \n",
+       "299522    815.0  1188380.00  \n",
+       "299540    815.0  1363900.00  \n",
+       "299543    815.0  1318070.00  \n",
+       "431159    834.0   211968.00  \n",
+       "645613    824.0  1500550.00  \n",
+       "645638    813.0  1476270.00  \n",
+       "645892    825.0        0.00  \n",
+       "645912    825.0        0.00  \n",
+       "773253    999.0        0.00  \n",
+       "773272    999.0        0.00  \n",
+       "948320    824.0    18956.50  \n",
+       "948361    825.0        0.00  \n",
+       "948369    825.0  1286230.00  \n",
+       "948390    825.0  1009390.00  \n",
+       "1297247   999.0        0.00  \n",
+       "1297573   999.0        0.00  \n",
+       "1343496   815.0   991400.00  \n",
+       "1343509   815.0  1433760.00  "
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clean_airdf[(clean_airdf[\"Specie\"]==\"pm25\") & (clean_airdf[\"median\"]>=800)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x1008 with 7 Axes>"
       ]
@@ -1051,60 +1975,464 @@
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "specie_median_distribution(clean_airdf, \"AU\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clean_airdf[clean_airdf[\"Specie\"]==\"pm25\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clean_airdf[(clean_airdf[\"Specie\"]==\"pm25\") & (clean_airdf[\"median\"]>=800)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "specie_median_distribution(clean_airdf, \"no2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clean_airdf[(clean_airdf[\"Specie\"]==\"no2\") & (clean_airdf[\"median\"]>=150)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "specie_median_distribution(clean_airdf, \"pm10\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "source": [
+    "specie_median_distribution(clean_airdf, \"no2\")"
+   ]
+  },
+  {
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>122673</th>\n",
+       "      <td>2020-01-18</td>\n",
+       "      <td>PH</td>\n",
+       "      <td>Manila</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>149.9</td>\n",
+       "      <td>253.1</td>\n",
+       "      <td>166.6</td>\n",
+       "      <td>14687.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256157</th>\n",
+       "      <td>2020-05-07</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>Petaẖ Tiqwa</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>164.3</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>164.3</td>\n",
+       "      <td>36722.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297462</th>\n",
+       "      <td>2019-11-06</td>\n",
+       "      <td>GR</td>\n",
+       "      <td>Thessaloníki</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297617</th>\n",
+       "      <td>2019-11-06</td>\n",
+       "      <td>GR</td>\n",
+       "      <td>Athens</td>\n",
+       "      <td>no2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>357.7</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>500.0</td>\n",
+       "      <td>25311.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Date Country          City Specie  count    min    max  median  \\\n",
+       "122673  2020-01-18      PH        Manila    no2      6  149.9  253.1   166.6   \n",
+       "256157  2020-05-07      IL   Petaẖ Tiqwa    no2      2  164.3  250.0   164.3   \n",
+       "1297462 2019-11-06      GR  Thessaloníki    no2      4  500.0  500.0   500.0   \n",
+       "1297617 2019-11-06      GR        Athens    no2      8  357.7  500.0   500.0   \n",
+       "\n",
+       "         variance  \n",
+       "122673    14687.7  \n",
+       "256157    36722.4  \n",
+       "1297462       0.0  \n",
+       "1297617   25311.6  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clean_airdf[(clean_airdf[\"Specie\"]==\"no2\") & (clean_airdf[\"median\"]>=150)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1008 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "specie_median_distribution(clean_airdf, \"pm10\")"
+   ]
+  },
+  {
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>293676</th>\n",
+       "      <td>2020-03-04</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>868.0</td>\n",
+       "      <td>868.0</td>\n",
+       "      <td>736719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299188</th>\n",
+       "      <td>2020-06-01</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>2198710.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299190</th>\n",
+       "      <td>2020-01-10</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>883.0</td>\n",
+       "      <td>882.0</td>\n",
+       "      <td>1376010.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307469</th>\n",
+       "      <td>2020-05-17</td>\n",
+       "      <td>TR</td>\n",
+       "      <td>Kütahya</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>577027</th>\n",
+       "      <td>2019-03-07</td>\n",
+       "      <td>TR</td>\n",
+       "      <td>Kütahya</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>1886520.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581093</th>\n",
+       "      <td>2019-02-15</td>\n",
+       "      <td>TR</td>\n",
+       "      <td>Denizli</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>581100</th>\n",
+       "      <td>2019-02-14</td>\n",
+       "      <td>TR</td>\n",
+       "      <td>Denizli</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>642563</th>\n",
+       "      <td>2019-01-23</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>48</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>882.0</td>\n",
+       "      <td>855.0</td>\n",
+       "      <td>1715900.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645508</th>\n",
+       "      <td>2019-03-28</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>645553</th>\n",
+       "      <td>2019-03-29</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>880.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>828071</th>\n",
+       "      <td>2019-05-12</td>\n",
+       "      <td>CN</td>\n",
+       "      <td>Lanzhou</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>72</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>592125.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>945550</th>\n",
+       "      <td>2019-04-14</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>48</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>882.0</td>\n",
+       "      <td>868.0</td>\n",
+       "      <td>1384820.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>948165</th>\n",
+       "      <td>2019-05-15</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>867.0</td>\n",
+       "      <td>867.0</td>\n",
+       "      <td>1.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>948196</th>\n",
+       "      <td>2019-05-16</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>Aguascalientes</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>22</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>867.0</td>\n",
+       "      <td>866.0</td>\n",
+       "      <td>0.87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1139535</th>\n",
+       "      <td>2019-08-12</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>Shillong</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>21</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>895.0</td>\n",
+       "      <td>878.0</td>\n",
+       "      <td>1023390.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297317</th>\n",
+       "      <td>2019-11-06</td>\n",
+       "      <td>GR</td>\n",
+       "      <td>Thessaloníki</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297664</th>\n",
+       "      <td>2019-11-06</td>\n",
+       "      <td>GR</td>\n",
+       "      <td>Athens</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>999.0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1343337</th>\n",
+       "      <td>2019-12-01</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>867.0</td>\n",
+       "      <td>868.0</td>\n",
+       "      <td>868.0</td>\n",
+       "      <td>1.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1343360</th>\n",
+       "      <td>2019-11-30</td>\n",
+       "      <td>MX</td>\n",
+       "      <td>San Luis Potosí</td>\n",
+       "      <td>pm10</td>\n",
+       "      <td>24</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>868.0</td>\n",
+       "      <td>868.0</td>\n",
+       "      <td>1507350.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Date Country             City Specie  count    min    max  \\\n",
+       "293676  2020-03-04      MX  San Luis Potosí   pm10      9   71.0  868.0   \n",
+       "299188  2020-06-01      MX   Aguascalientes   pm10      5   14.0  880.0   \n",
+       "299190  2020-01-10      MX   Aguascalientes   pm10     24   23.0  883.0   \n",
+       "307469  2020-05-17      TR          Kütahya   pm10      3  880.0  880.0   \n",
+       "577027  2019-03-07      TR          Kütahya   pm10      7   57.0  880.0   \n",
+       "581093  2019-02-15      TR          Denizli   pm10      3  880.0  880.0   \n",
+       "581100  2019-02-14      TR          Denizli   pm10      3  880.0  880.0   \n",
+       "642563  2019-01-23      MX  San Luis Potosí   pm10     48   17.0  882.0   \n",
+       "645508  2019-03-28      MX   Aguascalientes   pm10      3  880.0  880.0   \n",
+       "645553  2019-03-29      MX   Aguascalientes   pm10      3  880.0  880.0   \n",
+       "828071  2019-05-12      CN          Lanzhou   pm10     72   46.0  999.0   \n",
+       "945550  2019-04-14      MX  San Luis Potosí   pm10     48   32.0  882.0   \n",
+       "948165  2019-05-15      MX   Aguascalientes   pm10     24  866.0  867.0   \n",
+       "948196  2019-05-16      MX   Aguascalientes   pm10     22  866.0  867.0   \n",
+       "1139535 2019-08-12      IN         Shillong   pm10     21   47.0  895.0   \n",
+       "1297317 2019-11-06      GR     Thessaloníki   pm10      8  999.0  999.0   \n",
+       "1297664 2019-11-06      GR           Athens   pm10      8  999.0  999.0   \n",
+       "1343337 2019-12-01      MX  San Luis Potosí   pm10     24  867.0  868.0   \n",
+       "1343360 2019-11-30      MX  San Luis Potosí   pm10     24   44.0  868.0   \n",
+       "\n",
+       "         median    variance  \n",
+       "293676    868.0   736719.00  \n",
+       "299188    880.0  2198710.00  \n",
+       "299190    882.0  1376010.00  \n",
+       "307469    880.0        0.00  \n",
+       "577027    880.0  1886520.00  \n",
+       "581093    880.0        0.00  \n",
+       "581100    880.0        0.00  \n",
+       "642563    855.0  1715900.00  \n",
+       "645508    880.0        0.00  \n",
+       "645553    880.0        0.00  \n",
+       "828071    999.0   592125.00  \n",
+       "945550    868.0  1384820.00  \n",
+       "948165    867.0        1.14  \n",
+       "948196    866.0        0.87  \n",
+       "1139535   878.0  1023390.00  \n",
+       "1297317   999.0        0.00  \n",
+       "1297664   999.0        0.00  \n",
+       "1343337   868.0        1.45  \n",
+       "1343360   868.0  1507350.00  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "clean_airdf[(clean_airdf[\"Specie\"]==\"pm10\") & (clean_airdf[\"median\"]>=800)]"
    ]
@@ -4648,7 +5976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -5350,13 +6678,7 @@
       "                        'Date reported': '26/04/2020',\n",
       "                        'Death No (in state)': '17',\n",
       "                        'Details': 'Man in 90s - died in hospital',\n",
-      "                        'Name (if known)': '',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                        'Name (if known)': '',\n",
       "                        'Source': 'media release',\n",
       "                        'State': 'VIC'},\n",
       "                       {'Date of death': '',\n",
@@ -5708,7 +7030,13 @@
       "                         'email': '',\n",
       "                         'media releases': '',\n",
       "                         'state': 'SA'},\n",
-      "                        {'daily update': 'https://ww2.health.wa.gov.au/Articles/A_E/Coronavirus',\n",
+      "                        {'daily update': 'https://ww2.health.wa.gov.au/Articles/A_E/Coronavirus',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                         'dashboard': 'https://dohwa.maps.arcgis.com/apps/opsdashboard/index.html#/744650bd230546928a0df2e87fd5b8e5',\n",
       "                         'email': 'media@health.wa.gov.au',\n",
       "                         'media releases': 'https://ww2.health.wa.gov.au/News/Media-releases-listing-page',\n",
@@ -6075,13 +7403,7 @@
       "                         'Cumulative deaths': '',\n",
       "                         'Date': '06/02/2020',\n",
       "                         'Hospitalisations (count)': '',\n",
-      "                         'Intensive care (count)': '',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '',\n",
       "                         'State': 'QLD',\n",
@@ -7032,13 +8354,7 @@
       "                         'Cumulative deaths': '2',\n",
       "                         'Date': '11/03/2020',\n",
       "                         'Hospitalisations (count)': '',\n",
-      "                         'Intensive care (count)': '',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '',\n",
       "                         'State': 'NSW',\n",
@@ -7091,7 +8407,13 @@
       "                         'Date': '12/03/2020',\n",
       "                         'Hospitalisations (count)': '',\n",
       "                         'Intensive care (count)': '',\n",
-      "                         'Notes': '',\n",
+      "                         'Notes': '',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                         'Recovered (cumulative)': '',\n",
       "                         'State': 'QLD',\n",
       "                         'Tests conducted (negative)': '',\n",
@@ -8015,13 +9337,7 @@
       "                         'Ventilator usage (count)': ''},\n",
       "                        {'Cumulative case count': '533',\n",
       "                         'Cumulative deaths': '',\n",
-      "                         'Date': '21/03/2020',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Date': '21/03/2020',\n",
       "                         'Hospitalisations (count)': '',\n",
       "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
@@ -8398,7 +9714,13 @@
       "                         'Ventilator usage (count)': ''},\n",
       "                        {'Cumulative case count': '197',\n",
       "                         'Cumulative deaths': '',\n",
-      "                         'Date': '25/03/2020',\n",
+      "                         'Date': '25/03/2020',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                         'Hospitalisations (count)': '',\n",
       "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
@@ -8968,13 +10290,7 @@
       "                         'Time': '08:40',\n",
       "                         'Update Source': 'media release',\n",
       "                         'Ventilator usage (count)': ''},\n",
-      "                        {'Cumulative case count': '821',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                        {'Cumulative case count': '821',\n",
       "                         'Cumulative deaths': '4',\n",
       "                         'Date': '30/03/2020',\n",
       "                         'Hospitalisations (count)': '29',\n",
@@ -9713,13 +11029,7 @@
       "                         'State': 'ACT',\n",
       "                         'Tests conducted (negative)': '5329',\n",
       "                         'Tests conducted (total)': '',\n",
-      "                         'Time': '',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Time': '',\n",
       "                         'Update Source': 'media release',\n",
       "                         'Ventilator usage (count)': ''},\n",
       "                        {'Cumulative case count': '411',\n",
@@ -9825,7 +11135,13 @@
       "                         'Tests conducted (total)': '',\n",
       "                         'Time': '',\n",
       "                         'Update Source': 'media release',\n",
-      "                         'Ventilator usage (count)': ''},\n",
+      "                         'Ventilator usage (count)': ''},\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                        {'Cumulative case count': '470',\n",
       "                         'Cumulative deaths': '6',\n",
       "                         'Date': '07/04/2020',\n",
@@ -10548,13 +11864,7 @@
       "                         'Intensive care (count)': '12',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '296',\n",
-      "                         'State': 'WA',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'State': 'WA',\n",
       "                         'Tests conducted (negative)': '23870',\n",
       "                         'Tests conducted (total)': '',\n",
       "                         'Time': '16:55',\n",
@@ -11410,13 +12720,7 @@
       "                        {'Cumulative case count': '104',\n",
       "                         'Cumulative deaths': '',\n",
       "                         'Date': '22/04/2020',\n",
-      "                         'Hospitalisations (count)': '1',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Hospitalisations (count)': '1',\n",
       "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '',\n",
@@ -11781,7 +13085,13 @@
       "                         'Cumulative deaths': '',\n",
       "                         'Date': '25/04/2020',\n",
       "                         'Hospitalisations (count)': '',\n",
-      "                         'Intensive care (count)': '',\n",
+      "                         'Intensive care (count)': '',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '',\n",
       "                         'State': 'TAS',\n",
@@ -12380,13 +13690,7 @@
       "                         'Ventilator usage (count)': ''},\n",
       "                        {'Cumulative case count': '3031',\n",
       "                         'Cumulative deaths': '',\n",
-      "                         'Date': '01/05/2020',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Date': '01/05/2020',\n",
       "                         'Hospitalisations (count)': '',\n",
       "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
@@ -12959,7 +14263,13 @@
       "                        {'Cumulative case count': '226',\n",
       "                         'Cumulative deaths': '',\n",
       "                         'Date': '07/05/2020',\n",
-      "                         'Hospitalisations (count)': '',\n",
+      "                         'Hospitalisations (count)': '',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '',\n",
@@ -13349,13 +14659,7 @@
       "                        {'Cumulative case count': '1509',\n",
       "                         'Cumulative deaths': '',\n",
       "                         'Date': '12/05/2020',\n",
-      "                         'Hospitalisations (count)': '8',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Hospitalisations (count)': '8',\n",
       "                         'Intensive care (count)': '4',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '1376',\n",
@@ -14187,13 +15491,7 @@
       "                        {'Cumulative case count': '',\n",
       "                         'Cumulative deaths': '50',\n",
       "                         'Date': '22/05/2020',\n",
-      "                         'Hospitalisations (count)': '',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Hospitalisations (count)': '',\n",
       "                         'Intensive care (count)': '',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '',\n",
@@ -14460,7 +15758,13 @@
       "                         'State': 'VIC',\n",
       "                         'Tests conducted (negative)': '',\n",
       "                         'Tests conducted (total)': '430000',\n",
-      "                         'Time': '10:00',\n",
+      "                         'Time': '10:00',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                         'Update Source': 'media release',\n",
       "                         'Ventilator usage (count)': ''},\n",
       "                        {'Cumulative case count': '107',\n",
@@ -15091,13 +16395,7 @@
       "                         'Cumulative deaths': '',\n",
       "                         'Date': '02/06/2020',\n",
       "                         'Hospitalisations (count)': '11',\n",
-      "                         'Intensive care (count)': '1',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Intensive care (count)': '1',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '',\n",
       "                         'State': 'NSW',\n",
@@ -15830,13 +17128,7 @@
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '1051',\n",
       "                         'State': 'QLD',\n",
-      "                         'Tests conducted (negative)': '',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Tests conducted (negative)': '',\n",
       "                         'Tests conducted (total)': '249627',\n",
       "                         'Time': '12:00',\n",
       "                         'Update Source': 'media release',\n",
@@ -16003,7 +17295,13 @@
       "                         'Hospitalisations (count)': '49',\n",
       "                         'Intensive care (count)': '0',\n",
       "                         'Notes': '',\n",
-      "                         'Recovered (cumulative)': '2770',\n",
+      "                         'Recovered (cumulative)': '2770',\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                         'State': 'NSW',\n",
       "                         'Tests conducted (negative)': '',\n",
       "                         'Tests conducted (total)': '661226',\n",
@@ -16744,13 +18042,7 @@
       "                         'Cumulative deaths': '',\n",
       "                         'Date': '29/06/2020',\n",
       "                         'Hospitalisations (count)': '9',\n",
-      "                         'Intensive care (count)': '1',\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "                         'Intensive care (count)': '1',\n",
       "                         'Notes': '',\n",
       "                         'Recovered (cumulative)': '1789',\n",
       "                         'State': 'VIC',\n",
@@ -17147,7 +18439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -17156,7 +18448,7 @@
        "dict_keys(['sheets'])"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17167,7 +18459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -17176,7 +18468,7 @@
        "dict_keys(['updates', 'deaths', 'latest totals', 'locations', 'sources', 'about', 'data validation'])"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17187,7 +18479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -17208,7 +18500,7 @@
        " 'Notes': ''}"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17220,7 +18512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17238,7 +18530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -17317,7 +18609,7 @@
        "4   QLD  28/01/2020                     0                           "
       ]
      },
-     "execution_count": 48,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17334,7 +18626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -17361,7 +18653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17371,7 +18663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17381,7 +18673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17392,7 +18684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -17419,7 +18711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17428,7 +18720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -17455,7 +18747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17465,7 +18757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -17492,7 +18784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -17691,7 +18983,7 @@
        "869   VIC 2020-07-07                   2824                        2028"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17702,7 +18994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -17711,7 +19003,7 @@
        "Timestamp('2020-01-23 00:00:00')"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17723,7 +19015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -17732,7 +19024,7 @@
        "Timestamp('2020-07-07 00:00:00')"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17744,7 +19036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17753,7 +19045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17762,7 +19054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -17771,7 +19063,7 @@
        "Timestamp('2020-07-05 00:00:00')"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17870,9 +19162,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:PythonData] *",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-PythonData-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {