Botswana Covid-19 Analysis .ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=center><font size = 7>Data Modelling and Analysis of Covid-19  Botswana</font></h1>\n",
    "<h3 align=center><font size = 3>My journey as a story teller – Thato Seeletso Mmusi</font></h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To begin we first start with some of the tools we are going to use for the analysis as well as the datasets "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing datasets\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing of the modules I will use for the report\n",
    "\n",
    "from pandas import Series, DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pycountry_convert as pc\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta,date\n",
    "import plotly as px\n",
    "from IPython.core.display import display, HTML\n",
    "from cycler import cycler\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#RETREIVING DATASETS\n",
    "\n",
    "confirmed_df = pd.read_csv('C:/Users/y4...4#1#1/Desktop/Data Science Study/Exercises\\/COVID-19 Botswana/covid-19/time_series_covid19_confirmed_global.csv')\n",
    "deaths_df = pd.read_csv('C:/Users/y4...4#1#1/Desktop/Data Science Study/Exercises\\/COVID-19 Botswana/covid-19/time_series_covid19_deaths_global.csv')\n",
    "recovered_df = pd.read_csv('C:/Users/y4...4#1#1/Desktop/Data Science Study/Exercises\\/COVID-19 Botswana/covid-19/time_series_covid19_recovered_global.csv')\n",
    "cases_df = pd.read_csv('C:/Users/y4...4#1#1/Desktop/Data Science Study/Exercises\\/COVID-19 Botswana/covid-19/cases_country.csv')\n",
    "\n",
    "bots_confirmed = pd.read_csv('C:/Users/y4...4#1#1/Desktop/Data Science Study/Exercises\\/COVID-19 Botswana/covid-19/COVID-19-Botswana.csv')\n",
    "\n",
    "\n",
    "# Total number of confirmed, death and recovered cases\n",
    "confirmed_total = int(cases_df['Confirmed'].sum())\n",
    "deaths_total = int(cases_df['Deaths'].sum())\n",
    "recovered_total = int(cases_df['Recovered'].sum())\n",
    "active_total = int(cases_df['Active'].sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents :\n",
    "\n",
    " * [Importing datasets](#Importing-datasets) \n",
    " * [Introduction to Covid-19](#Introduction-to-Covid-19)\n",
    " * [Purpose](#Purpose) \n",
    "* [Pre Processing ](#Pre-Processing)\n",
    "* [Functions Definitions](#Functions-Definitions)\n",
    "\n",
    "* [Quick Analysis of Botswana](#Quick-Analysis-of-Botswana)\n",
    "   * [Botswana ](#Botswana )\n",
    "   * [Botswana vs World](#Botswana-vs-World)\n",
    "   * [Working on local data](#Working-on-local-data )\n",
    "   * **[Analysis of tests done in Botswana vs South-Africa](#Analysis-of-Tests-done-in-India-and-Comparission-with-South-Korea)** \n",
    "\n",
    "   \n",
    "* [COVID-19 Spread Analysis](#COVID-19-Spread-Analysis)\n",
    "    * [Spread Analysis Botswana](#COVID-19-Spread-Analysis)\n",
    "    * [Infection Rate](#Infection-Rate)\n",
    "    * [Quick Comparison Botswana Vs World Spread Analysis](#Quick-Comparison-Botswana-Vs-World-Spread-Analysis)\n",
    "    * [Testing capacity](#Testing-capacity)\n",
    "    * [Total people tested](#Total-people-tested)\n",
    "* [Correlation Analysis](#Correlation-Analysis) \n",
    "\n",
    "\n",
    "* **[Visualization on Map](#Visualization-on-Map)** \n",
    "    * [Botswana Confirmed Cases on Map](#Botswana-Confirmed-Cases-on-Map)\n",
    "\n",
    "* [Botswana Prediction](#Global-Prediction)\n",
    "    * **[Tabulation of prediction and actual figures that may be after 30th May](#Tabulation-of-prediction-and-actual-figure-after-24th-March-Global)** \n",
    "    \n",
    " * **<span style=\"color:red\"> To be updated as we get recoveries in Botswana. Currently we are at zero</span> ** \n",
    "\n",
    "* [Valuable Feedback is Welcome](#Valuable-Feedback)\n",
    "* [References](#Valuable-Feedback)\n",
    "\n",
    "**You can also follow my work on github [Version 1.0.1](https://www.kaggle.com/tarunkr/covid-19-case-study-analysis-viz-comparisons?scriptVersionId=30677812) of this notebook**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tracking Covid-19 in Botswana ver 1.0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "## Analysis, Visualization, Comparison between trends with other countries as well as attempting to predict what is next in terms of the pandemic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction to Covid-19"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The novel coronavirus (SARS – CoV -2) is a viral strain closely related to the SARS – COV – 1 virus\n",
    "which was detected in China in 2019 (EU – European Center for Disease Prevention &amp; Control, 2020).\n",
    "\n",
    "Corona viruses are a group of viruses that usually have animals as host but sometimes they also\n",
    "infect humans. The disease caused by SARS – COV -2 was deemed COVID – 19. Although the disease\n",
    "caused by this virus has similar symptoms to those caused by the seasonal flu (common cold) the\n",
    "viruses are different since they do not behave the same way. COVID -19 is associated with Severe\n",
    "Acute Respiratory Tract Syndrome (SARS), rapid transmission among humans that resulted in a\n",
    "global pandemic – about 2, 000, 000.00 infections worldwide with a space of about 4 months (by\n",
    "April 2020). So far the COVID – 19 disease has killed &gt;500, 000.00people globally.\n",
    "\n",
    "Botswana was hit by the virus in 2020 with the first 3 confirmed positive cases reported on 29\n",
    "March. By 1 April there was a 4 th case and two more were reported the next day on April 5. Up to\n",
    "date, 13 cases were reported as at 9 April. Out of the 13 positive cases 1 death was reported, with\n",
    "results for the deceased learned posthumously. In response, the government of Botswana has since\n",
    "3 April declared a 6 month state of emergency (SoE) to foster extreme social distancing in a 28 day\n",
    "period in-line with the World Health Organization response guidelines to curb the pandemic. Given\n",
    "the rate of spread of the virus in first world countries (China, Italy, Spain, USA and UK) and the\n",
    "fatalities from COVID-19, Botswana faces a grim situation of inadequate infrastructure, health\n",
    "system and technical expertise incapacities to handle an epidemic in the country, if it were to occur.\n",
    "\n",
    "Therefore, it is imperative to devise technological monitoring solutions to track the corona virus\n",
    "trends in the country to inform policy directions on the response.\n",
    "Lessons from abroad teach us that trend indicators can be mainly categorized into three; viral\n",
    "spread, recovery and death of the infected. For purposes of this paper, analysis of the corona virus\n",
    "trends in Botswana will focus on the rate of transmission and death for the mere reason that no\n",
    "recoveries were recorded yet. Therefore, the intention of the paper is analyse trends of the corona\n",
    "virus based on the two indicators; compare and contrast the trends with those in the mentioned first\n",
    "world countries and neighbouring South Africa; and highlight emerging patterns for the\n",
    "government’s strategic attention for the national response. Summarily the paper seeks to achieve\n",
    "the following:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Purpose:</h3>\n",
    "    \n",
    "Analyse trends of the corona virus, compare and contrast the trends with those of first\n",
    "world countries and neighbouring South Africa to highlight emerging patterns to strategically inform\n",
    "the decison maker's national response\n",
    "\n",
    "Specific Objectives:\n",
    "1. Analyse the spread of the corona virus in Botswana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    " As it is now, the pandemic is devastating the whole world: The COUNTER BELOW illustrates  the current figures as they stand\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style = 'background-color:Transparent; padding: 30px '><span style='padding:18px; color: #D3D3D3; font-size:18px;margin-left:0px float:left; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16); '> CONFIRMED CASES 3769150</span><span style='padding:18px; color: #D3D3D3; font-size:18px;margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> GLOBAL DEATHS 264111</span><span style='padding:18px; color: #D3D3D3; font-size:18px; margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> RECOVERED CASES 1250579</span></div>"
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    "\n",
    "# Displaying the total stats\n",
    "\n",
    "display(HTML(\"<div style = 'background-color:Transparent; padding: 30px '>\" +\n",
    "             \"<span style='padding:18px; color: #D3D3D3; font-size:18px;margin-left:0px float:left; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16); '> CONFIRMED CASES \"  + str(confirmed_total) +\"</span>\" +\n",
    "             \"<span style='padding:18px; color: #D3D3D3; font-size:18px;margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> GLOBAL DEATHS \" + str(deaths_total) + \"</span>\"+\n",
    "             \"<span style='padding:18px; color: #D3D3D3; font-size:18px; margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> RECOVERED CASES \" + str(recovered_total) + \"</span>\"+\n",
    "             \"</div>\")\n",
    "       )"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "<h3>Methodology</h3>\n",
    "\n",
    "Due to data paucity and the maiden nature of the pandemic in Botswana, data munging shall be used to scrap for available information on the pandemic locally. \n",
    "\n",
    "I will demonstrate first world nation –epicentres and include them for comparative analysis and South Africa as the main trading partner in the region for Botswana.\n",
    "  \n",
    "Further I will inlude a model for prediction of the infection in Botswana over time.\n",
    "  \n",
    "NB: Please refer to the end of the report to see some of the references given to some of these sources.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre Processing \n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping some column names and renaming them\n",
    "\n",
    "confirmed_df = confirmed_df.rename(columns={\"Province/State\":\"State\",\"Country/Region\": \"Country\"})\n",
    "deaths_df = deaths_df.rename(columns={\"Province/State\":\"State\",\"Country/Region\": \"Country\"})\n",
    "cases_df = cases_df.rename(columns={\"Country_Region\": \"Country\"})\n",
    "cases_df[\"Active\"] = cases_df[\"Confirmed\"]-cases_df[\"Recovered\"]-cases_df[\"Deaths\"]\n",
    "\n",
    "bots_confirmed = bots_confirmed.rename(columns = {\"Suspected;Cases\":\"Suspected Cases\", \"Tested;Positive\":\"Tested Positive\", \"Tested;Negative\":\"Tested Negative\", \"Pending\":\"Pending\",\"Testing;per;;%;of;population\":\"% Testing Ratio\",\"Increase;since;last;Last;Death\": \"Days since last death\", \"Increase;since;last;Confirmed\": \"Days since last case\", \"Death\":\"Death\", \"Source\":\"Source\"})\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if the cleaning and renaming of the cloumns was successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "  State      Country      Lat     Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
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    "confirmed_df.head(3)"
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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       "      <td>;;;;;;;;;;;;;;;</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>www.africanews.com/statistics/2020/01/04botswa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Quarantined  Suspected Cases  Tested Positive  Tested Negative  \\\n",
       "0  26/03/2020          0.0              0.0              0.0              0.0   \n",
       "1  27/03/2020          0.0              0.0              0.0              0.0   \n",
       "2  28/03/2020       1753.0            259.0              0.0             90.0   \n",
       "3  29/03/2020          NaN             93.0              3.0             90.0   \n",
       "4  30/03/2020          NaN              NaN              3.0              0.0   \n",
       "\n",
       "   Pending  Deaths  Unnamed: 7  % Testing Ratio  Days since last death  \\\n",
       "0      0.0     0.0         NaN              NaN                    0.0   \n",
       "1      0.0     0.0         NaN              NaN                    0.0   \n",
       "2    169.0     0.0         NaN              NaN                    0.0   \n",
       "3      NaN     0.0         NaN              NaN                    0.0   \n",
       "4      NaN     0.0         NaN              NaN                    0.0   \n",
       "\n",
       "   Days since last case                                             Source  \n",
       "0                   NaN                                    ;;;;;;;;;;;;;;;  \n",
       "1                   NaN                                    ;;;;;;;;;;;;;;;  \n",
       "2                   NaN                                    ;;;;;;;;;;;;;;;  \n",
       "3                   NaN                                    ;;;;;;;;;;;;;;;  \n",
       "4                   NaN  www.africanews.com/statistics/2020/01/04botswa...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bots_confirmed.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Last_Update</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Incident_Rate</th>\n",
       "      <th>People_Tested</th>\n",
       "      <th>People_Hospitalized</th>\n",
       "      <th>Mortality_Rate</th>\n",
       "      <th>UID</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-05-07 10:32:27</td>\n",
       "      <td>-25.0000</td>\n",
       "      <td>133.0000</td>\n",
       "      <td>6896</td>\n",
       "      <td>97</td>\n",
       "      <td>6035</td>\n",
       "      <td>764</td>\n",
       "      <td>27.085944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.406613</td>\n",
       "      <td>36</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-05-07 10:32:27</td>\n",
       "      <td>47.5162</td>\n",
       "      <td>14.5501</td>\n",
       "      <td>15752</td>\n",
       "      <td>609</td>\n",
       "      <td>13698</td>\n",
       "      <td>1445</td>\n",
       "      <td>174.897850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.866176</td>\n",
       "      <td>40</td>\n",
       "      <td>AUT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country          Last_Update      Lat     Long_  Confirmed  Deaths  \\\n",
       "0  Australia  2020-05-07 10:32:27 -25.0000  133.0000       6896      97   \n",
       "1    Austria  2020-05-07 10:32:27  47.5162   14.5501      15752     609   \n",
       "\n",
       "   Recovered  Active  Incident_Rate  People_Tested  People_Hospitalized  \\\n",
       "0       6035     764      27.085944            NaN                  NaN   \n",
       "1      13698    1445     174.897850            NaN                  NaN   \n",
       "\n",
       "   Mortality_Rate  UID ISO3  \n",
       "0        1.406613   36  AUS  \n",
       "1        3.866176   40  AUT  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything looks fine, the columns I changed names on the dataframes have indeed been changed.I will start working with the dataframes. So I will move onto defining some functions I am going to use for the analysis of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions Definitions\n",
    "\n",
    "<hr>\n",
    "\n",
    "I will also assign many variables values also here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#country cases with Mortality Rates\n",
    "country_cases_mort_df = cases_df.copy().drop(['Lat','Long_','Last_Update'],axis =1)\n",
    "country_cases_mort_df = country_cases_df.groupby([\"Country\"]).sum()\n",
    "country_cases_mort_df[\"Mortality Rate (per 100)\"] = np.round(100*country_cases_mort_df[\"Deaths\"]/country_cases_mort_df[\"Confirmed\"],2)\n",
    "\n",
    "#Southern African country cases\n",
    "countries_Southern_Africa = DataFrame(country_cases_df, columns=['Confirmed', 'Deaths', 'Recorvered', 'Active'],index=['Botswana', 'South Africa', 'Zimbabwe', 'Namibia', 'Zambia'])\n",
    "\n",
    "#Southern African country cases with Mortality Rates\n",
    "countries_Southern_Africa_mortality = DataFrame(country_cases_mort_df, columns=['Confirmed', 'Deaths', 'Recorvered', 'Active','Mortality Rate (per 100)'],index=['Botswana', 'South Africa', 'Zimbabwe', 'Namibia', 'Zambia'])\n",
    "\n",
    "\n",
    "#Plotting pie chart for Southern African country cases with Mortality Rates\n",
    "countries_SA_mort = pd.DataFrame({'Mortality Rates': [16.67, 0.98, 27.27 , 0, 2.56]},\n",
    "                  index=['Botswana', 'South Africa', 'Zimbabwe', 'Namibia', 'Zambia'])\n",
    "\n",
    "#World cases with Mortality Rates\n",
    "countries_world_mortality = DataFrame(country_cases_mort_df, columns=['Confirmed', 'Deaths', 'Recorvered', 'Active','Mortality Rate (per 100)'],index=['US', 'China', 'Italy', 'Spain', 'United Kingdom','Botswana'])\n",
    "\n",
    "#Plotting pie chart for World country cases with Mortality Rates\n",
    "countries_world_mort = pd.DataFrame({'Mortality Rates': [3.43, 4.03, 12.67 , 9.98, 11.57, 16.67]},\n",
    "                  index=['US', 'China', 'Italy', 'Spain', 'United Kingdom','Botswana'])\n",
    "\n",
    "\n",
    "#defining dates and converting to datetime from string (will later create function to convert) \n",
    "\n",
    "\n",
    "str_28 = datetime(2020,3,28,0,0) \n",
    "str_29 = datetime(2020,3,29,0,0)\n",
    "str_1 = datetime(2020,4,1,0,0) \n",
    "str_2 = datetime(2020,4,2,0,0) \n",
    "str_4 = datetime(2020,4,4,0,0) \n",
    "str_5 = datetime(2020,4,5,0,0)\n",
    "str_6 = datetime(2020,4,6,0,0) \n",
    "str_9 = datetime(2020,4,9,0,0) \n",
    "\n",
    "str_10 = datetime(2020,4,15,0,0) \n",
    "str_11 = datetime(2020,4,16,0,0)\n",
    "str_12 = datetime(2020,4,17,0,0) \n",
    "str_13 = datetime(2020,4,20,0,0) \n",
    "str_14 = datetime(2020,4,23,0,0) \n",
    "str_15 = datetime(2020,4,28,0,0)\n",
    "str_16 = datetime(2020,4,29,0,0) \n",
    "str_17 = datetime(2020,5,1,0,0)\n",
    "str_18 = datetime(2020,5,7,0,0)\n",
    "\n",
    "datetime_string_format = '%b %d %Y, %H:%M:%S' \n",
    "\n",
    "da_28 = datetime.strftime(str_28,datetime_string_format) \n",
    "da_29 = datetime.strftime(str_29,datetime_string_format) \n",
    "da_1 = datetime.strftime(str_1,datetime_string_format) \n",
    "da_2 = datetime.strftime(str_2,datetime_string_format) \n",
    "da_4 = datetime.strftime(str_4,datetime_string_format) \n",
    "da_5 = datetime.strftime(str_5,datetime_string_format)  \n",
    "da_6 = datetime.strftime(str_6,datetime_string_format) \n",
    "da_9 = datetime.strftime(str_9,datetime_string_format) \n",
    "da_10 = datetime.strftime(str_10,datetime_string_format) \n",
    "da_11 = datetime.strftime(str_11,datetime_string_format) \n",
    "da_12 = datetime.strftime(str_12,datetime_string_format) \n",
    "da_13 = datetime.strftime(str_13,datetime_string_format) \n",
    "da_14 = datetime.strftime(str_14,datetime_string_format) \n",
    "da_15 = datetime.strftime(str_15,datetime_string_format)  \n",
    "da_16 = datetime.strftime(str_16,datetime_string_format) \n",
    "da_17 = datetime.strftime(str_17,datetime_string_format)\n",
    "da_18 = datetime.strftime(str_18,datetime_string_format)\n",
    "#datetime.strftime(str_28,datetime_string_format) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Analysis of Botswana"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Botswana \n",
    "\n",
    "<hr>\n",
    "\n",
    "Latest confirmed, death & recovered cases on Botswana as per datasets\n",
    "\n",
    "- <a href=\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\">time_series_covid19_confirmed_global.csv </a>\n",
    "\n",
    "- <a href=\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\">time_series_covid19_deaths_global.csv </a>\n",
    "\n",
    " I will also work on the dataset I am currently working on.\n",
    " \n",
    "\n",
    "- <a href=\"#\"> Self developed datasets from various news outlets Botswana </a>\n",
    "\n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Confirmed cases in Botswana\n",
    "\n",
    "confirmed_Botswana = cases_df[cases_df['Country'] == \"Botswana\"]\n",
    "confirmed_Botswana = confirmed_Botswana.drop(['Lat', 'Long_', 'Country', 'Last_Update'], axis = 1)\n",
    "Botswana_Summary = pd.DataFrame(confirmed_Botswana.sum()).transpose()\n",
    "#Botswana_Summary.style.format(\"{:,.0f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col0 {\n",
       "            background-color:  #abbfdc;\n",
       "            color:  #000000;\n",
       "        }    #T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col1 {\n",
       "            background-color:  #f6eff7;\n",
       "            color:  #000000;\n",
       "        }    #T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col2 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col3 {\n",
       "            background-color:  #c0c9e2;\n",
       "            color:  #000000;\n",
       "        }    #T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col4 {\n",
       "            background-color:  #023858;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_cf3e8c54_7d69_11ea_a3d6_8c89a599befc\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Confirmed</th>        <th class=\"col_heading level0 col1\" >Deaths</th>        <th class=\"col_heading level0 col2\" >Recovered</th>        <th class=\"col_heading level0 col3\" >Active</th>        <th class=\"col_heading level0 col4\" >Mortality Rate (per 100)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_cf3e8c54_7d69_11ea_a3d6_8c89a599befclevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col0\" class=\"data row0 col0\" >6</td>\n",
       "                        <td id=\"T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col1\" class=\"data row0 col1\" >1</td>\n",
       "                        <td id=\"T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col2\" class=\"data row0 col2\" >0</td>\n",
       "                        <td id=\"T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col3\" class=\"data row0 col3\" >5</td>\n",
       "                        <td id=\"T_cf3e8c54_7d69_11ea_a3d6_8c89a599befcrow0_col4\" class=\"data row0 col4\" >16.670000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ead586c3c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Computation of mortality rate for Botswana\n",
    "\n",
    "confirmed_Botswana = cases_df[cases_df['Country'] == \"Botswana\"]\n",
    "confirmed_Botswana = confirmed_Botswana.drop(['Lat', 'Long_', 'Country', 'Last_Update'], axis = 1)\n",
    "Botswana_Summary = pd.DataFrame(confirmed_Botswana.sum()).transpose()\n",
    "Botswana_Summary[\"Mortality Rate (per 100)\"] = np.round(100*Botswana_Summary[\"Deaths\"]/Botswana_Summary[\"Confirmed\"],2)\n",
    "Botswana_Summary.style.background_gradient(axis=1).format(\"{:.0f}\",subset=[\"Confirmed\"])\n",
    "#Botswana_Summary.style.format(\"{:,.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whilst at it we have to keep an eye on our biggest neighbour South Africa as well as our neighbours. This will will give us an idea of where our region stand and help in decision making by our leaders such as opening of borders. I have decided to include South Africa as everything that happens there affects our small country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### South Africa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Confirmed cases in South Africa\n",
    "\n",
    "confirmed_SouthAfrica = cases_df[cases_df['Country'] == \"South Africa\"]\n",
    "confirmed_SouthAfrica = confirmed_SouthAfrica.drop(['Lat', 'Long_', 'Country', 'Last_Update'], axis = 1)\n",
    "SA_Summary = pd.DataFrame(confirmed_SouthAfrica.sum()).transpose()\n",
    "#SA_Summary.style.format(\"{:,.0f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Incident_Rate</th>\n",
       "      <th>People_Tested</th>\n",
       "      <th>People_Hospitalized</th>\n",
       "      <th>Mortality_Rate</th>\n",
       "      <th>UID</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7808</td>\n",
       "      <td>153</td>\n",
       "      <td>3153</td>\n",
       "      <td>4502</td>\n",
       "      <td>13.165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.95953</td>\n",
       "      <td>710</td>\n",
       "      <td>ZAF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Confirmed Deaths Recovered Active Incident_Rate People_Tested  \\\n",
       "0      7808    153      3153   4502        13.165             0   \n",
       "\n",
       "  People_Hospitalized Mortality_Rate  UID ISO3  \n",
       "0                   0        1.95953  710  ZAF  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Computation of maortality rate for South Africa\n",
    "\n",
    "confirmed_SouthAfrica = cases_df[cases_df['Country'] == \"South Africa\"]\n",
    "confirmed_SouthAfrica = confirmed_SouthAfrica.drop(['Lat', 'Long_', 'Country', 'Last_Update'], axis = 1)\n",
    "SA_Summary = pd.DataFrame(confirmed_SouthAfrica.sum()).transpose()\n",
    "SA_Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As seen above,  the numbers clearly shows Botswana has a higher mortality rate then South Africa. This is so because of the relation between the confirmed cases and death figures. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now for the world the figures are as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Incident_Rate</th>\n",
       "      <th>People_Tested</th>\n",
       "      <th>People_Hospitalized</th>\n",
       "      <th>Mortality_Rate</th>\n",
       "      <th>UID</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3769150.0</td>\n",
       "      <td>264111.0</td>\n",
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       "      <td>2254460.0</td>\n",
       "      <td>16556.192133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>777.086761</td>\n",
       "      <td>97103.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Confirmed    Deaths  Recovered     Active  Incident_Rate  People_Tested  \\\n",
       "0  3769150.0  264111.0  1250579.0  2254460.0   16556.192133            0.0   \n",
       "\n",
       "   People_Hospitalized  Mortality_Rate      UID  \n",
       "0                  0.0      777.086761  97103.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Computation of mortality rate for World\n",
    "\n",
    "global_data = cases_df.copy().drop(['Lat', 'Long_', 'Country', 'Last_Update'], axis = 1)\n",
    "global_summary = pd.DataFrame(global_data.sum()).transpose()\n",
    "global_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "            color:  #000000;\n",
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       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col6 {\n",
       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }    #T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col7 {\n",
       "            background-color:  #fff7fb;\n",
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       "        }    #T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col8 {\n",
       "            background-color:  #fbf4f9;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befc\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Confirmed</th>        <th class=\"col_heading level0 col1\" >Deaths</th>        <th class=\"col_heading level0 col2\" >Recovered</th>        <th class=\"col_heading level0 col3\" >Active</th>        <th class=\"col_heading level0 col4\" >Incident_Rate</th>        <th class=\"col_heading level0 col5\" >People_Tested</th>        <th class=\"col_heading level0 col6\" >People_Hospitalized</th>        <th class=\"col_heading level0 col7\" >Mortality_Rate</th>        <th class=\"col_heading level0 col8\" >UID</th>        <th class=\"col_heading level0 col9\" >Mortality Rate (per 100)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befclevel0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col0\" class=\"data row0 col0\" >3769150</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col1\" class=\"data row0 col1\" >264111.000000</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col2\" class=\"data row0 col2\" >1250579.000000</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col3\" class=\"data row0 col3\" >2254460.000000</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col4\" class=\"data row0 col4\" >16556.192133</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col5\" class=\"data row0 col5\" >0.000000</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col6\" class=\"data row0 col6\" >0.000000</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col7\" class=\"data row0 col7\" >777.086761</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col8\" class=\"data row0 col8\" >97103.000000</td>\n",
       "                        <td id=\"T_1f103f86_9055_11ea_b4a8_8c89a599befcrow0_col9\" class=\"data row0 col9\" >7.010000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29914089e88>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#computation of mortality rate for world\n",
    "\n",
    "global_data = cases_df.copy().drop(['Lat', 'Long_', 'Country', 'Last_Update'], axis = 1)\n",
    "global_summary = pd.DataFrame(global_data.sum()).transpose()\n",
    "global_summary[\"Mortality Rate (per 100)\"] = np.round(100*global_summary[\"Deaths\"]/global_summary[\"Confirmed\"],2)\n",
    "global_summary.style.background_gradient(axis=1).format(\"{:.0f}\",subset=[\"Confirmed\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As seen above clearly the death rate in comparison to South Africa and the rest of the World is very high.. This clearly shows that we are in danger and should be more careful. it gives weight to the request by government message to it citizens to stay home. I will as below do a thorough comparison and analysis of this fact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below I will look at cases for all countries in Africa to get a better picture of severity of disease in; before comparing the moratality rate for interconnected Southern Africa neighbours South Africa , Zimbabwe, Namibia and Zambia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Botswana vs World"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Incident_Rate</th>\n",
       "      <th>People_Tested</th>\n",
       "      <th>People_Hospitalized</th>\n",
       "      <th>Mortality_Rate</th>\n",
       "      <th>UID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>3392</td>\n",
       "      <td>104</td>\n",
       "      <td>458</td>\n",
       "      <td>2830</td>\n",
       "      <td>8.713446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.066038</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>832</td>\n",
       "      <td>31</td>\n",
       "      <td>595</td>\n",
       "      <td>206</td>\n",
       "      <td>28.910974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.725962</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>4997</td>\n",
       "      <td>476</td>\n",
       "      <td>2197</td>\n",
       "      <td>2324</td>\n",
       "      <td>11.395396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.525715</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>751</td>\n",
       "      <td>46</td>\n",
       "      <td>521</td>\n",
       "      <td>184</td>\n",
       "      <td>971.979551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.125166</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>23</td>\n",
       "      <td>0.109535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>24</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>West Bank and Gaza</th>\n",
       "      <td>374</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>198</td>\n",
       "      <td>7.331298</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534759</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Western Sahara</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.004470</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0.083820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>146</td>\n",
       "      <td>4</td>\n",
       "      <td>101</td>\n",
       "      <td>41</td>\n",
       "      <td>0.794171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.739726</td>\n",
       "      <td>894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0.228757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.764706</td>\n",
       "      <td>716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Confirmed  Deaths  Recovered  Active  Incident_Rate  \\\n",
       "Country                                                                   \n",
       "Afghanistan              3392     104        458    2830       8.713446   \n",
       "Albania                   832      31        595     206      28.910974   \n",
       "Algeria                  4997     476       2197    2324      11.395396   \n",
       "Andorra                   751      46        521     184     971.979551   \n",
       "Angola                     36       2         11      23       0.109535   \n",
       "...                       ...     ...        ...     ...            ...   \n",
       "West Bank and Gaza        374       2        174     198       7.331298   \n",
       "Western Sahara              6       0          5       1       1.004470   \n",
       "Yemen                      25       5          1      19       0.083820   \n",
       "Zambia                    146       4        101      41       0.794171   \n",
       "Zimbabwe                   34       4          5      25       0.228757   \n",
       "\n",
       "                    People_Tested  People_Hospitalized  Mortality_Rate  UID  \n",
       "Country                                                                      \n",
       "Afghanistan                   0.0                  0.0        3.066038    4  \n",
       "Albania                       0.0                  0.0        3.725962    8  \n",
       "Algeria                       0.0                  0.0        9.525715   12  \n",
       "Andorra                       0.0                  0.0        6.125166   20  \n",
       "Angola                        0.0                  0.0        5.555556   24  \n",
       "...                           ...                  ...             ...  ...  \n",
       "West Bank and Gaza            0.0                  0.0        0.534759  275  \n",
       "Western Sahara                0.0                  0.0        0.000000  732  \n",
       "Yemen                         0.0                  0.0       20.000000  887  \n",
       "Zambia                        0.0                  0.0        2.739726  894  \n",
       "Zimbabwe                      0.0                  0.0       11.764706  716  \n",
       "\n",
       "[187 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confirmed cases in the world \n",
    "\n",
    "country_cases_df = cases_df.copy().drop(['Lat','Long_','Last_Update'],axis =1)\n",
    "country_cases_df = country_cases_df.groupby([\"Country\"]).sum()\n",
    "country_cases_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Country Cases with Mortality Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>Incident_Rate</th>\n",
       "      <th>People_Tested</th>\n",
       "      <th>People_Hospitalized</th>\n",
       "      <th>Mortality_Rate</th>\n",
       "      <th>UID</th>\n",
       "      <th>ISO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>6896</td>\n",
       "      <td>97</td>\n",
       "      <td>6035</td>\n",
       "      <td>764</td>\n",
       "      <td>27.085944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.406613</td>\n",
       "      <td>36</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>15752</td>\n",
       "      <td>609</td>\n",
       "      <td>13698</td>\n",
       "      <td>1445</td>\n",
       "      <td>174.897850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.866176</td>\n",
       "      <td>40</td>\n",
       "      <td>AUT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>64694</td>\n",
       "      <td>4366</td>\n",
       "      <td>28184</td>\n",
       "      <td>32144</td>\n",
       "      <td>170.896316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.748694</td>\n",
       "      <td>124</td>\n",
       "      <td>CAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>83970</td>\n",
       "      <td>4637</td>\n",
       "      <td>78929</td>\n",
       "      <td>404</td>\n",
       "      <td>5.977890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.522210</td>\n",
       "      <td>156</td>\n",
       "      <td>CHN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>10281</td>\n",
       "      <td>506</td>\n",
       "      <td>7689</td>\n",
       "      <td>2086</td>\n",
       "      <td>177.497232</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.921700</td>\n",
       "      <td>208</td>\n",
       "      <td>DNK</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>374</td>\n",
       "      <td>2</td>\n",
       "      <td>174</td>\n",
       "      <td>198</td>\n",
       "      <td>7.331298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.534759</td>\n",
       "      <td>275</td>\n",
       "      <td>PSE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.004470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>732</td>\n",
       "      <td>ESH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0.083820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>887</td>\n",
       "      <td>YEM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>146</td>\n",
       "      <td>4</td>\n",
       "      <td>101</td>\n",
       "      <td>41</td>\n",
       "      <td>0.794171</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.739726</td>\n",
       "      <td>894</td>\n",
       "      <td>ZMB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>0.228757</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.764706</td>\n",
       "      <td>716</td>\n",
       "      <td>ZWE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Country  Confirmed  Deaths  Recovered  Active  Incident_Rate  \\\n",
       "0             Australia       6896      97       6035     764      27.085944   \n",
       "1               Austria      15752     609      13698    1445     174.897850   \n",
       "2                Canada      64694    4366      28184   32144     170.896316   \n",
       "3                 China      83970    4637      78929     404       5.977890   \n",
       "4               Denmark      10281     506       7689    2086     177.497232   \n",
       "..                  ...        ...     ...        ...     ...            ...   \n",
       "182  West Bank and Gaza        374       2        174     198       7.331298   \n",
       "183      Western Sahara          6       0          5       1       1.004470   \n",
       "184               Yemen         25       5          1      19       0.083820   \n",
       "185              Zambia        146       4        101      41       0.794171   \n",
       "186            Zimbabwe         34       4          5      25       0.228757   \n",
       "\n",
       "     People_Tested  People_Hospitalized  Mortality_Rate  UID ISO3  \n",
       "0              NaN                  NaN        1.406613   36  AUS  \n",
       "1              NaN                  NaN        3.866176   40  AUT  \n",
       "2              NaN                  NaN        6.748694  124  CAN  \n",
       "3              NaN                  NaN        5.522210  156  CHN  \n",
       "4              NaN                  NaN        4.921700  208  DNK  \n",
       "..             ...                  ...             ...  ...  ...  \n",
       "182            NaN                  NaN        0.534759  275  PSE  \n",
       "183            NaN                  NaN        0.000000  732  ESH  \n",
       "184            NaN                  NaN       20.000000  887  YEM  \n",
       "185            NaN                  NaN        2.739726  894  ZMB  \n",
       "186            NaN                  NaN       11.764706  716  ZWE  \n",
       "\n",
       "[187 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_cases_mort_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let us now compare the Southern African countries cases"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recorvered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>7808</td>\n",
       "      <td>153</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Namibia</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>146</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Confirmed  Deaths  Recorvered  Active\n",
       "Botswana             23       1         NaN      14\n",
       "South Africa       7808     153         NaN    4502\n",
       "Zimbabwe             34       4         NaN      25\n",
       "Namibia              16       0         NaN       7\n",
       "Zambia              146       4         NaN      41"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_Southern_Africa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next let us now compare the Southern African countries' mortality rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda\\lib\\site-packages\\matplotlib\\colors.py:527: RuntimeWarning: invalid value encountered in less\n",
      "  xa[xa < 0] = -1\n"
     ]
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       "            background-color:  #fff7fb;\n",
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       "            background-color:  #cacee5;\n",
       "            color:  #000000;\n",
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       "            background-color:  #fff7fb;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_697ad198_9055_11ea_89db_8c89a599befc\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Confirmed</th>        <th class=\"col_heading level0 col1\" >Deaths</th>        <th class=\"col_heading level0 col2\" >Recorvered</th>        <th class=\"col_heading level0 col3\" >Active</th>        <th class=\"col_heading level0 col4\" >Mortality Rate (per 100)</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_697ad198_9055_11ea_89db_8c89a599befclevel0_row0\" class=\"row_heading level0 row0\" >Botswana</th>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow0_col0\" class=\"data row0 col0\" >23</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow0_col1\" class=\"data row0 col1\" >1</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow0_col2\" class=\"data row0 col2\" >nan</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow0_col3\" class=\"data row0 col3\" >14</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow0_col4\" class=\"data row0 col4\" >4.350000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_697ad198_9055_11ea_89db_8c89a599befclevel0_row1\" class=\"row_heading level0 row1\" >South Africa</th>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow1_col0\" class=\"data row1 col0\" >7808</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow1_col1\" class=\"data row1 col1\" >153</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow1_col3\" class=\"data row1 col3\" >4502</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow1_col4\" class=\"data row1 col4\" >1.960000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_697ad198_9055_11ea_89db_8c89a599befclevel0_row2\" class=\"row_heading level0 row2\" >Zimbabwe</th>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow2_col0\" class=\"data row2 col0\" >34</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow2_col1\" class=\"data row2 col1\" >4</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow2_col3\" class=\"data row2 col3\" >25</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow2_col4\" class=\"data row2 col4\" >11.760000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_697ad198_9055_11ea_89db_8c89a599befclevel0_row3\" class=\"row_heading level0 row3\" >Namibia</th>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow3_col0\" class=\"data row3 col0\" >16</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow3_col1\" class=\"data row3 col1\" >0</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow3_col2\" class=\"data row3 col2\" >nan</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow3_col3\" class=\"data row3 col3\" >7</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_697ad198_9055_11ea_89db_8c89a599befclevel0_row4\" class=\"row_heading level0 row4\" >Zambia</th>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow4_col0\" class=\"data row4 col0\" >146</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow4_col1\" class=\"data row4 col1\" >4</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow4_col3\" class=\"data row4 col3\" >41</td>\n",
       "                        <td id=\"T_697ad198_9055_11ea_89db_8c89a599befcrow4_col4\" class=\"data row4 col4\" >2.740000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x29916f60548>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_Southern_Africa_mortality.style.background_gradient(axis=1).format(\"{:.0f}\",subset=[\"Confirmed\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I have just proven visually what i mentioned earlier: the Mortality rate is higher in Botswana. \n",
    "\n",
    "NB: Please note the mortality rate is basically the death rate in simple terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = countries_SA_mort.plot.pie(y='Mortality Rates', figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notwithstanding that if you take a look at the figures in terms of confirmed cases, South Africa would lead, in Southern Africa. This shows that more people in our neighbouring country are getting the virus compared to us(Botswana); but because of population size in relation; their mortality rate is lower. Though in actual fact more people would have died that side.\n",
    "\n",
    "The following vertical bar graphs clearly illustrate this fact."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirmed Cases Comparison between Botswana & it neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Confirmed Cases Comparison between Botswana & it neighbours\n",
    "confirmed_cases = plt.figure(figsize=(10,5))\n",
    "confirmed_cases.add_subplot(111)\n",
    "\n",
    "plt.axes(axisbelow=True)\n",
    "plt.barh(countries_Southern_Africa.sort_values('Confirmed')[\"Confirmed\"].index[-10:],countries_Southern_Africa.sort_values('Confirmed')[\"Confirmed\"].values[-10:],color=\"darkcyan\")\n",
    "plt.tick_params(size=5,labelsize = 13)\n",
    "plt.xlabel(\"Confirmed Covid-19 Cases\",fontsize=18)\n",
    "plt.title(\"Southern Africa Countries (Covid-19 Cases)\",fontsize=20)\n",
    "\n",
    "plt.grid(alpha=0.3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Death Cases Comparison between Botswana & it neighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5E9OnT1eZG2Zvbw8Aavvm7++vVNfS1iMrKwsAlJ6/0khOTsapU6fQqlUrpeFq4PmUCTMzM/z111+4f//+G+33RZ07d1Y6K92wYUM0btwYycnJaocd1enUqVOJ6/Py8hAVFQW5XI4+ffoorevYsSMsLCywd+9ecUiysLAQ6enpSnWtX78+9u7di40bN5bykZW94rNw2dnZAJ7/vmloaGDChAlKF+p88skn6NOnD1JSUsRRAXNzc8yYMQO+vr5K+6xSpQrMzMxQUFCAx48fl6offn5+Sr+Xrq6u0NDQULrFVlFREZ4+fao01cXIyAjbt29/7ftT8S2S3vT1Cjz/nZg6dSq6deumtLxBgwaoX7++0oWHxe+rCQkJ4jJdXV2sWbMGcXFx4vGL39devjCoevXq+Oabb5Cbm4u9e/cCgDiV5uUzyXPnzkVcXBw+++yzN35M9O7wKm/64Lm4uMDFxQU5OTk4ffo0YmNjcejQIdy6dQurVq1CUVERxowZg+zsbNy5cwfW1tbi7UVeZGNjg7Vr16qdPyVVo0aNlH7W0dGBoaGh0rygS5cuQU9PT+39Am/cuAHg+fxCFxcXcXn9+vVfeczGjRsr/VylShUAzwPC6zx+/BiHDx9GnTp1xOFC4PkfjI4dOyI8PBxRUVEq81NfpaR+Fiuuu7phOkdHRzg6Ooo/f/HFFwCe1+XatWu4c+cOrl69KgaAl+d6AarPQWnrYWxsDAClDgzFiu89+uIQ84usra1x+fJlXLlyRe1V86Xx8mMCIL628/PzoaOjU+L2BgYGqFGjRoltrl+/jqdPn6KwsBBLlixRWZ+fn4+CggJcu3YNFhYW6NOnD9auXYuuXbuiZcuWaNeuHVxcXGBubl76B1YOin/XisPcpUuXoK+vjz/++EOlbXEY/vfff9GuXTuYmprC1NQUCoUCZ8+exc2bN3Hr1i1cunRJnELy4j+uJfn000+VftbT04OBgQFyc3PFZd7e3pg2bRp8fHzw+eefw8nJCc7OzrC2toamZsnngd729Qr8784L2dnZuHLlCm7evImbN2/iwoULuHPnjtLFaH369MGRI0cwevRoLFmyBE5OTnBycoKDg4PS6+7SpUsAgL1796pMKUpJSQHwv98VT09PhIaGYv78+QgJCRH32bZt2xKnm9D7gYGSPhqGhoZwdnaGs7Mzxo4di+3bt2PSpEnYtGkTAgMDxT8oxUHiZbVq1QLw/KbEZU1PT++1bZ48eYKCggIsXbr0lW1e/iNR0n6Lz8gUK/5jILx0QY06MTExyMvLw/379/H555+rbRMWFlbqQPnymUR1is8Eqgv7L4uLi8Ovv/4q/iHS09NDs2bNYGZmhvv376t9jK+qx+sUnwG8ffv2a9vevHkTDRo0gLa2tngm7HWvt+ILOt7Gy48JeLPnuTSvy+Ln5dq1ayW+NjMzMwE8n5f42WefITQ0FOfOncPZs2exZMkSfPbZZ5g6dap4Flmd7du34969e0rLzMzM0L59+9f2syTp6enIzc1FjRo1xED55MkTCIJQqt+3oqIiBAcHY/369WI9atSoAWtra9SrVw83btwoVb2BV9f8xe19fHxQs2ZNbNq0CQkJCbhy5QpWrVqFunXrYty4cejcufMr91+3bl1oa2vj7t27KCwsLPE2Sffu3YORkZF4NvHZs2eYP38+tm3bJr4P1qlTB3Z2dqhWrRoyMjLEbV1dXbF+/XqsXbsWJ06cwMaNG7Fx40YYGxtj+PDh4j1Pi+u1YsWKV/ajuM7NmzfHtm3bsHLlShw7dgxbt27F1q1bUalSJfj5+WH48OGl/r2ld4+Bkj5I2dnZ8PLyQuPGjdVOUtfQ0ECvXr2wd+9ecVjRxMQEwPN7sKlT/MZX/B9+SW9cUkLAq1SqVAmGhobi7ZAqUvHV3V999ZXaMHjo0CFcu3YNZ86cgZWVVZkcs/gPvbqrOYuHb3V0dHDnzh189913MDAwwIwZM2BtbY1GjRpBS0sLUVFROHToUJn0p5i9vT309fVx+vRpKBSKVwYChUIh3p/y8OHD4h/p1NRUte2L/4i+/Hp7OZiUxz84b6L4efHy8sLs2bNf215TUxO9evUSL9Y5ceIE9u/fjz///BODBw/G4cOHxcf8svDwcJXhzp49e0oOlMXDsi++Vg0MDFC9evVSfaLW6tWrERQUhNatW+Pbb7/F559/jpo1awJ4PpWieAShLHXq1AmdOnXC48ePER8fj4MHD2L37t0YNWoUmjZt+sobzxsaGsLa2honT57E2bNnlUYYXjZx4kTEx8dj1apVaNOmDWbNmoWtW7eiS5cu4icQFT9XHTt2VAqUAODg4AAHBwfk5ubi1KlTOHLkCCIjIzFt2jQ0atQIbdu2haGhIXR0dHDu3LlS3QPUzMwMixcvRl5eHv755x8cPXoUO3bswPLly1G3bl3eM/M9xjmU9EGqXLkynjx5ghMnTrz2JsKampowMTFB5cqV0aBBA9y4cUPtHLtTp04BAJo0aQIA4rDNywFHEIQSP1Lwbcnlcty/f1/ldjIAcPjwYSxcuLBchuNfduvWLZw5cwaNGzfG3LlzMW3aNJWv4rl0YWFhZXZcmUwGACq3bwKAXbt2wcLCArt27cL+/fuhUCgwcuRI9OrVC6ampuIfqqSkJAClOztXWsV3C8jJycGGDRte2S48PBzZ2dlo2bIlqlSpIt5d4MU5Zi86ffo0NDQ0xGBQ/Hp7cegTKN2Z0ZJIPaNjamoKHR0dcejyZevWrcPy5cvx+PFjpKenIygoSPyHpGbNmujWrRuWLFmC7t27Izc3t8SPId2yZQsSExOVvmbOnCmp/wDEaSQeHh7iMrlcjpSUFLXvBQcPHsTChQuRmJgI4PnrT0dHB8uWLYOjo6MYJgVBwPXr18Xvi0mpuUKhQHBwsPhaq1q1Kjp27Ig5c+bgu+++Q2FhIc6cOVPiPjw9PQE8v+L+VRITExEbG4tKlSqJd12Ijo6GiYkJFi1ahFatWolhMjc3F/fu3VN6jOvXr0dQUBCA5/90FN/zsvj2YcVTAWQyGfLz89W+dyUkJOC3334T24aHh2PGjBkAnp99d3BwwNixY7Fo0SKlfdL7iYGSPlg+Pj7Iy8vD8OHD1Z51PHjwIE6cOAF3d3dxGNXT0xPPnj3DrFmzlG7me+nSJWzatAlGRkbibT6KJ4AfP35caU5eSEiIOLz3Ih0dHUk3CPb09IQgCJg+fbrSvL4HDx5gypQpWLVqldJk/vISGRkJQPmPr7q+amhoYM+ePeLQrlStWrVCrVq1EBERofTH59mzZ1i/fj00NTXRqlUr8Qzhy/9IXL58WbynZVnfqHnkyJEwNDREUFCQ2hu7HzlyBL/++it0dHTEj7Jr2LAhbG1tce7cOZXgvWXLFpw7dw5t27YVh76LX28vnqEuLCwscaiwNLS0tKChoVHqi3ReZmBggE6dOiExMVElUMfGxmLu3LmIjIyEkZERKleujA0bNmDRokXiGX/gedgqni/3rj7+sPi4wcHBiI2NRfPmzdGxY0dxnZeXF4qKijBt2jSl37fU1FRMmTIFq1evFt83dHV1xY9zfNGSJUvEi6pefM1pa2u/db319PQQGRmJxYsXq9xW6u7duwBePye5e/fuaNGiBY4fP46pU6eqzBO+fv06hg0bhsLCQvE2R8XHVigUSp99XlhYKL4nFRUVie+Fx44dQ3BwMM6fP6+2j8XPc/EFabNmzVJ6r8jOzhbrXOzs2bP4448/VC48enmf9H7ikDd9sAICAnD16lXs27cPHTt2hKOjIxo1aoSCggKcO3cO//zzDz777DNMmTJF3GbQoEH466+/EB0djcTERDg4OCAtLQ0HDhyAIAhYuHCh+EfEzMwMzZs3x5kzZ9C3b1/Y2dmJF35YWFjg3LlzSv2pVasWbt68iR9//BGOjo7o3r37Gz0eLy8vHDp0CPv27UNiYiLatWuHgoIC7NmzB5mZmRg9ejQ++eQTyXUriSAI4tXdL1/p+aIGDRrA3t4e8fHxiImJKZNhKB0dHcyaNQsBAQHw9vaGu7s7qlWrhsOHD+P27duYNGkSTExM4ObmhgULFmD58uW4du0aGjZsiJs3b+Lw4cMwMjJCdna22sAvRe3atbF27VoMHjwYEyZMwLp162BnZwdtbW1cunQJCQkJ0NXVxZw5c5QuPpk+fTp8fHwwceJE7N27F02bNsWVK1cQGxuL2rVrY+rUqWJbDw8PLFmyBCtXrsTNmzdRv359/PXXX8jJyXnri3Ze7P+1a9cwdepUODs7K13YVRrjx4/H2bNnMWvWLOzfvx8tWrTA/fv3sX//fujq6mLWrFnQ0NCArq4uhg0bhtmzZ+PLL7+Eu7s79PT0cPLkSVy8eBFeXl4qF6WUlXXr1ql8lnd8fDxu3ryJhg0bIigoSOmClp49e+LQoUPYs2cPrly5AkdHR+Tn52Pv3r3IzMzETz/9JAa3bt264eLFi+K9YbW1tREfH4/Lly+jRo0aSEtLQ0ZGhjjftnbt2khOTsZPP/2Etm3b4quvvnqjx/Ljjz9i2LBh8PLyQqdOnWBkZIQLFy4gPj4erVu3hoODQ4nba2pqYsWKFRg0aBBCQkKwb98+ODs7o1q1arh58yaOHTuG/Px8+Pr6Kl253q1bN2zYsAE9evSAm5sbCgoKcPz4cdy6dUucQ/n48WNUr14dw4cPx6lTp9C/f3907twZJiYmuHbtGo4cOYKmTZuKd4do27Yt+vbti5CQEHTt2hXOzs7Q0dHB/v37cf/+ffTr10+8d+ugQYOwb98+jBw5Eh06dMAnn3yC5ORk7N+/H7Vq1SrVZ9FTxWGgpA+WlpYWgoKCsH//fkRFReH8+fM4duwYdHR08Omnn2L06NHw9fVVmgOop6eH9evXY82aNYiOjsaWLVtgZGQEV1dXDB48GGZmZkrHWLlyJebPn4/Dhw8jMTER5ubm2LBhA/bs2aMSKMeMGYMJEyZg7969SEtLe+NAqaGhgaCgIGzevBkREREICwuDvr4+mjRpAn9//3fyeeQJCQlITk6GlZWV0u1o1PHy8kJ8fDzCwsLKbF5Tu3btEBISguXLl+PIkSN4+vQpmjZtinnz5okBt27duli3bh0WLlyIEydOoLCwEPXq1YOfnx8GDRqEDh064NixY2XSnxdZWlpi9+7dCA0NFYNIdnY2atWqhV69emHgwIEqtzX57LPPEBERgWXLluHo0aOIj49HrVq14Ovri4CAAFSvXl1sW7t2bWzcuBELFizA0aNHoauri3bt2uGnn37CN998I6nvv/zyC2bNmoWwsDAUFha+caCsWbMmtm/fjhUrVuDAgQM4e/YsqlevDjc3NwwdOlTpwi0/Pz/xgpLdu3fj6dOnaNy4MX7++edyDQQvfqKVpqYmDA0N8dlnn2HkyJHo16+fysVempqaWLp0KTZt2oQdO3Zg27ZtMDAwQNOmTTFw4EClG5L7+vpCQ0MDoaGhCAsLg5GRERo1aiR+QMLw4cNx7Ngx8XZXP/30E37++WfExMQgPT39jQOlu7s7Vq9ejd9//x2HDh1CVlYW6tevj2HDhuHbb78t1ZB6zZo1ERoaiqioKERFReHEiRNIS0tDlSpV0K5dO/Tv3x9t2rRR2ubHH39ElSpVEB0djZCQEFSvXh1NmjTB5MmTceXKFcyZMwdHjx6Fp6cnLC0tsWnTJvEMcHp6OmrVqoUBAwZg6NChSldlT548GS1btkRoaCh27twJbW1tNG7cGD/88IPS++Qnn3yCLVu2YPny5Thz5gwOHjyIatWqoXv37ggMDBTnwdP7SUMoy8lGRERERPT/DudQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJLwKu9yUlRUhJycHOjo6PCjooiIiOi9JggC8vPzYWho+NrPjFeHgbKc5OTk4OrVqxXdDSIiIqJSk8lkqFKlyhtvx0BZToo/Rk0mk0FXV7eCe/P+yMrKgpGRUUV3473DuqhiTVSxJuqxLqpYE/VYF1XFNcnLy8PVq1fF/PKmGCjLSfEwt66urvhRcfQ8aLMeqlgXVayJKtZEPdZFFWuiHuui6uWavO00PV6UQ0RERESSMFASERERkSQMlEREREQkCQMlEREREV5ADU4AACAASURBVEnCQElEREREkjBQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJIwUNI7ZWxsXNFdeC+xLqpYE1WsiXqsiyrWRL0PsS5FzxQV3YVS4UcvlrNbNr2hlZpe0d0gIiKiD5Dpw+MV3YVS4RlKIiIiIpKEgZKIiIiIJGGgJCIiIiJJGCiJiIiISBIGSiIiIiKShIGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgZKIiIiIJKnQQPn06VM8evSoIrtARERERBK9NlDeuHEDAQEBsLOzg5WVFbp164awsLAyObiPjw8uXLgAAIiIiICXl9cbbZ+dnQ0rKyt89913Kuvi4uLQrl072NraIiEhQWX9l19+iWPHjr1dx4mIiIhIpF3SyqKiInz77bfw8vLCwoULoauri9OnTyMwMBBGRkbo1KmTpINnZmZK2j4qKgpOTk74+++/cfv2bXzyySfiul27dqF169aYO3eu2m13794t6dhERERE9FyJZygzMjKQnJyMbt26QV9fH5qamrC3t8eYMWOQn58PACgoKMCiRYvg5OSEVq1aYfjw4UhNTQWgetYxJycHcrkcycnJ+P7775GSkoIRI0Zg48aNAACFQoFJkyahdevWcHJyws6dO0vsfFhYGDw8PNClSxds3rxZXP7zzz9jx44diImJQbdu3RAfH48uXbpg0KBBsLe3R3x8PNzc3HD48GEAwNWrV9GvXz9YWVmhffv2iIqKAgA8e/YMU6ZMgbu7OywtLdGxY0ccOHDgTWtMRERE9FErMVDWqFED9vb28Pf3R1BQEOLi4pCbm4tevXqha9euAICgoCAcPHgQISEhOHLkCIyMjDBixAgIglDigZctW4Z69eph8eLF8PX1BQBcu3YNzZs3x4kTJxAQEIDJkycjLy9P7fbnz5/HgwcP4OzsjD59+iAiIgK5ubkAgJkzZ8LDwwP9+/cXw+H169fRuXNnHD16FDY2NuJ+8vLyMHjwYDg4OCA+Ph4LFy7E5MmTkZSUhDVr1iApKQkRERFISEiAl5cXpk+fXsrSEhEREUmXmZlZbl85OTnIzMxEVlaWpD6WOOQNAL///ju2bNmCP//8E6tWrQIAdOzYEZMmTUK1atWwc+dOTJgwAQ0aNAAATJgwAXZ2drh+/fobd6Zhw4bw9vYGAHTp0gVTpkxBeno66tSpo9I2LCwMnp6e0NHRgbm5OT799FNERUWJ279MQ0MDHh4e0NXVVVr+zz//IDc3F0OHDoWmpiZatmyJkJAQ1K5dGz4+Pujbty8qVaqEe/fuwdDQUDz7SkRERPQuGBsbl/v+FQqFpH28NlDq6enBz88Pfn5+UCgUSEhIwLx58zBhwgQEBwcjLS0N9erVE9tXqlQJxsbGbxW8jIyMxO91dHQAPB9Sf1lOTg527doFbW1t7NixQ1y2efPmVwbKqlWrqoRJAEhLS0OtWrWgqfm/k7XNmjUDANy5cwdTp07F+fPn0bBhQzRs2PC1Z16JiIiI/r8pccg7JiYGbm5uYojS09NDmzZtEBAQgCtXrgAA6tWrh7t374rb5OTkICMjAzVq1ICmpqY41xKQfhFOsV27dqFx48aIiYlBZGQkIiMjER0djdu3byM+Pv6N9lW7dm08ePAARUVF4rLNmzfj3LlzmDx5Mho3bowTJ04gPDwcPj4+ZdJ/IiIioo9JiYGydevWyM3NxcyZM5GWlgZBEHDr1i2EhobC1dUVANC9e3csW7YMd+/exdOnTzF79mw0adIEMpkMjRs3xo0bN3Du3DkoFAqsWrUKGhoa4v51dHSQnZ39xp3eunUrunXrBhMTE/GrYcOG6NChAzZt2vRG+2rZsiWqVq2K1atXo6CgAOfPn8eiRYtQuXJlZGdnQ19fH1paWrh37x4WL14MAEohmYiIiOj/uxIDZbVq1RASEoIHDx6ga9eusLS0hL+/P1q0aIFx48YBAAYNGgQ3Nzf07dsXjo6OSE9PF4OjhYUF+vfvj4CAALi5uaFRo0aoWrWquH9PT09MmjQJy5cvL3WHL1++jH///RddunRRWde9e3ccPHgQ9+7dK/X+dHV1ERwcjBMnTsDBwQFjxozBzJkzYWpqivHjx+PIkSOwtrZGv3794OzsjEqVKiEpKanU+yciIiL62GkInBRYLhQKBS5evIgqA6ZBKzW9ortDREREHyDTh8fLdf+ZmZniRTkXL16Eubk59PT03ng//CxvIiIiIpKEgZKIiIiIJGGgJCIiIiJJGCiJiIiISBIGSiIiIiKShIGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgZKIiIiIJNGu6A587D5N2AY9Pb2K7gYRERF9gIqeKaCp//7nCJ6hpHcqMzOzorvwXmJdVLEmqlgT9VgXVayJeh9iXT6EMAkwUBIRERGRRAyURERERCQJAyURERERScJASURERESSMFASERERkSQMlEREREQkCQMlEREREUnCQElEREREkjBQvmeKnikqugtEREREb4QfvVjObtn0hlZqeqnbmz48Xo69ISIiIip7PENJRERERJIwUBIRERGRJAyURERERCQJAyURERERScJASURERESSMFASERERkSQMlEREREQkCQMlEREREUnCQElEREREkjBQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJIwUBIRERGRJAyURERERCTJexUoT58+DSsrK6UvS0tLyOVy7Nixo8yOI5fLcfXqVbXrvv32W2zdurXMjkVERET0sdOu6A68yNbWFmfOnFFaNmPGDBw5cgSurq7vpA+///77OzkOERER0cfivTpD+bJt27YhPDwcy5cvh7GxMXbv3g0vLy/Y2dnB3t4ev/zyCwRBAAC4ublhw4YN6NixIywtLfHLL7/g6NGjcHd3h42NDWbNmqW07127dsHNzQ1OTk4ICgpCYWEhAKB///7YtGkTAODy5cvw8/ODo6MjLCwsMHDgQDx69OjdFoGIiIjoPffeBsrz589jxowZ+PXXXyGTyZCcnIyJEydiypQpOHXqFEJCQrBr1y7ExcWJ2+zduxdhYWEIDw/H9u3b8fvvvyM8PBybN2/G5s2b8d9//4ltz5w5g4iICGzevBk7d+5EeHi4Sh9GjBiB9u3b4/jx4zhy5AiePHkihk0iIiIieu69GvIulpaWhmHDhsHf3x+dOnUCANSqVQvR0dFo0KABMjIykJmZiapVqyI1NVXcrnfv3qhatSqqVq0KExMT9OzZE0ZGRjAyMoKJiQlSUlLQtGlTAMCoUaNgbGwMY2Nj9O/fH7t370bv3r2V+rFmzRo0aNAAT58+RWpqKqpVq6Z0vPKSmZlZ7seoKDk5ORXdhfcS66KKNVHFmqjHuqhiTdRjXVQV1yQ/P1/Sft67QFlQUIAffvgBzZo1w4gRI8Tl2traCAsLw/bt21GpUiWYmZkhPz8fRUVFYpuqVauK32tpacHIyEj8WVNTU6ltvXr1xO/r1KmDhw8fqvTl/PnzGDRoEHJyciCXy/H48WNUr169zB7rqxgbG5f7MSrSx/743hbrooo1UcWaqMe6qGJN1GNdVBkbG0OhUEjax3sXKOfMmYOHDx8iODgYmpr/G5HfvXs3YmJiEBkZCRMTEwBA+/btlbbV0NAo9XEePXqE2rVrAwBSUlKUAiYA3L9/H2PHjkVISAgsLCwAAOPHjxfnbBIRERHRc+/VHMro6GhERkZi+fLlqFy5stK67OxsaGtrQ1dXF3l5eVi9ejWSk5NRUFDwVsdavHgxsrKycP36dWzcuBE9evRQWl98ClhfXx+CIODo0aPYu3ev5FPCRERERB+b9+oM5bZt25Cbm6sS7gDAw8MDTZs2haurK/T19WFnZwd3d3ckJSW91bHMzc3RsWNH6Onpwc/PD126dFFab2pqioCAAAwYMACFhYUwNTWFt7e30kVARERERARoCBzDLRcKhQIXL15ElQHToJWaXurtTB8eL8deVbzMzEzOX1GDdVHFmqhiTdRjXVSxJuqxLqqKa1KcW8zNzaGnp/fG+3mvhryJiIiI6MPDQElEREREkjBQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJIwUBIRERGRJAyURERERCQJAyURERERScJASURERESSMFASERERkSQMlEREREQkCQMlEREREUnCQElEREREkjBQEhEREZEk2hXdgY/dpwnboKenV+r2Rc8U0NQvfXsiIiKiisYzlO8ZhkkiIiL60DBQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJIwUBIRERGRJAyURERERCQJAyURERERScJASURERESSMFC+Z4qeKSq6C0RERERvhB+9WM5u2fSGVmp6qdubPjxejr0hIiIiKns8Q0lEREREkjBQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJIwUBIRERGRJAyURERERCQJAyURERERScJASURERESSMFASERERkSQMlEREREQkCQMlEREREUnCQElEREREkjBQEhEREZEkDJREREREJEmZBcrTp0/DyspK6cvS0hJyuRw7duyAlZUVcnNz32ifERER8PLyeqv+uLm54fDhw2+1LRERERGVnnZZ7cjW1hZnzpxRWjZjxgwcOXIErq6u8PT0LKtDEREREdF7pNyGvLdt24bw8HAsX74c2dnZkMvlyMnJQXx8PDw9PTF37lzY2dnByckJhw4dwsyZM2Fraws3NzfExsaK+8nLy8P48eNhb28PT09PnDx5Uly3e/dueHl5wc7ODvb29vjll18gCIK4PjY2Fp07d0abNm0wdepUPHv2DAcOHECnTp3ENgsWLICjo6P488qVKzF27FgAwKlTp9CjRw/Y2tqiV69eOH/+fHmVi4iIiOiDVWZnKF90/vx5zJgxA/PmzYNMJkNycrLS+suXL6Nr166Ij4/H4sWLERgYiJ9++gmxsbEICgrCb7/9hvDwcADAf//9h969e2PatGnYtWsXhgwZgoMHDyInJwcTJ07Ehg0b0LJlS1y7dg29e/dGly5d0Lp1awDAiRMnsG7dOujq6uK7775DcHAwBg8ejJSUFKSkpKBevXqIi4vDkydPkJSUBFNTUxw7dgz9+/dHSkoKBg8ejLlz58LFxQX79+/HoEGDsG/fPhgbG5dH2USZmZnluv+KlJOTU9FdeC+xLqpYE1WsiXqsiyrWRD3WRVVxTfLz8yXtp8wDZVpaGoYNGwZ/f3+lM4Ev0tHRwYABA6CpqQkHBwesWbMGvr6+0NTURJs2bbB9+3axbaNGjeDr6wsA8PT0xPr163H06FF88cUXiI6ORoMGDZCRkYHMzExUrVoVqamp4rZDhgxB3bp1AQABAQH49ddfMXLkSNjb2+PEiRPo3Lkz7t69i/bt2+PkyZMwMTHBpUuX4OjoiJCQELRq1QodOnQAAHTp0gUhISHYt28f+vTpU9ZlU1LegbWifeyP722xLqpYE1WsiXqsiyrWRD3WRZWxsTEUCoWkfZRpoCwoKMAPP/yAZs2aYcSIEa9sZ2hoCG3t54fW1NSEoaEhNDU1xZ+LiorEtvXr11fatk6dOnj48CG0tbURFhaG7du3o1KlSjAzM0N+fr7StvXq1RO/r127Nh4+fAgAcHV1RWxsLKpXrw5ra2vY29sjPj4exsbGsLKyQuXKlZGSkoLjx4/D1tZW6fHZ2NhIqBARERHRx6dMA+WcOXPw8OFDBAcHiwFRquIQWCwlJQX169fH7t27ERMTg8jISJiYmAAA2rdvr9T20aNHStsVB0xnZ2cEBwejZs2asLe3h4ODA5YsWQJ9fX24uLgAAExMTPDFF19g7ty54j7u3LmDatWqlcnjIiIiIvpYlNlFOdHR0YiMjMTy5ctRuXLlstotrl69irCwMOTl5SE0NBTp6elwcnJCdnY2tLW1oauri7y8PKxevRrJyckoKCgQt12xYgUePHiA1NRUBAcHo0ePHgCAhg0bwtjYGJGRkWjVqhUaNWoEHR0d7NmzB25ubgCAL7/8EocPH0ZsbCwEQUBCQgK6deuGCxculNljIyIiIvoYlNkZym3btiE3N1cMbS+ytrZ+6/3a2Njg8OHDmDVrFpo2bYrVq1ejcuXK8PT0RGxsLFxdXaGvrw87Ozu4u7sjKSlJ3LZ169bo2bMnCgsL4eXlBT8/P3Gds7MzIiIi0LRpUwBAq1atcOHCBTRs2BDA87mbixYtwrx583Dz5k1Ur14d48ePFy/4ISIiIqLnNIQX77NDZUahUODixYuoMmAatFLTS72d6cPj5diripeZmckJ0WqwLqpYE1WsiXqsiyrWRD3WRVVxTYpzi7m5OfT09N54P/zoRSIiIiKShIGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgZKIiIiIJGGgJCIiIiJJGCiJiIiISBIGSiIiIiKShIGSiIiIiCTRrugOfOw+TdgGPT29UrcveqaApn7p2xMRERFVNJ6hfM8wTBIREdGHhoGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgfIdKXqmqOguEBEREZULBspydsumN5JM2vETcIiIiOijxUBJRERERJIwUBIRERGRJAyURERERCQJAyURERERScJASURERESSMFASERERkSQMlEREREQkCQMlEREREUnCQElEREREkjBQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJIwUBIRERGRJO9toMzMzER2dnZFd4OIiIiIXqPcAqVcLoeFhQWsrKxgaWkJFxcXrFixotTbd+rUCSkpKeXVPSIiIiIqI9rlufOwsDDIZDIAQGJiIgYMGABTU1O4u7u/dtvMzMzy7BoRERERlZF3NuQtl8thZWWFy5cvi8s2bNiA9u3bw87ODgMHDsT169cBAF5eXgCAXr164cCBA0hJSYGvry9sbW3RoUMHzJ07F4Ig4Pvvv8fKlSvF/bVr1w7z588Xf+7UqRPi4uKQkZGB0aNHw83NDRYWFvDw8EBCQgIAICIiAt988w3GjBkDa2trdOjQAZGRkeI+du/eDS8vL9jZ2cHe3h6//PILBEEo11oRERERfUjeWaD8999/ceHCBTg5OQEAtm7dijVr1mDZsmX4+++/YW1tjUGDBuHZs2eIiIgA8PwMZ4cOHbBw4ULIZDKcPHkSmzZtwu7duxEbGwsXFxf8/fffAICkpCRkZWUhPj4eAHDnzh2kp6fD1tYW8+bNAwDExMTg1KlTsLGxUQqef/31F9q2bYuTJ0+if//+mD59OhQKBZKTkzFx4kRMmTIFp06dQkhICHbt2oW4uLh3VTYiIiKi9165Dnl7e3tDU1MT+fn5ePbsGdq1awe5XA4A2LlzJ/z8/PD5558DAIYOHYqtW7fi5MmTYugsVqVKFZw6dQr79u1D27ZtcfjwYWhqauLBgweYPn06nj17hri4OHz11VfYuXMncnJycPToUTg6OkJbWxsjR46EgYEBtLS0cPfuXRgZGSE1NVXcf7169dC9e3cAQPfu3TFr1iykpaWhVq1aiI6ORoMGDZCRkYHMzExUrVpVads3wWF8ICcnp6K78F5iXVSxJqpYE/VYF1WsiXqsi6rimuTn50vaT7kGytDQUHEO5cOHDzFhwgSMGjUKK1asQFpaGurVqye21dTURN26ddWGtTFjxiAoKAgLFizA6NGj4eTkhBkzZqBWrVpo0qQJTp8+jbi4OHh6eiIxMREJCQk4duwYunbtCgB48OABZs6ciaSkJDRu3BjGxsZKw9bVqlUTv9fWfl6SoqIiaGtrIywsDNu3b0elSpVgZmaG/Px8FBUVvVU9jI2N32q7jw3roB7rooo1UcWaqMe6qGJN1GNdVBkbG0OhUEjaxzsb8jYxMUHfvn0RGxsL4PlZwbt374rri4qKkJKSgho1aqhsm5iYiEGDBmH//v3Yu3cvcnJyEBQUBADisHdCQgLs7Ozg4OCA48eP4/Tp0+KZzlGjRqFDhw6Ii4tDaGgoOnfuXKo+7969GzExMYiMjMT+/fuxePFi6OnpSS0FERER0UflnQXKrKwshIeHw8rKCsDzoeUNGzYgMTEReXl5WL58OQDAwcEBAKCjoyPehzI4OBi//fYbFAoFatSoAS0tLfGsoouLCyIiIlCnTh1UqVIFDg4OCAsLQ7NmzcT/QrKzs2FgYAANDQ0kJSVh9erVpTq1m52dDW1tbejq6iIvLw+rV69GcnIyCgoKyrw+RERERB+qch3y7tWrFzQ1n2dWXV1dODg4YO7cuQCAr776ChkZGfj++++RlpaGFi1aYN26dahUqRKA51d6+/v7Y8qUKZgyZQomTZoER0dHAICrqysGDx4MAGjRogW0tbXRqlUrAIC1tTUKCwvh4uIi9mPatGmYPXs25s2bh9q1a6NHjx5YtGgRMjIySuy/p6cnYmNj4erqCn19fdjZ2cHd3R1JSUllWiciIiKiD5mGwHvglAuFQoGLFy+iyoBp0EpNh+nD4xXdpfdCZmYm56+owbqoYk1UsSbqsS6qWBP1WBdVxTUpzi3m5uZvNb3vvf3oRSIiIiL6MDBQEhEREZEkDJREREREJAkDJRERERFJwkBJRERERJIwUBIRERGRJAyURERERCQJAyURERERScJASURERESSMFASERERkSQMlEREREQkCQMlEREREUnCQElEREREkjBQEhEREZEk2hXdgY/dpwnboKenh6JnCmjq61V0d4iIiIjKHM9QviMMk0RERPSxYqAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgZKIiIiIJGGgJCIiIiJJGCiJiIiISBIGSiIiIiKShIGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgZKIiIiIJGGgJCIiIiJJGCiJiIiISBIGSiIiIiKShIGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIkgoNlHK5HJMnT1ZZ7ubmhsOHD5fpsX755RcsXLgQANC/f39s2rRJbbsVK1ZgzJgxZXpsIiIioo+ZdkV3YOvWrXBzc4Ozs3O5HmfatGmlajdkyJBy7QcRERHRx6bCh7x79eqFn3/+GZmZmWrX37lzB0OGDIGzszNatmwJb29vJCUlAQCWLFmCCRMmYPDgwbCyskL37t1x7tw5fPvtt7CyskKvXr1w7949AMC4ceMwZ84ccb9XrlyBp6cnWrVqhdGjR4vHX7JkCYYPHw4AyMjIwOjRo+Hm5gYLCwt4eHggISGhPMtBRERE9MGp8DOU/fr1w507dzB58mQsXrxYZf3EiRPRvHlzLF26FHl5eRg9ejRWrFiBefPmAQCioqKwfv16LF26FAMHDsSAAQOwdu1amJmZYdCgQdi4cSPGjh2rst8jR45g7dq1qFevHkaPHo3p06dj/vz5Sm2KjxETEwNNTU3MmjUL8+fPR0hISKkfX1ZWFnR0dN6kJB+1nJyciu7Ce4l1UcWaqGJN1GNdVLEm6rEuqoprkp+fL2k/FR4oNTQ0MHv2bHh4eCA6OhoeHh5K63/99VdUq1YNhYWFSElJgbGxMe7evSuut7Kygq2tLQDAxsYGWlpasLa2BgDY2tri+vXrao/bv39/yGQyAMAPP/yAXr16Ye7cuUptRo4cCQMDA2hpaeHu3bswMjJCamrqGz0+IyMj6OnpvdE2HztjY+OK7sJ7iXVRxZqoYk3UY11UsSbqsS6qjI2NoVAoJO2jwgMlANStWxcTJ07EtGnTYGdnp7Tu+vXrmDdvHlJTU9GkSRNoaGhAEARx/YsvDC0tLRgZGYk/a2pqKrV9Ub169cTva9eujfz8fJVh9wcPHmDmzJlISkpC48aNYWxs/Mr9EREREf1/VeFzKIt1794dDg4OGD9+vBja8vLyEBgYiCFDhiA2NhZ//PEH7O3tlbbT0NB4q+M9evRI/D4lJQX6+vqoXr26UptRo0ahQ4cOiIuLQ2hoKDp37vxWxyIiIiL6mL03gRIApk6diqtXryIlJQXA8/F8hUIBAwMDAMDZs2cRGhoqeZwfAP744w/cuHEDWVlZWLhwIby8vFTCaXZ2NgwMDKChoYGkpCSsXr26TI5NRERE9DF5rwJl9erVMX36dPFnQ0NDTJ06FRMnToSNjQ2mTp0Kb29v3Lp1CwUFBZKO5erqiu+++w7u7u6oW7eu2ntPTps2DWvWrIG1tTUCAwPh6emJjIwMZGRkSDo2ERER0cdEQ+CkwHKhUChw8eJFmJub86KcF2RmZnJCtBqsiyrWRBVroh7rooo1UY91UVVcE6m55b06Q0lEREREHx4GSiIiIiKShIGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgZKIiIiIJGGgJCIiIiJJGCiJiIiISBIGSiIiIiKShIGSiIiIiCRhoCQiIiIiSRgoiYiIiEgSBkoiIiIikoSBkoiIiIgkYaAkIiIiIkkYKImIiIhIEgZKIiIiIpKEgZKIiIiIJGGgJCIiIiJJGCiJiIiISBLtiu7Ax0oQBABAXl5eBffk/ZKfnw+F4v/au/e4mvL9f+Cv0pUmnJTbNBR2hqKtq1DSdYbqiC6oXM6Qy6F41NAZZtCMmojEUWoMk3upZJJLpUJG5BbKMCLJJemm6263P98//FrH1mVQ6vew38/HE5rCygAAGqtJREFUYz8e9md99lrv9fZp926tz1qrvqvD+P8O5aU5yklzlJOWUV6ao5y0jPLSXFNOmuqVpvrlfVFB+ZE0NDQAAO7evdvFkRBCCCGEvJuGhgYoKCi89+ek2IeWoqRNIpEI1dXVkJWVhZSUVFeHQwghhBDSKsYYGhoa0KNHD0hLv/+MSCooCSGEEEJIu9BFOYQQQgghpF2ooCSEEEIIIe1CBSUhhBBCCGkXKigJIYQQQki7UEFJCCGEEELahQpKQgghhBDSLlRQEkIIIYSQdqGC8iPIzc3F9OnToaurCwcHB1y/fr2rQ+p02dnZcHJygp6eHiwtLXHo0CEAQEVFBZYsWQI9PT1MnDgRMTExXRxp1ygpKcHYsWORlpYGAHj8+DFmz54NPp8PGxsbrl0SPHv2DJ6enhgzZgxMTU0RFRUFgMbK1atX4ejoiDFjxsDGxga///47AMnNS05ODsaPH8+9bysPAoEA//nPf2BoaAgTExOEhYV1Rcgf3ds5efbsGRYvXgwjIyOMGzcO/v7+Yo/TCw4OhrGxMQwMDPDjjz+isbGxq0L/qN7OSxORSAR3d3f8/PPPXJukjhWBQAB/f38YGRnByMgI3333XfvHCiMdqq6ujk2YMIHt37+fCQQCFhMTw8aNG8fq6+u7OrROU15ezgwMDFhCQgJrbGxkt27dYgYGBiwzM5MtXbqU+fj4sLq6Onbjxg1maGjI8vLyujrkTrdgwQI2fPhwdubMGcYYY46OjmzTpk1MIBCw9PR0xufz2cuXL7s4yo9PJBKxqVOnssDAQCYQCNjdu3eZgYEBu3LlikSPFaFQyIyNjdmJEycYY4xdvnyZjRgxghUWFkpcXkQiEYuJiWF6enrM0NCQa28rD4GBgWz27NmssrKSPXjwgJmbm7PU1NSu2oUO11pO3Nzc2Lp161hdXR0rLi5mTk5ObPPmzYwxxvbu3cumTJnCnj9/zoqLi9nUqVNZVFRUV+3CR9FaXppERkay4cOHs8DAQK5NUsdKQEAAc3d3Z2VlZaysrIw5OzuzsLAwxtiHjxU6QtnBLl68CGlpacycOROysrKYPn06evfuLVFHnJ48eQIzMzPY29tDWloaI0eOhJGREa5evYqUlBQsW7YM8vLyGDVqFKZMmSIxR1iaHDx4EIqKiujfvz8A4P79+7h79y6WLFkCWVlZmJmZwdDQEEePHu3iSD++GzduoLi4GD4+PpCVlcWwYcNw6NAh9O3bV6LHSmVlJUpLS9HY2AjGGKSkpCArK4tu3bpJXF7Cw8MRFRWFhQsXcm3V1dVt5uHYsWPw9PTEZ599hsGDB8PNzQ3R0dFdtQsdrqWcCAQCKCoqYtGiRZCXl4eqqirs7Oxw7do1AEBCQgJmz54NNTU1qKqqwtPT85PKCdByXprcuXMHcXFxsLKyEmuXxLHS0NCAw4cP4/vvv0evXr3Qq1cvhIaGws7ODsCHjxUqKDvYgwcPMGTIELE2DQ0N3Lt3r4si6nxffvklNm7cyL2vqKhAdnY2AEBGRgbq6urcMknLzcOHD7F7926sXbuWa8vPz8fAgQOhoKDAtUlKXm7fvo1hw4Zh48aNGDduHGxsbHDjxg1UVFRI9Fjp3bs3Zs6ciRUrVmDkyJGYNWsW1qxZg7KyMonLy7Rp05CQkAAdHR2uraCgoNU8VFRUoKSkBEOHDm227FPRUk7k5OQQEREBVVVVri0tLQ3Dhw8H8Pp75u2c/PXXX2Cf0NOXW8oL8LrYXrlyJdavX4/u3btz7ZI6VgoKCtDY2IgbN27A2toaEyZMwJ49e6Cmpgbgw8cKFZQdrKamBoqKimJtCgoKqKur66KIutarV6+wcOFC7ijlm0UTIFm5EQqF8PX1xXfffYdevXpx7ZI8ZioqKpCVlcUdxQ8ICIC/vz9qamokeqyIRCIoKChg69atuH79OsLDw7FhwwZUVVVJXF7U1NQgJSUl1tbW+KitrQUAsZ+pTy1HLeXkTYwx/Pjjj8jPz4enpycAoLa2VixnioqKEIlE3Ly5T0FreQkODsb48eOhr68v1i6pY6W8vBwNDQ1IS0vDkSNHEB0djczMTERGRgL48LFCBWUHU1RUbDYY6+rqxP4qkhSFhYVwdXVFz549sX37dnTv3l2ic7Njxw58+eWXMDMzE2uX5DEjJyeHnj17wtPTE3JyctwFKKGhoRKbEwA4ffo0cnJyYGtrCzk5OUycOBETJ07Etm3bJDovTdr6mWn6RfjmcknKUV1dHby8vHDu3Dns3bsXKioqAF4XSvX19Vy/2tpayMjIQF5evqtC7RR//PEHLl68CC8vr2bLJHWsyMnJQSQSwdvbG8rKyujfvz/mzp2LlJQUAB8+Vqig7GCampp48OCBWNuDBw/EDh9Lgtu3b8PZ2Rnjx4/Hjh07oKCggEGDBkEoFOLJkydcP0nKTVJSEo4fPw59fX3o6+vjyZMnWLFiBR48eICioiKxv/4kJS8aGhqora2FUCjk2hobGzFixAiJHitPnz5tdjRARkYGI0eOlOi8NGnru6RXr15QUVER+x5uaSrSp6i8vBxubm4oLy/H4cOHxaYEDBkypFlONDU1uyLMTpWUlIRHjx7BxMQE+vr6SExMxL59++Dp6SmxY2Xw4MGQlpZGZWUl1/bmVdwfOlaooOxgY8eOhUAgwN69e9HQ0IAjR46gpKSkxVsYfKpKSkrwzTffYO7cufDz84O09OthpqSkBAsLCwQHB6O2thY5OTlITEzkJgJ/6k6ePIkrV64gOzsb2dnZGDBgADZv3gxPT08MHToUISEhEAgEyMjIQFZWFmxtbbs65I9u3LhxUFZWRnBwMIRCIa5evYrk5GTY2tpK9FgxMTFBXl4eYmNjwRjDpUuXkJycjMmTJ0t0Xpr83XeJvb09tm3bhvLycjx8+BD79u2Dg4NDF0f9cTHGsHTpUvTp0we7du0Sm1YDvM7Jrl278OzZM5SUlGDnzp2ffE4AwN/fH9euXeO+d6dMmQI3Nzfs3LkTgGSOFWVlZVhaWmLLli2orKzE8+fP8dtvv3G/cz54rHyEq9QlXl5eHnNxcWG6urrMwcGBXbt2ratD6lRhYWGMx+MxXV1dsdfmzZtZWVkZW7ZsGTMwMGBmZmYsJiamq8PtMubm5txtgx4/fszmzZvHxowZw6ytrbl2SfDw4UM2b948ZmBgwMzNzdmRI0cYY0zix0pqaiqzt7dnfD6fTZ48mZ0+fZoxJrl5uXjxothtT9rKQ21tLVuzZg0zNjZmY8eO5W6H8ql5MydXrlxhPB6P6ejoiH3vzpw5kzH2+lZUmzdvZuPGjWOGhobM39+fCYXCrgz/o3l7rLxp5cqVYrcNksSxwhhjr169YqtXr+bGw4YNG1hDQwNj7MPHihRjn9AlXoQQQgghpNPRKW9CCCGEENIuVFASQgghhJB2oYKSEEIIIYS0CxWUhBBCCCGkXaigJIQQQggh7UIFJSGkw6xatQpaWlpiL21tbZiZmcHX17fTnpH78uVL1NTUcO/d3d0xadKkdq1TIBAgOjoarq6uMDIygo6ODuzs7BAREfHJPL4uIyMDCxcuhKmpKbS1tTFp0iSsX78eL168+GjbjIuLg5aWFrKysjqk39sqKipgYmKCuLi4ZstEIhEiIiJgYWGBUaNGwd7eHjExMe+1fkLIazJdHQAh5NPj5+eH3r17A3j92K6CggLExcXh1KlTiIyMhJGR0UfbdkZGBnx8fBAfH99hj1B78eIFlixZgpycHFhbW8PW1hbdunVDZmYmgoODcf78efzyyy+Qk5PrkO11toaGBqxduxZHjhyBrq4uZs2ahZ49eyI3NxfR0dFITk7GwYMH8fnnn3f4tg0MDBAUFPRRnk4iEAjg7e2Nly9ftrj8hx9+QHR0NAwNDTF37lzk5uZi9erVePjwIXx9fTs8HkI+ZVRQEkI6nKWlZbPiw8PDA9OmTYO3tzdSUlLQo0ePj7LtnJwcsUeKtRdjDCtWrMCdO3fw66+/wsTEhFvm7u6OyMhIbNq0CcHBwfDz8+uw7XamsLAwHDlyBN7e3li0aJHYMnt7e8yZMwdLly5FfHx8h29bXV1d7BGBHeX58+fw8vLCtWvXWlx+69YtREdHw9TUFOHh4ejWrRsAoG/fvggPD4ednR2GDx/e4XER8qmiU96EkE7Rv39/rFy5EqWlpYiNje3qcN5ZcnIyLl26hAULFogVk03mz58PTU1NHD16FHV1dV0QYfuUlJQgIiICRkZGzYpJANDX14ejoyPy8vJw/fr1Lojw/Z0/fx62trb4888/4e7u3mKftLQ0AMDixYu5YhIA/vWvf0EkEiEhIaFTYiXkU0EFJSGk09ja2kJOTg7nzp0Ta7927Rrmzp0LPp8PPp+PefPmIScnR6wPYwwHDx7E9OnTwefzoaOjA1tbW0RERKDpgV+rVq3C9u3bAQAWFhbNionz58/D0dEROjo6mDhxInbs2AGRSNRmzMePHwcAODs7t9onMjIS6enpUFBQ4NpOnjwJNzc36OnpcfMRg4KCxOZbCgQC/PTTT7CwsODmmq5btw4VFRVi63/27Bm+/fZbGBsbQ0dHB//85z9x7NixZvnZvn07bGxsoKOjAxMTE/j6+uLp06dt7t+pU6fQ0NAAFxeXVvt4e3sjMzMTurq6XFtZWRnWrl2LCRMmQFtbGzY2NoiIiEBjYyMA4MaNG9DS0sLu3bubrW/VqlXg8/mora1tcW7ky5cv4efnB2NjY+jp6eH7779/r3mq9+/fh5GRERISEmBlZdVin+fPnwMAeDyeWLuSkhJUVFSQm5v7ztsjhNApb0JIJ5KXl8cXX3yBO3fucG2ZmZnw9PTE8OHD4eXlBYFAgLi4OMyaNQu7d++Gvr4+ACAkJATh4eGYOnUqnJ2dUV1djaNHjyI4OBiqqqqYOnUqXFxcUFVVheTkZPj5+WHYsGHcdl68eIGlS5dixowZcHJywu+//46tW7eie/fumDNnTqsx3759GwMHDoSamlqrfd4+vR8TE4PVq1dj0qRJ8PHxQUNDA5KTk7Fr1y50794d//73vwEA69evR2JiIjw8PKCuro579+5h//79KCgowK+//grgdeHj5OQExhjc3d3Rs2dPpKamwtfXF8XFxfjmm28AAOHh4fjvf/+LWbNmQUtLC48fP0ZUVBRu3bqFxMREsaNwb+8fAIwePbrV/fvHP/4h9r6iogKurq4oKiqCq6srNDQ0uPmkubm5CAkJwejRozFo0CCcOHECc+fO5T4rEAiQkpICS0tLKCoqNttWfX093Nzc8PjxY3h4eEBVVRXx8fFISkpqNb63zZgxA7NnzwaAVgvqpm1XV1eLTb8QiUSorKxESUnJO2+PEAKg4x9BTgiRVCtXrmQ8Ho8VFha22sfV1ZVpa2szxhhrbGxkFhYWzNXVlQmFQq5PdXU1s7KyYg4ODowxxgQCARszZgxbvny52LpevXrFtLW1maenJ9cWGhraLAY3NzfG4/HY6dOnxT47ZswYNnPmzDb3afTo0czZ2fkd9v5/bG1tmYuLCxOJRFxbQ0MDMzU1ZVOmTOHaRo0axdatWyf22S1btjBHR0dWVVXFGHudU0NDQ/b8+XOxfitWrGDa2tqspKSEMcbYV199xRYsWCDW5+DBg8ze3p4VFBS0Guv8+fMZj8dj9fX177x/GzduZDwejyUnJ4u1r127lvF4PJaens4YY2zr1q2Mx+OxoqIirk9KSgrj8XgsIyODMcZYbGws4/F47OLFi4wxxvbu3dts3dXV1ezrr78W6/euLl68yHg8HouNjRVrT0xMZDwej0VFRYm1nzlzhvF4PGZhYfFe2yFE0tEpb0JIpxIKhZCSkgIA5ObmorCwEJaWlqioqEBpaSlKS0tRV1cHc3Nz5OXl4dmzZ5CVlcWFCxewfv16sXWVlZVBSUlJ7BZBrVFUVBS7dZCSkhI0NTX/9kiUtLQ0dxr3XR07dgwRERHcfgKvT+MqKyuLxdqvXz8kJSUhLi6Ou5DI29sbsbGx6NGjB0QiEVJSUqCvrw8ZGRkuP6WlpbC2toZAIEBmZia3rqysLPz222/cPrm6uiIhIQFffPFFm/sH4L328cyZMxgyZAgsLS3F2hcvXgwASE1NBQDY2dkBeH36v0lSUhJUVFRanI8KAGfPnkWfPn3E1t29e3c4OTm9c3zvwsrKChoaGggODsahQ4dQWFiI5ORkrFmzBsrKyq0e0SWEtIxOeRNCOlV5eTl3CvXRo0cAgKCgIAQFBbXY/+nTp+jXrx9kZWWRnp6O1NRUPHjwAAUFBdxcQ/b/5lC2pVevXs2KBAUFhVZvKdNEVVUVpaWlf7v+N8nKyuLy5ctITExEfn4+Hj16xG1n4MCBXL+1a9fC29sbfn5+WLNmDXR1dWFlZYVp06bhs88+Q1lZGV69eoWUlBSkpKS0uK2mU7rffvstFi1ahA0bNiAgIAAjR47EpEmT4OzsDFVV1Tb3D3hd8L7rbYEeP36MCRMmtLguZWVlFBUVAQA0NDQwcuRInDx5EvPmzUNdXR3OnDmDadOmQUam5V8/RUVFLV71raGhIfa+tLS0WRHc1n6+TU5ODpGRkfDy8sIPP/wA4HXhunz5ciQlJb3TmCKE/A8VlISQTlNVVYXCwkJMnDgRALgLYry8vMQu+HiTpqYmGGPw9fVFYmIi9PT0wOfz4eLiAgMDA26u3N9pOhL3vvh8PuLj41FcXNzqPMr9+/fjwoULWLFiBYYMGYLg4GBERERgxIgR0NXVhYODA/h8Pvz9/cXm9I0dOxZpaWncKzMzEwEBAdizZw/i4uK4gsnGxgaurq4tbrup+Bo+fDhOnTqFc+fOIS0tDefOnUNoaCj27NmDQ4cOtXqfRz6fj+joaNy4caPVgvLmzZsICgrC7NmzYWlp2WaxJRKJICsry723t7dHQEAAioqKcPPmTdTU1GDKlCmtfl5KSgr19fXN2t/e5vTp07nCtcmff/7Z6npboq6ujri4ONy9exfV1dUYNmwYlJSUEBkZCUNDw/daFyGSjgpKQkinOXnyJBhjsLCwAPC/o3Xdu3dvdgo0JycHFRUVUFBQQHZ2NhITE7F48WJ4eXlxfYRCIcrLyz/KfQybWFlZIT4+HrGxsS3eVkckEiEmJgZ//fUX1q1bh6KiIkRERMDBwaHZUdc3T68LBALk5eWhX79+mDx5MiZPngyRSITdu3cjKCgIx48fx4wZM6CoqAihUNgsP0+ePEFubi4UFRXR2NiIO3fuQElJCRYWFlx+k5KSsHz5csTExGDVqlUt7p+ZmRnk5ORw5MgRTJ48ucU+CQkJuHTpEjw8PAC8/n/Lz89v1u/FixeoqqpC//79ubavv/4aP//8M1JTU3HlyhWoq6u3+scD8PoCp+zsbAiFQrGjmIWFhWL9Nm7c2GLh+a6ePn2K8+fPw9zcXOxK70ePHqG4uBh6enofvG5CJBHNoSSEdIri4mKEhoaib9++3Nw6bW1tqKqqYu/evaiurub6VlVVcaeCu3XrhvLycgDA0KFDxdYZHR2N2tpaCIVCrq3pSGRHnbK0sLCArq4uIiIiWnzs37Zt25CXlwdnZ2f06dOHOw3/dqwZGRl4+PAhF2t5eTlcXFywc+dOsdh1dHS4f8vIyMDU1BQZGRliV8YDQGBgIJYsWYKysjI0NjbCw8MDGzZsEOvTdOV2W0dnVVRUMHv2bFy4cAG7du1qtvz8+fM4cOAAtLS0uELV3Nwc+fn5zU7DR0REAAB3BBoA1NTUYGxsjOTkZJw9e5b7v2+NtbU1Xr16JfYIxIaGBkRHR4v109PTg4mJidjrfVRXV2P16tVi90RljGHLli1QVlaGvb39e62PEElHRygJIR0uJSWFe/RifX098vPzcfToUdTX1yMyMpK7X6OsrCzWrFkDb29vODo6Yvr06ZCXl0dMTAyePHmCTZs2QUZGBnw+H0pKSggICMCTJ0+grKyMrKwsJCUlQV5eXqwYbZqf+csvv8DU1JQrgtpj8+bNmDNnDubOnQsbGxvo6+ujtrYW6enpuHz5MvT19eHj4wPgdSE5YMAAhIeHo76+Hv369UNOTg7i4+PFYlVTU4OdnR0OHDiA2tpa8Pl8lJeXY9++fejTpw+++uorAICPjw+ysrIwa9YszJo1CwMGDEB6ejrS0tLg4uLC3RrJ3d0dYWFhWLJkCSZMmIC6ujocPnwYioqKmDZtWpv7t3TpUty7dw9BQUFISUmBlZUV5OXlce3aNRw/fhwqKioICQnhClNPT0+cPn0a3t7emDFjBgYPHoyLFy/i9OnTsLa2hpmZmdj67ezsuKcItXW6GwAcHBwQHR0Nf39/3L9/H4MHD8axY8c6/HniQ4cOhbm5ObZv347KykoMGjQIKSkpOHv2LAIDA6GkpNSh2yPkUyfFaOYxIaSDrFq1qtnj+Xr06IH+/ftj9OjRmD9/frOLKwDgjz/+QFhYGG7evAlpaWkMGzYMnp6eMDc35/pcuXIFmzZtwp07dyAnJwcNDQ14eHggJycHUVFR3NXBlZWV8PLyQnZ2Nj7//HOcOHEC7u7uKCoqwpkzZ8S221p7S8rLy3HgwAGcOnUKRUVFEAgE0NTUhIODA9zc3MTmDd67dw+BgYHIyckBYwxffPEFpk+fDqFQiJ9++gmxsbHQ1tZGXV0dIiIicPz4cTx9+hSKiooYO3Ysli9fjkGDBnHrKygoQGhoKDIzM1FTUwN1dXU4OTnB3d2du9BIJBIhKioKsbGxePz4Mbp164YxY8Zg2bJl0NbW/tv9EwqFOHbsGGJjY/Hw4UNUVFSgb9++sLCwwMKFC5vdi7KkpAQhISFIS0tDZWUl1NXVMW3aNMyZM6fZxU9VVVUwMTHB0KFDERcXJ7YsLi4Ofn5+iIqK4p7xXlVVhS1btuDkyZOoqamBqakpbGxssHz5crF+7yIrKwseHh4ICAiAo6Njs7hCQkKQnJyMyspKaGlpNRt3hJB3QwUlIYQQQghpF5pDSQghhBBC2oUKSkIIIYQQ0i5UUBJCCCGEkHahgpIQQgghhLQLFZSEEEIIIaRdqKAkhBBCCCHtQgUlIYQQQghpFyooCSGEEEJIu1BBSQghhBBC2oUKSkIIIYQQ0i7/B442rrTPGAz2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting Death Cases Comparison between Botswana & it neighbours\n",
    "confirmed_deaths = plt.figure(figsize=(10,5))\n",
    "confirmed_deaths.add_subplot(111)\n",
    "\n",
    "plt.axes(axisbelow=True)\n",
    "plt.barh(countries_Southern_Africa.sort_values('Deaths')[\"Deaths\"].index[-10:],countries_Southern_Africa.sort_values('Deaths')[\"Deaths\"].values[-10:],color=\"crimson\")\n",
    "plt.tick_params(size=5,labelsize = 13)\n",
    "plt.xlabel(\"Death Cases Covid-19\",fontsize=18)\n",
    "plt.title(\"Southern African Countries - Deaths Cases\",fontsize=20)\n",
    "plt.grid(alpha=0.3,which='both')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As clearly seen above; with the death cases it seems Botswana is on par with the other three Southern African Countries and South Africa is far in a bigger crisis then us Botswana. Yes South Africa are in a crisis but when the figures are taken properly in relation to the confirmed cases in each country as shown on pie chart and Table above Botswana scores first in mortality rates.\n",
    "\n",
    "That simply means the rate at which people die in relation to confirmed cases is higher in Botswana. This rate if one take it and comapre it to the rest of the world is still relatively high.\n",
    "\n",
    "For illustration purposes of this fact I will plot our mortality rate amongst some of the hardest hit nations in the world.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                        <th id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befclevel0_row1\" class=\"row_heading level0 row1\" >China</th>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow1_col0\" class=\"data row1 col0\" >82867</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow1_col1\" class=\"data row1 col1\" >3339</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow1_col3\" class=\"data row1 col3\" >1889</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow1_col4\" class=\"data row1 col4\" >4.030000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befclevel0_row2\" class=\"row_heading level0 row2\" >Italy</th>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow2_col0\" class=\"data row2 col0\" >139422</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow2_col1\" class=\"data row2 col1\" >17669</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow2_col2\" class=\"data row2 col2\" >nan</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow2_col3\" class=\"data row2 col3\" >95262</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow2_col4\" class=\"data row2 col4\" >12.670000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befclevel0_row3\" class=\"row_heading level0 row3\" >Spain</th>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow3_col0\" class=\"data row3 col0\" >148220</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow3_col1\" class=\"data row3 col1\" >14792</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow3_col2\" class=\"data row3 col2\" >nan</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow3_col3\" class=\"data row3 col3\" >85407</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow3_col4\" class=\"data row3 col4\" >9.980000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befclevel0_row4\" class=\"row_heading level0 row4\" >United Kingdom</th>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow4_col0\" class=\"data row4 col0\" >61474</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow4_col1\" class=\"data row4 col1\" >7111</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow4_col2\" class=\"data row4 col2\" >nan</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow4_col3\" class=\"data row4 col3\" >54018</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow4_col4\" class=\"data row4 col4\" >11.570000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befclevel0_row5\" class=\"row_heading level0 row5\" >Botswana</th>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow5_col1\" class=\"data row5 col1\" >1</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow5_col2\" class=\"data row5 col2\" >nan</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow5_col3\" class=\"data row5 col3\" >5</td>\n",
       "                        <td id=\"T_2444e8b0_7d6a_11ea_98f2_8c89a599befcrow5_col4\" class=\"data row5 col4\" >16.670000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ead5a3a448>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_world_mortality .style.background_gradient(axis=1).format(\"{:.0f}\",subset=[\"Confirmed\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above further illustrates my point that we in Botswana have to be careful on how we deal with this epidemic and put in strenous measures in place to mitigate. This is emphasized by fact that already we have a high mortality rates compared to some of the hardest hit countries in the world."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An illustration of the above as a pie chart.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = countries_world_mort.plot.pie(y='Mortality Rates', figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This clearly is evident that should the disease/virus follow the same trends as the most developed nations  we could reach catastrophic troubles here as we are no match to them in terms of facilities such as health specialists, equipment etc. So it is clear for me; Botswana has no option but to stay home.. It also indicates that the country leadership could be spot on on the 6 months monitoring and managing this situation. \n",
    "\n",
    "To further get the facts to support such an idea I will start by looking at the Covid-19 trends in some of these hard hit countries in relation to Botswana and South Africa:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working on local data\n",
    "\n",
    "This data as mentioned above is developend by munging from various sources, and mostly where possible from Government Facebook Page"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will start by loading the datasets into dataframes and testing below to check if everything is ok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Quarantined</th>\n",
       "      <th>Suspected;Cases</th>\n",
       "      <th>Tested;Positive</th>\n",
       "      <th>Tested;Negative</th>\n",
       "      <th>Pending</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Testing;per;;%;of;population</th>\n",
       "      <th>Increase;since;last;Last;Death</th>\n",
       "      <th>Increase;since;last;Confirmed</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26/03/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28/03/2020</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>www.africanews.com/statistics/2020/01/04botswa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BwGovernment;;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01/04/20</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>02/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>DumaFm;FB;page;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>03/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>04/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BwGovernment;;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>05/04/20</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BwGovernment;;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>06/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1154.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DumaFm;page;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>07/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>08/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>09/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2527.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4434.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4414.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5328.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5306.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6016.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>26/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>27/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>28/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6548.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6525.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6809.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6876.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>30/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>31/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>01/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7675.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7652.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>02/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>03/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>04/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>05/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>06/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>07/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9066.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9043.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Quarantined  Suspected;Cases  Tested;Positive  \\\n",
       "0   26/03/2020          0.0              0.0              0.0   \n",
       "1   27/03/2020          0.0              0.0              0.0   \n",
       "2   28/03/2020       1753.0            259.0              0.0   \n",
       "3   29/03/2020          NaN             93.0              3.0   \n",
       "4   30/03/2020          NaN              NaN              3.0   \n",
       "5   31/03/2020          NaN              NaN              NaN   \n",
       "6     01/04/20       1856.0            593.0              4.0   \n",
       "7     02/04/20          NaN            670.0              4.0   \n",
       "8     03/04/20          NaN              NaN              NaN   \n",
       "9     04/04/20          NaN            820.0              4.0   \n",
       "10    05/04/20       2065.0            966.0              6.0   \n",
       "11    06/04/20          NaN           1154.0              6.0   \n",
       "12    07/04/20          NaN              NaN              NaN   \n",
       "13    08/04/20          NaN              NaN              NaN   \n",
       "14    09/04/20          NaN           2527.0             13.0   \n",
       "15    10/04/20          NaN              NaN             13.0   \n",
       "16    11/04/20          NaN              NaN              NaN   \n",
       "17    12/04/20          NaN              NaN              NaN   \n",
       "18  13/04/2020          NaN              NaN              NaN   \n",
       "19  14/04/2020          NaN              NaN              NaN   \n",
       "20  15/04/2020          NaN           3602.0             15.0   \n",
       "21  16/04/2020          NaN           3923.0             15.0   \n",
       "22  17/04/2020          NaN           4434.0             15.0   \n",
       "23  18/04/2020          NaN              NaN              NaN   \n",
       "24  19/04/2020          NaN              NaN              NaN   \n",
       "25  20/04/2020          NaN           5328.0             20.0   \n",
       "26  21/04/2020          NaN              NaN              NaN   \n",
       "27  22/04/2020          NaN              NaN              NaN   \n",
       "28  23/04/2020          NaN           6016.0             22.0   \n",
       "29  24/04/2020          NaN              NaN              NaN   \n",
       "30  25/04/2020          NaN              NaN              NaN   \n",
       "31  26/04/2020          NaN              NaN              NaN   \n",
       "32  27/04/2020          NaN              NaN              NaN   \n",
       "33  28/04/2020          NaN           6548.0             23.0   \n",
       "34  29/04/2020          NaN           6809.0             23.0   \n",
       "35  30/04/2020          NaN              NaN              NaN   \n",
       "36  31/04/2020          NaN              NaN              NaN   \n",
       "37    01/05/20          NaN           7675.0             23.0   \n",
       "38    02/05/20          NaN              NaN              NaN   \n",
       "39    03/05/20          NaN              NaN              NaN   \n",
       "40    04/05/20          NaN              NaN              NaN   \n",
       "41    05/05/20          NaN              NaN              NaN   \n",
       "42    06/05/20          NaN              NaN              NaN   \n",
       "43    07/05/20          NaN           9066.0             23.0   \n",
       "\n",
       "    Tested;Negative  Pending  Deaths  Unnamed: 7  \\\n",
       "0               0.0      0.0       0         NaN   \n",
       "1               0.0      0.0       0         NaN   \n",
       "2              90.0    169.0       0         NaN   \n",
       "3              90.0      NaN       0         NaN   \n",
       "4               0.0      NaN       0         NaN   \n",
       "5               NaN      NaN       0         NaN   \n",
       "6             189.0    404.0       1         NaN   \n",
       "7             562.0     96.0       1         NaN   \n",
       "8               NaN      NaN       1         NaN   \n",
       "9             656.0    164.0       1         NaN   \n",
       "10            804.0    156.0       1         NaN   \n",
       "11           1148.0      NaN       1         NaN   \n",
       "12              NaN      NaN       1         NaN   \n",
       "13              NaN      NaN       1         NaN   \n",
       "14           2514.0      NaN       1         NaN   \n",
       "15              NaN      NaN       1         NaN   \n",
       "16              NaN      NaN       1         NaN   \n",
       "17              NaN      NaN       1         NaN   \n",
       "18              NaN      NaN       1         NaN   \n",
       "19              NaN      NaN       1         NaN   \n",
       "20           3587.0      NaN       1         NaN   \n",
       "21           3908.0      NaN       1         NaN   \n",
       "22           4414.0      5.0       1         NaN   \n",
       "23              NaN      NaN       1         NaN   \n",
       "24              NaN      NaN       1         NaN   \n",
       "25           5306.0      2.0       1         NaN   \n",
       "26              NaN      NaN       1         NaN   \n",
       "27              NaN      NaN       1         NaN   \n",
       "28           5994.0      NaN       1         NaN   \n",
       "29              NaN      NaN       1         NaN   \n",
       "30              NaN      NaN       1         NaN   \n",
       "31              NaN      NaN       1         NaN   \n",
       "32              NaN      NaN       1         NaN   \n",
       "33           6525.0      NaN       1         NaN   \n",
       "34           6876.0      NaN       1         NaN   \n",
       "35              NaN      NaN       1         NaN   \n",
       "36              NaN      NaN       1         NaN   \n",
       "37           7652.0      NaN       1         NaN   \n",
       "38              NaN      NaN       1         NaN   \n",
       "39              NaN      NaN       1         NaN   \n",
       "40              NaN      NaN       1         NaN   \n",
       "41              NaN      NaN       1         NaN   \n",
       "42              NaN      NaN       1         NaN   \n",
       "43           9043.0      0.0       1         NaN   \n",
       "\n",
       "    Testing;per;;%;of;population  Increase;since;last;Last;Death  \\\n",
       "0                            NaN                               0   \n",
       "1                            NaN                               0   \n",
       "2                            NaN                               0   \n",
       "3                            NaN                               0   \n",
       "4                            NaN                               0   \n",
       "5                            NaN                               0   \n",
       "6                            NaN                               0   \n",
       "7                            NaN                               1   \n",
       "8                            NaN                               0   \n",
       "9                            NaN                               0   \n",
       "10                           NaN                               0   \n",
       "11                           NaN                               0   \n",
       "12                           NaN                               0   \n",
       "13                           NaN                               0   \n",
       "14                           NaN                               0   \n",
       "15                           NaN                               0   \n",
       "16                           NaN                               0   \n",
       "17                           NaN                               0   \n",
       "18                           NaN                               0   \n",
       "19                           NaN                               0   \n",
       "20                           NaN                               0   \n",
       "21                           NaN                               0   \n",
       "22                           NaN                               0   \n",
       "23                           NaN                               0   \n",
       "24                           NaN                               0   \n",
       "25                           NaN                               0   \n",
       "26                           NaN                               0   \n",
       "27                           NaN                               0   \n",
       "28                           NaN                               0   \n",
       "29                           NaN                               0   \n",
       "30                           NaN                               0   \n",
       "31                           NaN                               0   \n",
       "32                           NaN                               0   \n",
       "33                           NaN                               0   \n",
       "34                           NaN                               0   \n",
       "35                           NaN                               0   \n",
       "36                           NaN                               0   \n",
       "37                           NaN                               0   \n",
       "38                           NaN                               0   \n",
       "39                           NaN                               0   \n",
       "40                           NaN                               0   \n",
       "41                           NaN                               0   \n",
       "42                           NaN                               0   \n",
       "43                           NaN                               0   \n",
       "\n",
       "    Increase;since;last;Confirmed  \\\n",
       "0                             NaN   \n",
       "1                             NaN   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "5                             NaN   \n",
       "6                             NaN   \n",
       "7                             1.0   \n",
       "8                             0.0   \n",
       "9                             0.0   \n",
       "10                            0.0   \n",
       "11                            2.0   \n",
       "12                            0.0   \n",
       "13                            0.0   \n",
       "14                            7.0   \n",
       "15                            0.0   \n",
       "16                            0.0   \n",
       "17                            0.0   \n",
       "18                            0.0   \n",
       "19                            0.0   \n",
       "20                            0.0   \n",
       "21                            0.0   \n",
       "22                            2.0   \n",
       "23                            0.0   \n",
       "24                            0.0   \n",
       "25                            5.0   \n",
       "26                            0.0   \n",
       "27                            0.0   \n",
       "28                            2.0   \n",
       "29                            0.0   \n",
       "30                            0.0   \n",
       "31                            0.0   \n",
       "32                            0.0   \n",
       "33                            1.0   \n",
       "34                            0.0   \n",
       "35                            0.0   \n",
       "36                            0.0   \n",
       "37                            0.0   \n",
       "38                            0.0   \n",
       "39                            0.0   \n",
       "40                            0.0   \n",
       "41                            0.0   \n",
       "42                            0.0   \n",
       "43                            0.0   \n",
       "\n",
       "                                               Source  \n",
       "0   https://web.facebook.com/109466677345255/photo...  \n",
       "1   https://web.facebook.com/109466677345255/photo...  \n",
       "2   https://web.facebook.com/109466677345255/photo...  \n",
       "3   https://web.facebook.com/109466677345255/photo...  \n",
       "4   www.africanews.com/statistics/2020/01/04botswa...  \n",
       "5                         BwGovernment;;;;;;;;;;;;;;;  \n",
       "6   https://web.facebook.com/109466677345255/photo...  \n",
       "7                         DumaFm;FB;page;;;;;;;;;;;;;  \n",
       "8   https://web.facebook.com/109466677345255/photo...  \n",
       "9                         BwGovernment;;;;;;;;;;;;;;;  \n",
       "10                        BwGovernment;;;;;;;;;;;;;;;  \n",
       "11                          DumaFm;page;;;;;;;;;;;;;;  \n",
       "12  https://web.facebook.com/109466677345255/photo...  \n",
       "13  https://web.facebook.com/109466677345255/photo...  \n",
       "14  https://web.facebook.com/109466677345255/photo...  \n",
       "15  https://web.facebook.com/109466677345255/photo...  \n",
       "16  https://web.facebook.com/109466677345255/photo...  \n",
       "17  https://web.facebook.com/109466677345255/photo...  \n",
       "18  https://web.facebook.com/109466677345255/photo...  \n",
       "19  https://web.facebook.com/109466677345255/photo...  \n",
       "20  https://web.facebook.com/109466677345255/photo...  \n",
       "21  https://web.facebook.com/109466677345255/photo...  \n",
       "22  https://web.facebook.com/109466677345255/photo...  \n",
       "23  https://web.facebook.com/109466677345255/photo...  \n",
       "24  https://web.facebook.com/109466677345255/photo...  \n",
       "25  https://web.facebook.com/109466677345255/photo...  \n",
       "26  https://web.facebook.com/109466677345255/photo...  \n",
       "27  https://web.facebook.com/109466677345255/photo...  \n",
       "28  https://web.facebook.com/109466677345255/photo...  \n",
       "29  https://web.facebook.com/109466677345255/photo...  \n",
       "30  https://web.facebook.com/109466677345255/photo...  \n",
       "31  https://web.facebook.com/109466677345255/photo...  \n",
       "32  https://web.facebook.com/109466677345255/photo...  \n",
       "33  https://web.facebook.com/109466677345255/photo...  \n",
       "34  https://web.facebook.com/109466677345255/photo...  \n",
       "35  https://web.facebook.com/109466677345255/photo...  \n",
       "36  https://web.facebook.com/109466677345255/photo...  \n",
       "37  https://web.facebook.com/109466677345255/photo...  \n",
       "38  https://web.facebook.com/109466677345255/photo...  \n",
       "39  https://web.facebook.com/109466677345255/photo...  \n",
       "40  https://web.facebook.com/109466677345255/photo...  \n",
       "41  https://web.facebook.com/109466677345255/photo...  \n",
       "42  https://web.facebook.com/109466677345255/photo...  \n",
       "43  https://web.facebook.com/109466677345255/photo...  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bots_confirmed.head(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Quarantined', 'Suspected;Cases', 'Tested;Positive',\n",
       "       'Tested;Negative', 'Pending', 'Deaths', 'Unnamed: 7',\n",
       "       'Testing;per;;%;of;population', 'Increase;since;last;Last;Death',\n",
       "       'Increase;since;last;Confirmed', 'Source'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bots_confirmed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is all tangled and need some cleaning and rearrangement before we can move on, I will start by deleting all rows without any values, that is NAN value rows.  But first I will rename some columns and start by dropping some rows, I wont need for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>Quarantined</th>\n",
       "      <th>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>Tested Negative</th>\n",
       "      <th>Pending</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Testing;per;;%;of;population</th>\n",
       "      <th>Increase;since;last;Last;Death</th>\n",
       "      <th>Increase;since;last;Confirmed</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26/03/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28/03/2020</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>www.africanews.com/statistics/2020/01/04botswa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BwGovernment;;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01/04/20</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>02/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>DumaFm;FB;page;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>03/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>04/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BwGovernment;;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>05/04/20</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BwGovernment;;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>06/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1154.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DumaFm;page;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>07/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>08/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>09/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2527.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4434.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4414.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5328.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5306.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6016.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>26/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>27/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>28/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6548.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6525.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6809.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6876.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>30/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>31/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>01/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7675.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7652.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>02/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>03/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>04/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>05/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>06/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>07/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9066.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9043.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Quarantined  Suspected Cases  Tested Positive  \\\n",
       "0   26/03/2020          0.0              0.0              0.0   \n",
       "1   27/03/2020          0.0              0.0              0.0   \n",
       "2   28/03/2020       1753.0            259.0              0.0   \n",
       "3   29/03/2020          NaN             93.0              3.0   \n",
       "4   30/03/2020          NaN              NaN              3.0   \n",
       "5   31/03/2020          NaN              NaN              NaN   \n",
       "6     01/04/20       1856.0            593.0              4.0   \n",
       "7     02/04/20          NaN            670.0              4.0   \n",
       "8     03/04/20          NaN              NaN              NaN   \n",
       "9     04/04/20          NaN            820.0              4.0   \n",
       "10    05/04/20       2065.0            966.0              6.0   \n",
       "11    06/04/20          NaN           1154.0              6.0   \n",
       "12    07/04/20          NaN              NaN              NaN   \n",
       "13    08/04/20          NaN              NaN              NaN   \n",
       "14    09/04/20          NaN           2527.0             13.0   \n",
       "15    10/04/20          NaN              NaN             13.0   \n",
       "16    11/04/20          NaN              NaN              NaN   \n",
       "17    12/04/20          NaN              NaN              NaN   \n",
       "18  13/04/2020          NaN              NaN              NaN   \n",
       "19  14/04/2020          NaN              NaN              NaN   \n",
       "20  15/04/2020          NaN           3602.0             15.0   \n",
       "21  16/04/2020          NaN           3923.0             15.0   \n",
       "22  17/04/2020          NaN           4434.0             15.0   \n",
       "23  18/04/2020          NaN              NaN              NaN   \n",
       "24  19/04/2020          NaN              NaN              NaN   \n",
       "25  20/04/2020          NaN           5328.0             20.0   \n",
       "26  21/04/2020          NaN              NaN              NaN   \n",
       "27  22/04/2020          NaN              NaN              NaN   \n",
       "28  23/04/2020          NaN           6016.0             22.0   \n",
       "29  24/04/2020          NaN              NaN              NaN   \n",
       "30  25/04/2020          NaN              NaN              NaN   \n",
       "31  26/04/2020          NaN              NaN              NaN   \n",
       "32  27/04/2020          NaN              NaN              NaN   \n",
       "33  28/04/2020          NaN           6548.0             23.0   \n",
       "34  29/04/2020          NaN           6809.0             23.0   \n",
       "35  30/04/2020          NaN              NaN              NaN   \n",
       "36  31/04/2020          NaN              NaN              NaN   \n",
       "37    01/05/20          NaN           7675.0             23.0   \n",
       "38    02/05/20          NaN              NaN              NaN   \n",
       "39    03/05/20          NaN              NaN              NaN   \n",
       "40    04/05/20          NaN              NaN              NaN   \n",
       "41    05/05/20          NaN              NaN              NaN   \n",
       "42    06/05/20          NaN              NaN              NaN   \n",
       "43    07/05/20          NaN           9066.0             23.0   \n",
       "\n",
       "    Tested Negative  Pending  Deaths  Unnamed: 7  \\\n",
       "0               0.0      0.0       0         NaN   \n",
       "1               0.0      0.0       0         NaN   \n",
       "2              90.0    169.0       0         NaN   \n",
       "3              90.0      NaN       0         NaN   \n",
       "4               0.0      NaN       0         NaN   \n",
       "5               NaN      NaN       0         NaN   \n",
       "6             189.0    404.0       1         NaN   \n",
       "7             562.0     96.0       1         NaN   \n",
       "8               NaN      NaN       1         NaN   \n",
       "9             656.0    164.0       1         NaN   \n",
       "10            804.0    156.0       1         NaN   \n",
       "11           1148.0      NaN       1         NaN   \n",
       "12              NaN      NaN       1         NaN   \n",
       "13              NaN      NaN       1         NaN   \n",
       "14           2514.0      NaN       1         NaN   \n",
       "15              NaN      NaN       1         NaN   \n",
       "16              NaN      NaN       1         NaN   \n",
       "17              NaN      NaN       1         NaN   \n",
       "18              NaN      NaN       1         NaN   \n",
       "19              NaN      NaN       1         NaN   \n",
       "20           3587.0      NaN       1         NaN   \n",
       "21           3908.0      NaN       1         NaN   \n",
       "22           4414.0      5.0       1         NaN   \n",
       "23              NaN      NaN       1         NaN   \n",
       "24              NaN      NaN       1         NaN   \n",
       "25           5306.0      2.0       1         NaN   \n",
       "26              NaN      NaN       1         NaN   \n",
       "27              NaN      NaN       1         NaN   \n",
       "28           5994.0      NaN       1         NaN   \n",
       "29              NaN      NaN       1         NaN   \n",
       "30              NaN      NaN       1         NaN   \n",
       "31              NaN      NaN       1         NaN   \n",
       "32              NaN      NaN       1         NaN   \n",
       "33           6525.0      NaN       1         NaN   \n",
       "34           6876.0      NaN       1         NaN   \n",
       "35              NaN      NaN       1         NaN   \n",
       "36              NaN      NaN       1         NaN   \n",
       "37           7652.0      NaN       1         NaN   \n",
       "38              NaN      NaN       1         NaN   \n",
       "39              NaN      NaN       1         NaN   \n",
       "40              NaN      NaN       1         NaN   \n",
       "41              NaN      NaN       1         NaN   \n",
       "42              NaN      NaN       1         NaN   \n",
       "43           9043.0      0.0       1         NaN   \n",
       "\n",
       "    Testing;per;;%;of;population  Increase;since;last;Last;Death  \\\n",
       "0                            NaN                               0   \n",
       "1                            NaN                               0   \n",
       "2                            NaN                               0   \n",
       "3                            NaN                               0   \n",
       "4                            NaN                               0   \n",
       "5                            NaN                               0   \n",
       "6                            NaN                               0   \n",
       "7                            NaN                               1   \n",
       "8                            NaN                               0   \n",
       "9                            NaN                               0   \n",
       "10                           NaN                               0   \n",
       "11                           NaN                               0   \n",
       "12                           NaN                               0   \n",
       "13                           NaN                               0   \n",
       "14                           NaN                               0   \n",
       "15                           NaN                               0   \n",
       "16                           NaN                               0   \n",
       "17                           NaN                               0   \n",
       "18                           NaN                               0   \n",
       "19                           NaN                               0   \n",
       "20                           NaN                               0   \n",
       "21                           NaN                               0   \n",
       "22                           NaN                               0   \n",
       "23                           NaN                               0   \n",
       "24                           NaN                               0   \n",
       "25                           NaN                               0   \n",
       "26                           NaN                               0   \n",
       "27                           NaN                               0   \n",
       "28                           NaN                               0   \n",
       "29                           NaN                               0   \n",
       "30                           NaN                               0   \n",
       "31                           NaN                               0   \n",
       "32                           NaN                               0   \n",
       "33                           NaN                               0   \n",
       "34                           NaN                               0   \n",
       "35                           NaN                               0   \n",
       "36                           NaN                               0   \n",
       "37                           NaN                               0   \n",
       "38                           NaN                               0   \n",
       "39                           NaN                               0   \n",
       "40                           NaN                               0   \n",
       "41                           NaN                               0   \n",
       "42                           NaN                               0   \n",
       "43                           NaN                               0   \n",
       "\n",
       "    Increase;since;last;Confirmed  \\\n",
       "0                             NaN   \n",
       "1                             NaN   \n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "4                             NaN   \n",
       "5                             NaN   \n",
       "6                             NaN   \n",
       "7                             1.0   \n",
       "8                             0.0   \n",
       "9                             0.0   \n",
       "10                            0.0   \n",
       "11                            2.0   \n",
       "12                            0.0   \n",
       "13                            0.0   \n",
       "14                            7.0   \n",
       "15                            0.0   \n",
       "16                            0.0   \n",
       "17                            0.0   \n",
       "18                            0.0   \n",
       "19                            0.0   \n",
       "20                            0.0   \n",
       "21                            0.0   \n",
       "22                            2.0   \n",
       "23                            0.0   \n",
       "24                            0.0   \n",
       "25                            5.0   \n",
       "26                            0.0   \n",
       "27                            0.0   \n",
       "28                            2.0   \n",
       "29                            0.0   \n",
       "30                            0.0   \n",
       "31                            0.0   \n",
       "32                            0.0   \n",
       "33                            1.0   \n",
       "34                            0.0   \n",
       "35                            0.0   \n",
       "36                            0.0   \n",
       "37                            0.0   \n",
       "38                            0.0   \n",
       "39                            0.0   \n",
       "40                            0.0   \n",
       "41                            0.0   \n",
       "42                            0.0   \n",
       "43                            0.0   \n",
       "\n",
       "                                               Source  \n",
       "0   https://web.facebook.com/109466677345255/photo...  \n",
       "1   https://web.facebook.com/109466677345255/photo...  \n",
       "2   https://web.facebook.com/109466677345255/photo...  \n",
       "3   https://web.facebook.com/109466677345255/photo...  \n",
       "4   www.africanews.com/statistics/2020/01/04botswa...  \n",
       "5                         BwGovernment;;;;;;;;;;;;;;;  \n",
       "6   https://web.facebook.com/109466677345255/photo...  \n",
       "7                         DumaFm;FB;page;;;;;;;;;;;;;  \n",
       "8   https://web.facebook.com/109466677345255/photo...  \n",
       "9                         BwGovernment;;;;;;;;;;;;;;;  \n",
       "10                        BwGovernment;;;;;;;;;;;;;;;  \n",
       "11                          DumaFm;page;;;;;;;;;;;;;;  \n",
       "12  https://web.facebook.com/109466677345255/photo...  \n",
       "13  https://web.facebook.com/109466677345255/photo...  \n",
       "14  https://web.facebook.com/109466677345255/photo...  \n",
       "15  https://web.facebook.com/109466677345255/photo...  \n",
       "16  https://web.facebook.com/109466677345255/photo...  \n",
       "17  https://web.facebook.com/109466677345255/photo...  \n",
       "18  https://web.facebook.com/109466677345255/photo...  \n",
       "19  https://web.facebook.com/109466677345255/photo...  \n",
       "20  https://web.facebook.com/109466677345255/photo...  \n",
       "21  https://web.facebook.com/109466677345255/photo...  \n",
       "22  https://web.facebook.com/109466677345255/photo...  \n",
       "23  https://web.facebook.com/109466677345255/photo...  \n",
       "24  https://web.facebook.com/109466677345255/photo...  \n",
       "25  https://web.facebook.com/109466677345255/photo...  \n",
       "26  https://web.facebook.com/109466677345255/photo...  \n",
       "27  https://web.facebook.com/109466677345255/photo...  \n",
       "28  https://web.facebook.com/109466677345255/photo...  \n",
       "29  https://web.facebook.com/109466677345255/photo...  \n",
       "30  https://web.facebook.com/109466677345255/photo...  \n",
       "31  https://web.facebook.com/109466677345255/photo...  \n",
       "32  https://web.facebook.com/109466677345255/photo...  \n",
       "33  https://web.facebook.com/109466677345255/photo...  \n",
       "34  https://web.facebook.com/109466677345255/photo...  \n",
       "35  https://web.facebook.com/109466677345255/photo...  \n",
       "36  https://web.facebook.com/109466677345255/photo...  \n",
       "37  https://web.facebook.com/109466677345255/photo...  \n",
       "38  https://web.facebook.com/109466677345255/photo...  \n",
       "39  https://web.facebook.com/109466677345255/photo...  \n",
       "40  https://web.facebook.com/109466677345255/photo...  \n",
       "41  https://web.facebook.com/109466677345255/photo...  \n",
       "42  https://web.facebook.com/109466677345255/photo...  \n",
       "43  https://web.facebook.com/109466677345255/photo...  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bots_confirmed.rename(columns={'Suspected;Cases':'Suspected Cases','Tested;Positive':'Tested Positive','Tested;Negative':'Tested Negative'}, inplace=True)\n",
    "bots_confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>Quarantined</th>\n",
       "      <th>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>Tested Negative</th>\n",
       "      <th>Pending</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Testing;per;;%;of;population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26/03/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27/03/2020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28/03/2020</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01/04/20</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>02/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>03/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>04/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>820.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>656.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>05/04/20</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>06/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1154.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1148.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>07/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>08/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>09/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2527.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4434.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4414.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5328.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5306.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>21/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>22/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6016.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>24/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>26/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>27/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>28/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6548.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6525.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6809.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6876.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>30/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>31/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>01/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7675.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7652.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>02/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>03/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>04/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>05/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>06/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>07/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9066.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9043.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Quarantined  Suspected Cases  Tested Positive  \\\n",
       "0   26/03/2020          0.0              0.0              0.0   \n",
       "1   27/03/2020          0.0              0.0              0.0   \n",
       "2   28/03/2020       1753.0            259.0              0.0   \n",
       "3   29/03/2020          NaN             93.0              3.0   \n",
       "4   30/03/2020          NaN              NaN              3.0   \n",
       "5   31/03/2020          NaN              NaN              NaN   \n",
       "6     01/04/20       1856.0            593.0              4.0   \n",
       "7     02/04/20          NaN            670.0              4.0   \n",
       "8     03/04/20          NaN              NaN              NaN   \n",
       "9     04/04/20          NaN            820.0              4.0   \n",
       "10    05/04/20       2065.0            966.0              6.0   \n",
       "11    06/04/20          NaN           1154.0              6.0   \n",
       "12    07/04/20          NaN              NaN              NaN   \n",
       "13    08/04/20          NaN              NaN              NaN   \n",
       "14    09/04/20          NaN           2527.0             13.0   \n",
       "15    10/04/20          NaN              NaN             13.0   \n",
       "16    11/04/20          NaN              NaN              NaN   \n",
       "17    12/04/20          NaN              NaN              NaN   \n",
       "18  13/04/2020          NaN              NaN              NaN   \n",
       "19  14/04/2020          NaN              NaN              NaN   \n",
       "20  15/04/2020          NaN           3602.0             15.0   \n",
       "21  16/04/2020          NaN           3923.0             15.0   \n",
       "22  17/04/2020          NaN           4434.0             15.0   \n",
       "23  18/04/2020          NaN              NaN              NaN   \n",
       "24  19/04/2020          NaN              NaN              NaN   \n",
       "25  20/04/2020          NaN           5328.0             20.0   \n",
       "26  21/04/2020          NaN              NaN              NaN   \n",
       "27  22/04/2020          NaN              NaN              NaN   \n",
       "28  23/04/2020          NaN           6016.0             22.0   \n",
       "29  24/04/2020          NaN              NaN              NaN   \n",
       "30  25/04/2020          NaN              NaN              NaN   \n",
       "31  26/04/2020          NaN              NaN              NaN   \n",
       "32  27/04/2020          NaN              NaN              NaN   \n",
       "33  28/04/2020          NaN           6548.0             23.0   \n",
       "34  29/04/2020          NaN           6809.0             23.0   \n",
       "35  30/04/2020          NaN              NaN              NaN   \n",
       "36  31/04/2020          NaN              NaN              NaN   \n",
       "37    01/05/20          NaN           7675.0             23.0   \n",
       "38    02/05/20          NaN              NaN              NaN   \n",
       "39    03/05/20          NaN              NaN              NaN   \n",
       "40    04/05/20          NaN              NaN              NaN   \n",
       "41    05/05/20          NaN              NaN              NaN   \n",
       "42    06/05/20          NaN              NaN              NaN   \n",
       "43    07/05/20          NaN           9066.0             23.0   \n",
       "\n",
       "    Tested Negative  Pending  Unnamed: 7  Testing;per;;%;of;population  \n",
       "0               0.0      0.0         NaN                           NaN  \n",
       "1               0.0      0.0         NaN                           NaN  \n",
       "2              90.0    169.0         NaN                           NaN  \n",
       "3              90.0      NaN         NaN                           NaN  \n",
       "4               0.0      NaN         NaN                           NaN  \n",
       "5               NaN      NaN         NaN                           NaN  \n",
       "6             189.0    404.0         NaN                           NaN  \n",
       "7             562.0     96.0         NaN                           NaN  \n",
       "8               NaN      NaN         NaN                           NaN  \n",
       "9             656.0    164.0         NaN                           NaN  \n",
       "10            804.0    156.0         NaN                           NaN  \n",
       "11           1148.0      NaN         NaN                           NaN  \n",
       "12              NaN      NaN         NaN                           NaN  \n",
       "13              NaN      NaN         NaN                           NaN  \n",
       "14           2514.0      NaN         NaN                           NaN  \n",
       "15              NaN      NaN         NaN                           NaN  \n",
       "16              NaN      NaN         NaN                           NaN  \n",
       "17              NaN      NaN         NaN                           NaN  \n",
       "18              NaN      NaN         NaN                           NaN  \n",
       "19              NaN      NaN         NaN                           NaN  \n",
       "20           3587.0      NaN         NaN                           NaN  \n",
       "21           3908.0      NaN         NaN                           NaN  \n",
       "22           4414.0      5.0         NaN                           NaN  \n",
       "23              NaN      NaN         NaN                           NaN  \n",
       "24              NaN      NaN         NaN                           NaN  \n",
       "25           5306.0      2.0         NaN                           NaN  \n",
       "26              NaN      NaN         NaN                           NaN  \n",
       "27              NaN      NaN         NaN                           NaN  \n",
       "28           5994.0      NaN         NaN                           NaN  \n",
       "29              NaN      NaN         NaN                           NaN  \n",
       "30              NaN      NaN         NaN                           NaN  \n",
       "31              NaN      NaN         NaN                           NaN  \n",
       "32              NaN      NaN         NaN                           NaN  \n",
       "33           6525.0      NaN         NaN                           NaN  \n",
       "34           6876.0      NaN         NaN                           NaN  \n",
       "35              NaN      NaN         NaN                           NaN  \n",
       "36              NaN      NaN         NaN                           NaN  \n",
       "37           7652.0      NaN         NaN                           NaN  \n",
       "38              NaN      NaN         NaN                           NaN  \n",
       "39              NaN      NaN         NaN                           NaN  \n",
       "40              NaN      NaN         NaN                           NaN  \n",
       "41              NaN      NaN         NaN                           NaN  \n",
       "42              NaN      NaN         NaN                           NaN  \n",
       "43           9043.0      0.0         NaN                           NaN  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bots_confirmed.drop(['Deaths','Increase;since;last;Last;Death','Source', 'Increase;since;last;Confirmed'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next I delete all rows without any value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>Quarantined</th>\n",
       "      <th>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>Tested Negative</th>\n",
       "      <th>Pending</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Testing;per;;%;of;population</th>\n",
       "      <th>Increase;since;last;Last;Death</th>\n",
       "      <th>Increase;since;last;Confirmed</th>\n",
       "      <th>Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28/03/2020</td>\n",
       "      <td>1753.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29/03/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01/04/20</td>\n",
       "      <td>1856.0</td>\n",
       "      <td>593.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>404.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>02/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>670.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>DumaFm;FB;page;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>05/04/20</td>\n",
       "      <td>2065.0</td>\n",
       "      <td>966.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BwGovernment;;;;;;;;;;;;;;;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>09/04/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2527.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2514.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3587.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3923.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3908.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4434.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4414.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>20/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5328.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5306.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6016.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>28/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6548.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6525.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6809.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>6876.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>01/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7675.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7652.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>07/05/20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9066.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>9043.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>https://web.facebook.com/109466677345255/photo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Quarantined  Suspected Cases  Tested Positive  \\\n",
       "2   28/03/2020       1753.0            259.0              0.0   \n",
       "3   29/03/2020          NaN             93.0              3.0   \n",
       "6     01/04/20       1856.0            593.0              4.0   \n",
       "7     02/04/20          NaN            670.0              4.0   \n",
       "10    05/04/20       2065.0            966.0              6.0   \n",
       "14    09/04/20          NaN           2527.0             13.0   \n",
       "20  15/04/2020          NaN           3602.0             15.0   \n",
       "21  16/04/2020          NaN           3923.0             15.0   \n",
       "22  17/04/2020          NaN           4434.0             15.0   \n",
       "25  20/04/2020          NaN           5328.0             20.0   \n",
       "28  23/04/2020          NaN           6016.0             22.0   \n",
       "33  28/04/2020          NaN           6548.0             23.0   \n",
       "34  29/04/2020          NaN           6809.0             23.0   \n",
       "37    01/05/20          NaN           7675.0             23.0   \n",
       "43    07/05/20          NaN           9066.0             23.0   \n",
       "\n",
       "    Tested Negative  Pending  Deaths  Unnamed: 7  \\\n",
       "2              90.0    169.0       0         NaN   \n",
       "3              90.0      NaN       0         NaN   \n",
       "6             189.0    404.0       1         NaN   \n",
       "7             562.0     96.0       1         NaN   \n",
       "10            804.0    156.0       1         NaN   \n",
       "14           2514.0      NaN       1         NaN   \n",
       "20           3587.0      NaN       1         NaN   \n",
       "21           3908.0      NaN       1         NaN   \n",
       "22           4414.0      5.0       1         NaN   \n",
       "25           5306.0      2.0       1         NaN   \n",
       "28           5994.0      NaN       1         NaN   \n",
       "33           6525.0      NaN       1         NaN   \n",
       "34           6876.0      NaN       1         NaN   \n",
       "37           7652.0      NaN       1         NaN   \n",
       "43           9043.0      0.0       1         NaN   \n",
       "\n",
       "    Testing;per;;%;of;population  Increase;since;last;Last;Death  \\\n",
       "2                            NaN                               0   \n",
       "3                            NaN                               0   \n",
       "6                            NaN                               0   \n",
       "7                            NaN                               1   \n",
       "10                           NaN                               0   \n",
       "14                           NaN                               0   \n",
       "20                           NaN                               0   \n",
       "21                           NaN                               0   \n",
       "22                           NaN                               0   \n",
       "25                           NaN                               0   \n",
       "28                           NaN                               0   \n",
       "33                           NaN                               0   \n",
       "34                           NaN                               0   \n",
       "37                           NaN                               0   \n",
       "43                           NaN                               0   \n",
       "\n",
       "    Increase;since;last;Confirmed  \\\n",
       "2                             NaN   \n",
       "3                             NaN   \n",
       "6                             NaN   \n",
       "7                             1.0   \n",
       "10                            0.0   \n",
       "14                            7.0   \n",
       "20                            0.0   \n",
       "21                            0.0   \n",
       "22                            2.0   \n",
       "25                            5.0   \n",
       "28                            2.0   \n",
       "33                            1.0   \n",
       "34                            0.0   \n",
       "37                            0.0   \n",
       "43                            0.0   \n",
       "\n",
       "                                               Source  \n",
       "2   https://web.facebook.com/109466677345255/photo...  \n",
       "3   https://web.facebook.com/109466677345255/photo...  \n",
       "6   https://web.facebook.com/109466677345255/photo...  \n",
       "7                         DumaFm;FB;page;;;;;;;;;;;;;  \n",
       "10                        BwGovernment;;;;;;;;;;;;;;;  \n",
       "14  https://web.facebook.com/109466677345255/photo...  \n",
       "20  https://web.facebook.com/109466677345255/photo...  \n",
       "21  https://web.facebook.com/109466677345255/photo...  \n",
       "22  https://web.facebook.com/109466677345255/photo...  \n",
       "25  https://web.facebook.com/109466677345255/photo...  \n",
       "28  https://web.facebook.com/109466677345255/photo...  \n",
       "33  https://web.facebook.com/109466677345255/photo...  \n",
       "34  https://web.facebook.com/109466677345255/photo...  \n",
       "37  https://web.facebook.com/109466677345255/photo...  \n",
       "43  https://web.facebook.com/109466677345255/photo...  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Longer way of dropping unnecessary rows that i do not need.\n",
    "\n",
    "bots_confirmed.drop([bots_confirmed.index[0] , bots_confirmed.index[1],\n",
    "                     bots_confirmed.index[4] , bots_confirmed.index[5],\n",
    "                    bots_confirmed.index[8] , bots_confirmed.index[9],\n",
    "                    bots_confirmed.index[11] , bots_confirmed.index[12], \n",
    "                    bots_confirmed.index[13] , bots_confirmed.index[15], \n",
    "                    bots_confirmed.index[16] , bots_confirmed.index[17], \n",
    "                    bots_confirmed.index[17] , bots_confirmed.index[18],\n",
    "                    bots_confirmed.index[19] , bots_confirmed.index[23],\n",
    "                    bots_confirmed.index[24] , bots_confirmed.index[26],\n",
    "                    bots_confirmed.index[27] , bots_confirmed.index[29], \n",
    "                    bots_confirmed.index[30] , bots_confirmed.index[31], \n",
    "                    bots_confirmed.index[32] , bots_confirmed.index[35],\n",
    "                    bots_confirmed.index[36] , bots_confirmed.index[38],\n",
    "                    bots_confirmed.index[38] , bots_confirmed.index[39],\n",
    "                    bots_confirmed.index[40] , bots_confirmed.index[41],\n",
    "                    bots_confirmed.index[42]\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>28/03/2020</th>\n",
       "      <th>29/03/2020</th>\n",
       "      <th>01/04/2020</th>\n",
       "      <th>02/04/2020</th>\n",
       "      <th>04/04/2020</th>\n",
       "      <th>05/04/2020</th>\n",
       "      <th>06/04/2020</th>\n",
       "      <th>09/04/2020</th>\n",
       "      <th>15/04/2020</th>\n",
       "      <th>16/04/2020</th>\n",
       "      <th>17/04/2020</th>\n",
       "      <th>20/04/2020</th>\n",
       "      <th>23/04/2020</th>\n",
       "      <th>28/04/2020</th>\n",
       "      <th>29/04/2020</th>\n",
       "      <th>01/05/2020</th>\n",
       "      <th>07/05/2020</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Suspected Cases</td>\n",
       "      <td>259</td>\n",
       "      <td>259</td>\n",
       "      <td>593</td>\n",
       "      <td>670</td>\n",
       "      <td>820</td>\n",
       "      <td>966</td>\n",
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       "      <td>3602</td>\n",
       "      <td>3923</td>\n",
       "      <td>4434</td>\n",
       "      <td>5328</td>\n",
       "      <td>6016</td>\n",
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       "      <td>6809</td>\n",
       "      <td>7675</td>\n",
       "      <td>9066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tested Positive</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tested Negative</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>189</td>\n",
       "      <td>562</td>\n",
       "      <td>656</td>\n",
       "      <td>804</td>\n",
       "      <td>1148</td>\n",
       "      <td>2514</td>\n",
       "      <td>3587</td>\n",
       "      <td>3908</td>\n",
       "      <td>4414</td>\n",
       "      <td>5306</td>\n",
       "      <td>5994</td>\n",
       "      <td>6525</td>\n",
       "      <td>6876</td>\n",
       "      <td>7652</td>\n",
       "      <td>9043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pending</td>\n",
       "      <td>169</td>\n",
       "      <td>166</td>\n",
       "      <td>404</td>\n",
       "      <td>96</td>\n",
       "      <td>164</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Death</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>% Testing Ratio</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "           columns  28/03/2020  29/03/2020  01/04/2020  02/04/2020  \\\n",
       "0  Suspected Cases         259         259         593         670   \n",
       "1  Tested Positive           0           3           4           4   \n",
       "2  Tested Negative          90          90         189         562   \n",
       "3          Pending         169         166         404          96   \n",
       "4            Death           0           0           1           1   \n",
       "5  % Testing Ratio           0           0           0           0   \n",
       "\n",
       "   04/04/2020  05/04/2020  06/04/2020  09/04/2020  15/04/2020  16/04/2020  \\\n",
       "0         820         966        1154        2527        3602        3923   \n",
       "1           4           6           6          13          15          15   \n",
       "2         656         804        1148        2514        3587        3908   \n",
       "3         164         156           0           0           0           0   \n",
       "4           1           1           1           1           1           1   \n",
       "5           0           0           0           0           0           0   \n",
       "\n",
       "   17/04/2020  20/04/2020  23/04/2020  28/04/2020  29/04/2020  01/05/2020  \\\n",
       "0        4434        5328        6016        6548        6809        7675   \n",
       "1          15          20          22          23          23          23   \n",
       "2        4414        5306        5994        6525        6876        7652   \n",
       "3           5           2           0           0           0           0   \n",
       "4           1           1           1           1           1           1   \n",
       "5           0           0           0           0           0           0   \n",
       "\n",
       "   07/05/2020  \n",
       "0        9066  \n",
       "1          23  \n",
       "2        9043  \n",
       "3           0  \n",
       "4           1  \n",
       "5           0  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'columns': ['Suspected Cases', 'Tested Positive', 'Tested Negative','Pending','Death','% Testing Ratio'],\n",
    "       '28/03/2020':[259,0,90,169,0,0],\n",
    "       '29/03/2020':[259,3,90,166,0,0], \n",
    "       '01/04/2020':[593,4,189,404,1,0],\n",
    "       '02/04/2020':[670,4,562,96,1,0],\n",
    "       '04/04/2020':[820,4,656,164,1,0],\n",
    "       '05/04/2020':[966,6,804,156,1,0],\n",
    "       '06/04/2020':[1154,6,1148,0,1,0],\n",
    "       '09/04/2020':[2527,13,2514,0,1,0], \n",
    "       '15/04/2020':[3602,15,3587,0,1,0],\n",
    "       '16/04/2020':[3923,15,3908,0,1,0],\n",
    "       '17/04/2020':[4434,15,4414,5,1,0],\n",
    "       '20/04/2020':[5328,20,5306,2,1,0],\n",
    "       '23/04/2020':[6016,22,5994,0,1,0],\n",
    "       '28/04/2020':[6548,23,6525,0,1,0],\n",
    "       '29/04/2020':[6809,23,6876,0,1,0],\n",
    "       '01/05/2020':[7675,23,7652,0,1,0],\n",
    "       '07/05/2020':[9066,23,9043,0,1,0],}\n",
    "cases = DataFrame(data)\n",
    "cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything looks fine and I have cleaned the data. I can now analyze the data I collected locally. \n",
    "\n",
    "<hr>\n",
    "\n",
    "Please note that the NaN stands for Not a Number which is what pandas uses as a stand in value for missing data. Wherever there is the NaN ; data is missing If you going through this notebook and can contribute to completing the data you would most welcome. Data collection in Botswana is a tedious and not straight forward job though generally data collection and preparation is tricky the world over."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font:12px;\">NOTE:</span>\n",
    "Now that the data is incosistent and have so many gaps, I am planning on retrieving the available data from the dataset into my own dataFrame before I can work with it in my analysis, in what is known as data wrangling. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, the above table now looks better then the above data farame: bots_confirmed and will enable for proper analysis but for the use of graphs I will further change it again to look as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>Tested Negative</th>\n",
       "      <th>Death</th>\n",
       "      <th>Pending</th>\n",
       "      <th>% Testing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>3</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>593</td>\n",
       "      <td>4</td>\n",
       "      <td>189</td>\n",
       "      <td>1</td>\n",
       "      <td>404</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>670</td>\n",
       "      <td>4</td>\n",
       "      <td>562</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>820</td>\n",
       "      <td>4</td>\n",
       "      <td>656</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>966</td>\n",
       "      <td>6</td>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>1154</td>\n",
       "      <td>6</td>\n",
       "      <td>1148</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>2527</td>\n",
       "      <td>13</td>\n",
       "      <td>2514</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15/04/2020</th>\n",
       "      <td>3602</td>\n",
       "      <td>15</td>\n",
       "      <td>3587</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/04/2020</th>\n",
       "      <td>3923</td>\n",
       "      <td>15</td>\n",
       "      <td>3908</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/04/2020</th>\n",
       "      <td>4434</td>\n",
       "      <td>15</td>\n",
       "      <td>4415</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/04/2020</th>\n",
       "      <td>5328</td>\n",
       "      <td>20</td>\n",
       "      <td>5306</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23/04/2020</th>\n",
       "      <td>6016</td>\n",
       "      <td>22</td>\n",
       "      <td>5994</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28/04/2020</th>\n",
       "      <td>6548</td>\n",
       "      <td>23</td>\n",
       "      <td>6525</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/04/2020</th>\n",
       "      <td>6809</td>\n",
       "      <td>23</td>\n",
       "      <td>6876</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2020</th>\n",
       "      <td>7675</td>\n",
       "      <td>23</td>\n",
       "      <td>7652</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/05/2020</th>\n",
       "      <td>9066</td>\n",
       "      <td>23</td>\n",
       "      <td>9043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Suspected Cases  Tested Positive  Tested Negative  Death  Pending  \\\n",
       "28/03/2020              259                0               90      0      169   \n",
       "29/03/2020              259                3               90      0      166   \n",
       "01/04/2020              593                4              189      1      404   \n",
       "02/04/2020              670                4              562      1       96   \n",
       "04/04/2020              820                4              656      1      164   \n",
       "05/04/2020              966                6              804      1      156   \n",
       "06/04/2020             1154                6             1148      1        0   \n",
       "09/04/2020             2527               13             2514      1        0   \n",
       "15/04/2020             3602               15             3587      1        0   \n",
       "16/04/2020             3923               15             3908      1        0   \n",
       "17/04/2020             4434               15             4415      1        5   \n",
       "20/04/2020             5328               20             5306      1        2   \n",
       "23/04/2020             6016               22             5994      1        0   \n",
       "28/04/2020             6548               23             6525      1        0   \n",
       "29/04/2020             6809               23             6876      1        0   \n",
       "01/05/2020             7675               23             7652      1        0   \n",
       "07/05/2020             9066               23             9043      1        0   \n",
       "\n",
       "            % Testing Ratio  \n",
       "28/03/2020                0  \n",
       "29/03/2020                0  \n",
       "01/04/2020                0  \n",
       "02/04/2020                0  \n",
       "04/04/2020                0  \n",
       "05/04/2020                0  \n",
       "06/04/2020                0  \n",
       "09/04/2020                0  \n",
       "15/04/2020                0  \n",
       "16/04/2020                0  \n",
       "17/04/2020                0  \n",
       "20/04/2020                0  \n",
       "23/04/2020                0  \n",
       "28/04/2020                0  \n",
       "29/04/2020                0  \n",
       "01/05/2020                0  \n",
       "07/05/2020                0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reaarranging the above data frame\n",
    "case_bots = pd.DataFrame( {\"Suspected Cases\" : [259,259,593,670,820,966,1154,2527,3602,3923,4434,5328,6016,6548,6809,7675,9066],\n",
    "                           \"Tested Positive\" : [0,3,4,4,4,6,6,13,15,15,15,20,22,23,23,23,23],\n",
    "                           \"Tested Negative\" : [90,90,189,562,656,804,1148,2514,3587,3908,4415,5306,5994,6525,6876,7652,9043],\n",
    "                           \"Death\"           : [0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],\n",
    "                           \"Pending\"         : [169,166,404,96,164,156,0,0,0,0,5,2,0,0,0,0,0],\n",
    "                           \"% Testing Ratio\" : [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]},\n",
    "                           index=['28/03/2020', '29/03/2020', '01/04/2020', '02/04/2020', '04/04/2020', '05/04/2020', '06/04/2020', '09/04/2020',\n",
    "                                 '15/04/2020', '16/04/2020', '17/04/2020', '20/04/2020', '23/04/2020', '28/04/2020', '29/04/2020', '01/05/2020',\n",
    "                                  '07/05/2020'])\n",
    "\n",
    "\n",
    "case_bots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will now make a basic plot of the above reaaranged data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x299198ea4c8>"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "case_bots.plot(kind='bar', figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A basic graph. Everything eems to be working but now let me get back to graphs that are more meaningful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>Suspected Cases</th>\n",
       "      <th>Tested Negative</th>\n",
       "      <th>Tested Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>90</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>593</td>\n",
       "      <td>189</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>670</td>\n",
       "      <td>562</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>820</td>\n",
       "      <td>656</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>966</td>\n",
       "      <td>804</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>1154</td>\n",
       "      <td>1148</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>2527</td>\n",
       "      <td>2514</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15/04/2020</th>\n",
       "      <td>3602</td>\n",
       "      <td>3587</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/04/2020</th>\n",
       "      <td>3923</td>\n",
       "      <td>3908</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/04/2020</th>\n",
       "      <td>4434</td>\n",
       "      <td>4415</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/04/2020</th>\n",
       "      <td>5328</td>\n",
       "      <td>5306</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23/04/2020</th>\n",
       "      <td>6016</td>\n",
       "      <td>5994</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28/04/2020</th>\n",
       "      <td>6548</td>\n",
       "      <td>6525</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/04/2020</th>\n",
       "      <td>6809</td>\n",
       "      <td>6876</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2020</th>\n",
       "      <td>7675</td>\n",
       "      <td>7652</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/05/2020</th>\n",
       "      <td>9066</td>\n",
       "      <td>9043</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Suspected Cases  Tested Negative  Tested Positive\n",
       "28/03/2020              259               90                0\n",
       "29/03/2020              259               90                3\n",
       "01/04/2020              593              189                4\n",
       "02/04/2020              670              562                4\n",
       "04/04/2020              820              656                4\n",
       "05/04/2020              966              804                6\n",
       "06/04/2020             1154             1148                6\n",
       "09/04/2020             2527             2514               13\n",
       "15/04/2020             3602             3587               15\n",
       "16/04/2020             3923             3908               15\n",
       "17/04/2020             4434             4415               15\n",
       "20/04/2020             5328             5306               20\n",
       "23/04/2020             6016             5994               22\n",
       "28/04/2020             6548             6525               23\n",
       "29/04/2020             6809             6876               23\n",
       "01/05/2020             7675             7652               23\n",
       "07/05/2020             9066             9043               23"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suspected_cases =  case_bots[['Suspected Cases', 'Tested Negative', 'Tested Positive']]\n",
    "suspected_cases.head(18)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29919e59d08>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "suspected_cases.plot(kind='bar',figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Cases')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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16rFw4ULmzp3Ln3/+ibOzM5UrV+a1116jfv36QO4vje7du+Pu7k67du2svwCee+45QkNDWb16Nfb29nTo0IGWLVuyZ88eqlWrxtChQzl//jwtW7a03tAQFRXFq6++ir+/P1lZWfj5+REQEGBd9vLLL2M2m3F1dWXu3LlAbqEQEhLClClTCA0NJSIiAn9/f7Kzs3n00UcZMmQIjo6OREVFMW7cOBwdHWnZsmW+x+zv749hGIwdO5acnBwyMzNp0qQJ7733Hk5OTjzzzDNMnjzZ+ove09OTQ4cO4eDgwMSJExk3bhwODg6YTCZee+01nJycCszVpUsXunfvTsWKFXFxcSEsLCzfbH379qVXr155RpkAxo0bx2uvvca8efMwmUyMGjWK2rVr39Rn3aNHD06fPk3Pnj2xWCzUrVuXqKioAo/rald/DldzcnKia9euxMfH89BDDwG5/0CYP38+r7/+OpmZmRiGweuvv35N5ivrTZo0iezsbOzt7YmKiuLw4cPMnz/f5oiRm5sbkZGRDBkyxJolOjqaiIgIli5dSk5ODmPGjKFFixZA7iha7969cXFx4f7777eODL/44ouMHDmSihUr4urqSsuWLa3/KGnfvj1z5szJMxp9Rc+ePYmOjqZDhw431P/VvLy8mD59OhEREVy6dIns7Gzuuece/u///o9q1aoRHBzMSy+9hL+/Pzk5OTz22GN88sknWCyWfL8Pzz33HDNnzqRbt26YzWYefPBBJkyYgKurKyNGjGDgwIG4uLhgb2/P9OnTC3xvRYqLybA13i0iperNN9/k/PnzTJ48ubSjiFj99NNPfP/99wwYMACAZcuWsX//fubNm1fKyURuLxqRExGRm1avXj2WLFlCTEwMJpOJmjVr8uqrr5Z2LJHbjkbkRERERMop3ewgIiIiUk6pkBMREREpp267a+QsFgvp6ek4OjrmmQNKREREpKwxDIPs7GwqVapknYT9arddIZeens6hQ4dKO4aIiIjIDWvYsOF1J8O+7Qo5R0dHIPcN+ee8TjcjMTGxwAeJl4TSzlDa/SuDMpS1DKXdvzIoQ1nqXxmKJkNWVhaHDh2y1i//dNsVcldOpzo5OV3zOJ6bVdjti0JpZyjt/pVBGcpahtLuXxmUoSz1rwxFlyG/y8F0s4OIiIhIOaVCTkRERKScuu1OrRbEYrFw4sQJ0tPTba7r4OBgfTB1aSntDKXdf2EzVKpUidq1a1/3LiAREZHyQIXcVVJTUzGZTDzwwAM2f7mnp6dTqVKlEkpWNjOUdv+FyWCxWDh58iSpqal4eHgUQzIREZHip6GIq/z1119Ur15dIzS3ATs7O6pXr87ff/9d2lFERERumSqWq5jN5nxv75V/H0dHR3Jycko7hoiIyC1TIfcPetrD7UOftYiIlHcq5AqQlW3Od1lhrg0raL9Xi4uLIygoiICAAPz9/Vm6dOkt91kUPv/8c5YtW3ZT2/Tv3589e/Zc056WlsbUqVPx8/MjMDCQ/v378/PPPxdVVBERkduCbnYogJOjPf4vxRb5fjfNDrS5TnJyMjNnzmTdunVUqVKF9PR0+vfvT7169XjiiSeKPNONSExMLJL9WCwWhg4dyiOPPMKGDRtwcHBg9+7dDB06lM2bN1OlSpUi6UdEROTfToVcGXX+/Hmys7PJyMgAckcAIyMjrTNDt2/fnrfeeov777+fPXv2sGDBAlasWMGyZctYv349dnZ2PPTQQ0ybNo1169axc+dOzp49S0pKCj4+PkyYMAGTycTbb7/N1q1bMZvNtGnThpCQEEwmE8uXL+fDDz/E3t4eHx8funXrxqpVqwC4++676dy5M5MnT+b333/HbDYzdOhQ/Pz8yMrKIjQ0lMTERGrVqsX58+evObY9e/Zw+vRpnn/+eeuNJa1atWLGjBlYLBZycnKYMmUKv/76K6mpqTzwwAPMmTOHnJwcxo4dS2pqKgAjR46kVatWHDt2jClTpvDXX3/h4uLCpEmTaNy4MZs2bWLp0qXY29tTu3ZtZs2aVSZm9xYRESkqKuTKqEaNGvHEE0/QoUMHHnzwQR555BH8/f2pW7duvtuYzWbeeustvvrqK+zt7QkNDSU5ORmAhIQEYmNjueOOOxgwYACffvopLi4uJCYmsmbNGkwmEyEhIWzcuJF69eqxcuVK1q5dS4UKFRgyZAhdunQhODgYgO7duxMVFcWDDz7I7NmzSUtLIzg4mGbNmvHJJ58AsHXrVo4ePUpAQMA1OX/55RcaNWp0zd3Bbdu2BWDv3r04OjqyevVqLBYL//vf//jiiy+4dOkStWrV4u233yYpKYmNGzfSqlUrXn75ZSZPnkzjxo357bffGDlyJNu2bWPevHnExMRQtWpVZs6cyZEjR3jwwQeL5PMREREpC1TIlWFTp07lueee4+uvv+brr7+mV69eREVF0alTp+uub29vz8MPP0yPHj144oknGDRoENWrVwfgiSeeoFq1agB07dqV3bt34+zszI8//khQUBAAGRkZ3H333aSmpuLj40PlypUBWL58OZB7jdwV8fHxXLp0iU2bNgFw6dIlfv31V7799lt69+4NwL333svDDz98TU47O7sCR8ZatmzJXXfdxQcffMCRI0c4evQoly5d4uGHH2bOnDkkJyfTrl07Ro4cyaVLl0hMTOSVV16xbn/p0iXOnz+Pj48Pffr0oUOHDvj6+qqIExGRfx0VcmXUzp07uXTpEl27dqV79+50796dmJgY1qxZYy3kDMMAyDOFRnR0ND/88ANffvklQ4YMISoqCsgt8q6wWCzY29tjNpv53//+x6BBgwC4cOEC9vb21hG6K5KTk6lQoUKefBaLhenTp+Pl5QXkTqZ85513EhMTY80FuU9e+KemTZuycuVKDMPI08+cOXN49NFHSU9PZ/78+QwYMICgoCDOnz+PYRjce++9bN26la+++orPP/+cd999l+XLl+Pk5ERs7P+/lvHPP//krrvuIiwsjAMHDvDFF18QEhLCqFGjCAy0fX2iiIjIzcgyZ+Nkn//0ZS1atCjU9gXRXatllIuLC7Nnz+bEiRNAbtGWlJRkHVWqUqUKhw8fBmD79u0AnDt3jq5du9KwYUPGjBnDY489xsGDBwH46quvuHjxIpmZmWzevBlvb29atWpFbGws6enp5OTkWE9Jenl58cUXX1jbX3rpJRITE7G3t7cWja1atWLNmjUAnDlzhoCAAE6fPk3r1q3ZtGmT9ckJ+/btu+bYvLy8qFq1KgsWLMBsNlvzrVu3jvvuu49vvvmGLl260L17d+644w727NmD2Wzm/fff580336RLly6Eh4dz7tw5IHfk70oht2vXLvr160dOTg6dOnWiSpUqDBs2jMDAwFJ/nJiIiPxLXTWAUdLba0SujGrVqhWjRo1i+PDhZGdnA/D4448zcuRIAJ5//nmmTZvG0qVLadOmDQBubm707t2bHj16UKFCBerVq0f37t2Ji4vDzc2NoUOHcv78eQICAnj88ccBOHDgAL169cJsNvP444/TrVs3TCYTTz/9NMHBwVgsFjp27Mijjz6Ko6MjL7/8MtWqVWPUqFGEhYXh5+eH2WwmJCSEe+65h759+/Lrr7/SpUsXatWqRcOGDa85NpPJRHR0NDNmzMDPzw8HBweqVKnC22+/TbVq1ejZsyfjxo1j8+bNODo60rx5c06cOMHQoUMZO3Ys/v7+2NvbExISQuXKlZk1axZTpkxh6dKlODo6MnfuXBwdHXn++ed55plncHZ2pmrVqkRGRpbQpyciIrcTJwcnjkR0v+Xt64euveVtTYZR2DKyfMnMzCQxMZGmTZtec53W1SNekDvfm5Oj/T93UWhFtd8bfc7ounXr+Pbbb4u8kCnPz1q94p+f+a1ISEiwOWxe3JShbGQo7f6VQRnKUv+3W4biKuQKqltAp1YLVFCxlZ6eXiz7FREREblROrV6GwgKCrLemSoiIiL/HhqRExERESmnVMiJiIiIlFPFfmr1yqz/ixcv5vDhw8yZM8e6LDk5mWbNmvHWW2+xYMEC1q5dyx133AFAr1696NevH0lJSYSGhpKeno6XlxdTp07FwcGBU6dOERISwtmzZ6lXrx5RUVGlfuG9iIiISEkq1hG5/fv306dPH44ePQrkPoIpNjaW2NhYli5diqurq3VG/sTERObMmWNd3q9fPwBCQkKYPHky27ZtwzAMYmJigNynHvTt25e4uDiaNm1KdHR0cR6KiIiISJlTrIVcTEwM4eHheHh4XLPs9ddfJzg4mHvvvRfILeTeeust/P39mTZtGpmZmZw8eZKMjAw8PT2B3Iv24+LiyM7OZu/evfj6+uZpFxEREbmdFOup1YiIiOu2Hz16lG+//da6PD09nQcffJCQkBDq1q3LhAkTiI6Opl27dri7u1u3c3d3Jzk5mfPnz+Pq6mp9/NOV9puRmJh4TZuDg0OeaUVcnBywd7z+M0ELcxrXnJ1JRlZOgevMmDGD/fv3k52dzfHjx6lfvz4Affr0yfOYqRuZBiUsLIznn3/+ugX19fzxxx+MGjWKjRs35mlfuHAh27dv58MPP7TOZbNjxw6WLVvG4sWLb2jfN+LChQtMmzaNqKgo/vzzTyIjI5k3b16+6xdmKpisrCwSEhJuefsrimIfyvDvyFDa/SuDMpSl/m+XDEUxT92tZiyV6UdWr15N3759cXJyAnKLoiVLlliXP/PMM0ycOBFvb+88z+K88mzOfz6jE7jmtS35TQj8zwKtMBP85ad+6Foq5VMgXjF9+nQATpw4wYABA6wPp7/ajU6Gm5CQQIUKFW64+KxYsSJ2dnbXrO/o6MjJkydZsmQJL7/8Munp6bi4uGBvb1+k1yempqby22+/UalSJRo0aJDnu/FPhZ0Q2MnJiWbNmt3y9nB7TXipDGW7f2VQhrLUvzLcnPwyXpkQOD+lctfq9u3b6dq1q/X1qVOnrM/thNyCzcHBgRo1apCSkmJtT01NxcPDAzc3Ny5evGh9TmdKSsoNjzb9G6SlpTF+/Hj69u3LU089xZYtWwD45Zdf6NmzJ0FBQfTt25fjx4+zaNEizp07x+DBg7lw4QL79+8nODiYbt26MXjwYE6ePAnATz/9xFNPPUW3bt1YtGhRvn0HBwezadMmfvjhh2uWpaSkMGLECIKCgujRowe7d+8GckfYhg8fjp+fH8OHDycgIIA///yTixcvMnr0aHr37o2Pjw+TJk0CcovY06dP8/zzz3Ps2DE6duzI2bNnadOmjfVZr7/88ot1bry1a9fSrVs3AgMDCQsLIysrq+jebBERkTKsxAu5c+fOkZGRQZ06daxtLi4uzJo1i+PHj2MYBh988AEdO3akVq1aODs7W4cbY2Nj8fb2xtHRES8vL2sBs2HDBry9vUv6UErNwoULadasGStXrmTFihUsXLiQkydPsmzZMp599lnWrVtHUFAQP/zwAyNGjMDNzY133nkHFxcXwsLCmDdvHuvXr6d///5MnjwZgPHjxzNhwgTWr1/P3XffnW/fbm5uhIWFMWHCBDIzM/Mse/XVVwkODmbdunUsXLiQsLAwLl++zPz582nYsCEff/wxI0aM4NdffwVyT8v+5z//YfXq1Wzbto1du3Zx4MABwsLCqFmzJvPnz7fuu2rVqjRu3Jj4+HgANm/eTEBAAIcOHWLDhg2sXr2a2NhYKleuzPLly4v4HRcRESmbSvzU6okTJ6hRo0aeNjc3N6ZNm8aIESPIzs6mefPmDBo0CICoqCjCwsJIS0ujSZMmDBgwAIDw8HAmTJjAokWLqFmzZp5pTf7t4uPjyc7OZtWqVdjZ2XH58mV+++032rVrR3h4ODt37sTHx4f27dvn2e7w4cOcOHGCYcOGAbkjn5mZmaSkpPD333/TqlUrALp163bdU7lXdO7cma1btxIdHZ2nj2+++YZjx45ZP4sr1/fFx8fz5ptvAtCsWTPuu+8+AAIDA/nhhx9Yvnw5hw8f5sKFC6Snp1OhQoXr9hsQEMCWLVt4/PHH2bZtGx9++CHr16/nyJEj9OzZE8i95q2wp0pFRETKixIp5Hbs2GH9+aGHHrJOIXI1X19f612oV2vUqFGe065X1KpVixUrVhRt0HLCYrEwZ84c6lhUWBYAACAASURBVNSpQ6VKlUhNTeXOO+/E0dGRFi1asGPHDt59912++uorpk6dat3ObDZz7733sn79euvrs2fPWq87vOLKTSQFCQ8Px9/fHzc3tzz7f//996lcuTKQO0+gu7s79vb2WCyWa/axfPlytm/fTq9evWjTpg0HDhzIk+OfOnTowKxZs/j222+pW7cu7u7uWCwW/Pz8rNPYpKWlXbcvERGRfyM92aEceuSRR/jwww+B3GLJ39+fM2fOMHr0aJKSkujbty+jR4/ml19+AXILs5ycHO677z5SUlLYt28fkHvTyfjx46lWrRrVqlXjyy+/BChwNO4KNzc3xo8fn2f+vqtzHTx4kICAADIyMmjdujUff/wxkHtDyeHDhzGZTOzatYs+ffrg7+/PpUuXOHjwIBaLBXt7e+v1j1dzcXHh0UcfJTIyEn9/fyD34tBPPvmEc+fOYRgGkydP5v3337/Vt1ZERKRcKZW7VssLS04W9UPXFst+7Rycbnn7MWPGMGXKFHr16oVhGEyYMIFatWoxYsQIwsLCeOONN3B2drZe/9auXTsGDx7MsmXLmDdvHhEREWRlZXHHHXcQGRkJ5J7CnjhxInPnzuWhhx66oRwdOnTg888/56+//gJgypQpTJo0yVoIzp49m4oVKzJ69GgmTJhAQEAAdevWpWrVqjg7OzNw4ECmTZvGokWLqFy5Mg8//DAnTpzA09OTqlWrMnDgwDwjipB7enXr1q107NgRgMaNGzNs2DAGDBiAxWKhSZMmDBky5JbfWxERkfJEhVwBCiq2CjPtxc0UcbVr185zahqgcuXKzJ49+5oMjRs3Zt26ddfs40pBB3D33Xezdu21xekDDzxw3farvfjii9e0XT2/W/Xq1Xn77bevWeezzz5jyJAh1kLtf//7H3feeSePPfYY27Ztu25fH330kfXnTz/91Ppz69atr7ljNjg4mODg4AKzi4iI/BupkJNiV79+faZOnYrFYsHOzo5XX331puf9ExERkWupkJNi16xZs+uOFIqIiEjh6GYHERERkXJKhZyIiIhIOaVCTkRERKScUiEnIiIiUk6pkCtAljk732W3OvWIrf1eMXXqVAIDA+natStNmzYlMDCQwMBAm1OEXE9ISAjJyck3vP6VB9X/09y5c2nTpo01S7du3fI8D/VmvPLKKyQlJfH3338zevRoAE6fPs3w4cNvaX8iIiK3I921WgAne0d6rR5R5PuN6b3I5jrh4eFA7rNpBwwYQGxs7C33t2fPngIffXUz+vbty3PPPQdASkoKQUFBtGzZktatW9/UfmbMmAHkFo0HDhwAoGbNmixevLhIcoqIiNwONCJXDqWlpTF+/Hj69u3LU089xZYtWwD45Zdf6NmzJ0FBQfTt25fjx4+zaNEizp07x+DBg7lw4QL79+8nODiYbt26MXjwYE6ePAnATz/9xFNPPUW3bt1YtMh2oQlQsWJF/vOf/3Do0CEAFi5cSNeuXfH392fmzJlYLBYuXLjAkCFDCAoKIigoiJ07dwLQp08fvvvuO6ZPn87p06d5/vnnrSOBZ8+epU2bNuTk5FiPKygoCIC1a9fSrVs3AgMDCQsLIysrqyjfWhERkXJFhVw5tHDhQpo1a8bKlStZsWIFCxcu5OTJkyxbtoxnn32WdevWERQUxA8//MCIESNwc3PjnXfewcXFhbCwMObNm8f69evp37+/9akP48ePZ8KECaxfv5677777hnKcOnWK/fv34+npyfbt2/nqq69Yt24d69ev58iRI8TExLBt2zbuvfde1q1bx7Rp0/juu+/y7CMsLIyaNWvmOUVbtWpVGjduTHx8PACbN28mICCAAwcOsGHDBlavXk1sbCyVK1fmgw8+KKJ3VUREpPzRqdVyKD4+nuzsbFatWoWdnR2XL1/mt99+o127doSHh7Nz5058fHxo3759nu0OHz7MiRMnGDZsGACGYZCZmUlKSgp///03rVq1AqBbt27W56X+08qVK9m2bZv1KQ0jR46kWbNmRERE4O/vj4uLCwBBQUFs2bKF559/njfeeIM///yTtm3b3vA1cAEBAWzZsoXHH3+cbdu28eGHH7J582aOHDlCz549AcjKyqJJkya39B6KiIj8G6iQK4csFgtz5syhTp06VKpUidTUVO68804cHR1p0aIFO3bs4N133+Wrr77K89B5s9nMvffey/r1662vz549i8lkynMNnYND/l+Lq6+Ru/pZr/+8Bs8wDMxmMw0aNCAuLo4vv/ySHTt2sHz5cj7++GObx9ihQwdmzZrFt99+S926dXF3d8diseDn58crr7wC5J5iTktLu8F3TURE5N9Hp1bLoUceeYQPP/wQgOTkZPz9/Tlz5gyjR48mKSmJvn37Mnr0aH755RcgtzDLycnhvvvuIyUlhX379gGwevVqxo8fT7Vq1ahWrRpffvklQL6jcQVp1aoVmzZtIjMzk5ycHNatW8cjjzzCe++9R3R0NF27dmXKlCkkJyeTnp5u3c7e3h6z2XzN/lxcXHj00UeJjIzE398fgP/+97988sknnDt3DsMwmDx5MqtXr77prCIiIv8WGpErh8aMGcOUKVPo1asXhmEwYcIEatWqxYgRIwgLC+ONN97A2dnZev1bu3btGDx4MMuWLWPevHlERESQlZXFHXfcQWRkJABRUVFMnDiRuXPn8tBDD910pg4dOnDgwAG6d+9OdnY23t7e9OnTh0uXLjF27Fj8/f1xcHBg4sSJuLq6Wrfz8PCgatWqDBw4MM/oIeSeXt26dat1KpSmTZsybNgwBgwYgMVioUmTJgwYMOBW30YREZFyT4VcAbLM2Tc0Vcit7NfJ3vGG1q1duzY7duzI01a5cmVmz56d59QmQOPGja/7cPorBR3A3Xfffd256B544AGbc9S9+OKLBS4fNWoUo0aNytN2xx13sHTp0mvWvTKiCPDRRx9Zf/7000+tP7du3Zoffvghz3bBwcEEBwdbX189uiciInK70anVAhRUbBWmgLjRIk5ERESkICrkRERERMopFXIiIiIi5ZQKuX8oqkdZSdmnz1pERMo7FXJXcXFx4ezZs/oFfxswDIOzZ89aJzAWEREpj3TX6lVq167NiRMnSElJsbluVlYWTk5OJZCq7GYo7f4Lm8HFxYXatWsXcSIREZGSo0LuKo6OjtSrV++G1k1ISKBZs2bFnKhsZyjt/stKBhERkdKiU6siIiIi5ZQKOREREZFySoWciIiISDmlQk5ERESknFIhJyIiIlJOqZATERERKadUyImIiEi5ZVjMBS5v0aJFobYv6zSPnIiIiJRbJjt7jkR0v+Xt64euLcI0Ja/YR+TS0tLw8/PjxIkTALzyyit06tSJwMBAAgMD+fTTTwGIj4/H39+fTp06MXfuXOv2SUlJBAUF4evrS2hoKDk5OQCcOnWKfv360blzZ0aMGEF6enpxH4qIiIhImVKshdz+/fvp06cPR48etbYlJiby/vvvExsbS2xsLB07diQjI4OJEycSHR3Nli1bSExM5IsvvgAgJCSEyZMns23bNgzDICYmBoCpU6fSt29f4uLiaNq0KdHR0cV5KCIiIiJlTrEWcjExMYSHh+Ph4QHA5cuXOXXqFBMnTsTf35/58+djsVj48ccfqVu3LnXq1MHBwQF/f3/i4uI4efIkGRkZeHp6AhAUFERcXBzZ2dns3bsXX1/fPO0iIiIit5NivUYuIiIiz+vU1FRatWpFeHg4lStXZtiwYaxZs4aKFSvi7u5uXc/Dw4Pk5GTOnDmTp93d3Z3k5GTOnz+Pq6srDg4OedpFREREbiclerNDnTp1WLhwofV1//792bBhA76+vphMJmu7YRiYTCYsFst126/8ebV/vrYlMTHxFo/i/0tISCj0Psp7htLuXxmUoaxlKO3+lUEZylL/JZHB1l2pN6KwGUszQ4kWcgcPHuTo0aPWU6KGYeDg4ECNGjVISUmxrpeSkoKHh8c17ampqXh4eODm5sbFixcxm83Y29tb178ZTZs2xdnZ+ZaPJSEhoUg+uMIo7Qyl3b8yKENZy1Da/SuDMpSl/stKhhtRFjLmlyEzM7PAwacSnUfOMAxee+01/v77b7Kzs1m9ejUdO3akWbNm/P777xw7dgyz2czHH3+Mt7c3tWrVwtnZ2VqlxsbG4u3tjaOjI15eXmzZsgWADRs24O3tXZKHIiIiIlLqSnRErlGjRjz77LP06dOHnJwcOnXqhJ+fHwCRkZGMHj2azMxM2rZtS+fOnQGIiooiLCyMtLQ0mjRpwoABAwAIDw9nwoQJLFq0iJo1azJnzpySPBQRERGRUlcihdyOHTusP/fr149+/fpds07r1q3ZuHHjNe2NGjVizZo117TXqlWLFStWFG1QERERkXJEj+gSERERKadUyImIiIiUUyrkRERERMopFXIiIiIi5ZQKOREREZFySoWciIiISDmlQk5ERESknFIhJyIiIlJOqZATERERKadUyImIiIiUUyrkRERERMopFXIiIiIi5ZQKOREREZFySoWciIiISDmlQk5ERESknFIhJyIiIlJOqZATERERKadUyImIiMgtyTJnF7i8RYsWhdpebFMhJyIiIrfGMEp3e8GhtAOIiIhI+eTk4MSRiO63vH390LVFmOb2pBE5ERERkXJKhZyIiIhIOaVCTkRERKScUiEnIiIiUk6pkBMREREpp1TIiYiIiJRTKuREREREyikVciIiIiLllAo5ERGRcsiwmAtcbuvxWDeyDyn79GQHERGRcshkZ1+opyqAnqzwb6AROREREZFySoWciIiISDmlQk5ERESknFIhJyIiIlJOFXshl5aWhp+fHydOnABg9erV+Pn54e/vzyuvvEJWVhYACxYswMfHh8DAQAIDA/nggw8ASEpKIigoCF9fX0JDQ8nJyQHg1KlT9OvXj86dOzNixAjS09OL+1BEREREypRiLeT2799Pnz59OHr0KAC///4777zzDqtWrWLjxo1YLBZWrlwJQGJiInPmzCE2NpbY2Fj69esHQEhICJMnT2bbtm0YhkFMTAwAU6dOpW/fvsTFxdG0aVOio6OL81BEREREypxiLeRiYmIIDw/Hw8MDACcnJ8LDw3F1dcVkMtGwYUNOnToF5BZyb731Fv7+/kybNo3MzExOnjxJRkYGnp6eAAQFBREXF0d2djZ79+7F19c3T7uIiIjI7aRYC7mIiAi8vLysr2vVqsVjjz0GwLlz5/jggw944oknSE9P58EHHyQkJIT169dz4cIFoqOjOXPmDO7u7tbt3d3dSU5O5vz587i6uuLg4JCnXUREROR2UioTAicnJzNkyBC6d+/OI488AsCSJUusy5955hkmTpyIt7c3JpPJ2m4YBiaTyfrn1f752pbExMRCHEGuhISEQu+jvGco7f6VQRnKWobS7l8Zbp8MN/LkhhtRmIxFkaGw79HtnqHEC7nDhw8zZMgQ+vfvzzPPPAPk3rgQHx9Pjx49gNyCzcHBgRo1apCSkmLdNjU1FQ8PD9zc3Lh48SJmsxl7e3tSUlKsp29vVNOmTXF2dr7l40hISCiyv0TlNUNp968MylDWMpR2/8qgDLeitDOWdv9lPUNmZmaBg08lOv1IWloagwcPZsyYMdYiDsDFxYVZs2Zx/PhxDMPggw8+oGPHjtSqVQtnZ2drlRobG4u3tzeOjo54eXmxZcsWADZs2IC3t3dJHoqIiIhIqSvREbk1a9aQmprKsmXLWLZsGQDt27dnzJgxTJs2jREjRpCdnU3z5s0ZNGgQAFFRUYSFhZGWlkaTJk0YMGAAAOHh4UyYMIFFixZRs2ZN5syZU5KHIiIiIlLqSqSQ27FjBwADBw5k4MCB113H19fXehfq1Ro1asSaNWuuaa9VqxYrVqwo0pwiIiIi5clNn1rNzs4ujhwiIiIicpNsFnLfffcd0dHRZGVl0bNnzzzXpomIiIhI6bFZyM2aNQtPT08+++wz7rrrLjZv3sy7775bEtlEREREpAA2Czmz2cyjjz5KfHw8HTp0oHbt2lgslpLIJiIiUiYZFrPNdWxNaXEj+xCxxebNDhaLhR9//JGdO3cybNgwDh06pOvkRETktmays+dIRPdC7aN+6NoiSiO3M5uF3PDhw3nppZfo0aMHderUoX379oSGhpZENhEREREpgM1CrlOnTnTq1Mn6+tNPP8Xe3r5YQ4mIiIiIbTavkUtJSeHZZ5/F19eX1NRUnn32Wc6cOVMS2URERESkADYLualTp9KhQwecnZ258847adSoEWFhYSWRTUREREQKYLOQO3nyJL169cLOzg5HR0dCQkI4ffp0SWQTERERkQLYLORMJlOe6UbS0tI0/YiIiIhIGXBDNzuMGzeOixcvsmrVKj766CO6dOlSEtlEREREpAA3NP3Ihg0bsFgsxMfH07t3b3r27FkS2URERESkADYLOYCnnnqKp556iuTkZI4fP47JZCruXCIiIiJig81r5FauXMlLL73EuXPnCAoKIjQ0lNmzZ5dENhEREREpgM1Cbs2aNbzyyivExcXRvn17Nm/ezK5du0oim4iIiIgU4IbuWq1WrRrffPMNrVu3xsHBQXetioiIiJQBNgs5JycnlixZwrfffstjjz3GypUrqVChQklkExEREZEC2CzkIiIiOHr0KDNnzuTOO+8kISGB6dOnl0Q2ERERESmAzbtW69evT0REhPX17NmzuXTpUrGGEhERERHbbBZyn332GfPnz+fSpUsYhoHFYuGvv/7i+++/L4l8IiIiIpIPm4Xc66+/zgsvvMCHH37I0KFD+eyzz6hUqVJJZBMRERGRAti8Rq5ChQp07doVT09PnJ2dmTJlCjt37iyBaCIiIiJSEJuFnLOzM1lZWdxzzz0kJSVhZ2enJzuIiIiIlAE2T622b9+eZ599lpkzZ9K7d28SEhKoUqVKSWQTERERkQLYLOSGDx9OQEAA1atXJzo6mr179+Ln51cS2URERESkAAUWcocPH8bZ2ZnatWsDcOTIER577DGqVq1aIuFEREREJH/5XiOXkJBAnz59+P33361tx44d4+mnn2b//v0lEk5EROR6DIu5wOUtWrQo1PYi5UW+I3Lz5s3jjTfeoHXr1ta2kSNH8p///Ic5c+bw3nvvlUhAERGRfzLZ2XMkovstb18/dG0RphEpPfmOyF28eDFPEXeFt7c3586dK9ZQIiIiImJbvoWcYRj5b2Rnc9YSERERESlm+VZkdevWJT4+/pr2+Ph4qlWrVqyhRERERMS2fK+RGzNmDAMHDqR79+40b94cwzD4/vvvWbNmDYsXLy7JjCIiIiJyHfkWcg0aNOC9995jyZIlzJo1Czs7Ozw9PVm5ciX33HNPSWYUERERkesocB65+vXrM2PGjFveeVpaGsHBwSxevJjatWsTHx/PjBkzyMzMpEuXLrz44osAJCUlERoaSnp6Ol5eXkydOhUHBwdOnTpFSEgIZ8+epV69ekRFRVGpUiUuXLjAuHHjOH78OG5ubsybNw93d/dbzikiIiJSHhXbXQv79++nT58+HD16FICMjAwmTpxIdHQ0W7ZsITExkS+++AKAkJAQJk+ezLZt2zAMg5iYGACmTp1K3759iYuLo2nTpkRHRwO5U6N4eXmxdetWevbsSURERHEdhoiIiEiZVWyFXExMDOHh4Xh4eADw448/UrduXerUqYODgwP+/v7ExcVx8uRJMjIy8PT0BCAoKIi4uDiys7PZu3cvvr6+edoBdu7cib+/PwB+fn58+eWXZGdnF9ehiIiIiJRJ+RZyn332GQBZWVm3tOOIiAi8vLysr8+cOZPn9KeHhwfJycnXtLu7u5OcnMz58+dxdXXFwcEhT/s/9+Xg4ICrq6vmthMREZHbTr7XyL3xxht06NCB3r17s379+kJ3ZLFYMJlM1teGYWAymfJtv/Ln1f75+uptbnZuu8TExJta/3oSEhIKvY/ynqG0+1cGZShrGUq7/9slg61HcN2IwmQsiv6VofD9K0MBhVylSpXw9fUlOTnZehrzaps2bbqpjmrUqEFKSor1dUpKCh4eHte0p6am4uHhgZubGxcvXsRsNmNvb29dH3JH81JTU6lRowY5OTmkp6dz11133VSepk2b4uzsfFPbXC0hIaHI/hKV1wyl3b8yKENZy1Da/SvDzSkLGZWh9Psv6xkyMzMLHHzKt5BbunSp9W7SSZMmFTpgs2bN+P333zl27Bi1a9fm448/pnv37tSqVQtnZ2frX/zY2Fi8vb1xdHTEy8uLLVu24O/vz4YNG/D29gagbdu2bNiwgeHDh7Nlyxa8vLxwdHQsdEYRERGR8iTfQs7V1ZWWLVvy1ltv4eHhwc8//0xOTg4PPfQQrq6uN92Rs7MzkZGRjB49mszMTNq2bUvnzp0BiIqKIiwsjLS0NJo0acKAAQMACA8PZ8KECSxatIiaNWsyZ84cIHey4gkTJvDkk09SuXJloqKibuXYRURERMq1AueRA7h48SL9+/enWrVqmM1mkpOTWbx4Mc2bN7+hDnbs2GH9uXXr1mzcuPGadRo1asSaNWuuaa9VqxYrVqy4pv2uu+7S0yVERETktmezkJs5cyZRUVG0atUKgG+++YbIyEjrXG8iIiIiUjps3uqZnp5uLeIgd1Tt8uXLxRpKRERERGyzWciZTCZOnjxpfX3ixAns7e2LNZSIiIiI2Gbz1OrIkSPp3bs3rVu3xmQy8fXXXxMeHl4S2URERESkADYLuQ4dOlC/fn12796NxWJh2LBhNGjQoCSyiYiIiEgBbBZyAPXr16d+/frFnUVEREREbsLNPddKRERERMoMFXIiIiIi5ZTNQm78+PElkUNEREREbpLNQi4pKQnDMEoii4iIiIjcBJs3O3h4ePDkk0/SrFkzKlWqZG0PCwsr1mAiIiIiUjCbhdzDDz/Mww8/XBJZRERERModS3YW9UPXFmp7O0enW9rWZiE3atQoMjIyOHbsGPfffz+ZmZlUqFDhljoTERER+bexc3TC/6XYW95+0+zAW+/b1gr79++nQ4cODBs2jDNnztCuXTv27dt3yx2KiIiISNGwWcjNnDmT5cuXc9ddd1GjRg1ef/11IiIiSiKbiIiUQYbFXODyFi1aFGp7EblxNk+tZmRkcN9991lft23blrlz5xZrKBERKbtMdvYcieh+y9sX5loiEcnL5oicg4MDf//9NyaTCYAjR44UeygRERERsc3miNyIESN4+umnSUlJYezYsezatYtp06aVRDYRERERKYDNQs7Hx4f69euza9cuLBYLI0eOpEGDBiWRTUREREQKcEPPWs3JycFiseDg4ICDg83aT0RERERKgM1Cbu3atQwYMICffvqJ7777jn79+rFt27aSyCYiIiIiBbA5vLZ8+XLWr1+Ph4cHAKdOnWLYsGH4+voWezgRERERyZ/NETlHR0drEQdw99134+joWKyhRERERMS2fEfkfv75ZwAeeOABpk2bRu/evbG3t2fdunU0b968xAKKiIiIyPXlW8iNHj06z+udO3dafzaZTISFhRVbKBERERGxLd9CbseOHSWZQ0RERERuks2bHVJSUli/fj1//fVXnvbx48cXWygRERERsc3mzQ4jRozgxx9/xDCMPP+JiIiISOmyOSKXnZ3NggULSiKLiIiIiNwEmyNyTZo04dChQyWRRURERERugs0RuebNm/PUU0/h7u6e5/Fc27dvL9ZgIiIiIlIwm4XcO++8Q1RUFPfcc09J5BERERGRG2SzkLvjjjvo2rVrSWQREREbDIsZk519vstbtGhRqO1FpHyxWci1atWKmTNn0qlTJ5ycnKztTZo0KdZgIiJyLZOdPUciut/y9vVD1xZhGhEpbTYLuU2bNgGwbds2a5vJZLrla+Q++ugj3n//fevrEydOEBgYyOXLl0lISKBChQoAjBo1io4dOxIfH8+MGTPIzMykS5cuvPjiiwAkJSURGhpKeno6Xl5eTJ06Nc81fCIiIiL/djYrn6J+wkPPnj3p2bMnAL/++isjR45k1KhR/O9//+P999/Hw8PDum5GRgYTJ05kxYoV1KxZk2HDhvHFF1/Qtm1bQkJCmD59Op6enkycOJGYmBj69u1bpFlFRESkbLNkZxVqpNmSnYWdo5PtFcsom4XcsmXLrts+aNCgQnc+ZcoUXnzxRSpUqMCpU6eYOHEiycnJdOzYkVGjRvHjjz9St25d6tSpA4C/vz9xcXHcd999ZGRk4OnpCUBQUBDz589XISciInKbsXN0wv+l2FveftPswCJMU/JsFnJXzyGXlZXF3r17ad26daE7jo+PJyMjgy5dunD8+HFatWpFeHg4lStXZtiwYaxZs4aKFSvi7u5u3cbDw4Pk5GTOnDmTp93d3Z3k5ORCZxIREREpT2wWcjNmzMjzOjk5mdDQ0EJ3vGrVKuuoXp06dVi4cKF1Wf/+/dmwYQO+vr6YTCZru2EYmEwmLBbLddtvRmJiYiGPABISEgq9j/KeobT7VwZlKGsZirt/W3el3ojCZlSGoulfGQrfvzLcQCH3T9WrV+fkyZO31NkVV0b2IiMjATh48CBHjx7F19cXyC3MHBwcqFGjBikpKdbtUlJS8PDwuKY9NTU1z7V1N6Jp06Y4Ozvf8jEkJCQU2V+i8pqhtPtXBmUoaxlKu/8bVRYyKoMylJX+y3qGzMzMAgefbuoaOcMwSExMpGrVqrcQ8f87ePAg9957LxUrVrTu97XXXqNVq1ZUrFiR1atX061bN5o1a8bvv//OsWPHqF27Nh9//DHdu3enVq1aODs7W/+nGRsbi7e3d6EyiYiIiJQ3N3WNHEDNmjUZP358oTo9fvw4NWrUsL5u9P/au/Owqqq9D+DfwyCK8t4kQa5DmiM3SXNKUTloV0BlEjPTEBXpOrxqTqmIY3avmuKUQw43u9ehEjVACwH1TTFRU7AQ5wwsU3VGRgAAIABJREFUleEIpSAynvX+4cMJQkLDs/c68P08j0+6D/D7HtbeZ/86Z++1HB0xbtw4jBgxAsXFxXB3d4eXlxcAYPny5ZgyZQoKCgrg6uqKAQMGAABCQ0Mxf/585ObmokOHDhg1alS1MhERERGZmqe+Ru5ZGDRoUIXVIvz9/eHv71/ha52dnXHgwIEK2x0dHbFv375nno2IiIjIVFTayM2dO7fSb9JoNFi6dKlRAhERyaq6y2M9yc8gInoalTZybdu2rbDtl19+wX//+180bdrUqKGIiGRU3eWxAC6RRUTPVqWN3NixY8v9Oz4+HnPmzIG3tzfmz59v9GBERERE9MeqvEauuLgYq1atQnh4ON577z3DFCFEREREpK4/bORSU1MxY8YM1K9fHxEREeXuNCUiIiIidZlV9sD+/fsxbNgwuLm5YefOnWziiIiIiCRT6Tty8+bNg5mZGbZu3Ypt27YZtpcuh5WYmKhIQCIiIpKTvqiwWjfw6IsKYWZZ5xkmqn0qbeSOHj2qZA4iIiIyMWaWdeA9M/JPf//BVb7PME3tVGkjxylGiIiIiORW6TVyRERERCQ3NnJEREREJqrKeeSIiIhIPtW90aD0Z/BmA9PGRo6IiMgEVfdGA4A3G9QE/GiViIiIyESxkSMiIiIyUWzkiIiIiEwUGzkiIiIiE8VGjoiIiMhEsZEjIiIiMlFs5IiIiIhMFOeRIyIiekqcjJdkwUaOiIjoKXEyXpIFP1olIiIiMlFs5IiIiIhMFBs5IiIiIhPFRo6IiIjIRLGRIyIiIjJRbOSIiIiITBQbOSIiIiITxUaOiIiIyESxkSMiIiIyUWzkiIiIiEwUl+giIiKTU921TrnOKdUUbOSIiMjkVHetU65zSjWFKo1cQEAAsrOzYWHxqPySJUvw008/4aOPPkJxcTFGjx4Nf39/AEB8fDyWLVuGgoICDBw4ENOnTwcAXL58GfPmzcODBw/QrVs3vPfee4afR0RERFQbKH6NnBACqampiIyMNPxxcHDAmjVr8OmnnyIiIgJ79uzBDz/8gPz8fISEhGDTpk2IiopCcnIyjh8/DgCYNWsWFi5ciJiYGAghEBYWpvRTISIiIlKV4o3cjz/+CAAYO3YsfHx8sGvXLsTHx6Nnz5547rnnYG1tDQ8PD0RHRyMpKQktWrRA8+bNYWFhAW9vb0RHR+P27dvIz8/HK6+8AgAYMmQIoqOjlX4qRKSgwpKiKr+ma9eu1f4ZRESmRPHPIu/fvw9nZ2csWLAARUVFGDVqFAYOHAg7OzvD19jb2yMpKQmZmZkVtmdkZFTYbmdnh4yMjKfKkZycXO3nkpCQUO2fYeoZ1K7PDLUnw8udXq7+DxGiWhmrahSflNoZqjtOzFBz9gUZMpj6vqB2BsUbuc6dO6Nz586Gfw8dOhTLli3DxIkTDduEENBoNNDr9dBoNE+8/Wk4OTnBysrqTz+PhISEZ3YQmWoGteszQ+3L8OO/Xq/W97eat1/13xPw7E7AplqfGZhBpvqyZygoKPjDN58U/2j13LlzOHXqlOHfQgg0bdoUOp3OsE2n08He3h4ODg5PtP3u3buwt7dX5gkQERERSULxRi4nJwcrVqxAQUEBcnNzER4ejpUrV+LUqVPIzs7Gw4cPERsbC61Wi06dOiElJQU3b95ESUkJvvzyS2i1WjRt2hRWVlaGtyEjIyOh1WqVfipEREREqlL8o9V+/frh+++/x+DBg6HX6/HWW2+ha9eumD59OkaNGoWioiIMHToUHTt2BAAsX74cU6ZMQUFBAVxdXTFgwAAAQGhoKObPn4/c3Fx06NABo0aNUvqpEBEREalKlYnXpk2bhmnTppXb5u3tDW9v7wpf6+zsjAMHDlTY7ujoiH379hktIxEREZHsuNYqERERkYliI0dERERkotjIEREREZkoNnJEREREJoqrzBMR0VPRFxWi1bz91fp+M8s6zzARUe3FRo6IiJ6KmWUdeM+M/NPff3CV7zNMQ1S78aNVIiIiIhPFRo6IiIjIRLGRIyIiIjJRbOSIiIiITBQbOSIiIiITxUaOiIiIyESxkSMiIiIyUZxHjojIhHAyXiIqi40cEZEJ4WS8RFQWP1olIiIiMlFs5IiIiIhMFBs5IiIiIhPFa+SIiJ5QdW80KP0ZvNmAiJ4VNnJERE+oujcaALzZgIieLX60SkRERGSi2MgRERERmSg2ckREREQmio0cERERkYliI0dERERkotjIEREREZkoNnJEREREJoqNHBEREZGJYiNHREREZKLYyBERERGZKDZyRERERCaKjRwRERGRiWIjR0RERGSi2MgRERERmShVGrkNGzbA09MTnp6eWLFiBQBg7ty5cHd3h6+vL3x9fXH48GEAQHx8PLy9veHu7o41a9YYfsbly5cxZMgQeHh4YN68eSguLlbjqRARERGpRvFGLj4+Ht988w3Cw8MRERGBixcv4vDhw0hOTsauXbsQGRmJyMhIuLm5IT8/HyEhIdi0aROioqKQnJyM48ePAwBmzZqFhQsXIiYmBkIIhIWFKf1UiIiIiFSleCNnZ2eH4OBg1KlTB5aWlmjdujXu3LmDO3fuICQkBN7e3vjwww+h1+uRlJSEFi1aoHnz5rCwsIC3tzeio6Nx+/Zt5Ofn45VXXgEADBkyBNHR0Uo/FSIiIiJVWShdsG3btoa/p6am4tChQ9i9eze+/fZbLFq0CDY2Nhg/fjz27dsHa2tr2NnZGb7e3t4eGRkZyMzMLLfdzs4OGRkZT5UjOTm52s8lISGh2j/D1DOoXZ8Zak+GVzo6odW8/dX6GSVFBfgu6c8f+127dq1W/VLV+T09iwzVHSdmqDn7ggwZTH1fUDuD4o1cqevXr2P8+PGYPXs2WrVqhY0bNxoeCwgIQEREBDw8PKDRaAzbhRDQaDTQ6/WP3f40nJycYGVl9afzJyQkPLODyFQzqF2fGWpfBu+ZkdX6/oOrfFX/PQHP7gRsqvWZgRlkqi97hoKCgj9880mVmx0SEhIwZswYzJw5E35+frh69SpiYmIMjwshYGFhAQcHB+h0OsN2nU4He3v7Ctvv3r0Le3t7RZ8DERERkdoUb+TS0tIwadIkhIaGwtPTE8Cjxm3p0qW4d+8eioqKsGfPHri5uaFTp05ISUnBzZs3UVJSgi+//BJarRZNmzaFlZWV4W3IyMhIaLVapZ8KERERkaoU/2j1448/RkFBAZYvX27YNnz4cIwbNw4jRoxAcXEx3N3d4eXlBQBYvnw5pkyZgoKCAri6umLAgAEAgNDQUMyfPx+5ubno0KEDRo0apfRTIao1hL4EGjPzP/yaqj6aeJKfQURET0fxRm7+/PmYP3/+Yx/z9/evsM3Z2RkHDhyosN3R0RH79u175vmIqCKNmTl+/Nfr1foZ1b1RgYiIKuLKDkREREQmio0cERERkYliI0dERERkotjIEREREZkoNnJEREREJoqNHBEREZGJYiNHREREZKLYyBGZAKEv+cPHn2QyXiIiqnkUnxCYiJ5edSfk5WS8REQ1E9+RIyIiIjJRbOSIiIiITBQbOSIiIiITxUaOqAq80YCIiGTFmx2IqsAbDYiISFZ8R46IiIjIRLGRIyIiIjJRbORIarw+jYiIqHK8Ro6kxuvT5KAvKqz271JfVAgzyzrPKBEREQFs5IjoCZhZ1oH3zMhq/YyDq3yfURoiIirFj1aJiIiITBQbOSIiIiITxY9WiUxAda9R4/VpREQ1Exs5IhNQ3WvUeH0aEVHNxEaOqAp8N4yIiGTFRo4qJfQl0JiZV/p4VXO4PcnPMAV8N4yIiGTFRo4qVd053ADO40ZERGRMbORIavxYk4iIqHJs5KhSMszmz481iYiIKsdGjirF2fyJiIjkxgmBiYiIiEwUGzkiIiIiE8VGrhL64sI/fLyqqTeq+v6qCH1JlV9TVYYn+RlERERkuniNXCXMLOpUa+qN6t4kwKk/iIiIqCom3cgdPHgQH330EYqLizF69Gj4+/s/s5+t9rQXMtwxSkRERHIz2UYuIyMDa9aswRdffIE6depg+PDh6NGjB9q0afNMfr7a017wjlEiIiKqisleIxcfH4+ePXviueeeg7W1NTw8PBAdHa12LCIiIiLFmOw7cpmZmbCzszP8297eHklJSVV+nxACAFBYWPXNCM/V//NrhBYUFPzp730W9Znh2dVnhurXZwZ5MtSU/VGGDKa+L8iQoabsC8bMUNqvlPYvv6cRlT0iuY8++ggFBQWYNm0aACAsLAzJyclYsmTJH35fTk4Orl27pkREIiIiomeiXbt2sLGxqbDdZN+Rc3BwwLlz5wz/1ul0sLe3r/L76tevj3bt2sHS0hIajcaYEYmIiIiqRQiBoqIi1K9f/7GPm2wj16tXL6xfvx7Z2dmoV68eYmNj8f7771f5fWZmZo/taImIiIhkVLdu3UofM9lGrnHjxpg+fTpGjRqFoqIiDB06FB07dlQ7FhEREZFiTPYaOSIiIqLazmSnHyEiIiKq7djIEREREZkoNnJEREREJoqNHBEREZGJYiNHREREZKLYyBERERGZKDZyRERERCbKZCcEVtKNGzcQExOD9PR0mJmZwd7eHi4uLnj55ZeZoRZmOHHiBKKjo8tl0Gq18PDwUKT+/fv38fXXXyMjIwMajQb29vZwdnZ+oiXqnhWOA8lEhv1RhgwyHBMyZNDr9UhMTCz3GtmxY0fUqVOnRtY3X7x48WKj/OQaYvfu3Vi9ejXatGmDF198Efb29sjLy8O///1v5OXloXPnzsxQizKsW7cOMTExeO211+Di4oJXXnkFDRs2REREBC5fvow+ffoYtf7hw4cxdepUaDQaWFpaorCwENevX8e6detga2uL9u3bG7U+wHH4vfv37yM6OhpxcXFITEzErVu3YGtrW+m6iM/ajRs38Pnnn+PgwYM4fvw4Ll26hLp166Jx48aK1Fc7gwz7owwZZDgmZMiQmJiIwMBAXLlyBZmZmUhJScGxY8ewYcMGtG/fHs2bN6959QX9IXd3d5GXl1dhe15envDw8GCGWpihpKSkwvbi4mIxYMAAo9f38PAQWVlZFbZnZWUpUl8IjkNZsbGxon///mLevHlizZo1YvXq1SIkJES89tpr4sCBA0avv2vXLuHj4yM2btwowsLCRFhYmNi4caPw9fUVH3/8sdHry5BBlv1RhgxqHxMyZPD09BQpKSkVtqempgovL68aWZ8frVbBwsICxcXFFbbn5+fD0tKSGWpZBisrK6Snp6NJkybltt+5c0eRt+01Gg1sbGwqbK9fvz7Mzc2NXh/gOJS1atUq7NmzB7a2tuW2Z2dnw9/fH97e3katv2PHDkRERKBevXrltgcGBsLPzw9jx441an0ZMsiwP8qQQYZjQoYMJSUlaNmyZYXtzZs3h1BgRVI16rORq8KECRMwePBgODs7w87ODhqNBpmZmTh9+jSmT5/ODLUsQ3BwMPz9/dGyZctyGVJTU7Fs2TKj13/jjTfw5ptvws3NzVBfp9MhNjYWQ4cONXp9gONQltqNtQwNhNoZZNgfZcggwzEhQ4a+fftiwoQJGDRoULnXyIMHD8LFxaVG1tcIJVpUE5eRkYFTp04hMzMTer0eDg4OcHZ2VvQaFGaQJ0NBQQGSkpLKZejUqZNi/8d54cIFHD9+HJmZmRBCoHHjxtBqtejYsaMi9QGOQ6nt27fjyy+/fGxj7ePjgzFjxhi1/sGDB7F27dpKGwhPT0+j1pclgwz7owwZZDgmZMgQHR1d4TXS1dUVAwYMqJH12cg9gcuXLyMtLQ3du3cv93/fX3/9Nfr168cMtSzDkSNHkJaWBldXV7zwwguG7Xv27MGbb75p9PrZ2dnQ6XRo27YtzMx+m0Ho4sWL6NChg9HrAxyHstRurGVoINTOIMP+KEMGGY4JGTIIIfDgwQM0aNCg3HadTgc7O7uaV98oV97VIP/5z3/EoEGDxPjx40WvXr1EfHy84bHBgwczQy3LsHLlSjF69GixZMkS0atXLxEREaFohq+++kr06dNHeHl5CXd3d3H16lVF6wvBcfi9rKwsceXKlQoXeScnJytS/9KlS+Lo0aPi/v375bb/3//9nyL11c4gw/4oQwYZjgkZMpw6dUr07t1bdO/eXYwePVqkp6crmkGN+mzkquDl5WW4GykhIUH07t1bnD17VgghhK+vLzPUwgxFRUVCCCFSUlJEv379RFRUlGIZvL29DXetljZ1169fV6y+EByHstRurGVoINTOIMv+KEMGtY8JGTL4+fmJH3/8UZSUlIgtW7aI/v37i4yMDMUyqFGfNzs8gdK7sbp06YI1a9Zg2rRp2L59OzQaDTPUsgxCCEOtli1bYsuWLQgMDIStra1iGUrvkBw0aBA0Gg3GjRuHzz77jOOg8DgAwObNmxEZGQlbW1tERUUhKCgIn3zyCdq0aaPIHXL79u3Dvn37UK9ePSQmJuKdd97B2rVr0a1bN0Xqy5JB7f1RhgwyHBMyZNDr9XjxxRcBAOPGjUOdOnUQFBSk2GukGvW5RFcVunbtiunTp+OHH34AAHTv3h0LFy5EYGAg0tLSmKGWZRgwYAACAgKQlJQEAGjbti3WrVuHadOm4aeffjJ6/VatWmHFihVIT08HAAwcOBCBgYHw9/fH3bt3jV4f4Dj8XtnGOiQkBOPGjTPM6K6ExzUQ165dU72JUSqDDPujDBlkOCZkyNCoUSN8+umnyMnJAQCMGTMGLi4uCAwMxL1792pkfa7sUAWtVovc3Fz85S9/MVy427p1a3Tv3h23b99W5I4sZpAnw6uvvoomTZqgYcOGhhN4kyZN4Onpifz8fGi1WqPW12q1+O6771C/fn3DDOGdOnVCkyZNcOHCBYwYMcKo9Usz1PZxKHXmzBkkJSWhdevWaNCggeEGlIULFyInJ8foc6hdu3YNkZGRaNOmDWxtbdG0aVM0a9YM06dPx/379/H2228btb4MGWTYH2XIIMMxIUMGZ2dn7N+/H1ZWVmjdujUAoHfv3sjLy8PJkyfxj3/8o8bV512rT0DtdduYQa4MMqx1mp6eXm4tQwcHB8VqAxyHUnl5edi6dSt69OgBZ2dnw/YjR45g/fr1iIyMNGp9vV6P/fv3o3379uXukk1KSsLmzZuxadMmo9aXKYPa+6MMGWQ4JmTIADyamDc7OxtmZmZ47rnnFJswXY36bOSqkJiYiLlz56JJkyZo1KgRhBC4e/cubt68iaVLl5Z78WaGmp/h8OHDWLFiBXr06FEuw+nTpzFt2jSjz+SfkpKC4OBg/PLLL+Xq161bFytXroSjo6NR6wMch8dRs7GWoYFQM4MM+6MMGWQ4JmTIkJWVhX/+8584ceIEGjRoAL1ej7y8PHTr1g2LFi3CX//615pX3yi3UNQgaq/bxgxyZVB7rVM/Pz/D3XBlnT17Vvj5+Rm9vhAch7J+/PFHMWzYMOHm5iZGjBghhg8fLvr37y+8vLzE5cuXjV4/ISFBuLu7izFjxoh3331XzJw5U4wePVr07du33N2jNTmDDPujDBlkOCZkyDBy5EgREREhiouLDduKi4tFZGSkGDFiRI2sz7tWq6D2um3MIFcGtZdkys/PR7du3Sps79atGwoLC41eH+A4lDVz5kyEhIRUGJNz584hJCQEX3zxhVHrL1y4EFu2bKkwHjdv3sTkyZNx8OBBo9aXIYMM+6MMGWQ4JmTIkJWVBV9f33LbzM3N4ePjg61bt9bI+mzkqqD2um3MIFcGtdc6dXJywuLFi+Ht7W245kSn0yEiIgJOTk5Grw9wHMpSu7GWoYFQO4MM+6MMGWQ4JmTI0Lx5c2zbtg0+Pj6GVRR0Oh0iIyMNN4jVtPq8Ru4JqL1uGzPIlUHNJZmKioqwY8eOcvUdHByg1WoREBCg2HVRtX0cSs2ePRvW1taPbawLCwuxfPlyo9b/4IMPkJKS8tgGonXr1pgzZ45R68uSQYb9UYYMMhwTamfIycnB2rVrcezYMWRmZgKAYSzeeecd/OUvf6lx9dnIPQGh8rptzCBXBhnWOlUbx+ERGRprGRoItTPIsD/KkEGGY0KGDLWOUa68q0HUXreNGeTKoPaSTJcvXxY+Pj7i1VdfFSEhISI3N1fR+kJwHGSj1+tFTk5Ohe2ZmZm1IoMM+6MMGWQ4JmTIcOfOHTFx4kTh5+cnNm7cWO6mg3HjxtXI+mzkqqD2um3MIFcGtdc6ffPNN8WpU6dEVlaWWLRokfDz8zM0cxwHZdecFUL9xlqGBkLtDDLsjzJkkOGYkCHDmDFjxL59+8SFCxfE+PHjxdtvvy0KCwsVy6BGfS7RVYXSddPMzMwwbtw4+Pv7IygoCLm5uYqvHccM6mcA1F2SKT8/Hz179oStrS0WL16MHj16YOLEiSgqKjJ67VIch98sXrwYc+fOxaFDh2BpaYmAgAA8ePAAABS50H/FihXYuXMnTp8+jV69emHkyJGG63KUqC9DBhn2RxkyAHIcE2pn+PXXX/H666/DyckJH330EWxsbDB79mxFaqtVn41cFdRet40Z5Mqg9lqnDRo0QFxcnOEEOWfOHNjZ2WHKlCl4+PCh0esDHIey1G6sZWgg1M4gw/4oQwYZjgkZMlhYWOD69esAHk2H8sEHHyA7OxsLFy5ESUlJzaxvlPf5apDMzEzx7rvvitjY2HLbP/nkE9G9e3dmqGUZHjx4INasWVNhotPDhw8LHx8fo9f/4YcfxIgRI0R4eLhhW3FxsfjnP/8pXnrpJaPXF4LjUJa/v784fvy40Ov1hm0zZswQ48ePF+7u7kavHxQUJHbv3i3u379v2PbBBx+IoUOHin79+hm9vgwZZNgfZcggwzEhQ4aEhATRr18/ceDAgXK5Jk6cKBwdHWtkfTZyVVi5cqU4c+aMKCoqYgZmEFOnThVffPGF0Ol0qtT/9NNPxc8///zYxx43o7oxcBx+o3ZjLUMDoXYGGfZHGTLIcEzIkOH48eMiPz/fcF1aWZcuXaqR9Tn9SBXOnTuHEydOICEhAba2tnBxcYGLi4uiaykygzwZ0tPTERcXhxMnTuDXX39Fly5doNVq0blz53K32htLZGQk4uLikJ6ejpdffhlarRbdunVTdF1NjsNvPvvsM7i4uKBZs2YVHsvOzjZcL2QsoaGh0Gq16NKlCyws1JnfXe0MMuyPMmSQ4ZiQIcOmTZvw7bffom7duujduze0Wi1atGihSG216rORewo6nQ4nTpxAXFwcdDodOnXqpOhFlDJmuHv3Ljp27FgrMxQXFyMhIQFxcXE4f/487O3tsXbtWkVqCyGQlJSEuLg4fPvtt7CxsUHv3r3h7++vSP1StX0c1G6sZWggZMhQSob9UYYMah4TsmS4d+8eTp48ibi4OKSmpsLJyQkuLi5wdXWtcfXZyD0lIQTu3buH//mf/8H58+fRtWvXWpkBAB48eIB69erV+gypqalo2bIlMjIy0LhxY1UyZGdn45tvvoGPj48q9Wv7OMjQWMvQQMiQAZBjf5QhgwyvTTJkAB5NSBwXF4eJEyfWvPpG+cC2Brlz546YMWOGWLJkibh8+bLo27evcHZ2FoMGDRI//PBDrcnwODJMvKp0htu3b1f44+npKe7cuSNu376taJZS/v7+qtQti+NQXlZWloiMjFSldm5urigpKRHnzp1Tpb4MGfjapM4xIUOGNWvWCCGEuHfvnpg5c6Z49dVXRa9evcTChQsfO2l1TajPd+SqMHr0aAwcOBB37tzB559/jlWrVsHFxQWnT5/Gxo0bsXPnzlqR4eWXX0ZxcTGAR+8+aDQawxQYGo0Gly9frhUZevTogaKiIjRs2NBQOzMzE/b29tBoNDh69KhR6//973+vsK3s/+kauz7AcajKyJEjsWvXLtXq+/n5ITw8XLX6SmeQYX+UIYMMx4QMGUr3vVmzZuGvf/0r3n77bej1euzevRuXLl3Cxo0ba159o7SHNUjpLdMlJSWiT58+5R5T6v+4ZMhw8eJFMWLECBETE2PYpuQM+rJkSEtLE0FBQWL79u2qZPj666/FwIEDxVdffSVu3bolfv75ZzFo0CBx69YtcevWLUUycBx+89prr1X406FDB8Pfjc3JyUk4OjoKR0dH0b59e8N/S/+uBLUzyLA/ypBBhmNChgyl50QvL68Kjw0aNKhG1ueEwFWoV68eTp48CTMzM0RFRRm2HzlyBPXq1as1GV566SVs374d8fHxmDt3Lh48eKDobOGyZHBwcMC2bdtQVFSEoKAgpKenK5qhb9++2LlzJw4cOIDw8HA0adIEderUQdOmTdG0aVNFMnAcfrNgwQJYWVlh5syZ2LFjB/773/+iRYsW2LFjB3bs2GH0+nv27EHnzp2xbt06XLlyBZcvX4ajo6Ph70pQO4MM+6MMGWQ4JmTIoNPpEBUVBQcHB3z33XeG7UlJSbCysqqZ9Y3SHtYg169fFwEBAaKkpMSwLSoqSgwdOlSx69NkyFDWkSNHxNChQ4Wbm5vitWXKcOHCBfHGG28o8s7L4+zYsUOMHDlSeHh4qFJfCI6DEELcvXtXjB8/Xqxfv16UlJQofm3Uw4cPxaJFi0RwcLDIzc1V5fowGTIIIcf+KEMGtY8JNTOEh4eL999/XwwbNkxMnjxZCPFoTsPevXsrcs2mGvV5jRz9KXfv3sWxY8cwdOjQWp0hPz8fCQkJ6N27tyr1r1+/jpiYGEyePFmV+gDHodTOnTsRGxsLnU6H6OhoxesfPXoUmzdvxr179xAbG6t4fVkyyLA/ypBBhmNChgwAkJubC2tra0XnmFSyvjozSJqQhw8fYtOmTTh06BAyMjJgZmYGe3t7uLq6YurUqbCxsakVGfR6PcLCwipkuH//PgICAmBpaVkrMjx8+BAbN25EdHR0uQxarRbTpk1TZCxOnDiB6OhopKenG+rHxMTAw8PD6LUBjkNlAgIC0LNnT8RKvggfAAAP5UlEQVTExCheG3h0I0ynTp1w7NgxVeqrlUGG/VGGDDIcEzJkKB2L379Gurq6YuTIkUYfCzXq8x25KkyaNAkdOnTAkCFDYGdnB+DRZ+Dh4eFITEzEtm3bakWGBQsWQK/Xw8/PD/b29hBCQKfT4cCBA8jLy0NoaGityFDZWERERCAhIcHoY7Fu3TokJSXBx8en3O/gyy+/RJs2bTBnzhyj1gc4Dr/3uMZaq9Uq0lhX1kBotVrVmxilMsiwP8qQQYZjQoYMao+FKvWN8oFtDTJw4MBKH/P09Kw1Gf7oOqw/ylfTMqg9Fu7u7uWulSxVXFwsBgwYYPT6QnAcylq7dq0YO3asiIiIEPHx8eLkyZMiIiJCvP3222L58uVGrz9//nwREhIizp49K27evClSU1PF2bNnxYIFC8TMmTONXl+GDDLsjzJkkOGYkCGD2mOhRn3etVoFW1tbHDp0CHq93rBNCIGvvvoKDRs2rDUZ6tevj6SkpArbz58/j/r169eaDGqPhZWVFdLT0ytsv3PnjmLLQnEcfhMVFYVt27bB19cXzs7O6NWrF3x9fbF582ZFPl48e/Ys/vWvf6Fbt2544YUX0KJFC3Tr1g1LlizBpUuXjF5fhgwy7I8yZJDhmJAhg9pjoUZ9frRahbS0NLz33ns4e/YsbGxsoNFokJOTg27dumHhwoVo0qRJrchw5coVzJo1CwUFBbCzs4NGo0FmZiasrKwQGhqK9u3b14oMao9FfHw85s2bh5YtW5b7HaSmpmLZsmXo2bOnUesDHIeyfHx8sHnz5gr1fv75Z0yePBmRkZFGrf/6669j0aJF6NixY7nt58+fx9KlS7F3716j1pchgwz7owwZZDgmZMig9lioUZ+NXBXi4uLQo0cPmJub45dffoFer8fzzz8PCwvl7hORIcONGzfQunVr3LlzB5mZmdDr9XBwcFDshClLBrXHIjc3F5aWlkhKSir3O+jUqZNi78hxHH6jdmOt9klLhgwy7I8yZJDhmJAhg9pjoUZ93rVaheTkZGzfvh1169ZF7969odVqFT9ZyJBh+/btuHHjBhwdHaHVatGzZ09YW1vXugxqj8XUqVMBAD179oRWq1XkRP17HIffdOzYEdHR0ao11paWljh48KCqDYTaGWTYH2XIIMMxIUMGtcdCjfp8R+4J3bt3DydPnkRcXBxSU1Ph5OQEFxcXuLq61qoMV65cQVxcHE6fPg1zc3P06tULLi4uaNOmTa3KoOZY5OXl4dSpU4iLi8PVq1fRpk0baLVa9OrVCw0aNDB6/VK1fRwAICgoCIB6jfW8efNUbyBkyADIsT/KkEHtY0KWDGqPhZL12cj9SRcvXkRcXBwmTpxYazPk5uYiPj4eJ06cwPvvv19rMwDqjsWNGzcQFxeH+Ph4RafdKKs2j4MMjbXaJy1ZMpSSYX+UIQOg/nlChgxqj4Wx67ORq0JxcTEiIiJQt25deHh4YNmyZTh79iycnJwwZ84cPPfcc0bPoNfrsWPHDhw9ehQ6nQ6WlpZ44YUXMGjQIHh6ehq9viyKi4uxe/dupKWloX///ujWrZvhsfXr12PKlCmq5JoxYwZWr16tSK0jR46gf//+AIC9e/ciLi4OFhYWcHNzw6BBgxTJUFxcjH379sHNzQ02NjbYunUrLly4gA4dOmD8+PGKrGco8zGhdmOt9klLjQxqzuVXKj4+HjY2Nvjb3/6G9evX4+rVq+jatSvGjh0Lc3Nzo9fnuUoOEyZMwLx589C8eXPFarKRq0JwcDDy8vJQWFiIX3/9FR07dsSwYcNw9OhRXLx4ER9++KHRMyxduhRFRUXo27cvYmJi4OjoCHt7e+zatQvOzs6YNGmS0TOcPXv2Dx/v3r270TOEhIRAr9ejXbt22LVrF4YNG4YJEyYAAPz8/BAeHm70DAEBARUWgU5OToaTkxMAGH2h9NLnuX79epw7dw4BAQEQQmDPnj3o0KEDpk+fbtT6ADBz5kwAwPz587Fhwwbk5eVhwIABOHbsGO7fv49Vq1YZPYMMxwSgfmMtQ1OtxomrLBkmyV65ciUSExORm5sLe3t7PP/88/D09ER0dDTq16+P+fPnGz0Dz1WPqH2ucnZ2ho2NDYYPH67YpNycELgKXl5eQohHE6727t273GM+Pj6KZPD29jb8vaSkRAwfPlwIIURBQYFiC6aPHj1adOrUSQQEBIiRI0eW+xMQEKBIhrK/h6ysLOHt7S0++eQTIYQQvr6+imTYuXOn0Gq1Yv/+/eLMmTPi9OnTws3NTZw5c0acOXPG6PVLFyP39vYW+fn5hu2FhYXC3d3d6PWF+O2YEOLR712v1xv+rdTkpzIcE0L8Nh4ffvihGDVqlDh8+LCIjY0VQUFBYvXq1UavP2PGDDFjxgyRnZ0tlixZIoKDg8WxY8fE4sWLxYwZM4xeXwghevbsKdzc3MTHH38sCgsLFalZlgyTZHt6egq9Xi+ys7NF165dDXn0er1i5wmeqx5R+1zl6+srdDqdmDVrlujbt6/YsmWLuHXrllFr8q7VKpiZmSElJQU5OTnIycnBrVu30KxZM2RnZ6O4uFiRDCUlJcjKysLzzz8PnU6H/Px8AEBRUZFidwRt27YNo0aNwujRo/H3v/9dkZq/J4RAXl4erK2tYWtri23btmHEiBGwtbWt8C6ZsYwcORI9e/bEokWL8MYbb2Dw4MGwtrbGq6++qkj9vLw83L17F40bN0Zubq7hHZf8/HzF9gVra2tcv34dbdu2RatWrZCWloYmTZogIyNDsSlQyh4TmZmZqhwTZR0+fBh79+41jEffvn3h5eVl9HdIr127hoMHDwIAEhISEB4eDo1GA1dXV8U+am/cuDH+/e9/Y8WKFXB3d8eIESPg6emJpk2bKlK/dJLs398lq+Qk2QBQWFiIhg0bYs6cOYbF0R88eKDYeYLnqkfUPldpNBo0atQIK1asQGpqKsLCwjB27FgUFBTAwcEBn3/++TOvyUauCrNmzUJgYCD0ej1WrVqFf/zjH2jXrh0uXLiAd955R5EMQUFBeP311/HKK6/g+++/x8yZM3Hz5k2MGTMGkydPViSDpaUlli5div3796vWyI0cORJ+fn5YvHgxnJ2dDSeQoKAgZGVlKZajTZs2+OSTT7B69WpMnToVRUVFitXu0qULAgMDDRNvfvjhh4iNjcXSpUsxfvx4RTIEBwcjMDAQXbp0gbW1NYYNG4ZOnTrh4sWLeO+99xTJEBQUhCFDhqBz586qHRPAb421g4ODKo21DE21GieusoKDg+Hv71/pXH5KeOutt+Dj44OoqCi88cYbAIDExES8++67hss/jI3nqkfUPleJMlertWzZErNnz8bs2bPxyy+/4OeffzZKTV4j9wSOHDmCO3fuoG/fvrC2tsa5c+fQtm1bnDt3Dm+++aYiGbZt24bCwkJ4enqiZcuWKCwsxIMHDxAbG6tYhhs3biA6OrrcwtguLi54+eWXFakPAFevXsXhw4eRmZlpyNC9e3dcvHgRY8aMUSTDjRs3EBMTg/T0dJw6dQq3b99GWFiY4To5JepHR0cjMTERzZs3h52dHXr06FHu5g9jKx2HDRs2wMLCAhMnTlQ8w5UrV3DkyBGcPXsWL774Iuzt7dGnT58KKwwY09y5c5GcnIy0tDQ4Oztj/fr1iI2NxbJlyzBu3DiMGDHCqPXPnz+PKVOmoEuXLqhXrx5OnjxZrqlWYrqHwYMHIyIiosL20hOXEuPx1VdfISUlBebm5mjWrJlhLr/w8HDFXh//85//wNzcHK6urnjhhReQnp6OnJwcJCYmKpaB56pH1DxXHT9+HM2aNTOcI8refGOscwQbuSqEhobi4sWLaNWqFaKjozF79mz4+voCUO4C+9DQUCQnJ6N169aqZdi9ezfCwsIwYMAANGrUCACg0+kQGxsLHx8fjB071ugZPv30U4SFhcHd3R12dnblMnh7exvm9DKm0t+Dh4dHhQxK/B5KfwceHh6qjcPu3buxd+9eVTNU9ns4fPgwvL29FclQVn5+PnQ6HZo3b45r165BCKHYnHKld4jevHkTJSUlaNSoEfr06QMHBwdF6h8/flzR+cF+r+xr9KFDhzBnzpxa+RrNc9Ujap+rVDlHGPUKvBrAy8tLFBUVCSGESElJEf369RNRUVFCCOUusPfy8hLFxcWqZnB3dxd5eXkVtufl5Sl2ESszqF+fGcq7ffv2H/6p6fVlyCDLazQz8FylVn1eI1cFIYThQvqWLVtiy5YtCAwMVPQCe/G7z9zVyGBhYfHYC2bz8/OVub2aGaSozwzljR8/HqmpqYZpL8rSaDQ4evSo4vU1Go3hdcvY9SvLUEqJDLK8RjMDz1Vq1WcjV4UBAwYgICAAwcHB6NixI9q2bYt169Zh8uTJKCwsrDUZJkyYgMGDB8PZ2bncBcWnT59WZO4yZpCjPjOU99lnn+Gtt97CokWL0LVrV8XqylJfhgwyvD4ygzwZ1H5tUKM+r5F7AqdOnYK9vT1at25t2JaWlobt27dj3rx5tSZDRkYGTp06VW5h7NK7R5XCDOrXZ4bykpKSsHfvXtVWUVC7vgwZZHh9ZAZ5Mqj92qB0fTZyRERERCbKTO0ARERERPTnsJEjIiIiMlG82YGI6A/cunULbm5uaNeuHQBAr9ejfv36GDVqVJXLYG3YsAGOjo7o37+/ElGJqBZiI0dEVIW6desiMjLS8O/bt29jzJgxMDc3h4eHR6Xfd+bMGbRp00aJiERUS7GRIyJ6Sk2bNsU777yDjz/+GO3atcOSJUvw4MED6HQ6ODo6Yu3atdi3bx+Sk5OxYsUKw9JNoaGhOHv2LEpKSvDSSy9h/vz5aNCggdpPh4hMGK+RIyL6ExwdHXHt2jWEhYVh8ODBCAsLQ2xsLG7duoVjx47B398fTk5OmD17Ntzc3LB161aYm5vjiy++wIEDB2Bvb4/Q0FC1nwYRmTi+I0dE9CdoNBrUrVsXs2bNwsmTJ7Ft2zakpqYiMzMTeXl5Fb7+2LFjyMnJQXx8PACgqKgIzz//vNKxiaiGYSNHRPQnXLhwAe3atcOMGTNQUlKCgQMHom/fvkhLS6uwVBXw6CaJkJAQwwLzDx48QEFBgdKxiaiG4UerRERPKSUlBZs2bcLYsWPxzTffYNKkSYY7WL///nuUlJQAAMzNzQ3rLvbp0we7d+9GYWEh9Ho9FixYgNWrV6v2HIioZuA7ckREVcjPz4evry8AwMzMDFZWVpgxYwb69u2L6dOnY9KkSbC2tkaDBg3QvXt3/PTTTwCA1157DatXr0ZRURH+93//Fx988AH8/PxQUlKCv/3tbwgODlbzaRFRDcAluoiIiIhMFD9aJSIiIjJRbOSIiIiITBQbOSIiIiITxUaOiIiIyESxkSMiIiIyUWzkiIiIiEwUGzkiIiIiE8VGjoiIiMhE/T99anPpc/7OdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Stacked bar graph to compare suspected cases vs Posistive vs Negative\n",
    "sus_plot = suspected_cases.plot(kind='bar',stacked=True,title=\"Suspected Cases Vs Positive & Negative Cases\",figsize=(10, 6))\n",
    "sus_plot.set_xlabel(\"Date\")\n",
    "sus_plot.set_ylabel(\"Number of Cases\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 2.1.1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<span style=\"font-size:20px; \">Analysis</span>\n",
    "\n",
    "<h1>Suspected Cases(Tested) Vs Positive & Negative Cases(Results) </h1>\n",
    "\n",
    "<hr>\n",
    "\n",
    "The graphs above make it is clear that for tests that are currently done almost 90% return in the negative. This is a clear indication that the disease is still under control in Botswana. \n",
    "\n",
    "It is also can be derived from the graph that when the testing began on the 28/03/2020 the testing was very slow and the country had a high number of suspected cases that tested for covid-19 who did not know their status. As time went on especially from the 02/04/2020 the testing pace or efficiency greatly increased as can be seen by the almost even number of stacked graphs(that is the Orange (Tested Negative) and Blue (suspected Cases)).\n",
    "\n",
    "NB: Please note that in relation to those who tested Negative vs Suspected Cases,  those who tested positive are only a fraction of the aforementioned Suspected Cases and those who tested Negative as whole and therefore it will not be clear on the above bar graph(appears that those who tested positive are available).\n",
    "\n",
    "Let me illustrate Positive tests over time in the following bar graph alone for clarity.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> Bar graph: illustration of deaths since 28/03/2020 </h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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>Tested Positive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15/04/2020</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/04/2020</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/04/2020</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/04/2020</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23/04/2020</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28/04/2020</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/04/2020</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2020</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/05/2020</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Tested Positive\n",
       "28/03/2020                0\n",
       "29/03/2020                3\n",
       "01/04/2020                4\n",
       "02/04/2020                4\n",
       "04/04/2020                4\n",
       "05/04/2020                6\n",
       "06/04/2020                6\n",
       "09/04/2020               13\n",
       "15/04/2020               15\n",
       "16/04/2020               15\n",
       "17/04/2020               15\n",
       "20/04/2020               20\n",
       "23/04/2020               22\n",
       "28/04/2020               23\n",
       "29/04/2020               23\n",
       "01/05/2020               23\n",
       "07/05/2020               23"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suspected_cases_a =  case_bots[['Tested Positive']]\n",
    "suspected_cases_a.head(18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Positive Cases')"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "positive_plot = suspected_cases_a.plot(kind='bar',stacked=False,title=\"Positive Cases since 28/03/2020\",figsize=(10, 6))\n",
    "positive_plot.set_xlabel(\"Date\")\n",
    "positive_plot.set_ylabel(\"Number of Positive Cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<span style=\"font-size:20px; \">Analysis</span>\n",
    "\n",
    "It is clear from the above graph that there has been a steady increase in the number of Positive Cases increases over the days since first date of data release. A closer look reveals that number of positive cases have been steadly increasing at about <span style=\"font-size:18px;\" > 2 cases per every 3days </span> from the 29/03/2020 to 06/04/2020 before finaly more than doubling up to <span style=\"font-size:18px;\" > 13 cases </span> on the 09/03/2020. The trend then follows for following days\n",
    "\n",
    "The histograph below clearly too illustrate the above finding. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average number of days per Case Testing Positive')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram help us to look at the positive cases in more detail.\n",
    "hist_plot = suspected_cases['Tested Positive'].hist()\n",
    "hist_plot.set_title(\"Positive Cases Patterns\")\n",
    "hist_plot.set_xlabel(\"Number of Positive Cases\")\n",
    "hist_plot.set_ylabel(\"Average number of days per Case Testing Positive\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line graph below clearly illustrate the rate of increase in confirmed positive cases:\n",
    "\n",
    "<span style=\"font-size:14px; color:blue;\"> Note</span>\n",
    "But before I can plot the line series I need to have the \"date\" data on above graph in the format of a datetime datatype for the graph to work. The above data as it is now in a string format. For quick analysis I will hardcode the data as follows:\n",
    "\n",
    "But note with a large set of data this could be tedious , it would better to have the date in datetime format so you can use some of the pandas function to easily do the job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### COVID-19 Spread Analysis\n",
    "<hr>\n",
    "\n",
    "The spread Analysis will be two sections as below\n",
    "1. The spread across Botswana\n",
    "2. The spread trends in the world"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Botswana Spread Analysis</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a line graph\n",
    "\n",
    "plt.plot(['28/03', '29/03', '01/04', '02/04', '04/04', '05/04', '06/04', '09/04'],[0,3,4,4,4,6,6,13])\n",
    "plt.ylabel('Number Of Positive Cases')\n",
    "plt.xlabel('Date Confirmed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph clearly indicates the rate at which confirmed cases are revealed in Botswana. As seen the graph is steadily moving up and starkly jumped upward on the 09/04/2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a line graph\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(['28/03', '29/03', '01/04', '02/04', '04/04', '05/04', '06/04', '09/04','15/04', '16/04', '17/04', '20/04', '23/04', '28/04', '29/04', '01/05','01/05'],[0,3,4,4,4,6,6,13,15,15,15,20,22,23,23,23,23])\n",
    "plt.ylabel('Number Of Positive Cases')\n",
    "plt.xlabel('Date Confirmed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph clearly indicates the rate at which confirmed cases are revealed in Botswana. As seen the graph is steadily moving up and starkly jumped upward on the 09/05/2020, before stabilizing prior to another upward surge."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Infection Rate</h5>\n",
    "\n",
    "Below is the infection rate formula commonly used by health specialists as per information on:\n",
    "\n",
    "    - www.wikipedia\n",
    "    \n",
    "    \n",
    " <img style=\"margin-left:25px;\" src=\"img/infection_rate.png\"/> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use this formula I first have to create it as a function so we be able to use it later on in our analysis. the function is as below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rate_infection(number_infections, number_atrisk):\n",
    "    K = 100\n",
    "    infection_rate =  K * number_infections/number_atrisk\n",
    "    \n",
    "    return infection_rate\n",
    "\n",
    "#this function in a more straight forward report or notebook we will have it under the functions definition sections as ealier on in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  rate_infection(4,50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will calculate the Covid-19 rate of infection in Botswana by basing those Tested Positive(number of infections) over the Suspected Cases(Number of those in a population at risks of infection). To get a clear picture of the movement of this rate it is best to express the rates as per the dates in which they were released so we can be able to express the rate(infection) over a period and show it as a graph. \n",
    "\n",
    " First let show Tested Cases and Positive Cases as a table as below:\n",
    " \n",
    " NB:Please note \n",
    " \n",
    "  - Suspected Cases: Number of those in a population at risk of infection\n",
    "  - Tested Positive: Infected\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>Infection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>3</td>\n",
       "      <td>1.158301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>593</td>\n",
       "      <td>4</td>\n",
       "      <td>0.674536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>670</td>\n",
       "      <td>4</td>\n",
       "      <td>0.597015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>820</td>\n",
       "      <td>4</td>\n",
       "      <td>0.487805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>966</td>\n",
       "      <td>6</td>\n",
       "      <td>0.621118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>1154</td>\n",
       "      <td>6</td>\n",
       "      <td>0.519931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>2527</td>\n",
       "      <td>13</td>\n",
       "      <td>0.514444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15/04/2020</th>\n",
       "      <td>3602</td>\n",
       "      <td>15</td>\n",
       "      <td>0.416435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/04/2020</th>\n",
       "      <td>3923</td>\n",
       "      <td>15</td>\n",
       "      <td>0.382360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/04/2020</th>\n",
       "      <td>4434</td>\n",
       "      <td>15</td>\n",
       "      <td>0.338295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/04/2020</th>\n",
       "      <td>5328</td>\n",
       "      <td>20</td>\n",
       "      <td>0.375375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23/04/2020</th>\n",
       "      <td>6016</td>\n",
       "      <td>22</td>\n",
       "      <td>0.365691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28/04/2020</th>\n",
       "      <td>6548</td>\n",
       "      <td>23</td>\n",
       "      <td>0.351252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/04/2020</th>\n",
       "      <td>6809</td>\n",
       "      <td>23</td>\n",
       "      <td>0.337788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2020</th>\n",
       "      <td>7675</td>\n",
       "      <td>23</td>\n",
       "      <td>0.299674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/05/2020</th>\n",
       "      <td>9066</td>\n",
       "      <td>23</td>\n",
       "      <td>0.253695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Suspected Cases  Tested Positive  Infection Rate\n",
       "28/03/2020              259                0        0.000000\n",
       "29/03/2020              259                3        1.158301\n",
       "01/04/2020              593                4        0.674536\n",
       "02/04/2020              670                4        0.597015\n",
       "04/04/2020              820                4        0.487805\n",
       "05/04/2020              966                6        0.621118\n",
       "06/04/2020             1154                6        0.519931\n",
       "09/04/2020             2527               13        0.514444\n",
       "15/04/2020             3602               15        0.416435\n",
       "16/04/2020             3923               15        0.382360\n",
       "17/04/2020             4434               15        0.338295\n",
       "20/04/2020             5328               20        0.375375\n",
       "23/04/2020             6016               22        0.365691\n",
       "28/04/2020             6548               23        0.351252\n",
       "29/04/2020             6809               23        0.337788\n",
       "01/05/2020             7675               23        0.299674\n",
       "07/05/2020             9066               23        0.253695"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case_bots['Infection Rate'] =  rate_infection(case_bots['Tested Positive'],case_bots['Suspected Cases'])\n",
    "suspected_cases =  case_bots[['Suspected Cases', 'Tested Positive','Infection Rate']]\n",
    "suspected_cases.head(18)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting a line graph\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(['28/03', '29/03', '01/04', '02/04', '04/04', '05/04', '06/04', '09/04','15/04', '16/04', '17/04', '20/04', '23/04', '28/04', '29/04', '01/05','01/05'],[0,1.15,0.67,0.59,0.49,0.62,0.51,0.51,0.41,0.38,0.33,0.37,0.36,0.35,0.33,0.29,0.25])\n",
    "plt.ylabel('Rate of Infection')\n",
    "plt.xlabel('Date Recorded')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graph shows the rate of infection over a period of time. It is certainly declining."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Quick Comparison Botswana Vs World Spread Analysis</h5>\n",
    "\n",
    "Below we show a graph showing the spread of the disease in the world."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "case_nums_country = confirmed_df.groupby(\"Country\").sum().drop(['Lat','Long'],axis =1).apply(lambda x: x[x > 0].count(), axis =0)\n",
    "d = [datetime.strptime(date,'%m/%d/%y').strftime(\"%d %b\") for date in case_nums_country.index]\n",
    "\n",
    "f = plt.figure(figsize=(16,8))\n",
    "f.add_subplot(111)\n",
    "marker_style = dict(c=\"crimson\",linewidth=2, linestyle='-', marker='3',markersize=8, markerfacecolor='#ffffff')\n",
    "plt.plot(d, case_nums_country,**marker_style)\n",
    "plt.tick_params(labelsize = 14)\n",
    "plt.xticks(list(np.arange(0,len(d),int(len(d)/5))),d[:-1:int(len(d)/5)]+[d[-1]])\n",
    "\n",
    "#labels\n",
    "plt.xlabel(\"Dates\",fontsize=18)\n",
    "plt.ylabel(\"Number of Countries\",fontsize=18)\n",
    "plt.grid(alpha = 0.3)\n",
    "\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Pie Chart Analysis: comparing Total suspected cases vs Positive Cases vs Negative Cases</h5> \n",
    "\n",
    "Below I plot Total Suspected Cases against Positive Cases vs Negative Cases to show the severity of the pandemic in Botswana. This data will be plotted on the last date cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9066\n",
       "1      23\n",
       "2    9043\n",
       "3       0\n",
       "4       1\n",
       "Name: 07/05/2020, dtype: int64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading last confirmed data\n",
    "pie_tb = cases['07/05/2020']\n",
    "pie_tb.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pie_total_cases = pd.DataFrame({'Suspected Cases': [9066, 23, 9043 , 0, 1]},\n",
    "                  index=['Suspected Cases', '', 'Tested Negative', 'Pending Results', 'Death'])\n",
    "plot = pie_total_cases.plot.pie(y='Suspected Cases', figsize=(5, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pie chart simply illustrate what has already been alluded before; that it seems for people that are tested for the disease most of them, their results come out negative. This is a good thing for Botswana. It would be great then if now all the 13 cases recorded could recover. As it is, no data has been recorded of any recovery in Botswana yet.\n",
    "\n",
    "Now going over the graphs and analysis it may seem that the testing and rate of results coming out seems to be adequate. But for us to have a better appreciation of this we should look at the some variables that better determines whether \n",
    "\n",
    " - the testing being done is sufficient ?\n",
    " - and satisfactory?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine the above I will look at the\n",
    "\n",
    "- Testing Centres in Botswana\n",
    "- Total Tested(Suspected Cases) in relation to the population\n",
    "- % Testing Ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5> Total tested people in relation to country population</h5>\n",
    "\n",
    "I will compare the tested numbers in relation to population size of Botswana as percentage to see if the numbers being tested are really satisfactory as figure 2.1.1 seems to suggest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "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>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>% Testing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>593</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>670</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>820</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>966</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>1154</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>2527</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15/04/2020</th>\n",
       "      <td>3602</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/04/2020</th>\n",
       "      <td>3923</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/04/2020</th>\n",
       "      <td>4434</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/04/2020</th>\n",
       "      <td>5328</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23/04/2020</th>\n",
       "      <td>6016</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28/04/2020</th>\n",
       "      <td>6548</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/04/2020</th>\n",
       "      <td>6809</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2020</th>\n",
       "      <td>7675</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/05/2020</th>\n",
       "      <td>9066</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Suspected Cases  Tested Positive  % Testing Ratio\n",
       "28/03/2020              259                0                0\n",
       "29/03/2020              259                3                0\n",
       "01/04/2020              593                4                0\n",
       "02/04/2020              670                4                0\n",
       "04/04/2020              820                4                0\n",
       "05/04/2020              966                6                0\n",
       "06/04/2020             1154                6                0\n",
       "09/04/2020             2527               13                0\n",
       "15/04/2020             3602               15                0\n",
       "16/04/2020             3923               15                0\n",
       "17/04/2020             4434               15                0\n",
       "20/04/2020             5328               20                0\n",
       "23/04/2020             6016               22                0\n",
       "28/04/2020             6548               23                0\n",
       "29/04/2020             6809               23                0\n",
       "01/05/2020             7675               23                0\n",
       "07/05/2020             9066               23                0"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suspected_cases =  case_bots[['Suspected Cases', 'Tested Positive','% Testing Ratio']]\n",
    "suspected_cases.head(18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous version I just calculated the testing ratio manually because the data was next to nothing, ut now it is becoming more. so I have to explore the power of python and pandas I will calculate it as follows:.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manual calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>Suspected(Total Tested)</th>\n",
       "      <th>% Testing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>593</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>670</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>820</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>966</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>1154</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>2527</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Suspected(Total Tested)  % Testing Ratio\n",
       "28/03/2020                      259             0.01\n",
       "29/03/2020                      259             0.01\n",
       "01/04/2020                      593             0.02\n",
       "02/04/2020                      670             0.03\n",
       "04/04/2020                      820             0.03\n",
       "05/04/2020                      966             0.04\n",
       "06/04/2020                     1154             0.05\n",
       "09/04/2020                     2527             0.11"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding the testing capcity as percentage to the data frame\n",
    "\n",
    "testing_capacity = pd.DataFrame( {\"Suspected(Total Tested)\" : [259,259,593,670,820,966,1154,2527],\n",
    "                           \"% Testing Ratio\" : [0.01,0.01,0.02,0.03,0.03,0.04,0.05,0.11]},\n",
    "                           index=['28/03/2020', '29/03/2020', '01/04/2020', '02/04/2020', '04/04/2020', '05/04/2020', '06/04/2020', '09/04/2020'])\n",
    "testing_capacity.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28/03/2020    0.011014\n",
       "29/03/2020    0.011014\n",
       "01/04/2020    0.025217\n",
       "02/04/2020    0.028491\n",
       "04/04/2020    0.034869\n",
       "05/04/2020    0.041078\n",
       "06/04/2020    0.049072\n",
       "09/04/2020    0.107458\n",
       "15/04/2020    0.153171\n",
       "16/04/2020    0.166821\n",
       "17/04/2020    0.188550\n",
       "20/04/2020    0.226567\n",
       "23/04/2020    0.255823\n",
       "28/04/2020    0.278446\n",
       "29/04/2020    0.289544\n",
       "01/05/2020    0.326370\n",
       "07/05/2020    0.385520\n",
       "Name: % Testing Ratio, dtype: float64"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case_bots['% Testing Ratio'] = (case_bots['Suspected Cases']/2351627)*100\n",
    "case_bots['% Testing Ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>% Testing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>259</td>\n",
       "      <td>3</td>\n",
       "      <td>0.011014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>593</td>\n",
       "      <td>4</td>\n",
       "      <td>0.025217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>670</td>\n",
       "      <td>4</td>\n",
       "      <td>0.028491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>820</td>\n",
       "      <td>4</td>\n",
       "      <td>0.034869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>966</td>\n",
       "      <td>6</td>\n",
       "      <td>0.041078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>1154</td>\n",
       "      <td>6</td>\n",
       "      <td>0.049072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>2527</td>\n",
       "      <td>13</td>\n",
       "      <td>0.107458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15/04/2020</th>\n",
       "      <td>3602</td>\n",
       "      <td>15</td>\n",
       "      <td>0.153171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/04/2020</th>\n",
       "      <td>3923</td>\n",
       "      <td>15</td>\n",
       "      <td>0.166821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/04/2020</th>\n",
       "      <td>4434</td>\n",
       "      <td>15</td>\n",
       "      <td>0.188550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/04/2020</th>\n",
       "      <td>5328</td>\n",
       "      <td>20</td>\n",
       "      <td>0.226567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23/04/2020</th>\n",
       "      <td>6016</td>\n",
       "      <td>22</td>\n",
       "      <td>0.255823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28/04/2020</th>\n",
       "      <td>6548</td>\n",
       "      <td>23</td>\n",
       "      <td>0.278446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/04/2020</th>\n",
       "      <td>6809</td>\n",
       "      <td>23</td>\n",
       "      <td>0.289544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2020</th>\n",
       "      <td>7675</td>\n",
       "      <td>23</td>\n",
       "      <td>0.326370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/05/2020</th>\n",
       "      <td>9066</td>\n",
       "      <td>23</td>\n",
       "      <td>0.385520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Suspected Cases  Tested Positive  % Testing Ratio\n",
       "28/03/2020              259                0         0.011014\n",
       "29/03/2020              259                3         0.011014\n",
       "01/04/2020              593                4         0.025217\n",
       "02/04/2020              670                4         0.028491\n",
       "04/04/2020              820                4         0.034869\n",
       "05/04/2020              966                6         0.041078\n",
       "06/04/2020             1154                6         0.049072\n",
       "09/04/2020             2527               13         0.107458\n",
       "15/04/2020             3602               15         0.153171\n",
       "16/04/2020             3923               15         0.166821\n",
       "17/04/2020             4434               15         0.188550\n",
       "20/04/2020             5328               20         0.226567\n",
       "23/04/2020             6016               22         0.255823\n",
       "28/04/2020             6548               23         0.278446\n",
       "29/04/2020             6809               23         0.289544\n",
       "01/05/2020             7675               23         0.326370\n",
       "07/05/2020             9066               23         0.385520"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "case_bots['% Testing Ratio'] = (case_bots['Suspected Cases']/2351627)*100\n",
    "suspected_cases =  case_bots[['Suspected Cases', 'Tested Positive','% Testing Ratio']]\n",
    "suspected_cases.head(18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure 2.1.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far the tested numbers expressed as a percentage from the table (figure 2.1.2) above clearly are not satisfactory. The table clearly shows that as of the last updated data on the 09/04/2020 only about 0.11% of the population was tested. \n",
    "\n",
    "This could also be interpreted that given the little data we have we do not yet have a proper appreciation of extent of the pandemic in Botswana. \n",
    "\n",
    "To put everything in perspective we have to appreciate that as of 09/04/2020 Botswana only had two testing centres with a testing capacity of 900people per day. \n",
    "\n",
    "Up to 07/05/2020 only 0.39% have been tested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing capacity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1 style='padding:5px; font-size: 25px;'>BOTSWANA'S TESTING CAPACITY</h1><div style = 'background-color:Transparent; padding: 30px '><span style='background-color:#023246; padding:20px; color: #ffff; font-size:20px;margin-left:0px float:left; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16); '> TESTING FACILITIES 2</span><span style='background-color:#D4D4CE; padding:20px; color: #ffff; font-size:20px;margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> CAPACITY 900/people per day</span><span style='background-color:PURPLE; padding:20px; color: #ffff; font-size:20px;margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> POPULATION 2351627</span></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing Capacity Botswana\n",
    "testing_Centres = 2\n",
    "a = 2351627\n",
    "b = '900/people per day'\n",
    "\n",
    "# Displaying the Capacity\n",
    "\n",
    "display(HTML(\"<h1 style='padding:5px; font-size: 25px;'>BOTSWANA'S TESTING CAPACITY</h1>\"\n",
    "             \"<div style = 'background-color:Transparent; padding: 30px '>\" \n",
    "             \"<span style='background-color:#023246; padding:20px; color: #ffff; font-size:20px;margin-left:0px float:left; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16); '> TESTING FACILITIES \"  + str(testing_Centres) +\"</span>\" +\n",
    "             \"<span style='background-color:#D4D4CE; padding:20px; color: #ffff; font-size:20px;margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> CAPACITY \" + str(b) + \"</span>\"+\n",
    "              \"<span style='background-color:PURPLE; padding:20px; color: #ffff; font-size:20px;margin-left:5px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'> POPULATION \" + str(a) + \"</span>\"+\n",
    "             \"</div>\")\n",
    "       )\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means that Botswana have a 0.03% capacity to test for covid-19 per day.  Let take a closer look at the following graph to test if it currently such capacity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Quick analysis</h5>\n",
    "\n",
    "Pandas has a quick tool in the form of a command known as describe(). If we run it as follows; we will get quick analysis of table (figure 2.1. ) above in the notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "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>Suspected(Total Tested)</th>\n",
       "      <th>% Testing Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>906.000000</td>\n",
       "      <td>0.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>726.146778</td>\n",
       "      <td>0.032404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>259.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>509.500000</td>\n",
       "      <td>0.017500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>745.000000</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1013.000000</td>\n",
       "      <td>0.042500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2527.000000</td>\n",
       "      <td>0.110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suspected(Total Tested)  % Testing Ratio\n",
       "count                 8.000000         8.000000\n",
       "mean                906.000000         0.037500\n",
       "std                 726.146778         0.032404\n",
       "min                 259.000000         0.010000\n",
       "25%                 509.500000         0.017500\n",
       "50%                 745.000000         0.030000\n",
       "75%                1013.000000         0.042500\n",
       "max                2527.000000         0.110000"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_capacity.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8.000000\n",
       "mean     0.037500\n",
       "std      0.032404\n",
       "min      0.010000\n",
       "25%      0.017500\n",
       "50%      0.030000\n",
       "75%      0.042500\n",
       "max      0.110000\n",
       "Name: % Testing Ratio, dtype: float64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_capacity['% Testing Ratio'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A look at the above figures shows that the mean % testing ratio is about 0.037500. This is slightly above our (Botswana) testing capacity. So it conclude the analysis above by clearly outlining:\n",
    "\n",
    "- Testing in Botswana though relatively low in relation to the population, it is satisfactory given the resources at our  \n",
    "  disposal\n",
    "- Testing was ramped since first dates of data reporting and as of date now slightly above what the country resources can   \n",
    "  achieve per day\n",
    "\n",
    "All of this is captured and illustrated in the table figure (2.1.3) and bar graph figure (2.1.4) below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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>% Testing Ratio</th>\n",
       "      <th>% Testing Ratio Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mar 28 2020, 00:00:00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mar 29 2020, 00:00:00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apr 01 2020, 00:00:00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr 02 2020, 00:00:00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apr 04 2020, 00:00:00</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Apr 05 2020, 00:00:00</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Apr 06 2020, 00:00:00</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Apr 09 2020, 00:00:00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Date  % Testing Ratio  % Testing Ratio Average\n",
       "0  Mar 28 2020, 00:00:00             0.01                    0.037\n",
       "1  Mar 29 2020, 00:00:00             0.01                    0.037\n",
       "2  Apr 01 2020, 00:00:00             0.02                    0.037\n",
       "3  Apr 02 2020, 00:00:00             0.03                    0.037\n",
       "4  Apr 04 2020, 00:00:00             0.03                    0.037\n",
       "5  Apr 05 2020, 00:00:00             0.04                    0.037\n",
       "6  Apr 06 2020, 00:00:00             0.05                    0.037\n",
       "7  Apr 09 2020, 00:00:00             0.11                    0.037"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#As of 09/04/2020\n",
    "testing_capacity_Ave = pd.DataFrame( {\"Date\" : [da_28 ,da_29,da_1,da_2,da_4,da_5,da_6,da_9],\n",
    "                           \"% Testing Ratio\" : [0.01,0.01,0.02,0.03,0.03,0.04,0.05,0.11],\n",
    "                           \"% Testing Ratio Average\" : [0.037,0.037,0.037,0.037,0.037,0.037,0.037,0.037]        },\n",
    "                           )\n",
    "testing_capacity_Ave"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure 2.1.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1346.4x451.44 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(18.7,6.27))\n",
    "color = 'tab:red'\n",
    "\n",
    "ax2 = sns.set(style=\"ticks\", rc={\"lines.linewidth\": 3.7})\n",
    "ax1 = sns.barplot(x='Date', y='% Testing Ratio', data = testing_capacity_Ave, palette='summer')\n",
    "ax1.tick_params(axis='y')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('% Testing Ratio Average', fontsize=16)\n",
    "ax2 = sns.lineplot(x='Date', y='% Testing Ratio Average', data = testing_capacity_Ave, sort=False, color=color)\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "#show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure 2.1.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the old example I formatted the date manually and line by line as can be seen on the cleaning section on this note book, below I will formatt it using Python code.#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>Suspected Cases</th>\n",
       "      <th>Tested Positive</th>\n",
       "      <th>% Testing Ratio</th>\n",
       "      <th>% Testing Ratio Average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28/03/2020</th>\n",
       "      <td>Mar 28 2020, 00:00:00</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011014</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/03/2020</th>\n",
       "      <td>Mar 29 2020, 00:00:00</td>\n",
       "      <td>259</td>\n",
       "      <td>3</td>\n",
       "      <td>0.011014</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/04/2020</th>\n",
       "      <td>Apr 01 2020, 00:00:00</td>\n",
       "      <td>593</td>\n",
       "      <td>4</td>\n",
       "      <td>0.025217</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02/04/2020</th>\n",
       "      <td>Apr 02 2020, 00:00:00</td>\n",
       "      <td>670</td>\n",
       "      <td>4</td>\n",
       "      <td>0.028491</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04/04/2020</th>\n",
       "      <td>Apr 04 2020, 00:00:00</td>\n",
       "      <td>820</td>\n",
       "      <td>4</td>\n",
       "      <td>0.034869</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05/04/2020</th>\n",
       "      <td>Apr 05 2020, 00:00:00</td>\n",
       "      <td>966</td>\n",
       "      <td>6</td>\n",
       "      <td>0.041078</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06/04/2020</th>\n",
       "      <td>Apr 06 2020, 00:00:00</td>\n",
       "      <td>1154</td>\n",
       "      <td>6</td>\n",
       "      <td>0.049072</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09/04/2020</th>\n",
       "      <td>Apr 09 2020, 00:00:00</td>\n",
       "      <td>2527</td>\n",
       "      <td>13</td>\n",
       "      <td>0.107458</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15/04/2020</th>\n",
       "      <td>Apr 15 2020, 00:00:00</td>\n",
       "      <td>3602</td>\n",
       "      <td>15</td>\n",
       "      <td>0.153171</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16/04/2020</th>\n",
       "      <td>Apr 16 2020, 00:00:00</td>\n",
       "      <td>3923</td>\n",
       "      <td>15</td>\n",
       "      <td>0.166821</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17/04/2020</th>\n",
       "      <td>Apr 17 2020, 00:00:00</td>\n",
       "      <td>4434</td>\n",
       "      <td>15</td>\n",
       "      <td>0.188550</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20/04/2020</th>\n",
       "      <td>Apr 20 2020, 00:00:00</td>\n",
       "      <td>5328</td>\n",
       "      <td>20</td>\n",
       "      <td>0.226567</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23/04/2020</th>\n",
       "      <td>Apr 23 2020, 00:00:00</td>\n",
       "      <td>6016</td>\n",
       "      <td>22</td>\n",
       "      <td>0.255823</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28/04/2020</th>\n",
       "      <td>Apr 28 2020, 00:00:00</td>\n",
       "      <td>6548</td>\n",
       "      <td>23</td>\n",
       "      <td>0.278446</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29/04/2020</th>\n",
       "      <td>Apr 29 2020, 00:00:00</td>\n",
       "      <td>6809</td>\n",
       "      <td>23</td>\n",
       "      <td>0.289544</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/05/2020</th>\n",
       "      <td>May 01 2020, 00:00:00</td>\n",
       "      <td>7675</td>\n",
       "      <td>23</td>\n",
       "      <td>0.326370</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07/05/2020</th>\n",
       "      <td>May 07 2020, 00:00:00</td>\n",
       "      <td>9066</td>\n",
       "      <td>23</td>\n",
       "      <td>0.385520</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Date  Suspected Cases  Tested Positive  \\\n",
       "28/03/2020  Mar 28 2020, 00:00:00              259                0   \n",
       "29/03/2020  Mar 29 2020, 00:00:00              259                3   \n",
       "01/04/2020  Apr 01 2020, 00:00:00              593                4   \n",
       "02/04/2020  Apr 02 2020, 00:00:00              670                4   \n",
       "04/04/2020  Apr 04 2020, 00:00:00              820                4   \n",
       "05/04/2020  Apr 05 2020, 00:00:00              966                6   \n",
       "06/04/2020  Apr 06 2020, 00:00:00             1154                6   \n",
       "09/04/2020  Apr 09 2020, 00:00:00             2527               13   \n",
       "15/04/2020  Apr 15 2020, 00:00:00             3602               15   \n",
       "16/04/2020  Apr 16 2020, 00:00:00             3923               15   \n",
       "17/04/2020  Apr 17 2020, 00:00:00             4434               15   \n",
       "20/04/2020  Apr 20 2020, 00:00:00             5328               20   \n",
       "23/04/2020  Apr 23 2020, 00:00:00             6016               22   \n",
       "28/04/2020  Apr 28 2020, 00:00:00             6548               23   \n",
       "29/04/2020  Apr 29 2020, 00:00:00             6809               23   \n",
       "01/05/2020  May 01 2020, 00:00:00             7675               23   \n",
       "07/05/2020  May 07 2020, 00:00:00             9066               23   \n",
       "\n",
       "            % Testing Ratio  % Testing Ratio Average  \n",
       "28/03/2020         0.011014                    0.037  \n",
       "29/03/2020         0.011014                    0.037  \n",
       "01/04/2020         0.025217                    0.037  \n",
       "02/04/2020         0.028491                    0.037  \n",
       "04/04/2020         0.034869                    0.037  \n",
       "05/04/2020         0.041078                    0.037  \n",
       "06/04/2020         0.049072                    0.037  \n",
       "09/04/2020         0.107458                    0.037  \n",
       "15/04/2020         0.153171                    0.037  \n",
       "16/04/2020         0.166821                    0.037  \n",
       "17/04/2020         0.188550                    0.037  \n",
       "20/04/2020         0.226567                    0.037  \n",
       "23/04/2020         0.255823                    0.037  \n",
       "28/04/2020         0.278446                    0.037  \n",
       "29/04/2020         0.289544                    0.037  \n",
       "01/05/2020         0.326370                    0.037  \n",
       "07/05/2020         0.385520                    0.037  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "case_bots['Date'] = [da_28 ,da_29,da_1,da_2,da_4,da_5,da_6,da_9,da_10 ,da_11,da_12,da_13,da_14,da_15,da_16,da_17,da_18]\n",
    "case_bots['% Testing Ratio'] = (case_bots['Suspected Cases']/2351627)*100\n",
    "case_bots['% Testing Ratio Average'] = 0.037\n",
    "suspected_cases =  case_bots[['Date','Suspected Cases', 'Tested Positive','% Testing Ratio','% Testing Ratio Average']]\n",
    "suspected_cases.head(18)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1346.4x451.44 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(18.7,6.27))\n",
    "color = 'tab:red'\n",
    "\n",
    "ax2 = sns.set(style=\"ticks\", rc={\"lines.linewidth\": 3.7})\n",
    "ax1 = sns.barplot(x='Date', y='% Testing Ratio', data = suspected_cases, palette='summer')\n",
    "ax1.tick_params(axis='y')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.set_ylabel('% Testing Ratio Average', fontsize=16)\n",
    "ax2 = sns.lineplot(x='Date', y='% Testing Ratio Average', data = suspected_cases, sort=False, color=color)\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "#show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The red line on the graph illustrate the average testing which is slightly above the testing capacity possible at present in relation to the actual testing rate represented by the green bars. The changing of the bars from the dark green to yellow also illustrate the increasing in testing rate as the days go on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Summary analysis capacity</h5>\n",
    "\n",
    "Testing is going as per our capacity given the current facilities, and as of 07/05/2020 been above our testing capacity. In short so far the following people in Botswana have been tested:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    9066\n",
       "1      23\n",
       "2    9043\n",
       "3       0\n",
       "4       1\n",
       "5       0\n",
       "Name: 07/05/2020, dtype: int64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_tested = cases['07/05/2020']\n",
    "total_tested"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total people tested"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1 style='padding:5px; font-size: 25px;'>Total People Tested %</h1><div style = 'background-color:Transparent; padding: 50px '><span style='background-color:PURPLE; width:100%; padding:25px 150px 25px 150px; color: #ffff; font-size:20px;margin-left:280px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'>0.004 % </span></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "per = 9066/2351627\n",
    "'{0:.3f}'.format(per)\n",
    "\n",
    "# Displaying total people tested as a percentage\n",
    "\n",
    "display(HTML(\"<h1 style='padding:5px; font-size: 25px;'>Total People Tested %</h1>\"\n",
    "             \"<div style = 'background-color:Transparent; padding: 50px '>\" +\n",
    "              \"<span style='background-color:PURPLE; width:100%; padding:25px 150px 25px 150px; color: #ffff; font-size:20px;margin-left:280px; border: 1px solid #e0e0e0;box-shadow: 0 3px 6px 1px rgba(0, 0, 0, 0.16);'>\" + str('{0:.3f}'.format(per)) + \" % </span>\"+\n",
    "             \"</div>\")\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So as of now are not really clear with the real picture of the pandemic in the country. We are basically banking on the figures coming out negative after tests as well as not many people getting sick locally.\n",
    "\n",
    "So it follows logically that this to some extent might be true, most people are not sick or the disease has not yet taken foot in the country.\n",
    "\n",
    "<span style=\"font-size:18px;\">Mapping Covid-19 in Botswana</span>\n",
    "\n",
    "With this in mind it would make sense in looking at confirmed cases. Where in the country are they; so as to allow for proper decisions to be made. For example where it appears to be prevalent the government might cordon off the areas where most cases have benn confirmed.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Geographic Presentation of Covid-19  \n",
    "\n",
    "<hr>\n",
    "\n",
    "The map of the whole country will show graphically which places in country are affected the most or have confirmed cases. It will gives us a broader picture say which places need to be coordoned off from others as per cases found in the different areas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Botswana Confirmed Cases on Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But before we import the Botswana map let us create a data frame for all the confirmed cases according to their locations as confirmed on public available data: Please note the data I am using below is only provided as in place not exact locations or coordinates provided.. so it is an assumption."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Address</th>\n",
       "      <th>y</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.6282</td>\n",
       "      <td>25.9448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.5999</td>\n",
       "      <td>25.8875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.6548</td>\n",
       "      <td>25.9548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.6222</td>\n",
       "      <td>25.9564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.5729</td>\n",
       "      <td>25.9863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.6428</td>\n",
       "      <td>25.9687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.4582</td>\n",
       "      <td>25.9863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.6899</td>\n",
       "      <td>25.9899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.7448</td>\n",
       "      <td>25.9448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.6112</td>\n",
       "      <td>25.9562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.7529</td>\n",
       "      <td>25.9529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.8528</td>\n",
       "      <td>25.9448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gaborone</td>\n",
       "      <td>-24.9756</td>\n",
       "      <td>25.9448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Ramotswa</td>\n",
       "      <td>-24.6282</td>\n",
       "      <td>25.5864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Molepolole</td>\n",
       "      <td>-24.3661</td>\n",
       "      <td>25.3661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Molepolole</td>\n",
       "      <td>-24.3211</td>\n",
       "      <td>25.4990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Molepolole</td>\n",
       "      <td>-24.3635</td>\n",
       "      <td>25.4685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Metsimotlhabe</td>\n",
       "      <td>-24.5421</td>\n",
       "      <td>25.5421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Metsimotlhabe</td>\n",
       "      <td>-24.3612</td>\n",
       "      <td>25.5012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Metsimotlhabe</td>\n",
       "      <td>-24.3311</td>\n",
       "      <td>25.4814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Metsimotlhabe</td>\n",
       "      <td>-24.5533</td>\n",
       "      <td>25.4023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mahalapye</td>\n",
       "      <td>-23.1078</td>\n",
       "      <td>26.8321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Siviya</td>\n",
       "      <td>-21.0831</td>\n",
       "      <td>27.6743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Address         y        x\n",
       "0        Gaborone  -24.6282  25.9448\n",
       "1        Gaborone  -24.5999  25.8875\n",
       "2        Gaborone  -24.6548  25.9548\n",
       "3        Gaborone  -24.6222  25.9564\n",
       "4        Gaborone  -24.5729  25.9863\n",
       "5        Gaborone  -24.6428  25.9687\n",
       "6        Gaborone  -24.4582  25.9863\n",
       "7        Gaborone  -24.6899  25.9899\n",
       "8        Gaborone  -24.7448  25.9448\n",
       "9        Gaborone  -24.6112  25.9562\n",
       "10       Gaborone  -24.7529  25.9529\n",
       "11       Gaborone  -24.8528  25.9448\n",
       "12       Gaborone  -24.9756  25.9448\n",
       "13       Ramotswa  -24.6282  25.5864\n",
       "14     Molepolole  -24.3661  25.3661\n",
       "15     Molepolole  -24.3211  25.4990\n",
       "16     Molepolole  -24.3635  25.4685\n",
       "17  Metsimotlhabe  -24.5421  25.5421\n",
       "18  Metsimotlhabe  -24.3612  25.5012\n",
       "19  Metsimotlhabe  -24.3311  25.4814\n",
       "20  Metsimotlhabe  -24.5533  25.4023\n",
       "21      Mahalapye  -23.1078  26.8321\n",
       "22         Siviya  -21.0831  27.6743"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Address': ['Gaborone', 'Gaborone', 'Gaborone','Gaborone', 'Gaborone', 'Gaborone','Gaborone', 'Gaborone', 'Gaborone',\n",
    "                   'Gaborone', 'Gaborone', 'Gaborone','Gaborone', 'Ramotswa','Molepolole', 'Molepolole', 'Molepolole','Metsimotlhabe',\n",
    "                    'Metsimotlhabe', 'Metsimotlhabe','Metsimotlhabe', 'Mahalapye','Siviya'],\n",
    "        'y': ['-24.6282','-24.5999','-24.6548','-24.6222','-24.5729','-24.6428','-24.4582','-24.6899','-24.7448',\n",
    "              '-24.6112','-24.7529','-24.8528','-24.9756','-24.6282', '-24.3661','-24.3211','-24.3635','-24.5421','-24.3612','-24.3311',\n",
    "              '-24.5533','-23.1078','-21.0831'],\n",
    "        \n",
    "        \n",
    "        'x': ['25.9448','25.8875','25.9548','25.9564','25.9863','25.9687','25.9863','25.9899','25.9448',\n",
    "              '25.9562','25.9529','25.9448','25.9448','25.5864', '25.3661','25.4990','25.4685','25.5421','25.5012','25.4814',\n",
    "              '25.4023','26.8321','27.6743']\n",
    "    \n",
    "       }\n",
    "dataPlaces = pd.DataFrame(data=data)\n",
    "dataPlaces"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the coordinates, of the places of confirmed cases; I import folium to start dealing with maps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folium imported!\n"
     ]
    }
   ],
   "source": [
    "import folium\n",
    "\n",
    "print('Folium imported!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a0b3b68048>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Botswana latitude and longitude values\n",
    "latitude = -22.550\n",
    "longitude = 27.133\n",
    "\n",
    "# create map and display it\n",
    "botswana_map = folium.Map(location=[latitude, longitude], zoom_start=8)\n",
    "\n",
    "# display the map of Botswana\n",
    "botswana_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    
        <script>
            L_NO_TOUCH = false;
            L_DISABLE_3D = false;
        </script>
    
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.js"></script>
    <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.5.1/dist/leaflet.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css"/>
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css"/>
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>
    <link rel="stylesheet" href="https://rawcdn.githack.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css"/>
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <meta name="viewport" content="width=device-width,
                initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />
            <style>
                #map_c53d7d2aa563414180240aa736742b8b {
                    position: relative;
                    width: 100.0%;
                    height: 100.0%;
                    left: 0.0%;
                    top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_c53d7d2aa563414180240aa736742b8b" ></div>
        
</body>
<script>    
    
            var map_c53d7d2aa563414180240aa736742b8b = L.map(
                "map_c53d7d2aa563414180240aa736742b8b",
                {
                    center: [-22.55, 27.133],
                    crs: L.CRS.EPSG3857,
                    zoom: 8,
                    zoomControl: true,
                    preferCanvas: false,
                }
            );

            

        
    
            var tile_layer_18fcbe00734f4864a85c7cb0c2fb74c5 = L.tileLayer(
                "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
                {"attribution": "Data by \u0026copy; \u003ca href=\"http://openstreetmap.org\"\u003eOpenStreetMap\u003c/a\u003e, under \u003ca href=\"http://www.openstreetmap.org/copyright\"\u003eODbL\u003c/a\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}
            ).addTo(map_c53d7d2aa563414180240aa736742b8b);
        
    
            var feature_group_8a9fc1cb458f49a882283ac0d288a822 = L.featureGroup(
                {}
            ).addTo(map_c53d7d2aa563414180240aa736742b8b);
        
    
            var circle_marker_7e721ea52ca94be99385f61b1bb97bce = L.circleMarker(
                [-24.6282, 25.9448],
                {"bubblingMouseEvents": true, "color": "Yellow", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "Red", "fillOpacity": 0.6, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(feature_group_8a9fc1cb458f49a882283ac0d288a822);
        
    
            var circle_marker_7544c049c4004bf8b48ecc5387c1cce3 = L.circleMarker(
                [-24.5999, 25.8875],
                {"bubblingMouseEvents": true, "color": "Yellow", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "Red", "fillOpacity": 0.6, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 5, "stroke": true, "weight": 3}
            ).addTo(feature_group_8a9fc1cb458f49a882283ac0d288a822);
        
    
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x1a0b3b68048>"
      ]
     },
     "execution_count": 10,
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   ],
   "source": [
    "\n",
    "# instantiate a feature group for the incidents in the dataframe\n",
    "incidents = folium.map.FeatureGroup()\n",
    "\n",
    "# loop through the 100 crimes and add each to the incidents feature group\n",
    "for lat, lng, in zip(dataPlaces.y, dataPlaces.x):\n",
    "    incidents.add_child(\n",
    "        folium.CircleMarker(\n",
    "            [lat, lng],\n",
    "            radius=5, # define how big you want the circle markers to be\n",
    "            color='Yellow',\n",
    "            fill=True,\n",
    "            fill_color='Red',\n",
    "            fill_opacity=0.6\n",
    "        )\n",
    "    )\n",
    "\n",
    "# add incidents to map\n",
    "botswana_map.add_child(incidents)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Botswana Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please refer to an earlier piece I wrote on an estimation model.\n",
    "\n",
    "The link is as follows:\n",
    "\n",
    "<a href=\"https://github.com/Mmusi/COVID-19-Botswana/blob/master/Botswana%20Covid-19%20Infections%20Estimations%20-%20Model%20Development.ipynb\"> Botswana Covid-19 Infections Estimations - Model Development </a>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- Logistic Growth Model\n",
    "  https/www.maa.org/book/export/html\n",
    "  \n",
    "- https:statista.com/chart/21331/share-of-covid-19-cases-deaths-in-us-states-counties\n",
    "\n",
    "- https//link.springer.com/chapter/10/111\n",
    "\n",
    "- Humanitarian Data Exchange\n",
    "  https:data.humdata.org/m/dataset\n",
    "  \n",
    "- https://pythonprogramming.net/live-graphs-matplotbib-tutorial\n",
    "\n",
    "- Italian Corona Viruspressures and flattening the curve -an epidemiology expert explains \n",
    "  https://www.weforum.org/agenda/2020/03/covid-19coronavirus-epidemiology-expert-explains-Italian-death-rate/\n",
    "  \n",
    "- https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\n",
    "\n",
    "- https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\n",
    "  \n",
    "- https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv\n",
    "\n",
    "- Use logistic growth models \n",
    "https://courses.lumenlearning.com/ivytech-collegealgebra/chapter/use-logistic-growth-models/\n",
    "\n",
    " - BwGovernment Facebook page\n",
    " https://www.facebook.com/BwGovernment\n",
    " \n",
    " - Covid-19 MHW\n",
    " https://www.facebook.com/Covid-19 \n",
    "\n",
    "- https://news.google.com/covid19/map?hl=en-BW&gl=BW&ceid=BW%3Aen&mid=%2Fm%2F0166v\n",
    "    \n",
    "- https://www.gov.bw/#covid-19-map\n",
    "    \n",
    "- https://www.cdc.gov/\n",
    "\n",
    "- https://coronavirus.jhu.edu/map.html"
   ]
  },
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   "metadata": {},
   "source": []
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