Analysis_Covid_19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries such as pandas matplotlib and seaborn \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of data\n",
    "For this report, we decided to use two databases: a quantitative database from **https://covid.ourworldindata.org** and a qualitative database from a survey on **https://data.humdata.org**. This analysis will be done on two countries: $ Ghana $ and $ Rwanda $ . We have extracted from the two global databases the parameters measured for these two countries. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### _Dataset n°1_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_excel(\"https://covid.ourworldindata.org/data/owid-covid-data.xlsx\", \n",
    "                    parse_dates=['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .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>iso_code</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>total_tests_per_thousand</th>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <th>tests_units</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.733</td>\n",
       "      <td>18.733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.465</td>\n",
       "      <td>18.733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112.395</td>\n",
       "      <td>74.930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code location       date  total_cases  new_cases  total_deaths  \\\n",
       "0      ABW    Aruba 2020-03-13            2          2             0   \n",
       "1      ABW    Aruba 2020-03-20            4          2             0   \n",
       "2      ABW    Aruba 2020-03-24           12          8             0   \n",
       "\n",
       "   new_deaths  total_cases_per_million  new_cases_per_million  \\\n",
       "0           0                   18.733                 18.733   \n",
       "1           0                   37.465                 18.733   \n",
       "2           0                  112.395                 74.930   \n",
       "\n",
       "   total_deaths_per_million  new_deaths_per_million  total_tests  new_tests  \\\n",
       "0                       0.0                     0.0          NaN        NaN   \n",
       "1                       0.0                     0.0          NaN        NaN   \n",
       "2                       0.0                     0.0          NaN        NaN   \n",
       "\n",
       "   total_tests_per_thousand  new_tests_per_thousand tests_units  \n",
       "0                       NaN                     NaN         NaN  \n",
       "1                       NaN                     NaN         NaN  \n",
       "2                       NaN                     NaN         NaN  "
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14092 entries, 0 to 14091\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   iso_code                  13907 non-null  object        \n",
      " 1   location                  14092 non-null  object        \n",
      " 2   date                      14092 non-null  datetime64[ns]\n",
      " 3   total_cases               14092 non-null  int64         \n",
      " 4   new_cases                 14092 non-null  int64         \n",
      " 5   total_deaths              14092 non-null  int64         \n",
      " 6   new_deaths                14092 non-null  int64         \n",
      " 7   total_cases_per_million   13804 non-null  float64       \n",
      " 8   new_cases_per_million     13804 non-null  float64       \n",
      " 9   total_deaths_per_million  13804 non-null  float64       \n",
      " 10  new_deaths_per_million    13804 non-null  float64       \n",
      " 11  total_tests               3462 non-null   float64       \n",
      " 12  new_tests                 3082 non-null   float64       \n",
      " 13  total_tests_per_thousand  3462 non-null   float64       \n",
      " 14  new_tests_per_thousand    3086 non-null   float64       \n",
      " 15  tests_units               3462 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(8), int64(4), object(3)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The database (data1) provides a trend in the number of infections, number of deaths, etc. from different countries. The database contains 13883 lines and 16 variables.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### _Dataset n°2_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.read_excel(\"https://data.humdata.org/dataset/24144abc-c60c-4bdd-b123-2bb08f184765/resource/ab241169-4467-4a89-8e85-e9384ad22c52/download/geopoll-coronavirus-round2-data.xlsx\", \n",
    "                      header=2, parse_dates=['Survey Date'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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>Survey Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>Admin1</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Awareness</th>\n",
       "      <th>Urban/Rural</th>\n",
       "      <th>BiggestChallenge</th>\n",
       "      <th>LevelConcern</th>\n",
       "      <th>Concerns</th>\n",
       "      <th>...</th>\n",
       "      <th>InformationSources: Friends/family</th>\n",
       "      <th>InformationSources: Government messages</th>\n",
       "      <th>InformationSources: Newspapers</th>\n",
       "      <th>InformationSources: Other</th>\n",
       "      <th>InformationSources: Radio</th>\n",
       "      <th>InformationSources: Social Media</th>\n",
       "      <th>InformationSources: TV</th>\n",
       "      <th>SocialMedia</th>\n",
       "      <th>MediaConsumption</th>\n",
       "      <th>StayPositive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>Machakos</td>\n",
       "      <td>1999</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Rural area</td>\n",
       "      <td>Corona</td>\n",
       "      <td>5</td>\n",
       "      <td>Contracting the disease</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Consuming more</td>\n",
       "      <td>Nothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>Uasin Gishu</td>\n",
       "      <td>1991</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban area</td>\n",
       "      <td>High prices of goods</td>\n",
       "      <td>5</td>\n",
       "      <td>Local infections</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Media outlets</td>\n",
       "      <td>Consuming more</td>\n",
       "      <td>Hope</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04-02</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Benue</td>\n",
       "      <td>1999</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Urban area</td>\n",
       "      <td>Light</td>\n",
       "      <td>5</td>\n",
       "      <td>Contracting the disease</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Health organizations</td>\n",
       "      <td>Consuming more</td>\n",
       "      <td>Will go back to school soon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Survey Date  Country       Admin1  BirthYear Gender Awareness Urban/Rural  \\\n",
       "0  2020-04-02    Kenya     Machakos       1999   Male       Yes  Rural area   \n",
       "1  2020-04-02    Kenya  Uasin Gishu       1991   Male       Yes  Urban area   \n",
       "2  2020-04-02  Nigeria        Benue       1999   Male       Yes  Urban area   \n",
       "\n",
       "       BiggestChallenge  LevelConcern                 Concerns  ...  \\\n",
       "0                Corona             5  Contracting the disease  ...   \n",
       "1  High prices of goods             5         Local infections  ...   \n",
       "2                 Light             5  Contracting the disease  ...   \n",
       "\n",
       "  InformationSources: Friends/family InformationSources: Government messages  \\\n",
       "0                              False                                    True   \n",
       "1                              False                                    True   \n",
       "2                              False                                   False   \n",
       "\n",
       "  InformationSources: Newspapers  InformationSources: Other  \\\n",
       "0                          False                      False   \n",
       "1                          False                      False   \n",
       "2                          False                      False   \n",
       "\n",
       "   InformationSources: Radio  InformationSources: Social Media  \\\n",
       "0                      False                             False   \n",
       "1                      False                              True   \n",
       "2                      False                              True   \n",
       "\n",
       "   InformationSources: TV           SocialMedia MediaConsumption  \\\n",
       "0                   False                   NaN   Consuming more   \n",
       "1                    True         Media outlets   Consuming more   \n",
       "2                   False  Health organizations   Consuming more   \n",
       "\n",
       "                  StayPositive  \n",
       "0                      Nothing  \n",
       "1                         Hope  \n",
       "2  Will go back to school soon  \n",
       "\n",
       "[3 rows x 55 columns]"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The database (data2) examine perceptions and the impact of COVID-19 in 12 countries throughout sub-Saharan Africa. \n",
    "Topics covered include greatest concerns surrounding coronavirus, preventative measures being taken, \n",
    "changes in food market operability and food security, etc.\n",
    "The database contains 4788 lines and 55 variables.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A- Trend analysis of the disease in the two countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question\n",
    "**1**. What is the progression of the disease (in terms of mortality and number of infected persons) in these two countries?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = data1[(data1.location.isin([\"Ghana\", \"Rwanda\"]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1= sns.lineplot(x='date', y='total_cases', \n",
    "                   hue='location', data=df1, legend = False)\n",
    "fig1.set(xlabel=\"Date\", ylabel=\"Total cases\", \n",
    "         title=\"Trend in the number of cases of infected persons\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title=\"Country\", loc='upper left', labels=['Ghana', 'Rwanda'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totat of infected persons in Rwanda: 261\n",
      "To now, almost 2719 people have been infected in Ghana\n"
     ]
    }
   ],
   "source": [
    "print('Totat of infected persons in Rwanda:',df1[df1.location == 'Rwanda'].total_cases.max())\n",
    "print('To now, almost {} people have been infected in Ghana'.format(df1[df1.location == 'Ghana'].total_cases.max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2= sns.lineplot(x='date', y='total_deaths',\n",
    "                   hue='location', data=df1, legend = False)\n",
    "fig2.set(xlabel=\"Date\", ylabel=\"total_deaths\",\n",
    "         title=\"Trend in the number of cases of death\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title=\"Country\", loc='upper left', labels=['Ghana', 'Rwanda'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Totat of death in Rwanda: 0\n",
      "To now, almost 18 people dead in Ghana\n"
     ]
    }
   ],
   "source": [
    "print('Totat of death in Rwanda:',df1[df1.location == 'Rwanda'].total_deaths.max())\n",
    "print('To now, almost {} people dead in Ghana'.format(df1[df1.location == 'Ghana'].total_deaths.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "The results show a net evolution of the contamination of the disease in Ghana than in Rwanda. After more than a month, Ghana has registered nearly 2719 infected persons with 18 deaths while Rwanda remains at 0 with nearly 261 cases of infections. We can conclude that the virus evolves more rapidly in Ghana than in Rwanda. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B- Perceptions and the impact of COVID-19 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Question\n",
    "**2**. What is their level of involvement and **3**. knowledge of the risk of this virus in these countries? \n",
    "The degree of their involvement was measured from 1 (less concerned) to 5 (very concerned)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2=data2[(data2.Country.isin([\"Ghana\", \"Rwanda\"]))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,''), Text(0.5,0,'Risk Awareness')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(2,2,1)\n",
    "q2 = sns.countplot(x=\"LevelConcern\", data=df2, hue=\"Country\")\n",
    "q2.set(xlabel=\"Level of concern\")\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "q3 = sns.countplot(x=\"RiskAwareness\", data=df2, hue=\"Country\")\n",
    "q3.legend_.remove()\n",
    "q3.set(ylabel='', xlabel=\"Risk Awareness\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country  LevelConcern\n",
      "Ghana    5               315\n",
      "         4                43\n",
      "         3                21\n",
      "         1                16\n",
      "         2                 5\n",
      "Rwanda   5               324\n",
      "         1                43\n",
      "         4                16\n",
      "         2                11\n",
      "         3                 6\n",
      "Name: LevelConcern, dtype: int64\n",
      "Country  RiskAwareness\n",
      "Ghana    Yes              259\n",
      "         No               141\n",
      "Rwanda   No               254\n",
      "         Yes              146\n",
      "Name: RiskAwareness, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2.groupby('Country')[\"LevelConcern\"].value_counts())\n",
    "print(df2.groupby('Country')[\"RiskAwareness\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "Both countries have an estimated average of more than 300 people claiming to be affected by the presence of the virus. While in general both countries show the same trend, the number of people in Rwanda who say they are not concerned about the virus is higher than in Ghana. However, against all expectations, 254 people in Rwanda say they do not know the risks involved, compared to 141 people in Ghana. It appears that for this study, 65% of the population in Ghana (against 35%  in Rwanda) are aware of the dangerousness of the contrary illness.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question\n",
    " **4**.The majority of people living either in the informal sector or in daily work, what is their perception of the impact (measured on a scale of 1 to 5) of the virus on the economy of their country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Impact of the virus on the economie')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"EconomicImpact\", data=df2, hue=\"Country\")\n",
    "plt.xlabel(\"Perception\")\n",
    "plt.title(\"Impact of the virus on the economie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country  EconomicImpact\n",
      "Ghana    5                 277\n",
      "         4                  60\n",
      "         3                  33\n",
      "         1                  15\n",
      "         2                  15\n",
      "Rwanda   5                 335\n",
      "         1                  44\n",
      "         4                  10\n",
      "         3                   7\n",
      "         2                   4\n",
      "Name: EconomicImpact, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2.groupby('Country')[\"EconomicImpact\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "Respectively 277 people in Ghana and 335 people in Rwanda said that the impact of the virus on their country's economy is very high. Beyond this analysis, it is important to note that the Ghanaians (perception levels (2 to 4)) perceive more the probably very negative effects of the virus on the aconomy of their country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The perception of the population of the virus in terms of health or economic impact seems to be practically the same in both countries. However, these data do not allow us to make a decision on the factors that have influenced the number of cases and mortality observed in the database (Dataset n°1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0,0.5,''), Text(0.5,0,'Risk Awareness')]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country  LevelConcern\n",
      "Ghana    5               315\n",
      "         4                43\n",
      "         3                21\n",
      "         1                16\n",
      "         2                 5\n",
      "Rwanda   5               324\n",
      "         1                43\n",
      "         4                16\n",
      "         2                11\n",
      "         3                 6\n",
      "Name: LevelConcern, dtype: int64\n",
      "Country  RiskAwareness\n",
      "Ghana    Yes              259\n",
      "         No               141\n",
      "Rwanda   No               254\n",
      "         Yes              146\n",
      "Name: RiskAwareness, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2.groupby('Country')[\"LevelConcern\"].value_counts())\n",
    "print(df2.groupby('Country')[\"RiskAwareness\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "Both countries have an estimated average of more than 300 people claiming to be affected by the presence of the virus. While in general both countries show the same trend, the number of people in Rwanda who say they are not concerned about the virus is higher than in Ghana. However, against all expectations, 254 people in Rwanda say they do not know the risks involved, compared to 141 people in Ghana. It appears that for this study, 65% of the population in Ghana (against 35%  in Rwanda) are aware of the dangerousness of the contrary illness.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question\n",
    " **4**.The majority of people living either in the informal sector or in daily work, what is their perception of the impact (measured on a scale of 1 to 5) of the virus on the economy of their country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Impact of the virus on the economie')"
      ]
     },
     "execution_count": 208,
     "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": [
    "sns.countplot(x=\"EconomicImpact\", data=df2, hue=\"Country\")\n",
    "plt.xlabel(\"Perception\")\n",
    "plt.title(\"Impact of the virus on the economie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country  EconomicImpact\n",
      "Ghana    5                 277\n",
      "         4                  60\n",
      "         3                  33\n",
      "         1                  15\n",
      "         2                  15\n",
      "Rwanda   5                 335\n",
      "         1                  44\n",
      "         4                  10\n",
      "         3                   7\n",
      "         2                   4\n",
      "Name: EconomicImpact, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df2.groupby('Country')[\"EconomicImpact\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "Respectively 277 people in Ghana and 335 people in Rwanda said that the impact of the virus on their country's economy is very high. Beyond this analysis, it is important to note that the Ghanaians (perception levels (2 to 4)) perceive more the probably very negative effects of the virus on the aconomy of their country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The perception of the population of the virus in terms of health or economic impact seems to be practically the same in both countries. However, these data do not allow us to make a decision on the factors that have influenced the number of cases and mortality observed in the database (Dataset n°1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.10 64-bit ('base': conda)",
   "language": "python",
   "name": "python361064bitbaseconda23104e51003a44379d7af35983baa4d7"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}