nga_covid19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot\n",
    "import seaborn as sns\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib.dates import DateFormatter\n",
    "import plotly.express as px\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the neccesary libraries.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading the files and inspecting the data..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga = pd.read_csv(\"nga_subnational_covid19_hera.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'DATE', 'ISO_3', 'PAYS', 'ID_PAYS', 'REGION', 'ID_REGION',\n",
       "       'CONTAMINES', 'DECES', 'GUERIS', 'CONTAMINES_FEMME', 'CONTAMINES_HOMME',\n",
       "       'CONTAMINES_GENRE_NON_SPECIFIE', 'SOURCE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the columns\n",
    "nga.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26866, 14)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ID        DATE ISO_3     PAYS  ID_PAYS       REGION  ID_REGION  CONTAMINES  \\\n",
      "0   1  27/02/2020   NGA  Nigéria       15         Abia        170         0.0   \n",
      "1   2  27/02/2020   NGA  Nigéria       15      Adamawa        171         0.0   \n",
      "2   3  27/02/2020   NGA  Nigéria       15    Akwa Ibom        172         0.0   \n",
      "3   4  27/02/2020   NGA  Nigéria       15      Anambra        173         0.0   \n",
      "4   5  27/02/2020   NGA  Nigéria       15       Bauchi        174         0.0   \n",
      "5   6  27/02/2020   NGA  Nigéria       15      Bayelsa        175         0.0   \n",
      "6   7  27/02/2020   NGA  Nigéria       15        Benue        176         0.0   \n",
      "7   8  27/02/2020   NGA  Nigéria       15        Borno        177         0.0   \n",
      "8   9  27/02/2020   NGA  Nigéria       15  Cross River        178         0.0   \n",
      "9  10  27/02/2020   NGA  Nigéria       15        Delta        179         0.0   \n",
      "\n",
      "   DECES  GUERIS  CONTAMINES_FEMME  CONTAMINES_HOMME  \\\n",
      "0    0.0     0.0               0.0               0.0   \n",
      "1    0.0     0.0               0.0               0.0   \n",
      "2    0.0     0.0               0.0               0.0   \n",
      "3    0.0     0.0               0.0               0.0   \n",
      "4    0.0     0.0               0.0               0.0   \n",
      "5    0.0     0.0               0.0               0.0   \n",
      "6    0.0     0.0               0.0               0.0   \n",
      "7    0.0     0.0               0.0               0.0   \n",
      "8    0.0     0.0               0.0               0.0   \n",
      "9    0.0     0.0               0.0               0.0   \n",
      "\n",
      "   CONTAMINES_GENRE_NON_SPECIFIE                              SOURCE  \n",
      "0                            0.0  Nigeria Centre for Disease Control  \n",
      "1                            0.0  Nigeria Centre for Disease Control  \n",
      "2                            0.0  Nigeria Centre for Disease Control  \n",
      "3                            0.0  Nigeria Centre for Disease Control  \n",
      "4                            0.0  Nigeria Centre for Disease Control  \n",
      "5                            0.0  Nigeria Centre for Disease Control  \n",
      "6                            0.0  Nigeria Centre for Disease Control  \n",
      "7                            0.0  Nigeria Centre for Disease Control  \n",
      "8                            0.0  Nigeria Centre for Disease Control  \n",
      "9                            0.0  Nigeria Centre for Disease Control  \n"
     ]
    }
   ],
   "source": [
    "print(nga.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>DATE</th>\n",
       "      <th>ISO_3</th>\n",
       "      <th>PAYS</th>\n",
       "      <th>ID_PAYS</th>\n",
       "      <th>REGION</th>\n",
       "      <th>ID_REGION</th>\n",
       "      <th>CONTAMINES</th>\n",
       "      <th>DECES</th>\n",
       "      <th>GUERIS</th>\n",
       "      <th>CONTAMINES_FEMME</th>\n",
       "      <th>CONTAMINES_HOMME</th>\n",
       "      <th>CONTAMINES_GENRE_NON_SPECIFIE</th>\n",
       "      <th>SOURCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26861</th>\n",
       "      <td>26862</td>\n",
       "      <td>02/02/2022</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Sokoto</td>\n",
       "      <td>203</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26862</th>\n",
       "      <td>26863</td>\n",
       "      <td>02/02/2022</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Taraba</td>\n",
       "      <td>204</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26863</th>\n",
       "      <td>26864</td>\n",
       "      <td>02/02/2022</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Yobe</td>\n",
       "      <td>205</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26864</th>\n",
       "      <td>26865</td>\n",
       "      <td>02/02/2022</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Zamfara</td>\n",
       "      <td>206</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26865</th>\n",
       "      <td>26866</td>\n",
       "      <td>02/02/2022</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Non spécifié</td>\n",
       "      <td>207</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID        DATE ISO_3     PAYS  ID_PAYS        REGION  ID_REGION  \\\n",
       "26861  26862  02/02/2022   NGA  Nigéria       15        Sokoto        203   \n",
       "26862  26863  02/02/2022   NGA  Nigéria       15        Taraba        204   \n",
       "26863  26864  02/02/2022   NGA  Nigéria       15          Yobe        205   \n",
       "26864  26865  02/02/2022   NGA  Nigéria       15       Zamfara        206   \n",
       "26865  26866  02/02/2022   NGA  Nigéria       15  Non spécifié        207   \n",
       "\n",
       "       CONTAMINES  DECES  GUERIS  CONTAMINES_FEMME  CONTAMINES_HOMME  \\\n",
       "26861         0.0    0.0     0.0               0.0               0.0   \n",
       "26862         0.0    0.0     0.0               0.0               0.0   \n",
       "26863         0.0    0.0     0.0               0.0               0.0   \n",
       "26864         0.0    0.0     0.0               0.0               0.0   \n",
       "26865         0.0    0.0     0.0               0.0               0.0   \n",
       "\n",
       "       CONTAMINES_GENRE_NON_SPECIFIE                              SOURCE  \n",
       "26861                            0.0  Nigeria Centre for Disease Control  \n",
       "26862                            0.0  Nigeria Centre for Disease Control  \n",
       "26863                            0.0  Nigeria Centre for Disease Control  \n",
       "26864                            0.0  Nigeria Centre for Disease Control  \n",
       "26865                            0.0  Nigeria Centre for Disease Control  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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>ID</th>\n",
       "      <th>ID_PAYS</th>\n",
       "      <th>ID_REGION</th>\n",
       "      <th>CONTAMINES</th>\n",
       "      <th>DECES</th>\n",
       "      <th>GUERIS</th>\n",
       "      <th>CONTAMINES_FEMME</th>\n",
       "      <th>CONTAMINES_HOMME</th>\n",
       "      <th>CONTAMINES_GENRE_NON_SPECIFIE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.0</td>\n",
       "      <td>26866.00000</td>\n",
       "      <td>26802.000000</td>\n",
       "      <td>26463.000000</td>\n",
       "      <td>26280.000000</td>\n",
       "      <td>17734.000000</td>\n",
       "      <td>17734.000000</td>\n",
       "      <td>23663.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13433.500000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>188.50000</td>\n",
       "      <td>9.440452</td>\n",
       "      <td>0.118467</td>\n",
       "      <td>8.714574</td>\n",
       "      <td>2.197023</td>\n",
       "      <td>1.259276</td>\n",
       "      <td>8.102481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7755.690502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.96606</td>\n",
       "      <td>45.747271</td>\n",
       "      <td>0.942725</td>\n",
       "      <td>107.723565</td>\n",
       "      <td>25.365332</td>\n",
       "      <td>15.169063</td>\n",
       "      <td>46.874095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>170.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6717.250000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>179.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13433.500000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>188.50000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20149.750000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>198.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26866.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>207.00000</td>\n",
       "      <td>3393.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>10946.000000</td>\n",
       "      <td>696.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>3393.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  ID_PAYS    ID_REGION    CONTAMINES         DECES  \\\n",
       "count  26866.000000  26866.0  26866.00000  26802.000000  26463.000000   \n",
       "mean   13433.500000     15.0    188.50000      9.440452      0.118467   \n",
       "std     7755.690502      0.0     10.96606     45.747271      0.942725   \n",
       "min        1.000000     15.0    170.00000     -1.000000      0.000000   \n",
       "25%     6717.250000     15.0    179.00000      0.000000      0.000000   \n",
       "50%    13433.500000     15.0    188.50000      0.000000      0.000000   \n",
       "75%    20149.750000     15.0    198.00000      3.000000      0.000000   \n",
       "max    26866.000000     15.0    207.00000   3393.000000     79.000000   \n",
       "\n",
       "             GUERIS  CONTAMINES_FEMME  CONTAMINES_HOMME  \\\n",
       "count  26280.000000      17734.000000      17734.000000   \n",
       "mean       8.714574          2.197023          1.259276   \n",
       "std      107.723565         25.365332         15.169063   \n",
       "min      -46.000000          0.000000        -90.000000   \n",
       "25%        0.000000          0.000000          0.000000   \n",
       "50%        0.000000          0.000000          0.000000   \n",
       "75%        0.000000          0.000000          0.000000   \n",
       "max    10946.000000        696.000000        493.000000   \n",
       "\n",
       "       CONTAMINES_GENRE_NON_SPECIFIE  \n",
       "count                   23663.000000  \n",
       "mean                        8.102481  \n",
       "std                        46.874095  \n",
       "min                        -1.000000  \n",
       "25%                         0.000000  \n",
       "50%                         0.000000  \n",
       "75%                         0.000000  \n",
       "max                      3393.000000  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.describe()\n",
    "#negative numbers present in contamines,gueris, homme, non_specifie columns\n",
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for null values..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                                  0\n",
       "DATE                                0\n",
       "ISO_3                               0\n",
       "PAYS                                0\n",
       "ID_PAYS                             0\n",
       "REGION                              0\n",
       "ID_REGION                           0\n",
       "CONTAMINES                         64\n",
       "DECES                             403\n",
       "GUERIS                            586\n",
       "CONTAMINES_FEMME                 9132\n",
       "CONTAMINES_HOMME                 9132\n",
       "CONTAMINES_GENRE_NON_SPECIFIE    3203\n",
       "SOURCE                              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling in the null values with the median values per column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga['CONTAMINES']=nga.CONTAMINES.fillna(nga.CONTAMINES.median())\n",
    "nga['DECES']=nga.DECES.fillna(nga.DECES.median())\n",
    "nga['GUERIS']=nga.GUERIS.fillna(nga.GUERIS.median())\n",
    "nga['CONTAMINES_FEMME']=nga.CONTAMINES_FEMME.fillna(nga.CONTAMINES_FEMME.median())\n",
    "nga['CONTAMINES_HOMME']=nga.CONTAMINES_HOMME.fillna(nga.CONTAMINES_HOMME.median())\n",
    "nga['CONTAMINES_GENRE_NON_SPECIFIE']=nga.CONTAMINES_GENRE_NON_SPECIFIE.fillna(nga.CONTAMINES_GENRE_NON_SPECIFIE.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                               0\n",
       "DATE                             0\n",
       "ISO_3                            0\n",
       "PAYS                             0\n",
       "ID_PAYS                          0\n",
       "REGION                           0\n",
       "ID_REGION                        0\n",
       "CONTAMINES                       0\n",
       "DECES                            0\n",
       "GUERIS                           0\n",
       "CONTAMINES_FEMME                 0\n",
       "CONTAMINES_HOMME                 0\n",
       "CONTAMINES_GENRE_NON_SPECIFIE    0\n",
       "SOURCE                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values\n",
    "nga.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ID_PAYS</th>\n",
       "      <th>ID_REGION</th>\n",
       "      <th>CONTAMINES</th>\n",
       "      <th>DECES</th>\n",
       "      <th>GUERIS</th>\n",
       "      <th>CONTAMINES_FEMME</th>\n",
       "      <th>CONTAMINES_HOMME</th>\n",
       "      <th>CONTAMINES_GENRE_NON_SPECIFIE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.0</td>\n",
       "      <td>26866.00000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13433.500000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>188.50000</td>\n",
       "      <td>9.417963</td>\n",
       "      <td>0.116690</td>\n",
       "      <td>8.524492</td>\n",
       "      <td>1.450234</td>\n",
       "      <td>0.831237</td>\n",
       "      <td>7.136492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7755.690502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.96606</td>\n",
       "      <td>45.695065</td>\n",
       "      <td>0.935739</td>\n",
       "      <td>106.549817</td>\n",
       "      <td>20.634374</td>\n",
       "      <td>12.338562</td>\n",
       "      <td>44.069426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>170.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-90.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6717.250000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>179.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13433.500000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>188.50000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20149.750000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>198.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26866.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>207.00000</td>\n",
       "      <td>3393.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>10946.000000</td>\n",
       "      <td>696.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>3393.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  ID_PAYS    ID_REGION    CONTAMINES         DECES  \\\n",
       "count  26866.000000  26866.0  26866.00000  26866.000000  26866.000000   \n",
       "mean   13433.500000     15.0    188.50000      9.417963      0.116690   \n",
       "std     7755.690502      0.0     10.96606     45.695065      0.935739   \n",
       "min        1.000000     15.0    170.00000     -1.000000      0.000000   \n",
       "25%     6717.250000     15.0    179.00000      0.000000      0.000000   \n",
       "50%    13433.500000     15.0    188.50000      0.000000      0.000000   \n",
       "75%    20149.750000     15.0    198.00000      3.000000      0.000000   \n",
       "max    26866.000000     15.0    207.00000   3393.000000     79.000000   \n",
       "\n",
       "             GUERIS  CONTAMINES_FEMME  CONTAMINES_HOMME  \\\n",
       "count  26866.000000      26866.000000      26866.000000   \n",
       "mean       8.524492          1.450234          0.831237   \n",
       "std      106.549817         20.634374         12.338562   \n",
       "min      -46.000000          0.000000        -90.000000   \n",
       "25%        0.000000          0.000000          0.000000   \n",
       "50%        0.000000          0.000000          0.000000   \n",
       "75%        0.000000          0.000000          0.000000   \n",
       "max    10946.000000        696.000000        493.000000   \n",
       "\n",
       "       CONTAMINES_GENRE_NON_SPECIFIE  \n",
       "count                   26866.000000  \n",
       "mean                        7.136492  \n",
       "std                        44.069426  \n",
       "min                        -1.000000  \n",
       "25%                         0.000000  \n",
       "50%                         0.000000  \n",
       "75%                         0.000000  \n",
       "max                      3393.000000  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I observe from the code above that some columns contain negative values. These are probable input errors.\n",
    "I replace them with the median values per column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "nga.GUERIS = np.abs(nga.GUERIS)\n",
    "nga.CONTAMINES = np.abs(nga.CONTAMINES)\n",
    "nga.CONTAMINES_HOMME = np.abs(nga.CONTAMINES_HOMME)\n",
    "nga.CONTAMINES = np.abs(nga.CONTAMINES)\n",
    "nga.CONTAMINES_GENRE_NON_SPECIFIE = np.abs(nga.CONTAMINES_GENRE_NON_SPECIFIE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>ID_PAYS</th>\n",
       "      <th>ID_REGION</th>\n",
       "      <th>CONTAMINES</th>\n",
       "      <th>DECES</th>\n",
       "      <th>GUERIS</th>\n",
       "      <th>CONTAMINES_FEMME</th>\n",
       "      <th>CONTAMINES_HOMME</th>\n",
       "      <th>CONTAMINES_GENRE_NON_SPECIFIE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.0</td>\n",
       "      <td>26866.00000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "      <td>26866.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13433.500000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>188.50000</td>\n",
       "      <td>9.418038</td>\n",
       "      <td>0.116690</td>\n",
       "      <td>8.528735</td>\n",
       "      <td>1.450234</td>\n",
       "      <td>0.837936</td>\n",
       "      <td>7.136567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7755.690502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.96606</td>\n",
       "      <td>45.695049</td>\n",
       "      <td>0.935739</td>\n",
       "      <td>106.549477</td>\n",
       "      <td>20.634374</td>\n",
       "      <td>12.338109</td>\n",
       "      <td>44.069414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>170.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6717.250000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>179.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13433.500000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>188.50000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>20149.750000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>198.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26866.000000</td>\n",
       "      <td>15.0</td>\n",
       "      <td>207.00000</td>\n",
       "      <td>3393.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>10946.000000</td>\n",
       "      <td>696.000000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>3393.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID  ID_PAYS    ID_REGION    CONTAMINES         DECES  \\\n",
       "count  26866.000000  26866.0  26866.00000  26866.000000  26866.000000   \n",
       "mean   13433.500000     15.0    188.50000      9.418038      0.116690   \n",
       "std     7755.690502      0.0     10.96606     45.695049      0.935739   \n",
       "min        1.000000     15.0    170.00000      0.000000      0.000000   \n",
       "25%     6717.250000     15.0    179.00000      0.000000      0.000000   \n",
       "50%    13433.500000     15.0    188.50000      0.000000      0.000000   \n",
       "75%    20149.750000     15.0    198.00000      3.000000      0.000000   \n",
       "max    26866.000000     15.0    207.00000   3393.000000     79.000000   \n",
       "\n",
       "             GUERIS  CONTAMINES_FEMME  CONTAMINES_HOMME  \\\n",
       "count  26866.000000      26866.000000      26866.000000   \n",
       "mean       8.528735          1.450234          0.837936   \n",
       "std      106.549477         20.634374         12.338109   \n",
       "min        0.000000          0.000000          0.000000   \n",
       "25%        0.000000          0.000000          0.000000   \n",
       "50%        0.000000          0.000000          0.000000   \n",
       "75%        0.000000          0.000000          0.000000   \n",
       "max    10946.000000        696.000000        493.000000   \n",
       "\n",
       "       CONTAMINES_GENRE_NON_SPECIFIE  \n",
       "count                   26866.000000  \n",
       "mean                        7.136567  \n",
       "std                        44.069414  \n",
       "min                         0.000000  \n",
       "25%                         0.000000  \n",
       "50%                         0.000000  \n",
       "75%                         0.000000  \n",
       "max                      3393.000000  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>ID</th>\n",
       "      <th>DATE</th>\n",
       "      <th>ISO_3</th>\n",
       "      <th>PAYS</th>\n",
       "      <th>ID_PAYS</th>\n",
       "      <th>REGION</th>\n",
       "      <th>ID_REGION</th>\n",
       "      <th>CONTAMINES</th>\n",
       "      <th>DECES</th>\n",
       "      <th>GUERIS</th>\n",
       "      <th>CONTAMINES_FEMME</th>\n",
       "      <th>CONTAMINES_HOMME</th>\n",
       "      <th>CONTAMINES_GENRE_NON_SPECIFIE</th>\n",
       "      <th>SOURCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Abia</td>\n",
       "      <td>170</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Adamawa</td>\n",
       "      <td>171</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Akwa Ibom</td>\n",
       "      <td>172</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Anambra</td>\n",
       "      <td>173</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Bauchi</td>\n",
       "      <td>174</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID        DATE ISO_3     PAYS  ID_PAYS     REGION  ID_REGION  CONTAMINES  \\\n",
       "0   1  27/02/2020   NGA  Nigéria       15       Abia        170         0.0   \n",
       "1   2  27/02/2020   NGA  Nigéria       15    Adamawa        171         0.0   \n",
       "2   3  27/02/2020   NGA  Nigéria       15  Akwa Ibom        172         0.0   \n",
       "3   4  27/02/2020   NGA  Nigéria       15    Anambra        173         0.0   \n",
       "4   5  27/02/2020   NGA  Nigéria       15     Bauchi        174         0.0   \n",
       "\n",
       "   DECES  GUERIS  CONTAMINES_FEMME  CONTAMINES_HOMME  \\\n",
       "0    0.0     0.0               0.0               0.0   \n",
       "1    0.0     0.0               0.0               0.0   \n",
       "2    0.0     0.0               0.0               0.0   \n",
       "3    0.0     0.0               0.0               0.0   \n",
       "4    0.0     0.0               0.0               0.0   \n",
       "\n",
       "   CONTAMINES_GENRE_NON_SPECIFIE                              SOURCE  \n",
       "0                            0.0  Nigeria Centre for Disease Control  \n",
       "1                            0.0  Nigeria Centre for Disease Control  \n",
       "2                            0.0  Nigeria Centre for Disease Control  \n",
       "3                            0.0  Nigeria Centre for Disease Control  \n",
       "4                            0.0  Nigeria Centre for Disease Control  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the values are all integers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The columns are in French.\n",
    "I rename each accordingly below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga.rename(columns={\n",
    "    'ID': 'id',\n",
    "    'DATE': 'date',\n",
    "    'ISO_3': 'iso_3',\n",
    "    'PAYS': 'country',\n",
    "    'ID_PAYS': 'country_id',\n",
    "    'REGION': 'state',\n",
    "    'ID_REGION': 'state_id',\n",
    "    'CONTAMINES': 'confirmed',\n",
    "    'DECES': 'deaths',\n",
    "    'GUERIS': 'recovered',\n",
    "    'CONTAMINES_FEMME': 'confirmed_female',\n",
    "    'CONTAMINES_HOMME': 'confirmed_male',\n",
    "    'CONTAMINES_GENRE_NON_SPECIFIE': 'confirmed_gender_ns',\n",
    "    'SOURCE': 'source'\n",
    "}, inplace=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>iso_3</th>\n",
       "      <th>country</th>\n",
       "      <th>country_id</th>\n",
       "      <th>state</th>\n",
       "      <th>state_id</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>confirmed_female</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>confirmed_gender_ns</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Abia</td>\n",
       "      <td>170</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Adamawa</td>\n",
       "      <td>171</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Akwa Ibom</td>\n",
       "      <td>172</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Anambra</td>\n",
       "      <td>173</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigéria</td>\n",
       "      <td>15</td>\n",
       "      <td>Bauchi</td>\n",
       "      <td>174</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id        date iso_3  country  country_id      state  state_id  confirmed  \\\n",
       "0   1  27/02/2020   NGA  Nigéria          15       Abia       170        0.0   \n",
       "1   2  27/02/2020   NGA  Nigéria          15    Adamawa       171        0.0   \n",
       "2   3  27/02/2020   NGA  Nigéria          15  Akwa Ibom       172        0.0   \n",
       "3   4  27/02/2020   NGA  Nigéria          15    Anambra       173        0.0   \n",
       "4   5  27/02/2020   NGA  Nigéria          15     Bauchi       174        0.0   \n",
       "\n",
       "   deaths  recovered  confirmed_female  confirmed_male  confirmed_gender_ns  \\\n",
       "0     0.0        0.0               0.0             0.0                  0.0   \n",
       "1     0.0        0.0               0.0             0.0                  0.0   \n",
       "2     0.0        0.0               0.0             0.0                  0.0   \n",
       "3     0.0        0.0               0.0             0.0                  0.0   \n",
       "4     0.0        0.0               0.0             0.0                  0.0   \n",
       "\n",
       "                               source  \n",
       "0  Nigeria Centre for Disease Control  \n",
       "1  Nigeria Centre for Disease Control  \n",
       "2  Nigeria Centre for Disease Control  \n",
       "3  Nigeria Centre for Disease Control  \n",
       "4  Nigeria Centre for Disease Control  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Nigeria' is misspelt.\n",
    "Correcting the error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>iso_3</th>\n",
       "      <th>country</th>\n",
       "      <th>country_id</th>\n",
       "      <th>state</th>\n",
       "      <th>state_id</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>15</td>\n",
       "      <td>Adamawa</td>\n",
       "      <td>171</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",
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       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>15</td>\n",
       "      <td>Akwa Ibom</td>\n",
       "      <td>172</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>15</td>\n",
       "      <td>Anambra</td>\n",
       "      <td>173</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>27/02/2020</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>15</td>\n",
       "      <td>Bauchi</td>\n",
       "      <td>174</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>0.0</td>\n",
       "      <td>Nigeria Centre for Disease Control</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id        date iso_3  country  country_id      state  state_id  confirmed  \\\n",
       "0   1  27/02/2020   NGA  Nigeria          15       Abia       170        0.0   \n",
       "1   2  27/02/2020   NGA  Nigeria          15    Adamawa       171        0.0   \n",
       "2   3  27/02/2020   NGA  Nigeria          15  Akwa Ibom       172        0.0   \n",
       "3   4  27/02/2020   NGA  Nigeria          15    Anambra       173        0.0   \n",
       "4   5  27/02/2020   NGA  Nigeria          15     Bauchi       174        0.0   \n",
       "\n",
       "   deaths  recovered  confirmed_female  confirmed_male  confirmed_gender_ns  \\\n",
       "0     0.0        0.0               0.0             0.0                  0.0   \n",
       "1     0.0        0.0               0.0             0.0                  0.0   \n",
       "2     0.0        0.0               0.0             0.0                  0.0   \n",
       "3     0.0        0.0               0.0             0.0                  0.0   \n",
       "4     0.0        0.0               0.0             0.0                  0.0   \n",
       "\n",
       "                               source  \n",
       "0  Nigeria Centre for Disease Control  \n",
       "1  Nigeria Centre for Disease Control  \n",
       "2  Nigeria Centre for Disease Control  \n",
       "3  Nigeria Centre for Disease Control  \n",
       "4  Nigeria Centre for Disease Control  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.country=nga.country.replace(['Nigéria'],'Nigeria')\n",
    "nga.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nigeria    26866\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check that there is only one instance of each country\n",
    "nga['country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                Abia\n",
      "1             Adamawa\n",
      "2           Akwa Ibom\n",
      "3             Anambra\n",
      "4              Bauchi\n",
      "             ...     \n",
      "26861          Sokoto\n",
      "26862          Taraba\n",
      "26863            Yobe\n",
      "26864         Zamfara\n",
      "26865    Non spécifié\n",
      "Name: state, Length: 26866, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#checking that there are 36 states\n",
    "states = nga['state']\n",
    "print(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                Abia\n",
      "1             Adamawa\n",
      "2           Akwa Ibom\n",
      "3             Anambra\n",
      "4              Bauchi\n",
      "             ...     \n",
      "26861          Sokoto\n",
      "26862          Taraba\n",
      "26863            Yobe\n",
      "26864         Zamfara\n",
      "26865    Non spécifié\n",
      "Name: state, Length: 26866, dtype: object\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Non spécifié'"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for this unwanted value\n",
    "nga.state[26865]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting date datattype to datetime\n",
    "nga.date = pd.to_datetime(nga.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                              int64\n",
       "date                   datetime64[ns]\n",
       "iso_3                          object\n",
       "country                        object\n",
       "country_id                      int64\n",
       "state                          object\n",
       "state_id                        int64\n",
       "confirmed                     float64\n",
       "deaths                        float64\n",
       "recovered                     float64\n",
       "confirmed_female              float64\n",
       "confirmed_male                float64\n",
       "confirmed_gender_ns           float64\n",
       "source                         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking if conversion is successfull\n",
    "nga.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping unwanted columns\n",
    "nga.drop(['iso_3', 'country_id', 'state_id', 'id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing an error value (row)\n",
    "nga.drop([26865], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing an unwanted column\n",
    "nga.drop(['source'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26865 entries, 0 to 26864\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   date                 26865 non-null  datetime64[ns]\n",
      " 1   country              26865 non-null  object        \n",
      " 2   state                26865 non-null  object        \n",
      " 3   confirmed            26865 non-null  float64       \n",
      " 4   deaths               26865 non-null  float64       \n",
      " 5   recovered            26865 non-null  float64       \n",
      " 6   confirmed_female     26865 non-null  float64       \n",
      " 7   confirmed_male       26865 non-null  float64       \n",
      " 8   confirmed_gender_ns  26865 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(6), object(2)\n",
      "memory usage: 1.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(nga.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "253025.0\n",
      "3135.0\n",
      "229133.0\n"
     ]
    }
   ],
   "source": [
    "#number of confirmed cases\n",
    "print(nga.confirmed.sum())\n",
    "print(nga.deaths.sum())\n",
    "print(nga.recovered.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Nigeria</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>1552.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1552.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12868</th>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25066</th>\n",
       "      <td>2021-12-17</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>906.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12374</th>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>901.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>901.0</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>10456</th>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Benue</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10455</th>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bayelsa</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10454</th>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bauchi</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10453</th>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Anambra</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26864</th>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Zamfara</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>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26865 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  country    state  confirmed  deaths  recovered  \\\n",
       "25256 2021-12-22  Nigeria    Lagos     3393.0     0.0        0.0   \n",
       "25218 2021-12-21  Nigeria    Lagos     1552.0     0.0        0.0   \n",
       "12868 2021-01-30  Nigeria    Lagos     1040.0     0.0      461.0   \n",
       "25066 2021-12-17  Nigeria    Lagos      906.0     0.0        0.0   \n",
       "12374 2021-01-17  Nigeria    Lagos      901.0     2.0      717.0   \n",
       "...          ...      ...      ...        ...     ...        ...   \n",
       "10456 2020-11-28  Nigeria    Benue        0.0     0.0        0.0   \n",
       "10455 2020-11-28  Nigeria  Bayelsa        0.0     0.0        0.0   \n",
       "10454 2020-11-28  Nigeria   Bauchi        0.0     0.0        0.0   \n",
       "10453 2020-11-28  Nigeria  Anambra        0.0     0.0        0.0   \n",
       "26864 2022-02-02  Nigeria  Zamfara        0.0     0.0        0.0   \n",
       "\n",
       "       confirmed_female  confirmed_male  confirmed_gender_ns  \n",
       "25256               0.0             0.0               3393.0  \n",
       "25218               0.0             0.0               1552.0  \n",
       "12868               0.0             0.0               1040.0  \n",
       "25066               0.0             0.0                906.0  \n",
       "12374               0.0             0.0                901.0  \n",
       "...                 ...             ...                  ...  \n",
       "10456               0.0             0.0                  0.0  \n",
       "10455               0.0             0.0                  0.0  \n",
       "10454               0.0             0.0                  0.0  \n",
       "10453               0.0             0.0                  0.0  \n",
       "26864               0.0             0.0                  0.0  \n",
       "\n",
       "[26865 rows x 9 columns]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking most states with confirmed cases\n",
    "nga.sort_values(by=['confirmed'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the data\n",
    "out_nga = 'nga_covid19.csv'\n",
    "nga.to_csv(out_nga)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 26865 entries, 0 to 26864\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   date                 26865 non-null  datetime64[ns]\n",
      " 1   country              26865 non-null  object        \n",
      " 2   state                26865 non-null  object        \n",
      " 3   confirmed            26865 non-null  float64       \n",
      " 4   deaths               26865 non-null  float64       \n",
      " 5   recovered            26865 non-null  float64       \n",
      " 6   confirmed_female     26865 non-null  float64       \n",
      " 7   confirmed_male       26865 non-null  float64       \n",
      " 8   confirmed_gender_ns  26865 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(6), object(2)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "nga.info()"
   ]
  },
  {
   "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>state</th>\n",
       "      <th>population_(2022)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lagos</td>\n",
       "      <td>21,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kano</td>\n",
       "      <td>12,550,598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kaduna</td>\n",
       "      <td>8,252,366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Oyo</td>\n",
       "      <td>7,840,864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Katsina</td>\n",
       "      <td>7,831,319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state population_(2022)\n",
       "0    Lagos        21,000,000\n",
       "1     Kano        12,550,598\n",
       "2   Kaduna         8,252,366\n",
       "3      Oyo         7,840,864\n",
       "4  Katsina         7,831,319"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading population table. The goal is to merge with nga\n",
    "import pandas as pd\n",
    "pop_nga = pd.read_csv(r\"C:\\Users\\eduna\\Documents\\state_pop.txt\", sep='\\s+')\n",
    "pop_nga.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "#correcting some states misspellings\n",
    "pop_nga.state=pop_nga.state.replace(['Akwa-Ibom'],'Akwa Ibom')\n",
    "pop_nga.state=pop_nga.state.replace(['Cross-River'],'Cross River')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Akwa Ibom'"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the correction\n",
    "pop_nga.state[13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37 entries, 0 to 36\n",
      "Data columns (total 2 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   state              37 non-null     object\n",
      " 1   population_(2022)  37 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 720.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "#checking that data is clean\n",
    "pop_nga.isna().sum()\n",
    "pop_nga.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "#exporting the data\n",
    "pop_nga.to_csv('pop_nga.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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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>date</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>confirmed_female</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>confirmed_gender_ns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Abia</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Adamawa</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Akwa Ibom</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Anambra</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>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bauchi</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>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  country      state  confirmed  deaths  recovered  \\\n",
       "0 2020-02-27  Nigeria       Abia        0.0     0.0        0.0   \n",
       "1 2020-02-27  Nigeria    Adamawa        0.0     0.0        0.0   \n",
       "2 2020-02-27  Nigeria  Akwa Ibom        0.0     0.0        0.0   \n",
       "3 2020-02-27  Nigeria    Anambra        0.0     0.0        0.0   \n",
       "4 2020-02-27  Nigeria     Bauchi        0.0     0.0        0.0   \n",
       "\n",
       "   confirmed_female  confirmed_male  confirmed_gender_ns  \n",
       "0               0.0             0.0                  0.0  \n",
       "1               0.0             0.0                  0.0  \n",
       "2               0.0             0.0                  0.0  \n",
       "3               0.0             0.0                  0.0  \n",
       "4               0.0             0.0                  0.0  "
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             Abia\n",
       "1          Adamawa\n",
       "2        Akwa Ibom\n",
       "3          Anambra\n",
       "4           Bauchi\n",
       "           ...    \n",
       "26860       Rivers\n",
       "26861       Sokoto\n",
       "26862       Taraba\n",
       "26863         Yobe\n",
       "26864      Zamfara\n",
       "Name: state, Length: 26159, dtype: object"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping rows with 'Non specifie'\n",
    "nga.drop(nga.index[nga['state'] == 'Non spécifié'], inplace=True)\n",
    "nga['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>date</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>confirmed_female</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>confirmed_gender_ns</th>\n",
       "      <th>population_(2022)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Abia</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>0.0</td>\n",
       "      <td>3,727,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Adamawa</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>0.0</td>\n",
       "      <td>4,248,436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Akwa Ibom</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>0.0</td>\n",
       "      <td>5,482,177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Anambra</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>0.0</td>\n",
       "      <td>5,527,809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bauchi</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>0.0</td>\n",
       "      <td>6,537,314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bayelsa</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>0.0</td>\n",
       "      <td>2,277,961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Benue</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>0.0</td>\n",
       "      <td>5,741,815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Borno</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>0.0</td>\n",
       "      <td>5,860,183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Cross River</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>0.0</td>\n",
       "      <td>3,866,269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Delta</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>0.0</td>\n",
       "      <td>5,663,362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ebonyi</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>0.0</td>\n",
       "      <td>2,880,383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Edo</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>0.0</td>\n",
       "      <td>4,235,595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ekiti</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>0.0</td>\n",
       "      <td>3,270,798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Enugu</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>0.0</td>\n",
       "      <td>4,411,119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Federal Capital Territory</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>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Gombe</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>0.0</td>\n",
       "      <td>3,256,962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Imo</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>0.0</td>\n",
       "      <td>5,408,756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Jigawa</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>0.0</td>\n",
       "      <td>5,828,163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Kaduna</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>0.0</td>\n",
       "      <td>8,252,366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Kano</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>0.0</td>\n",
       "      <td>12,550,598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Katsina</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>0.0</td>\n",
       "      <td>7,831,319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Kebbi</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>0.0</td>\n",
       "      <td>4,440,050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Kogi</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>0.0</td>\n",
       "      <td>4,473,490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Kwara</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>0.0</td>\n",
       "      <td>3,192,893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Lagos</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>0.0</td>\n",
       "      <td>21,000,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Nasarawa</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>0.0</td>\n",
       "      <td>2,523,395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Niger</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>0.0</td>\n",
       "      <td>5,556,247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ogun</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5,217,716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Osun</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>0.0</td>\n",
       "      <td>4,705,589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ondo</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>0.0</td>\n",
       "      <td>4,671,695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Oyo</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>0.0</td>\n",
       "      <td>7,840,864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Plateau</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>0.0</td>\n",
       "      <td>4,200,442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Rivers</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>0.0</td>\n",
       "      <td>7,303,924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Sokoto</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>0.0</td>\n",
       "      <td>4,998,090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Taraba</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>0.0</td>\n",
       "      <td>3,066,834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Yobe</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>0.0</td>\n",
       "      <td>3,294,137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Zamfara</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>0.0</td>\n",
       "      <td>4,515,427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Abia</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>0.0</td>\n",
       "      <td>3,727,347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Adamawa</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>0.0</td>\n",
       "      <td>4,248,436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Akwa Ibom</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>0.0</td>\n",
       "      <td>5,482,177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Anambra</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>0.0</td>\n",
       "      <td>5,527,809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bauchi</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>0.0</td>\n",
       "      <td>6,537,314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bayelsa</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>0.0</td>\n",
       "      <td>2,277,961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Benue</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>0.0</td>\n",
       "      <td>5,741,815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Borno</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>0.0</td>\n",
       "      <td>5,860,183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  country                      state  confirmed  deaths  \\\n",
       "0  2020-02-27  Nigeria                       Abia        0.0     0.0   \n",
       "1  2020-02-27  Nigeria                    Adamawa        0.0     0.0   \n",
       "2  2020-02-27  Nigeria                  Akwa Ibom        0.0     0.0   \n",
       "3  2020-02-27  Nigeria                    Anambra        0.0     0.0   \n",
       "4  2020-02-27  Nigeria                     Bauchi        0.0     0.0   \n",
       "5  2020-02-27  Nigeria                    Bayelsa        0.0     0.0   \n",
       "6  2020-02-27  Nigeria                      Benue        0.0     0.0   \n",
       "7  2020-02-27  Nigeria                      Borno        0.0     0.0   \n",
       "8  2020-02-27  Nigeria                Cross River        0.0     0.0   \n",
       "9  2020-02-27  Nigeria                      Delta        0.0     0.0   \n",
       "10 2020-02-27  Nigeria                     Ebonyi        0.0     0.0   \n",
       "11 2020-02-27  Nigeria                        Edo        0.0     0.0   \n",
       "12 2020-02-27  Nigeria                      Ekiti        0.0     0.0   \n",
       "13 2020-02-27  Nigeria                      Enugu        0.0     0.0   \n",
       "14 2020-02-27  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "15 2020-02-27  Nigeria                      Gombe        0.0     0.0   \n",
       "16 2020-02-27  Nigeria                        Imo        0.0     0.0   \n",
       "17 2020-02-27  Nigeria                     Jigawa        0.0     0.0   \n",
       "18 2020-02-27  Nigeria                     Kaduna        0.0     0.0   \n",
       "19 2020-02-27  Nigeria                       Kano        0.0     0.0   \n",
       "20 2020-02-27  Nigeria                    Katsina        0.0     0.0   \n",
       "21 2020-02-27  Nigeria                      Kebbi        0.0     0.0   \n",
       "22 2020-02-27  Nigeria                       Kogi        0.0     0.0   \n",
       "23 2020-02-27  Nigeria                      Kwara        0.0     0.0   \n",
       "24 2020-02-27  Nigeria                      Lagos        0.0     0.0   \n",
       "25 2020-02-27  Nigeria                   Nasarawa        0.0     0.0   \n",
       "26 2020-02-27  Nigeria                      Niger        0.0     0.0   \n",
       "27 2020-02-27  Nigeria                       Ogun        1.0     0.0   \n",
       "28 2020-02-27  Nigeria                       Osun        0.0     0.0   \n",
       "29 2020-02-27  Nigeria                       Ondo        0.0     0.0   \n",
       "30 2020-02-27  Nigeria                        Oyo        0.0     0.0   \n",
       "31 2020-02-27  Nigeria                    Plateau        0.0     0.0   \n",
       "32 2020-02-27  Nigeria                     Rivers        0.0     0.0   \n",
       "33 2020-02-27  Nigeria                     Sokoto        0.0     0.0   \n",
       "34 2020-02-27  Nigeria                     Taraba        0.0     0.0   \n",
       "35 2020-02-27  Nigeria                       Yobe        0.0     0.0   \n",
       "36 2020-02-27  Nigeria                    Zamfara        0.0     0.0   \n",
       "37 2020-02-28  Nigeria                       Abia        0.0     0.0   \n",
       "38 2020-02-28  Nigeria                    Adamawa        0.0     0.0   \n",
       "39 2020-02-28  Nigeria                  Akwa Ibom        0.0     0.0   \n",
       "40 2020-02-28  Nigeria                    Anambra        0.0     0.0   \n",
       "41 2020-02-28  Nigeria                     Bauchi        0.0     0.0   \n",
       "42 2020-02-28  Nigeria                    Bayelsa        0.0     0.0   \n",
       "43 2020-02-28  Nigeria                      Benue        0.0     0.0   \n",
       "44 2020-02-28  Nigeria                      Borno        0.0     0.0   \n",
       "\n",
       "    recovered  confirmed_female  confirmed_male  confirmed_gender_ns  \\\n",
       "0         0.0               0.0             0.0                  0.0   \n",
       "1         0.0               0.0             0.0                  0.0   \n",
       "2         0.0               0.0             0.0                  0.0   \n",
       "3         0.0               0.0             0.0                  0.0   \n",
       "4         0.0               0.0             0.0                  0.0   \n",
       "5         0.0               0.0             0.0                  0.0   \n",
       "6         0.0               0.0             0.0                  0.0   \n",
       "7         0.0               0.0             0.0                  0.0   \n",
       "8         0.0               0.0             0.0                  0.0   \n",
       "9         0.0               0.0             0.0                  0.0   \n",
       "10        0.0               0.0             0.0                  0.0   \n",
       "11        0.0               0.0             0.0                  0.0   \n",
       "12        0.0               0.0             0.0                  0.0   \n",
       "13        0.0               0.0             0.0                  0.0   \n",
       "14        0.0               0.0             0.0                  0.0   \n",
       "15        0.0               0.0             0.0                  0.0   \n",
       "16        0.0               0.0             0.0                  0.0   \n",
       "17        0.0               0.0             0.0                  0.0   \n",
       "18        0.0               0.0             0.0                  0.0   \n",
       "19        0.0               0.0             0.0                  0.0   \n",
       "20        0.0               0.0             0.0                  0.0   \n",
       "21        0.0               0.0             0.0                  0.0   \n",
       "22        0.0               0.0             0.0                  0.0   \n",
       "23        0.0               0.0             0.0                  0.0   \n",
       "24        0.0               0.0             0.0                  0.0   \n",
       "25        0.0               0.0             0.0                  0.0   \n",
       "26        0.0               0.0             0.0                  0.0   \n",
       "27        0.0               0.0             0.0                  1.0   \n",
       "28        0.0               0.0             0.0                  0.0   \n",
       "29        0.0               0.0             0.0                  0.0   \n",
       "30        0.0               0.0             0.0                  0.0   \n",
       "31        0.0               0.0             0.0                  0.0   \n",
       "32        0.0               0.0             0.0                  0.0   \n",
       "33        0.0               0.0             0.0                  0.0   \n",
       "34        0.0               0.0             0.0                  0.0   \n",
       "35        0.0               0.0             0.0                  0.0   \n",
       "36        0.0               0.0             0.0                  0.0   \n",
       "37        0.0               0.0             0.0                  0.0   \n",
       "38        0.0               0.0             0.0                  0.0   \n",
       "39        0.0               0.0             0.0                  0.0   \n",
       "40        0.0               0.0             0.0                  0.0   \n",
       "41        0.0               0.0             0.0                  0.0   \n",
       "42        0.0               0.0             0.0                  0.0   \n",
       "43        0.0               0.0             0.0                  0.0   \n",
       "44        0.0               0.0             0.0                  0.0   \n",
       "\n",
       "   population_(2022)  \n",
       "0          3,727,347  \n",
       "1          4,248,436  \n",
       "2          5,482,177  \n",
       "3          5,527,809  \n",
       "4          6,537,314  \n",
       "5          2,277,961  \n",
       "6          5,741,815  \n",
       "7          5,860,183  \n",
       "8          3,866,269  \n",
       "9          5,663,362  \n",
       "10         2,880,383  \n",
       "11         4,235,595  \n",
       "12         3,270,798  \n",
       "13         4,411,119  \n",
       "14               NaN  \n",
       "15         3,256,962  \n",
       "16         5,408,756  \n",
       "17         5,828,163  \n",
       "18         8,252,366  \n",
       "19        12,550,598  \n",
       "20         7,831,319  \n",
       "21         4,440,050  \n",
       "22         4,473,490  \n",
       "23         3,192,893  \n",
       "24        21,000,000  \n",
       "25         2,523,395  \n",
       "26         5,556,247  \n",
       "27         5,217,716  \n",
       "28         4,705,589  \n",
       "29         4,671,695  \n",
       "30         7,840,864  \n",
       "31         4,200,442  \n",
       "32         7,303,924  \n",
       "33         4,998,090  \n",
       "34         3,066,834  \n",
       "35         3,294,137  \n",
       "36         4,515,427  \n",
       "37         3,727,347  \n",
       "38         4,248,436  \n",
       "39         5,482,177  \n",
       "40         5,527,809  \n",
       "41         6,537,314  \n",
       "42         2,277,961  \n",
       "43         5,741,815  \n",
       "44         5,860,183  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging the data with population data\n",
    "nga_merged = pd.merge(nga, pop_nga, on='state', how='left')\n",
    "nga_merged.head(45)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "ename": "PermissionError",
     "evalue": "[Errno 13] Permission denied: 'nga_merged.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\eduna\\Documents\\ProjectExample\\nga_covid19\\nga_covid19.ipynb Cell 48'\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/eduna/Documents/ProjectExample/nga_covid19/nga_covid19.ipynb#ch0000048?line=0'>1</a>\u001b[0m \u001b[39m#exporting the data\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/eduna/Documents/ProjectExample/nga_covid19/nga_covid19.ipynb#ch0000048?line=1'>2</a>\u001b[0m nga_merged\u001b[39m.\u001b[39;49mto_csv(\u001b[39m'\u001b[39;49m\u001b[39mnga_merged.csv\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[1;32mc:\\Users\\eduna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\core\\generic.py:3551\u001b[0m, in \u001b[0;36mNDFrame.to_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3539'>3540</a>\u001b[0m df \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(\u001b[39mself\u001b[39m, ABCDataFrame) \u001b[39melse\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mto_frame()\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3541'>3542</a>\u001b[0m formatter \u001b[39m=\u001b[39m DataFrameFormatter(\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3542'>3543</a>\u001b[0m     frame\u001b[39m=\u001b[39mdf,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3543'>3544</a>\u001b[0m     header\u001b[39m=\u001b[39mheader,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3547'>3548</a>\u001b[0m     decimal\u001b[39m=\u001b[39mdecimal,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3548'>3549</a>\u001b[0m )\n\u001b[1;32m-> <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3550'>3551</a>\u001b[0m \u001b[39mreturn\u001b[39;00m DataFrameRenderer(formatter)\u001b[39m.\u001b[39;49mto_csv(\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3551'>3552</a>\u001b[0m     path_or_buf,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3552'>3553</a>\u001b[0m     line_terminator\u001b[39m=\u001b[39;49mline_terminator,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3553'>3554</a>\u001b[0m     sep\u001b[39m=\u001b[39;49msep,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3554'>3555</a>\u001b[0m     encoding\u001b[39m=\u001b[39;49mencoding,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3555'>3556</a>\u001b[0m     errors\u001b[39m=\u001b[39;49merrors,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3556'>3557</a>\u001b[0m     compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3557'>3558</a>\u001b[0m     quoting\u001b[39m=\u001b[39;49mquoting,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3558'>3559</a>\u001b[0m     columns\u001b[39m=\u001b[39;49mcolumns,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3559'>3560</a>\u001b[0m     index_label\u001b[39m=\u001b[39;49mindex_label,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3560'>3561</a>\u001b[0m     mode\u001b[39m=\u001b[39;49mmode,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3561'>3562</a>\u001b[0m     chunksize\u001b[39m=\u001b[39;49mchunksize,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3562'>3563</a>\u001b[0m     quotechar\u001b[39m=\u001b[39;49mquotechar,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3563'>3564</a>\u001b[0m     date_format\u001b[39m=\u001b[39;49mdate_format,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3564'>3565</a>\u001b[0m     doublequote\u001b[39m=\u001b[39;49mdoublequote,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3565'>3566</a>\u001b[0m     escapechar\u001b[39m=\u001b[39;49mescapechar,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3566'>3567</a>\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/core/generic.py?line=3567'>3568</a>\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\eduna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\formats\\format.py:1180\u001b[0m, in \u001b[0;36mDataFrameRenderer.to_csv\u001b[1;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1158'>1159</a>\u001b[0m     created_buffer \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1160'>1161</a>\u001b[0m csv_formatter \u001b[39m=\u001b[39m CSVFormatter(\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1161'>1162</a>\u001b[0m     path_or_buf\u001b[39m=\u001b[39mpath_or_buf,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1162'>1163</a>\u001b[0m     line_terminator\u001b[39m=\u001b[39mline_terminator,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1177'>1178</a>\u001b[0m     formatter\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfmt,\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1178'>1179</a>\u001b[0m )\n\u001b[1;32m-> <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1179'>1180</a>\u001b[0m csv_formatter\u001b[39m.\u001b[39;49msave()\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1181'>1182</a>\u001b[0m \u001b[39mif\u001b[39;00m created_buffer:\n\u001b[0;32m   <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/format.py?line=1182'>1183</a>\u001b[0m     \u001b[39massert\u001b[39;00m \u001b[39misinstance\u001b[39m(path_or_buf, StringIO)\n",
      "File \u001b[1;32mc:\\Users\\eduna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\formats\\csvs.py:241\u001b[0m, in \u001b[0;36mCSVFormatter.save\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=236'>237</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=237'>238</a>\u001b[0m \u001b[39mCreate the writer & save.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=238'>239</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=239'>240</a>\u001b[0m \u001b[39m# apply compression and byte/text conversion\u001b[39;00m\n\u001b[1;32m--> <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=240'>241</a>\u001b[0m \u001b[39mwith\u001b[39;00m get_handle(\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=241'>242</a>\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfilepath_or_buffer,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=242'>243</a>\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmode,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=243'>244</a>\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mencoding,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=244'>245</a>\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49merrors,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=245'>246</a>\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcompression,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=246'>247</a>\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstorage_options,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=247'>248</a>\u001b[0m ) \u001b[39mas\u001b[39;00m handles:\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=248'>249</a>\u001b[0m \n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=249'>250</a>\u001b[0m     \u001b[39m# Note: self.encoding is irrelevant here\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=250'>251</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwriter \u001b[39m=\u001b[39m csvlib\u001b[39m.\u001b[39mwriter(\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=251'>252</a>\u001b[0m         handles\u001b[39m.\u001b[39mhandle,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=252'>253</a>\u001b[0m         lineterminator\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mline_terminator,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=257'>258</a>\u001b[0m         quotechar\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mquotechar,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=258'>259</a>\u001b[0m     )\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/formats/csvs.py?line=260'>261</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_save()\n",
      "File \u001b[1;32mc:\\Users\\eduna\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\pandas\\io\\common.py:789\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=783'>784</a>\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=784'>785</a>\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=785'>786</a>\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=786'>787</a>\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=787'>788</a>\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[1;32m--> <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=788'>789</a>\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=789'>790</a>\u001b[0m             handle,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=790'>791</a>\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=791'>792</a>\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=792'>793</a>\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=793'>794</a>\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=794'>795</a>\u001b[0m         )\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=795'>796</a>\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=796'>797</a>\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m    <a href='file:///c%3A/Users/eduna/AppData/Local/Programs/Python/Python310/lib/site-packages/pandas/io/common.py?line=797'>798</a>\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'nga_merged.csv'"
     ]
    }
   ],
   "source": [
    "#exporting the data\n",
    "nga_merged.to_csv('nga_merged.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26159 entries, 0 to 26158\n",
      "Data columns (total 10 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   date                 26159 non-null  datetime64[ns]\n",
      " 1   country              26159 non-null  object        \n",
      " 2   state                26159 non-null  object        \n",
      " 3   confirmed            26159 non-null  float64       \n",
      " 4   deaths               26159 non-null  float64       \n",
      " 5   recovered            26159 non-null  float64       \n",
      " 6   confirmed_female     26159 non-null  float64       \n",
      " 7   confirmed_male       26159 non-null  float64       \n",
      " 8   confirmed_gender_ns  26159 non-null  float64       \n",
      " 9   population_(2022)    25452 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(6), object(3)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#checking the newly merged column\n",
    "nga_merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                     0\n",
       "country                  0\n",
       "state                    0\n",
       "confirmed                0\n",
       "deaths                   0\n",
       "recovered                0\n",
       "confirmed_female         0\n",
       "confirmed_male           0\n",
       "confirmed_gender_ns      0\n",
       "population_(2022)      707\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for the columns with null values\n",
    "nga_merged.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The population column has some missing values. Let's check that out..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>26025</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Federal Capital Territory</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26062</th>\n",
       "      <td>2022-01-31</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Federal Capital Territory</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>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26099</th>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Federal Capital Territory</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>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26136</th>\n",
       "      <td>2022-02-02</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Federal Capital Territory</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>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>707 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  country                      state  confirmed  deaths  \\\n",
       "14    2020-02-27  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "51    2020-02-28  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "88    2020-02-29  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "125   2020-01-03  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "162   2020-02-03  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "...          ...      ...                        ...        ...     ...   \n",
       "25988 2022-01-29  Nigeria  Federal Capital Territory        3.0     0.0   \n",
       "26025 2022-01-30  Nigeria  Federal Capital Territory        6.0     0.0   \n",
       "26062 2022-01-31  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "26099 2022-01-02  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "26136 2022-02-02  Nigeria  Federal Capital Territory        0.0     0.0   \n",
       "\n",
       "       recovered  confirmed_female  confirmed_male  confirmed_gender_ns  \\\n",
       "14           0.0               0.0             0.0                  0.0   \n",
       "51           0.0               0.0             0.0                  0.0   \n",
       "88           0.0               0.0             0.0                  0.0   \n",
       "125          0.0               0.0             0.0                  0.0   \n",
       "162          0.0               0.0             0.0                  0.0   \n",
       "...          ...               ...             ...                  ...   \n",
       "25988        0.0               0.0             0.0                  3.0   \n",
       "26025      245.0               0.0             0.0                  6.0   \n",
       "26062        0.0               0.0             0.0                  0.0   \n",
       "26099        0.0               0.0             0.0                  0.0   \n",
       "26136        0.0               0.0             0.0                  0.0   \n",
       "\n",
       "      population_(2022)  \n",
       "14                  NaN  \n",
       "51                  NaN  \n",
       "88                  NaN  \n",
       "125                 NaN  \n",
       "162                 NaN  \n",
       "...                 ...  \n",
       "25988               NaN  \n",
       "26025               NaN  \n",
       "26062               NaN  \n",
       "26099               NaN  \n",
       "26136               NaN  \n",
       "\n",
       "[707 rows x 10 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga_merged[nga_merged['population_(2022)'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem seems to be that values for the Federal Capital Territory were not inputed during the merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
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       "      <th>confirmed_female</th>\n",
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       "      <th>population_(2022)</th>\n",
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       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date, country, state, confirmed, deaths, recovered, confirmed_female, confirmed_male, confirmed_gender_ns, population_(2022)]\n",
       "Index: []"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fixing the nans in the population column by inserting with the value of the population of the FCT\n",
    "nga_merged['population_(2022)'] = nga_merged['population_(2022)'].fillna(3564126)\n",
    "#confirming that no nulls are present\n",
    "nga_merged[nga_merged['population_(2022)'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26159 entries, 0 to 26158\n",
      "Data columns (total 10 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   date                 26159 non-null  datetime64[ns]\n",
      " 1   country              26159 non-null  object        \n",
      " 2   state                26159 non-null  object        \n",
      " 3   confirmed            26159 non-null  float64       \n",
      " 4   deaths               26159 non-null  float64       \n",
      " 5   recovered            26159 non-null  float64       \n",
      " 6   confirmed_female     26159 non-null  float64       \n",
      " 7   confirmed_male       26159 non-null  float64       \n",
      " 8   confirmed_gender_ns  26159 non-null  float64       \n",
      " 9   population_(2022)    26159 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(6), object(3)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "nga_merged.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating separate columns for year, month and date in preparation for aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>confirmed_female</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>confirmed_gender_ns</th>\n",
       "      <th>population_(2022)</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Abia</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>0.0</td>\n",
       "      <td>3,727,347</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Adamawa</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>0.0</td>\n",
       "      <td>4,248,436</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Akwa Ibom</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>0.0</td>\n",
       "      <td>5,482,177</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Anambra</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>0.0</td>\n",
       "      <td>5,527,809</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bauchi</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>0.0</td>\n",
       "      <td>6,537,314</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  country      state  confirmed  deaths  recovered  \\\n",
       "0 2020-02-27  Nigeria       Abia        0.0     0.0        0.0   \n",
       "1 2020-02-27  Nigeria    Adamawa        0.0     0.0        0.0   \n",
       "2 2020-02-27  Nigeria  Akwa Ibom        0.0     0.0        0.0   \n",
       "3 2020-02-27  Nigeria    Anambra        0.0     0.0        0.0   \n",
       "4 2020-02-27  Nigeria     Bauchi        0.0     0.0        0.0   \n",
       "\n",
       "   confirmed_female  confirmed_male  confirmed_gender_ns population_(2022)  \\\n",
       "0               0.0             0.0                  0.0         3,727,347   \n",
       "1               0.0             0.0                  0.0         4,248,436   \n",
       "2               0.0             0.0                  0.0         5,482,177   \n",
       "3               0.0             0.0                  0.0         5,527,809   \n",
       "4               0.0             0.0                  0.0         6,537,314   \n",
       "\n",
       "   year  \n",
       "0  2020  \n",
       "1  2020  \n",
       "2  2020  \n",
       "3  2020  \n",
       "4  2020  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracting the year from the date column\n",
    "nga_merged['year'] = pd.DatetimeIndex(nga_merged['date']).year\n",
    "#confirming\n",
    "nga_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>confirmed_female</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>confirmed_gender_ns</th>\n",
       "      <th>population_(2022)</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Abia</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>0.0</td>\n",
       "      <td>3,727,347</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Adamawa</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>0.0</td>\n",
       "      <td>4,248,436</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Akwa Ibom</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>0.0</td>\n",
       "      <td>5,482,177</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Anambra</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>0.0</td>\n",
       "      <td>5,527,809</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Bauchi</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>0.0</td>\n",
       "      <td>6,537,314</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  country      state  confirmed  deaths  recovered  \\\n",
       "0 2020-02-27  Nigeria       Abia        0.0     0.0        0.0   \n",
       "1 2020-02-27  Nigeria    Adamawa        0.0     0.0        0.0   \n",
       "2 2020-02-27  Nigeria  Akwa Ibom        0.0     0.0        0.0   \n",
       "3 2020-02-27  Nigeria    Anambra        0.0     0.0        0.0   \n",
       "4 2020-02-27  Nigeria     Bauchi        0.0     0.0        0.0   \n",
       "\n",
       "   confirmed_female  confirmed_male  confirmed_gender_ns population_(2022)  \\\n",
       "0               0.0             0.0                  0.0         3,727,347   \n",
       "1               0.0             0.0                  0.0         4,248,436   \n",
       "2               0.0             0.0                  0.0         5,482,177   \n",
       "3               0.0             0.0                  0.0         5,527,809   \n",
       "4               0.0             0.0                  0.0         6,537,314   \n",
       "\n",
       "   year  month  \n",
       "0  2020      2  \n",
       "1  2020      2  \n",
       "2  2020      2  \n",
       "3  2020      2  \n",
       "4  2020      2  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extracting month\n",
    "nga_merged['month'] = pd.DatetimeIndex(nga_merged['date']).month\n",
    "#confirming\n",
    "nga_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26159 entries, 0 to 26158\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   date                 26159 non-null  datetime64[ns]\n",
      " 1   country              26159 non-null  object        \n",
      " 2   state                26159 non-null  object        \n",
      " 3   confirmed            26159 non-null  float64       \n",
      " 4   deaths               26159 non-null  float64       \n",
      " 5   recovered            26159 non-null  float64       \n",
      " 6   confirmed_female     26159 non-null  float64       \n",
      " 7   confirmed_male       26159 non-null  float64       \n",
      " 8   confirmed_gender_ns  26159 non-null  float64       \n",
      " 9   population_(2022)    26159 non-null  object        \n",
      " 10  year                 26159 non-null  int64         \n",
      " 11  month                26159 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(6), int64(2), object(3)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "nga_merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_merged.to_csv(\"nga_merged_month_year.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26159 entries, 0 to 26158\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   date                 26159 non-null  datetime64[ns]\n",
      " 1   country              26159 non-null  object        \n",
      " 2   state                26159 non-null  object        \n",
      " 3   confirmed            26159 non-null  float64       \n",
      " 4   deaths               26159 non-null  float64       \n",
      " 5   recovered            26159 non-null  float64       \n",
      " 6   confirmed_female     26159 non-null  float64       \n",
      " 7   confirmed_male       26159 non-null  float64       \n",
      " 8   confirmed_gender_ns  26159 non-null  float64       \n",
      " 9   population_(2022)    26159 non-null  object        \n",
      " 10  year                 26159 non-null  int64         \n",
      " 11  month                26159 non-null  int64         \n",
      " 12  active_cases         26159 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(7), int64(2), object(3)\n",
      "memory usage: 2.8+ MB\n"
     ]
    }
   ],
   "source": [
    "nga_merged['active_cases'] = nga_merged['confirmed']-(nga_merged['recovered']+nga_merged['deaths'])\n",
    "nga_merged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>state</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>confirmed_female</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>confirmed_gender_ns</th>\n",
       "      <th>population_(2022)</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>active_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>2020-03-18</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ogun</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5,217,716</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ogun</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5,217,716</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ekiti</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3,270,798</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1356</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21,000,000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>-4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467</th>\n",
       "      <td>2020-06-04</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21,000,000</td>\n",
       "      <td>2020</td>\n",
       "      <td>6</td>\n",
       "      <td>-1.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26029</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Kaduna</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8,252,366</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26030</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Kano</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12,550,598</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26038</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Ogun</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5,217,716</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26039</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Osun</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4,705,589</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26043</th>\n",
       "      <td>2022-01-30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Rivers</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7,303,924</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3372 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  country   state  confirmed  deaths  recovered  \\\n",
       "767   2020-03-18  Nigeria    Ogun        0.0     0.0        1.0   \n",
       "878   2020-03-21  Nigeria    Ogun        0.0     0.0        1.0   \n",
       "1233  2020-03-31  Nigeria   Ekiti        0.0     0.0        1.0   \n",
       "1356  2020-03-04  Nigeria   Lagos        7.0     0.0       11.0   \n",
       "1467  2020-06-04  Nigeria   Lagos        0.0     0.0        1.0   \n",
       "...          ...      ...     ...        ...     ...        ...   \n",
       "26029 2022-01-30  Nigeria  Kaduna        0.0     0.0        7.0   \n",
       "26030 2022-01-30  Nigeria    Kano        1.0     0.0        6.0   \n",
       "26038 2022-01-30  Nigeria    Ogun        0.0     0.0        3.0   \n",
       "26039 2022-01-30  Nigeria    Osun        9.0     0.0       15.0   \n",
       "26043 2022-01-30  Nigeria  Rivers        2.0     0.0       11.0   \n",
       "\n",
       "       confirmed_female  confirmed_male  confirmed_gender_ns  \\\n",
       "767                 0.0             0.0                  0.0   \n",
       "878                 0.0             0.0                  0.0   \n",
       "1233                0.0             0.0                  0.0   \n",
       "1356                0.0             0.0                  0.0   \n",
       "1467                0.0             0.0                  0.0   \n",
       "...                 ...             ...                  ...   \n",
       "26029               0.0             0.0                  0.0   \n",
       "26030               0.0             0.0                  1.0   \n",
       "26038               0.0             0.0                  0.0   \n",
       "26039               0.0             0.0                  9.0   \n",
       "26043               0.0             0.0                  2.0   \n",
       "\n",
       "      population_(2022)  year  month  active_cases  \n",
       "767           5,217,716  2020      3          -1.0  \n",
       "878           5,217,716  2020      3          -1.0  \n",
       "1233          3,270,798  2020      3          -1.0  \n",
       "1356         21,000,000  2020      3          -4.0  \n",
       "1467         21,000,000  2020      6          -1.0  \n",
       "...                 ...   ...    ...           ...  \n",
       "26029         8,252,366  2022      1          -7.0  \n",
       "26030        12,550,598  2022      1          -5.0  \n",
       "26038         5,217,716  2022      1          -3.0  \n",
       "26039         4,705,589  2022      1          -6.0  \n",
       "26043         7,303,924  2022      1          -9.0  \n",
       "\n",
       "[3372 rows x 13 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nga_merged[nga_merged['active_cases'].values < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The issue seems to be from the formula. And the negative values occur at dates when 'recovered' is greater than 'confirmed'. At such cases, active cases should be zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>confirmed_female</th>\n",
       "      <th>confirmed_male</th>\n",
       "      <th>confirmed_gender_ns</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>active_cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26159.000000</td>\n",
       "      <td>26159.000000</td>\n",
       "      <td>26159.000000</td>\n",
       "      <td>26159.0</td>\n",
       "      <td>26159.0</td>\n",
       "      <td>26159.000000</td>\n",
       "      <td>26159.000000</td>\n",
       "      <td>26159.000000</td>\n",
       "      <td>26159.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.672579</td>\n",
       "      <td>0.119844</td>\n",
       "      <td>8.759242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.329447</td>\n",
       "      <td>2020.609618</td>\n",
       "      <td>6.595474</td>\n",
       "      <td>6.319584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>46.281864</td>\n",
       "      <td>0.948101</td>\n",
       "      <td>107.970440</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.645168</td>\n",
       "      <td>0.575629</td>\n",
       "      <td>3.429761</td>\n",
       "      <td>38.039798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3393.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>10946.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3393.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3393.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          confirmed        deaths     recovered  confirmed_female  \\\n",
       "count  26159.000000  26159.000000  26159.000000           26159.0   \n",
       "mean       9.672579      0.119844      8.759242               0.0   \n",
       "std       46.281864      0.948101    107.970440               0.0   \n",
       "min        0.000000      0.000000      0.000000               0.0   \n",
       "25%        0.000000      0.000000      0.000000               0.0   \n",
       "50%        0.000000      0.000000      0.000000               0.0   \n",
       "75%        3.000000      0.000000      0.000000               0.0   \n",
       "max     3393.000000     79.000000  10946.000000               0.0   \n",
       "\n",
       "       confirmed_male  confirmed_gender_ns          year         month  \\\n",
       "count         26159.0         26159.000000  26159.000000  26159.000000   \n",
       "mean              0.0             7.329447   2020.609618      6.595474   \n",
       "std               0.0            44.645168      0.575629      3.429761   \n",
       "min               0.0             0.000000   2020.000000      1.000000   \n",
       "25%               0.0             0.000000   2020.000000      4.000000   \n",
       "50%               0.0             0.000000   2021.000000      7.000000   \n",
       "75%               0.0             0.000000   2021.000000     10.000000   \n",
       "max               0.0          3393.000000   2022.000000     12.000000   \n",
       "\n",
       "       active_cases  \n",
       "count  26159.000000  \n",
       "mean       6.319584  \n",
       "std       38.039798  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max     3393.000000  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using clip function to correct this\n",
    "nga_merged['active_cases'] = nga_merged['active_cases'].clip(lower=0)\n",
    "nga_merged.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3135.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total deaths\n",
    "nga_merged['deaths'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "253025.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total cases\n",
    "nga_merged['confirmed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229133.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total recovered\n",
    "nga_merged['recovered'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date', ylabel='confirmed'>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "sns.lineplot(x='date', y='confirmed', data=nga_merged)\n",
    "#number of confirmed cases peaked early 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date', ylabel='deaths'>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PD2PTpk3YsWMHFEVBNBpt9L4RERERLXtVBWMf/ehH8Xd/93e47LLL8Mgjj+Cyyy7DxRdf3Oh9IyJanjiuBRFZVNWA//LLL8fll18OQG8/dvz4cWzZsqWhO0ZERETUDqrKjCUSCfyf//N/8Ed/9EfIZDL4z//8TySTyUbvGxEREdGyV1Uw9rnPfQ7d3d2YmZmB3+9HPB7HZz7zmUbvGxEREdGyV1Uwtm/fPtxyyy3weDwIBoP4yle+gn379jV634iIlh82FyOiAlUFYy6X/WOqqhb9jIiIiIhqV1UD/gsuuAB///d/j3Q6jSeffBI/+MEPcOGFFzZ634iIiIiWvarSW3/5l3+JUCiErq4ufO1rX8PWrVtx6623NnrfiIiIiJa9ipmxP/zDP4QkSea/A4EAAOCll17Chz70IXzve99r7N4RERERLXMVg7E/+IM/AAA88sgjiMfjuPnmm+F2u3H//feju7t7SXaQiGh5Ykt+ItJVDMauuuoqAMB3vvMd3H333Waj/csuuwzvfve7G793RERERMtcVW3GwuEwMpmM+e9EIoG5ubmG7RQRERFRu6iqN+Xb3/52vOtd78KVV14JIQQeeughvOtd72r0vhEREREte1UFY3/+53+Os88+G88++ywA4NZbb8W2bdsaumNERMuRYFsxIipQVTAGAFdccQWuuOKKRu4LEVHbEIJBGRHpOIw+ERERkYMYjBERERE5iMEYEdESYwUlEVkxGCMiIiJyUEODsa9//eu45pprcO211+K73/1uIzdFRERE1JKq7k1Zq9/+9rd49tln8dOf/hSKouCaa67Btm3bsGnTpkZtkoiIiKjlNCwzduGFF+J73/sePB4PZmZmoKoqQqFQozZHRNQSOKIFERWSRIMHu/nGN76Bf/u3f8PVV1+NL3zhC5AkqZGbIyJqaoqq4cUDk+ju8GHrxn6nd4eImkDDgzEASKVS+NjHPoZrrrmm6gnGZ2bi0LTG7NrQUBempmINWXczaZdyAu1T1nYo53Ivo6Jq2Ht0Fp1BDy4+b/2yLqthuZ9ToD3KaGiXslZbTpdLwsBA54K21bBqysOHD2Pfvn0AgGAwiLe+9a04cOBAozZHRERE1JIaFoyNjIzgtttuQzabRTabxWOPPYbzzz+/UZsjImoZgjNUEpFFw3pTbtu2DTt37sQNN9wAt9uNt771rbj22msbtTkiIiKiltSwYAwA/uzP/gx/9md/1shNEBEREbU0jsBPRERE5CAGY0REREQOYjBGRERE5CAGY0REREQOYjBGRERE5CAGY0REREQOYjBGRLTUOOIrEVkwGCMiIiJyEIMxIiIiIgcxGCMiWkaSaQXprOL0bhBRDRiMEREtIdHg9mLj4SQmw6nGboSIFhWDMSKi5UQAisoeAkSthMEYEdEy4pIA0ej0GxEtKgZjREQOaFS85HJJ0BiMEbUUBmNEREuskaGSS5KYGSNqMQzGiIiWEZckQdOc3gsiqgWDMSKiZURyCVZTErUYBmNERMuIJEmcbYmoxTAYIyJaUnqo1NDkFaMxopbCYIyIiIjIQQzGiIiWmKywhT0R5TEYIyJaQsfHY/in+/fi1RORhm2DtZRErYXBGBHREjo6FrX9uegYiRG1HAZjRERLiLESERViMEZEtJRy0ZgkSc7uBxE1DQZjRERLSDA3RkQFGIwRES0hYWbGlmAjRNQSGIwRES0hxklEVIjBGBHREjJiMbYYIyIDgzEioqW0JPWURNRKGIwRES0hZsaIqBCDMSKiJbQUbcbYLI2otTAYIyJyAGspicjAYIxqpmqc5JioXoLdKYmoAIMxqtmJyTgDMqIFalRijLEeUethMEZ14Q2fqD7md4f1lESUw2CMasZAjKh+S9Gbkt9RotbCYIyIaAlxbkoiKsRgjGrGRwk1o0xWRSItO70b8+OYr0RUgMEY1Y7RGC2hansfzkTTmIqkGrw3C5evpmxsNMZem0Stg8EYETW14xMxyEp1vXdbIf4wg6SGxWItcBCIyIbBGNWBN3taWlorRFlVWkZFIaJFwmCMiJpaLcFLK8U5jWoz1krHgIh0DMaoZnyzJ6ofvz9EVIjBGBE1tWUXu+SisYY34G/o2oloMTEYI6LlowUiEFHib0TU3hiMEVFzY8xSE1aDErUeTyNXftddd+EXv/gFAGDbtm34q7/6q0ZujpaIELzhU7Nq/gszPzVlA6sp+QUlaikNy4xt374dTz31FO69917cd9992Lt3Lx555JFGbY6Ilq3lFVhwMFYiKtSwzNjQ0BBuvfVW+Hw+AMBpp52G0dHRRm2Oloj+IOHDhKhuSzUdkkBjZyMnokXTsGDs9NNPN/9+7NgxPPjgg7j77rsbtTkiWqZqif9bIenUArtIREusoW3GAODgwYP46Ec/ir/+67/GKaecUvVyAwOdjdspAENDXQ1df7NY7HIKITCdkDE42Amf172o614ontPlw1rG2aR+vQX8lW9XaQ1IpuWmPz7BoFf/M6DXGiz2/iZVAUVyYXCoC25X86TGmv28LIZ2KKOhXcq6VOVsaDD2wgsv4M/+7M/w6U9/Gtdee21Ny87MxKFpjXmHHBrqwtRUrCHrbiaNKKcQApFIEtMBN7ye5gnGeE6Xj8IyhsNJdHhc8PsqX2+zswmkMgqmQt5G7+KCJJJZAEA6IwPAop/P2dkE5ubSmJ6KwdUkwVg7XrfLWbuUtdpyulzSghNIDQvGxsbG8IlPfAJ33nknLrnkkkZthhzAahZacs0RUywOfoGIqEDDgrHvfOc7yGQy+OIXv2j+7D3veQ9+//d/v1GbpCVgPEdaoW0OLQ9sM1bj+oWxHbbgJ2oVDQvGbrvtNtx2222NWj05qQUeeETNKzcdEuMkIsrhCPxE1ORqif6b/03ByFw1em5KImodDMaoDs3/wCNqVmZVaiMH4G/cqomoARiMUW2E7Q8iqpEwqikbtwEiajEMxqhmrdBImpqLEAKyota3bIM+u9zxe0rUOhiMEVHDxVMyTk4nnN6N5tDwIIlRGFGrYTBG9eH9nmogRP2ZmuWWGWuFfSSipcVgjIiWhVYZKmIpqg8FQz6ilsJgjGoiINgWhepS92WzzFJjgl8gIirAYIzqwjdvovqIgj8btX4iah0MxqgOvN1THeq4bEQtcyG1ikZHY0TUchiMEVHTW06ZWKMsjaquXE7HiqhdMBijmvFWT/Wp78qpNmZpmSzaUgyc3AKHgYjyGIw5pFUb8Rq73aK7T+Q4fnWIqBCDMQekMgqOT8Sc3o26MRCjWgnwuinU6BcyHm+i1sFgzAGKqvFGSVQFs637Mvq+MAgjokIMxoioqS234GK5lYeIFo7BmEN4PyZaXK1SFboUU1O2wGEgIgsGY07h3ZLqkEzLSGUUp3ejLvUHSsvzy9IKgSMRLQ0GY0QtZHQqgYlw0undWDrLMWBZsihsOR48ouWJwZhjWvdG2SrVQctRaw/oWec4Y4u8F07LD8C/3EqWl5VVHBuPOr0bRC2DwRhRi2Eg3NpEPhpbthRV8DolqgGDMQe0cmapVQY5X65a9boBlqbheitp5XNJRIuLwRjViU8SpzTyyDdj9ZIQyytwafg4Y7n/ZEVz7FxKEu8QRLVgMEY14012+ZIbOCBxPatdju2qliqwzCoODy69/E4dUcMwGHPIcnrTp6WzFJdNK1+arfC9aoMmY0RUIwZjjmnhW3ErPPGWsxoP/2Q4iVgy25h9qQUvG90SzvHk7CHnCSeqFoMxqpGw/J9aQSKtIJ6Sq1+gxMlVNQ2Kqi3eTtVs/iuuVao0RYO/Q7YYz8FDwnc2ouoxGHNIK9+nWnnfW96SdGctXv/4bAojU/FFX++8SyzDi80oU0OLJji0BFErYTBGdRECTdfrjkoTACRJqv7zJR7iQnPu4V7tZiVINXzaOWJJojFza0uxESJaIAZjDmnlW6QQgLbAh7MQwuFqrzbSJBcbMzW6pTwOPOZErYHBmFOWwU1yIUWIxLOLUO3Vnhr9gC23fscuWbEsvi4mbVmVpjwGgkTVYzBGNbHVsCzgZrvQzBrVotYD3WzR2DKzlLWUjlr+JSRaLAzGHLMcblQLK0OzHQFZad1qU1nRyo7sXmvQ22znpdr9abb9LiffZqxh/Skt22rQJmraCyKaD4MxJ7T6XSo3P82Cb/RNdBw0TbREtWm5Y35yOl7b8BX1bX1hSy/B+W6iS6qsJW2/T0QtgcGYQ1q9is6Y7Lzeefb0zn3NcxAEWrvaVAhAW6T9b7rDsMyGadAaPTelEACM3rPOHbhldMqIGo7BGNXEPnj4wobZbKYH7EICy6VVOjCptOe1FKtSjLzww9MKx7fxlnAAfh5xohbBYIzqtuBayiZ6UuRqXluXyOdCSv6y1pU1CbHMelICrRL0L4I2KSbRYmAw5pBWvh8L658LKkfzHAQhRK6qsnn2qbxS+7h4+92oQ7AUR7YVTt98+ygrKiLxzIK20YhKSk0TmJ5L1bBEC5wMoibBYMwxrX2jEouQsmimI9Aq1ZRlxwCrsOtNUaoWOLZLxbjOjOC/8LqLJWXMRusPxkTZfyxMVlERSza6kwhRe2IwRjUTltzYQlqNNdPjWWuhRuL1VDpWPxlSmemQFqGDQ73Lt8p5qZaW//pgeDyGyYg92+R2SdAWoTdGI14ualnjcjtvRI3EYMwhLXufsgyRtJyGtgCMqsrWVPFcNEOhapgbsxnMxTMNm67LOrRFVlGRyaq230suCZoov21V02qoxlzk6utmuJaIliEGYw5p6bdGkc+OLawczXMQhBALGhpiqao4y29lcbavLbwhYGl1Hx9nrpHZWAbJjNKQdVuvlVKHZb7MWCqjIhyrLhhb7Muy1uu8Far+iZoBgzGqzyK0sWqm+7S16qhWybSM4xOxRd2fSmpvvl9dobKyium5VG4MOfsyTp+qaq61ZrqeKrEOwF9ulzWhZ8DKrmO+jTRBIrJVzgdRM2AwVqdMVkWqQW/OzU5Y/lsuxALajOlTEdW3bDSRrSmolcpU9y3GOGNqbr7Q8p936Iw78FRv5BatbS7LlU3VROV2Y01+KoxxCImoOgzG6jQRTmJ8NlnXssvhFqVnTxa4jiY6EAILGxm93iVnouma2iaV7cW6SMeSU/XkNPAAzHeZGQFx5WrzChXWovTfF0OtrSrb/joiqhKDsTq1Uu+7xWT2nzS65y+bNmNGtVG9Xf4Wsthi1CmJRaqaKlOQRThV9azCue9Y4zacH9qiQvnmKbhTx6Wm3pQN2wui5YfB2IIsfSalWSzG/jfTMRAL7h5abzQmautoWC5WqlSjVcOumYm3Ess4eb6WetuNDHbMdc+XhazznNp/t+ipseo/2kxfcKImx2CsTprWnpkxAOYTO/9+X/9qmoWqiQVVvS7tcrUvVFNni0adl4YnHZvogqrAmhkrt89VdFeo6telOmMsRPXrYn03US0aHozF43G8/e1vx8jISKM3taSW2xhbdVkOZchRFG1BA9jWYyGdBorXtVjrMUKEZXRyqyArGo6NR81/NzQzNu8P6lhHIam5XnaIqLKGBmM7d+7E7//+7+PYsWON3IwjFlqr1er3SeONe7nc8CsNI1CNeo5DPW3UymfvKq2nttxS6f4BCw/P6l1+KQJDRS3sEdu4bRodRSrdQ8Q8R7zq/NQiFsNsV2lZaSqjcCwxokXQ0GDsRz/6ET772c9ixYoVjdyMIwRqbOuzTOTHSHLu4dwIqioWuEN1VB1ivqEkGrn1CutpooerKPpLlZ9fyLYaTBQUqvCbVNW8r1XsrDkwc227N+86reubCCeRSBcP8SMsnyei+XkaufLPf/7zdS87MNC5iHtSbGioy/bvQyci2Li6C16Pu6rlJ+NZuCWpaD3V8ATSiKaVupat1WJvI5mW0R1Ooa+vA7KQMDDYiYCv9stI8nqQVMSi7t9C1hVOKZAlFwYHu+D11PaO4vF7kUXt14KiaphJKBga6qz+uovNoLc3VLSt0UgagwOd6On0235+ciqOnp4Q+nsC8+5fMi0jltWQzioYHOyCx50/DtGMirSs1n2MPX4vopnqlzc+l84q6A6nMTDQid4uf8Vl4rIGlzdT1z4m0zJSav56nIpnSx7PxeBy6W9xPp8HAkBfwfn0xTOYimUxMNCJUMBbtHwgma343ZmMZeFJZdHf34mMBgwNdpnbXIh4SkYkrdjWF0krGBzoQFfIZ/tsMJlFJKV/1p27jpbifue0diijoV3KulTlbGgwthAzM/FFmSy3lKGhLkxN2UdMnw0n4JMEgv7qDkkknIBLkjDVUXyznM9cPIO5uRQmJ6NlB/FcDKXKuVCpjILoXAqzHhcic2lMT8fh91YXSFjNRtOIzKUWbf8WWtbpmTgSKRlTU7Gag7G5RBbhSBJTU775P2yhqBrC4QSmgu6qgzEhBCKRZFFZI5Ekpn0uZFNZ289HxqLIZBVAU+Gb51JLZRSEwwlkZA1TUzFbMDYbTiCraDWX0WBc89WcI+u5zMgqonNJzMx4IaezFZebnU0gljuHtdLLnsRUSP8+hyNJ+F0oOp6LQVH0KvFsVs8ozYaTmArk7zvRZBaRaArT0/GS96N4StavmzLnIhJJIpmWMetz68d8OgbXItxnEmnZvPaMYGw2nIRHCKSD3hKf1bftdrkaci9qNu1QRkO7lLXacrpc0oITSOxNmVNr2CcEFjT5cSsn7812RdVUp1RcUfMcBUXVFjaZdb1txhp9CBa10dDCFq23Z58jV8kSDG1R8etT5zhjqYyC3+wc1dvAofwwJfUoub/l2rwJ+59EVFnTZsaWXI03DSHqH2NzIUMoNI0Fz0vZXAdAVQX0l/36m5nXvISor1l84aGr1H5P03Kfr7bNVbNdnLWOkLCQgHGJGvCLBUYqUoXr9NEXRrB9zzhcErBxVXd9O1iJ2elEyv+g/EeJqErMjJlq7dW28P5lrXqzMt6Q9f/qfaBITVV+ASws01nn8ASL1oC/TMBVy/mx9u5sRDxW91hsi7sbFbaT39JSDPpqZK2KtjVPrFYpo9aZqy6cjWUWNStWsGu2fSn/4Wb6hhM1tyXJjD3++ONLsZkFqfW+0fb3mYIu7nUs3nTHUMIS79NiVR8BZXe8tvLMN1RJk52wRbZUpdOq6wpZ+de5719hm9OuXDAWiWXM1dgzWfXTh7JZPsPZEDUTZsYWpL4bnBNVQbPRNKKJxWuMnG8DVOfy5lhLzXFnFwtMI9SXGRN1NrsrXkIASGYUTEZSBfslyi5j/czx8XwjVTHP5+thywY1K8vOLUlmzNxG6fO5kCsjkVaqTrsqqj7g7cmp+Pzrr2GXmvpcEzUZBmM5NWfGsEjzMi+RSCKLaHIRg7FFGGesWSxOQFhf269FOYa5laSzCuIF57iaogkBqJbzWfLMOnmya6jLXbyGA42PxsptoZqXnHLNAOu5lrOyPuBtPCXrHVkqbLN4e+XXuyQdVIiWCQZjphrbjC3gRiOcSBMIsSjB4/BEHKPTCXOd9e+P7Q/HSRIgof52bPUsVzia+UK2ZaxGLRgOppqWjUJvlZ2/pstc2048WJcy5F+q8uWTlaLiAK/z1hiX+IB1OKDqs9d6hjaraMjKFWaiEMY+V72XRFQl9qbMqa/KrXVuRAsdisPwpf98EQBw7uaBhe1PuSe+A+avyKtiHXUtLGrq6YgyH7Uey8Kx+SoFV/bPCTPbuVhnZS6egcsloSvkywUdzXG+SyncvUbu6bxtxipknWejachK+TUU97Stdq+MIKtCdXaJ35bfj+Y910TNiJmxnNrjsNaqphOLlBkz15f734J7yJVZvlJ1SUM4kK0U1u3WslyZRVySVJQZ0z9f/DPj+GqagKpa2q4tYkA2E01jKpJe0Dq+99ABfP+Xry7C3lRDWNoyNnArhQFTlZ8D9AGGU1kld56KP6DVEVGaL6JV3NMKr47K2bvFDO2JljcGY4Y67hnN3lXfShOLkhgzLVZ7p1K3/0xWxYnJ+RsTLxqHnheLdgwtK9GE/YFc7nE4MhVHKqNgIpzE6EzC3pmg5IVd+566XFJxUF3jap59ZQJziWz1i9UdRS1uVrDilixxf9nhK+pcVquz/FVlskS+Ojv/s7o2R0QFGIwBuR5otd1VFlallXv7XtpGY4tadZCv1smvU1G1qntsCq38MVQ1B7rPOzDruzHoa03XgbAf78lwEkC+6ktTNWiawNhMwhyGQF/EXj5N6NuXFS33d9jbAy3C8fe6XUuf4VyApWszVjkLWyljOl/mzhasVfpgwTLG5ys2yAdQ7dlkjEZUGwZjABIpuZmbsywKIfQG6pUs9MGZlTXEU3JNy5Q+7osbOFZLQv0Bcj1Lzffwm09GVvUhDHIrEwJQNA2yoiGVVSt3ELBWSeU+p6il651Vrf7rYqlqf6PJLG7/7vMYqWJ4hlIKq80bub/27Fa96fXSy5XKjFWd9Jrng6WrMssvs9Drm6idMBhDnQ9SUX+VQL3bXAghBMQ8yZ+RqXgNZRLQUFgOUfWDe76M0FIGYwIiF4gtaCV1LbPQB1bh8ooqIKua2aZIVjQcG4sWL2fbpp4RM4c+Efag9MRkHLJa+05a13FsLIpkRql5HdU6PDIHANhxYKqu5a1VxpV6OC6GfGbc+K/ExsrG0FUETFV+tnCD8wWHpX7HYItocbR1b8pwLIPOoMdePVOFBQ9YarwZL+GNTGD+cdE0rcoPIv+WXLiNwt589ailOmSxiFy5l7IdYLXVSOVIluUF9JcDTRPIZIysGPDrl09i95FZrB3qwJrBjvy2c7+XJCkXiOjZSK1EaKDVeb1aY5pv3rsHPR0+XHDmCtQzQt+8216MWmZhBOa1r2x6LoX+rgBcrvmXzVcFlx7R3syclQp+LH+WbDNWNLTJ/Mzq7Co+W/1K688yE7Wjts6MhWMZRBNGFaXZyqJ6Dtxr6m+gO9/vjaENalu//U0csNZ0lttXVdMqdrQSAhCLENRVywjE6o/EhPlgrX25+jZpXYWxEgHA7ZaQzCjm+ZyN6tPipAqyUvk5KIW5Hq041WndSJ37lje3iDNAVLvNmpa1LG85rFWJJWVkZLWqz9o6WJRKihVVBVp/Z+xf6S9Q6UnI5y9I6SpIOy13vVY9h2djE4xEy0pbB2Oappk3xtoS+vY/a7WQScaHJ2LIVnnTr4XxIKr2YVa6ykLYHjTHxmNF1ZbJtILhifi82cGlr/5Y2AbrzYwtVjGN4MHrdiGZUaAhn9HSScWfN/4ujGykwNRcGqmMUhRk172f9QSpdchns+oNHC3DWug/qHH5Gj5r+bNiVX2Zn1Z6kSl8h6nmPmPuRxUXZPlcHREtRFtXUxrVOkYQUtNtRRgtjWq3kNuXptWXHdOrpCp9IH/jTqRleFwu+H3u8vuRW6ZgFWbwpagaFFUrekhp5kPPzM2UXr8TjVHq6FGpahoi8ax5/dSyhlqCX0DPbmkF16l9HQJutwRZ0eB2uWyZzqKimZlQfTkjG/lfjx5EKODB+VtW2LZhrL8W5iCyi3Aq5w3oFqGa0haAoubSlv1NNJlF0OeB1+PKbSeffiv/MlImFDMD5/JZNetnqy2Esd6KH5/nBarMx4moCm2dGRMCkNV8K5mqHxq5p269VWkLqkqpu8qocrSpiXxQOjGbxEx0nsE6S7WbE7mqLgCyokFRilt+SZLersW48c/3QCn174aocxOpjLrgsZ2qXXwinCzRJij/hDTagGlCg6YVrrfwmOYuYym/L0Y5kumCzFiFg1Op7Mav5quWWwylYrFarpvCQ1VPjXO57U/PpTGXyBR91joNlZWW/0D5nS1RpWhtfyYA/OSJw9h3PDx/WaxBVoUP/+TXh7HnyGypRcutkhHZMrUYbYPJru2DsWRGqet+saDedwsMxuppYCyAebMHRsZKUTW452mIrKolHgbIf0kVVQ/GCh+IEvKBHwCMTCVK7Ifxn0A8JeP4RKzyji+QsS/1NhszH2K1PsBr3I4+Ur51O/pcmqmsgnAsowdj5q5Urs4yy2lkRAqCgvHc+GWlGpgbUhkFwxXOjbHeUrMCNIp1X09OJTAdSVW5IMyAtuTK5t2usGUfFVUzr1vrS1vhy0vhFuIpGdHkfMPDlN6v4xMxKJZmAS8fmsYjO0bm3/eya7T77b5J/HrnqH3ZMgvOzKWRyi5+cwpyniYEDgzPYt/xWZyYjGE2mkYyrSxoCBxq82pKAEilFQT9nhrbjAnA6IUmBKQaqre0XIBRb0BWz+Ve6YGa/0z+P0UVJWvsrBkO4wFrzygIsxpSCL0NUvF2JbNqWFG0oi+wEALhWEZfD/QM25J8x3Pnsx4LyVZWu2Q0kTWPiX0lwD/8ZDeOjEXx6T94fa5HqMgHZLmPFV6jZlbG+IwQtuNsZDXNsyyKJ1FXVK2ojZK9fPqf6iIM/FrPMc6qGqQahr0zymocj1oUZ6nyLyaFMyJY/64VRMyykk9pltoDW9BclFFbWLa+XP+NUp/V/yz/6c99bwdCAQ9ef/pgXftDzSuRkpFIyfB5XUikZERiWSD3TPR73egKetER9MLvdcPrdcHlwIDaraitgzGPW0I8pSHgqz2rkbv2apbJqgusaqx92YysQlFUiIrPRGE+7DWtTPbNsmm1RNc742FgPF5KJ4sENAGouXIECtqlKaqw9baUpPqDneoVPiJrXNp8eArU0nhJlHuqljAdTesP94KPCiFwJDeOmLV6S0DPTozNJEtuQhQUWYOAsIwlZg4AnLvkyl7rFXZdy11wi9H+b75hzszqVlhekGoIdvXLrf791LOS+YOkV8fr+2I9b6IgMCtdvVnpa57PeBbtrygz6GsVO2/cWxbzq5ZMN25cOXKGEAITs0l0d4eQVFS4/S4E/PnfK4qGcDyDqbkUJEmCJAEhvxedIQ9Cfi/8Xhe8nvJtkdtZW1dTShLM6jjjJl6N/DO09puXJOld/GWluHF7NdvVtNofGqmMAkUrXxlxdCyqv5FDD8Q0TZjT61gVZ8Yk2yrzDbaF+V+J54U5FY/f6yr6vSRB39fctvQ2UI0OxoyN17dYiRhp0ZcTmh4sT0VSZZfRRK4aGPrhu+/JI/lfloutc/8rrE4UAI6NR2GMP1Zyn1D5O2NmxhahmrKe7Jqe5dUQjmWq+7CwB0JVnxvjercsYWSGNSHKt5kTArMlzmc4lp7nxUvfkhlo2/ajxHbm23/L56u7B4oSf6N2kMooSGaUsp27PB4XQgEPujt86Ap50RHwQFFVTIZTODoWxb7jYUzMJksu2+7aOhizZnBqj6r0dEGtgZEkSfjmvXtw75NHa9sejCql2gNAVdOrHUstZry5a+aDqPxI3NYfGeu00kR+/4z/CgMpo/pGU/XjVyroUxTNzFRIZfalmdTaED+/YPXr19tpRPDNn+zEoZNzlt/lP6cHLFIuqMhNb1Tic+a/bUGCsLU3Ms5TPp6uvnDxlIy4McWYkRlaoPkDcktqLMcoQ7VDwYgyf69lOdvPLT21zSF0LOU4dDKKf7xnF4bH8+3usrKKf7xvL554ebRofca2jDWoJdKFJY/1PIXRCoLwktutkHGrJiCvhaJqRePiLRVNE4gls0ve/imekltiHtfJSAo+T/VhgyRJ8Hnd6Ax60RXyojPoxXg4iWS6tmnz2kF7B2PmG3C5IRSrX0etxut4O6izlhKaEHCVaROlavkpjPSHvn48Sj9ArZkxFAVK1ompy7VTE0JA1VQ8smME05FUySBBUfNBnVHd00hCAHuOzGD/8XDd10A9VVyVB/coWL8QmAjrjdGnwsa1Y8/GqLm2fkIICE3Ybu7FQXFxoKYo9nNpXAOVsi2lHsRTkRSmIilompFtnaeAVZg/oDOOpT2kqnbbv3rpJI6NRfMBRi0vPSWvd5EfOgf5Y1eqGNaey0YW8dDJuTIvT/n/VFvwLMzvr/HvatmTdaWXK8yalv7H4piJpuu6Py6EJgQisTQODIdxeHQOR0djkJXGd0DQhMD4bAKvnog0fcYonVUQS2QrDnk0H5dLQtDnxvBknA3+CzAYs2Ryan8druMBrNkfFjUta7kR17ZNlM2MqZqeQdGMQCy38lIvadZdT6VlKKpqu0kbbWSMTE6phKOA3pbkuX0TuOc3R0rslNAf4rmfL1U15SM7RvCL54brWrbuoS3KDRZVYv2alq9ptC5SmK00fw7YMmOlpsmxP4RRnBkzq98qlKHUz4xskLlfC7/pzneMrcU7PhEzP1/tufnp08fwwPbjlnRP9fs2PBkrHmxVWI+hNXtaecXVVOmmMjJkVSs63wLW7VS//8JMi6NsueUSw9Sg8iILslTJcE0IROIZvHoighOTCXg9LvR2+pFVVBwamWtohk5RNZyYiGNyNoXeTh+m51J6564mNTOXhtst1dRhrRSf1w1F0TAxW2VP5zbR1sGYbiHz5NXeA09dwF0mP0p4bevQNGHOQVi0P6pmVkfBeLsX+cbX9h3I//X7D7+K//jlqwVVHPkHgXVAUUDvRKD3FMuvR9VEyUyB2+2Cx+2yZWQaGZAVBiULXknVi1T34DQf6gWbKlzMeDgbQbU1uFI1vRevPoaYMKvwjGvYyEhaN6ppAknLPJfl9q34Z/pJFrnTXeu50zRR9BDU5mnBLwpeCoxAQTMa0tUgf91Vt9/lYk3r90oTenubUlWL1h20fZ/KbP7rP9mNn28/VrDT9u+JLbAuuFJkRUPGMuyEtalAqWYDAGxZVtt+NeJ7Kao/9nVvQghEk1kcPDGH4YkY3C4JXR1eeHJVcKGABy63hEMjc4gmqmhzWKOMrOLoWBSxZBbdnT64XBJCAQ+GJ2JNWV2ZlVXMRtMI+henz19H0IOpSBKxZOOnSGsVbR+MmbfBeTIAxQvWN/1KqYFQq6U/2Gp/7mtC2CaVtu2PKnIj5Vsa36P0A6bwoZqVNftNXzNesHPVQ5ZjenI6gclcY2VZLR+EmNlK5P4U+eqeRql2TsFyFjKRdnXrz1XZmguW/pyqaeZnNNivtUxWxcRsEuPhZIlgTkADsPdofkBPI1M6E01DVVQ89sII0tmCLEGJh6atqhr6PqsV2q6VMhfPFFVTzXf6bT1JjewQag8Eq606ti1TIgtsfFeNFw4hBEam4iUHU7Yew2r3d3QmadtHo6OF8T0pHfTpZmNpDE/m26kZ712FZbBSSqzPXklu+flSpbXqYIxdeOjkHI6NRiFJAt0dPnN2BCu/142g342jYzFMzy1eFieRlnFoZA6KqqEz5DV/7vW4oWmiKasrZ2NpuCTXgrNiBkmSEAp4cWIyXjrr2obaOhgrV5U2H0VVcw+62hvTL+itx5q9qoEm9JkGSknLiq0qxQj2tBLRWKntRpOyOQF0KqtANRt92wOufFVIvi2TnjkoGGcM9iECRG7ZRt7fpyPzzDZQQb4nXe00UXrIiNHphO2NsXDOT+PBW5ixUlQjYhOAJuxVyEIgq6j5DJIQub8Lc2deOjid/7zRXkwT2DccwY4DU3hy59i8ZRKW/TMiyHqn77KaLxi3/troSFJqPdVQNU3v7Vzl50u2jzS+T0a2UtMzUnPx8pmATFbFXNw6Un+pACjPeulYs8j69srvvc/jtg2xY7t6yywm2zJjBcF1wTK1HvNsLkt0YjJuDodh3cax8WhtKywjnpJx+GQUR0bnIIRAd6cPPm/l9k8ejwtdIS9GphIYnU4sOEM/G03j8Mk5+LxSySxTR9CD6bl0U2WMFFXDdCSNYGBxh6TwevTaj7GZREMD+Nm5NMLxxc9uLra2DsZ0lsb7VV4QX/jBi/iHe3ajnmrKSj3c5qOJ0kNOzGfX4Wl86769GJ21j3afyar6zU/YH/gS8m3GNCEsVRrF25ZlDdGE3vvoc997AU/tGtMfTkZVkXHDF/njrKr2n9mqpMzMn6XK1Hi4F352kfzoV4fqXtYIFvP/qmXh0uO5pTKKbYwmIfRtWNuMldqidXw4TdgDf00TUFWhV5MbD2xLFqjwIaPmPqZqlmxLiQd80U00F4MZo9BJUn1DWxSuV608SF5R9Z557VgPVlXbBb7z8334p/v3VvWAyGRVPRAsEZXkh3cRUIU+lE3J9nO5E5uWVds5y5QYwd42mn/Rvue/w7asWVGwlJ+TF8jPplE4PIeVNcs631FRC87FfGRVM//LyKp+PVr3dYHP6WRaxpHRORwZnYMqNHR3zB+EWblcEno6vJieS2N4vL5qRE0IjE4nMDwZR0fQU3asLT1j5MGJyXjTVFdG4hkI6MdhsXUEPQjHM7aXkMU0G0vjyFh0STpjLFRbB2PGzXZ0Oo59x2fn+XSeMYVPpZtXOQv9glkb2VfrwHAEADBZ0GDy5ExCD24kCQeGIzg6GoUxrYtxU5+NpnFiMm5uu5CiasgqKrKyXq69R2fzD3dLpwG91lJ/CBhv2ZrQMwZjs8kSVYX5Ed+tGaCx2eSip7WN49nX5Z/nk6UWNrJBtTsyHrUNU2FQtcKxqYR5PG0bRYkqLskoh7BVVSlGdZn1QZlr03VwZA5P7bJnvYyBSvXegOVLV5wQygcg+4fDODERr2tU+MJrfP7MWD4IsQ6mawxCXHHZgnW/cGCq6v0czb3VFzXgzx01LfcyYQ4AW+G7WzgcSSqrFr182AKdwgAU1QU/+WOl/6kVXBOl2O5b5rVn+2d+/TWebyHsMw9YVy5y/6snc5LKKDg6FsWhk1FkFRXdHT74awjCrCRJQneHF/GUjKNj0aqHSwFyU2ONRzEVSaGnwwu3q/Jj1+txQdP0XpZO0zSBydkkQv7GDdTaGfBgZCpR0zGtxlw8gxOTcfh9rRHmtMZeNoj+Mivh7scO4eHnR2p+oMaT8pJWU5qjZFfx2eGJfNdsI/jxuO2n26j+kyTg2z97BT9/dlgfBiM3ZREAxBLZivusqHqVjmyZPkcIy8CtlhROVtYQS8lFN2tNzWe+hPEAzS1b+MauN+Re4KtyAeu0NbXe9PXskVa6qmoe//GLA3j4+RPF6xSFVYwoCM7yD1LrNrOyBkXV4PO64fd5cgP95pbRhD6BuLUKL/fnfU8exSvHw7Z9MIZkKDkFk7HOgu2b+5b77xfPDuMnvz5i249qz13hFafOc58WlsigcPqh+d7ny1XhVxNECmN7BcdJCGtmTL9GjDZkxSvJL2P9rpUa6LbwurAFnqK6QKiweYZiC7hLL2+rpjTLKCBKZPpqzYSqqgZZUUvFYmYgVus3fmI2iYMnIkhnFXR3eBHwLU7D886QF4qq4dDJuapmGEhnFRw+GUUyraKn01d1m6uOoAczc5mGdB6oRTSRgaLpnaoaxe12weWScHI6vmjVlcacxp0BT0Myeo3Q1sEYANudutrrwPg+zVYzsncBpY4sgSGdVc2eYvOx9pgyqhhcLglCCDMLZa2WNAiB3DyN+u/0agPjwV+8YUUV+rYsbzWaEPj2A3vxrz/fb3tYqEZVmeUGboxQLiwPpK/9ZBfuf+qo3s5JVs2qL7Mxf5nya0LUlY42zkldnQSEwF337MFvygzSWd0qRMG/C6qFSgR6xvGw+q/HDuKrd+/EniOzegBQMM6YHvTmlkf5LAgArBoIAsaxtmymms4OAgLpTP5zdd1fC6sp53mJMS6pUoMOz/coV23toazrrCYYs1+79l/mg1pVEyXPY+G6rB1i5FKBjmVfSw1XU00zBlEwi4f9+1h6GVvHo1whvv2zV/D1/9ldVHB79m/+/ckoql5VaryQ2YLpfHOFamVkFZORJLo6vIvW+88q6PfA45ZweDRSMViKJjI4NDIHAQ0dwdr2w6iuHJlKOFZdqQmB8XAKwQZmxQyhgAexhIzZ6MKDz2RaxtGxOYT8noYGkYutdfa0AYTl/7UY6g0C0Kvwas6M1dD2otDuIzM4NBKpajlVy9+WjbdalyQhlVEwOq2nv40HhfVlLZWW8cKBCbO6RB8oPz9lVCGfV/+d7S1RAKPTSb2XpvEj861f2Ib3yLdfsa/96FgMsdxI7tbeipqmlX1IzsWzZhVyLaxtomo9J0Ygt/vI7CLm60TxGFIlUjwCKBEBAM++Mp5rOJ//mSby+5oPyETZi1Af303/sPV4G9eOuQ9lMmMzsXyVeKnOIBVJxeudt81YQebQ/HZXkbEs1VMQsA8PUY45TEiJAyly+6UPXKy3+StVxa5arj/rc1ct8dmilznLd6PqzFiuCto4LuZQIBUWtbV1zf25Y3/p6txaX2oURYMqrO3ELNeoyAVkNXy7piMpuBex518pPq8bQb9H72kZSdkDSCEwPZfC0bEYAj533Vk5r8cFoQHjM85UVyZSMrKyWrKnaSN0hrwYnY4X99quQSqj4MhoFAGf2xympFW01t4uMpGrXjL/XWVkFcq9bemZsRpvPPPNeFyGpgn8x0MH8PCOkaq3U9jN3Qg2rAOzwhJsAcD9Tx/Dk7vGcWIyZjb6dhnBWImbrHGjiRb0/sv/Pf+n2dPSshoje1Gy9kYYPTPzWTG1wkPD2nutFub4XHUsuxjt14qyG6JwzC+Y800an7f+vZDRU87WnkyztyEzMw7l9knkp9ASBT+3Zy5Klyaa0AevDPrddTXgL2xbNd/3xvr5wkzrfI9ke+Yhv55qstj53sf2zxrXuZrrVCEEILlKB6bmi4+m2cZTM16ihi2D2BZeF9ahOARKN/ov3md7aGOUX5LKZ8bkMtnDUsqO1l/u86qAC8LS09e+ZOE9o5J0VsFMdPF7/pXices9LU9OxzE6nTA7u5ycTmB0KoGukHfBAUEo6MZsbOmrK4XQh9gILGC0/Vq5XBK8XhdOTMbruhdnsiqOjEXha9HJyNs6GPvVS6P41v2vmP+u9vQbN5twLFN7JqXOlLNcopqgHCMoyd/A9WVVVdUnktYsI+4XLGuM7yRBgqxo0DTNrMb4lwdeQaGsoge0aUtDY+tDzNpYWM2NZ6bZfm/vsWf7EgqY7UWMAMRarlI0IWo+xkb5tFIHxLpurXjdtmrRGi6G+UalN9qvybnjC60w0BS2h7GhM+hBVtGKAhChiVyblfzPxmeTuOM/dlTcvhCA15Lqt0yOAJifKX5wGo1x3S5XVQOZFir83Hw3Z+NBLgTMRvu5wzZ/ZqzMuqt5cRJC4MFnjuGb9+4p/l1uvyWz2l8quS3z5UizB2tKrlrZeLEqetmQLAF17rtS6uXA+IxxreYv8/x3Ln9OS5ezsNq8sJxWtT5IVc0+KLWGwuul+vVNhFPwuBubFbNyuSR0d/gwE03jyEgER8eiCEfT6OrwLkpbJUmSEPLr1ZVLOR6XMSF4Lb1OF0PA50Eqo2AqUtu4bllZxZHRObhdWPJ9XixtHYy9dHDaduOYr12KwVgmI6tVP1wMu4/M5P9R58N7vsWMQVKN8hgPFbNtlMg/tArrv4zpONKyog8i6srfJPcVNPIGAFlW9epPyxt53JolszwkNU1gLpHF/oL1CEuAVdiY2mwcLYqDzELGQ2841/uzWpWGbrCaCCfNnqWGensAJVLlU/ECernjKRnDEzHz39aA1foQtgoFvIglZYxM2cfu0YSA2/pwEMCxsRjKUXNjW8zMpTA8kf+cZqnaNAIeW4iYO0/Z3IPD7ZbqG9qi4N/zVRkam7AP/2KMAVd5+2qJxun6z+ff79loBvuHIyXa8OlbjcSz2D8cNtdbbnJv4/thfd7q1fz5YShSGQW7Dk9blsx/N41MZ6UZPk5MJZBIyxCW75QmBI6MRTGbG4y27NAWFeY5LR4WpbbgW9U0/btrrVrOr6HqzFgyrWAunlmSNk5Wek9LH+JpGVlZQ1dH9Q31q+FxoLqy1gnBF1Otk4nLioajYzFAwqJ11HBC2wZjpU50tXPoGQ8XRdGK3hLn8/iLJ2v6vMHW/sfyj3AsUzSfmXGTNR6IijXzAykf1Gh6dkyy3PqMISoyWX3uO2vPylIysgpJgi0zFkvm9ydryRwpQh/D6fhEPqBJpWVoEOabtzW4SWYUCMvTXq/20SremDVNQLM8OOYSWVsVainGA9I6nEfJ68PSAN4sk2J7bFTcjlXCun5h308jUJYkCcmMgmwu6LdWpwrkgraC9XYE9JvRj584bNtXo+2S9cEmVfj2z0b1rO/3H34Vr47kh98Qlp53wojGLAT0a8o4jx6Xq65xovTgxNqzsPTnMlkVk5GUbVJza4azmge5bMv65H9eqa3b9FwKqYyCf3twX37Z3J8jk3Hz7z9+4hAeem441/NalAzG1NwxLeydqKrGd1TvBfufjx60Dcxr3apxLko1JZiM6NluTRW2LLS+iMB9Tx7FDx85qFenlrmGbdWUBduwlkkTAmPT+e93NfdHVYN5bRo7Zl2s2nvs+GwCPs/SZcUKdQV9DaseXcrqylRGQXSBE4IvRC2TiSuqhmPjUWia1pDOGkupbYOxsRJvGdYHayXGBWJtoF6rWu8X5dq2hWMZRApG7ZYVDRpgVnEZDxslNyyEqgkk0kq+N6VlZ9xu/e/6yPyYt+lsWlbhcknIyPkvTdqSJTN+LgBoqmb7HQAcG48BAmZmLWt5MGZlNR94CGNapPx8moWMNi/WTMxMNI3pucoj7KuWB3laVhCJZ20BpVXhgyE735gLZaQy9uVs+ynyAY2sqMgqGp7ZO24+iNVcsHHbt5/FUzvtvTg7AvnpVQob8BttwP7n14fxrz/bB3eFi/BfHnilxIPfqFLL/13/i/X3+v+M8240Xq+JgPmiYCiXXUtmFMRTsrkvHrcEt9tlXreF6yml3LortRmLJmXEUnLRssZL0Jd++CIe23HCfFFKpvVhcPp7fEXrMtq4aRBQLNeXnBs2xnhxMma6yLOXTaB0my8jMFY1LZeFzi9gbWP27L7xCuOM5VesFHwHbMGYJpDOzt870768BpftlTDPqGqerz4gnuvsE2jxB3I5kiShI+DBiSWorpyNpuFZhAnBF6KaycRVTcPx8RiysoZgoPXPexsHY8Xzf1XTewrI37xlVZvvHlFWrZe59f5nfUst7IkYiWf1sgkBWVYRS8pmT0ezAT+AExMxTM+lsfPQNI5PFFdXZbKa2d5F337pgmayejWlNQNmzSplsgpkRcW+4+GSxzeRViCQH1nfGKDW2N+DI3P6gwz5KrFIPIuT08VVkRKMHmn26GC+8aKsn5+YTZadXLpUo/f9x/P7W0uS1NpjyKyKNdoF5QobiWehqAITM0nsscwbaWRSEmkFLx+asa23w3JTKhxCRIIEoQk8/PwJJHOD/VayfY99INjCh33h4KHWz+XbJ9XeqUJ/9ha0Lazw3bQGGNNzaUyFUxBC7z0sCtMsJdgnwc5/tlKzhXwVv33HjazzyFQCB0fm4M81JI6lsrlq3+LMhvkyoNkDV1XVMJfIms0Kipsg5ce2ELkXp1LH2tpbUxOAJjQzC2Z9kdt5aKbs7czaZkwteGlVCl5I7HORVpMZE7lhd/R/F44Tp6+n/PJCCIzPJBzL5CwVj8cFiMZWVy72hOALUWkycU0TODERRyqj1DxsSLNq22BspsR4JtX2dDRueIpS21AI1huTtY1Ercvah8ewBwjprGI+XPS69Py8bmZvRk3vlfjQc8P41Usn8T+/PmJ+xniLTmctVYSiOAjJb0+Fy2WvXrRWC6azKu75zRE8+MxxTJdolKmoGtySlGt/J/DDR161/e7eJ4/iZ88ch9FY3RzFv1RDZSDXSSD/M0mSKrajAewPwKysmQ+DjKzaGpLqgax9Xb94bjj/+5qCMcu4bLmgV821lZEgcmOsKdA0gW/eZ28cbmszViBkCcZsVT2aKIq90vNMLVWqAbItBhH2d5FURtGnTrFkYzXNPq5cNYfIaDNkXa5Slsqolgf04/qfjx4EoJ/73k7fvGNvlRq2oZptFv7WzHBZDrzXq99iY0kZkAR2Hp5BIbPNmLBfi4oqLFWVomSmwjpYa+G2DcaI7/r6NX0oi1y1YOFYieWuK2vAqmiq7ftemB20vnRVM3OCAHKdEYR57i0FNF8gy4mlZCQzat2j67eSUECvrmzk9EGLOSH4QpSbTFwTAiNTccSSWdtE662ubYOxUoODKlWmf62ZsVIjUM+3HICaU2PWZbMF3cytNzxjWAO3ywVZ1WxtkxRLb8aXD06VHLTWeDBlsip2HZ7BXffuxoETEchlquN+tv04XjgwZcv0RK1txmQNkdzkyD99+njx9hRNPxYlHiRGBjAcy+Srx4SRbSneFy2XFTMGktXHdqouM2bce4xxzCQAsWTWPrFzwQO4uFdZ9dGYdZobVRNwSXrA9PLBKXzzvj1I56aYMqqN7ftbvq1iKFD65mQEfNZDEZnnhp6Viw+yfq3pAdaX/vMlvHBg0nyAx1IyEikFGoCMJRirtZbyU//vCew6PG0fbb7CSrQSGaEjo1F88Ycv4it370RqnpHSyw76WuHlTCuRJTXaY1n3xeiJqgdjEhJpGRtXdtrXlcuI3nXPLrz0ar5qWFE1vb1Y7pouDI4Lx1ATKB1MmRlMIXI9NPPznBZeA1qZekprNl5V7NlnpSATZj2eGVmpmB0rbBMqcgWxhJjm974UvY1aEoEWmfLGStMEXj0RwX89ehD/dP9eHDgRmXcZo7pyZHrxqysbNSF4OqNg1+EZ/OjxQ/jGT3Zh+57xqttnF04mLoQ+x2cknkVXR3GVfytrvSt4kZS6kOcSxVP1lGJNwydqmLhaKXPTr4Z11nlZtler2IKxXIbJJek3SetgrOYUNxB4smAuwkLp3JgtsqJhYjZZceqPh58fsQ1WGbcEY2lZgadMF2+PW8JsLJObM7M4wJJtjeP17JvRBqlUBuDVExG90b8mMBlOYXgyBoH5e/OpmmY+NFWRnxEgq2j2c1YQjWULrqF6M2PGw0vVBH769DFkZf2mmFW0km+oqibKZnHLDdC45+gs9h6d1dP6uexZpKgNUhVy7cGGJ+KYiabx1O5x81cel6QH7bkqckA/nrX0plRUPXj/1Uujtu9ZpXWIEtfDEy/lO8qMV2h3om/TGtFYt1m5mjKesh8/JTelj3VfErnvzWwsg92HZzAVTqO74CGiCQFF06ulx2bzzSdUNf9yUaqaMpaUzW1NzaWKBh/NFyk/TImiqth9ZAaTEb0qN5awt40s14PUnhmzvwxYM2FC2M9VLCHbXs4K6YsaY7FZer9aAzTjZyVE4xlk5MUZgiGa6+n9yrHZBQ08Op9EWsbTu8dw1z27cfdjhzA2k4SqCvz3Y4fwkycOF3XIKmRUV5Zq97wQizkheDwp44UDU/jBw6/iK3fvxH1PHsXJ6QS6Qj48umME//rAvqKe6eVYJxMfn01idi6NrtDyqJq0Wn4lqlKpYCyRzmIinMTqgY6Ky2q5N0sBYDqSxtrBzoqfL7XNWrsNf+Mnu/LrKchYaEKvGvR73JYHgd4GI2H5Yhs9ESu98RvSsmqmwrOKNu88bNaGwNbG75ky2R1ADxwOnZzDoZNzuOVd5xY9SGRLVYgQ+gj7fp9eRkkAU5EkhnpDAPRy/ssDr2DNYAfeuW0TsrIKRdF7ohU+n8LxDCQAvZ36xOCaJuDzuZFVNFtAmMmqtoeAMF7bzbLZs4WFAYGRlewoka2y3uyt4ywFcg+VdFqvbvZ6io+dPgVVbR0HokkZ9z55FKsHQgj5PUik9R5TVhefvRLP7p2ouB4tl/nIWs53OJZBb6cfbpcEWdEf+tZqylrajFmzcdbjWbH9lijOnHks19x8NS62+SCtQUaF74kQwLd/ah93T1WLp+syMk9HRqM4Mqo3GegK2YMxVRMls3fGiPzGMZcK0ukvH5zGuZsHAQD/57v6eHGnru4qua8ilzYbnU7iwWeHEfC58cbXril68KfKBCHWzk2FzTPUgmpea/VuOtcJpxwhhHmCjE46hVXA5v4X0DSBsdmkOQh3LZJpfSaS0ZkERqeTGJ1O2I6FyyVh0+oubN3Yhy3re9ERXFh1mBB6O8Id+yfxyrEwVE1g46ouXPGGddiyoRcQwNN7xvHkzjEcGY3iygvW47zNA2WrC0MBN8KxDHo6fOjJ3ccWYjEmBI/EMtg/HMa+4xEz0Orv8uPis1di68ZerB3Un6sHhiN46LlhfPfB/Xj9GYN4y/nr5m2j1hnw6EMWCaCrw9sU1aiLrX2DsRI394DPjclwCh0BD7o7yl/gqirg87qRkVX7EAXzsN7cPW5XTZkU23ARqjVI0auMJmaTCHjdEALYdXgGz+wdx7uvPAPxtAJ3bk5KNTd8fTUji2eyqvlGm1WKe0EW2nN0Fj0dPswlsnpj5RxF1cq+aekTl6tmOQqPx3/88oCtnI+9OIJYMosrL9gANdfTxgjGjOBmKpyC2yVZBn/VH2Sqpj/MXC4Jk7NJCKEHY0aDeY/HBWSsjZyFrf1ZNJHF1360EzdvOw1nrO8zj5FV4f5PhFOQAJy6uvhGXlhNqXftF/DnxslxuSUM9ASKsi/GMS03sK17nrfauUQWXbkHi/Wc/u/fPw9BvwdvvWA9Pve9F0o+QPcenYXXLWHjqm6kZftQJj6PCwLAk7tG8cKB/DQ5mlYQpM5z6Vk7gqi2h3r5YKzUDA4ey0C12XkCV1vWx9o2pVLVqBCYKuilK+eaAVgDx1Lfm66Cdi6aJhBPF5/nXz4/jM3reiCEfY5Ygz5VWEFVeYldVnPjlQnoVcn5/RJFwVipxtJG2QwZxf6CY6/mtQ/fkc2qJToeWPYXwC+ePY51Q53YurEXmYyKf7x3D97yhvV4zan9ZhBZSiSegawIBP2VX2wzsoqx6QRGZ/Sgy6jmMgz2BLBpTTdWD4SwJhcw7D8exv7hCH62/Th+Lh3H+hWdOHNjH7Zu6K0p+MnKKnYfmcWO/ZOYCKfg97px/pYhnL9lyJxWz/Cmc9fgrI19+Nkzx/HA08ew+8gM3n7JRvR3B4rWK0kSOoL6YLChgGfBI84bE4KHapjLUZ/2KY19x8M4MBwxO8Wt7Ati23lrcObGPgz1BooCp60b+3Dqmm78+uVRPPfKBA4MR3DlBevx2k39ZYMst9uVm2vS2V6ejdS+wViJzJgm9JTo8EQcp6/zlO2do2oagn4vMrKqj44t8lMGVdxm7qZlBEfVKs4Y2buOCyHglfQbrRDAwZE5xJIyjo1GkUzL8HvdkFUNiqpP1ZGYJw0O6DdrI3OSzqoley++6/LTMDwZx7N7J5CRVZyxvge7j8zabvCqKspO0WId2T2dUYp6W9oaMysatu/Rq8Tecv56KKoKj9ttHntzUnRNg8stQclVMUouCYqqIZVRMRlO4tTV3ZAkew8z674oioqRqTgGuvy2Z8CuwzOIJmX8dt8EfvfcNQCKM2NF51QIiDKNA62D5Oq9yfLnEgAOnZzDKau6MDxRfNxlRYNckLW5YOsKrBkM4cyNfVjZF8REuHTVXLclK2MNFKrtPfXyoRm890qtqAOC8ac1EANq701pbRhu61hR4WVAf14XZMYsmWfrUAslt2m5F9jaRtXY2M0I5OfLWvZ12R/mqiZKDgKsqAKReBqKItDT4Uc8mf9Mb6dPH9KjYB9LVf0omkA0kcVUJGn7LgqgOBhLlL43WAPWbFaxZy0t+6BXU1oDN7ViajKTVbB/OIL9wxH0dfkhKxpmYxk8uXMUb7toA6bnkphLZIviMUXVMD6bREeJ9k3pjILdR2b1aYmmE7ahbXo7fVg90IHztwxhzWAH1gx0lLzPr1/RiSvesA4T4ZQZmP3ytyfwy9+ewJqBELZu7MPWjX0Y7CkOlABgMpzCCwcmsfPwDLKyhlX9Qbz9dzbiNaf2V6xSHewN4o+u3oIXX53GoztG8E/378W289bgkrNXFb3UetwuyJKGgyciGOwNoqfTX1cnhlonBJ+JprF97wReOjCFmdxgweuGOnDFG9Zh64beksFjIb/XjbdesB7nbBrAz545jvuePIqXD03jmos3lj2m9UwtlUjLODYWLfrONaO2DsZcLvuApqqqweN2wePWMDwZw6Y13WZPJIORSdG/UHJ+4NcqgjHjrdvj0aeIiSaz8Hpctrf4UpIF7dIKs3rJtILukBdpWYXX7TLfVMOxDBRF72Wkz+so8JW7X553V4N+N1IZ1bzJZmUV6YJxsToCHvNmZFRt9XfrF7w12EtnFVvgYWX9csVTinl8SgUT8YylI4KWa9yem2rH5ZbM4EBvW6NnxmRVg1voPYPSWQXJjJoLoPQALZ6SzTZWRrXW4y+O4uR0Aqv7gwj6vRWzl4XBWGGAX1itGUtmEfC54fW4bT0Zw7E0+roCgKV91f7jEduwGYXbKdzW2qEOnHPaAADg8tevxd2PHSq5bGfQa2ZzM7lsos/jghACsaSM7g4fLjpzBZ6pUF2pavYA23gwl2rHZ3SqqJa1mtLaTjJTIcApbKcF2DOEmXna/1jLYs2MFVZTCqG36+rr8pdM8BmN4+drWG192KwZ7CgbjAHAfzyk9y7+X+8+DxlLNnKoN4ipSKooI1jqWAsh8KX/fAmT4RSuvmiD5eclgrEy7busxyWjqLYMp9F2zXghLcwMzsUzZTO51u/5j584jPdftQVA/vt4e6769fwtK2zLRWIZqKoGd8H4UsfHY7jvyaOYS2TREfBgzWAHzj61Xw+8BkMlmwyUI0kSVvWHsKo/hMtetxYz0bQemB2P4PEXT+LxF09iqDeArRv0wKyrK4i9R2fx/P5JDE/E4XZJOPvUfrxhyxDWDnVUndGRJAnnbxnC6et68IvnhvHYCyex92gY1126sagJTTDggappmAynMD6bRHeHD4M9QYQCnqoSBEB+QvDCtoyFIrEMfrNrDDsPTUMCsHFVFy46awW2bOgtqnqv1qqBED54zVa8+OoUHnvhJP75/r249LWr8MbXrq57Xk9jlpf9x8MYnoxDCGDtUCfWDFTXnMgp7RuMqRoCXrct0DFuZAG/B7GkjPGZJNYO2U+g8Rlfrst6Vql+SiTjJu3zuJCVNSRSCvxe2Wy7VI5xgzz71D7sPRq23ewl6A17hRBQFQ0Pbj9uTgcUjmZyc3W5kFUk8yY53/6G/B6kMmp+TsuCBtLnnT6It1+yEUII9HXn972vS3/IZGQNrtwArLOxDNK5RuOJgnYx1iD06T1jeE0umNi6oa8oGEtaHhrff+hV/P4Vm3NjE+kPQGu13/hsEqv7Q4jEMvjVSydx1YUbkJU1ZGXFzGQqmoaxmYTZ3s8Iyk5O641ik2kVAZ+n6KFr/Xdhxi+ZkSFEPi1vzEdomIqk0RXyYqg3aMssKbmG2gLFAV4pslJcTWnNMlr//vZLNupDg0BvvwHkG/8bz+1bfu8cvd1SRkVXSOCKN6zDTDSDV8v07lIUzbafxlAj//rz4rlLC6+d+XqcWoMua4Ai54YcKXzAmI2+K8Q/8x3TtOX31u9WYYZLUQXCsQxm5tL4ya8OF61HVlQ8/NsT8zZMtn7fA34PMlkFyUzlbHVWVpCRNazsD+KPrtqC3+waw9GxaNEYX6UcHJnDZO77VDgkQmGWPFYma2693jJZDd9+YK/5b1nVkMmqCPo9uWYT+XOsCXv700LjBeM9Gm3s3EUvqNa2fBomwilbOy5V0/Drl0fx1K5x9Hf58YFrtmJdDQFQNQa6A7j0tatx6WtX6439hyPYfzyMp3aP4cldY/rUX6pAb6cPV7xhHc7bPFC2d3M1ujt8ePebN2Pf8TB+8eww/vVn+3DxWStx2evW2Kol3S4XOkP6C1U6o+DIaBRej4Sh3iB6OvxlO/UA+vdnvgnBY8ksntw1hhdf1YOwC89cgWsuPQ2qXH0TnUpcLglv2LoCWzf04eHnT+A3O8ew5+gsrrl4Izat6a5qHTO56tL9w2GMTuvX1FBvAG88ZzVOWdVlVj83s/YNxhQNPq8LScs9wtqWqjPowdRcGqGAxwwygHww1t8VwOh0EienE8XVU2UYb7ZetwvprFp1916jd2Jf7iZuHVBzbDaJwZ6A3og/kcURy7his7E0OvweBP365KvlMlSAnkkwyhbyezADe+9N6wj7QtO72U+F0/BYGpj3dvjMbGPA74HbJSEczSCTe+sqDMa8lkbW4VjGbLAvSfqbsTUzMTKV7zk0l8hi56EZXHz2KqQyKmaiCXh8+Zveb3aO4Y3nrMavXzqJkakE9hyZwar+kN4YOtegPJNR0eH3mOdgZX/Ito2sqk+zU6lxcOFDfjqchqYBp67WbyD6cE7W6hw9GxfwuW0N+GVFxcnJGHo6/RUDB5/Hhe5OP+bimaLsSzqrIp1VkJU123n2WqotAn5PUSDncklw57qPe91Sbg5LV8Wb80Q4ZRsNXhMCYzMJvHpiruTnrW2IxmaS5vEpxZoZswbYWSMbU/BsHZtJICvnJ7Mf7Algei5tW3a+9o7WqlFrMJgpaqemt8/815+9gqlI8awOqibw82eLh28xvO3iDXBJkq26Kej3IBrPzN/2NNcpoqfTh4Dfg86gF4oqkMqqCJcYosbKGvBYq+wUVSvKhKVzVZCFQa+s5jstxdMy9h4L59djuRZnomnELUG0qmrIyhpm5tIIuIqrw8fD9mDMmCPTU9CUw3qHnZ7Te40ax3Emmsa9vzmC0ekkXnf6IK66cH3DJ4vu7vDhwjNX4MIzVyCZlvHqiTnMxLLYuKIDp63tXtQg8MyNfTh1VRcefWEEz+ydwL7jYVz7Oxtx2poe2+ckSULA70HAr5/bsZkkxqaT6O3yYaA7gKDfU7RfydyE4KWyYomUjKf3jGPH/kloGvC6Mwbxu+esRneHD90dPoQjixOMGTpDXty0bRPOPX0Av3hmGD94+FW8ZlM/3vqG9UXjiQkhMD5rVCGHze/jmsEQ3vz6tbYq5FradTupbYMxRdHnsrI25LT2MpQkCV0hD0am4gj4POZNxHiw9HToF/iJyXjV4ygZDxpXroF5tW1pjAbxxhcmm+vZ9OjzJ3D344fw7jefhrVDXYgUzFsWjWegqRrWDHXC7XbZemytHewws0CAXjUZ9HswFcl1vc8FJi5JfxBaG0GfmntbERCQZYELz1yB3+6bRGfQa2YbvW4Xejp85ptuZ0FvpHNO60c0Ya3OVM22O/qDwLj163752xO25Y0erVlFhaoKyJbgZjqSwg8fzg8em5ZVfYgFyzhrAz1+WwDaFfLizI292JerGpydy+CB7cdw5sY+vP6MoZKBs5EZe/Pr1+DxF0eRyir2oQEK2oxpmoAKPYtkDRBeORbGM3sn8JY3rKs48fjmdT1Yt7ILDz83XFRVHfK7c9NfAQOW9hHWgNfrcZlt6Qxul95HTxMCHrcLiiqQyearb7s7fEW9Lu+6Z7ft35ooXT3m87iQtWTRJKn80AmpjN7RxFp+603UGIzXZTmesqLhq/+9E5tWd+PUNd3we9047/RBPLpjxNb7N1smq2awnouYpayFDf/1QVPLf2fnq548Y11PUePvoN+T6wg0/wC8qqaZg/oaLwnxhGyO8l+ONcCfsQRjmaxa1AQiky0d9CqqhoDPjVRWtY+9B3uzic/+2/O236WyKrKyPr+qv8RYYIXT3RiDcXvcrqIelYAeOE+F0+gI6lm4lw9O46HfnoDbJeH3LjsNZ57SV/IYNFIo4MV5pw+ir7cD4UhjRscP+D14+++cgtdsGsDPth/DDx8+iHNPG8CVF6y3DfRs8Lhd6Arlmx+EYxkEfB6s6A2gM+QzayWmSkwInsooeGbvOJ57ZRKKquGc0wbwpnPXLFm7q9PW9OBj15+Np3aP4end4zh4Yg5vOX8tXn/GEEam4mZGMhLPQpKADSu7cPVFQ9iyoQ89LTz2WNsGY7KqIehz4+oLN+Ch3+qjqB86OYeNqzrN1LLb5YLP48bwRBynre2Gx+0yq1zcbgldIS8Slnnx5mPc3L0elz4KfpXBmJEZywdj+npOTOnVIf/9+GGzDYlhoNuPmWgG0aSM03xuuFyS7cb7mk39tmDM7XKZAac1td4R9OqdAcb0KZM+9PYzzZSvJEmABFx14Xq86dw1UFUBv08PxoIBDzqCXr3Hk6phVX/IXOcH3rYVXSEv7n3yaG7bkpnZAZDrMWM/BnOJLEJ+j1mG516ZRFfQhxV9QaiaZg6E2xXyFr3tpzIKfvXCCDqDXpy2phs/e+Y4xmcSuOlNp5lZT6/bbSv37iP6SOn6gLPFVZJA/iFntJdIWbKd0WQW9/zmCK66YIMZOOrnWx87LJVR4fW4ICua2W7nsR0jtvX/yQ1n41v35auDXC7JfONPFrQx8vs9Zns561ukpyAYS2UUW9BgtK3SNAFP7veqKszlgj43ovM8Xx7dcQIXnrmi6OdG5sJ4eLtdrrLZ4NHpBAK54UUMRjWl26VPt6VpArDEHcZxOz4Rw8ZVXWZGFdDf+EMBPROoNyUo367THqzkX2j0cb40uCS9B5fR/q3c171cVtM4z6UemkG/B5ns/MHYT7cfQySexTmn6Q9EYy6+eCqLns7yDyC/161PWZbLWFsHep5LZIruQZkSQS+gB5pGMFYYnBu9SEt54Olj+Og7zkIyo6DTV7yfE7NJdAQ95rk2GoSXq86enkvDlbtf/OyZY9h/PIJTVnfhhjeeOm+bp+XglFVd+Ng7zsZvdo1i++4JHByZw+nresw2cSv7Q7bmH/oo9vq1IisqhifjcEkSBnsCCAW8iCayZu/eTFbFc/sm8MwevUPW2af2Y9t5a8o2qG8kj8eFy163Fq85tR8PPjuMB58dxsPPj+gztrgknLqmG797zmqcsaG3pnaAzax9gzFFgy/gtd3IXjo4jd1HZvDaTQO46KyVWNEXhN/nRjwpY3Q6gfUrOs12Vx63C6GAB9NzaUxH0tCEwOqBUMUuxkYqVW9Qrz/oS9Vlj07H4fO60RX0YXQmP/7NQK59ViqjP1ysb/9TBVMNregLmW+ZAZ8bHreEqdy/V/YFi6qK3G7JfNu2PsBPX9uDFw9Om6NDK7lhItwuF1wSoAr97XbVQAixRNas3uoIeNAR8CCe0qs9vB4XLj5rJdat6EBH0GOrqunr8mN6Lo3Z3P65XMXBGKC3mXt+f7633suHpvH6MwaRzqrI5J7hF2xdgcdfPGlbbmQyX4W2dqjTbAv1q5f1BrgA4PVI6LBUoRjtfrpCXggI80FrfeYYQV9/7o1xbDqBVf0hDE/EsOPAFF49MYeh3im4XXr1wXQkDbdbwoq+ENJZBQGfns0qnJprw4pOrF/ZabsJnrd5AGefOoBkNh/sWQW8bmRlDS4X0OHN35ysVexet0s/f5bslMulB9Sa0K/peEpGT4fPPI/l2hBZ7To8g3VDxY1jjUzUXC5jq6ha2YBF1fTBZI1qKiB/Dno6fcgqWm6wWw0nJuM4dXW3rfG5pumZL+NBlMwoCPo8CPj0Mbys5y0cyyCeyiLg8+iDzFoClCnL9jUhcHwiDllWMdATwNhM0jbdWKFwiSnWAOCP37YFr56YK3lvEEKfWDxecD6N6laD8TI00BNAVlbNLHcsJWO4xNyyRiccn9eFtKzC73EVNVMo7KnrcUvmC1EknkEsmUXQ50FWUZFIyblhV7JFGfi5eAaaAI6Mlq6mPjoaA1xuDHYUPzRnY2ms6g/haEovg1FmWdFwzNLkQsm1S5ueS2EqksJPnz6GRFrBFW9Yh0vOXrlshzsoxeNx4c2vX4ezT+nHEy+P4tDJOXOaLZdLwsq+INYM6MHZmsEODPUG4XJJ8Hr0zkOaJjATTWMyksplwzU8v38KT+8eRyqjYMuGXlx23hqstLxAO2WwN4g/vOoM7D4yi2PjMWxa3YXN63oQ8C2/0KWhJXrggQfwrW99C7Is44//+I/xvve9r5Gbq4ms6IORGina1QMhXHPxBrx8cAY7D8/gpYPTOHV1Fy46ayVOX9eDSCyDjoDXDBI8bhc6Ap5cLz0FybSMWFLG6sEQ+jr9JW8O//243sPNyG585+f78IWPXlz0uWRagawIBHz6m30knoHHrWdFJEkPAlRNVKwLX9kXxL7jersOr8eF3g6/WSVw9cUbioIdt0vC5rU92D8cwdqhfIC4aU03Xh2ZMx983UEf4il9VH1J0hMVgYAXmVw1o/ElCfn1zJiRSezp8OGsU/uRzirwuN0QQkNPhw8nAKzoC2J6Lm2bvL1UldKqgptDLCnjy//1Mlb2BXHVJacAAM46ta8oGLNmBF8+mJ9upsPvMd/E3W4XeixpeCNjoM/RmW/kbc0kROIZ+H1uc1qOJ14eRTSRxVsv3GBmClRVzzg99NwwXj6kb/vzH7oQGVlFR8CLGPRA32r1QAfedO4aWzbu2ks2Yi6eRcpoiF2QnegIeuF1u+ByS/C5XebD2NpY3MjQ2NqMSRKSaQWpjGoGf51BL9YPdQEYw8q+II6OFT/sC01FkkU/Mwb7te7rQ88N45zTBos+q2kCGU2xtfUyjldfpx/h3JyXaq6zgCaEGcAI5KqtLZmxVFqB1+NCwO9FPK3YsmrprGJ2UFFVfaBTvR2faqvGS6aVXGcFDcm0jDt/tBNBn9ucb7LQdLT0cCJ9XX68YetQLgD3IGn53hpDERROIH71hevxg0cOFq1rqDcIRdXMLNDIVBy/ftk+m4bH7cLrzxjC07vH0RHQh+ApHAoFgDmHpyHg8+Rm8NCrblMZFZqmZ1RkVYPP44IkwTbEBgD8+uUxXP/GTWUHhn54xwlgxwl89U8vtf08I+vbGOgOmNeYUQ0sK5p9jltZRTiaxlO7xrDjwBQGewJ4z1tOx+oB5wMGp6zsD+Hdb94MIfShS4w2zGMzCew5OosXXtVfXL0eF1b1h/TgbKADawY70N/th6oJvPjqFJ7aNY54SsZpa7tx2evWmoOzNgtJknDOaQNmb/HlqmHB2MTEBO68807cc8898Pl8eM973oOLLroImzdvbtQmayLn0p1G91khgP7uAK79nY24/PVr8eKrU9ixfxJ3P3YI/V1+XLB1BbKKihV9+pff45bQkWtEe/djB7GyL4jVAyFMzaWwfqgD64a6bOPXWHtmWevopyIprOzL31Bmo2nc/9RRXHPJRjyzfRydQS/GZ5Po7fQjk9XQEfAinsrikR0n8OqJOb0aokS2wTqgYMDnsdX3d/g9RW/3HrcL55w2iP4eP05ZmR/FO5hrLBxPyXC7JISCHkQTMrwBDzwuFxQh0BHwYDaaQX+PH8ZIIJ0Br62dWFeHD0GfG0KI3Bs4cOUb1uGc0/SB/l45FsZk7oHu87pLjs61aW2+weplr1uDJ14aBaA3KDce4qpSuep315EZdAQ9UFWBV46HsaJPP04elwtnrO/BYHcA//7QfjOTomchgWwuI2U9buFYBp0Bj62x+4sHp3Hu6YNm0PbsKxMYnUnYshDjkRTSWf0hVEpnyJtry5Ffr8gNp2I8VB+yTFAOAN0hL1b0haBqArFkFn/w1i1wSfbpkbweF7KyaqsASqZlvTdW0AWXS8+O9nT60d/tx4euPRMz0XRVwZiRsXz9GYN4MTe/ohFQW6u19g9H9CyWJTM6HUnh+w8f0IPAlcUZts6QF8fGY1A0gUd36O3lTl3dbcvaGRNpWzNjA/4AukJeDE/EbW2OfvzEYZx9Sh92HprB+VsGcfhkFN0dPqSzqpmZ83lcePaVCRw4EUF3yGtmCVJZtWxHmPGZ0sFYOqMfc1nR4Pe6YW2KZgRVM9E01gx2mIH5UF8Il523Bk+8PIot63ux7XVrEPC60dvlRzwpozeX0T82Zs9ubVzZhfdcsRk+jwuvObUfT+0aszW2B1CyZzMA+H1uZGQNWVnFT544hK0b+rD7yAw6g95cx4sueNwu8wXG6omXT+IXz9qvyXe/ebP5AgoAs3NpsxMSADMj2d9d3BZpZCqB8el8gH/oRAQ/ffoYpufSeMOWIVx5wboFD3S6XEiShJ5OP3o6/WabOSEEZqMZc5DbkzMJvHBgGs+pkwD0lwCPW0IirWDjyk6887JN2GC579PSa1gwtn37dlx88cXo7e0FAFx11VV46KGH8Kd/+qeN2mTVhBCYi2dxysouBC3pTrM3YcCDN56zGpe8ZiX2H4/g2Vcm8MvnT8D/ssusjvG4XQj59WDj2HgMJybjtmEvVvQGsWlNN7Zu6MNgb9B843a7JJy+rgd7js4CAF45Grbtw8+fOY69x8Lwetz5zECXH4M9AXQGvegKeTETzeBnTx9DV8iLbeetwc+26z24LjprJZ57RR8fytp+YqAnYGtH0NPpQzqj4oKtK9AZ9OJXL52E1+1CR9CDkN9rtkcB8sGYse+e3OeCPg9SkgK/JKGvy4+5ZBY9HT6czDX8H+gNoMcyi8HK/hB8Pg80oQeH6ayCUMCHFX0dmI2mEfR7sCP3QA/5PZByD+vT1nbD7ZLQ0+FDKq3gut/ZiERaKWrHsOfwDHo6fLb2Um9+3VpIkgRF03BoZM5sI6co+WrHJ17WAzq3GxjqCSKekrGiNz/OWSIt4/DJOUzP2f8NwOxeXzg8wANPH7NVP08X9Lzbc3hG771Y5mHSGdTHCOoMevG2izYgnpIxPZdGX5ffFrgDwOWvW6sHhUGv2eZPVjSs7AtiMpyCx+XC5a9bi66QFyenE0U9BDWhd9lXNA0+jxuapj+svV43uj1u27APhVbktmF13uZBc9ypjSs68fhLo0VjWR06GbGN33fPb46YWdFSg9z2depv8TsPTuO+p/R2hmdt7MPx8Xw2JZrI6tXmqpHRVOGxtOs8PKq/uLx6IoKXD06bGVLje2gEjEZV+ZYNvdh9ZBbhWAbhWAbHS+xXoZPTpRvXuV16mzO3JGFiNoXuDh+uvmgDFFXDutwDMJ6Ssao/hHddvhlHRufgdbtw/pYhzMbSOGfTAFbkqpr0ZgLCPN/Hc1WUXrdLH67Hrz9kJ2ZT+vfUUvVudNrxed22YMzopOHzuBBNZPHEyyfx4qvTZlBt6Onwwed148BwpKiMP33qWNHPNq7swvlnDOKFV41jPWP7fb4pgA+XvnYVnrbMcwrAHJIFAL77i/0I+Nx491s2Y8v63pLHuVkZsxKouc5Dqibyg/XqU2/k/gQgCUBIuQ4UAm6XpDcJcUlwu6Sq542UJH0Gj4GeAF6byyhpmsBkJKXPRjCdRCIt4w1bV+DU1V2LUs1rTGelaZbymrcPYS+rvoBZbpcLZhmNcrZT1TPQwGBscnISQ0ND5r9XrFiBXbt2VVhi6Ty3V//SB/we8413w8pOZGUNcWF/cGxc2YWNK7swNpPQ207lbkTBgMds5Lp6IIR3XXYaZqJpTIRT+n+zSTy1exy/2WmvQrjpTZtw1il9ZuP1h347bHYgsDICMUDPwGzd0IuA343+Lr/5pnvVeWtwxrpeAMfR1+XHpa9ZheGJGMZmkgj63Wa7k6GegNke7NRVXchkNWQVDddcvBGj0wn86qWTOHV1d+6tXYPX7Ta/NwM9fgx0+3HopF4V5nJJ8LrdWDvUieEJfWBcSZIwFUnB7XJh87oe7Dkyi/UrO9EV0APCvi4/1g91orfTj9HpBFb06VUtG1d14ZVjenbqXZefhv94SJ/+qLfTjzM39OGFV6dwzcUb0dPpx1Q4ha6QF95cVYnI9Y4zqkH3HZvF+VuG0Bn0mVVOZ53aB0h6td3rTx/EnT/Wr791Qx2QFQ3Dk3GzR2XQ50F/TwCRRAanr+sxg7FURsWX/+sl81xMzKbw+e+/YP77tZv60detZ0WNgGJ6Lm1r73PhWSvMLB4A/Cr394FuP46WmK+9tysAv8+NgN+Ns0/tRywpoyvkhdvlwqa13bbMxtaNvRAC5jRKxvEOBTyIJLJweyRces5qc2ymUjpDXrMRrJFR9XvckFW1aOoe/fz4EIlnMdAdKArGukM+vP13NuojqUeNtmL2bOUXf/gSysnIKs7ZPIhduev/7FP6sH6F/gL03V/sNz/393e/bFtu95FZDPQEbPOg+n1uhHweqJo+2HElgz0BpLMq4ikZHreE150+hN1HZisu09flRziWwamru3B0LGYGVLOxtNlzuivkhdvtgjc30DMkCR0BL153ul5Ve+7WVfi3B/YgkVLQGfTilFV6W8HOkBcTs0lsO28t/F43IvEMfB69XeD6lZ3welzYsqEXLx+cRkdQn8bq3t8cxYreIObiWTPA6sq103r9GYM4Y0Mf7n70IFb1B83hMK6/9BQMT8bx0sFp9Hf5MTaTxP/8+kjJ8m5a04OLz16Jv/rWMxWPC6AH9F0hLy4+exUu2LoS//zAXtz/1DHcXyJo83lcuOjMlYinZOw8lA/YrM0L1g914m0XbUBH0Gt2aGo2bm/WMoNBPujQ75ku+D1u+L0ueL162y395VYPttxufSgPRdUDGUXToOQ6n8iyhoyi6sMhqVpRAFdLzNIZ8OL0db04fV2v+bNyAw5X4vJm8j2PLfvjcUvwez0I+Fzw+/QOcB63y1ZWQB/mxxhfUVX18smy/lzKyirkXI2ENTitNzSbbzq0ZiGJWublqcE//dM/IZVK4ZZbbgEA/PjHP8bu3btxxx13NGJzNVE1gWd2j2KwN4jT1/Xi1eEwunKNluc7GuFYGkdGozjntEH0dvmx6+A0An530eCwgF6ldXhkzrzxBf0enHVqP/p7Ajh8IoyJcKrkFyHo13skGm3GIATO2NCHDau7MRVO4qVXp+Bzu3DWpn4EfHonAiEEhnqDyMgqwtEMVuU6E4xMRrFmsBPdHT785uWTGOoNYtVAByQJ6OrwQ1U1nJyKo69bz7zFk1l0dfgxNZvE4ZE5XPTaVYglsvjtK+MY6A7ivDOGIHJliSayZgZOb+DrRlZWceTkHFb2hzDQE8AL+yfhcgGvPW0IHrcLibSsDyqbVdEZ9NqqsMan4xieiOHc01egI+jBkZNzWDvUiY6gF9FEBj6vB1lZMd8Ox6YSODkdz01OLXD6+l6csqYH49NxzEbT2LiqG7BkSybDKcxG9WphvUF9Cj2dfhweieD8M1dioCeIVEZBNJHBroPTWDkQwshk3Fy+I+hFKiPbJhM/dU03ztjQh0g8g4PD+gTA+gCXeq/GgW4/hvpC2HN4Bn6fHuwmUgo8bgmv37oCkVgGw+Mx9Pf4oSj6zen09X258+PT2+LJCrweN3xe/fhG4xnsPx5GKODB1lP69cF3uwJFA2XORlMI5drzAXpWb9erU9CEPnp2NKmPUv7azUNFb6GJlGz2ujx4IoxESkZvbuT5we4AxmcTWNEXwqvDYbhyN9jeLj9OXdMNVdXbb4UCXrx4YBKRaAbrV3Uim9UwEUmWHKC1M6QHk4mkgtPW6/MxzsylsGFVF3o6/NhzeAbhWAY+rx7UGAFeKOBBKqtAaMBQXwCb1/Xi6GgM4zMJrF/VhRW9Qew5PG3LCOq9cmUEc71zu0I+rBkMweVyY/+xWfR2+fGaTQM4MhpBMq2gryuAk5MxhAL6y0h3hw9z8QyG+kIQ0DM7s3MpHBuLYeVAEN0hP05MxnD6+l6kMwrWDHXpQ1Pkqri9Hpc5Wn93hw/7js3i8EgEm9b2YO1QFxRVRVeHH7FExgy0s7JiPn97OgNwuSRMR1J4cf8EersC2LS2ByfGo1g5EELQ7zG/K5oGvHxwEqes6sbKgQ4cHolgqC+I8ZkkwrEMztzYB7/fjRPjMQz2hXBwOIxURrUdI0XV0BH0YsuGPgz2hrD78BSicRn93QHEUlkkUzKysoZQLqPb2+mH5AI2r+9DPJmFpgkcOTlXFLgb52/rxn69o0FWwStHZ7F2sAPheAaRWAY+rztXfd1V9ZRdTpEkwOVy5WZxkeB2u8xAZLEYwYuSm97OmHe3MU/x8iRJb2frdbvMcnoWcd5Io32o0eFIUbXc0D31FbQz5Gv+66dRwdi9996LHTt24POf/zwA4Jvf/CaEEFVXU87MxGua064WQ0NdmJqavx1Mq2uXcgLtU9Z2KGc7lNHQLmVth3K2QxkN7VLWasvpckkYWOB0S/VN/lSF3/md38EzzzyD2dlZpFIpPPzww3jTm97UqM0RERERtaSG5e1WrlyJW265Be9///shyzLe+c534pxzzmnU5oiIiIhaUkMrUa+77jpcd911jdwEERERUUtrWDUlEREREc2PwRgRERGRgxiMERERETmIwRgRERGRgxiMERERETmIwRgRERGRgxiMERERETmIwRgRERGRgxiMERERETmIwRgRERGRgxiMERERETmIwRgRERGRgxiMERERETnI4/QOlONySS29/mbRLuUE2qes7VDOdiijoV3K2g7lbIcyGtqlrNWUczGOhSSEEAteCxERERHVhdWURERERA5iMEZERETkIAZjRERERA5iMEZERETkIAZjRERERA5iMEZERETkIAZjRERERA5iMEZERETkIAZjRERERA5iMEZERETkoJYJxu666y5ce+21uPbaa/HlL38ZALB9+3Zcd911eOtb34o777zT/Oyjjz6K66+/Hu94xzvw8Y9/HHNzcwCA0dFRvO9978PVV1+NP/mTP0EikSi5rX379uHmm2/GVVddhb/927+Foii233/961/HP/zDPyzbct5333144xvfiOuvvx7XX3+9bZvLray7du3CzTffjOuuuw4f/ehHMTU11ZTlNMx37Tl17TZDGVvpujXUW9ZWuW5feOEF3Hzzzbj++uvxR3/0Rzh58mRN5TQ0+z13oeVcimu3GcrZKtftjh07cNNNN+G6667Dxz72saLv7XzlNNR03YoW8PTTT4t3v/vdIpPJiGw2K97//veLBx54QGzbtk0MDw8LWZbFBz/4QfHEE0+IWCwmLr30UjE+Pi6EEOJrX/ua+L//9/8KIYT4yEc+In72s58JIYS46667xJe//OWS27v22mvFSy+9JIQQ4m/+5m/ED3/4QyGEENFoVPzN3/yNOOecc8Q3vvGNZVvOO+64QzzwwAOLXr5mK6umaWLbtm3imWeeEUII8fOf/1x89KMfbcpyVnvtOXHtNksZW+m6XUhZW+m6vfzyy8W+ffuEEEL8+Mc/Fh/72MeqLqcQrXPPXWg5G33tNkM5W+m6veKKK8TBgweFEEL8/d//vfjqV79adTmFqO+6bYnM2NDQEG699Vb4fD54vV6cdtppOHbsGDZu3Ij169fD4/Hguuuuw0MPPQRZlnH77bdj5cqVAIAtW7ZgbGwMsizj+eefx1VXXQUAuOmmm/DQQw8VbevkyZNIp9M477zzij732GOP4ZRTTsEHPvCBZV3O3bt347777sM73vEO/OVf/mXZt4JWL2s4HEY6ncbFF18MALj88svx1FNPIZvNNlU5gequPaeu3WYpY6tctwsta6tct9lsFn/+53+OrVu3FpW/mnICrXHPXYxyNvrabYZytsp1CwAPPvggNm/eDFmWMTExge7u7qrLCdR33bZEMHb66aebBT527BgefPBBSJKEoaEh8zMrVqzAxMQE+vr6cMUVVwAA0uk0/uVf/gVXXHEFwuEwOjs74fF4AOgnbWJiomhbk5OTtvVaP3fDDTfgIx/5CNxu97Iu59DQED75yU/i/vvvx+rVq3HHHXcsy7L29fUhFArhqaeeAgD8/Oc/hyzLCIfDTVVOoLprz6lrt1nK2CrXLbCwsrbKdevz+XD99dcDADRNw1133WV+rppyAq1xz12Mcjb62m2GcrbKdQsAXq8XBw4cwLZt2/Dcc8/h2muvrbqcQH3XbUsEY4aDBw/igx/8IP76r/8aGzZsKPq9JEnm32OxGD784Q9j69atuPHGGyGEqPh5Q7WfaySny/nNb34T5557LiRJwoc+9CH85je/WUhxKnKyrJIk4Rvf+Ab++Z//GTfccANisRh6e3vh9XoXWKpiCylntZy+dp0uY6tct9VaLtdtNpvFX/7lX0JRFHz0ox8tWofT1y3gfDmX6tp1spytdt1u2bIF27dvx8c//nHccsstRetY7Ou2ZYKxF154AX/8x3+Mv/iLv8CNN96IlStXYnp62vz95OQkVqxYYf79ve99L7Zu3YrPf/7zAID+/n7E43GoqgoAmJqaMiNko9Hkhz/84aL1Gp9rl3LGYjH8+7//u/lzIYSZeVpuZQUAj8eD73//+7jvvvtw4403QtM09Pb2NlU5y2mma9fpMrbSdVvOcrxuE4kEPvShD0FRFHzrW9+C1+ttquu2Gcq5VNeu0+UEWuO6zWQyePTRR83Pv+Md78CBAwcaf93W0jjOKaOjo+Kiiy4S27dvN3+WTqfFm970JnHs2DGhKIr4//6//088+OCDQlEUceONN4pvfvObRev58Ic/LH76058KIYT4x3/8R3H77beX3N61114rduzYIYQQ4m//9m/Ft7/9bdvvv/GNbzSkMWkzlFNRFHHppZeKl19+WQghxD/8wz+Iv/u7v1vsojZFWYUQ4sYbbxQ7d+4UQgjx1a9+Vdx2221NWU7DfNeeE9duM5Sx1a5bQ73ns1Wu2z/5kz8Rt912m9A0reL2Wv2eu5ByLsW12wzlFKI1rltFUcQb3/hGsXv3biGEED/5yU/EBz/4wZrKaajlum3Mq+Mi+853voNMJoMvfvGL5s/e85734Itf/CI++clPIpPJYNu2bbj66qvx6KOP4pVXXoGqqvjlL38JAHjNa16Dz3/+8/jsZz+LW2+9Fd/61rewevVq/L//9/9Kbu8rX/kKbrvtNiQSCZx11ll4//vf3zbldLvd+NrXvobbb78d6XQap5xyitk9eLmVFQBuv/12fPazn0UqlcKWLVvmzV44Vc5qOXHtNkMZW+26XUhZgda4bt/3vvfhsccew+bNm3HDDTcA0NvrfPvb3666nI3WDOVcimu3GcoJtMZ1+/nPfx533nknPvOZz0BVVaxcubLsfi7mdSsJUaLik4iIiIiWRMu0GSMiIiJajhiMERERETmIwRgRERGRgxiMERERETmIwRgRERGRgxiMEdGy8tBDD+EP//APK37mrrvusg3sSETkJAZjRNR2nnvuOSiK4vRuEBEBAFpi0Fciokq+/vWv44EHHkBvby82btwIADh69CjuuOMOJJNJTE5OYuvWrfja176Gn/zkJ9izZw++/OUvw+12Y9u2bfjKV76C559/Hqqq4qyzzsJtt92Gzs5Oh0tFRO2CmTEiammPPvooHn74Ydx33324++67EY/HAQA/+tGPcMMNN+C///u/8fDDD2NkZARPPPEE3ve+9+E1r3kN/uqv/gpXXnkl/uVf/gVutxv33HMPfvrTn2LFihX4yle+4nCpiKidMDNGRC3tmWeewZVXXmlmsm6++WZ8//vfx//+3/8bTz/9NL797W/j2LFjmJycRDKZLFr+iSeeQCwWw/bt2wEAsixjYGBgSctARO2NwRgRtTRJkmCd1c3tdgMA/tf/+l9QVRVve9vbcNlll2FsbAylZn/TNA2f/vSnsW3bNgBAIpFAJpNZmp0nIgKrKYmoxf3u7/4uHnroIUSjUWiahvvvvx8A8NRTT+ETn/gErrnmGkiShJ07d0JVVQB6wGY04H/jG9+IH/7wh8hms9A0DX/3d39XdsJ5IqJGYGaMiFratm3bcODAAdx8883o7u7G1q1bEQ6Hccstt+ATn/gEenp6EAwGccEFF2B4eBgAcPnll+NLX/oSZFnGxz/+cXzpS1/CjTfeCFVVceaZZ+LWW291uFRE1E4kUSpvT0RERERLgtWURERERA5iMEZERETkIAZjRERERA5iMEZERETkIAZjRERERA5iMEZERETkIAZjRERERA76/wHyXXRtmyFCngAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "sns.lineplot(x='date', y='deaths', data=nga_merged)\n",
    "#number of deaths peaked mid 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='state', ylabel='confirmed'>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(30,16)})\n",
    "sns.barplot(x='state', y='confirmed', data=nga_merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='state', ylabel='deaths'>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2160x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(30,16)})\n",
    "sns.barplot(x='state', y='deaths', data=nga_merged)\n",
    "#Lagos by far suffered the most fatalities. Not surprising given its massive population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_20 = nga_merged2[nga_merged2['year']==2020] # Considering data for the year 2020 only\n",
    "nga_21 = nga_merged2[nga_merged2['year']==2021] # Considering data for the year 2021 only\n",
    "#nga_22 = nga_merged2[nga_merged2['year']==2022] # Considering data for the year 2022 only\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping by confirmed cases, recovered and deaths by month for each year since the pandemic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 2020..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_confirmed_20 = nga_20['confirmed'].groupby(nga_20['month']).sum()\n",
    "nga_confirmed_20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f1cd9bd60>]"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nga_confirmed_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_rec_20 = nga_20['recovered'].groupby(nga_20['month']).sum()\n",
    "nga_rec_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f1d6f1510>]"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nga_rec_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_dea_20 = nga_20['deaths'].groupby(nga_20['month']).sum()\n",
    "nga_dea_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f1d72b670>]"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nga_dea_20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 2021..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_confirmed_21 = nga_21['confirmed'].groupby(nga_21['month']).sum()\n",
    "nga_confirmed_21.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f1d7a2170>]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nga_confirmed_21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_rec_21 = nga_21['recovered'].groupby(nga_21['month']).sum()\n",
    "nga_rec_21.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f1d8142e0>]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nga_rec_21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nga_dea_21 = nga_21['deaths'].groupby(nga_21['month']).sum()\n",
    "nga_dea_21.head\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f1d851f90>]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nga_dea_21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings:\n",
    "2020\n",
    "> Cases surged in the middle of year, spiking at the end of the year\n",
    "\n",
    ">Mosts of the deaths occurred during the middle of the year "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Findings:\n",
    "2021\n",
    "> The beginning saw high number of cases, sharply falling and surging for a while in the middle of the year before rising sharply again towards the end of the year\n",
    "\n",
    ">Mosts of the deaths occurred during the in the beginning of the year, paralleling the rise in cases. Deaths lowered throughout the first quarter until August, its peak."
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "0363b4ed3252edb5e43d70082e8fbe44fb518c325324907bd437a8594ba4f6c2"
  },
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}