data-cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"font-family:'Fira Sans Condensed';font-size:18px;\">\n",
    "\n",
    "<h2>Covid-19 - Data Cleaning</h2>\n",
    "        \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "# Full display rows option\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'\n",
    "death_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'\n",
    "recovered_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed = pd.read_csv(confirmed_url) #.fillna(\"N/A\", inplace = True)\n",
    "deaths = pd.read_csv(death_url)\n",
    "recovered = pd.read_csv(recovered_url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>8/31/20</th>\n",
       "      <th>9/1/20</th>\n",
       "      <th>9/2/20</th>\n",
       "      <th>9/3/20</th>\n",
       "      <th>9/4/20</th>\n",
       "      <th>9/5/20</th>\n",
       "      <th>9/6/20</th>\n",
       "      <th>9/7/20</th>\n",
       "      <th>9/8/20</th>\n",
       "      <th>9/9/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>38165</td>\n",
       "      <td>38196</td>\n",
       "      <td>38243</td>\n",
       "      <td>38288</td>\n",
       "      <td>38304</td>\n",
       "      <td>38324</td>\n",
       "      <td>38398</td>\n",
       "      <td>38494</td>\n",
       "      <td>38520</td>\n",
       "      <td>38544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9513</td>\n",
       "      <td>9606</td>\n",
       "      <td>9728</td>\n",
       "      <td>9844</td>\n",
       "      <td>9967</td>\n",
       "      <td>10102</td>\n",
       "      <td>10255</td>\n",
       "      <td>10406</td>\n",
       "      <td>10553</td>\n",
       "      <td>10704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>44494</td>\n",
       "      <td>44833</td>\n",
       "      <td>45158</td>\n",
       "      <td>45469</td>\n",
       "      <td>45773</td>\n",
       "      <td>46071</td>\n",
       "      <td>46364</td>\n",
       "      <td>46653</td>\n",
       "      <td>46938</td>\n",
       "      <td>47216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1176</td>\n",
       "      <td>1184</td>\n",
       "      <td>1199</td>\n",
       "      <td>1199</td>\n",
       "      <td>1215</td>\n",
       "      <td>1215</td>\n",
       "      <td>1215</td>\n",
       "      <td>1261</td>\n",
       "      <td>1261</td>\n",
       "      <td>1301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2654</td>\n",
       "      <td>2729</td>\n",
       "      <td>2777</td>\n",
       "      <td>2805</td>\n",
       "      <td>2876</td>\n",
       "      <td>2935</td>\n",
       "      <td>2965</td>\n",
       "      <td>2981</td>\n",
       "      <td>3033</td>\n",
       "      <td>3092</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 236 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  8/31/20  9/1/20  9/2/20  9/3/20  \\\n",
       "0        0        0        0        0  ...    38165   38196   38243   38288   \n",
       "1        0        0        0        0  ...     9513    9606    9728    9844   \n",
       "2        0        0        0        0  ...    44494   44833   45158   45469   \n",
       "3        0        0        0        0  ...     1176    1184    1199    1199   \n",
       "4        0        0        0        0  ...     2654    2729    2777    2805   \n",
       "\n",
       "   9/4/20  9/5/20  9/6/20  9/7/20  9/8/20  9/9/20  \n",
       "0   38304   38324   38398   38494   38520   38544  \n",
       "1    9967   10102   10255   10406   10553   10704  \n",
       "2   45773   46071   46364   46653   46938   47216  \n",
       "3    1215    1215    1215    1261    1261    1301  \n",
       "4    2876    2935    2965    2981    3033    3092  \n",
       "\n",
       "[5 rows x 236 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(266, 236)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(266, 236)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(253, 236)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n",
       "       '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       ...\n",
       "       '8/31/20', '9/1/20', '9/2/20', '9/3/20', '9/4/20', '9/5/20', '9/6/20',\n",
       "       '9/7/20', '9/8/20', '9/9/20'],\n",
       "      dtype='object', length=236)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_confirmed.head()\n",
    "confirmed.shape\n",
    "deaths.shape\n",
    "# df_recovered.head()\n",
    "recovered.shape\n",
    "confirmed.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "confirmed['Province/State'].fillna('N/A', inplace=True)\n",
    "deaths['Province/State'].fillna('N/A', inplace=True)\n",
    "recovered['Province/State'].fillna('N/A', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking inconsistency as the Recovered dataset doesn't have the same shape as \n",
    "# Death and Confirmed datasets\n",
    "def dataframe_difference(df1, df2, which=None):\n",
    "    \"\"\"Find rows which are different between two DataFrames.\"\"\"\n",
    "    comparison_df = df1.merge(df2,\n",
    "                              indicator=True,\n",
    "                              how='outer')\n",
    "    if which is None:\n",
    "        diff_df = comparison_df[comparison_df['_merge'] != 'both']\n",
    "    else:\n",
    "        diff_df = comparison_df[comparison_df['_merge'] == which]\n",
    "    return diff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_confirm = ['Province/State', 'Country/Region'] # used as index \n",
    "inner_confirm_rec = dataframe_difference(confirmed[cols_to_confirm],\n",
    "                                         recovered[cols_to_confirm],\n",
    "                                         which='both')\n",
    "inner_death_rec = dataframe_difference(deaths[cols_to_confirm],\n",
    "                                       recovered[cols_to_confirm],\n",
    "                                       which='both')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>N/A</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State      Country/Region _merge\n",
       "261            N/A  West Bank and Gaza   both\n",
       "262            N/A      Western Sahara   both\n",
       "263            N/A               Yemen   both\n",
       "264            N/A              Zambia   both\n",
       "265            N/A            Zimbabwe   both"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_confirm_rec.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Melt \n",
    "# recovered = recovered.melt(id_vars=['Province/State', \n",
    "#                                        'Country/Region', \n",
    "#                                        'Lat', \n",
    "#                                        'Long'])\n",
    "# recovered.head()\n",
    "# # Rename\n",
    "# recovered.rename(columns={'variable': 'Date', \n",
    "#                           'value': 'Confirmed',\n",
    "#                           'Province/State': 'Province_State',\n",
    "#                           'Country/Region': 'Country_Region'\n",
    "#                          }, \n",
    "#                  inplace=True)\n",
    "# recovered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch and Reshape\n",
    "def melt_data(df, case_type):\n",
    "    df_melt = df.melt(id_vars=['Province/State', \n",
    "                                 'Country/Region', \n",
    "                                 'Lat', \n",
    "                                 'Long'])\n",
    "    \n",
    "    df_melt.rename(columns={'variable': 'Date', \n",
    "                              'value': case_type,\n",
    "                              'Province/State': 'Province_State',\n",
    "                              'Country/Region': 'Country_Region'\n",
    "                             }, \n",
    "                     inplace=True)\n",
    "    \n",
    "    return df_melt\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_confirmed = melt_data(confirmed, \"Confirmed\")\n",
    "df_recovered = melt_data(recovered, \"Recovered\")\n",
    "df_deaths = melt_data(deaths, \"Death\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61707</th>\n",
       "      <td>N/A</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>31.952200</td>\n",
       "      <td>35.233200</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>27919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61708</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>24.215500</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61709</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61710</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>13112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61711</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>7429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61712 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Province_State      Country_Region        Lat       Long     Date  \\\n",
       "0                N/A         Afghanistan  33.939110  67.709953  1/22/20   \n",
       "1                N/A             Albania  41.153300  20.168300  1/22/20   \n",
       "2                N/A             Algeria  28.033900   1.659600  1/22/20   \n",
       "3                N/A             Andorra  42.506300   1.521800  1/22/20   \n",
       "4                N/A              Angola -11.202700  17.873900  1/22/20   \n",
       "...              ...                 ...        ...        ...      ...   \n",
       "61707            N/A  West Bank and Gaza  31.952200  35.233200   9/9/20   \n",
       "61708            N/A      Western Sahara  24.215500 -12.885800   9/9/20   \n",
       "61709            N/A               Yemen  15.552727  48.516388   9/9/20   \n",
       "61710            N/A              Zambia -13.133897  27.849332   9/9/20   \n",
       "61711            N/A            Zimbabwe -19.015438  29.154857   9/9/20   \n",
       "\n",
       "       Confirmed  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "...          ...  \n",
       "61707      27919  \n",
       "61708         10  \n",
       "61709       1999  \n",
       "61710      13112  \n",
       "61711       7429  \n",
       "\n",
       "[61712 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
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       "      <td>N/A</td>\n",
       "      <td>Afghanistan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
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       "      <th>2</th>\n",
       "      <td>N/A</td>\n",
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       "      <th>3</th>\n",
       "      <td>N/A</td>\n",
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       "      <td>42.506300</td>\n",
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       "      <td>1/22/20</td>\n",
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       "      <th>4</th>\n",
       "      <td>N/A</td>\n",
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       "      <th>61707</th>\n",
       "      <td>N/A</td>\n",
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       "      <td>31.952200</td>\n",
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       "      <td>192</td>\n",
       "    </tr>\n",
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       "      <th>61708</th>\n",
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       "      <td>24.215500</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>1</td>\n",
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       "      <td>N/A</td>\n",
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       "      <td>48.516388</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>576</td>\n",
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       "    <tr>\n",
       "      <th>61710</th>\n",
       "      <td>N/A</td>\n",
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       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61711</th>\n",
       "      <td>N/A</td>\n",
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       "      <td>222</td>\n",
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       "  </tbody>\n",
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       "<p>61712 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Province_State      Country_Region        Lat       Long     Date  Death\n",
       "0                N/A         Afghanistan  33.939110  67.709953  1/22/20      0\n",
       "1                N/A             Albania  41.153300  20.168300  1/22/20      0\n",
       "2                N/A             Algeria  28.033900   1.659600  1/22/20      0\n",
       "3                N/A             Andorra  42.506300   1.521800  1/22/20      0\n",
       "4                N/A              Angola -11.202700  17.873900  1/22/20      0\n",
       "...              ...                 ...        ...        ...      ...    ...\n",
       "61707            N/A  West Bank and Gaza  31.952200  35.233200   9/9/20    192\n",
       "61708            N/A      Western Sahara  24.215500 -12.885800   9/9/20      1\n",
       "61709            N/A               Yemen  15.552727  48.516388   9/9/20    576\n",
       "61710            N/A              Zambia -13.133897  27.849332   9/9/20    300\n",
       "61711            N/A            Zimbabwe -19.015438  29.154857   9/9/20    222\n",
       "\n",
       "[61712 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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       "      <th>58691</th>\n",
       "      <td>N/A</td>\n",
       "      <td>West Bank and Gaza</td>\n",
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       "      <td>9/9/20</td>\n",
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       "      <td>27.849332</td>\n",
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       "      <td>5542</td>\n",
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      ],
      "text/plain": [
       "      Province_State      Country_Region        Lat       Long     Date  \\\n",
       "0                N/A         Afghanistan  33.939110  67.709953  1/22/20   \n",
       "1                N/A             Albania  41.153300  20.168300  1/22/20   \n",
       "2                N/A             Algeria  28.033900   1.659600  1/22/20   \n",
       "3                N/A             Andorra  42.506300   1.521800  1/22/20   \n",
       "4                N/A              Angola -11.202700  17.873900  1/22/20   \n",
       "...              ...                 ...        ...        ...      ...   \n",
       "58691            N/A  West Bank and Gaza  31.952200  35.233200   9/9/20   \n",
       "58692            N/A      Western Sahara  24.215500 -12.885800   9/9/20   \n",
       "58693            N/A               Yemen  15.552727  48.516388   9/9/20   \n",
       "58694            N/A              Zambia -13.133897  27.849332   9/9/20   \n",
       "58695            N/A            Zimbabwe -19.015438  29.154857   9/9/20   \n",
       "\n",
       "       Recovered  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  \n",
       "...          ...  \n",
       "58691      18466  \n",
       "58692          8  \n",
       "58693       1209  \n",
       "58694      11839  \n",
       "58695       5542  \n",
       "\n",
       "[58696 rows x 6 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm datasets have the same shape \n",
    "df_confirmed\n",
    "df_deaths\n",
    "df_recovered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Join Confirmed & Deaths \n",
    "df = df_confirmed.join(df_deaths['Death'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "  Province_State Country_Region       Lat       Long     Date  Confirmed  \\\n",
       "0            N/A    Afghanistan  33.93911  67.709953  1/22/20          0   \n",
       "1            N/A        Albania  41.15330  20.168300  1/22/20          0   \n",
       "2            N/A        Algeria  28.03390   1.659600  1/22/20          0   \n",
       "3            N/A        Andorra  42.50630   1.521800  1/22/20          0   \n",
       "4            N/A         Angola -11.20270  17.873900  1/22/20          0   \n",
       "\n",
       "   Death  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
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     },
     "execution_count": 55,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.merge(df_recovered, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1/22/20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51963</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>6.423800</td>\n",
       "      <td>-66.589700</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>45318</td>\n",
       "      <td>56751</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51964</th>\n",
       "      <td>N/A</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>31.952200</td>\n",
       "      <td>35.233200</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>18466</td>\n",
       "      <td>27919</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51965</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>24.215500</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51966</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>-13.133897</td>\n",
       "      <td>27.849332</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>11839</td>\n",
       "      <td>13112</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51967</th>\n",
       "      <td>N/A</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>-19.015438</td>\n",
       "      <td>29.154857</td>\n",
       "      <td>9/9/20</td>\n",
       "      <td>5542</td>\n",
       "      <td>7429</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51968 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Province_State      Country_Region        Lat       Long     Date  \\\n",
       "0                N/A         Afghanistan  33.939110  67.709953  1/22/20   \n",
       "1                N/A             Albania  41.153300  20.168300  1/22/20   \n",
       "2                N/A             Algeria  28.033900   1.659600  1/22/20   \n",
       "3                N/A             Andorra  42.506300   1.521800  1/22/20   \n",
       "4                N/A              Angola -11.202700  17.873900  1/22/20   \n",
       "...              ...                 ...        ...        ...      ...   \n",
       "51963            N/A           Venezuela   6.423800 -66.589700   9/9/20   \n",
       "51964            N/A  West Bank and Gaza  31.952200  35.233200   9/9/20   \n",
       "51965            N/A      Western Sahara  24.215500 -12.885800   9/9/20   \n",
       "51966            N/A              Zambia -13.133897  27.849332   9/9/20   \n",
       "51967            N/A            Zimbabwe -19.015438  29.154857   9/9/20   \n",
       "\n",
       "       Recovered  Confirmed  Death  \n",
       "0              0          0      0  \n",
       "1              0          0      0  \n",
       "2              0          0      0  \n",
       "3              0          0      0  \n",
       "4              0          0      0  \n",
       "...          ...        ...    ...  \n",
       "51963      45318      56751    452  \n",
       "51964      18466      27919    192  \n",
       "51965          8         10      1  \n",
       "51966      11839      13112    300  \n",
       "51967       5542       7429    222  \n",
       "\n",
       "[51968 rows x 8 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percentage</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Total, Percentage]\n",
       "Index: []"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fiigure out the percentage of missing values \n",
    "total = new_df.isnull().sum().sort_values(ascending=False) \n",
    "percentage = (new_df.isnull().sum() / new_df.isnull().count()).sort_values(ascending=False)\n",
    "missing_data = pd.concat([total, percentage], axis=1, keys=['Total', 'Percentage'])\n",
    "missing_data[missing_data['Total'] > 0] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"font-family:'Fira Sans Condensed';font-size:18px;\">\n",
    "\n",
    "<h2> Wrap-up </h2> \n",
    "    As given by the last table and at the time of performing this analysis, the merge data shape is different from the source shape after melting. The Confirmed and Death datasets are having the same shape which is different from the Recovered. Some observation in the first 2 datasets are missing the last one. Similarly, the Recovered set has some observation which is not present in the first 2 datasets. This is due to the inconsistency of the Recovered set pulled from the official <a href='https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series'>GitHub of JHU</a>. I will later try to figure out the problem by comparing this dataset to other sources.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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