ebola_scrub_old.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook to scrub ebola dataset for Project 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-1-e775c3c9271a>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-1-e775c3c9271a>\"\u001b[1;36m, line \u001b[1;32m4\u001b[0m\n\u001b[1;33m    import\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import \n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "# File to Load (Remember to Change These)\n",
    "ebola_data_to_load = \"Data/ebola_2014_2016_clean.csv\"\n",
    "centroid_data_to_load = \"Data/clean_country_centroids_az8.csv\"\n",
    "pop_data_to_load = \"Data/pop_data_2009-2019.csv\"\n",
    "\n",
    "# Read Population Data from \"Estimates\" sheet\n",
    "ebola_data = pd.read_csv(ebola_data_to_load, encoding=\"ISO-8859-1\")\n",
    "centroid_data = pd.read_csv(centroid_data_to_load)\n",
    "pop_data = pd.read_csv(pop_data_to_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-08-29 00:00:00\n",
      "2016-03-23 00:00:00\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Date</th>\n",
       "      <th>No_of_suspected_cases</th>\n",
       "      <th>No_of_probable_cases</th>\n",
       "      <th>No_of_confirmed_cases</th>\n",
       "      <th>No_of_confirmed,_probable_and_suspected_cases</th>\n",
       "      <th>No_of_suspected_deaths</th>\n",
       "      <th>No_of_probable_deaths</th>\n",
       "      <th>No_of_confirmed_deaths</th>\n",
       "      <th>No_of_confirmed,_probable_and_suspected_deaths</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>25.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>648.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>54.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2014-08-29</td>\n",
       "      <td>382.0</td>\n",
       "      <td>674.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>1378.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>694.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>78.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1146.0</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2480</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>5636.0</td>\n",
       "      <td>1879.0</td>\n",
       "      <td>3151.0</td>\n",
       "      <td>10666.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4806.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2481</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2482</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2483</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2484</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2016-03-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2485 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Country       Date  No_of_suspected_cases  No_of_probable_cases  No_of_confirmed_cases  No_of_confirmed,_probable_and_suspected_cases  No_of_suspected_deaths  No_of_probable_deaths  No_of_confirmed_deaths  No_of_confirmed,_probable_and_suspected_deaths  year\n",
       "0                       Guinea 2014-08-29                   25.0                 141.0                  482.0                                          648.0                     2.0                  141.0                   287.0                                           430.0  2014\n",
       "1                      Nigeria 2014-08-29                    3.0                   1.0                   15.0                                           19.0                     0.0                    1.0                     6.0                                             7.0  2014\n",
       "2                 Sierra Leone 2014-08-29                   54.0                  37.0                  935.0                                         1026.0                     8.0                   34.0                   380.0                                           422.0  2014\n",
       "3                      Liberia 2014-08-29                  382.0                 674.0                  322.0                                         1378.0                   168.0                  301.0                   225.0                                           694.0  2014\n",
       "4                 Sierra Leone 2014-09-05                   78.0                  37.0                 1146.0                                         1261.0                    11.0                   37.0                   443.0                                           491.0  2014\n",
       "...                        ...        ...                    ...                   ...                    ...                                            ...                     ...                    ...                     ...                                             ...   ...\n",
       "2480                   Liberia 2016-03-23                 5636.0                1879.0                 3151.0                                        10666.0                     NaN                    NaN                     NaN                                          4806.0  2016\n",
       "2481                     Italy 2016-03-23                    0.0                   0.0                    1.0                                            1.0                     NaN                    NaN                     NaN                                             0.0  2016\n",
       "2482                   Liberia 2016-03-23                    0.0                   3.0                    2.0                                            5.0                     NaN                    3.0                     1.0                                             4.0  2016\n",
       "2483                   Nigeria 2016-03-23                    0.0                   1.0                   19.0                                           20.0                     0.0                    1.0                     7.0                                             8.0  2016\n",
       "2484  United States of America 2016-03-23                    0.0                   0.0                    4.0                                            4.0                     0.0                    0.0                     1.0                                             1.0  2016\n",
       "\n",
       "[2485 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove spaces and '.' in column names\n",
    "ebola_data.columns = [c.replace(' ', '_') for c in ebola_data.columns]\n",
    "ebola_data.columns = [c.replace('.', '') for c in ebola_data.columns]\n",
    "\n",
    "# Add year column to dataframe\n",
    "ebola_data['year'] = pd.DatetimeIndex(ebola_data['Date']).year\n",
    "\n",
    "ebola_data['Date'] = pd.to_datetime(ebola_data['Date'], infer_datetime_format=True)\n",
    "\n",
    "# Find earliest and latest dates\n",
    "print(ebola_data.Date.min())\n",
    "print(ebola_data.Date.max())\n",
    "ebola_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Liberia                     365\n",
       "Guinea                      259\n",
       "Sierra Leone                259\n",
       "Nigeria                     255\n",
       "Senegal                     254\n",
       "United States of America    245\n",
       "Mali                        243\n",
       "Spain                       243\n",
       "United Kingdom              221\n",
       "Italy                       141\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many occurrences of each country in dataset?\n",
    "ebola_data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>year</th>\n",
       "      <th>No_of_confirmed_cases</th>\n",
       "      <th>No_of_confirmed_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>2014</td>\n",
       "      <td>2397.0</td>\n",
       "      <td>1433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>2015</td>\n",
       "      <td>3351.0</td>\n",
       "      <td>2083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guinea</td>\n",
       "      <td>2016</td>\n",
       "      <td>3351.0</td>\n",
       "      <td>2083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2014</td>\n",
       "      <td>3110.0</td>\n",
       "      <td>1241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2015</td>\n",
       "      <td>3153.0</td>\n",
       "      <td>3858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Liberia</td>\n",
       "      <td>2016</td>\n",
       "      <td>3151.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mali</td>\n",
       "      <td>2014</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mali</td>\n",
       "      <td>2015</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mali</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2014</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2015</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2016</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>2014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Senegal</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>2014</td>\n",
       "      <td>7354.0</td>\n",
       "      <td>2392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>2015</td>\n",
       "      <td>8704.0</td>\n",
       "      <td>3589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>2016</td>\n",
       "      <td>8704.0</td>\n",
       "      <td>3589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2014</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2016</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Country  year  No_of_confirmed_cases  No_of_confirmed_deaths\n",
       "0                     Guinea  2014                 2397.0                  1433.0\n",
       "1                     Guinea  2015                 3351.0                  2083.0\n",
       "2                     Guinea  2016                 3351.0                  2083.0\n",
       "3                      Italy  2015                    1.0                     0.0\n",
       "4                      Italy  2016                    1.0                     NaN\n",
       "5                    Liberia  2014                 3110.0                  1241.0\n",
       "6                    Liberia  2015                 3153.0                  3858.0\n",
       "7                    Liberia  2016                 3151.0                     3.0\n",
       "8                       Mali  2014                    7.0                     5.0\n",
       "9                       Mali  2015                    7.0                     NaN\n",
       "10                      Mali  2016                    7.0                     NaN\n",
       "11                   Nigeria  2014                   19.0                     7.0\n",
       "12                   Nigeria  2015                   19.0                     7.0\n",
       "13                   Nigeria  2016                   19.0                     7.0\n",
       "14                   Senegal  2014                    1.0                     0.0\n",
       "15                   Senegal  2015                    1.0                     0.0\n",
       "16                   Senegal  2016                    1.0                     0.0\n",
       "17              Sierra Leone  2014                 7354.0                  2392.0\n",
       "18              Sierra Leone  2015                 8704.0                  3589.0\n",
       "19              Sierra Leone  2016                 8704.0                  3589.0\n",
       "20                     Spain  2014                    1.0                     0.0\n",
       "21                     Spain  2015                    1.0                     0.0\n",
       "22                     Spain  2016                    1.0                     0.0\n",
       "23            United Kingdom  2015                    1.0                     0.0\n",
       "24            United Kingdom  2016                    1.0                     NaN\n",
       "25  United States of America  2014                    4.0                     1.0\n",
       "26  United States of America  2015                    4.0                     1.0\n",
       "27  United States of America  2016                    4.0                     1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by country and year\n",
    "ebola_1 = ebola_data.groupby([\"Country\", \"year\"], as_index = False).agg(\n",
    "    {\n",
    "        'No_of_confirmed_cases':'max',    \n",
    "        'No_of_confirmed_deaths': 'max'\n",
    "    }\n",
    ")\n",
    "\n",
    "ebola_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge longitude and latitude data by country\n",
    "# Get name, Longitude, and Latitude from centroid_data\n",
    "centroid_data_1 = centroid_data[['name', 'Longitude', 'Latitude']]\n",
    "centroid_data_1['name'].replace(['United States'], 'United States of America', inplace=True)\n",
    "centroid_data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge centroid lon, lat \n",
    "ebola_2 = pd.merge(ebola_1, centroid_data_1, left_on='Country', right_on='name', how = 'left')\n",
    "ebola_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get subset of pop_data, Country, 2014, 2015, 2016\n",
    "pop_data_1 = pop_data[['Country', '2014', '2015', '2016']]\n",
    "pop_data_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge Population Data\n",
    "# Merge centroid lon, lat \n",
    "ebola_3 = pd.merge(ebola_2, pop_data_1, on='Country', how = 'left')\n",
    "ebola_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add population column\n",
    "ebola_3['population'] = ebola_3['2014'] * 1000\n",
    "ebola_3['population'] = np.where(ebola_3['year'] == 2015, ebola_3['2015'], ebola_3['population'] * 1000)\n",
    "ebola_3['population'] = np.where(ebola_3['year'] == 2016, ebola_3['2016'], ebola_3['population'] * 1000)\n",
    "ebola_3['Pandemic'] = \"Ebola\"\n",
    "# Replace Nan values in No_of_confirmed_deaths column to 0\n",
    "ebola_3['No_of_confirmed_deaths'].fillna(0, inplace=True)\n",
    "ebola_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get columns in following order: Pandemic, Country, Year, Cases, Deaths, lon,lat, population\n",
    "ebola_4 = ebola_3[['Pandemic', 'Country', 'year', 'No_of_confirmed_cases', 'No_of_confirmed_deaths', 'Longitude', 'Latitude', 'population']]\n",
    "ebola_5 = ebola_4.rename(columns={\"year\": \"Year\", \"Longitude\": \"Lon\", \"Latitude\": \"Lat\", \"No_of_confirmed_cases\": \"Cases\", \"No_of_confirmed_deaths\": \"Deaths\"  })\n",
    "ebola_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save scrubbed file to .csv\n",
    "ebola_5.to_csv(r'Data/ebola_data.csv', index = False, header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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