population_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FIT1043 Introduction to Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Assignment 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"text-align: center\"> Yo Kogure </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <div style=\"text-align: center\"> 32134541 </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: right\"> 31st March 2021 </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will be my first assignment, Assignment 1 for FIT1043 by Yo Kogure.\n",
    "I will be performing tasks in Jupyter Notebook, and will be submitting the file as well as the printed PDF version of my codes. I will do my best to carry out the tasks smoothly and explain necessary steps ahead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to import the library **pandas**, which is an open source data analysis tool for the python programming language. The purpose of the importin this library is that we would like to use the data structure such as *DataFrame* and it's associat4ed functions such as reading from CSV files and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pd allows us to use functions of pandas with a shortcut word pd, for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a matplot library to allow us to visualize data by plotting on graph. I am importing pyplot from matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is just a magic line to make sure the graphs are showed inline in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to read the files. There are in a folder called data, which is the same folder that this document is in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccinations = pd.read_csv('Vaccinations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = pd.read_csv('2020-Population.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = pd.read_csv('2019-GDP.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have read 3 csv files accordingly and named in comprehensive names.\n",
    "To make sure they are read correctly, I will use head(or tail), which prints out data for the first 5 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>128.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-13</td>\n",
       "      <td>188.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>266.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "0  Albania      ALB  2021-01-10                 0.0                0.0   \n",
       "1  Albania      ALB  2021-01-11                 NaN                NaN   \n",
       "2  Albania      ALB  2021-01-12               128.0              128.0   \n",
       "3  Albania      ALB  2021-01-13               188.0              188.0   \n",
       "4  Albania      ALB  2021-01-14               266.0              266.0   \n",
       "\n",
       "   people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "0                      NaN                     NaN                 NaN   \n",
       "1                      NaN                     NaN                64.0   \n",
       "2                      NaN                     NaN                64.0   \n",
       "3                      NaN                    60.0                63.0   \n",
       "4                      NaN                    78.0                66.0   \n",
       "\n",
       "   total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "0                            0.00                           0.00   \n",
       "1                             NaN                            NaN   \n",
       "2                            0.00                           0.00   \n",
       "3                            0.01                           0.01   \n",
       "4                            0.01                           0.01   \n",
       "\n",
       "   people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                                  NaN                             NaN   \n",
       "1                                  NaN                            22.0   \n",
       "2                                  NaN                            22.0   \n",
       "3                                  NaN                            22.0   \n",
       "4                                  NaN                            23.0   \n",
       "\n",
       "          vaccines         source_name  \\\n",
       "0  Pfizer/BioNTech  Ministry of Health   \n",
       "1  Pfizer/BioNTech  Ministry of Health   \n",
       "2  Pfizer/BioNTech  Ministry of Health   \n",
       "3  Pfizer/BioNTech  Ministry of Health   \n",
       "4  Pfizer/BioNTech  Ministry of Health   \n",
       "\n",
       "                                      source_website  \n",
       "0  https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "1  https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "2  https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "3  https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "4  https://shendetesia.gov.al/covid19-ministria-e...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4146, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shape tells us that vaccinations has 4146 rows and 15 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'iso_code', 'date', 'total_vaccinations',\n",
       "       'people_vaccinated', 'people_fully_vaccinated',\n",
       "       'daily_vaccinations_raw', 'daily_vaccinations',\n",
       "       'total_vaccinations_per_hundred', 'people_vaccinated_per_hundred',\n",
       "       'people_fully_vaccinated_per_hundred', 'daily_vaccinations_per_million',\n",
       "       'vaccines', 'source_name', 'source_website'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".columns shows us basic info about each columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I will be doing similar operations for the other 2 files, to make sure they are read correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 68</th>\n",
       "      <th>Unnamed: 69</th>\n",
       "      <th>Unnamed: 70</th>\n",
       "      <th>Unnamed: 71</th>\n",
       "      <th>Unnamed: 72</th>\n",
       "      <th>Unnamed: 73</th>\n",
       "      <th>Unnamed: 74</th>\n",
       "      <th>Unnamed: 75</th>\n",
       "      <th>Unnamed: 76</th>\n",
       "      <th>Unnamed: 77</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Nations</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Population Division</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0 Unnamed: 1 Unnamed: 2 Unnamed: 3 Unnamed: 4 Unnamed: 5  \\\n",
       "0                  NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1                  NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2                  NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "3       United Nations        NaN        NaN        NaN        NaN        NaN   \n",
       "4  Population Division        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "  Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9  ... Unnamed: 68 Unnamed: 69  \\\n",
       "0        NaN        NaN        NaN        NaN  ...         NaN         NaN   \n",
       "1        NaN        NaN        NaN        NaN  ...         NaN         NaN   \n",
       "2        NaN        NaN        NaN        NaN  ...         NaN         NaN   \n",
       "3        NaN        NaN        NaN        NaN  ...         NaN         NaN   \n",
       "4        NaN        NaN        NaN        NaN  ...         NaN         NaN   \n",
       "\n",
       "  Unnamed: 70 Unnamed: 71 Unnamed: 72 Unnamed: 73 Unnamed: 74 Unnamed: 75  \\\n",
       "0         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4         NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "\n",
       "  Unnamed: 76 Unnamed: 77  \n",
       "0         NaN         NaN  \n",
       "1         NaN         NaN  \n",
       "2         NaN         NaN  \n",
       "3         NaN         NaN  \n",
       "4         NaN         NaN  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because I found a lot of NaN(Not a Number), I will be doing .tails() to show the bottom 5 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 68</th>\n",
       "      <th>Unnamed: 69</th>\n",
       "      <th>Unnamed: 70</th>\n",
       "      <th>Unnamed: 71</th>\n",
       "      <th>Unnamed: 72</th>\n",
       "      <th>Unnamed: 73</th>\n",
       "      <th>Unnamed: 74</th>\n",
       "      <th>Unnamed: 75</th>\n",
       "      <th>Unnamed: 76</th>\n",
       "      <th>Unnamed: 77</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>285</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Bermuda</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>286</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>13 733</td>\n",
       "      <td>14 078</td>\n",
       "      <td>14 445</td>\n",
       "      <td>...</td>\n",
       "      <td>34 539</td>\n",
       "      <td>34 922</td>\n",
       "      <td>35 297</td>\n",
       "      <td>35 664</td>\n",
       "      <td>36 027</td>\n",
       "      <td>36 383</td>\n",
       "      <td>36 732</td>\n",
       "      <td>37 075</td>\n",
       "      <td>37 411</td>\n",
       "      <td>37 742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>287</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Greenland</td>\n",
       "      <td>26</td>\n",
       "      <td>304</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>288</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>2</td>\n",
       "      <td>666</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>289</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>35</td>\n",
       "      <td>840</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>158 804</td>\n",
       "      <td>160 872</td>\n",
       "      <td>163 266</td>\n",
       "      <td>...</td>\n",
       "      <td>311 584</td>\n",
       "      <td>314 044</td>\n",
       "      <td>316 401</td>\n",
       "      <td>318 673</td>\n",
       "      <td>320 878</td>\n",
       "      <td>323 016</td>\n",
       "      <td>325 085</td>\n",
       "      <td>327 096</td>\n",
       "      <td>329 065</td>\n",
       "      <td>331 003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 Unnamed: 1                 Unnamed: 2 Unnamed: 3 Unnamed: 4  \\\n",
       "300        285  Estimates                    Bermuda         14         60   \n",
       "301        286  Estimates                     Canada        NaN        124   \n",
       "302        287  Estimates                  Greenland         26        304   \n",
       "303        288  Estimates  Saint Pierre and Miquelon          2        666   \n",
       "304        289  Estimates   United States of America         35        840   \n",
       "\n",
       "       Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9  ...  \\\n",
       "300  Country/Area        918         37         38         38  ...   \n",
       "301  Country/Area        918     13 733     14 078     14 445  ...   \n",
       "302  Country/Area        918         23         23         24  ...   \n",
       "303  Country/Area        918          5          5          5  ...   \n",
       "304  Country/Area        918    158 804    160 872    163 266  ...   \n",
       "\n",
       "    Unnamed: 68 Unnamed: 69 Unnamed: 70 Unnamed: 71 Unnamed: 72 Unnamed: 73  \\\n",
       "300          65          65          64          64          64          63   \n",
       "301      34 539      34 922      35 297      35 664      36 027      36 383   \n",
       "302          57          56          56          56          56          56   \n",
       "303           6           6           6           6           6           6   \n",
       "304     311 584     314 044     316 401     318 673     320 878     323 016   \n",
       "\n",
       "    Unnamed: 74 Unnamed: 75 Unnamed: 76 Unnamed: 77  \n",
       "300          63          63          63          62  \n",
       "301      36 732      37 075      37 411      37 742  \n",
       "302          56          57          57          57  \n",
       "303           6           6           6           6  \n",
       "304     325 085     327 096     329 065     331 003  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305, 78)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "306 rows and 78 columns, is a lot so I will not list .columns or .rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Unnamed: 0</th>\n",
       "      <th>Gross domestic product 2019</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>.. Not available.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>TON</td>\n",
       "      <td>196</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tonga</td>\n",
       "      <td>450</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>MNE</td>\n",
       "      <td>156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>5,495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>CRI</td>\n",
       "      <td>78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>61,774</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>IRN</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iran, Islamic Rep.</td>\n",
       "      <td>445,345</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>VIR</td>\n",
       "      <td>164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin Islands (U.S.)</td>\n",
       "      <td>3,855</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>IRL</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>388,699</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>COG</td>\n",
       "      <td>142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Congo, Rep.</td>\n",
       "      <td>10,821</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>MDA</td>\n",
       "      <td>139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Moldova</td>\n",
       "      <td>11,955</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>KNA</td>\n",
       "      <td>189</td>\n",
       "      <td>NaN</td>\n",
       "      <td>St. Kitts and Nevis</td>\n",
       "      <td>1,051</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 Gross domestic product 2019  Unnamed: 2             Unnamed: 3  \\\n",
       "239        NaN         .. Not available.           NaN                    NaN   \n",
       "199        TON                         196         NaN                  Tonga   \n",
       "159        MNE                         156         NaN             Montenegro   \n",
       "81         CRI                          78         NaN             Costa Rica   \n",
       "31         IRN                          28         NaN     Iran, Islamic Rep.   \n",
       "167        VIR                         164         NaN  Virgin Islands (U.S.)   \n",
       "35         IRL                          32         NaN                Ireland   \n",
       "145        COG                         142         NaN            Congo, Rep.   \n",
       "142        MDA                         139         NaN                Moldova   \n",
       "192        KNA                         189         NaN    St. Kitts and Nevis   \n",
       "\n",
       "    Unnamed: 4 Unnamed: 5  \n",
       "239        NaN        NaN  \n",
       "199        450        NaN  \n",
       "159      5,495        NaN  \n",
       "81      61,774        NaN  \n",
       "31     445,345        NaN  \n",
       "167      3,855        NaN  \n",
       "35     388,699        NaN  \n",
       "145     10,821        NaN  \n",
       "142     11,955          g  \n",
       "192      1,051        NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.sample(10) # gives sample for the first 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Unnamed: 0</th>\n",
       "      <th>Gross domestic product 2019</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>.. Not available.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Note: Rankings include only those economies wi...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>NaN</td>\n",
       "      <td>a. Based on data from official statistics of U...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GDP data source: http://data.worldbank.org/dat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>NaN</td>\n",
       "      <td>GDP projections:  http://data.worldbank.org/da...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                        Gross domestic product 2019  Unnamed: 2  \\\n",
       "239        NaN                                .. Not available.           NaN   \n",
       "240        NaN  Note: Rankings include only those economies wi...         NaN   \n",
       "241        NaN  a. Based on data from official statistics of U...         NaN   \n",
       "242        NaN  GDP data source: http://data.worldbank.org/dat...         NaN   \n",
       "243        NaN  GDP projections:  http://data.worldbank.org/da...         NaN   \n",
       "\n",
       "    Unnamed: 3 Unnamed: 4 Unnamed: 5  \n",
       "239        NaN        NaN        NaN  \n",
       "240        NaN        NaN        NaN  \n",
       "241        NaN        NaN        NaN  \n",
       "242        NaN        NaN        NaN  \n",
       "243        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(244, 6)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it is only 6 columns, I will be printing them out. Looks like they are read nicely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Gross domestic product 2019', 'Unnamed: 2', 'Unnamed: 3',\n",
       "       'Unnamed: 4', 'Unnamed: 5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dtype, data types are object which means that for all of these 3 csv file, data are stored in String format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I can conclude that 3 files are read correctly, moving forward to next. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wrangling the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will be folllowing the instructions from the speficiations worksheets to wrangle the data. It will be less Markdowns, but some of them are self-explanatory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Later on, I will apply casting to change type from a string to integer, on population and GDP to find GDP per capita later on in the assignment. I will use astype()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Pfizer/BioNTech</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4143</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4144</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1312.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4146 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  people_fully_vaccinated  daily_vaccinations           vaccines\n",
       "0      Albania                      NaN                 NaN    Pfizer/BioNTech\n",
       "1      Albania                      NaN                64.0    Pfizer/BioNTech\n",
       "2      Albania                      NaN                64.0    Pfizer/BioNTech\n",
       "3      Albania                      NaN                63.0    Pfizer/BioNTech\n",
       "4      Albania                      NaN                66.0    Pfizer/BioNTech\n",
       "...        ...                      ...                 ...                ...\n",
       "4141  Zimbabwe                      NaN               328.0  Sinopharm/Beijing\n",
       "4142  Zimbabwe                      NaN               328.0  Sinopharm/Beijing\n",
       "4143  Zimbabwe                      NaN               328.0  Sinopharm/Beijing\n",
       "4144  Zimbabwe                      NaN               808.0  Sinopharm/Beijing\n",
       "4145  Zimbabwe                      NaN              1312.0  Sinopharm/Beijing\n",
       "\n",
       "[4146 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc_subset = vaccinations[['country', 'people_fully_vaccinated', 'daily_vaccinations','vaccines']]\n",
    "\n",
    "#vc will stand short for vaccinations.\n",
    "vc_subset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A subset with necessary columns has been created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare to aggregate. **total_vaccine** is found by the sum of daily_vaccinations for each **country** like Albania.\n",
    "I will be using \"vc\" which stands for vaccines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>611.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69070.0</td>\n",
       "      <td>Sputnik V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1672.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>267061.0</td>\n",
       "      <td>748593.0</td>\n",
       "      <td>Sputnik V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2187849.0</td>\n",
       "      <td>4849929.0</td>\n",
       "      <td>Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>669105.0</td>\n",
       "      <td>18212968.0</td>\n",
       "      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>United States</td>\n",
       "      <td>20607261.0</td>\n",
       "      <td>62329417.0</td>\n",
       "      <td>Moderna, Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Wales</td>\n",
       "      <td>59279.0</td>\n",
       "      <td>891714.0</td>\n",
       "      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3432.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  people_fully_vaccinated  daily_vaccinations  \\\n",
       "0                 Albania                    611.0              2336.0   \n",
       "1                 Algeria                      NaN             69070.0   \n",
       "2                 Andorra                      NaN              1541.0   \n",
       "3                Anguilla                      NaN              1672.0   \n",
       "4               Argentina                 267061.0            748593.0   \n",
       "..                    ...                      ...                 ...   \n",
       "102  United Arab Emirates                2187849.0           4849929.0   \n",
       "103        United Kingdom                 669105.0          18212968.0   \n",
       "104         United States               20607261.0          62329417.0   \n",
       "105                 Wales                  59279.0            891714.0   \n",
       "106              Zimbabwe                      NaN              3432.0   \n",
       "\n",
       "                                              vaccines  \n",
       "0                                      Pfizer/BioNTech  \n",
       "1                                            Sputnik V  \n",
       "2                                      Pfizer/BioNTech  \n",
       "3                                   Oxford/AstraZeneca  \n",
       "4                                            Sputnik V  \n",
       "..                                                 ...  \n",
       "102  Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...  \n",
       "103                Oxford/AstraZeneca, Pfizer/BioNTech  \n",
       "104                           Moderna, Pfizer/BioNTech  \n",
       "105                Oxford/AstraZeneca, Pfizer/BioNTech  \n",
       "106                                  Sinopharm/Beijing  \n",
       "\n",
       "[107 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc_groupbycountry = vc_subset.groupby(['country'], as_index=False).agg({'people_fully_vaccinated': 'max','daily_vaccinations': 'sum', 'vaccines': 'first'})\n",
    "vc_groupbycountry #as_ndex=False prevents the first column to be treated as an index.\n",
    "#people_fully_vaccinated is in cumulative form, which means that I only have to consider the largest data for each country to find the total people who have completed fully.\n",
    "#daily_vaccinations, I will be finding sum of it and then renaming it later as total_vaccinations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now there are no duplicates, and values has been summed up. For vaccines I took the first value as a company.\n",
    "rename daily_vaciinations to total_vaccinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>611.0</td>\n",
       "      <td>2336.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69070.0</td>\n",
       "      <td>Sputnik V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1672.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>267061.0</td>\n",
       "      <td>748593.0</td>\n",
       "      <td>Sputnik V</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2187849.0</td>\n",
       "      <td>4849929.0</td>\n",
       "      <td>Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>669105.0</td>\n",
       "      <td>18212968.0</td>\n",
       "      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>United States</td>\n",
       "      <td>20607261.0</td>\n",
       "      <td>62329417.0</td>\n",
       "      <td>Moderna, Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Wales</td>\n",
       "      <td>59279.0</td>\n",
       "      <td>891714.0</td>\n",
       "      <td>Oxford/AstraZeneca, Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3432.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  country  people_fully_vaccinated  total_vaccinations  \\\n",
       "0                 Albania                    611.0              2336.0   \n",
       "1                 Algeria                      NaN             69070.0   \n",
       "2                 Andorra                      NaN              1541.0   \n",
       "3                Anguilla                      NaN              1672.0   \n",
       "4               Argentina                 267061.0            748593.0   \n",
       "..                    ...                      ...                 ...   \n",
       "102  United Arab Emirates                2187849.0           4849929.0   \n",
       "103        United Kingdom                 669105.0          18212968.0   \n",
       "104         United States               20607261.0          62329417.0   \n",
       "105                 Wales                  59279.0            891714.0   \n",
       "106              Zimbabwe                      NaN              3432.0   \n",
       "\n",
       "                                              vaccines  \n",
       "0                                      Pfizer/BioNTech  \n",
       "1                                            Sputnik V  \n",
       "2                                      Pfizer/BioNTech  \n",
       "3                                   Oxford/AstraZeneca  \n",
       "4                                            Sputnik V  \n",
       "..                                                 ...  \n",
       "102  Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm...  \n",
       "103                Oxford/AstraZeneca, Pfizer/BioNTech  \n",
       "104                           Moderna, Pfizer/BioNTech  \n",
       "105                Oxford/AstraZeneca, Pfizer/BioNTech  \n",
       "106                                  Sinopharm/Beijing  \n",
       "\n",
       "[107 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc_groupbycountry = vc_groupbycountry.rename(columns={'daily_vaccinations':'total_vaccinations'})\n",
    "vc_groupbycountry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vc_groupbycountry.reset_index()   I used to reset index, but now no need to because of as_index becoming False\n",
    "#vc_groupbycountry.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it has been aggregated, renamed, and indexed properly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare a list of South East Asian countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will use a list instead of tuple. List is mutable while tuple is immutable.\n",
    "I might be adding new data or removing countries from the list of countries, so it has to be mutable.\n",
    "Furthermore, list is more comfortable with indexing. I can convert or merge or filter later on, by managing neatly each data for the list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_country = ['Brunei Darussalam', 'Cambodia', 'Indonesia', 'Laos', 'Malaysia', 'Myanmar', 'Philippines', 'Singapore','Thailand','Timor-Leste', 'Vietnam']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now my step is to merge the 2 lists correctly. country will be acting as a key. In terms of the operations to perform, it will be a filter or locate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Brunei Darussalam',\n",
       " 'Cambodia',\n",
       " 'Indonesia',\n",
       " 'Laos',\n",
       " 'Malaysia',\n",
       " 'Myanmar',\n",
       " 'Philippines',\n",
       " 'Singapore',\n",
       " 'Thailand',\n",
       " 'Timor-Leste',\n",
       " 'Vietnam']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "vc_sea = vc_groupbycountry[vc_groupbycountry['country'].isin(sea_country)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  people_fully_vaccinated  total_vaccinations            vaccines\n",
       "17   Cambodia                      NaN              8171.0   Sinopharm/Beijing\n",
       "47  Indonesia                 825650.0           2022788.0             Sinovac\n",
       "68    Myanmar                      NaN             82823.0  Oxford/AstraZeneca\n",
       "90  Singapore                 110000.0            329630.0     Pfizer/BioNTech"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc_sea"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It correctly outputs datas for countries in south east asian region.\n",
    "Note that people_fully_vaccinated > tota_vaccinations in Indonesia which is rare. This could be fault during data collection like the according part of spreadsheet being empty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next I will deal with GDP and then population. Merge it with sea_country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Gross domestic product 2019', 'Unnamed: 2', 'Unnamed: 3',\n",
       "       'Unnamed: 4', 'Unnamed: 5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>Gross domestic product 2019</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>FRA</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>France</td>\n",
       "      <td>2,715,518</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>GRC</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greece</td>\n",
       "      <td>209,853</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>BGD</td>\n",
       "      <td>41</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>302,571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>FIN</td>\n",
       "      <td>44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finland</td>\n",
       "      <td>268,761</td>\n",
       "      <td>NaN</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>130</th>\n",
       "      <td>BEN</td>\n",
       "      <td>127</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Benin</td>\n",
       "      <td>14,391</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ITA</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2,001,244</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>ERI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eritrea</td>\n",
       "      <td>-</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>TUV</td>\n",
       "      <td>203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuvalu</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>MNP</td>\n",
       "      <td>186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>1,323</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0 Gross domestic product 2019  Unnamed: 2  \\\n",
       "225        NaN                         NaN         NaN   \n",
       "10         FRA                           7         NaN   \n",
       "54         GRC                          51         NaN   \n",
       "44         BGD                          41         NaN   \n",
       "47         FIN                          44         NaN   \n",
       "..         ...                         ...         ...   \n",
       "130        BEN                         127         NaN   \n",
       "11         ITA                           8         NaN   \n",
       "210        ERI                         NaN         NaN   \n",
       "206        TUV                         203         NaN   \n",
       "189        MNP                         186         NaN   \n",
       "\n",
       "                   Unnamed: 3 Unnamed: 4 Unnamed: 5  \n",
       "225                       NaN        NaN        NaN  \n",
       "10                     France  2,715,518        NaN  \n",
       "54                     Greece    209,853        NaN  \n",
       "44                 Bangladesh    302,571        NaN  \n",
       "47                    Finland    268,761        NaN  \n",
       "..                        ...        ...        ...  \n",
       "130                     Benin     14,391        NaN  \n",
       "11                      Italy  2,001,244        NaN  \n",
       "210                   Eritrea       -           NaN  \n",
       "206                    Tuvalu         47        NaN  \n",
       "189  Northern Mariana Islands      1,323        NaN  \n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp.sample(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of country code, ranking, country and GDP value, we only need to deal with the last two columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = gdp.rename(columns={'Unnamed: 3':'country','Unnamed: 4':'GDP'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#After renaming, filter out the others because we only care about 2 columns.\n",
    "gdp = gdp[['country','GDP']]\n",
    "\n",
    "#Now, the csv file specifies that the value stated here are in millions of US dollars.\n",
    "#We have to convert by multiplying a million to every fields in column GDP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp['GDP'] = gdp['GDP'].fillna(0) #I replaced all NaN into 0s. to perform conversion of types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>21,427,700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>China</td>\n",
       "      <td>14,342,903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Japan</td>\n",
       "      <td>5,081,770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3,845,630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>India</td>\n",
       "      <td>2,875,142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           country         GDP\n",
       "4    United States  21,427,700\n",
       "5            China  14,342,903\n",
       "6            Japan   5,081,770\n",
       "7          Germany   3,845,630\n",
       "8            India   2,875,142\n",
       "..             ...         ...\n",
       "239            NaN           0\n",
       "240            NaN           0\n",
       "241            NaN           0\n",
       "242            NaN           0\n",
       "243            NaN           0\n",
       "\n",
       "[240 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp = gdp.iloc[4:] #delete first rows because they are invalid\n",
    "gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gdp['GDP'] = 1000000 * gdp['GDP'] \n",
    "#gdp\n",
    "\n",
    "#I was thinking of converting it to millions of dollars, but decided not to for easier visualization. We can do it later when finding\n",
    "#GDP per capita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = gdp.apply(lambda x: x.str.replace(',', ''))\n",
    "#I have referred to this page to remove commas from the GDP values.\n",
    "#https://stackoverflow.com/questions/56947333/how-to-remove-commas-from-all-the-column-in-pandas-at-once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = gdp[gdp['GDP']!='-']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp = gdp[gdp['GDP']!=' -   ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp['GDP']=gdp['GDP'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Laos</td>\n",
       "      <td>18174.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country      GDP\n",
       "120    Laos  18174.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp['country']=gdp['country'].replace(['Lao PDR'],'Laos')\n",
    "gdp.loc[gdp['country']=='Laos']\n",
    "#Replace some countries that had different way of showing a country name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now merge it with sea_country using the key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdp_sea = gdp[gdp['country'].isin(sea_country)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>GDP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1119191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>543650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>376796.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>372063.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>364702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>261921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>76086.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>27089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Laos</td>\n",
       "      <td>18174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>13469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1674.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country        GDP\n",
       "19           Indonesia  1119191.0\n",
       "25            Thailand   543650.0\n",
       "36         Philippines   376796.0\n",
       "37           Singapore   372063.0\n",
       "39            Malaysia   364702.0\n",
       "48             Vietnam   261921.0\n",
       "74             Myanmar    76086.0\n",
       "106           Cambodia    27089.0\n",
       "120               Laos    18174.0\n",
       "136  Brunei Darussalam    13469.0\n",
       "185        Timor-Leste     1674.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_sea\n",
    "#Must note! that GDP here is in a million of USD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### success!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Nations</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Population Division</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>300</th>\n",
       "      <td>285</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Bermuda</td>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>38</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>286</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Canada</td>\n",
       "      <td>NaN</td>\n",
       "      <td>124</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>13 733</td>\n",
       "      <td>14 078</td>\n",
       "      <td>14 445</td>\n",
       "      <td>...</td>\n",
       "      <td>34 539</td>\n",
       "      <td>34 922</td>\n",
       "      <td>35 297</td>\n",
       "      <td>35 664</td>\n",
       "      <td>36 027</td>\n",
       "      <td>36 383</td>\n",
       "      <td>36 732</td>\n",
       "      <td>37 075</td>\n",
       "      <td>37 411</td>\n",
       "      <td>37 742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>287</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Greenland</td>\n",
       "      <td>26</td>\n",
       "      <td>304</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>288</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>2</td>\n",
       "      <td>666</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>289</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>35</td>\n",
       "      <td>840</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>158 804</td>\n",
       "      <td>160 872</td>\n",
       "      <td>163 266</td>\n",
       "      <td>...</td>\n",
       "      <td>311 584</td>\n",
       "      <td>314 044</td>\n",
       "      <td>316 401</td>\n",
       "      <td>318 673</td>\n",
       "      <td>320 878</td>\n",
       "      <td>323 016</td>\n",
       "      <td>325 085</td>\n",
       "      <td>327 096</td>\n",
       "      <td>329 065</td>\n",
       "      <td>331 003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Unnamed: 0 Unnamed: 1                 Unnamed: 2 Unnamed: 3  \\\n",
       "0                    NaN        NaN                        NaN        NaN   \n",
       "1                    NaN        NaN                        NaN        NaN   \n",
       "2                    NaN        NaN                        NaN        NaN   \n",
       "3         United Nations        NaN                        NaN        NaN   \n",
       "4    Population Division        NaN                        NaN        NaN   \n",
       "..                   ...        ...                        ...        ...   \n",
       "300                  285  Estimates                    Bermuda         14   \n",
       "301                  286  Estimates                     Canada        NaN   \n",
       "302                  287  Estimates                  Greenland         26   \n",
       "303                  288  Estimates  Saint Pierre and Miquelon          2   \n",
       "304                  289  Estimates   United States of America         35   \n",
       "\n",
       "    Unnamed: 4    Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9  ...  \\\n",
       "0          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "1          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "2          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "3          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "4          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "..         ...           ...        ...        ...        ...        ...  ...   \n",
       "300         60  Country/Area        918         37         38         38  ...   \n",
       "301        124  Country/Area        918     13 733     14 078     14 445  ...   \n",
       "302        304  Country/Area        918         23         23         24  ...   \n",
       "303        666  Country/Area        918          5          5          5  ...   \n",
       "304        840  Country/Area        918    158 804    160 872    163 266  ...   \n",
       "\n",
       "    Unnamed: 68 Unnamed: 69 Unnamed: 70 Unnamed: 71 Unnamed: 72 Unnamed: 73  \\\n",
       "0           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "300          65          65          64          64          64          63   \n",
       "301      34 539      34 922      35 297      35 664      36 027      36 383   \n",
       "302          57          56          56          56          56          56   \n",
       "303           6           6           6           6           6           6   \n",
       "304     311 584     314 044     316 401     318 673     320 878     323 016   \n",
       "\n",
       "    Unnamed: 74 Unnamed: 75 Unnamed: 76 Unnamed: 77  \n",
       "0           NaN         NaN         NaN         NaN  \n",
       "1           NaN         NaN         NaN         NaN  \n",
       "2           NaN         NaN         NaN         NaN  \n",
       "3           NaN         NaN         NaN         NaN  \n",
       "4           NaN         NaN         NaN         NaN  \n",
       "..          ...         ...         ...         ...  \n",
       "300          63          63          63          62  \n",
       "301      36 732      37 075      37 411      37 742  \n",
       "302          56          57          57          57  \n",
       "303           6           6           6           6  \n",
       "304     325 085     327 096     329 065     331 003  \n",
       "\n",
       "[305 rows x 78 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wrangle with population at last."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(305, 78)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon inspection, I found out that the last column, Unnamed: 77 contains 2020 population and Unnamed: 76 contains 2019. Since I am dealing with GDP from 2019, I have to use corresponding data althought not up-to-date. So I am using data from 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>37 411</td>\n",
       "      <td>37 742</td>\n",
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       "      <td>287</td>\n",
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       "      <td>23</td>\n",
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       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>288</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>2</td>\n",
       "      <td>666</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>289</td>\n",
       "      <td>Estimates</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>35</td>\n",
       "      <td>840</td>\n",
       "      <td>Country/Area</td>\n",
       "      <td>918</td>\n",
       "      <td>158 804</td>\n",
       "      <td>160 872</td>\n",
       "      <td>163 266</td>\n",
       "      <td>...</td>\n",
       "      <td>311 584</td>\n",
       "      <td>314 044</td>\n",
       "      <td>316 401</td>\n",
       "      <td>318 673</td>\n",
       "      <td>320 878</td>\n",
       "      <td>323 016</td>\n",
       "      <td>325 085</td>\n",
       "      <td>327 096</td>\n",
       "      <td>329 065</td>\n",
       "      <td>331 003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Unnamed: 0 Unnamed: 1                    country Unnamed: 3  \\\n",
       "0                    NaN        NaN                        NaN        NaN   \n",
       "1                    NaN        NaN                        NaN        NaN   \n",
       "2                    NaN        NaN                        NaN        NaN   \n",
       "3         United Nations        NaN                        NaN        NaN   \n",
       "4    Population Division        NaN                        NaN        NaN   \n",
       "..                   ...        ...                        ...        ...   \n",
       "300                  285  Estimates                    Bermuda         14   \n",
       "301                  286  Estimates                     Canada        NaN   \n",
       "302                  287  Estimates                  Greenland         26   \n",
       "303                  288  Estimates  Saint Pierre and Miquelon          2   \n",
       "304                  289  Estimates   United States of America         35   \n",
       "\n",
       "    Unnamed: 4    Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9  ...  \\\n",
       "0          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "1          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "2          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "3          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "4          NaN           NaN        NaN        NaN        NaN        NaN  ...   \n",
       "..         ...           ...        ...        ...        ...        ...  ...   \n",
       "300         60  Country/Area        918         37         38         38  ...   \n",
       "301        124  Country/Area        918     13 733     14 078     14 445  ...   \n",
       "302        304  Country/Area        918         23         23         24  ...   \n",
       "303        666  Country/Area        918          5          5          5  ...   \n",
       "304        840  Country/Area        918    158 804    160 872    163 266  ...   \n",
       "\n",
       "    Unnamed: 68 Unnamed: 69 Unnamed: 70 Unnamed: 71 Unnamed: 72 Unnamed: 73  \\\n",
       "0           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "1           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "2           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "3           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "4           NaN         NaN         NaN         NaN         NaN         NaN   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "300          65          65          64          64          64          63   \n",
       "301      34 539      34 922      35 297      35 664      36 027      36 383   \n",
       "302          57          56          56          56          56          56   \n",
       "303           6           6           6           6           6           6   \n",
       "304     311 584     314 044     316 401     318 673     320 878     323 016   \n",
       "\n",
       "    Unnamed: 74 Unnamed: 75 population Unnamed: 77  \n",
       "0           NaN         NaN        NaN         NaN  \n",
       "1           NaN         NaN        NaN         NaN  \n",
       "2           NaN         NaN        NaN         NaN  \n",
       "3           NaN         NaN        NaN         NaN  \n",
       "4           NaN         NaN        NaN         NaN  \n",
       "..          ...         ...        ...         ...  \n",
       "300          63          63         63          62  \n",
       "301      36 732      37 075     37 411      37 742  \n",
       "302          56          57         57          57  \n",
       "303           6           6          6           6  \n",
       "304     325 085     327 096    329 065     331 003  \n",
       "\n",
       "[305 rows x 78 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population = population.rename(columns={'Unnamed: 76':'population','Unnamed: 2':'country'})\n",
    "population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Canada</td>\n",
       "      <td>37 411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>Greenland</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>329 065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>305 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       country population\n",
       "0                          NaN        NaN\n",
       "1                          NaN        NaN\n",
       "2                          NaN        NaN\n",
       "3                          NaN        NaN\n",
       "4                          NaN        NaN\n",
       "..                         ...        ...\n",
       "300                    Bermuda         63\n",
       "301                     Canada     37 411\n",
       "302                  Greenland         57\n",
       "303  Saint Pierre and Miquelon          6\n",
       "304   United States of America    329 065\n",
       "\n",
       "[305 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population=population[['country', 'population']]\n",
    "population #first I am dealing with sea regions, so ignoring outliers for now. It does not affect our goal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>96 462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country population\n",
       "162  Vietnam     96 462"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vietnam was named Viet nam in population, so will change it.\n",
    "#population['country']=population['country'].replace(['Viet Nam'],'Vietnam')\n",
    "#population.loc['country'] == ['Viet Nam']\n",
    "#Nice!\n",
    "population=population.replace('Viet Nam','Vietnam')\n",
    "population.loc[population['country']=='Vietnam']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Laos</td>\n",
       "      <td>7 169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country population\n",
       "155    Laos      7 169"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population['country']=population['country'].replace(['Lao People\\'s Democratic Republic'],'Laos')\n",
    "population.loc[population['country']=='Laos']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = population.dropna(subset=['population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "population = population[population['population'] != '...']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "population['population'] = population['population'].str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "population['population'] = population['population'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://stackoverflow.com/questions/43332057/pandas-strip-white-space\n",
    "#I have refered to this page to replace an empty character within string to a ''(basically deletes it)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GDP is in float and population is casted to int, because they are discrete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "population_sea = population[population['country'].isin(sea_country)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>country</th>\n",
       "      <th>population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>16487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>270626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Laos</td>\n",
       "      <td>7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>31950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>54045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>108117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>69626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>96462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  population\n",
       "152  Brunei Darussalam         433\n",
       "153           Cambodia       16487\n",
       "154          Indonesia      270626\n",
       "155               Laos        7169\n",
       "156           Malaysia       31950\n",
       "157            Myanmar       54045\n",
       "158        Philippines      108117\n",
       "159          Singapore        5804\n",
       "160           Thailand       69626\n",
       "161        Timor-Leste        1293\n",
       "162            Vietnam       96462"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_sea      #Note! that this population is in THOUSANDS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining 3 kinds of data into sea_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
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       "      <td>Brunei Darussalam</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
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       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country\n",
       "0   Brunei Darussalam\n",
       "1            Cambodia\n",
       "2           Indonesia\n",
       "3                Laos\n",
       "4            Malaysia\n",
       "5             Myanmar\n",
       "6         Philippines\n",
       "7           Singapore\n",
       "8            Thailand\n",
       "9         Timor-Leste\n",
       "10            Vietnam"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I will convert the list, sea_country to DataFrame to perform merge as I want it to.\n",
    "sea_data = pd.DataFrame({'country': sea_country})\n",
    "sea_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      country  people_fully_vaccinated  total_vaccinations            vaccines\n",
       "17   Cambodia                      NaN              8171.0   Sinopharm/Beijing\n",
       "47  Indonesia                 825650.0           2022788.0             Sinovac\n",
       "68    Myanmar                      NaN             82823.0  Oxford/AstraZeneca\n",
       "90  Singapore                 110000.0            329630.0     Pfizer/BioNTech"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country\n",
       "0   Brunei Darussalam\n",
       "1            Cambodia\n",
       "2           Indonesia\n",
       "3                Laos\n",
       "4            Malaysia\n",
       "5             Myanmar\n",
       "6         Philippines\n",
       "7           Singapore\n",
       "8            Thailand\n",
       "9         Timor-Leste\n",
       "10            Vietnam"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sea_data = vc_sea.merge(sea_data, on=['country'],how='outer')\n",
    "sea_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>16487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>270626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Laos</td>\n",
       "      <td>7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>31950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>54045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>108117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>69626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>96462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  population\n",
       "152  Brunei Darussalam         433\n",
       "153           Cambodia       16487\n",
       "154          Indonesia      270626\n",
       "155               Laos        7169\n",
       "156           Malaysia       31950\n",
       "157            Myanmar       54045\n",
       "158        Philippines      108117\n",
       "159          Singapore        5804\n",
       "160           Thailand       69626\n",
       "161        Timor-Leste        1293\n",
       "162            Vietnam       96462"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>index</th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>152</td>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153</td>\n",
       "      <td>Cambodia</td>\n",
       "      <td>16487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>270626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>155</td>\n",
       "      <td>Laos</td>\n",
       "      <td>7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>31950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>157</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>54045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>158</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>108117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>159</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>5804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>160</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>69626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>161</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>162</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>96462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index            country  population\n",
       "0     152  Brunei Darussalam         433\n",
       "1     153           Cambodia       16487\n",
       "2     154          Indonesia      270626\n",
       "3     155               Laos        7169\n",
       "4     156           Malaysia       31950\n",
       "5     157            Myanmar       54045\n",
       "6     158        Philippines      108117\n",
       "7     159          Singapore        5804\n",
       "8     160           Thailand       69626\n",
       "9     161        Timor-Leste        1293\n",
       "10    162            Vietnam       96462"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_sea.reset_index(inplace=True)\n",
    "population_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "del population_sea['index']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>16487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>270626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>31950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>54045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>108117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>69626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>96462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  population\n",
       "0   Brunei Darussalam         433\n",
       "1            Cambodia       16487\n",
       "2           Indonesia      270626\n",
       "3                Laos        7169\n",
       "4            Malaysia       31950\n",
       "5             Myanmar       54045\n",
       "6         Philippines      108117\n",
       "7           Singapore        5804\n",
       "8            Thailand       69626\n",
       "9         Timor-Leste        1293\n",
       "10            Vietnam       96462"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_sea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  people_fully_vaccinated  total_vaccinations  \\\n",
       "0   Brunei Darussalam                      NaN                 NaN   \n",
       "1            Cambodia                      NaN              8171.0   \n",
       "2           Indonesia                 825650.0           2022788.0   \n",
       "3                Laos                      NaN                 NaN   \n",
       "4            Malaysia                      NaN                 NaN   \n",
       "5             Myanmar                      NaN             82823.0   \n",
       "6         Philippines                      NaN                 NaN   \n",
       "7           Singapore                 110000.0            329630.0   \n",
       "8            Thailand                      NaN                 NaN   \n",
       "9         Timor-Leste                      NaN                 NaN   \n",
       "10            Vietnam                      NaN                 NaN   \n",
       "\n",
       "              vaccines  \n",
       "0                  NaN  \n",
       "1    Sinopharm/Beijing  \n",
       "2              Sinovac  \n",
       "3                  NaN  \n",
       "4                  NaN  \n",
       "5   Oxford/AstraZeneca  \n",
       "6                  NaN  \n",
       "7      Pfizer/BioNTech  \n",
       "8                  NaN  \n",
       "9                  NaN  \n",
       "10                 NaN  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data = pd.merge(sea_data,vc_sea,on='country',how='outer')\n",
    "sea_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_data = pd.merge(sea_data, population_sea, on=['country'] )\n",
    "#One by one I have merged carefully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>16487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "      <td>270626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>54045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>5804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  people_fully_vaccinated  total_vaccinations  \\\n",
       "0   Brunei Darussalam                      NaN                 NaN   \n",
       "1            Cambodia                      NaN              8171.0   \n",
       "2           Indonesia                 825650.0           2022788.0   \n",
       "3                Laos                      NaN                 NaN   \n",
       "4            Malaysia                      NaN                 NaN   \n",
       "5             Myanmar                      NaN             82823.0   \n",
       "6         Philippines                      NaN                 NaN   \n",
       "7           Singapore                 110000.0            329630.0   \n",
       "8            Thailand                      NaN                 NaN   \n",
       "9         Timor-Leste                      NaN                 NaN   \n",
       "10            Vietnam                      NaN                 NaN   \n",
       "\n",
       "              vaccines  population  \n",
       "0                  NaN         433  \n",
       "1    Sinopharm/Beijing       16487  \n",
       "2              Sinovac      270626  \n",
       "3                  NaN        7169  \n",
       "4                  NaN       31950  \n",
       "5   Oxford/AstraZeneca       54045  \n",
       "6                  NaN      108117  \n",
       "7      Pfizer/BioNTech        5804  \n",
       "8                  NaN       69626  \n",
       "9                  NaN        1293  \n",
       "10                 NaN       96462  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_data = pd.merge(sea_data, gdp_sea, on=['country'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433</td>\n",
       "      <td>13469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>16487</td>\n",
       "      <td>27089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "      <td>270626</td>\n",
       "      <td>1119191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7169</td>\n",
       "      <td>18174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31950</td>\n",
       "      <td>364702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>54045</td>\n",
       "      <td>76086.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108117</td>\n",
       "      <td>376796.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>5804</td>\n",
       "      <td>372063.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69626</td>\n",
       "      <td>543650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1293</td>\n",
       "      <td>1674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96462</td>\n",
       "      <td>261921.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  people_fully_vaccinated  total_vaccinations  \\\n",
       "0   Brunei Darussalam                      NaN                 NaN   \n",
       "1            Cambodia                      NaN              8171.0   \n",
       "2           Indonesia                 825650.0           2022788.0   \n",
       "3                Laos                      NaN                 NaN   \n",
       "4            Malaysia                      NaN                 NaN   \n",
       "5             Myanmar                      NaN             82823.0   \n",
       "6         Philippines                      NaN                 NaN   \n",
       "7           Singapore                 110000.0            329630.0   \n",
       "8            Thailand                      NaN                 NaN   \n",
       "9         Timor-Leste                      NaN                 NaN   \n",
       "10            Vietnam                      NaN                 NaN   \n",
       "\n",
       "              vaccines  population        GDP  \n",
       "0                  NaN         433    13469.0  \n",
       "1    Sinopharm/Beijing       16487    27089.0  \n",
       "2              Sinovac      270626  1119191.0  \n",
       "3                  NaN        7169    18174.0  \n",
       "4                  NaN       31950   364702.0  \n",
       "5   Oxford/AstraZeneca       54045    76086.0  \n",
       "6                  NaN      108117   376796.0  \n",
       "7      Pfizer/BioNTech        5804   372063.0  \n",
       "8                  NaN       69626   543650.0  \n",
       "9                  NaN        1293     1674.0  \n",
       "10                 NaN       96462   261921.0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now sea_data, a new data frame for sea countries are successfully created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since I have already appled casting, and converted data type population and GDP to integer, I can perform division to find GDP per capita directly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that:\n",
    "## Population is in thousands\n",
    "## GDP is in millions\n",
    "## perCapitaGDP will be in in million / thousand = thousands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_data['perCapitaGDP']= sea_data['GDP'] / sea_data['population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>perCapitaGDP</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433</td>\n",
       "      <td>13469.0</td>\n",
       "      <td>31.106236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>16487</td>\n",
       "      <td>27089.0</td>\n",
       "      <td>1.643052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "      <td>270626</td>\n",
       "      <td>1119191.0</td>\n",
       "      <td>4.135563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7169</td>\n",
       "      <td>18174.0</td>\n",
       "      <td>2.535082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31950</td>\n",
       "      <td>364702.0</td>\n",
       "      <td>11.414773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>54045</td>\n",
       "      <td>76086.0</td>\n",
       "      <td>1.407827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108117</td>\n",
       "      <td>376796.0</td>\n",
       "      <td>3.485076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>5804</td>\n",
       "      <td>372063.0</td>\n",
       "      <td>64.104583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69626</td>\n",
       "      <td>543650.0</td>\n",
       "      <td>7.808146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1293</td>\n",
       "      <td>1674.0</td>\n",
       "      <td>1.294664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96462</td>\n",
       "      <td>261921.0</td>\n",
       "      <td>2.715276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  people_fully_vaccinated  total_vaccinations  \\\n",
       "0   Brunei Darussalam                      NaN                 NaN   \n",
       "1            Cambodia                      NaN              8171.0   \n",
       "2           Indonesia                 825650.0           2022788.0   \n",
       "3                Laos                      NaN                 NaN   \n",
       "4            Malaysia                      NaN                 NaN   \n",
       "5             Myanmar                      NaN             82823.0   \n",
       "6         Philippines                      NaN                 NaN   \n",
       "7           Singapore                 110000.0            329630.0   \n",
       "8            Thailand                      NaN                 NaN   \n",
       "9         Timor-Leste                      NaN                 NaN   \n",
       "10            Vietnam                      NaN                 NaN   \n",
       "\n",
       "              vaccines  population        GDP  perCapitaGDP  \n",
       "0                  NaN         433    13469.0     31.106236  \n",
       "1    Sinopharm/Beijing       16487    27089.0      1.643052  \n",
       "2              Sinovac      270626  1119191.0      4.135563  \n",
       "3                  NaN        7169    18174.0      2.535082  \n",
       "4                  NaN       31950   364702.0     11.414773  \n",
       "5   Oxford/AstraZeneca       54045    76086.0      1.407827  \n",
       "6                  NaN      108117   376796.0      3.485076  \n",
       "7      Pfizer/BioNTech        5804   372063.0     64.104583  \n",
       "8                  NaN       69626   543650.0      7.808146  \n",
       "9                  NaN        1293     1674.0      1.294664  \n",
       "10                 NaN       96462   261921.0      2.715276  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, I have made a new dataframe sea_data, with 11 rows and 7 columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General Analysis on the final data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will be providing a general analysis using basic statatistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>perCapitaGDP</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
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       "      <td>31.106236</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>16487</td>\n",
       "      <td>27089.0</td>\n",
       "      <td>1.643052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "      <td>270626</td>\n",
       "      <td>1119191.0</td>\n",
       "      <td>4.135563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7169</td>\n",
       "      <td>18174.0</td>\n",
       "      <td>2.535082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31950</td>\n",
       "      <td>364702.0</td>\n",
       "      <td>11.414773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>54045</td>\n",
       "      <td>76086.0</td>\n",
       "      <td>1.407827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108117</td>\n",
       "      <td>376796.0</td>\n",
       "      <td>3.485076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>5804</td>\n",
       "      <td>372063.0</td>\n",
       "      <td>64.104583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69626</td>\n",
       "      <td>543650.0</td>\n",
       "      <td>7.808146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1293</td>\n",
       "      <td>1674.0</td>\n",
       "      <td>1.294664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96462</td>\n",
       "      <td>261921.0</td>\n",
       "      <td>2.715276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  people_fully_vaccinated  total_vaccinations  \\\n",
       "0   Brunei Darussalam                      NaN                 NaN   \n",
       "1            Cambodia                      NaN              8171.0   \n",
       "2           Indonesia                 825650.0           2022788.0   \n",
       "3                Laos                      NaN                 NaN   \n",
       "4            Malaysia                      NaN                 NaN   \n",
       "5             Myanmar                      NaN             82823.0   \n",
       "6         Philippines                      NaN                 NaN   \n",
       "7           Singapore                 110000.0            329630.0   \n",
       "8            Thailand                      NaN                 NaN   \n",
       "9         Timor-Leste                      NaN                 NaN   \n",
       "10            Vietnam                      NaN                 NaN   \n",
       "\n",
       "              vaccines  population        GDP  perCapitaGDP  \n",
       "0                  NaN         433    13469.0     31.106236  \n",
       "1    Sinopharm/Beijing       16487    27089.0      1.643052  \n",
       "2              Sinovac      270626  1119191.0      4.135563  \n",
       "3                  NaN        7169    18174.0      2.535082  \n",
       "4                  NaN       31950   364702.0     11.414773  \n",
       "5   Oxford/AstraZeneca       54045    76086.0      1.407827  \n",
       "6                  NaN      108117   376796.0      3.485076  \n",
       "7      Pfizer/BioNTech        5804   372063.0     64.104583  \n",
       "8                  NaN       69626   543650.0      7.808146  \n",
       "9                  NaN        1293     1674.0      1.294664  \n",
       "10                 NaN       96462   261921.0      2.715276  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a data frame sea_data, with 7 columns describing a characteristics of 11 countries in South East Asia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From vaccinations.csv, I can see that the data for vaccinations for every country has not been done or input yet.\n",
    "Therefore only fields regarding vacciantions for *4 rows* are filled in. Myanmar has NaN for people_fully_vaccinated, because the data is simiply not input yet by the creator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.294663573085847"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data['perCapitaGDP'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.10458304617505"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data['perCapitaGDP'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.968207133624274"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data['perCapitaGDP'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.294664\n",
       "1      1.407827\n",
       "2      1.643052\n",
       "3      2.535082\n",
       "4      2.715276\n",
       "5      3.485076\n",
       "6      4.135563\n",
       "7      7.808146\n",
       "8     11.414773\n",
       "9     31.106236\n",
       "10    64.104583\n",
       "dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data['perCapitaGDP'].mode()  #We can visually see in ascending order."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note that, perCapitaGDP is in thousand dollars per person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data ranges from 1.294663573085847 to 64.10458304617505, Singapore having the greatest GDP per capita."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brunei having the second largest GDP per capita due to its small population. Indonesia and philippines in general have small GDP given the large population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each country currently have only 1 vaccine type being used in SEA region. In case there is a case where more than 1 vaccine type is used, I will assume that it will be equally distributed to the country's population."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If, for example, Indonesia has being using 2 vaccine types of both Pfizer/BioNTech **and** Sinovac;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022788.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data.iloc[2]['total_vaccinations'] #second row with index [2] represents Indonesia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#I have referred to this page to count comma seperated values\n",
    "#https://stackoverflow.com/questions/42152910/pandas-counting-quantity-of-commas-in-character-field\n",
    "sea_data['number_of_vaccine_types'] = (sea_data['vaccines'].str.count(',') + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is under assumption that vaccine types of more than 2 are written under 'vaccines' with comma separated, e.g., 'Pfizer/BioNTech, Sinovac'.\n",
    "If number_of_vaccine_types > 1, treat ['total_vaccinations'] / number_of_vaccine_types because they are evenly distributed among population, and then add that as a data for the types of vaccines stated in the cell.\n",
    "\n",
    "But in this example, we do not really need to implement it as only 1 type of vaccines used in South East Asia countries as of now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>perCapitaGDP</th>\n",
       "      <th>number_of_vaccine_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>433</td>\n",
       "      <td>13469.0</td>\n",
       "      <td>31.106236</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>16487</td>\n",
       "      <td>27089.0</td>\n",
       "      <td>1.643052</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>825650.0</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>Sinovac</td>\n",
       "      <td>270626</td>\n",
       "      <td>1119191.0</td>\n",
       "      <td>4.135563</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7169</td>\n",
       "      <td>18174.0</td>\n",
       "      <td>2.535082</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31950</td>\n",
       "      <td>364702.0</td>\n",
       "      <td>11.414773</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>54045</td>\n",
       "      <td>76086.0</td>\n",
       "      <td>1.407827</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108117</td>\n",
       "      <td>376796.0</td>\n",
       "      <td>3.485076</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>5804</td>\n",
       "      <td>372063.0</td>\n",
       "      <td>64.104583</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69626</td>\n",
       "      <td>543650.0</td>\n",
       "      <td>7.808146</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1293</td>\n",
       "      <td>1674.0</td>\n",
       "      <td>1.294664</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96462</td>\n",
       "      <td>261921.0</td>\n",
       "      <td>2.715276</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  people_fully_vaccinated  total_vaccinations  \\\n",
       "0   Brunei Darussalam                      NaN                 NaN   \n",
       "1            Cambodia                      NaN              8171.0   \n",
       "2           Indonesia                 825650.0           2022788.0   \n",
       "3                Laos                      NaN                 NaN   \n",
       "4            Malaysia                      NaN                 NaN   \n",
       "5             Myanmar                      NaN             82823.0   \n",
       "6         Philippines                      NaN                 NaN   \n",
       "7           Singapore                 110000.0            329630.0   \n",
       "8            Thailand                      NaN                 NaN   \n",
       "9         Timor-Leste                      NaN                 NaN   \n",
       "10            Vietnam                      NaN                 NaN   \n",
       "\n",
       "              vaccines  population        GDP  perCapitaGDP  \\\n",
       "0                  NaN         433    13469.0     31.106236   \n",
       "1    Sinopharm/Beijing       16487    27089.0      1.643052   \n",
       "2              Sinovac      270626  1119191.0      4.135563   \n",
       "3                  NaN        7169    18174.0      2.535082   \n",
       "4                  NaN       31950   364702.0     11.414773   \n",
       "5   Oxford/AstraZeneca       54045    76086.0      1.407827   \n",
       "6                  NaN      108117   376796.0      3.485076   \n",
       "7      Pfizer/BioNTech        5804   372063.0     64.104583   \n",
       "8                  NaN       69626   543650.0      7.808146   \n",
       "9                  NaN        1293     1674.0      1.294664   \n",
       "10                 NaN       96462   261921.0      2.715276   \n",
       "\n",
       "    number_of_vaccine_types  \n",
       "0                       NaN  \n",
       "1                       1.0  \n",
       "2                       1.0  \n",
       "3                       NaN  \n",
       "4                       NaN  \n",
       "5                       1.0  \n",
       "6                       NaN  \n",
       "7                       1.0  \n",
       "8                       NaN  \n",
       "9                       NaN  \n",
       "10                      NaN  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now I will be visualize display the data in bar graph. Although it is one of the most simple methods, It is easy to tell which vaccines is used and how big is the difference. If I need to find relations of some sort, I will use other form, but this is a simple cumulative-data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 2 columns I need to consider are 'total_vaccinations' and 'vaccines'. people_fully_vaccinated for Myanmar has not been filled, and in addition, total_vaccinations is more reliable and comprehensive data when just considering how much vaccinations has been made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>vaccines</th>\n",
       "      <th>total_vaccinations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Oxford/AstraZeneca</td>\n",
       "      <td>82823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>329630.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>8171.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sinovac</td>\n",
       "      <td>2022788.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             vaccines  total_vaccinations\n",
       "0  Oxford/AstraZeneca             82823.0\n",
       "1     Pfizer/BioNTech            329630.0\n",
       "2   Sinopharm/Beijing              8171.0\n",
       "3             Sinovac           2022788.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sea_data_q1 = sea_data.groupby('vaccines', as_index=False)['total_vaccinations'].sum()\n",
    "sea_data_q1 #as_index = false so that I dont have to reset index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1_vaccines = sea_data_q1['vaccines']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "q1_total = sea_data_q1['total_vaccinations']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(q1_vaccines, q1_total)\n",
    "plt.xlabel('Vaccine Types')\n",
    "plt.ylabel('Total Vaccinations')\n",
    "plt.xticks(rotation=40) #I had to rotate this as labels were overlapping.\n",
    "plt.title('No. of people vaccinated for each vaccine type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The above bar graph is for 4 types of vaccines, each aligned alphabetially.*\n",
    "To give my insight into this question, Sinovac currently has dominating portion of the overall vaccines in South East Asia. This is because Indonesia is one of the largest countries in the region to apply vaccinations, and the population of Indonesia itself is the largest in South East Asia.\n",
    "\n",
    "The second most popular vaccine as of now is from Pfizer/BioNTech which is mainly distributed in Singapore. Althought its population is not the biggest, it has showed itself up in the bar graph due to its high interest in the vaccines as a country."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this question, I will need to plot a bar graph for each of the country, with side-by-side bars for population, total vaccinations, and people_fully_vaccinated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>433</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>16487</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>270626</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>825650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>7169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>31950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>54045</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>108117</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5804</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>110000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>69626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>96462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  population  total_vaccinations  people_fully_vaccinated\n",
       "0   Brunei Darussalam         433                 NaN                      NaN\n",
       "1            Cambodia       16487              8171.0                      NaN\n",
       "2           Indonesia      270626           2022788.0                 825650.0\n",
       "3                Laos        7169                 NaN                      NaN\n",
       "4            Malaysia       31950                 NaN                      NaN\n",
       "5             Myanmar       54045             82823.0                      NaN\n",
       "6         Philippines      108117                 NaN                      NaN\n",
       "7           Singapore        5804            329630.0                 110000.0\n",
       "8            Thailand       69626                 NaN                      NaN\n",
       "9         Timor-Leste        1293                 NaN                      NaN\n",
       "10            Vietnam       96462                 NaN                      NaN"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using this table for the graph:\n",
    "data_q2 = sea_data[['country','population','total_vaccinations','people_fully_vaccinated']]\n",
    "#I have extracted necessary data.\n",
    "data_q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>population</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brunei Darussalam</td>\n",
       "      <td>433</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cambodia</td>\n",
       "      <td>16487</td>\n",
       "      <td>8171.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indonesia</td>\n",
       "      <td>270626</td>\n",
       "      <td>2022788.0</td>\n",
       "      <td>825650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Laos</td>\n",
       "      <td>7169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>31950</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>54045</td>\n",
       "      <td>82823.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>108117</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>5804</td>\n",
       "      <td>329630.0</td>\n",
       "      <td>110000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>69626</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>96462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              country  population  total_vaccinations  people_fully_vaccinated\n",
       "0   Brunei Darussalam         433                 0.0                      0.0\n",
       "1            Cambodia       16487              8171.0                      0.0\n",
       "2           Indonesia      270626           2022788.0                 825650.0\n",
       "3                Laos        7169                 0.0                      0.0\n",
       "4            Malaysia       31950                 0.0                      0.0\n",
       "5             Myanmar       54045             82823.0                      0.0\n",
       "6         Philippines      108117                 0.0                      0.0\n",
       "7           Singapore        5804            329630.0                 110000.0\n",
       "8            Thailand       69626                 0.0                      0.0\n",
       "9         Timor-Leste        1293                 0.0                      0.0\n",
       "10            Vietnam       96462                 0.0                      0.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_q2.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "#Import numpy to make calculations easy. I will need numpy.arange to generate a list of range(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "w = 0.2 #a width of the graph\n",
    "bar1 = np.arange(len(data_q2))\n",
    "bar2 = [w + i for i in bar1]\n",
    "bar3 = [w + i for i in bar2]\n",
    "q2_country=data_q2['country']\n",
    "q2_population=data_q2['population']\n",
    "q2_total=data_q2['total_vaccinations']\n",
    "q2_fully=data_q2['people_fully_vaccinated']\n",
    "\n",
    "plt.bar(bar1, q2_population, w, label = 'Population in Thousands')\n",
    "plt.bar(bar2, q2_total, w, label = 'Total Vaccinations')\n",
    "plt.bar(bar3, q2_fully, w, label = 'People fully vaccinated')\n",
    "\n",
    "plt.xticks(bar1, q2_country, rotation = 80)\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('Population')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Data for each country')\n",
    "plt.show()\n",
    "\n",
    "#Upon writing this code, I have looked on this video to get the general view. https://www.youtube.com/watch?v=ZjQCPMO7LBE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the printing side by side bar graph is done, however I see some challenges here as specified in the instruction pdf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, bars for indonesia is pulling up the maximum value of the graph too much. It is too dominant that it is hard to visually see the differences in other countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be solved by setting max and min values for the y value.\n",
    "For the practical codes, I have refered to:\n",
    "https://stackoverflow.com/questions/3777861/setting-y-axis-limit-in-matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = 0.2 #a width of the graph\n",
    "bar1 = np.arange(len(data_q2))\n",
    "bar2 = [w + i for i in bar1]\n",
    "bar3 = [w + i for i in bar2]\n",
    "q2_country=data_q2['country']\n",
    "q2_population=data_q2['population']\n",
    "q2_total=data_q2['total_vaccinations']\n",
    "q2_fully=data_q2['people_fully_vaccinated']\n",
    "\n",
    "plt.bar(bar1, q2_population, w, label = 'Population in Thousands')\n",
    "plt.bar(bar2, q2_total, w, label = 'Total Vaccinations')\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,380000])\n",
    "plt.bar(bar3, q2_fully, w, label = 'People fully vaccinated')\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,150000])\n",
    "\n",
    "plt.xticks(bar1, q2_country, rotation = 80)\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('Population')\n",
    "\n",
    "plt.legend()\n",
    "plt.title('Data for each country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-scaling has been done and the first challenge is solved. However, the downside of this is that It is hard to see how much value Indonesia and Singapore has, without actually seeing the raw data. Still, it is way better to be able to compare ignoring the dominating factors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Secondly, this visualization is not perfectly intuitive. That is because there are 3 types of data represented here, and the unit for 'population'(Thousands) is different from that of 'total_vaccinations' and 'people_fully_vaccinated'(One person).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can solve this second problem by adding a label on the right as well as a different scale. I will use subplots() to make this possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = 0.2 #a width of the graph\n",
    "bar1 = np.arange(len(data_q2)) #set an array of range(len)\n",
    "bar2 = [w + i for i in bar1]\n",
    "bar3 = [w + i for i in bar2]\n",
    "q2_country=data_q2['country']\n",
    "q2_population=data_q2['population']\n",
    "q2_total=data_q2['total_vaccinations']\n",
    "q2_fully=data_q2['people_fully_vaccinated'] #set variables so that it is easy to handle\n",
    "\n",
    "fig, ax1 = plt.subplots()  #prepare a subplot\n",
    "\n",
    "ax1.bar(bar1, q2_population, w, label = 'Population in Thousands', color='b')\n",
    "ax1.set_xlabel('Countries')\n",
    "ax1.set_ylabel('Population in Thousands')\n",
    "plt.xticks(bar1, q2_country, rotation = 80)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "ax2 = ax1.twinx()  #make a twin subplot, sharing the same x axis\n",
    "ax2.bar(bar2, q2_total, w, label = 'Total Vaccinations', color='g')\n",
    "ax2.set_ylabel('Number of people')\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,380000])\n",
    "ax2.bar(bar3, q2_fully, w, label = 'People fully vaccinated', color='orange')\n",
    "axes = plt.gca()\n",
    "axes.set_ylim([0,150000])\n",
    "\n",
    "\n",
    "plt.xticks(bar1, q2_country, rotation = 80)\n",
    "\n",
    "plt.legend(loc='upper right') #make sure legend is in different location\n",
    "plt.title('Data for each country')\n",
    "plt.show()\n",
    "\n",
    "#Reference to : https://matplotlib.org/stable/gallery/subplots_axes_and_figures/two_scales.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A lot better! 3 Bars are neatly seperated into 2 parts, right and left. Scales and legends are easy to see after overcoming 2 challenges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, I would note down some possibility why this data could be misleading."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Population on the left is in Thousands, while total vaccinations and people fully vaccinated are in One. But because of that, one reader might misinterrupt after seeing the green bar(total vaccinations) slightly less than blue bar(population) in Cambodia and consider that \"Oh, almost all population of cambodia has already done vaccinations\".\n",
    "Which is not true; you can say that one in a thousand of people in Cambodia has done vaccinations instead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Because of having data of only 4 countries out of 11 who has started vaccination process, It is not really easy to find correlations or relations to perform an actual analysis on these data. Maybe the good idea is to wait until the data is updated to collect and perform the graphing again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The first version of this bar graph, having one label on the left was very misleading, as we can not tell whether it is in thousands or not without looking carefully at the legend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the final question, I will be getting the non-aggregated data from 'Vaccination.csv'. I will extract data related to singapore and plot line graph daily_vaccinations against day. After that I will be comparing it to the cumulative line graph of that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>128.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.0</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-13</td>\n",
       "      <td>188.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>ALB</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>266.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://shendetesia.gov.al/covid19-ministria-e...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2021-02-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://twitter.com/MoHCCZim/status/1364676207...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2021-02-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://twitter.com/MoHCCZim/status/1364676207...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4143</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>1314.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>328.0</td>\n",
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       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://twitter.com/MoHCCZim/status/1364676207...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4144</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>4041.0</td>\n",
       "      <td>4041.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2727.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://twitter.com/MoHCCZim/status/1364676207...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4145</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>7872.0</td>\n",
       "      <td>7872.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3831.0</td>\n",
       "      <td>1312.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Sinopharm/Beijing</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://twitter.com/MoHCCZim/status/1364676207...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4146 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "0      Albania      ALB  2021-01-10                 0.0                0.0   \n",
       "1      Albania      ALB  2021-01-11                 NaN                NaN   \n",
       "2      Albania      ALB  2021-01-12               128.0              128.0   \n",
       "3      Albania      ALB  2021-01-13               188.0              188.0   \n",
       "4      Albania      ALB  2021-01-14               266.0              266.0   \n",
       "...        ...      ...         ...                 ...                ...   \n",
       "4141  Zimbabwe      ZWE  2021-02-20                 NaN                NaN   \n",
       "4142  Zimbabwe      ZWE  2021-02-21                 NaN                NaN   \n",
       "4143  Zimbabwe      ZWE  2021-02-22              1314.0             1314.0   \n",
       "4144  Zimbabwe      ZWE  2021-02-23              4041.0             4041.0   \n",
       "4145  Zimbabwe      ZWE  2021-02-24              7872.0             7872.0   \n",
       "\n",
       "      people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "0                         NaN                     NaN                 NaN   \n",
       "1                         NaN                     NaN                64.0   \n",
       "2                         NaN                     NaN                64.0   \n",
       "3                         NaN                    60.0                63.0   \n",
       "4                         NaN                    78.0                66.0   \n",
       "...                       ...                     ...                 ...   \n",
       "4141                      NaN                     NaN               328.0   \n",
       "4142                      NaN                     NaN               328.0   \n",
       "4143                      NaN                     NaN               328.0   \n",
       "4144                      NaN                  2727.0               808.0   \n",
       "4145                      NaN                  3831.0              1312.0   \n",
       "\n",
       "      total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "0                               0.00                           0.00   \n",
       "1                                NaN                            NaN   \n",
       "2                               0.00                           0.00   \n",
       "3                               0.01                           0.01   \n",
       "4                               0.01                           0.01   \n",
       "...                              ...                            ...   \n",
       "4141                             NaN                            NaN   \n",
       "4142                             NaN                            NaN   \n",
       "4143                            0.01                           0.01   \n",
       "4144                            0.03                           0.03   \n",
       "4145                            0.05                           0.05   \n",
       "\n",
       "      people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                                     NaN                             NaN   \n",
       "1                                     NaN                            22.0   \n",
       "2                                     NaN                            22.0   \n",
       "3                                     NaN                            22.0   \n",
       "4                                     NaN                            23.0   \n",
       "...                                   ...                             ...   \n",
       "4141                                  NaN                            22.0   \n",
       "4142                                  NaN                            22.0   \n",
       "4143                                  NaN                            22.0   \n",
       "4144                                  NaN                            54.0   \n",
       "4145                                  NaN                            88.0   \n",
       "\n",
       "               vaccines         source_name  \\\n",
       "0       Pfizer/BioNTech  Ministry of Health   \n",
       "1       Pfizer/BioNTech  Ministry of Health   \n",
       "2       Pfizer/BioNTech  Ministry of Health   \n",
       "3       Pfizer/BioNTech  Ministry of Health   \n",
       "4       Pfizer/BioNTech  Ministry of Health   \n",
       "...                 ...                 ...   \n",
       "4141  Sinopharm/Beijing  Ministry of Health   \n",
       "4142  Sinopharm/Beijing  Ministry of Health   \n",
       "4143  Sinopharm/Beijing  Ministry of Health   \n",
       "4144  Sinopharm/Beijing  Ministry of Health   \n",
       "4145  Sinopharm/Beijing  Ministry of Health   \n",
       "\n",
       "                                         source_website  \n",
       "0     https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "1     https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "2     https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "3     https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "4     https://shendetesia.gov.al/covid19-ministria-e...  \n",
       "...                                                 ...  \n",
       "4141  https://twitter.com/MoHCCZim/status/1364676207...  \n",
       "4142  https://twitter.com/MoHCCZim/status/1364676207...  \n",
       "4143  https://twitter.com/MoHCCZim/status/1364676207...  \n",
       "4144  https://twitter.com/MoHCCZim/status/1364676207...  \n",
       "4145  https://twitter.com/MoHCCZim/status/1364676207...  \n",
       "\n",
       "[4146 rows x 15 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Only extracting singapore:\n",
    "\n",
    "q3_data = vaccinations[vaccinations['country'] == 'Singapore']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, 15)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3467</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>3400.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.moh.gov.sg/news-highlights/details...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>479.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.moh.gov.sg/news-highlights/details...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3469</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>2021-01-13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4090.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>699.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.moh.gov.sg/news-highlights/details...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3470</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4520.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>773.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.moh.gov.sg/news-highlights/details...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4735.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>809.0</td>\n",
       "      <td>Pfizer/BioNTech</td>\n",
       "      <td>Ministry of Health</td>\n",
       "      <td>https://www.moh.gov.sg/news-highlights/details...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "3467  Singapore      SGP  2021-01-11              3400.0             3400.0   \n",
       "3468  Singapore      SGP  2021-01-12              6200.0             6200.0   \n",
       "3469  Singapore      SGP  2021-01-13                 NaN                NaN   \n",
       "3470  Singapore      SGP  2021-01-14                 NaN                NaN   \n",
       "3471  Singapore      SGP  2021-01-15                 NaN                NaN   \n",
       "\n",
       "      people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "3467                      NaN                     NaN                 NaN   \n",
       "3468                      NaN                  2800.0              2800.0   \n",
       "3469                      NaN                     NaN              4090.0   \n",
       "3470                      NaN                     NaN              4520.0   \n",
       "3471                      NaN                     NaN              4735.0   \n",
       "\n",
       "      total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "3467                            0.06                           0.06   \n",
       "3468                            0.11                           0.11   \n",
       "3469                             NaN                            NaN   \n",
       "3470                             NaN                            NaN   \n",
       "3471                             NaN                            NaN   \n",
       "\n",
       "      people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "3467                                  NaN                             NaN   \n",
       "3468                                  NaN                           479.0   \n",
       "3469                                  NaN                           699.0   \n",
       "3470                                  NaN                           773.0   \n",
       "3471                                  NaN                           809.0   \n",
       "\n",
       "             vaccines         source_name  \\\n",
       "3467  Pfizer/BioNTech  Ministry of Health   \n",
       "3468  Pfizer/BioNTech  Ministry of Health   \n",
       "3469  Pfizer/BioNTech  Ministry of Health   \n",
       "3470  Pfizer/BioNTech  Ministry of Health   \n",
       "3471  Pfizer/BioNTech  Ministry of Health   \n",
       "\n",
       "                                         source_website  \n",
       "3467  https://www.moh.gov.sg/news-highlights/details...  \n",
       "3468  https://www.moh.gov.sg/news-highlights/details...  \n",
       "3469  https://www.moh.gov.sg/news-highlights/details...  \n",
       "3470  https://www.moh.gov.sg/news-highlights/details...  \n",
       "3471  https://www.moh.gov.sg/news-highlights/details...  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "q3_data = q3_data[['date', 'daily_vaccinations']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now useful data has been extracted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-13</td>\n",
       "      <td>4090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>4520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>4735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-01-16</td>\n",
       "      <td>4864.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-01-17</td>\n",
       "      <td>4950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-01-18</td>\n",
       "      <td>5011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-01-19</td>\n",
       "      <td>5380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-01-20</td>\n",
       "      <td>5380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-01-21</td>\n",
       "      <td>5380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-01-22</td>\n",
       "      <td>5380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-01-23</td>\n",
       "      <td>6127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-01-24</td>\n",
       "      <td>6874.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-01-25</td>\n",
       "      <td>7621.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-01-26</td>\n",
       "      <td>8369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021-01-27</td>\n",
       "      <td>9116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021-01-28</td>\n",
       "      <td>9847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2021-01-29</td>\n",
       "      <td>10579.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-01-30</td>\n",
       "      <td>10563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-01-31</td>\n",
       "      <td>10547.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021-02-01</td>\n",
       "      <td>10885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2021-02-02</td>\n",
       "      <td>11223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2021-02-03</td>\n",
       "      <td>11046.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2021-02-04</td>\n",
       "      <td>10886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>10725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2021-02-06</td>\n",
       "      <td>10564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>10404.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-02-08</td>\n",
       "      <td>9889.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021-02-09</td>\n",
       "      <td>9375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2021-02-10</td>\n",
       "      <td>9375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2021-02-11</td>\n",
       "      <td>9893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>10411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2021-02-13</td>\n",
       "      <td>10929.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>11446.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2021-02-15</td>\n",
       "      <td>11964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>12482.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2021-02-17</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2021-02-18</td>\n",
       "      <td>13000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  daily_vaccinations\n",
       "0   2021-01-11                 0.0\n",
       "1   2021-01-12              2800.0\n",
       "2   2021-01-13              4090.0\n",
       "3   2021-01-14              4520.0\n",
       "4   2021-01-15              4735.0\n",
       "5   2021-01-16              4864.0\n",
       "6   2021-01-17              4950.0\n",
       "7   2021-01-18              5011.0\n",
       "8   2021-01-19              5380.0\n",
       "9   2021-01-20              5380.0\n",
       "10  2021-01-21              5380.0\n",
       "11  2021-01-22              5380.0\n",
       "12  2021-01-23              6127.0\n",
       "13  2021-01-24              6874.0\n",
       "14  2021-01-25              7621.0\n",
       "15  2021-01-26              8369.0\n",
       "16  2021-01-27              9116.0\n",
       "17  2021-01-28              9847.0\n",
       "18  2021-01-29             10579.0\n",
       "19  2021-01-30             10563.0\n",
       "20  2021-01-31             10547.0\n",
       "21  2021-02-01             10885.0\n",
       "22  2021-02-02             11223.0\n",
       "23  2021-02-03             11046.0\n",
       "24  2021-02-04             10886.0\n",
       "25  2021-02-05             10725.0\n",
       "26  2021-02-06             10564.0\n",
       "27  2021-02-07             10404.0\n",
       "28  2021-02-08              9889.0\n",
       "29  2021-02-09              9375.0\n",
       "30  2021-02-10              9375.0\n",
       "31  2021-02-11              9893.0\n",
       "32  2021-02-12             10411.0\n",
       "33  2021-02-13             10929.0\n",
       "34  2021-02-14             11446.0\n",
       "35  2021-02-15             11964.0\n",
       "36  2021-02-16             12482.0\n",
       "37  2021-02-17             13000.0\n",
       "38  2021-02-18             13000.0"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3_data = q3_data.reset_index(drop=True) #Wrangled the data nice and neat.\n",
    "q3_data = q3_data.fillna(0) #replaced NaN with 0 so that It can be dealt as numbers\n",
    "q3_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First plot a graph of relative frequency, before cumulative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q3_date = q3_data['date']\n",
    "#q3_index = np.arange(len(q3_data)), Index was not really useful here\n",
    "q3_vc = q3_data['daily_vaccinations']\n",
    "\n",
    "plt.plot(q3_date, q3_vc)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Daily Vaccinations')\n",
    "plt.xticks(fontsize=8, rotation=90) #Adjust fontsize as well as rotation to make xtick visible and not overlap\n",
    "plt.title('Daily number of vaccinations in Singapore')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Second part of Question 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add cumulative column to q3_data to plot cumulative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-13</td>\n",
       "      <td>4090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>4520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>4735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  daily_vaccinations\n",
       "0  2021-01-11                 0.0\n",
       "1  2021-01-12              2800.0\n",
       "2  2021-01-13              4090.0\n",
       "3  2021-01-14              4520.0\n",
       "4  2021-01-15              4735.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "q3_data['total_vaccinations'] = q3_data['daily_vaccinations'].cumsum()\n",
    "#This will be a total number of vaccinations up to that day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-12</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-13</td>\n",
       "      <td>4090.0</td>\n",
       "      <td>6890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-14</td>\n",
       "      <td>4520.0</td>\n",
       "      <td>11410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-15</td>\n",
       "      <td>4735.0</td>\n",
       "      <td>16145.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  daily_vaccinations  total_vaccinations\n",
       "0  2021-01-11                 0.0                 0.0\n",
       "1  2021-01-12              2800.0              2800.0\n",
       "2  2021-01-13              4090.0              6890.0\n",
       "3  2021-01-14              4520.0             11410.0\n",
       "4  2021-01-15              4735.0             16145.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now will be printing using this column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "q3_date = q3_data['date']\n",
    "#q3_index = np.arange(len(q3_data)), I dont need index for Question 3\n",
    "q3_total = q3_data['total_vaccinations']\n",
    "\n",
    "plt.plot(q3_date, q3_total)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Daily Vaccinations')\n",
    "plt.xticks(fontsize=8, rotation=90) #Adjust fontsize as well as rotation to make xtick visible and not overlap\n",
    "plt.title('Cumulative sum of vaccinations in Singapore')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Nice!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first graph, showing daily vaccinations everyday is useful to use when comparing number of new people each day, and for example judge which day has lowest/highest number of people taking the vaccine."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But that is not really helpful in determining which day number has just passed a certain milestone."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is where the second graph, total_vaccinations comes in. Since it is cumulative and shows sum of people up to a certain day, you can see when it reached a certain total number of people. In addition to that, **daily relative number of vaccinations is responsible for the gradient of the cumulative graph**. You can also determine which day had decrease or increase in the rate of increase(daily new vaccinations), from the cumulative graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This concludes the end of Assignment 1, for FIT1043.\n",
    "\n",
    "I personally found this assignment really interesting. I did not think that I could perform this much of data-manipulations inside Python; I thought required professional knowledges of softwares such as R or mySQL.\n",
    "This was like a puzzle, mix of python knowledges from FIT1045 and my Excel skills from secondary school and very fun to interpret questions in my own way.\n",
    "\n",
    "Althought it took 20 hours and I am very tired, I am very motivated for future weeks and the next one. Because I searched things I didn't know *on my will*, I probably wouldn't forget the techniques I learned here.\n",
    "\n",
    "\n",
    "I have imported necessary libraries, read the files, wrangled the data, answered 3 questions, showed all my working, formatted the entire file organized, and described as many steps as possible.\n",
    "\n",
    "Thank you for marking!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yo Kogure\n",
    "#### 32134541"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End of Assignment 1"
   ]
  }
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