scholarship_project.ipynb

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    "# Project: Investigate GAPMINDER WORLD\n",
    "\n",
    "# Table of Contents\n",
    "> Introduction\n",
    "\n",
    "> Data Wrangling And Exploratory Data Analysis\n",
    "\n",
    "> Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e30f9e9",
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   "source": [
    "# INTRODUCTION\n",
    "\n",
    "\n",
    "# PROBLEM DEFINITION\n",
    "> Analysing economic situation of different countries based on Inflation, Poplation, amount of Tax generated and Investment Rate of Each countries and finding correlations in in these datasets to know if the variables are dependent on each other.\n",
    "\n",
    "> This notebook looks into using python-based analytics and data science libraries in an attempt to understand which factors are important for a country economic growth.\n",
    "\n",
    "# DATA\n",
    "> All the Data came from Gapminder World http://www.gapminder.org/data/\n",
    "> There are Four Datasets in this project. they includes\n",
    "\n",
    "> * **INFLATION:** Inflation as measured by annual growth rate of GDP implicit deflator shows the rate of price change in the economy as a whole.  The GDP implicit deflator is the ratio of GDP in current local currency  to GDP in constant local currency\n",
    "> Inflation Dataset Link https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG\n",
    "\n",
    "> * **INVESTMENT:** Gross capital formation (formally gross domestic investment) consist of outlays on addition to the fix asset of the economy plus net changes in level of inventories. Fixed asset includes  Land improvemnet{ fences, ditches, drains and so on ]; plants, machineries and equipments purchases; and the construction of roads, railways and the likes, including Schools, Hospitals, Private residential dwellings, Commercial and Industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected flunctuation in production or sales and 'work in progress'. According to the 1993 SNA, net cquisition of valuables are also considered capital formation\n",
    ">  Investment dataset link: https://data.worldbank.org/indicator/NE.GDI.TOTL.ZS\n",
    "\n",
    "> * **POPULATION:** Total Population size of each country. Population Dataset was gotten from:  http://gapm.io/dpop\n",
    "\n",
    "> * **TAX REVENUE:** Refers to the compulsory transfers to the federal government for public purposes. Certain compulsory transfers such as fines, penalties and most social security contributions are excluded. Refund and corrections of erroneously collected tax revenue are treated as negative revenue.\n",
    "> Tax Revenue dataset link https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS\n",
    "\n",
    "# FEATURES\n",
    "> The features of the data set used for this project are constant. They include\n",
    "* COUNTRY: The country that the data was gotten from\n",
    "* YEAR: The year the data was collected\n",
    "\n",
    "# QUESTIONS FOR ANALYSIS\n",
    "* Do Population size of country affect Economic Rate or Situation of that country?\n",
    "* Has Economic Rate of different Countries Increased over the year?"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a8b0bc0f",
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   "outputs": [],
   "source": [
    "# import packages to be used for this project\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b78895d",
   "metadata": {},
   "source": [
    "# DATA WRANGLING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "265dc779",
   "metadata": {},
   "outputs": [],
   "source": [
    "inflation= pd.read_csv('inflation_annual_percent.csv')\n",
    "investment= pd.read_csv('investments_percent_of_gdp.csv')\n",
    "population= pd.read_csv('population_total.csv')\n",
    "tax_revenue= pd.read_csv('tax_revenue_percent_of_gdp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ed0ef17",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
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       "      <th>1965</th>\n",
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       "      <th>...</th>\n",
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       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</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>3.11</td>\n",
       "      <td>0.789</td>\n",
       "      <td>3.29</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>−1.37</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</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>16.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.567</td>\n",
       "      <td>2.45</td>\n",
       "      <td>−2.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.07</td>\n",
       "      <td>6.52</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Angola</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>31.8</td>\n",
       "      <td>7.26</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.56</td>\n",
       "      <td>−3.52</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</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>2.31</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.289</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.564</td>\n",
       "      <td>−0.632</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.477</td>\n",
       "      <td>−0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Andorra</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>0.197</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.448</td>\n",
       "      <td>−0.084</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 61 columns</p>\n",
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       "       country 1960 1961 1962 1963 1964 1965 1966 1967 1968  ...   2010  \\\n",
       "0        Aruba  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   3.11   \n",
       "1  Afghanistan  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   16.6   \n",
       "2       Angola  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   31.8   \n",
       "3      Albania  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   2.31   \n",
       "4      Andorra  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.197   \n",
       "\n",
       "    2011   2012    2013   2014    2015  2016  2017   2018    2019  \n",
       "0  0.789   3.29       2   0.44   −1.37  1.61   NaN    NaN     NaN  \n",
       "1    7.3   4.82   0.567   2.45    −2.2   2.4  2.07   6.52     5.5  \n",
       "2   7.26   2.84    3.56  −3.52    21.8  22.6  29.1   28.1    15.1  \n",
       "3   1.04  0.289    1.55  0.564  −0.632  1.45  1.36  0.477  −0.965  \n",
       "4  0.175  0.448  −0.084  0.661   0.348  1.14  1.01   1.37    1.06  \n",
       "\n",
       "[5 rows x 61 columns]"
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     "execution_count": 3,
     "metadata": {},
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    "inflation.head()"
   ]
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       "      <td>Aruba</td>\n",
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       "      <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>28.5</td>\n",
       "      <td>26.6</td>\n",
       "      <td>23.8</td>\n",
       "      <td>23.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>21.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>16.1</td>\n",
       "      <td>16.6</td>\n",
       "      <td>19.1</td>\n",
       "      <td>14.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>11.3</td>\n",
       "      <td>8.41</td>\n",
       "      <td>5.18</td>\n",
       "      <td>6.47</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",
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       "      <th>2</th>\n",
       "      <td>Angola</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>26.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</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>31.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>24.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</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>23.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>22.2</td>\n",
       "      <td>25.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.8</td>\n",
       "      <td>NaN</td>\n",
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       "                country  1959  1960  1961  1962  1963  1964  1965  1966  1967  \\\n",
       "0                 Aruba   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1           Afghanistan  16.1  16.6  19.1  14.2  13.9  11.3  8.41  5.18  6.47   \n",
       "2                Angola   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "3               Albania   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "4  United Arab Emirates   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "   ...  2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "0  ...  28.5  26.6  23.8  23.1  21.5  20.6  20.6  21.6   NaN   NaN  \n",
       "1  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "2  ...  26.4  26.7  26.1  27.5  34.2  27.2  24.1  21.4  19.8  22.8  \n",
       "3  ...  31.4  28.3  28.1  25.7  25.8  25.2  25.1  24.1   NaN   NaN  \n",
       "4  ...  23.2  21.9  19.3  22.2  25.6  26.0  25.3  23.5  23.8   NaN  \n",
       "\n",
       "[5 rows x 62 columns]"
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>3.28M</td>\n",
       "      <td>...</td>\n",
       "      <td>76.6M</td>\n",
       "      <td>76.4M</td>\n",
       "      <td>76.3M</td>\n",
       "      <td>76.1M</td>\n",
       "      <td>76M</td>\n",
       "      <td>75.8M</td>\n",
       "      <td>75.6M</td>\n",
       "      <td>75.4M</td>\n",
       "      <td>75.2M</td>\n",
       "      <td>74.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>1.57M</td>\n",
       "      <td>...</td>\n",
       "      <td>168M</td>\n",
       "      <td>170M</td>\n",
       "      <td>172M</td>\n",
       "      <td>175M</td>\n",
       "      <td>177M</td>\n",
       "      <td>179M</td>\n",
       "      <td>182M</td>\n",
       "      <td>184M</td>\n",
       "      <td>186M</td>\n",
       "      <td>188M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>400k</td>\n",
       "      <td>402k</td>\n",
       "      <td>404k</td>\n",
       "      <td>405k</td>\n",
       "      <td>407k</td>\n",
       "      <td>409k</td>\n",
       "      <td>411k</td>\n",
       "      <td>413k</td>\n",
       "      <td>414k</td>\n",
       "      <td>...</td>\n",
       "      <td>1.33M</td>\n",
       "      <td>1.3M</td>\n",
       "      <td>1.27M</td>\n",
       "      <td>1.25M</td>\n",
       "      <td>1.22M</td>\n",
       "      <td>1.19M</td>\n",
       "      <td>1.17M</td>\n",
       "      <td>1.14M</td>\n",
       "      <td>1.11M</td>\n",
       "      <td>1.09M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "      <td>...</td>\n",
       "      <td>63k</td>\n",
       "      <td>62.9k</td>\n",
       "      <td>62.9k</td>\n",
       "      <td>62.8k</td>\n",
       "      <td>62.7k</td>\n",
       "      <td>62.7k</td>\n",
       "      <td>62.6k</td>\n",
       "      <td>62.5k</td>\n",
       "      <td>62.5k</td>\n",
       "      <td>62.4k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>40.2k</td>\n",
       "      <td>...</td>\n",
       "      <td>12.3M</td>\n",
       "      <td>12.4M</td>\n",
       "      <td>12.5M</td>\n",
       "      <td>12.5M</td>\n",
       "      <td>12.6M</td>\n",
       "      <td>12.7M</td>\n",
       "      <td>12.7M</td>\n",
       "      <td>12.8M</td>\n",
       "      <td>12.8M</td>\n",
       "      <td>12.9M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 302 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                country   1799   1800   1801   1802   1803   1804   1805  \\\n",
       "0           Afghanistan  3.28M  3.28M  3.28M  3.28M  3.28M  3.28M  3.28M   \n",
       "1                Angola  1.57M  1.57M  1.57M  1.57M  1.57M  1.57M  1.57M   \n",
       "2               Albania   400k   402k   404k   405k   407k   409k   411k   \n",
       "3               Andorra   2650   2650   2650   2650   2650   2650   2650   \n",
       "4  United Arab Emirates  40.2k  40.2k  40.2k  40.2k  40.2k  40.2k  40.2k   \n",
       "\n",
       "    1806   1807  ...   2090   2091   2092   2093   2094   2095   2096   2097  \\\n",
       "0  3.28M  3.28M  ...  76.6M  76.4M  76.3M  76.1M    76M  75.8M  75.6M  75.4M   \n",
       "1  1.57M  1.57M  ...   168M   170M   172M   175M   177M   179M   182M   184M   \n",
       "2   413k   414k  ...  1.33M   1.3M  1.27M  1.25M  1.22M  1.19M  1.17M  1.14M   \n",
       "3   2650   2650  ...    63k  62.9k  62.9k  62.8k  62.7k  62.7k  62.6k  62.5k   \n",
       "4  40.2k  40.2k  ...  12.3M  12.4M  12.5M  12.5M  12.6M  12.7M  12.7M  12.8M   \n",
       "\n",
       "    2098   2099  \n",
       "0  75.2M  74.9M  \n",
       "1   186M   188M  \n",
       "2  1.11M  1.09M  \n",
       "3  62.5k  62.4k  \n",
       "4  12.8M  12.9M  \n",
       "\n",
       "[5 rows x 302 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c02e44e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197, 302)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "238062fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>1971</th>\n",
       "      <th>1972</th>\n",
       "      <th>1973</th>\n",
       "      <th>1974</th>\n",
       "      <th>1975</th>\n",
       "      <th>1976</th>\n",
       "      <th>1977</th>\n",
       "      <th>1978</th>\n",
       "      <th>1979</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</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>8.920</td>\n",
       "      <td>7.710</td>\n",
       "      <td>7.12</td>\n",
       "      <td>6.880</td>\n",
       "      <td>7.5900</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>9.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Angola</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>16.100</td>\n",
       "      <td>14.800</td>\n",
       "      <td>14.20</td>\n",
       "      <td>13.500</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>9.7300</td>\n",
       "      <td>9.2200</td>\n",
       "      <td>9.6400</td>\n",
       "      <td>9.380</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</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>18.000</td>\n",
       "      <td>17.500</td>\n",
       "      <td>16.50</td>\n",
       "      <td>18.300</td>\n",
       "      <td>18.5000</td>\n",
       "      <td>17.6000</td>\n",
       "      <td>18.9000</td>\n",
       "      <td>18.6000</td>\n",
       "      <td>18.300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Arab Emirates</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>0.321</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>0.0665</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>0.965</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</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>12.700</td>\n",
       "      <td>13.000</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.600</td>\n",
       "      <td>12.3000</td>\n",
       "      <td>12.1000</td>\n",
       "      <td>10.9000</td>\n",
       "      <td>10.1000</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                country  1971  1972  1973  1974  1975  1976  1977  1978  1979  \\\n",
       "0           Afghanistan   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "1                Angola   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "2               Albania   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "3  United Arab Emirates   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "4             Argentina   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "   ...    2010    2011   2012    2013     2014     2015     2016     2017  \\\n",
       "0  ...   8.920   7.710   7.12   6.880   7.5900   9.5000   9.9000      NaN   \n",
       "1  ...  16.100  14.800  14.20  13.500  11.0000   9.7300   9.2200   9.6400   \n",
       "2  ...  18.000  17.500  16.50  18.300  18.5000  17.6000  18.9000  18.6000   \n",
       "3  ...   0.321   0.364   0.37   0.356   0.0577   0.0435   0.0665   0.0593   \n",
       "4  ...  12.700  13.000  12.50  12.600  12.3000  12.1000  10.9000  10.1000   \n",
       "\n",
       "     2018  2019  \n",
       "0     NaN   NaN  \n",
       "1   9.380   NaN  \n",
       "2  18.300   NaN  \n",
       "3   0.965   NaN  \n",
       "4  10.600   NaN  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc16b48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(156, 50)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50ce223f",
   "metadata": {},
   "source": [
    "# DATA CLEANING AND EXPLORATORY DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "454db129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212, 61)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop data that are more than twenty years because the data above 20 years are not important\n",
    "inflation.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0d25ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country    object\n",
       "1960       object\n",
       "1961       object\n",
       "1962       object\n",
       "1963       object\n",
       "            ...  \n",
       "2015       object\n",
       "2016       object\n",
       "2017       object\n",
       "2018       object\n",
       "2019       object\n",
       "Length: 61, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0cb64f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>1968</th>\n",
       "      <th>1969</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
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       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</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>3.11</td>\n",
       "      <td>0.789</td>\n",
       "      <td>3.29</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>−1.37</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</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>16.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.567</td>\n",
       "      <td>2.45</td>\n",
       "      <td>−2.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.07</td>\n",
       "      <td>6.52</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</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>31.8</td>\n",
       "      <td>7.26</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.56</td>\n",
       "      <td>−3.52</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</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>2.31</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.289</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.564</td>\n",
       "      <td>−0.632</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.477</td>\n",
       "      <td>−0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</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>0.197</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.448</td>\n",
       "      <td>−0.084</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            1960 1961 1962 1963 1964 1965 1966 1967 1968 1969  ...   2010  \\\n",
       "country                                                        ...          \n",
       "Aruba        NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   3.11   \n",
       "Afghanistan  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   16.6   \n",
       "Angola       NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   31.8   \n",
       "Albania      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...   2.31   \n",
       "Andorra      NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  ...  0.197   \n",
       "\n",
       "              2011   2012    2013   2014    2015  2016  2017   2018    2019  \n",
       "country                                                                      \n",
       "Aruba        0.789   3.29       2   0.44   −1.37  1.61   NaN    NaN     NaN  \n",
       "Afghanistan    7.3   4.82   0.567   2.45    −2.2   2.4  2.07   6.52     5.5  \n",
       "Angola        7.26   2.84    3.56  −3.52    21.8  22.6  29.1   28.1    15.1  \n",
       "Albania       1.04  0.289    1.55  0.564  −0.632  1.45  1.36  0.477  −0.965  \n",
       "Andorra      0.175  0.448  −0.084  0.661   0.348  1.14  1.01   1.37    1.06  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reimport the inflation and use the country column as index\n",
    "inflation= pd.read_csv('inflation_annual_percent.csv', index_col= ('country'))\n",
    "inflation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3a038ac",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>1.05</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4.53</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.95</td>\n",
       "      <td>5.95</td>\n",
       "      <td>5.07</td>\n",
       "      <td>1.73</td>\n",
       "      <td>...</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.789</td>\n",
       "      <td>3.29</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>−1.37</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>22.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>−2.16</td>\n",
       "      <td>...</td>\n",
       "      <td>16.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.567</td>\n",
       "      <td>2.45</td>\n",
       "      <td>−2.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.07</td>\n",
       "      <td>6.52</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>418</td>\n",
       "      <td>106</td>\n",
       "      <td>197</td>\n",
       "      <td>93.9</td>\n",
       "      <td>33.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>17.1</td>\n",
       "      <td>4.31</td>\n",
       "      <td>19.4</td>\n",
       "      <td>−16.8</td>\n",
       "      <td>...</td>\n",
       "      <td>31.8</td>\n",
       "      <td>7.26</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.56</td>\n",
       "      <td>−3.52</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>5.64</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.64</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.48</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.289</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.564</td>\n",
       "      <td>−0.632</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.477</td>\n",
       "      <td>−0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>28.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.448</td>\n",
       "      <td>−0.084</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1999  2000  2001  2002  2003  2004  2005  2006  2007   2008  ...  \\\n",
       "country                                                                   ...   \n",
       "Aruba        1.05  5.63  4.53   2.1  2.17  3.33  2.95  5.95  5.07   1.73  ...   \n",
       "Afghanistan   NaN   NaN   NaN  11.7  11.3  10.9   7.2  22.5   2.1  −2.16  ...   \n",
       "Angola        418   106   197  93.9  33.4  42.4  17.1  4.31  19.4  −16.8  ...   \n",
       "Albania      5.64  3.81  3.64   5.2  3.16   3.3  2.48  4.39  4.12   2.42  ...   \n",
       "Andorra      28.7  3.08  3.24  3.18  3.04   3.4  3.46  3.21  2.27  0.132  ...   \n",
       "\n",
       "              2010   2011   2012    2013   2014    2015  2016  2017   2018  \\\n",
       "country                                                                      \n",
       "Aruba         3.11  0.789   3.29       2   0.44   −1.37  1.61   NaN    NaN   \n",
       "Afghanistan   16.6    7.3   4.82   0.567   2.45    −2.2   2.4  2.07   6.52   \n",
       "Angola        31.8   7.26   2.84    3.56  −3.52    21.8  22.6  29.1   28.1   \n",
       "Albania       2.31   1.04  0.289    1.55  0.564  −0.632  1.45  1.36  0.477   \n",
       "Andorra      0.197  0.175  0.448  −0.084  0.661   0.348  1.14  1.01   1.37   \n",
       "\n",
       "               2019  \n",
       "country              \n",
       "Aruba           NaN  \n",
       "Afghanistan     5.5  \n",
       "Angola         15.1  \n",
       "Albania      −0.965  \n",
       "Andorra        1.06  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we can slice the columns and leave the last 20 years\n",
    "inflation= inflation.loc[:, '1999': '2019']\n",
    "inflation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9b4a8dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now that we have the year we want for inflation, do the same for other data set and make country the index column\n",
    "investment= pd.read_csv('investments_percent_of_gdp.csv', index_col= ['country'] )\n",
    "population= pd.read_csv('population_total.csv', index_col= ['country'])\n",
    "tax_revenue= pd.read_csv('tax_revenue_percent_of_gdp.csv', index_col= ['country'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fe6f24d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "investment= investment.loc[:, '1999':'2019']\n",
    "population= population.loc[:, '1999': '2019']\n",
    "tax_revenue= tax_revenue.loc[:, '1999': '2019']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85f6f402",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
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       "      <th>2019</th>\n",
       "    </tr>\n",
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       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>25.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>29.2</td>\n",
       "      <td>28.8</td>\n",
       "      <td>33.6</td>\n",
       "      <td>35.2</td>\n",
       "      <td>32.5</td>\n",
       "      <td>33.3</td>\n",
       "      <td>30.3</td>\n",
       "      <td>...</td>\n",
       "      <td>28.5</td>\n",
       "      <td>26.6</td>\n",
       "      <td>23.8</td>\n",
       "      <td>23.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>21.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</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>Angola</th>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>27.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>25.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>...</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>30.8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>33.9</td>\n",
       "      <td>34.2</td>\n",
       "      <td>36.9</td>\n",
       "      <td>35.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>35.8</td>\n",
       "      <td>34.6</td>\n",
       "      <td>...</td>\n",
       "      <td>31.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>24.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>31.6</td>\n",
       "      <td>...</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>22.2</td>\n",
       "      <td>25.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1999  2000  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "country                                                                      \n",
       "Aruba                 25.5  23.7  26.9  29.2  28.8  33.6  35.2  32.5  33.3   \n",
       "Afghanistan            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                30.5  30.5  30.5  30.5  30.9  27.6  23.3  25.7  30.8   \n",
       "Albania               30.8  35.0  35.3  33.9  34.2  36.9  35.7  34.5  35.8   \n",
       "United Arab Emirates   NaN  22.5  22.5  22.1  20.0  20.2  19.3  25.2  25.1   \n",
       "\n",
       "                      2008  ...  2010  2011  2012  2013  2014  2015  2016  \\\n",
       "country                     ...                                             \n",
       "Aruba                 30.3  ...  28.5  26.6  23.8  23.1  21.5  20.6  20.6   \n",
       "Afghanistan            NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                42.8  ...  26.4  26.7  26.1  27.5  34.2  27.2  24.1   \n",
       "Albania               34.6  ...  31.4  28.3  28.1  25.7  25.8  25.2  25.1   \n",
       "United Arab Emirates  31.6  ...  23.2  21.9  19.3  22.2  25.6  26.0  25.3   \n",
       "\n",
       "                      2017  2018  2019  \n",
       "country                                 \n",
       "Aruba                 21.6   NaN   NaN  \n",
       "Afghanistan            NaN   NaN   NaN  \n",
       "Angola                21.4  19.8  22.8  \n",
       "Albania               24.1   NaN   NaN  \n",
       "United Arab Emirates  23.5  23.8   NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a847dc77",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2006</th>\n",
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       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>20.8M</td>\n",
       "      <td>21.6M</td>\n",
       "      <td>22.6M</td>\n",
       "      <td>23.7M</td>\n",
       "      <td>24.7M</td>\n",
       "      <td>25.7M</td>\n",
       "      <td>26.4M</td>\n",
       "      <td>27.1M</td>\n",
       "      <td>27.7M</td>\n",
       "      <td>28.4M</td>\n",
       "      <td>...</td>\n",
       "      <td>30.1M</td>\n",
       "      <td>31.2M</td>\n",
       "      <td>32.3M</td>\n",
       "      <td>33.4M</td>\n",
       "      <td>34.4M</td>\n",
       "      <td>35.4M</td>\n",
       "      <td>36.3M</td>\n",
       "      <td>37.2M</td>\n",
       "      <td>38M</td>\n",
       "      <td>38.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>16.4M</td>\n",
       "      <td>16.9M</td>\n",
       "      <td>17.5M</td>\n",
       "      <td>18.1M</td>\n",
       "      <td>18.8M</td>\n",
       "      <td>19.4M</td>\n",
       "      <td>20.1M</td>\n",
       "      <td>20.9M</td>\n",
       "      <td>21.7M</td>\n",
       "      <td>22.5M</td>\n",
       "      <td>...</td>\n",
       "      <td>24.2M</td>\n",
       "      <td>25.1M</td>\n",
       "      <td>26M</td>\n",
       "      <td>26.9M</td>\n",
       "      <td>27.9M</td>\n",
       "      <td>28.8M</td>\n",
       "      <td>29.8M</td>\n",
       "      <td>30.8M</td>\n",
       "      <td>31.8M</td>\n",
       "      <td>32.9M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3.13M</td>\n",
       "      <td>3.13M</td>\n",
       "      <td>3.13M</td>\n",
       "      <td>3.12M</td>\n",
       "      <td>3.1M</td>\n",
       "      <td>3.09M</td>\n",
       "      <td>3.06M</td>\n",
       "      <td>3.03M</td>\n",
       "      <td>3M</td>\n",
       "      <td>2.97M</td>\n",
       "      <td>...</td>\n",
       "      <td>2.93M</td>\n",
       "      <td>2.91M</td>\n",
       "      <td>2.9M</td>\n",
       "      <td>2.9M</td>\n",
       "      <td>2.89M</td>\n",
       "      <td>2.89M</td>\n",
       "      <td>2.88M</td>\n",
       "      <td>2.88M</td>\n",
       "      <td>2.88M</td>\n",
       "      <td>2.88M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>65.4k</td>\n",
       "      <td>67.3k</td>\n",
       "      <td>70k</td>\n",
       "      <td>73.2k</td>\n",
       "      <td>76.3k</td>\n",
       "      <td>78.9k</td>\n",
       "      <td>81k</td>\n",
       "      <td>82.7k</td>\n",
       "      <td>83.9k</td>\n",
       "      <td>84.5k</td>\n",
       "      <td>...</td>\n",
       "      <td>83.7k</td>\n",
       "      <td>82.4k</td>\n",
       "      <td>80.8k</td>\n",
       "      <td>79.2k</td>\n",
       "      <td>78k</td>\n",
       "      <td>77.3k</td>\n",
       "      <td>77k</td>\n",
       "      <td>77k</td>\n",
       "      <td>77.1k</td>\n",
       "      <td>77.3k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>3.13M</td>\n",
       "      <td>3.3M</td>\n",
       "      <td>3.48M</td>\n",
       "      <td>3.71M</td>\n",
       "      <td>4.07M</td>\n",
       "      <td>4.59M</td>\n",
       "      <td>5.3M</td>\n",
       "      <td>6.17M</td>\n",
       "      <td>7.09M</td>\n",
       "      <td>7.92M</td>\n",
       "      <td>...</td>\n",
       "      <td>8.95M</td>\n",
       "      <td>9.14M</td>\n",
       "      <td>9.2M</td>\n",
       "      <td>9.21M</td>\n",
       "      <td>9.26M</td>\n",
       "      <td>9.36M</td>\n",
       "      <td>9.49M</td>\n",
       "      <td>9.63M</td>\n",
       "      <td>9.77M</td>\n",
       "      <td>9.89M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       1999   2000   2001   2002   2003   2004   2005   2006  \\\n",
       "country                                                                        \n",
       "Afghanistan           20.8M  21.6M  22.6M  23.7M  24.7M  25.7M  26.4M  27.1M   \n",
       "Angola                16.4M  16.9M  17.5M  18.1M  18.8M  19.4M  20.1M  20.9M   \n",
       "Albania               3.13M  3.13M  3.13M  3.12M   3.1M  3.09M  3.06M  3.03M   \n",
       "Andorra               65.4k  67.3k    70k  73.2k  76.3k  78.9k    81k  82.7k   \n",
       "United Arab Emirates  3.13M   3.3M  3.48M  3.71M  4.07M  4.59M   5.3M  6.17M   \n",
       "\n",
       "                       2007   2008  ...   2010   2011   2012   2013   2014  \\\n",
       "country                             ...                                      \n",
       "Afghanistan           27.7M  28.4M  ...  30.1M  31.2M  32.3M  33.4M  34.4M   \n",
       "Angola                21.7M  22.5M  ...  24.2M  25.1M    26M  26.9M  27.9M   \n",
       "Albania                  3M  2.97M  ...  2.93M  2.91M   2.9M   2.9M  2.89M   \n",
       "Andorra               83.9k  84.5k  ...  83.7k  82.4k  80.8k  79.2k    78k   \n",
       "United Arab Emirates  7.09M  7.92M  ...  8.95M  9.14M   9.2M  9.21M  9.26M   \n",
       "\n",
       "                       2015   2016   2017   2018   2019  \n",
       "country                                                  \n",
       "Afghanistan           35.4M  36.3M  37.2M    38M  38.9M  \n",
       "Angola                28.8M  29.8M  30.8M  31.8M  32.9M  \n",
       "Albania               2.89M  2.88M  2.88M  2.88M  2.88M  \n",
       "Andorra               77.3k    77k    77k  77.1k  77.3k  \n",
       "United Arab Emirates  9.36M  9.49M  9.63M  9.77M  9.89M  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "population.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2349be6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>Afghanistan</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>6.97</td>\n",
       "      <td>5.28</td>\n",
       "      <td>6.09</td>\n",
       "      <td>8.48</td>\n",
       "      <td>...</td>\n",
       "      <td>8.920</td>\n",
       "      <td>7.710</td>\n",
       "      <td>7.12</td>\n",
       "      <td>6.880</td>\n",
       "      <td>7.5900</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>9.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>28.70</td>\n",
       "      <td>29.20</td>\n",
       "      <td>15.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.40</td>\n",
       "      <td>21.50</td>\n",
       "      <td>25.70</td>\n",
       "      <td>18.40</td>\n",
       "      <td>...</td>\n",
       "      <td>16.100</td>\n",
       "      <td>14.800</td>\n",
       "      <td>14.20</td>\n",
       "      <td>13.500</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>9.7300</td>\n",
       "      <td>9.2200</td>\n",
       "      <td>9.6400</td>\n",
       "      <td>9.380</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.0</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>18.000</td>\n",
       "      <td>17.500</td>\n",
       "      <td>16.50</td>\n",
       "      <td>18.300</td>\n",
       "      <td>18.5000</td>\n",
       "      <td>17.6000</td>\n",
       "      <td>18.9000</td>\n",
       "      <td>18.6000</td>\n",
       "      <td>18.300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</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>0.321</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>0.0665</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>0.965</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9.62</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.82</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.90</td>\n",
       "      <td>12.40</td>\n",
       "      <td>13.30</td>\n",
       "      <td>12.40</td>\n",
       "      <td>...</td>\n",
       "      <td>12.700</td>\n",
       "      <td>13.000</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.600</td>\n",
       "      <td>12.3000</td>\n",
       "      <td>12.1000</td>\n",
       "      <td>10.9000</td>\n",
       "      <td>10.1000</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                       1999   2000   2001  2002  2003  2004   2005   2006  \\\n",
       "country                                                                     \n",
       "Afghanistan             NaN    NaN    NaN   NaN   NaN   NaN   6.97   5.28   \n",
       "Angola                28.70  29.20  15.50  17.2  18.1  17.1  23.40  21.50   \n",
       "Albania                 NaN    NaN  16.50  17.2  18.0   NaN    NaN    NaN   \n",
       "United Arab Emirates    NaN    NaN    NaN   NaN   NaN   NaN    NaN    NaN   \n",
       "Argentina              9.62   9.32   9.82  12.5  13.1  13.1  12.90  12.40   \n",
       "\n",
       "                       2007   2008  ...    2010    2011   2012    2013  \\\n",
       "country                             ...                                  \n",
       "Afghanistan            6.09   8.48  ...   8.920   7.710   7.12   6.880   \n",
       "Angola                25.70  18.40  ...  16.100  14.800  14.20  13.500   \n",
       "Albania                 NaN    NaN  ...  18.000  17.500  16.50  18.300   \n",
       "United Arab Emirates    NaN    NaN  ...   0.321   0.364   0.37   0.356   \n",
       "Argentina             13.30  12.40  ...  12.700  13.000  12.50  12.600   \n",
       "\n",
       "                         2014     2015     2016     2017    2018  2019  \n",
       "country                                                                 \n",
       "Afghanistan            7.5900   9.5000   9.9000      NaN     NaN   NaN  \n",
       "Angola                11.0000   9.7300   9.2200   9.6400   9.380   NaN  \n",
       "Albania               18.5000  17.6000  18.9000  18.6000  18.300   NaN  \n",
       "United Arab Emirates   0.0577   0.0435   0.0665   0.0593   0.965   NaN  \n",
       "Argentina             12.3000  12.1000  10.9000  10.1000  10.600   NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "16533bf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 212 entries, Aruba to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   1999    192 non-null    object\n",
      " 1   2000    197 non-null    object\n",
      " 2   2001    198 non-null    object\n",
      " 3   2002    203 non-null    object\n",
      " 4   2003    203 non-null    object\n",
      " 5   2004    204 non-null    object\n",
      " 6   2005    204 non-null    object\n",
      " 7   2006    205 non-null    object\n",
      " 8   2007    203 non-null    object\n",
      " 9   2008    204 non-null    object\n",
      " 10  2009    204 non-null    object\n",
      " 11  2010    204 non-null    object\n",
      " 12  2011    204 non-null    object\n",
      " 13  2012    204 non-null    object\n",
      " 14  2013    206 non-null    object\n",
      " 15  2014    205 non-null    object\n",
      " 16  2015    204 non-null    object\n",
      " 17  2016    204 non-null    object\n",
      " 18  2017    203 non-null    object\n",
      " 19  2018    198 non-null    object\n",
      " 20  2019    180 non-null    object\n",
      "dtypes: object(21)\n",
      "memory usage: 36.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# perform Exploratoy Data Analysis on each of the dataset\n",
    "inflation.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "043c34ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 197 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   1999    197 non-null    object\n",
      " 1   2000    197 non-null    object\n",
      " 2   2001    197 non-null    object\n",
      " 3   2002    197 non-null    object\n",
      " 4   2003    197 non-null    object\n",
      " 5   2004    197 non-null    object\n",
      " 6   2005    197 non-null    object\n",
      " 7   2006    197 non-null    object\n",
      " 8   2007    197 non-null    object\n",
      " 9   2008    197 non-null    object\n",
      " 10  2009    197 non-null    object\n",
      " 11  2010    197 non-null    object\n",
      " 12  2011    197 non-null    object\n",
      " 13  2012    197 non-null    object\n",
      " 14  2013    197 non-null    object\n",
      " 15  2014    197 non-null    object\n",
      " 16  2015    197 non-null    object\n",
      " 17  2016    197 non-null    object\n",
      " 18  2017    197 non-null    object\n",
      " 19  2018    197 non-null    object\n",
      " 20  2019    197 non-null    object\n",
      "dtypes: object(21)\n",
      "memory usage: 33.9+ KB\n"
     ]
    }
   ],
   "source": [
    "population.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb82f900",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 188 entries, Aruba to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1999    166 non-null    float64\n",
      " 1   2000    167 non-null    float64\n",
      " 2   2001    168 non-null    float64\n",
      " 3   2002    169 non-null    float64\n",
      " 4   2003    169 non-null    float64\n",
      " 5   2004    170 non-null    float64\n",
      " 6   2005    171 non-null    float64\n",
      " 7   2006    173 non-null    float64\n",
      " 8   2007    175 non-null    float64\n",
      " 9   2008    174 non-null    float64\n",
      " 10  2009    176 non-null    float64\n",
      " 11  2010    176 non-null    float64\n",
      " 12  2011    175 non-null    float64\n",
      " 13  2012    174 non-null    float64\n",
      " 14  2013    175 non-null    float64\n",
      " 15  2014    176 non-null    object \n",
      " 16  2015    175 non-null    float64\n",
      " 17  2016    174 non-null    float64\n",
      " 18  2017    174 non-null    float64\n",
      " 19  2018    163 non-null    float64\n",
      " 20  2019    130 non-null    float64\n",
      "dtypes: float64(20), object(1)\n",
      "memory usage: 32.3+ KB\n"
     ]
    }
   ],
   "source": [
    "investment.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f710799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 156 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1999    85 non-null     float64\n",
      " 1   2000    91 non-null     float64\n",
      " 2   2001    96 non-null     float64\n",
      " 3   2002    100 non-null    float64\n",
      " 4   2003    102 non-null    float64\n",
      " 5   2004    102 non-null    float64\n",
      " 6   2005    105 non-null    float64\n",
      " 7   2006    105 non-null    float64\n",
      " 8   2007    113 non-null    float64\n",
      " 9   2008    118 non-null    float64\n",
      " 10  2009    122 non-null    float64\n",
      " 11  2010    127 non-null    float64\n",
      " 12  2011    131 non-null    float64\n",
      " 13  2012    128 non-null    float64\n",
      " 14  2013    135 non-null    float64\n",
      " 15  2014    138 non-null    float64\n",
      " 16  2015    136 non-null    float64\n",
      " 17  2016    136 non-null    float64\n",
      " 18  2017    129 non-null    float64\n",
      " 19  2018    116 non-null    float64\n",
      " 20  2019    3 non-null      float64\n",
      "dtypes: float64(21)\n",
      "memory usage: 26.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# for the dataset that have float datatypes, lets check some of their characterics\n",
    "tax_revenue.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ed8919",
   "metadata": {},
   "source": [
    "# Exploring population dataset\n",
    "\n",
    "* Remove the *k* and *M* strings making use of regex and multiply by the integer equivalent of the sign\n",
    "* Convert all columns to Float Data types for easy analyzing\n",
    "* Check for relationships between the columns \n",
    "* Communicate your findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5516ac61",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>...</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>186</td>\n",
       "      <td>189</td>\n",
       "      <td>186</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>184</td>\n",
       "      <td>191</td>\n",
       "      <td>189</td>\n",
       "      <td>...</td>\n",
       "      <td>190</td>\n",
       "      <td>189</td>\n",
       "      <td>186</td>\n",
       "      <td>187</td>\n",
       "      <td>184</td>\n",
       "      <td>187</td>\n",
       "      <td>183</td>\n",
       "      <td>190</td>\n",
       "      <td>186</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>11.1M</td>\n",
       "      <td>10.3M</td>\n",
       "      <td>48M</td>\n",
       "      <td>12M</td>\n",
       "      <td>74.2M</td>\n",
       "      <td>25.7M</td>\n",
       "      <td>12.2M</td>\n",
       "      <td>13.7M</td>\n",
       "      <td>6.08M</td>\n",
       "      <td>6.16M</td>\n",
       "      <td>...</td>\n",
       "      <td>4.08M</td>\n",
       "      <td>10.5M</td>\n",
       "      <td>2.06M</td>\n",
       "      <td>109k</td>\n",
       "      <td>10.7M</td>\n",
       "      <td>110k</td>\n",
       "      <td>4.06M</td>\n",
       "      <td>1.27M</td>\n",
       "      <td>11.5M</td>\n",
       "      <td>2.08M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1999   2000 2001 2002   2003   2004   2005   2006   2007   2008  ...  \\\n",
       "count     197    197  197  197    197    197    197    197    197    197  ...   \n",
       "unique    186    189  186  188    188    187    187    184    191    189  ...   \n",
       "top     11.1M  10.3M  48M  12M  74.2M  25.7M  12.2M  13.7M  6.08M  6.16M  ...   \n",
       "freq        3      3    2    3      2      3      2      3      2      2  ...   \n",
       "\n",
       "         2010   2011   2012  2013   2014  2015   2016   2017   2018   2019  \n",
       "count     197    197    197   197    197   197    197    197    197    197  \n",
       "unique    190    189    186   187    184   187    183    190    186    188  \n",
       "top     4.08M  10.5M  2.06M  109k  10.7M  110k  4.06M  1.27M  11.5M  2.08M  \n",
       "freq        2      2      2     3      3     2      2      2      3      2  \n",
       "\n",
       "[4 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "03aae979",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 197 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   1999    197 non-null    object\n",
      " 1   2000    197 non-null    object\n",
      " 2   2001    197 non-null    object\n",
      " 3   2002    197 non-null    object\n",
      " 4   2003    197 non-null    object\n",
      " 5   2004    197 non-null    object\n",
      " 6   2005    197 non-null    object\n",
      " 7   2006    197 non-null    object\n",
      " 8   2007    197 non-null    object\n",
      " 9   2008    197 non-null    object\n",
      " 10  2009    197 non-null    object\n",
      " 11  2010    197 non-null    object\n",
      " 12  2011    197 non-null    object\n",
      " 13  2012    197 non-null    object\n",
      " 14  2013    197 non-null    object\n",
      " 15  2014    197 non-null    object\n",
      " 16  2015    197 non-null    object\n",
      " 17  2016    197 non-null    object\n",
      " 18  2017    197 non-null    object\n",
      " 19  2018    197 non-null    object\n",
      " 20  2019    197 non-null    object\n",
      "dtypes: object(21)\n",
      "memory usage: 33.9+ KB\n"
     ]
    }
   ],
   "source": [
    "population.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8dd6aec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the *k* and *M* strings\n",
    "population_2= population.replace({\"k\":'1e3', \"M\":'1e6', \"B\":\"1e9\"}, regex= True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "725f76f4",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
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       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2008</th>\n",
       "      <th>...</th>\n",
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       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
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       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>20810000.0</td>\n",
       "      <td>21610000.0</td>\n",
       "      <td>22610000.0</td>\n",
       "      <td>23710000.0</td>\n",
       "      <td>24710000.0</td>\n",
       "      <td>25710000.0</td>\n",
       "      <td>26410000.0</td>\n",
       "      <td>27110000.0</td>\n",
       "      <td>27710000.0</td>\n",
       "      <td>28410000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30110000.0</td>\n",
       "      <td>31210000.0</td>\n",
       "      <td>32310000.0</td>\n",
       "      <td>33410000.0</td>\n",
       "      <td>34410000.0</td>\n",
       "      <td>35410000.0</td>\n",
       "      <td>36310000.0</td>\n",
       "      <td>37210000.0</td>\n",
       "      <td>381000000.0</td>\n",
       "      <td>38910000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>16410000.0</td>\n",
       "      <td>16910000.0</td>\n",
       "      <td>17510000.0</td>\n",
       "      <td>18110000.0</td>\n",
       "      <td>18810000.0</td>\n",
       "      <td>19410000.0</td>\n",
       "      <td>20110000.0</td>\n",
       "      <td>20910000.0</td>\n",
       "      <td>21710000.0</td>\n",
       "      <td>22510000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24210000.0</td>\n",
       "      <td>25110000.0</td>\n",
       "      <td>261000000.0</td>\n",
       "      <td>26910000.0</td>\n",
       "      <td>27910000.0</td>\n",
       "      <td>28810000.0</td>\n",
       "      <td>29810000.0</td>\n",
       "      <td>30810000.0</td>\n",
       "      <td>31810000.0</td>\n",
       "      <td>32910000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3121000.0</td>\n",
       "      <td>3110000.0</td>\n",
       "      <td>3091000.0</td>\n",
       "      <td>3061000.0</td>\n",
       "      <td>3031000.0</td>\n",
       "      <td>31000000.0</td>\n",
       "      <td>2971000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2931000.0</td>\n",
       "      <td>2911000.0</td>\n",
       "      <td>2910000.0</td>\n",
       "      <td>2910000.0</td>\n",
       "      <td>2891000.0</td>\n",
       "      <td>2891000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>65410.0</td>\n",
       "      <td>67310.0</td>\n",
       "      <td>701000.0</td>\n",
       "      <td>73210.0</td>\n",
       "      <td>76310.0</td>\n",
       "      <td>78910.0</td>\n",
       "      <td>811000.0</td>\n",
       "      <td>82710.0</td>\n",
       "      <td>83910.0</td>\n",
       "      <td>84510.0</td>\n",
       "      <td>...</td>\n",
       "      <td>83710.0</td>\n",
       "      <td>82410.0</td>\n",
       "      <td>80810.0</td>\n",
       "      <td>79210.0</td>\n",
       "      <td>781000.0</td>\n",
       "      <td>77310.0</td>\n",
       "      <td>771000.0</td>\n",
       "      <td>771000.0</td>\n",
       "      <td>77110.0</td>\n",
       "      <td>77310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3310000.0</td>\n",
       "      <td>3481000.0</td>\n",
       "      <td>3711000.0</td>\n",
       "      <td>4071000.0</td>\n",
       "      <td>4591000.0</td>\n",
       "      <td>5310000.0</td>\n",
       "      <td>6171000.0</td>\n",
       "      <td>7091000.0</td>\n",
       "      <td>7921000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8951000.0</td>\n",
       "      <td>9141000.0</td>\n",
       "      <td>9210000.0</td>\n",
       "      <td>9211000.0</td>\n",
       "      <td>9261000.0</td>\n",
       "      <td>9361000.0</td>\n",
       "      <td>9491000.0</td>\n",
       "      <td>9631000.0</td>\n",
       "      <td>9771000.0</td>\n",
       "      <td>9891000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            1999        2000        2001        2002  \\\n",
       "country                                                                \n",
       "Afghanistan           20810000.0  21610000.0  22610000.0  23710000.0   \n",
       "Angola                16410000.0  16910000.0  17510000.0  18110000.0   \n",
       "Albania                3131000.0   3131000.0   3131000.0   3121000.0   \n",
       "Andorra                  65410.0     67310.0    701000.0     73210.0   \n",
       "United Arab Emirates   3131000.0   3310000.0   3481000.0   3711000.0   \n",
       "\n",
       "                            2003        2004        2005        2006  \\\n",
       "country                                                                \n",
       "Afghanistan           24710000.0  25710000.0  26410000.0  27110000.0   \n",
       "Angola                18810000.0  19410000.0  20110000.0  20910000.0   \n",
       "Albania                3110000.0   3091000.0   3061000.0   3031000.0   \n",
       "Andorra                  76310.0     78910.0    811000.0     82710.0   \n",
       "United Arab Emirates   4071000.0   4591000.0   5310000.0   6171000.0   \n",
       "\n",
       "                            2007        2008  ...        2010        2011  \\\n",
       "country                                       ...                           \n",
       "Afghanistan           27710000.0  28410000.0  ...  30110000.0  31210000.0   \n",
       "Angola                21710000.0  22510000.0  ...  24210000.0  25110000.0   \n",
       "Albania               31000000.0   2971000.0  ...   2931000.0   2911000.0   \n",
       "Andorra                  83910.0     84510.0  ...     83710.0     82410.0   \n",
       "United Arab Emirates   7091000.0   7921000.0  ...   8951000.0   9141000.0   \n",
       "\n",
       "                             2012        2013        2014        2015  \\\n",
       "country                                                                 \n",
       "Afghanistan            32310000.0  33410000.0  34410000.0  35410000.0   \n",
       "Angola                261000000.0  26910000.0  27910000.0  28810000.0   \n",
       "Albania                 2910000.0   2910000.0   2891000.0   2891000.0   \n",
       "Andorra                   80810.0     79210.0    781000.0     77310.0   \n",
       "United Arab Emirates    9210000.0   9211000.0   9261000.0   9361000.0   \n",
       "\n",
       "                            2016        2017         2018        2019  \n",
       "country                                                                \n",
       "Afghanistan           36310000.0  37210000.0  381000000.0  38910000.0  \n",
       "Angola                29810000.0  30810000.0   31810000.0  32910000.0  \n",
       "Albania                2881000.0   2881000.0    2881000.0   2881000.0  \n",
       "Andorra                 771000.0    771000.0      77110.0     77310.0  \n",
       "United Arab Emirates   9491000.0   9631000.0    9771000.0   9891000.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_2= population_2.astype(float)\n",
    "population_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7064dbfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we can check the data to draw conclusion on the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "26d86d0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 197 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1999    197 non-null    float64\n",
      " 1   2000    197 non-null    float64\n",
      " 2   2001    197 non-null    float64\n",
      " 3   2002    197 non-null    float64\n",
      " 4   2003    197 non-null    float64\n",
      " 5   2004    197 non-null    float64\n",
      " 6   2005    197 non-null    float64\n",
      " 7   2006    197 non-null    float64\n",
      " 8   2007    197 non-null    float64\n",
      " 9   2008    197 non-null    float64\n",
      " 10  2009    197 non-null    float64\n",
      " 11  2010    197 non-null    float64\n",
      " 12  2011    197 non-null    float64\n",
      " 13  2012    197 non-null    float64\n",
      " 14  2013    197 non-null    float64\n",
      " 15  2014    197 non-null    float64\n",
      " 16  2015    197 non-null    float64\n",
      " 17  2016    197 non-null    float64\n",
      " 18  2017    197 non-null    float64\n",
      " 19  2018    197 non-null    float64\n",
      " 20  2019    197 non-null    float64\n",
      "dtypes: float64(21)\n",
      "memory usage: 33.9+ KB\n"
     ]
    }
   ],
   "source": [
    "population_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d0453fb",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "      <td>1.970000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.091477e+08</td>\n",
       "      <td>1.020427e+08</td>\n",
       "      <td>1.059143e+08</td>\n",
       "      <td>1.047406e+08</td>\n",
       "      <td>1.158462e+08</td>\n",
       "      <td>1.055147e+08</td>\n",
       "      <td>1.053391e+08</td>\n",
       "      <td>1.109643e+08</td>\n",
       "      <td>1.118679e+08</td>\n",
       "      <td>1.144271e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>1.248458e+08</td>\n",
       "      <td>1.223037e+08</td>\n",
       "      <td>1.217903e+08</td>\n",
       "      <td>1.289779e+08</td>\n",
       "      <td>1.342642e+08</td>\n",
       "      <td>1.436533e+08</td>\n",
       "      <td>1.378202e+08</td>\n",
       "      <td>1.378163e+08</td>\n",
       "      <td>1.502003e+08</td>\n",
       "      <td>1.437752e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.699621e+08</td>\n",
       "      <td>3.718716e+08</td>\n",
       "      <td>3.776451e+08</td>\n",
       "      <td>3.803914e+08</td>\n",
       "      <td>3.904380e+08</td>\n",
       "      <td>3.896424e+08</td>\n",
       "      <td>3.936131e+08</td>\n",
       "      <td>3.990090e+08</td>\n",
       "      <td>4.025280e+08</td>\n",
       "      <td>4.085911e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>4.207645e+08</td>\n",
       "      <td>4.264090e+08</td>\n",
       "      <td>4.279072e+08</td>\n",
       "      <td>4.382993e+08</td>\n",
       "      <td>4.455895e+08</td>\n",
       "      <td>4.545367e+08</td>\n",
       "      <td>4.568710e+08</td>\n",
       "      <td>4.619169e+08</td>\n",
       "      <td>4.720538e+08</td>\n",
       "      <td>4.775887e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.900000e+02</td>\n",
       "      <td>7.920000e+02</td>\n",
       "      <td>7.920000e+02</td>\n",
       "      <td>7.920000e+02</td>\n",
       "      <td>7.950000e+02</td>\n",
       "      <td>7.980000e+02</td>\n",
       "      <td>7.950000e+02</td>\n",
       "      <td>7.940000e+02</td>\n",
       "      <td>7.920000e+02</td>\n",
       "      <td>7.870000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>7.860000e+02</td>\n",
       "      <td>7.860000e+02</td>\n",
       "      <td>7.860000e+02</td>\n",
       "      <td>7.850000e+02</td>\n",
       "      <td>7.900000e+02</td>\n",
       "      <td>7.910000e+02</td>\n",
       "      <td>7.980000e+02</td>\n",
       "      <td>8.100000e+02</td>\n",
       "      <td>8.150000e+02</td>\n",
       "      <td>8.090000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.131000e+06</td>\n",
       "      <td>3.131000e+06</td>\n",
       "      <td>3.151000e+06</td>\n",
       "      <td>3.211000e+06</td>\n",
       "      <td>3.271000e+06</td>\n",
       "      <td>3.321000e+06</td>\n",
       "      <td>3.310000e+06</td>\n",
       "      <td>3.261000e+06</td>\n",
       "      <td>3.310000e+06</td>\n",
       "      <td>3.171000e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>3.371000e+06</td>\n",
       "      <td>3.381000e+06</td>\n",
       "      <td>3.461000e+06</td>\n",
       "      <td>3.481000e+06</td>\n",
       "      <td>3.741000e+06</td>\n",
       "      <td>3.421000e+06</td>\n",
       "      <td>3.441000e+06</td>\n",
       "      <td>3.451000e+06</td>\n",
       "      <td>3.510000e+06</td>\n",
       "      <td>3.551000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.871000e+06</td>\n",
       "      <td>7.081000e+06</td>\n",
       "      <td>7.310000e+06</td>\n",
       "      <td>7.451000e+06</td>\n",
       "      <td>7.531000e+06</td>\n",
       "      <td>7.691000e+06</td>\n",
       "      <td>7.631000e+06</td>\n",
       "      <td>7.861000e+06</td>\n",
       "      <td>7.981000e+06</td>\n",
       "      <td>7.921000e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>8.481000e+06</td>\n",
       "      <td>8.091000e+06</td>\n",
       "      <td>8.521000e+06</td>\n",
       "      <td>8.621000e+06</td>\n",
       "      <td>8.881000e+06</td>\n",
       "      <td>8.721000e+06</td>\n",
       "      <td>8.821000e+06</td>\n",
       "      <td>9.110000e+06</td>\n",
       "      <td>9.321000e+06</td>\n",
       "      <td>8.961000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.081000e+07</td>\n",
       "      <td>2.161000e+07</td>\n",
       "      <td>2.231000e+07</td>\n",
       "      <td>2.351000e+07</td>\n",
       "      <td>2.521000e+07</td>\n",
       "      <td>2.141000e+07</td>\n",
       "      <td>2.241000e+07</td>\n",
       "      <td>2.641000e+07</td>\n",
       "      <td>2.761000e+07</td>\n",
       "      <td>2.421000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.871000e+07</td>\n",
       "      <td>2.491000e+07</td>\n",
       "      <td>2.981000e+07</td>\n",
       "      <td>2.691000e+07</td>\n",
       "      <td>3.031000e+07</td>\n",
       "      <td>3.071000e+07</td>\n",
       "      <td>2.981000e+07</td>\n",
       "      <td>2.981000e+07</td>\n",
       "      <td>3.191000e+07</td>\n",
       "      <td>2.911000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.821000e+09</td>\n",
       "      <td>2.851000e+09</td>\n",
       "      <td>2.871000e+09</td>\n",
       "      <td>2.901000e+09</td>\n",
       "      <td>2.921000e+09</td>\n",
       "      <td>2.951000e+09</td>\n",
       "      <td>2.981000e+09</td>\n",
       "      <td>3.011000e+09</td>\n",
       "      <td>3.031000e+09</td>\n",
       "      <td>3.061000e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.121000e+09</td>\n",
       "      <td>3.141000e+09</td>\n",
       "      <td>3.161000e+09</td>\n",
       "      <td>3.191000e+09</td>\n",
       "      <td>3.211000e+09</td>\n",
       "      <td>3.231000e+09</td>\n",
       "      <td>3.251000e+09</td>\n",
       "      <td>3.271000e+09</td>\n",
       "      <td>3.291000e+09</td>\n",
       "      <td>3.311000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1999          2000          2001          2002          2003  \\\n",
       "count  1.970000e+02  1.970000e+02  1.970000e+02  1.970000e+02  1.970000e+02   \n",
       "mean   1.091477e+08  1.020427e+08  1.059143e+08  1.047406e+08  1.158462e+08   \n",
       "std    3.699621e+08  3.718716e+08  3.776451e+08  3.803914e+08  3.904380e+08   \n",
       "min    7.900000e+02  7.920000e+02  7.920000e+02  7.920000e+02  7.950000e+02   \n",
       "25%    3.131000e+06  3.131000e+06  3.151000e+06  3.211000e+06  3.271000e+06   \n",
       "50%    6.871000e+06  7.081000e+06  7.310000e+06  7.451000e+06  7.531000e+06   \n",
       "75%    2.081000e+07  2.161000e+07  2.231000e+07  2.351000e+07  2.521000e+07   \n",
       "max    2.821000e+09  2.851000e+09  2.871000e+09  2.901000e+09  2.921000e+09   \n",
       "\n",
       "               2004          2005          2006          2007          2008  \\\n",
       "count  1.970000e+02  1.970000e+02  1.970000e+02  1.970000e+02  1.970000e+02   \n",
       "mean   1.055147e+08  1.053391e+08  1.109643e+08  1.118679e+08  1.144271e+08   \n",
       "std    3.896424e+08  3.936131e+08  3.990090e+08  4.025280e+08  4.085911e+08   \n",
       "min    7.980000e+02  7.950000e+02  7.940000e+02  7.920000e+02  7.870000e+02   \n",
       "25%    3.321000e+06  3.310000e+06  3.261000e+06  3.310000e+06  3.171000e+06   \n",
       "50%    7.691000e+06  7.631000e+06  7.861000e+06  7.981000e+06  7.921000e+06   \n",
       "75%    2.141000e+07  2.241000e+07  2.641000e+07  2.761000e+07  2.421000e+07   \n",
       "max    2.951000e+09  2.981000e+09  3.011000e+09  3.031000e+09  3.061000e+09   \n",
       "\n",
       "       ...          2010          2011          2012          2013  \\\n",
       "count  ...  1.970000e+02  1.970000e+02  1.970000e+02  1.970000e+02   \n",
       "mean   ...  1.248458e+08  1.223037e+08  1.217903e+08  1.289779e+08   \n",
       "std    ...  4.207645e+08  4.264090e+08  4.279072e+08  4.382993e+08   \n",
       "min    ...  7.860000e+02  7.860000e+02  7.860000e+02  7.850000e+02   \n",
       "25%    ...  3.371000e+06  3.381000e+06  3.461000e+06  3.481000e+06   \n",
       "50%    ...  8.481000e+06  8.091000e+06  8.521000e+06  8.621000e+06   \n",
       "75%    ...  2.871000e+07  2.491000e+07  2.981000e+07  2.691000e+07   \n",
       "max    ...  3.121000e+09  3.141000e+09  3.161000e+09  3.191000e+09   \n",
       "\n",
       "               2014          2015          2016          2017          2018  \\\n",
       "count  1.970000e+02  1.970000e+02  1.970000e+02  1.970000e+02  1.970000e+02   \n",
       "mean   1.342642e+08  1.436533e+08  1.378202e+08  1.378163e+08  1.502003e+08   \n",
       "std    4.455895e+08  4.545367e+08  4.568710e+08  4.619169e+08  4.720538e+08   \n",
       "min    7.900000e+02  7.910000e+02  7.980000e+02  8.100000e+02  8.150000e+02   \n",
       "25%    3.741000e+06  3.421000e+06  3.441000e+06  3.451000e+06  3.510000e+06   \n",
       "50%    8.881000e+06  8.721000e+06  8.821000e+06  9.110000e+06  9.321000e+06   \n",
       "75%    3.031000e+07  3.071000e+07  2.981000e+07  2.981000e+07  3.191000e+07   \n",
       "max    3.211000e+09  3.231000e+09  3.251000e+09  3.271000e+09  3.291000e+09   \n",
       "\n",
       "               2019  \n",
       "count  1.970000e+02  \n",
       "mean   1.437752e+08  \n",
       "std    4.775887e+08  \n",
       "min    8.090000e+02  \n",
       "25%    3.551000e+06  \n",
       "50%    8.961000e+06  \n",
       "75%    2.911000e+07  \n",
       "max    3.311000e+09  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "67863e74",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
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       "      <th>2004</th>\n",
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       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>United States</th>\n",
       "      <td>2.821000e+09</td>\n",
       "      <td>2.851000e+09</td>\n",
       "      <td>2.871000e+09</td>\n",
       "      <td>2.901000e+09</td>\n",
       "      <td>2.921000e+09</td>\n",
       "      <td>2.951000e+09</td>\n",
       "      <td>2.981000e+09</td>\n",
       "      <td>3.011000e+09</td>\n",
       "      <td>3.031000e+09</td>\n",
       "      <td>3.061000e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.121000e+09</td>\n",
       "      <td>3.141000e+09</td>\n",
       "      <td>3.161000e+09</td>\n",
       "      <td>3.191000e+09</td>\n",
       "      <td>3.211000e+09</td>\n",
       "      <td>3.231000e+09</td>\n",
       "      <td>3.251000e+09</td>\n",
       "      <td>3.271000e+09</td>\n",
       "      <td>3.291000e+09</td>\n",
       "      <td>3.311000e+09</td>\n",
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       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       1999          2000          2001          2002  \\\n",
       "country                                                                 \n",
       "United States  2.821000e+09  2.851000e+09  2.871000e+09  2.901000e+09   \n",
       "\n",
       "                       2003          2004          2005          2006  \\\n",
       "country                                                                 \n",
       "United States  2.921000e+09  2.951000e+09  2.981000e+09  3.011000e+09   \n",
       "\n",
       "                       2007          2008  ...          2010          2011  \\\n",
       "country                                    ...                               \n",
       "United States  3.031000e+09  3.061000e+09  ...  3.121000e+09  3.141000e+09   \n",
       "\n",
       "                       2012          2013          2014          2015  \\\n",
       "country                                                                 \n",
       "United States  3.161000e+09  3.191000e+09  3.211000e+09  3.231000e+09   \n",
       "\n",
       "                       2016          2017          2018          2019  \n",
       "country                                                                \n",
       "United States  3.251000e+09  3.271000e+09  3.291000e+09  3.311000e+09  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# country with max populaton in 2009 and their opulation in 2019\n",
    "max_population_1999= population_2[population_2['1999']== max(population_2['1999'])]\n",
    "max_population_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "12cf9166",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
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       "      <th>2002</th>\n",
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       "      <th></th>\n",
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       "      <th>Holy See</th>\n",
       "      <td>790.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>...</td>\n",
       "      <td>786.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>785.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>809.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           1999   2000   2001   2002   2003   2004   2005   2006   2007  \\\n",
       "country                                                                   \n",
       "Holy See  790.0  792.0  792.0  792.0  795.0  798.0  795.0  794.0  792.0   \n",
       "\n",
       "           2008  ...   2010   2011   2012   2013   2014   2015   2016   2017  \\\n",
       "country          ...                                                           \n",
       "Holy See  787.0  ...  786.0  786.0  786.0  785.0  790.0  791.0  798.0  810.0   \n",
       "\n",
       "           2018   2019  \n",
       "country                 \n",
       "Holy See  815.0  809.0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_population_1999= population_2[population_2['1999']== min(population_2['1999'])]\n",
    "min_population_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "95952e7a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.031000e+09</td>\n",
       "      <td>3.061000e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>3.121000e+09</td>\n",
       "      <td>3.141000e+09</td>\n",
       "      <td>3.161000e+09</td>\n",
       "      <td>3.191000e+09</td>\n",
       "      <td>3.211000e+09</td>\n",
       "      <td>3.231000e+09</td>\n",
       "      <td>3.251000e+09</td>\n",
       "      <td>3.271000e+09</td>\n",
       "      <td>3.291000e+09</td>\n",
       "      <td>3.311000e+09</td>\n",
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       "                       1999          2000          2001          2002  \\\n",
       "country                                                                 \n",
       "United States  2.821000e+09  2.851000e+09  2.871000e+09  2.901000e+09   \n",
       "\n",
       "                       2003          2004          2005          2006  \\\n",
       "country                                                                 \n",
       "United States  2.921000e+09  2.951000e+09  2.981000e+09  3.011000e+09   \n",
       "\n",
       "                       2007          2008  ...          2010          2011  \\\n",
       "country                                    ...                               \n",
       "United States  3.031000e+09  3.061000e+09  ...  3.121000e+09  3.141000e+09   \n",
       "\n",
       "                       2012          2013          2014          2015  \\\n",
       "country                                                                 \n",
       "United States  3.161000e+09  3.191000e+09  3.211000e+09  3.231000e+09   \n",
       "\n",
       "                       2016          2017          2018          2019  \n",
       "country                                                                \n",
       "United States  3.251000e+09  3.271000e+09  3.291000e+09  3.311000e+09  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_population_2019= population_2[population_2['2019']== max(population_2['2019'])]\n",
    "max_population_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "639e54cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Holy See</th>\n",
       "      <td>790.0</td>\n",
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       "      <td>792.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>792.0</td>\n",
       "      <td>787.0</td>\n",
       "      <td>...</td>\n",
       "      <td>786.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>785.0</td>\n",
       "      <td>790.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>809.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           1999   2000   2001   2002   2003   2004   2005   2006   2007  \\\n",
       "country                                                                   \n",
       "Holy See  790.0  792.0  792.0  792.0  795.0  798.0  795.0  794.0  792.0   \n",
       "\n",
       "           2008  ...   2010   2011   2012   2013   2014   2015   2016   2017  \\\n",
       "country          ...                                                           \n",
       "Holy See  787.0  ...  786.0  786.0  786.0  785.0  790.0  791.0  798.0  810.0   \n",
       "\n",
       "           2018   2019  \n",
       "country                 \n",
       "Holy See  815.0  809.0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_population_2019= population_2[population_2['2019']== min(population_2['2019'])]\n",
    "min_population_2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d515b3c0",
   "metadata": {},
   "source": [
    "# Calculate the percentage at which the minimum population number increased over the year and Visualize it\n",
    "* Since the min population in 2019 and 1999 are the same, we will be using one dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8c6bcf8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_population_1= population_2['1999'].min()\n",
    "min_population_2= population_2['2019'].min()\n",
    "max_population_1= population_2['1999'].max()\n",
    "max_population_2= population_2['2019'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8df774ac",
   "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": [
    "plt.bar(['HolySee_1999', 'HolySee_2019'], [min_population_1, min_population_2]) \n",
    "plt.title('Visualization to show the relationship between country with the lowest population in 1999 and 2019')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Population Size');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "de0a2da7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Holy see which is the country with the minimum population size  increased by 2.41% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# Percentage rise of minimum population in 1999 and 2019\n",
    "difference= min_population_2- min_population_1\n",
    "percentage_rise= (difference/min_population_1)*100\n",
    "print (f' Holy see which is the country with the minimum population size  increased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c45c64b9",
   "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": [
    "plt.bar(['USA_1999', 'USA_2019'], [max_population_1, max_population_2], width= 0.8) \n",
    "plt.title('Relationship between country with the highest population size in 1999 and 2019')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Population Size');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8315f1f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " USA which is the country with the maximum population size increased by 17.37% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# Percentage rise of maximum population in 1999 and 2019\n",
    "difference= max_population_2- max_population_1\n",
    "percentage_rise= (difference/max_population_1)*100\n",
    "print (f' USA which is the country with the maximum population size increased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7e24d3",
   "metadata": {},
   "source": [
    "# Summary of Population Analysis\n",
    "* United State Of America has the highest Population size both in the year 2009 and 2019\n",
    "* The Highest Population size in 1999 increased by 17.37% in the year 2019\n",
    "* Holy see has the lowest population both in the year 1999 and 2019\n",
    "* Holy see population size in 1999 increased by 2.41% in the year 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e8d07e",
   "metadata": {},
   "source": [
    "# Analysing Inflation Dataset\n",
    "* Clean The data\n",
    "* Remove all missing rows\n",
    "* Replace all non ASCII characters to ASCII characters\n",
    "* Convert all columns to Float Data types for easy analyzing\n",
    "* Check for relationships between the columns \n",
    "* Communicate your findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c3e40e34",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>1.05</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4.53</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.95</td>\n",
       "      <td>5.95</td>\n",
       "      <td>5.07</td>\n",
       "      <td>1.73</td>\n",
       "      <td>...</td>\n",
       "      <td>3.11</td>\n",
       "      <td>0.789</td>\n",
       "      <td>3.29</td>\n",
       "      <td>2</td>\n",
       "      <td>0.44</td>\n",
       "      <td>−1.37</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>10.9</td>\n",
       "      <td>7.2</td>\n",
       "      <td>22.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>−2.16</td>\n",
       "      <td>...</td>\n",
       "      <td>16.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.567</td>\n",
       "      <td>2.45</td>\n",
       "      <td>−2.2</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.07</td>\n",
       "      <td>6.52</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>418</td>\n",
       "      <td>106</td>\n",
       "      <td>197</td>\n",
       "      <td>93.9</td>\n",
       "      <td>33.4</td>\n",
       "      <td>42.4</td>\n",
       "      <td>17.1</td>\n",
       "      <td>4.31</td>\n",
       "      <td>19.4</td>\n",
       "      <td>−16.8</td>\n",
       "      <td>...</td>\n",
       "      <td>31.8</td>\n",
       "      <td>7.26</td>\n",
       "      <td>2.84</td>\n",
       "      <td>3.56</td>\n",
       "      <td>−3.52</td>\n",
       "      <td>21.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>29.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>15.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>5.64</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.64</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.48</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.42</td>\n",
       "      <td>...</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.04</td>\n",
       "      <td>0.289</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.564</td>\n",
       "      <td>−0.632</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.477</td>\n",
       "      <td>−0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>28.7</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.448</td>\n",
       "      <td>−0.084</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1999  2000  2001  2002  2003  2004  2005  2006  2007   2008  ...  \\\n",
       "country                                                                   ...   \n",
       "Aruba        1.05  5.63  4.53   2.1  2.17  3.33  2.95  5.95  5.07   1.73  ...   \n",
       "Afghanistan   NaN   NaN   NaN  11.7  11.3  10.9   7.2  22.5   2.1  −2.16  ...   \n",
       "Angola        418   106   197  93.9  33.4  42.4  17.1  4.31  19.4  −16.8  ...   \n",
       "Albania      5.64  3.81  3.64   5.2  3.16   3.3  2.48  4.39  4.12   2.42  ...   \n",
       "Andorra      28.7  3.08  3.24  3.18  3.04   3.4  3.46  3.21  2.27  0.132  ...   \n",
       "\n",
       "              2010   2011   2012    2013   2014    2015  2016  2017   2018  \\\n",
       "country                                                                      \n",
       "Aruba         3.11  0.789   3.29       2   0.44   −1.37  1.61   NaN    NaN   \n",
       "Afghanistan   16.6    7.3   4.82   0.567   2.45    −2.2   2.4  2.07   6.52   \n",
       "Angola        31.8   7.26   2.84    3.56  −3.52    21.8  22.6  29.1   28.1   \n",
       "Albania       2.31   1.04  0.289    1.55  0.564  −0.632  1.45  1.36  0.477   \n",
       "Andorra      0.197  0.175  0.448  −0.084  0.661   0.348  1.14  1.01   1.37   \n",
       "\n",
       "               2019  \n",
       "country              \n",
       "Aruba           NaN  \n",
       "Afghanistan     5.5  \n",
       "Angola         15.1  \n",
       "Albania      −0.965  \n",
       "Andorra        1.06  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "074f51e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 212 entries, Aruba to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   1999    192 non-null    object\n",
      " 1   2000    197 non-null    object\n",
      " 2   2001    198 non-null    object\n",
      " 3   2002    203 non-null    object\n",
      " 4   2003    203 non-null    object\n",
      " 5   2004    204 non-null    object\n",
      " 6   2005    204 non-null    object\n",
      " 7   2006    205 non-null    object\n",
      " 8   2007    203 non-null    object\n",
      " 9   2008    204 non-null    object\n",
      " 10  2009    204 non-null    object\n",
      " 11  2010    204 non-null    object\n",
      " 12  2011    204 non-null    object\n",
      " 13  2012    204 non-null    object\n",
      " 14  2013    206 non-null    object\n",
      " 15  2014    205 non-null    object\n",
      " 16  2015    204 non-null    object\n",
      " 17  2016    204 non-null    object\n",
      " 18  2017    203 non-null    object\n",
      " 19  2018    198 non-null    object\n",
      " 20  2019    180 non-null    object\n",
      "dtypes: object(21)\n",
      "memory usage: 36.4+ KB\n"
     ]
    }
   ],
   "source": [
    "inflation.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6cd5c095",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Convert Object Datatype to Float, replace all non ASCII characters to ASCII characters\n",
    "inflation_2= inflation.replace(r\"[^\\x00-\\x7F]+\", \"-\", regex= True)\n",
    "inflation_2= inflation_2.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1999c0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>1.05</td>\n",
       "      <td>5.63</td>\n",
       "      <td>4.53</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.17</td>\n",
       "      <td>3.33</td>\n",
       "      <td>2.95</td>\n",
       "      <td>5.95</td>\n",
       "      <td>5.07</td>\n",
       "      <td>1.730</td>\n",
       "      <td>...</td>\n",
       "      <td>3.110</td>\n",
       "      <td>0.789</td>\n",
       "      <td>3.290</td>\n",
       "      <td>2.000</td>\n",
       "      <td>0.440</td>\n",
       "      <td>-1.370</td>\n",
       "      <td>1.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.70</td>\n",
       "      <td>11.30</td>\n",
       "      <td>10.90</td>\n",
       "      <td>7.20</td>\n",
       "      <td>22.50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>-2.160</td>\n",
       "      <td>...</td>\n",
       "      <td>16.600</td>\n",
       "      <td>7.300</td>\n",
       "      <td>4.820</td>\n",
       "      <td>0.567</td>\n",
       "      <td>2.450</td>\n",
       "      <td>-2.200</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.07</td>\n",
       "      <td>6.520</td>\n",
       "      <td>5.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>418.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>197.00</td>\n",
       "      <td>93.90</td>\n",
       "      <td>33.40</td>\n",
       "      <td>42.40</td>\n",
       "      <td>17.10</td>\n",
       "      <td>4.31</td>\n",
       "      <td>19.40</td>\n",
       "      <td>-16.800</td>\n",
       "      <td>...</td>\n",
       "      <td>31.800</td>\n",
       "      <td>7.260</td>\n",
       "      <td>2.840</td>\n",
       "      <td>3.560</td>\n",
       "      <td>-3.520</td>\n",
       "      <td>21.800</td>\n",
       "      <td>22.60</td>\n",
       "      <td>29.10</td>\n",
       "      <td>28.100</td>\n",
       "      <td>15.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>5.64</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.64</td>\n",
       "      <td>5.20</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.48</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.420</td>\n",
       "      <td>...</td>\n",
       "      <td>2.310</td>\n",
       "      <td>1.040</td>\n",
       "      <td>0.289</td>\n",
       "      <td>1.550</td>\n",
       "      <td>0.564</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.477</td>\n",
       "      <td>-0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>28.70</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1.060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1999    2000    2001   2002   2003   2004   2005   2006   2007  \\\n",
       "country                                                                         \n",
       "Aruba          1.05    5.63    4.53   2.10   2.17   3.33   2.95   5.95   5.07   \n",
       "Afghanistan     NaN     NaN     NaN  11.70  11.30  10.90   7.20  22.50   2.10   \n",
       "Angola       418.00  106.00  197.00  93.90  33.40  42.40  17.10   4.31  19.40   \n",
       "Albania        5.64    3.81    3.64   5.20   3.16   3.30   2.48   4.39   4.12   \n",
       "Andorra       28.70    3.08    3.24   3.18   3.04   3.40   3.46   3.21   2.27   \n",
       "\n",
       "               2008  ...    2010   2011   2012   2013   2014    2015   2016  \\\n",
       "country              ...                                                      \n",
       "Aruba         1.730  ...   3.110  0.789  3.290  2.000  0.440  -1.370   1.61   \n",
       "Afghanistan  -2.160  ...  16.600  7.300  4.820  0.567  2.450  -2.200   2.40   \n",
       "Angola      -16.800  ...  31.800  7.260  2.840  3.560 -3.520  21.800  22.60   \n",
       "Albania       2.420  ...   2.310  1.040  0.289  1.550  0.564  -0.632   1.45   \n",
       "Andorra       0.132  ...   0.197  0.175  0.448 -0.084  0.661   0.348   1.14   \n",
       "\n",
       "              2017    2018    2019  \n",
       "country                             \n",
       "Aruba          NaN     NaN     NaN  \n",
       "Afghanistan   2.07   6.520   5.500  \n",
       "Angola       29.10  28.100  15.100  \n",
       "Albania       1.36   0.477  -0.965  \n",
       "Andorra       1.01   1.370   1.060  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fbb7d6d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 212 entries, Aruba to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1999    192 non-null    float64\n",
      " 1   2000    197 non-null    float64\n",
      " 2   2001    198 non-null    float64\n",
      " 3   2002    203 non-null    float64\n",
      " 4   2003    203 non-null    float64\n",
      " 5   2004    204 non-null    float64\n",
      " 6   2005    204 non-null    float64\n",
      " 7   2006    205 non-null    float64\n",
      " 8   2007    203 non-null    float64\n",
      " 9   2008    204 non-null    float64\n",
      " 10  2009    204 non-null    float64\n",
      " 11  2010    204 non-null    float64\n",
      " 12  2011    204 non-null    float64\n",
      " 13  2012    204 non-null    float64\n",
      " 14  2013    206 non-null    float64\n",
      " 15  2014    205 non-null    float64\n",
      " 16  2015    204 non-null    float64\n",
      " 17  2016    204 non-null    float64\n",
      " 18  2017    203 non-null    float64\n",
      " 19  2018    198 non-null    float64\n",
      " 20  2019    180 non-null    float64\n",
      "dtypes: float64(21)\n",
      "memory usage: 36.4+ KB\n"
     ]
    }
   ],
   "source": [
    "inflation_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "26e37b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop all missing data\n",
    "inflation_2.dropna(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4d544047",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1999</th>\n",
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       "      <th>2019</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>170.000000</td>\n",
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       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>170.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.565241</td>\n",
       "      <td>8.462171</td>\n",
       "      <td>7.781159</td>\n",
       "      <td>6.324175</td>\n",
       "      <td>7.552104</td>\n",
       "      <td>8.200677</td>\n",
       "      <td>9.435947</td>\n",
       "      <td>6.928759</td>\n",
       "      <td>9.739364</td>\n",
       "      <td>3.277504</td>\n",
       "      <td>...</td>\n",
       "      <td>7.817045</td>\n",
       "      <td>5.243213</td>\n",
       "      <td>3.845928</td>\n",
       "      <td>3.573067</td>\n",
       "      <td>2.684615</td>\n",
       "      <td>3.604046</td>\n",
       "      <td>5.470644</td>\n",
       "      <td>5.446839</td>\n",
       "      <td>5.954814</td>\n",
       "      <td>7.310469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>204.102682</td>\n",
       "      <td>16.408422</td>\n",
       "      <td>18.922766</td>\n",
       "      <td>9.670270</td>\n",
       "      <td>9.738299</td>\n",
       "      <td>8.684602</td>\n",
       "      <td>17.281747</td>\n",
       "      <td>5.353072</td>\n",
       "      <td>7.319135</td>\n",
       "      <td>12.565882</td>\n",
       "      <td>...</td>\n",
       "      <td>8.266167</td>\n",
       "      <td>7.407848</td>\n",
       "      <td>6.433933</td>\n",
       "      <td>5.959206</td>\n",
       "      <td>7.848265</td>\n",
       "      <td>6.286108</td>\n",
       "      <td>7.381751</td>\n",
       "      <td>8.569805</td>\n",
       "      <td>27.424747</td>\n",
       "      <td>48.222917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-9.380000</td>\n",
       "      <td>-19.100000</td>\n",
       "      <td>-5.230000</td>\n",
       "      <td>-7.590000</td>\n",
       "      <td>-6.330000</td>\n",
       "      <td>-2.760000</td>\n",
       "      <td>-2.270000</td>\n",
       "      <td>-6.690000</td>\n",
       "      <td>-1.390000</td>\n",
       "      <td>-27.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.620000</td>\n",
       "      <td>-2.940000</td>\n",
       "      <td>-6.670000</td>\n",
       "      <td>-17.300000</td>\n",
       "      <td>-30.200000</td>\n",
       "      <td>-11.100000</td>\n",
       "      <td>-3.200000</td>\n",
       "      <td>-2.890000</td>\n",
       "      <td>-3.420000</td>\n",
       "      <td>-29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.037500</td>\n",
       "      <td>1.870000</td>\n",
       "      <td>1.492500</td>\n",
       "      <td>1.655000</td>\n",
       "      <td>2.462500</td>\n",
       "      <td>2.577500</td>\n",
       "      <td>2.820000</td>\n",
       "      <td>3.132500</td>\n",
       "      <td>4.015000</td>\n",
       "      <td>0.173750</td>\n",
       "      <td>...</td>\n",
       "      <td>2.435000</td>\n",
       "      <td>1.485000</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.660750</td>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.662250</td>\n",
       "      <td>1.527500</td>\n",
       "      <td>1.710000</td>\n",
       "      <td>0.978000</td>\n",
       "      <td>0.065200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.205000</td>\n",
       "      <td>4.070000</td>\n",
       "      <td>3.495000</td>\n",
       "      <td>3.925000</td>\n",
       "      <td>5.690000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>5.845000</td>\n",
       "      <td>5.855000</td>\n",
       "      <td>8.130000</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>2.525000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>2.275000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>3.565000</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>2.345000</td>\n",
       "      <td>1.755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.375000</td>\n",
       "      <td>8.192500</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>8.397500</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>11.075000</td>\n",
       "      <td>10.575000</td>\n",
       "      <td>8.887500</td>\n",
       "      <td>13.075000</td>\n",
       "      <td>6.175000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.375000</td>\n",
       "      <td>7.167500</td>\n",
       "      <td>4.945000</td>\n",
       "      <td>5.277500</td>\n",
       "      <td>4.925000</td>\n",
       "      <td>4.697500</td>\n",
       "      <td>6.317500</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>4.197500</td>\n",
       "      <td>4.892500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2630.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>93.900000</td>\n",
       "      <td>94.200000</td>\n",
       "      <td>59.300000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>27.200000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>95.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>75.300000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>40.300000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>58.200000</td>\n",
       "      <td>61.300000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>610.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1999        2000        2001        2002        2003  \\\n",
       "count   170.000000  170.000000  170.000000  170.000000  170.000000   \n",
       "mean     29.565241    8.462171    7.781159    6.324175    7.552104   \n",
       "std     204.102682   16.408422   18.922766    9.670270    9.738299   \n",
       "min      -9.380000  -19.100000   -5.230000   -7.590000   -6.330000   \n",
       "25%       2.037500    1.870000    1.492500    1.655000    2.462500   \n",
       "50%       5.205000    4.070000    3.495000    3.925000    5.690000   \n",
       "75%      12.375000    8.192500    6.700000    8.397500   10.100000   \n",
       "max    2630.000000  106.000000  197.000000   93.900000   94.200000   \n",
       "\n",
       "             2004        2005        2006        2007        2008  ...  \\\n",
       "count  170.000000  170.000000  170.000000  170.000000  170.000000  ...   \n",
       "mean     8.200677    9.435947    6.928759    9.739364    3.277504  ...   \n",
       "std      8.684602   17.281747    5.353072    7.319135   12.565882  ...   \n",
       "min     -2.760000   -2.270000   -6.690000   -1.390000  -27.600000  ...   \n",
       "25%      2.577500    2.820000    3.132500    4.015000    0.173750  ...   \n",
       "50%      5.875000    5.845000    5.855000    8.130000    2.430000  ...   \n",
       "75%     11.075000   10.575000    8.887500   13.075000    6.175000  ...   \n",
       "max     59.300000  175.000000   27.200000   35.000000   95.400000  ...   \n",
       "\n",
       "             2010        2011        2012        2013        2014        2015  \\\n",
       "count  170.000000  170.000000  170.000000  170.000000  170.000000  170.000000   \n",
       "mean     7.817045    5.243213    3.845928    3.573067    2.684615    3.604046   \n",
       "std      8.266167    7.407848    6.433933    5.959206    7.848265    6.286108   \n",
       "min     -1.620000   -2.940000   -6.670000  -17.300000  -30.200000  -11.100000   \n",
       "25%      2.435000    1.485000    1.030000    0.660750    0.575000    0.662250   \n",
       "50%      5.650000    3.580000    2.525000    2.300000    2.275000    1.860000   \n",
       "75%     10.375000    7.167500    4.945000    5.277500    4.925000    4.697500   \n",
       "max     71.000000   75.300000   54.000000   40.300000   42.500000   41.100000   \n",
       "\n",
       "             2016        2017        2018        2019  \n",
       "count  170.000000  170.000000  170.000000  170.000000  \n",
       "mean     5.470644    5.446839    5.954814    7.310469  \n",
       "std      7.381751    8.569805   27.424747   48.222917  \n",
       "min     -3.200000   -2.890000   -3.420000  -29.000000  \n",
       "25%      1.527500    1.710000    0.978000    0.065200  \n",
       "50%      3.565000    2.875000    2.345000    1.755000  \n",
       "75%      6.317500    5.550000    4.197500    4.892500  \n",
       "max     58.200000   61.300000  350.000000  610.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation_2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "31705aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>-9.38</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>1.94</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.28</td>\n",
       "      <td>3.23</td>\n",
       "      <td>2.02</td>\n",
       "      <td>...</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.34</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.26</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  ...  \\\n",
       "country                                                              ...   \n",
       "Malta   -9.38  4.42  2.73  1.87  2.11  1.94  2.16  2.28  3.23  2.02  ...   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "country                                                              \n",
       "Malta    1.12  2.15  2.28  2.34  4.22  1.76  2.22  2.26  2.31  1.45  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_inflation_1999= inflation_2[inflation_2['1999']== min(inflation_2['1999'])]\n",
    "min_inflation_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c0624392",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2006</th>\n",
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       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Congo, Dem. Rep.</th>\n",
       "      <td>2630.0</td>\n",
       "      <td>73.1</td>\n",
       "      <td>31.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>6.37</td>\n",
       "      <td>29.9</td>\n",
       "      <td>13.3</td>\n",
       "      <td>20.3</td>\n",
       "      <td>20.5</td>\n",
       "      <td>32.7</td>\n",
       "      <td>...</td>\n",
       "      <td>13.8</td>\n",
       "      <td>5.94</td>\n",
       "      <td>2.77</td>\n",
       "      <td>0.994</td>\n",
       "      <td>-1.16</td>\n",
       "      <td>4.35</td>\n",
       "      <td>43.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    1999  2000  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "country                                                                    \n",
       "Congo, Dem. Rep.  2630.0  73.1  31.7  13.4  6.37  29.9  13.3  20.3  20.5   \n",
       "\n",
       "                  2008  ...  2010  2011  2012   2013  2014  2015  2016  2017  \\\n",
       "country                 ...                                                    \n",
       "Congo, Dem. Rep.  32.7  ...  13.8  5.94  2.77  0.994 -1.16  4.35  43.1  29.0   \n",
       "\n",
       "                  2018  2019  \n",
       "country                       \n",
       "Congo, Dem. Rep.   4.7  10.3  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_inflation_1999= inflation_2[inflation_2['1999']== max(inflation_2['1999'])]\n",
    "max_inflation_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f13b3219",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
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       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Libya</th>\n",
       "      <td>13.3</td>\n",
       "      <td>7.18</td>\n",
       "      <td>27.2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>22.6</td>\n",
       "      <td>28.6</td>\n",
       "      <td>9.46</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.8</td>\n",
       "      <td>-25.3</td>\n",
       "      <td>...</td>\n",
       "      <td>18.3</td>\n",
       "      <td>8.98</td>\n",
       "      <td>-6.67</td>\n",
       "      <td>-17.3</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>-2.56</td>\n",
       "      <td>14.4</td>\n",
       "      <td>18.1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  ...  \\\n",
       "country                                                              ...   \n",
       "Libya    13.3  7.18  27.2  14.4  22.6  28.6  9.46  11.0  21.8 -25.3  ...   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "country                                                              \n",
       "Libya    18.3  8.98 -6.67 -17.3 -19.4 -2.56  14.4  18.1  -1.1 -29.0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_inflation_2019= inflation_2[inflation_2['2019']== inflation_2['2019'].min()]\n",
    "min_inflation_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3af455ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Zimbabwe</th>\n",
       "      <td>0.628</td>\n",
       "      <td>-0.131</td>\n",
       "      <td>2.71</td>\n",
       "      <td>8.8</td>\n",
       "      <td>7.61</td>\n",
       "      <td>5.14</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>0.895</td>\n",
       "      <td>1.35</td>\n",
       "      <td>95.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.55</td>\n",
       "      <td>4.03</td>\n",
       "      <td>9.37</td>\n",
       "      <td>-0.252</td>\n",
       "      <td>0.608</td>\n",
       "      <td>2.16</td>\n",
       "      <td>10.8</td>\n",
       "      <td>61.3</td>\n",
       "      <td>350.0</td>\n",
       "      <td>610.0</td>\n",
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       "           1999   2000  2001  2002  2003  2004  2005   2006  2007  2008  ...  \\\n",
       "country                                                                  ...   \n",
       "Zimbabwe  0.628 -0.131  2.71   8.8  7.61  5.14 -2.02  0.895  1.35  95.4  ...   \n",
       "\n",
       "          2010  2011  2012   2013   2014  2015  2016  2017   2018   2019  \n",
       "country                                                                   \n",
       "Zimbabwe  2.55  4.03  9.37 -0.252  0.608  2.16  10.8  61.3  350.0  610.0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_inflation_2019= inflation_2[inflation_2['2019']== inflation_2['2019'].max()]\n",
    "max_inflation_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ed2a8183",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>13.3</td>\n",
       "      <td>7.18</td>\n",
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       "      <td>14.4</td>\n",
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       "      <td>-19.4</td>\n",
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       "      <td>-29.0</td>\n",
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       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  ...  \\\n",
       "country                                                              ...   \n",
       "Libya    13.3  7.18  27.2  14.4  22.6  28.6  9.46  11.0  21.8 -25.3  ...   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "country                                                              \n",
       "Libya    18.3  8.98 -6.67 -17.3 -19.4 -2.56  14.4  18.1  -1.1 -29.0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_inflation_2019= inflation_2[inflation_2['2019'] == inflation_2['2019'].min()]\n",
    "min_inflation_2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "829b34bd",
   "metadata": {},
   "source": [
    "# Visualizing Inflation Dataset to understand the relationship between minimum inflation and maximum inflation in different years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a49de25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_inflation_1= inflation_2['1999'].min()\n",
    "min_inflation_2= inflation_2['2019'].min()\n",
    "max_inflation_1= inflation_2['1999'].max()\n",
    "max_inflation_2= inflation_2['2019'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "befc1cc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lXCPpSdKJ346tZ4HcY7I3qQ48RLpp7QN1B4VppK/+3FsZFulO1ZoPkBLdraS6fzFLu0ZfA/xZ0sIc4zERcXcum8zy9bjf5br9cdLB+FHSAfyKSvntpG7Vu3Is4+rmv4N0InIaaTvtRfpa5nP9FGKvjn29PK6uqDNJCekA0g12z5CObZC6U39N+jz/BjgrIqZU5n0b6XP+KKn7ePecH2pOJd1bcEf+/+EWcXwU+Equ318i7cue+jKpC/pu0gnjOS2m3Ql4B+lY+JjS93kXStoZICJm53U5j3Qv0Ro5tpqDSNvoh6RepGdI27CmN+sM5Ls3rXtKX2TfNCIOLB2LdU/SWcD9EfGF0rGYmTVT+ovTZv1O6caqfUk/LGNm1rGG7QMcJO0u6Q5JcyR9vnQ81j8knUjqhv9WpVvUzKwjDcvu6HzjwT9IX4KfT7p77YCIuLVoYGZmNqwM15bwDqQfV7gr33xxAelGCDMzs7YZrteE12PZL3fPp+5uU0mHk74vyujRo7ffcsst+/xmf7/v8T7Pa0PbK9dr9q2Z9nIdtWZWpI7efPPND0XE2H4MZ8gZrkm40ZfAl+mXz7fiTwHo6uqKGTNm9PnNNvp8oweUmMGMb+xZOgTAddSaW5E6KqnbX68a7oZrd/R8lv2Fle5++crMzKzfDdck/BdgM0kbS1qF9AMWzX5f2MzMbEAMy+7oiFgs6SjSk0lGkH4JZnbhsMzMbJgZlkkYICJ+TfpJNjMzsyKGa3e0mZlZcU7CZmZmhTgJm5mZFeIkbGZmVoiTsJmZWSFOwmZmZoU4CZuZmRXiJGxmZlaIk7CZmVkhTsJmZmaFOAmbmZkV4iRsZmZWiJOwmZlZIU7CZmZmhTgJm5mZFeIkbGZmVoiTsJmZWSFOwmZmZoU4CZuZmRXiJGxmZlaIk7CZmVkhTsJmZmaFOAmbmZkV4iRsZmZWiJOwmZlZIU7CZmZmhTgJm5mZFTLskrCkyZLukzQz/729dExmZjY8jSwdQCHfi4hvlw7CzMyGt2HXEjYzM+sUwzUJHyXpFklnSXpxowkkHS5phqQZCxYsaHd8ZmY2DAzJJCzpOkmzGvztA/wQ2ASYCDwAfKfRMiJiSkR0RUTX2LFj2xe8mZkNG0PymnBE7NaT6SSdCVw5wOGYmZk1NCRbwq1Ienll8F3ArFKxmJnZ8DYkW8LdOFnSRCCAe4AjikZjZmbD1rBLwhFxUOkYzMzMYBh2R5uZmXUKJ2EzM7NCnITNzMwKcRI2MzMrxEnYzMysECdhMzOzQpyEzczMCnESNjMzK8RJ2MzMrBAnYTMzs0KchM3MzApxEjYzMyvESdjMzKwQJ2EzM7NCnITNzMwKcRI2MzMrxEnYzMysECdhMzOzQpyEzczMCnESNjMzK8RJ2MzMrBAnYTMzs0KchM3MzApxEjYzMyvESdjMzKyQkaUDGA7u+caepUMwM7MO5JawmZlZIUMyCUvaT9JsSUskddWVHSdpjqQ7JL2tVIxmZmZDtTt6FrAv8KPqSEkTgP2BrYFxwHWSNo+I59sfopmZDXdDsiUcEbdFxB0NivYBLoiIRRFxNzAH2KG90ZmZmSVDMgm3sB4wrzI8P49bjqTDJc2QNGPBggVtCc7MzIaXQdsdLek6YN0GRcdHxOXNZmswLhpNGBFTgCkAXV1dDacxMzNbEYM2CUfEbn2YbT6wQWV4feD+/onIzMysd4Zbd/QVwP6SVpW0MbAZcFPhmMzMbJgakklY0rskzQdeB1wl6WqAiJgNXATcCvwW+JjvjDYzs1IGbXd0KxFxKXBpk7KTgJPaG5GZmdnyhmRL2MzMbDDoiCQsaXVJX5R0Zh7eTNI7SsdlZmY2kDoiCQM/BRaRruFCuov5q+XCMTMzG3idkoQ3iYiTgf8ARMQzNP5Or5mZ2ZDRKUn4OUmrkX84Q9ImpJaxmZnZkNUpd0dPJn1laANJ5wGvBz5YNCIzM7MB1hFJOCKukXQz8FpSN/QxEfFQ4bDMzMwGVEd0R0u6PiIejoirIuLKiHhI0vWl4zIzMxtIRVvCkkYBqwPrSHoxS2/GGkN63q+ZmdmQVbo7+gjgWFLCvZmlSfgJ4PuFYjIzM2uLokk4Ik4FTpV0dEScVjIWMzOzdivdEgYgIk6TtA0wARhVGX92uajMzMwGVkckYUknAJNISfjXwB7ADYCTsJmZDVkdcXc08B5gV+DBiPggsB2watmQzMzMBlanJOFnImIJsFjSGODfwCsKx2RmZjagOqI7GpghaS3gTNJd0guBm4pGZGZmNsCKJ2FJAr4eEY8BZ0j6LTAmIm4pG5mZmdnAKt4dHREBXFYZvscJ2MzMhoPiSTibLuk1pYMwMzNrp+Ld0dmbgCMkzQWeIv1yVkTEtmXDMjMzGzidkoT3aFUo6cUR8Wi7gjEzM2uHjkjCETG3m0muB17djljMzMzapVOuCXdH3U9iZmY2uAyWJBylAzAzM+tvgyUJm5mZDTmDJQm7O9rMzIacjknCkkZIGidpfO2vUrxrL5e1n6TZkpZI6qqM30jSM5Jm5r8z+m0FzMzMeqkj7o6WdDRwAvAvYEkeHcC2ABHxSC8XOQvYF/hRg7I7I2Ji3yI1MzPrPx2RhIFjgC0i4uH+WFhE3AaQfpbazMysM3VKd/Q84PE2vdfGkv4qaZqkndv0nmZmZsvplJbwXcBUSVcBi2ojI+K7zWaQdB2wboOi4yPi8iazPQCMj4iHJW0PXCZp64h4osHyDwcOBxg/fnx9sZmZ2QrrlCR8b/5bJf91KyJ26+2bRMQicpKPiJsl3QlsDsxoMO0UYApAV1eXv6dsZmb9riOScER8GUDSGmkwFg7E+0gaCzwSEc9LegWwGakVbmZm1nYdcU1Y0jaS/kq6q3m2pJslbb0Cy3uXpPnA64CrJF2di3YBbpH0N+Bi4Mg+3HltZmbWLzqiJUzq9v1kRPwOQNIk4Exgp74sLCIuBS5tMP4S4JI+R2lmZtaPOqIlDIyuJWCAiJgKjC4XjpmZ2cDrlJbwXZK+CJyThw8E7i4Yj5mZ2YDrlJbwocBY4JekbuSxwAeLRmRmZjbAOqIlHBGPAh8vHYeZmVk7FU3Ckk6JiGMl/YoGzwyOiL0LhGVmZtYWpVvCtWvA3y4ahZmZWQFFk3BE3JxfToyIU6tlko4BprU/KjMzs/bolBuzDm4w7pB2B2FmZtZOpa8JHwC8j/RkoysqRWsA/fJYQzMzs05V+prwjaQnG60DfKcy/kngliIRmZmZtUnpa8Jzgbmk33g2MzMbVjrimrCk10r6i6SFkp6T9Lyk5Z7xa2ZmNpR0RBIGTgcOAP4JrAYcBpxWNCIzM7MBVvqa8AsiYo6kERHxPPBTSTeWjsnMzGwgdUoSflrSKsBMSSeTbtbyU5TMzGxI65Tu6IOAEcBRwFPABsC7i0ZkZmY2wDqiJZzvkgZ4BvhyyVjMzMzapfSPdfydBg9uqImIbdsYjpmZWVuVbgnvR2r9mpmZDTulk/AvIuLVks6JiIMKx2JmZtZWpZPwKpIOBnaStG99YUT8skBMZmZmbVE6CR8JvB9YC9irriwAJ2EzMxuySv929A3ADZJmRMRPSsZiZmbWbqVbwgBExE8k7QRsRCWmiDi7WFBmZmYDrCOSsKRzgE2AmcDzeXQATsJmZjZkdUQSBrqACRHR9DvDZmZmQ02n/GzlLGDd0kGYmZm1U6e0hNcBbpV0E7CoNjIi9u7LwiR9i3S39XPAncAHI+KxXHYc8CFSt/fHI+LqFQvdzMysbzolCU/u5+VdCxwXEYslfRM4DvicpAnA/sDWwDjgOkmb58cnmpmZtVVHJOGImNbPy7umMjgdeE9+vQ9wQUQsAu6WNAfYAfhTf76/mZlZT5R+gMOTNH6Ag4CIiDH98DaHAhfm1+uRknLN/DyuUWyHA4cDjB8/vh/CMDMzW1bpH+tYo6/zSrqOxjdzHR8Rl+dpjgcWA+fVZmsURpPYpgBTALq6unzXtpmZ9buO6I7ui4jYrVV5/k3qdwC7Vr76NB/YoDLZ+sD9AxOhmZlZa53yFaV+JWl34HPA3hHxdKXoCmB/SatK2hjYDLipRIxmZmaDtiXcjdOBVYFrJQFMj4gjI2K2pIuAW0nd1B/zndFmZlbKkEzCEbFpi7KTgJPaGI6ZmVlDQ7I72szMbDBwEjYzMyvESdjMzKwQJ2EzM7NCnITNzMwKcRI2MzMrxEnYzMysECdhMzOzQpyEzczMCnESNjMzK8RJ2MzMrBAnYTMzs0KchM3MzApxEjYzMyvESdjMzKwQJ2EzM7NCnITNzMwKcRI2MzMrxEnYzMysECdhMzOzQpyEzczMCnESNjMzK8RJ2MzMrBAnYTMzs0KchM3MzApxEjYzMyvESdjMzKyQIZmEJX1L0u2SbpF0qaS18viNJD0jaWb+O6NwqGZmNowNySQMXAtsExHbAv8AjquU3RkRE/PfkWXCMzMzG6JJOCKuiYjFeXA6sH7JeMzMzBoZkkm4zqHAbyrDG0v6q6RpknZuNpOkwyXNkDRjwYIFAx+lmZkNOyNLB9BXkq4D1m1QdHxEXJ6nOR5YDJyXyx4AxkfEw5K2By6TtHVEPFG/kIiYAkwB6OrqioFYBzMzG94GbRKOiN1alUs6GHgHsGtERJ5nEbAov75Z0p3A5sCMAQ7XzMxsOUOyO1rS7sDngL0j4unK+LGSRuTXrwA2A+4qE6WZmQ13g7Yl3I3TgVWBayUBTM93Qu8CfEXSYuB54MiIeKRcmGZmNpwNySQcEZs2GX8JcEmbwzEzM2toSHZHm5mZDQZOwmZmZoU4CZuZmRXiJGxmZlaIk7CZmVkhTsJmZmaFOAmbmZkV4iRsZmZWiJOwmZlZIU7CZmZmhTgJm5mZFeIkbGZmVoiTsJmZWSFOwmZmZoU4CZuZmRXiJGxmZlaIk7CZmVkhTsJmZmaFOAmbmZkV4iRsZmZWiJOwmZlZIU7CZmZmhTgJm5mZFTKydABmVt4939izdAhmw5JbwmZmZoU4CZuZmRUyJJOwpBMl3SJppqRrJI2rlB0naY6kOyS9rWScZmY2vA3JJAx8KyK2jYiJwJXAlwAkTQD2B7YGdgd+IGlEsSjNzGxYG5JJOCKeqAyOBiK/3ge4ICIWRcTdwBxgh3bHZ2ZmBkP47mhJJwEfAB4H3pRHrwdMr0w2P48zMzNru0HbEpZ0naRZDf72AYiI4yNiA+A84KjabA0WFQ3GIelwSTMkzViwYMHArISZmQ1rg7YlHBG79XDSXwBXASeQWr4bVMrWB+5vsvwpwBSArq6uhonazMxsRQzalnArkjarDO4N3J5fXwHsL2lVSRsDmwE3tTs+MzMzAEUMvUaepEuALYAlwFzgyIi4L5cdDxwKLAaOjYjf9GB5C/JybMWtAzxUOgizFlxH+8+GETG2dBCdbEgmYetckmZERFfpOMyacR21dhqS3dFmZmaDgZOwmZlZIU7C1m5TSgdg1g3XUWsbXxM2MzMrxC1hMzOzQpyEzczMCnESNjMzK8RJeJiTFJLOqQyPlLRA0pXdzDepNk1+vVMf339LSX+StEjSp+vKjsm/Bz5b0rGV8dvlef4u6VeSxuTxq0j6aR7/N0mT+hKTdR5JCxuMO1LSB/LrqZIG5Lu9kt6fn09+i6QbJW1XKds9P5t8jqTPV8bvl+vtkmpcrqNWz0nYngK2kbRaHn4LcF8vlzEJ6FMSBh4BPg58uzpS0jbAh0mPmtwOeEfl50h/DHw+Il4JXAp8Jo//MEAe/xbgO5Jcx4eoiDgjIs5uw1vdDbwxIrYFTiTfPZ2fRf59YA9gAnBAfmY5wCxgX+D3dctyHbVleOcbwG+APfPrA4DzawWSdshn/3/N/7eozihpI+BI4BOSZkraWdJekv6c57lO0suavXFE/Dsi/gL8p65oK2B6RDwdEYuBacC7ctkWLD24XQu8O7+eAFxfWy7wGOBfPhqiJE2u6z05MNfRWbneriTpn5LG5ulXyi3WdXpZR2+MiEfz4HTSg18gnSDOiYi7IuI54ALSM8uJiNsi4o4Gi3MdtWU4CRukg8f+kkYB2wJ/rpTdDuwSEa8CvgR8rTpjRNwDnAF8LyImRsQfgBuA1+Z5LgA+24eYZgG7SFpb0urA21n6BKxZpAdzAOxXGf83YJ/cpb4xsD3LPjXLhrbREbET8FHgrIhYApwLvD+X7wb8LSIeou919EOkk1ZIzyKfVynryfPJXUdtGYP2UYbWfyLiltyiPQD4dV3xmsDPc1dwACv3YJHrAxdKejmwCqk7r7cx3Sbpm6SW7kLSwWtxLj4U+H+SvkR6MtZzefxZpBb0DNIDN26szGND3/kAEfF7SWMkrUWqE5cDp5DqzU/ztL2uo5LeRErCb6iNajBZdz+84Dpqy3BL2GquIF2XPb9u/InA7yJiG2AvYFQPlnUacHq+7nVED+dZTkT8JCJeHRG7kK4d/zOPvz0i3hoR2+d478zjF0fEJ3KLfB9grdo8NizUJ8CIiHnAvyS9GdiRpa3YXtVRSduS7kXYJyIezqN7/HzySkCuo7YMJ2GrOQv4SkT8vW78miy9UeuQJvM+CazRZJ6D+xqQpJfm/+NJN7mcXzd+JeALpO5wJK0uaXR+/RZgcUTc2tf3t0HnvwAkvQF4PCIez+N/TOqWvigins/jelxHc/37JXBQRPyjUvQXYDNJG0taBdifdDLbalmuo7YMd0cbABExHzi1QdHJpO7oTwL/22T2XwEXS9oHOBqYDPyPpPtIN7Js3Ox9Ja1L6pobAyzJX0WaEBFPAJdIWpt009bHKjfHHCDpY/n1L1naxfhS4GpJS0gH2IO6XXEbLFaXNL8y/N0G0zwq6UZSXTq0Mv4KUh35aWXcZHpYR0n3QqwN/EASpMTZFRGLJR0FXA2MIF2Hng0g6V2k1vZY4CpJMyPibbiOWh3/drSZDWn5e7rfi4idS8diVs8tYTMbsvIPaHyEpXdIm3UUt4StLSR9EDimbvQfI+JjjaY3azfXUSvBSdjMzKwQ3x1tZmZWiJOwmZlZIU7CZh1IyQ2S9qiMe6+k35aMy8z6l68Jm3Wo/CSp/wFeRfoe6kxg94i4sw/LGlH5oQoz6xBOwmYdTNLJpMdNjs7/NwReSfp64eSIuDz/7vc5eRqAoyLixvys2hOAB4CJETEBM+soTsJmHSz/xOH/kR5ScSUwOyLOzQ8nuInUSg5gSUQ8mx+0cX5EdOUkfBWwTUT0+iEaZjbw/GMdZh0sIp6SdCHpSVLvBfaqPEN3FDCe9NCA0yVNBJ4HNq8s4iYnYLPO5SRs1vmW5D8B765/WLykycC/gO1IN1s+Wyl+qk0xmlkf+O5os8HjauBo5acISHpVHr8m8EB+iP1BpJu4zGwQcBI2GzxOBFYGbpE0Kw8D/AA4WNJ0Ule0W79mg4RvzDIzMyvELWEzM7NCnITNzMwKcRI2MzMrxEnYzMysECdhMzOzQpyEzczMCnESNjMzK+T/A/0t0W3dWsc2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Malta_1999', 'Libya_2019'], [min_inflation_1, min_inflation_2]) \n",
    "#plt.(['min_1999', 'min_2019'], [max_inflation_1, max_inflation_2]) \n",
    "plt.title('Relationship between country with the lowest inflation rate in 1999 and 2019')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Inflation_rate');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "227cf6c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The minimum inflation rate in the year 1999 decreased by 209.17% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# The minimum inflation rate in 1999 and 2019 is on a downward trend\n",
    "difference= min_inflation_2- min_inflation_1\n",
    "percentage_rise= (difference/min_inflation_1)*100\n",
    "print (f' The minimum inflation rate in the year 1999 decreased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "aae329bb",
   "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": [
    "plt.bar(['Congo_Dem.republic_1999', 'Zimbabwe_2019'], [max_inflation_1, max_inflation_2]) \n",
    "plt.title('Relationship between country with the highest inflation rate in 1999 and 2019')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Inflation_rate');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "caac8142",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The maximum inflation rate in the year 1999 decreased by 76.81% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# The maximum inflation rate in 1999 and 2019 is on a downward trend\n",
    "difference= max_inflation_1- max_inflation_2\n",
    "percentage_rise= (difference/max_inflation_1)*100\n",
    "print (f' The maximum inflation rate in the year 1999 decreased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9866496e",
   "metadata": {},
   "source": [
    "# Summary of the Inflation Analysis\n",
    "* The minimum Inflation Rate in 1999 decreased by 209.17% in the year 2019\n",
    "* The maximum Inflation Rate in 1999 decreased by 79.81% in the year 2019\n",
    "\n",
    "\n",
    " This shows how much Economic rate of all the in the data set countries has improved over the years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10318c2d",
   "metadata": {},
   "source": [
    "# Exploring Investment Data Set\n",
    "* Clean The data\n",
    "* Remove missing rows\n",
    "* Convert all columns to Float Data types for easy analyzing\n",
    "* Check for relationships between the columns \n",
    "* Communicate your findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "6f975596",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>25.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>29.2</td>\n",
       "      <td>28.8</td>\n",
       "      <td>33.6</td>\n",
       "      <td>35.2</td>\n",
       "      <td>32.5</td>\n",
       "      <td>33.3</td>\n",
       "      <td>30.3</td>\n",
       "      <td>...</td>\n",
       "      <td>28.5</td>\n",
       "      <td>26.6</td>\n",
       "      <td>23.8</td>\n",
       "      <td>23.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>21.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</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>Angola</th>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>27.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>25.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>...</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>30.8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>33.9</td>\n",
       "      <td>34.2</td>\n",
       "      <td>36.9</td>\n",
       "      <td>35.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>35.8</td>\n",
       "      <td>34.6</td>\n",
       "      <td>...</td>\n",
       "      <td>31.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>24.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>31.6</td>\n",
       "      <td>...</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>22.2</td>\n",
       "      <td>25.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1999  2000  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "country                                                                      \n",
       "Aruba                 25.5  23.7  26.9  29.2  28.8  33.6  35.2  32.5  33.3   \n",
       "Afghanistan            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                30.5  30.5  30.5  30.5  30.9  27.6  23.3  25.7  30.8   \n",
       "Albania               30.8  35.0  35.3  33.9  34.2  36.9  35.7  34.5  35.8   \n",
       "United Arab Emirates   NaN  22.5  22.5  22.1  20.0  20.2  19.3  25.2  25.1   \n",
       "\n",
       "                      2008  ...  2010  2011  2012  2013  2014  2015  2016  \\\n",
       "country                     ...                                             \n",
       "Aruba                 30.3  ...  28.5  26.6  23.8  23.1  21.5  20.6  20.6   \n",
       "Afghanistan            NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                42.8  ...  26.4  26.7  26.1  27.5  34.2  27.2  24.1   \n",
       "Albania               34.6  ...  31.4  28.3  28.1  25.7  25.8  25.2  25.1   \n",
       "United Arab Emirates  31.6  ...  23.2  21.9  19.3  22.2  25.6  26.0  25.3   \n",
       "\n",
       "                      2017  2018  2019  \n",
       "country                                 \n",
       "Aruba                 21.6   NaN   NaN  \n",
       "Afghanistan            NaN   NaN   NaN  \n",
       "Angola                21.4  19.8  22.8  \n",
       "Albania               24.1   NaN   NaN  \n",
       "United Arab Emirates  23.5  23.8   NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9f264b5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 188 entries, Aruba to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1999    166 non-null    float64\n",
      " 1   2000    167 non-null    float64\n",
      " 2   2001    168 non-null    float64\n",
      " 3   2002    169 non-null    float64\n",
      " 4   2003    169 non-null    float64\n",
      " 5   2004    170 non-null    float64\n",
      " 6   2005    171 non-null    float64\n",
      " 7   2006    173 non-null    float64\n",
      " 8   2007    175 non-null    float64\n",
      " 9   2008    174 non-null    float64\n",
      " 10  2009    176 non-null    float64\n",
      " 11  2010    176 non-null    float64\n",
      " 12  2011    175 non-null    float64\n",
      " 13  2012    174 non-null    float64\n",
      " 14  2013    175 non-null    float64\n",
      " 15  2014    176 non-null    object \n",
      " 16  2015    175 non-null    float64\n",
      " 17  2016    174 non-null    float64\n",
      " 18  2017    174 non-null    float64\n",
      " 19  2018    163 non-null    float64\n",
      " 20  2019    130 non-null    float64\n",
      "dtypes: float64(20), object(1)\n",
      "memory usage: 32.3+ KB\n"
     ]
    }
   ],
   "source": [
    "investment.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7fea281f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# there is only one column that is not in float dataset, so we will convert the object datatype into float datatype\n",
    "for label, content in investment.items():\n",
    "    if not pd.api.types.is_numeric_dtype(content):\n",
    "        investment[label]= content.replace(r\"[^\\x00-\\x7F]+\", \"-\", regex= True)\n",
    "        investment[label]= investment[label].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e3c53f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 188 entries, Aruba to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1999    166 non-null    float64\n",
      " 1   2000    167 non-null    float64\n",
      " 2   2001    168 non-null    float64\n",
      " 3   2002    169 non-null    float64\n",
      " 4   2003    169 non-null    float64\n",
      " 5   2004    170 non-null    float64\n",
      " 6   2005    171 non-null    float64\n",
      " 7   2006    173 non-null    float64\n",
      " 8   2007    175 non-null    float64\n",
      " 9   2008    174 non-null    float64\n",
      " 10  2009    176 non-null    float64\n",
      " 11  2010    176 non-null    float64\n",
      " 12  2011    175 non-null    float64\n",
      " 13  2012    174 non-null    float64\n",
      " 14  2013    175 non-null    float64\n",
      " 15  2014    176 non-null    float64\n",
      " 16  2015    175 non-null    float64\n",
      " 17  2016    174 non-null    float64\n",
      " 18  2017    174 non-null    float64\n",
      " 19  2018    163 non-null    float64\n",
      " 20  2019    130 non-null    float64\n",
      "dtypes: float64(21)\n",
      "memory usage: 32.3+ KB\n"
     ]
    }
   ],
   "source": [
    "investment.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "71c4d7e9",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>25.5</td>\n",
       "      <td>23.7</td>\n",
       "      <td>26.9</td>\n",
       "      <td>29.2</td>\n",
       "      <td>28.8</td>\n",
       "      <td>33.6</td>\n",
       "      <td>35.2</td>\n",
       "      <td>32.5</td>\n",
       "      <td>33.3</td>\n",
       "      <td>30.3</td>\n",
       "      <td>...</td>\n",
       "      <td>28.5</td>\n",
       "      <td>26.6</td>\n",
       "      <td>23.8</td>\n",
       "      <td>23.1</td>\n",
       "      <td>21.5</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>21.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</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>Angola</th>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>27.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>25.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>...</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>30.8</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>33.9</td>\n",
       "      <td>34.2</td>\n",
       "      <td>36.9</td>\n",
       "      <td>35.7</td>\n",
       "      <td>34.5</td>\n",
       "      <td>35.8</td>\n",
       "      <td>34.6</td>\n",
       "      <td>...</td>\n",
       "      <td>31.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>24.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>22.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>19.3</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.1</td>\n",
       "      <td>31.6</td>\n",
       "      <td>...</td>\n",
       "      <td>23.2</td>\n",
       "      <td>21.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>22.2</td>\n",
       "      <td>25.6</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>23.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1999  2000  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "country                                                                      \n",
       "Aruba                 25.5  23.7  26.9  29.2  28.8  33.6  35.2  32.5  33.3   \n",
       "Afghanistan            NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                30.5  30.5  30.5  30.5  30.9  27.6  23.3  25.7  30.8   \n",
       "Albania               30.8  35.0  35.3  33.9  34.2  36.9  35.7  34.5  35.8   \n",
       "United Arab Emirates   NaN  22.5  22.5  22.1  20.0  20.2  19.3  25.2  25.1   \n",
       "\n",
       "                      2008  ...  2010  2011  2012  2013  2014  2015  2016  \\\n",
       "country                     ...                                             \n",
       "Aruba                 30.3  ...  28.5  26.6  23.8  23.1  21.5  20.6  20.6   \n",
       "Afghanistan            NaN  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "Angola                42.8  ...  26.4  26.7  26.1  27.5  34.2  27.2  24.1   \n",
       "Albania               34.6  ...  31.4  28.3  28.1  25.7  25.8  25.2  25.1   \n",
       "United Arab Emirates  31.6  ...  23.2  21.9  19.3  22.2  25.6  26.0  25.3   \n",
       "\n",
       "                      2017  2018  2019  \n",
       "country                                 \n",
       "Aruba                 21.6   NaN   NaN  \n",
       "Afghanistan            NaN   NaN   NaN  \n",
       "Angola                21.4  19.8  22.8  \n",
       "Albania               24.1   NaN   NaN  \n",
       "United Arab Emirates  23.5  23.8   NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fa370717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999    22\n",
       "2000    21\n",
       "2001    20\n",
       "2002    19\n",
       "2003    19\n",
       "2004    18\n",
       "2005    17\n",
       "2006    15\n",
       "2007    13\n",
       "2008    14\n",
       "2009    12\n",
       "2010    12\n",
       "2011    13\n",
       "2012    14\n",
       "2013    13\n",
       "2014    12\n",
       "2015    13\n",
       "2016    14\n",
       "2017    14\n",
       "2018    25\n",
       "2019    58\n",
       "dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets view if there is any missing data\n",
    "investment.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cda4cc4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now lets drop all missing data\n",
    "investment.dropna(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c6ccc69d",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.00000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>125.00000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.00000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.483520</td>\n",
       "      <td>21.556240</td>\n",
       "      <td>21.385920</td>\n",
       "      <td>22.044880</td>\n",
       "      <td>22.878720</td>\n",
       "      <td>23.497920</td>\n",
       "      <td>24.263920</td>\n",
       "      <td>25.608720</td>\n",
       "      <td>26.24008</td>\n",
       "      <td>23.219120</td>\n",
       "      <td>...</td>\n",
       "      <td>24.22504</td>\n",
       "      <td>23.976000</td>\n",
       "      <td>23.814800</td>\n",
       "      <td>23.760320</td>\n",
       "      <td>24.272720</td>\n",
       "      <td>23.84896</td>\n",
       "      <td>23.620800</td>\n",
       "      <td>23.920560</td>\n",
       "      <td>24.146400</td>\n",
       "      <td>22.956320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.503338</td>\n",
       "      <td>6.001869</td>\n",
       "      <td>7.042935</td>\n",
       "      <td>7.080528</td>\n",
       "      <td>7.130474</td>\n",
       "      <td>6.868932</td>\n",
       "      <td>6.641374</td>\n",
       "      <td>7.836245</td>\n",
       "      <td>7.73220</td>\n",
       "      <td>7.777686</td>\n",
       "      <td>...</td>\n",
       "      <td>6.85511</td>\n",
       "      <td>7.514592</td>\n",
       "      <td>7.500867</td>\n",
       "      <td>7.162567</td>\n",
       "      <td>8.490465</td>\n",
       "      <td>8.35368</td>\n",
       "      <td>6.634676</td>\n",
       "      <td>6.983195</td>\n",
       "      <td>7.854406</td>\n",
       "      <td>8.067561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.93000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>8.940000</td>\n",
       "      <td>8.090000</td>\n",
       "      <td>7.82000</td>\n",
       "      <td>7.790000</td>\n",
       "      <td>9.970000</td>\n",
       "      <td>12.200000</td>\n",
       "      <td>5.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.500000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>21.80000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.90000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>19.70000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>19.400000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>17.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.200000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>25.90000</td>\n",
       "      <td>21.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>23.90000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>22.500000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>22.70000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>22.700000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>22.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25.900000</td>\n",
       "      <td>25.200000</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>25.300000</td>\n",
       "      <td>26.200000</td>\n",
       "      <td>27.200000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>29.200000</td>\n",
       "      <td>30.30000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>26.90000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>26.40000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>26.800000</td>\n",
       "      <td>26.400000</td>\n",
       "      <td>26.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.200000</td>\n",
       "      <td>40.300000</td>\n",
       "      <td>60.200000</td>\n",
       "      <td>53.200000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>48.900000</td>\n",
       "      <td>42.200000</td>\n",
       "      <td>61.100000</td>\n",
       "      <td>47.90000</td>\n",
       "      <td>54.300000</td>\n",
       "      <td>...</td>\n",
       "      <td>58.20000</td>\n",
       "      <td>55.900000</td>\n",
       "      <td>53.300000</td>\n",
       "      <td>53.600000</td>\n",
       "      <td>79.400000</td>\n",
       "      <td>74.00000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>47.300000</td>\n",
       "      <td>54.700000</td>\n",
       "      <td>55.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1999        2000        2001        2002        2003        2004  \\\n",
       "count  125.000000  125.000000  125.000000  125.000000  125.000000  125.000000   \n",
       "mean    21.483520   21.556240   21.385920   22.044880   22.878720   23.497920   \n",
       "std      6.503338    6.001869    7.042935    7.080528    7.130474    6.868932   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%     18.500000   18.300000   18.400000   18.800000   19.700000   19.800000   \n",
       "50%     22.200000   22.200000   21.200000   21.300000   22.700000   22.600000   \n",
       "75%     25.900000   25.200000   24.700000   25.300000   26.200000   27.200000   \n",
       "max     35.200000   40.300000   60.200000   53.200000   58.000000   48.900000   \n",
       "\n",
       "             2005        2006       2007        2008  ...       2010  \\\n",
       "count  125.000000  125.000000  125.00000  125.000000  ...  125.00000   \n",
       "mean    24.263920   25.608720   26.24008   23.219120  ...   24.22504   \n",
       "std      6.641374    7.836245    7.73220    7.777686  ...    6.85511   \n",
       "min      0.000000    0.000000    0.00000    0.000000  ...    9.93000   \n",
       "25%     20.800000   21.200000   21.80000   18.900000  ...   19.90000   \n",
       "50%     23.500000   24.600000   25.90000   21.400000  ...   23.90000   \n",
       "75%     28.400000   29.200000   30.30000   26.400000  ...   26.90000   \n",
       "max     42.200000   61.100000   47.90000   54.300000  ...   58.20000   \n",
       "\n",
       "             2011        2012        2013        2014       2015        2016  \\\n",
       "count  125.000000  125.000000  125.000000  125.000000  125.00000  125.000000   \n",
       "mean    23.976000   23.814800   23.760320   24.272720   23.84896   23.620800   \n",
       "std      7.514592    7.500867    7.162567    8.490465    8.35368    6.634676   \n",
       "min      6.700000    7.220000    8.940000    8.090000    7.82000    7.790000   \n",
       "25%     19.400000   19.300000   19.500000   19.800000   19.70000   19.600000   \n",
       "50%     23.300000   22.500000   22.800000   23.300000   22.70000   22.800000   \n",
       "75%     27.000000   27.500000   27.000000   27.500000   26.40000   27.300000   \n",
       "max     55.900000   53.300000   53.600000   79.400000   74.00000   48.500000   \n",
       "\n",
       "             2017        2018        2019  \n",
       "count  125.000000  125.000000  125.000000  \n",
       "mean    23.920560   24.146400   22.956320  \n",
       "std      6.983195    7.854406    8.067561  \n",
       "min      9.970000   12.200000    5.790000  \n",
       "25%     19.400000   18.900000   17.900000  \n",
       "50%     22.700000   22.800000   22.800000  \n",
       "75%     26.800000   26.400000   26.200000  \n",
       "max     47.300000   54.700000   55.800000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investment.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "14f04c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
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       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ethiopia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.1</td>\n",
       "      <td>37.1</td>\n",
       "      <td>34.1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>37.3</td>\n",
       "      <td>38.4</td>\n",
       "      <td>34.7</td>\n",
       "      <td>35.3</td>\n",
       "      <td>30.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  ...  \\\n",
       "country                                                               ...   \n",
       "Ethiopia   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "\n",
       "          2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "country                                                               \n",
       "Ethiopia  32.1  37.1  34.1  38.0  40.7  37.3  38.4  34.7  35.3  30.8  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# There are 125 Rows and 21 columns in this dataset\n",
    "min_investment_1999= investment[investment['1999']== investment['1999'].min()]\n",
    "min_investment_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ee26afb2",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
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       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>35.2</td>\n",
       "      <td>27.6</td>\n",
       "      <td>25.1</td>\n",
       "      <td>17.2</td>\n",
       "      <td>22.9</td>\n",
       "      <td>21.5</td>\n",
       "      <td>22.4</td>\n",
       "      <td>23.1</td>\n",
       "      <td>30.2</td>\n",
       "      <td>27.4</td>\n",
       "      <td>...</td>\n",
       "      <td>26.7</td>\n",
       "      <td>29.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.4</td>\n",
       "      <td>25.4</td>\n",
       "      <td>26.5</td>\n",
       "      <td>27.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>22.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  ...  \\\n",
       "country                                                                ...   \n",
       "Singapore  35.2  27.6  25.1  17.2  22.9  21.5  22.4  23.1  30.2  27.4  ...   \n",
       "\n",
       "           2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "country                                                                \n",
       "Singapore  26.7  29.3  30.0  29.4  25.4  26.5  27.3  25.0  24.7  22.6  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_investment_1999= investment[investment['1999']== investment['1999'].max()]\n",
    "max_investment_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "22a989be",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sudan</th>\n",
       "      <td>24.9</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>29.3</td>\n",
       "      <td>28.1</td>\n",
       "      <td>28.9</td>\n",
       "      <td>27.6</td>\n",
       "      <td>24.6</td>\n",
       "      <td>24.5</td>\n",
       "      <td>...</td>\n",
       "      <td>26.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>14.8</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.83</td>\n",
       "      <td>11.4</td>\n",
       "      <td>9.97</td>\n",
       "      <td>14.3</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  ...  \\\n",
       "country                                                              ...   \n",
       "Sudan    24.9  24.5  26.4  26.7  29.3  28.1  28.9  27.6  24.6  24.5  ...   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  2019  \n",
       "country                                                              \n",
       "Sudan    26.7  20.0  17.8  14.8  13.0  9.83  11.4  9.97  14.3  5.79  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_investment_2019= investment[investment['2019']== investment['2019'].min()]\n",
    "min_investment_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ce2227be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
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       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>29.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>26.9</td>\n",
       "      <td>36.2</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.7</td>\n",
       "      <td>...</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>27.2</td>\n",
       "      <td>32.6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1999  2000  2001  2002  2003  2004  2005  2006  2007  \\\n",
       "country                                                                     \n",
       "Antigua and Barbuda  29.0  20.5  22.1  23.2  23.5  26.9  36.2  37.4  37.4   \n",
       "\n",
       "                     2008  ...  2010  2011  2012  2013  2014  2015  2016  \\\n",
       "country                    ...                                             \n",
       "Antigua and Barbuda  37.7  ...  20.7  22.1  23.7  25.0  23.9  27.2  32.6   \n",
       "\n",
       "                     2017  2018  2019  \n",
       "country                                \n",
       "Antigua and Barbuda  37.0  42.5  55.8  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_investment_2019= investment[investment['2019']== investment['2019'].max()]\n",
    "max_investment_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "eb7fe8a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_investment_1= investment['1999'].min()\n",
    "min_investment_2= investment['2019'].min()\n",
    "max_investment_1= investment['1999'].max()\n",
    "max_investment_2= investment['2019'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ea3de953",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Ethiopia_1999', 'Sudan_2019'], [min_investment_1, min_investment_2]) \n",
    "plt.title('Relationship between country with the lowest Investment rate in 1999 and 2019')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Investment_rate');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3dbb5126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The minimum investment rate in the year 1999 increased by 579.00% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# The minimum inflation rate in 1999 and 2019 is on a upward trend\n",
    "difference= min_investment_2- min_investment_1\n",
    "percentage_rise= (difference)*100\n",
    "print (f' The minimum investment rate in the year 1999 increased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "1ff17965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6vF227TDtI6QbparduO103a79jaODbnW92amkyw9XS5pJOrncph/vO4eI+C1wGumm0ftJl4ggncB2m7dbfToYmJLr4adJJ11ExL2krwM9kNd/IL73fDmwHqlVdEclxk772XBSsnua1AX6JLN7Kn8MbJzj+2Vfg+njcbF53v7Wr2NJPRozSet1UdP4sVSOKRExiXTz6PdJ2+B+0r0m88LVpAbW9qTE9CLpMguknsEbSMeMccBxEXF1Zd6DST1P00nXp3eLiEb9HEY6vswitfJvJJ0ovEmur0eSTm6fJm2rdpf0WjmC1I3/GCkp/1+HafcF3kE6iZ5V+Vsrx3Il6dtrfyDVvYeonNyQEv+LpG/afCy/Pr6v61yl2b1c1omksaSbUT5WdyzWnaSzgUcj4viuExuQHk5CSoaLN12rNLMFmJ91b8VRupluP3IPirUnad/ctbo8qfX7Kyd5s7I40VtRJJ1AapV+JyIerDueBcCnSN3Lfyddo+7PzZlmNh9z172ZmVnB3KI3MzMr2Pz+YwgLrJVWWilGjhxZdxhmZguUyZMnPxERI+qOoyRO9PPIyJEjmTRpUt1hmJktUCR1feqc9Y277s3MzArmRG9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70ZmZmBXOiNzMzK5gTvZmZWcGc6M3MzArmJ+OZWZ+NPO7XdYdg87Epp+xVdwhW4Ra9mZlZwZzozczMCuZEb2ZmVjAnejMzs4I50ZuZmRXMid7MzKxgTvRmZmYFc6I3MzMrmBO9mZlZwZzozczMCuZEb2ZmVjAnejMzs4I50ZuZmRXMid7MzKxgTvRmZmYFc6I3MzMrmBO9mZlZwYbWHcD8RtIUYCbwGvBqRIyStAJwETASmAIcEBFP1xWjmZlZr9yib+09EbFFRIzKw8cB4yNiPWB8HjYzM5vvOdH3Zm9gXH49DtinvlDMzMx650T/ZgFcLWmypDG5bJWImAaQ/6/cakZJYyRNkjRpxowZgxSumZlZe75G/2Y7RMSjklYGrpF0b68zRsRZwFkAo0aNinkVoJmZWa/com8SEY/m/9OBS4F3Ao9LWhUg/59eX4RmZma9c6KvkLSUpGUar4H3AncBlwOj82SjgcvqidDMzKxv3HU/p1WASyVB2jYXRMSVkm4BLpZ0KPAwsH+NMZqZmfXMib4iIh4ANm9R/iSwy+BHZGZmNnfcdW9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70ZmZmBXOiNzMzK5gTvZmZWcGc6M3MzArmRG9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70ZmZmBXOiNzMzK5gTvZmZWcGc6M3MzArmRG9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70ZmZmBXOiNzMzK5gTvZmZWcGc6M3MzArmRG9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70LUgaIuk2SVfk4RUkXSPpb/n/8nXHaGZm1gsn+taOAu6pDB8HjI+I9YDxedjMzGy+50TfRNIawF7AjyrFewPj8utxwD6DHJaZmVm/ONG/2X8DXwJer5StEhHTAPL/lVvNKGmMpEmSJs2YMWOeB2pmZtaNE32FpPcD0yNicn/mj4izImJURIwaMWLEAEdnZmbWd0PrDmBek7RURDzf4+Q7AB+UtCcwDBgu6TzgcUmrRsQ0SasC0+dVvGZmZgOp2Ba9pO0l/YV8U52kzSWd0WmeiPhKRKwRESOBjwC/j4iPAZcDo/Nko4HL5l3kZmZmA6fYRA/8F/A+4EmAiLgDeHc/l3UKsJukvwG75WEzM7P5XtFd9xExVVK16LU+zDsBmJBfPwnsMpCxmZmZDYaSE/1USdsDIWkx4Ejm/G68mZlZ8Uruuv808FlgdeARYAvg8DoDMjMzG2wlt+g3iIiDqgWSdgCurykeMzOzQVdyi/5/eiwzMzMrVnEteknbAdsDIyR9oTJqODCknqjMzMzqUVyiBxYDliat2zKV8ueAD9cSkZmZWU2KS/QRMRGYKOmciHio7njMzMzqVFyir3hB0neATUiPswUgInauLyQzM7PBVfLNeOcD9wJvBb4BTAFuqTMgMzOzwVZyol8xIn4MvBIREyPiE8C2dQdlZmY2mEruun8l/58maS/gUWCNGuMxMzMbdCUn+hMlLQscQ/r+/HDg8/WGZGZmNriKTPSShgDrRcQVwLPAe2oOqU9GHvfrukOw+dSUU/aqOwQzW8AUeY0+Il4DPlh3HGZmZnUrskWf3SDp+8BFwPONwoi4tb6QzMzMBlfJiX77/P+blbIA/D16MzNbaBSb6COi43V5SaMjYtxgxWNmZlaHIq/R9+iougMwMzOb1xbmRK+6AzAzM5vXFuZEH3UHYGZmNq8tzIneLXozMytesYle0lu7lF0/iOGYmZnVothED/y8RdkljRcRccQgxmJmZlaL4r5eJ2lD0m/QLytpv8qo4VR+l97MzGxhUFyiBzYA3g8sB3ygUj4T+GQdAZmZmdWluEQfEZcBl0naLiJurDseMzOzOhWX6Cvul/RVYCSV9YyIT9QWkZmZ2SArOdFfBvwR+B3wWs2xmJmZ1aLkRL9kRHy5LzNIGgZcCyxO2jaXRMTXJa1A+hW8kcAU4ICIeHpgwzUzMxt4JX+97gpJe/Zxnn8CO0fE5sAWwO6StgWOA8ZHxHrA+DxsZmY23ys50R9FSvYvSXpO0kxJz3WaIZJZeXDR/BfA3kDjl+7GAfvMo5jNzMwGVLFd9xGxTH/mkzQEmAysC5weEX+StEpETMvLnSZp5QEM1czMbJ4ptkWv5GOSvpaH15T0zm7zRcRrEbEFsAbwTkmb9uE9x0iaJGnSjBkz+h27mZnZQCk20QNnANsBH83Ds4DTe505Ip4BJgC7A49LWhUg/5/eZp6zImJURIwaMWJE/yM3MzMbICUn+m0i4rPASwD5LvnFOs0gaYSk5fLrJYBdgXuBy4HRebLRpK/umZmZzfeKvUYPvJKvtwekJA683mWeVYFxeb5FgIsj4gpJNwIXSzoUeBjYfx7GbWZmNmBKTvSnAZcCK0s6CfgwcHynGSLiTmDLFuVPArvMiyDNzMzmpWITfUScL2kyKUEL2Cci7qk5LDMzs0FVbKLPHic9BncosISkrSLi1ppjMjMzGzTFJnpJJwCHAH8nX6fP/3euKyYzM7PBVmyiBw4A1omIl+sOxMzMrC4lf73uLmC5uoMwMzOrU8kt+pOB2yTdRfqxGgAi4oP1hWRmZja4Sk7044BvA3+m+/fnzczMilRyon8iIk6rOwgzM7M6lZzoJ0s6mfT42mrXvb9eZ2ZmC42SE33jCXfbVsr89TozM1uolJzoD42IB6oFkt5WVzBmZmZ1KPnrdZe0KPvZoEdhZmZWo+Ja9JI2BDYBlpW0X2XUcGBYPVGZmZnVo7hED2wAvJ/0sJwPVMpnAp+sIyAzM7O6FJfoI+Iy4DJJ20XEjXXHY2ZmVqeSr9HvK2m4pEUljZf0hKSP1R2UmZnZYCo50b83Ip4jdeM/AqwPfLHekMzMzAZXyYl+0fx/T+DCiHiqzmDMzMzqUNw1+opfSboXeBE4XNII4KWaYzIzMxtUxbboI+I4YDtgVES8ArwA7F1vVGZmZoOr2EQvaUngs8CZuWg1YFR9EZmZmQ2+YhM98H/Ay8D2efgR4MT6wjEzMxt8JSf6dSLiP4FXACLiRUD1hmRmZja4Sk70L0tagvSLdUhah8rP1ZqZmS0MSr7rfixwJbCmpPOBHYBD6gzIzMxssBWb6CPiakmTSb9HL+CoiHii5rDMzMwGVbGJXtLlwIXA5RHxfN3xmJmZ1aHka/TfA/4F+Iukn0n6sCT/TK2ZmS1Uik30ETExIg4H3gacBRwATO80j6Q1Jf1B0j2S7pZ0VC5fQdI1kv6W/y8/79fAzMxs7hWb6AHyXfcfAj4NvAMY12WWV4FjImIj0rX9z0raGDgOGB8R6wHj87CZmdl8r+Rr9BcB25DuvD8dmBARr3eaJyKmAdPy65mS7gFWJz06d6c82ThgAvDleRK4mZnZACo20ZOejPfRiHitPzNLGglsCfwJWCWfBBAR0yStPGBRmpmZzUPFJvqIuFLS9jlhD62Un9ttXklLAz8Hjo6I56TeHqgnaQwwBmCttdbqT9hmZmYDqthEL+knwDrA7UCjVR9Ax0QvaVFSkj8/In6Rix+XtGpuza9Km5v6IuIs0o1/jBo1KuZ6JczMzOZSsYme9Et1G0dEzwlXqen+Y+CeiPh/lVGXA6OBU/L/ywYyUDMzs3ml5Lvu7wLe0sd5dgAOBnaWdHv+25OU4HeT9DdgtzxsZmY23yu5Rb8S6WE5N1P5MZuI+GC7GSLiOtr/wt0uAxuemZnZvFdyoh9bdwBmZmZ1KzbRR8TEumMwMzOrW3GJXtJM8m/QN48CIiKGD3JIZmZmtSku0UfEMnXHYGZmNr8o+a57MzOzhZ4TvZmZWcGc6M3MzArmRG9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70ZmZmBXOiNzMzK5gTvZmZWcGc6M3MzArmRG9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70ZmZmBXOiNzMzK5gTvZmZWcGc6M3MzArmRG9mZlYwJ3ozM7OCOdGbmZkVzInezMysYE70ZmZmBXOiNzMzK5gTfYWksyVNl3RXpWwFSddI+lv+v3ydMZqZmfWFE/2czgF2byo7DhgfEesB4/OwmZnZAsGJviIirgWeaireGxiXX48D9hnMmMzMzOaGE313q0TENID8f+V2E0oaI2mSpEkzZswYtADNzMzacaIfQBFxVkSMiohRI0aMqDscMzMzJ/oePC5pVYD8f3rN8ZiZmfXMib67y4HR+fVo4LIaYzEzM+sTJ/oKSRcCNwIbSHpE0qHAKcBukv4G7JaHzczMFghD6w5gfhIRB7YZtcugBmJmZjZA3KI3MzMrmBO9mZlZwZzozczMCuZEb2ZmVjAnejMzs4I50ZuZmRXMid7MzKxgTvRmZmYFc6I3MzMrmBO9mZlZwZzozczMCuZEb2ZmVjAnejMzs4I50ZuZmRXMid7MzKxgTvRmZmYFc6I3MzMrmBO9mZlZwZzozczMCuZEb2ZmVjAnejMzs4I50ZuZmRXMid7MzKxgTvRmZmYFc6I3MzMrmBO9mZlZwZzozczMCuZEb2ZmVjAn+h5J2l3SfZLul3Rc3fGYmZn1wom+B5KGAKcDewAbAwdK2rjeqMzMzLpzou/NO4H7I+KBiHgZ+Cmwd80xmZmZdTW07gAWEKsDUyvDjwDbNE8kaQwwJg/OknTfIMS2MFgJeKLuIOYH+nbdEVgbrqMVc1lP1x6gMCxzou+NWpTFmwoizgLOmvfhLFwkTYqIUXXHYdaO66jNz9x135tHgDUrw2sAj9YUi5mZWc+c6HtzC7CepLdKWgz4CHB5zTGZmZl15a77HkTEq5KOAK4ChgBnR8TdNYe1MPHlEJvfuY7afEsRb7rUbGZmZoVw172ZmVnBnOjNzMwK5kRvZmZWMCd6A0DSv0u6W9Kdkm6XtI2kHy3oj/qVdET+fYKQtFKlfHlJl+b1vVnSppVxR0m6K2+Poyvlm0u6UdKfJf1K0vBBXp35iqR983bdsMfpj5a0ZGX4N5KWm2cBDgBJs9qUv5b3kzsk3Spp+4Fadh/mnyCpz9/dl3R+/t2OuySdLWnRXC5Jp+X95U5JW1XmOVvSdEl3NS3L+8QCwInekLQd8H5gq4jYDNgVmBoRh0XEX+qNbk75dwf64nrS+jzUVP5V4Pa8vv8GnJqXvynwSdJjjzcH3i9pvTzPj4DjIuLtwKXAF/u1EuU4ELiO9HXTXhwNvJHoI2LPiHhm4MMaFC9GxBYRsTnwFeDkXmfMCbXOY+/5wIbA24ElgMNy+R7AevlvDHBmZZ5zgN1bLMv7xALAid4AVgWeiIh/AkTEExHxaLXFIGmWpJNyC+YmSavk8nXy8C2SvtlopUhaWtL43Nr5s6S9c/lISfdKGpdbDZc0WnmSdpF0W57+bEmL5/Ipkv5D0nXA/pLem1sRt0r6maSl261YRNwWEVNajNoYGJ+nuRcYmddpI+CmiHghIl4FJgL75nk2AK7Nr68BPtS/zb3gy9t8B+BQKole0k653lySP+fzc2I7ElgN+IOkP+RppzR6WSR9LU9/jaQLJR2by6t1cCVJU/LrkZL+mOtA2xa1pF9Kmpx7Z8ZUytvV57fmunWLpBN63BzDgacb26VDvb9H0hnAreQHcEn6Xp52vKQRXdZ5CUk/zfvNRaQk3VifMyVNyuv5jU7BRsRvIgNuJj0ADNLvd5ybR90ELCdp1TzPtcBTLRbnfWIB4ERvAFcDa0r6q6QzJO3YYpqlSAlwc9KO/clcfipwakS8gzmfFvgSsG9EbAW8B/iepMajhDcAzsqt6eeAwyUNI7Ua/jW3DoYCn6kuLyLeBfwOOB7YNS97EvCFfqzzHcB+AJLeSXq+9hrAXcC7Ja2YT0D2ZPZTEe8CPphf78+cT0tc2OwDXBkRfwWeUqWbF9iS1HrfGHgbsENEnEaqH++JiPdUF5ST2ofyfPsBvXRHTwd2y3XgX4HT2kz3iYjYOi/zSEkr5vJO9fnMXJ8f6/D+Syh13d9LatU2Tgq61ftzI2LLiHgox3BrnnYi8PUu6/wZ4IW835wEbF0Z9+/5EbybATtK2qzLslDqsj8YuDIXtfpNj9W7LMb7xALAid6IiFmkg8YYYAZwkaRDmiZ7Gbgiv54MjMyvtwN+ll9fUJlewLck3UlKzqsDq+RxUyPi+vz6POBdpIPggzlxAIwD3l1Z3kX5/7akBHK9pNuB0fTvRzBOAZbPy/gccBvwakTcA3yb1Dq5knRC8Gqe5xPAZyVNBpYhbZOF1YGkX3Ek/z+wMu7miHgkIl4Hbmd2XWnnXcBlEfFiRMwEftXD+y8K/FDSn0n1r929JEdKugO4iZSEGpdh2tXnHYAL8+ufdHj/Rtf9hqQu7XNzQu9U7x/KLeWG15ldrxv7QSfvztMREXcCd1bGHSDpVlI93oT226PqDODaiPhjHu7pNz2aeJ9YAPjJeAZARLwGTAAm5IPn6KZJXonZT1d6je515yBgBLB1RLySux+HNd6u+e1pfZCpej7/F3BNRBzYaeJuIuI54OOQrpkCD+Y/IuLHwI/zuG+RWjaNLv735vL1gb3mJoYFVW4V7wxsKilIT4sMSV/Kk/yzMnkvdaXTZ/8qsxskwyrlnwceJ91HsQipJd0c506k+zO2i4gXJE2oLKNTfe7TU8Qi4sZ8CWIEqQeoXb1/vs0imt+33Tq3jE3SW4FjgXdExNOSzmkxX/M8X89xfqpS3Off9PA+sWBwi96QtIFm33AGsAVvvnmtnZuYfV2uelPWssD0fLB7D3O2utdSugEQZt/Q1bhOvm4uP5jUndnq/XZoTCdpyXyA6RNJyyn9bgGkm5GuzckfSSvn/2uRupIvbCpfhHT54Ad9fd9CfJjUBb12RIyMiDVJJ0ndWqQzSa2+ZtcBH5A0TOnafzVZTGF2F/WHK+XLAtNyr8HBpJONZssCT+ckvyGpN6ib65ldjw/qYXrysocAT9K53jdbhNnr9FHSdoD263xtIyalm0Yb3fPDSScRz+Z7DfboEu9hwPuAA/P2a7gc+Dcl2wLPRsS0LsvyPrEAcKI3gKWBcZL+krscNwbG9jjv0cAXJN1Muqnv2Vx+PjBK0iTSweneyjz3AKPze61Auib6EqmF/bPco/A6LQ4aETEDOAS4MM9/E+kO4pYkHSnpEVLr5E5JP8qjNgLuztdY9wCOqsz2c0l/IXUhfzYins7lB0r6a16XR4H/67ZxCnUg6Q7rqp+TklUnZwG/Vb4ZryEibiElmTuAX5Duu2jUo+8Cn5F0A+k33xvOINWhm4D1ad1avhIYmuvJCaS60s1RpK7oW0hJu53GNfrbSd3vo3OvWKd63+x5YJPc7b0z8M1c3m6dzwSWzuvzJdKNdETEHaQu+7uBs0knK538gHQ54ca8Dv+Ry38DPADcD/wQOLwxg6QLgRuBDSQ9IunQPMr7xALAz7q3uZJvWHsxIkLSR0ithL07TD8SuCIiNm03jS18JC0dEbNyfboWGBMRt9Ydl1kJfI3e5tbWwPfzde5nSDfnmPXVWUoPZxoGjHOSNxs4btFbESRdCry1qfjLEXFVHfGY1c37hDU40ZuZmRXMN+OZmZkVzInezMysYE70ZoXI33++TtIelbIDJF3ZaT4zK5uv0ZsVJD9I5Wek58YPIT2CdveI+Hs/ljUkfzfczBZgTvRmhZH0n6SHsSyV/69N+knSocDYiLgsP8/gJ3kagCMi4ob82NivA9OALSKil2emm9l8zInerDCSliL9FGrjh1vujojzJC1HepralqRnpr8eES/lxx9fGBGjcqL/NbBpRDxYR/xmNrD8wByzwkTE80q/Vz4LOID0HPlj8+hhwFqkx5V+X9IWpB91qf5ewM1O8mblcKI3K9Pr+U/AhyLivupISWNp/+tv3X5lzcwWIL7r3qxsVwGfy48oRtKWubyXX38zswI40ZuV7QRgUdIv992Vh6G3X38zswL4ZjwzM7OCuUVvZmZWMCd6MzOzgjnRm5mZFcyJ3szMrGBO9GZmZgVzojczMyuYE72ZmVnB/j8/1B2R/Lz0OAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Singapore_1999', 'Antigua and Barbuda_2019'], [max_investment_1, max_investment_2]) \n",
    "plt.title('Relationship between country with the highest Investment rate in 1999 and 2019')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Investment_rate');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7de3d46b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The maximum investment rate in the year 1999 increased by 58.52% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# The maximum inflation rate in 1999 and 2019 is on a upward trend\n",
    "difference= max_investment_2- max_investment_1\n",
    "percentage_rise= (difference/ max_investment_1)*100\n",
    "print (f' The maximum investment rate in the year 1999 increased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e42a18",
   "metadata": {},
   "source": [
    "# Summary of the Investment Analysis\n",
    "* The minimum Inflation Rate in 1999 increased by 579.00% in the year 2019\n",
    "* The maximum Inflation Rate in 1999 increased by 58.52% in the year 2019\n",
    "\n",
    "\n",
    " This shows how much Economic rate of all the in the data set countries has improved over the years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926e2d3a",
   "metadata": {},
   "source": [
    "# Exploring Tax Revenue Data Set\n",
    "* Clean The data\n",
    "* Remove all missing values\n",
    "* Check for relationships between the columns\n",
    "* Communicate your findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8832c324",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</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>6.97</td>\n",
       "      <td>5.28</td>\n",
       "      <td>6.09</td>\n",
       "      <td>8.48</td>\n",
       "      <td>...</td>\n",
       "      <td>8.920</td>\n",
       "      <td>7.710</td>\n",
       "      <td>7.12</td>\n",
       "      <td>6.880</td>\n",
       "      <td>7.5900</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>9.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>28.70</td>\n",
       "      <td>29.20</td>\n",
       "      <td>15.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.40</td>\n",
       "      <td>21.50</td>\n",
       "      <td>25.70</td>\n",
       "      <td>18.40</td>\n",
       "      <td>...</td>\n",
       "      <td>16.100</td>\n",
       "      <td>14.800</td>\n",
       "      <td>14.20</td>\n",
       "      <td>13.500</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>9.7300</td>\n",
       "      <td>9.2200</td>\n",
       "      <td>9.6400</td>\n",
       "      <td>9.380</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.0</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>18.000</td>\n",
       "      <td>17.500</td>\n",
       "      <td>16.50</td>\n",
       "      <td>18.300</td>\n",
       "      <td>18.5000</td>\n",
       "      <td>17.6000</td>\n",
       "      <td>18.9000</td>\n",
       "      <td>18.6000</td>\n",
       "      <td>18.300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</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>0.321</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>0.0665</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>0.965</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9.62</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.82</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.90</td>\n",
       "      <td>12.40</td>\n",
       "      <td>13.30</td>\n",
       "      <td>12.40</td>\n",
       "      <td>...</td>\n",
       "      <td>12.700</td>\n",
       "      <td>13.000</td>\n",
       "      <td>12.50</td>\n",
       "      <td>12.600</td>\n",
       "      <td>12.3000</td>\n",
       "      <td>12.1000</td>\n",
       "      <td>10.9000</td>\n",
       "      <td>10.1000</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       1999   2000   2001  2002  2003  2004   2005   2006  \\\n",
       "country                                                                     \n",
       "Afghanistan             NaN    NaN    NaN   NaN   NaN   NaN   6.97   5.28   \n",
       "Angola                28.70  29.20  15.50  17.2  18.1  17.1  23.40  21.50   \n",
       "Albania                 NaN    NaN  16.50  17.2  18.0   NaN    NaN    NaN   \n",
       "United Arab Emirates    NaN    NaN    NaN   NaN   NaN   NaN    NaN    NaN   \n",
       "Argentina              9.62   9.32   9.82  12.5  13.1  13.1  12.90  12.40   \n",
       "\n",
       "                       2007   2008  ...    2010    2011   2012    2013  \\\n",
       "country                             ...                                  \n",
       "Afghanistan            6.09   8.48  ...   8.920   7.710   7.12   6.880   \n",
       "Angola                25.70  18.40  ...  16.100  14.800  14.20  13.500   \n",
       "Albania                 NaN    NaN  ...  18.000  17.500  16.50  18.300   \n",
       "United Arab Emirates    NaN    NaN  ...   0.321   0.364   0.37   0.356   \n",
       "Argentina             13.30  12.40  ...  12.700  13.000  12.50  12.600   \n",
       "\n",
       "                         2014     2015     2016     2017    2018  2019  \n",
       "country                                                                 \n",
       "Afghanistan            7.5900   9.5000   9.9000      NaN     NaN   NaN  \n",
       "Angola                11.0000   9.7300   9.2200   9.6400   9.380   NaN  \n",
       "Albania               18.5000  17.6000  18.9000  18.6000  18.300   NaN  \n",
       "United Arab Emirates   0.0577   0.0435   0.0665   0.0593   0.965   NaN  \n",
       "Argentina             12.3000  12.1000  10.9000  10.1000  10.600   NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e97e858b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 156 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 21 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   1999    85 non-null     float64\n",
      " 1   2000    91 non-null     float64\n",
      " 2   2001    96 non-null     float64\n",
      " 3   2002    100 non-null    float64\n",
      " 4   2003    102 non-null    float64\n",
      " 5   2004    102 non-null    float64\n",
      " 6   2005    105 non-null    float64\n",
      " 7   2006    105 non-null    float64\n",
      " 8   2007    113 non-null    float64\n",
      " 9   2008    118 non-null    float64\n",
      " 10  2009    122 non-null    float64\n",
      " 11  2010    127 non-null    float64\n",
      " 12  2011    131 non-null    float64\n",
      " 13  2012    128 non-null    float64\n",
      " 14  2013    135 non-null    float64\n",
      " 15  2014    138 non-null    float64\n",
      " 16  2015    136 non-null    float64\n",
      " 17  2016    136 non-null    float64\n",
      " 18  2017    129 non-null    float64\n",
      " 19  2018    116 non-null    float64\n",
      " 20  2019    3 non-null      float64\n",
      "dtypes: float64(21)\n",
      "memory usage: 26.8+ KB\n"
     ]
    }
   ],
   "source": [
    "tax_revenue.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "875b6dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop 2019 column because it contains only 3 non null value data\n",
    "tax_revenue.drop('2019', axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5a0b8231",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>1999</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</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>6.97</td>\n",
       "      <td>5.28</td>\n",
       "      <td>6.09</td>\n",
       "      <td>8.48</td>\n",
       "      <td>9.17</td>\n",
       "      <td>8.920</td>\n",
       "      <td>7.710</td>\n",
       "      <td>7.12</td>\n",
       "      <td>6.880</td>\n",
       "      <td>7.5900</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>9.9000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>28.70</td>\n",
       "      <td>29.20</td>\n",
       "      <td>15.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.40</td>\n",
       "      <td>21.50</td>\n",
       "      <td>25.70</td>\n",
       "      <td>18.40</td>\n",
       "      <td>16.60</td>\n",
       "      <td>16.100</td>\n",
       "      <td>14.800</td>\n",
       "      <td>14.20</td>\n",
       "      <td>13.500</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>9.7300</td>\n",
       "      <td>9.2200</td>\n",
       "      <td>9.6400</td>\n",
       "      <td>9.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.0</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>18.000</td>\n",
       "      <td>17.500</td>\n",
       "      <td>16.50</td>\n",
       "      <td>18.300</td>\n",
       "      <td>18.5000</td>\n",
       "      <td>17.6000</td>\n",
       "      <td>18.9000</td>\n",
       "      <td>18.6000</td>\n",
       "      <td>18.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</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>NaN</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.356</td>\n",
       "      <td>0.0577</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>0.0665</td>\n",
       "      <td>0.0593</td>\n",
       "      <td>0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9.62</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.82</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.90</td>\n",
       "      <td>12.40</td>\n",
       "      <td>13.30</td>\n",
       "      <td>12.40</td>\n",
       "      <td>12.90</td>\n",
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       "      <td>12.600</td>\n",
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       "      <td>10.9000</td>\n",
       "      <td>10.1000</td>\n",
       "      <td>10.600</td>\n",
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       "                       1999   2000   2001  2002  2003  2004   2005   2006  \\\n",
       "country                                                                     \n",
       "Afghanistan             NaN    NaN    NaN   NaN   NaN   NaN   6.97   5.28   \n",
       "Angola                28.70  29.20  15.50  17.2  18.1  17.1  23.40  21.50   \n",
       "Albania                 NaN    NaN  16.50  17.2  18.0   NaN    NaN    NaN   \n",
       "United Arab Emirates    NaN    NaN    NaN   NaN   NaN   NaN    NaN    NaN   \n",
       "Argentina              9.62   9.32   9.82  12.5  13.1  13.1  12.90  12.40   \n",
       "\n",
       "                       2007   2008   2009    2010    2011   2012    2013  \\\n",
       "country                                                                    \n",
       "Afghanistan            6.09   8.48   9.17   8.920   7.710   7.12   6.880   \n",
       "Angola                25.70  18.40  16.60  16.100  14.800  14.20  13.500   \n",
       "Albania                 NaN    NaN    NaN  18.000  17.500  16.50  18.300   \n",
       "United Arab Emirates    NaN    NaN    NaN   0.321   0.364   0.37   0.356   \n",
       "Argentina             13.30  12.40  12.90  12.700  13.000  12.50  12.600   \n",
       "\n",
       "                         2014     2015     2016     2017    2018  \n",
       "country                                                           \n",
       "Afghanistan            7.5900   9.5000   9.9000      NaN     NaN  \n",
       "Angola                11.0000   9.7300   9.2200   9.6400   9.380  \n",
       "Albania               18.5000  17.6000  18.9000  18.6000  18.300  \n",
       "United Arab Emirates   0.0577   0.0435   0.0665   0.0593   0.965  \n",
       "Argentina             12.3000  12.1000  10.9000  10.1000  10.600  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "32295688",
   "metadata": {},
   "outputs": [],
   "source": [
    "tax_revenue.dropna(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3c9bce0b",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
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       "      <th>Angola</th>\n",
       "      <td>28.70</td>\n",
       "      <td>29.20</td>\n",
       "      <td>15.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.73</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.64</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9.62</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.82</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>12.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.6</td>\n",
       "      <td>12.3</td>\n",
       "      <td>12.10</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.10</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>23.10</td>\n",
       "      <td>24.90</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.4</td>\n",
       "      <td>24.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>24.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.4</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.20</td>\n",
       "      <td>22.00</td>\n",
       "      <td>23.10</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>26.20</td>\n",
       "      <td>28.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>26.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.1</td>\n",
       "      <td>25.4</td>\n",
       "      <td>26.1</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>25.9</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.8</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>27.30</td>\n",
       "      <td>26.80</td>\n",
       "      <td>25.70</td>\n",
       "      <td>25.1</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>25.8</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.1</td>\n",
       "      <td>24.4</td>\n",
       "      <td>22.80</td>\n",
       "      <td>23.40</td>\n",
       "      <td>24.00</td>\n",
       "      <td>22.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1999   2000   2001  2002  2003  2004  2005  2006  2007  2008  \\\n",
       "country                                                                    \n",
       "Angola     28.70  29.20  15.50  17.2  18.1  17.1  23.4  21.5  25.7  18.4   \n",
       "Argentina   9.62   9.32   9.82  12.5  13.1  13.1  12.9  12.4  13.3  12.4   \n",
       "Australia  23.10  24.90  23.60  24.4  24.3  24.8  24.6  24.1  24.3  22.0   \n",
       "Austria    26.20  28.00  27.00  26.6  26.3  25.5  25.1  25.4  26.1  25.2   \n",
       "Belgium    27.30  26.80  25.70  25.1  25.5  25.5  25.3  24.8  25.3  23.8   \n",
       "\n",
       "           2009  2010  2011  2012  2013  2014   2015   2016   2017   2018  \n",
       "country                                                                    \n",
       "Angola     16.6  16.1  14.8  14.2  13.5  11.0   9.73   9.22   9.64   9.38  \n",
       "Argentina  12.9  12.7  13.0  12.5  12.6  12.3  12.10  10.90  10.10  10.60  \n",
       "Australia  20.5  20.4  21.1  22.0  21.9  21.9  22.20  22.00  23.10  23.30  \n",
       "Austria    25.3  25.4  25.9  26.4  26.4  26.8  25.40  25.40  25.40  25.60  \n",
       "Belgium    24.5  24.9  25.8  26.2  26.1  24.4  22.80  23.40  24.00  22.70  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "50d35c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 20)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "322a1fa1",
   "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>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.963788</td>\n",
       "      <td>18.773939</td>\n",
       "      <td>18.366515</td>\n",
       "      <td>18.692576</td>\n",
       "      <td>19.031061</td>\n",
       "      <td>19.693333</td>\n",
       "      <td>20.312576</td>\n",
       "      <td>20.795152</td>\n",
       "      <td>20.106667</td>\n",
       "      <td>18.622727</td>\n",
       "      <td>18.404394</td>\n",
       "      <td>18.739545</td>\n",
       "      <td>19.104394</td>\n",
       "      <td>19.152576</td>\n",
       "      <td>19.248333</td>\n",
       "      <td>18.909697</td>\n",
       "      <td>19.215303</td>\n",
       "      <td>19.187121</td>\n",
       "      <td>19.162121</td>\n",
       "      <td>19.240303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.401048</td>\n",
       "      <td>7.521921</td>\n",
       "      <td>7.505966</td>\n",
       "      <td>7.408773</td>\n",
       "      <td>7.508476</td>\n",
       "      <td>7.850744</td>\n",
       "      <td>8.061964</td>\n",
       "      <td>8.747530</td>\n",
       "      <td>6.833189</td>\n",
       "      <td>6.053404</td>\n",
       "      <td>5.735813</td>\n",
       "      <td>5.894124</td>\n",
       "      <td>6.377423</td>\n",
       "      <td>6.311409</td>\n",
       "      <td>6.507238</td>\n",
       "      <td>6.102691</td>\n",
       "      <td>6.195030</td>\n",
       "      <td>5.948940</td>\n",
       "      <td>5.943527</td>\n",
       "      <td>5.952132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.650000</td>\n",
       "      <td>7.350000</td>\n",
       "      <td>7.620000</td>\n",
       "      <td>7.030000</td>\n",
       "      <td>8.970000</td>\n",
       "      <td>9.080000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>9.540000</td>\n",
       "      <td>7.920000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>9.240000</td>\n",
       "      <td>9.090000</td>\n",
       "      <td>9.170000</td>\n",
       "      <td>8.990000</td>\n",
       "      <td>8.990000</td>\n",
       "      <td>9.180000</td>\n",
       "      <td>9.220000</td>\n",
       "      <td>9.560000</td>\n",
       "      <td>9.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.175000</td>\n",
       "      <td>13.325000</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>13.325000</td>\n",
       "      <td>13.325000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>14.525000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>14.825000</td>\n",
       "      <td>13.625000</td>\n",
       "      <td>13.475000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>14.725000</td>\n",
       "      <td>13.725000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>13.650000</td>\n",
       "      <td>13.850000</td>\n",
       "      <td>14.550000</td>\n",
       "      <td>14.350000</td>\n",
       "      <td>14.525000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>16.950000</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>19.900000</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>19.800000</td>\n",
       "      <td>18.250000</td>\n",
       "      <td>17.700000</td>\n",
       "      <td>18.050000</td>\n",
       "      <td>18.450000</td>\n",
       "      <td>17.800000</td>\n",
       "      <td>18.050000</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>18.350000</td>\n",
       "      <td>18.300000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>18.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.375000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>22.950000</td>\n",
       "      <td>23.700000</td>\n",
       "      <td>24.100000</td>\n",
       "      <td>24.150000</td>\n",
       "      <td>24.700000</td>\n",
       "      <td>24.175000</td>\n",
       "      <td>22.850000</td>\n",
       "      <td>22.375000</td>\n",
       "      <td>22.725000</td>\n",
       "      <td>23.125000</td>\n",
       "      <td>23.125000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>23.275000</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>23.575000</td>\n",
       "      <td>23.325000</td>\n",
       "      <td>23.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.600000</td>\n",
       "      <td>51.400000</td>\n",
       "      <td>53.700000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>58.800000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>34.400000</td>\n",
       "      <td>37.400000</td>\n",
       "      <td>38.100000</td>\n",
       "      <td>36.400000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>34.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            1999       2000       2001       2002       2003       2004  \\\n",
       "count  66.000000  66.000000  66.000000  66.000000  66.000000  66.000000   \n",
       "mean   18.963788  18.773939  18.366515  18.692576  19.031061  19.693333   \n",
       "std     7.401048   7.521921   7.505966   7.408773   7.508476   7.850744   \n",
       "min     7.650000   7.350000   7.620000   7.030000   8.970000   9.080000   \n",
       "25%    13.175000  13.325000  13.200000  13.325000  13.325000  13.800000   \n",
       "50%    17.500000  17.200000  16.950000  17.750000  18.250000  18.800000   \n",
       "75%    23.375000  23.400000  22.600000  22.950000  23.700000  24.100000   \n",
       "max    48.600000  51.400000  53.700000  54.100000  55.000000  58.800000   \n",
       "\n",
       "            2005       2006       2007       2008       2009       2010  \\\n",
       "count  66.000000  66.000000  66.000000  66.000000  66.000000  66.000000   \n",
       "mean   20.312576  20.795152  20.106667  18.622727  18.404394  18.739545   \n",
       "std     8.061964   8.747530   6.833189   6.053404   5.735813   5.894124   \n",
       "min     8.780000   9.010000   9.540000   7.920000   8.600000   9.240000   \n",
       "25%    14.525000  14.900000  14.825000  13.625000  13.475000  14.500000   \n",
       "50%    19.900000  20.250000  19.800000  18.250000  17.700000  18.050000   \n",
       "75%    24.150000  24.700000  24.175000  22.850000  22.375000  22.725000   \n",
       "max    60.000000  62.500000  46.000000  40.000000  34.400000  37.400000   \n",
       "\n",
       "            2011       2012       2013       2014       2015       2016  \\\n",
       "count  66.000000  66.000000  66.000000  66.000000  66.000000  66.000000   \n",
       "mean   19.104394  19.152576  19.248333  18.909697  19.215303  19.187121   \n",
       "std     6.377423   6.311409   6.507238   6.102691   6.195030   5.948940   \n",
       "min     9.090000   9.170000   8.990000   8.990000   9.180000   9.220000   \n",
       "25%    14.725000  13.725000  13.700000  13.650000  13.850000  14.550000   \n",
       "50%    18.450000  17.800000  18.050000  18.500000  18.350000  18.300000   \n",
       "75%    23.125000  23.125000  23.500000  23.275000  23.250000  23.575000   \n",
       "max    38.100000  36.400000  36.500000  33.900000  37.600000  33.400000   \n",
       "\n",
       "            2017       2018  \n",
       "count  66.000000  66.000000  \n",
       "mean   19.162121  19.240303  \n",
       "std     5.943527   5.952132  \n",
       "min     9.560000   9.380000  \n",
       "25%    14.350000  14.525000  \n",
       "50%    18.600000  18.950000  \n",
       "75%    23.325000  23.250000  \n",
       "max    32.300000  34.300000  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4ae61c26",
   "metadata": {},
   "outputs": [
    {
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       "      <th>country</th>\n",
       "      <th></th>\n",
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       "      <th>Georgia</th>\n",
       "      <td>7.65</td>\n",
       "      <td>7.35</td>\n",
       "      <td>7.62</td>\n",
       "      <td>7.03</td>\n",
       "      <td>9.93</td>\n",
       "      <td>12.1</td>\n",
       "      <td>15.4</td>\n",
       "      <td>17.7</td>\n",
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       "      <td>21.0</td>\n",
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       "      <td>23.2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>22.3</td>\n",
       "      <td>22.1</td>\n",
       "      <td>21.7</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  \\\n",
       "country                                                                     \n",
       "Georgia  7.65  7.35  7.62  7.03  9.93  12.1  15.4  17.7  23.8  23.1  21.0   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "country                                                        \n",
       "Georgia  22.8  23.2  22.0  22.0  22.2  22.3  22.1  21.7  19.6  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_tax_revenue_1999= tax_revenue[tax_revenue['1999'] == min(tax_revenue['1999'])]\n",
    "min_tax_revenue_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "293b22fd",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1999</th>\n",
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       "      <th>Angola</th>\n",
       "      <td>28.7</td>\n",
       "      <td>29.2</td>\n",
       "      <td>15.5</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.73</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.64</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  \\\n",
       "country                                                                     \n",
       "Angola   28.7  29.2  15.5  17.2  18.1  17.1  23.4  21.5  25.7  18.4  16.6   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "country                                                        \n",
       "Angola   16.1  14.8  14.2  13.5  11.0  9.73  9.22  9.64  9.38  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_tax_revenue_2018= tax_revenue[tax_revenue['2018'] == min(tax_revenue['2018'])]\n",
    "min_tax_revenue_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6261cbc5",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <td>48.6</td>\n",
       "      <td>51.4</td>\n",
       "      <td>53.7</td>\n",
       "      <td>54.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>58.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>26.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.7</td>\n",
       "      <td>25.7</td>\n",
       "      <td>26.0</td>\n",
       "      <td>24.3</td>\n",
       "      <td>25.1</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25.8</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  \\\n",
       "country                                                                     \n",
       "Malta    48.6  51.4  53.7  54.1  55.0  58.8  60.0  62.5  25.8  26.0  25.3   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "country                                                        \n",
       "Malta    25.8  25.7  25.7  26.0  24.3  25.1  25.6  25.8  25.5  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_tax_revenue_1999= tax_revenue[tax_revenue['1999'] == max(tax_revenue['1999'])]\n",
    "max_tax_revenue_1999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4828b574",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>31.6</td>\n",
       "      <td>30.2</td>\n",
       "      <td>30.1</td>\n",
       "      <td>30.1</td>\n",
       "      <td>31.3</td>\n",
       "      <td>33.1</td>\n",
       "      <td>31.9</td>\n",
       "      <td>35.1</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.2</td>\n",
       "      <td>32.7</td>\n",
       "      <td>32.8</td>\n",
       "      <td>33.4</td>\n",
       "      <td>33.8</td>\n",
       "      <td>36.5</td>\n",
       "      <td>33.9</td>\n",
       "      <td>33.3</td>\n",
       "      <td>33.4</td>\n",
       "      <td>32.2</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         1999  2000  2001  2002  2003  2004  2005  2006  2007  2008  2009  \\\n",
       "country                                                                     \n",
       "Denmark  31.6  30.2  30.1  30.1  31.3  33.1  31.9  35.1  33.4  33.2  32.7   \n",
       "\n",
       "         2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "country                                                        \n",
       "Denmark  32.8  33.4  33.8  36.5  33.9  33.3  33.4  32.2  34.3  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_tax_revenue_2018= tax_revenue[tax_revenue['2018'] == max(tax_revenue['2018'])]\n",
    "max_tax_revenue_2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3239c4ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_tax_revenue_1= tax_revenue['1999'].min()\n",
    "min_tax_revenue_2= tax_revenue['2018'].min()\n",
    "max_tax_revenue_1= tax_revenue['1999'].max()\n",
    "max_tax_revenue_2= tax_revenue['2018'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1895bc3c",
   "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": [
    "plt.bar (['Geogia_1999', 'Angola_2018'], [min_tax_revenue_1, min_tax_revenue_2]) \n",
    "plt.title('Relationship between country with the lowest tax revenue rate in 1999 and 2018')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Tax revenue generated');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "852ea22a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The minimum tax revenue rate in the year 1999 increased by 22.61% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# The minimum tax rate in 1999 and 2019 is on an upward trend\n",
    "difference= min_tax_revenue_2- min_tax_revenue_1\n",
    "percentage_rise= (difference/ min_tax_revenue_1)*100\n",
    "print (f' The minimum tax revenue rate in the year 1999 increased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "674f6f11",
   "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": [
    "plt.bar(['Malta_1999', 'Denmark_2018'], [max_tax_revenue_1, max_tax_revenue_2]) \n",
    "plt.title('Relationship between country with the highest tax revenue in 1999 and 2018')\n",
    "plt.xlabel ('Year')\n",
    "plt.ylabel('Tax Revenue Generated');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "de538c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The minimum tax revenue rate in the year 1999 increased by -186.93% in the year 2019\n"
     ]
    }
   ],
   "source": [
    "# The minimum tax rate in 1999 and 2019 is on a downward trend\n",
    "difference= max_tax_revenue_2- max_tax_revenue_1\n",
    "percentage_rise= (difference/ min_tax_revenue_1)*100\n",
    "print (f' The minimum tax revenue rate in the year 1999 increased by {(percentage_rise):.2f}% in the year 2019'  )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f425fbe7",
   "metadata": {},
   "source": [
    "# Summary of the Tax Revenue Analysis\n",
    "* The minimum tax revenue in 1999 increased by 22.61% in the year 2018\n",
    "* The maximum tax revenue in 1999 decreased by 186.93% in the year 2018\n",
    "\n",
    "\n",
    " This shows that total tax revenue of most countries decreased over the years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21073bd8",
   "metadata": {},
   "source": [
    "# SUMMARY \n",
    "* Malta has the lowest inflation rate in 1999 and the maximum tax revenue in 1999\n",
    "* "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bc08614",
   "metadata": {},
   "source": [
    "# Lets correlate the data\n",
    "* Change the column names\n",
    "* Merge the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f927da0c",
   "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>1999_pop</th>\n",
       "      <th>2000_pop</th>\n",
       "      <th>2001_pop</th>\n",
       "      <th>2002_pop</th>\n",
       "      <th>2003_pop</th>\n",
       "      <th>2004_pop</th>\n",
       "      <th>2005_pop</th>\n",
       "      <th>2006_pop</th>\n",
       "      <th>2007_pop</th>\n",
       "      <th>2008_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2010_pop</th>\n",
       "      <th>2011_pop</th>\n",
       "      <th>2012_pop</th>\n",
       "      <th>2013_pop</th>\n",
       "      <th>2014_pop</th>\n",
       "      <th>2015_pop</th>\n",
       "      <th>2016_pop</th>\n",
       "      <th>2017_pop</th>\n",
       "      <th>2018_pop</th>\n",
       "      <th>2019_pop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>20810000.0</td>\n",
       "      <td>21610000.0</td>\n",
       "      <td>22610000.0</td>\n",
       "      <td>23710000.0</td>\n",
       "      <td>24710000.0</td>\n",
       "      <td>25710000.0</td>\n",
       "      <td>26410000.0</td>\n",
       "      <td>27110000.0</td>\n",
       "      <td>27710000.0</td>\n",
       "      <td>28410000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30110000.0</td>\n",
       "      <td>31210000.0</td>\n",
       "      <td>32310000.0</td>\n",
       "      <td>33410000.0</td>\n",
       "      <td>34410000.0</td>\n",
       "      <td>35410000.0</td>\n",
       "      <td>36310000.0</td>\n",
       "      <td>37210000.0</td>\n",
       "      <td>381000000.0</td>\n",
       "      <td>38910000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>16410000.0</td>\n",
       "      <td>16910000.0</td>\n",
       "      <td>17510000.0</td>\n",
       "      <td>18110000.0</td>\n",
       "      <td>18810000.0</td>\n",
       "      <td>19410000.0</td>\n",
       "      <td>20110000.0</td>\n",
       "      <td>20910000.0</td>\n",
       "      <td>21710000.0</td>\n",
       "      <td>22510000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24210000.0</td>\n",
       "      <td>25110000.0</td>\n",
       "      <td>261000000.0</td>\n",
       "      <td>26910000.0</td>\n",
       "      <td>27910000.0</td>\n",
       "      <td>28810000.0</td>\n",
       "      <td>29810000.0</td>\n",
       "      <td>30810000.0</td>\n",
       "      <td>31810000.0</td>\n",
       "      <td>32910000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3121000.0</td>\n",
       "      <td>3110000.0</td>\n",
       "      <td>3091000.0</td>\n",
       "      <td>3061000.0</td>\n",
       "      <td>3031000.0</td>\n",
       "      <td>31000000.0</td>\n",
       "      <td>2971000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2931000.0</td>\n",
       "      <td>2911000.0</td>\n",
       "      <td>2910000.0</td>\n",
       "      <td>2910000.0</td>\n",
       "      <td>2891000.0</td>\n",
       "      <td>2891000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "      <td>2881000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>65410.0</td>\n",
       "      <td>67310.0</td>\n",
       "      <td>701000.0</td>\n",
       "      <td>73210.0</td>\n",
       "      <td>76310.0</td>\n",
       "      <td>78910.0</td>\n",
       "      <td>811000.0</td>\n",
       "      <td>82710.0</td>\n",
       "      <td>83910.0</td>\n",
       "      <td>84510.0</td>\n",
       "      <td>...</td>\n",
       "      <td>83710.0</td>\n",
       "      <td>82410.0</td>\n",
       "      <td>80810.0</td>\n",
       "      <td>79210.0</td>\n",
       "      <td>781000.0</td>\n",
       "      <td>77310.0</td>\n",
       "      <td>771000.0</td>\n",
       "      <td>771000.0</td>\n",
       "      <td>77110.0</td>\n",
       "      <td>77310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3310000.0</td>\n",
       "      <td>3481000.0</td>\n",
       "      <td>3711000.0</td>\n",
       "      <td>4071000.0</td>\n",
       "      <td>4591000.0</td>\n",
       "      <td>5310000.0</td>\n",
       "      <td>6171000.0</td>\n",
       "      <td>7091000.0</td>\n",
       "      <td>7921000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8951000.0</td>\n",
       "      <td>9141000.0</td>\n",
       "      <td>9210000.0</td>\n",
       "      <td>9211000.0</td>\n",
       "      <td>9261000.0</td>\n",
       "      <td>9361000.0</td>\n",
       "      <td>9491000.0</td>\n",
       "      <td>9631000.0</td>\n",
       "      <td>9771000.0</td>\n",
       "      <td>9891000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        1999_pop    2000_pop    2001_pop    2002_pop  \\\n",
       "country                                                                \n",
       "Afghanistan           20810000.0  21610000.0  22610000.0  23710000.0   \n",
       "Angola                16410000.0  16910000.0  17510000.0  18110000.0   \n",
       "Albania                3131000.0   3131000.0   3131000.0   3121000.0   \n",
       "Andorra                  65410.0     67310.0    701000.0     73210.0   \n",
       "United Arab Emirates   3131000.0   3310000.0   3481000.0   3711000.0   \n",
       "\n",
       "                        2003_pop    2004_pop    2005_pop    2006_pop  \\\n",
       "country                                                                \n",
       "Afghanistan           24710000.0  25710000.0  26410000.0  27110000.0   \n",
       "Angola                18810000.0  19410000.0  20110000.0  20910000.0   \n",
       "Albania                3110000.0   3091000.0   3061000.0   3031000.0   \n",
       "Andorra                  76310.0     78910.0    811000.0     82710.0   \n",
       "United Arab Emirates   4071000.0   4591000.0   5310000.0   6171000.0   \n",
       "\n",
       "                        2007_pop    2008_pop  ...    2010_pop    2011_pop  \\\n",
       "country                                       ...                           \n",
       "Afghanistan           27710000.0  28410000.0  ...  30110000.0  31210000.0   \n",
       "Angola                21710000.0  22510000.0  ...  24210000.0  25110000.0   \n",
       "Albania               31000000.0   2971000.0  ...   2931000.0   2911000.0   \n",
       "Andorra                  83910.0     84510.0  ...     83710.0     82410.0   \n",
       "United Arab Emirates   7091000.0   7921000.0  ...   8951000.0   9141000.0   \n",
       "\n",
       "                         2012_pop    2013_pop    2014_pop    2015_pop  \\\n",
       "country                                                                 \n",
       "Afghanistan            32310000.0  33410000.0  34410000.0  35410000.0   \n",
       "Angola                261000000.0  26910000.0  27910000.0  28810000.0   \n",
       "Albania                 2910000.0   2910000.0   2891000.0   2891000.0   \n",
       "Andorra                   80810.0     79210.0    781000.0     77310.0   \n",
       "United Arab Emirates    9210000.0   9211000.0   9261000.0   9361000.0   \n",
       "\n",
       "                        2016_pop    2017_pop     2018_pop    2019_pop  \n",
       "country                                                                \n",
       "Afghanistan           36310000.0  37210000.0  381000000.0  38910000.0  \n",
       "Angola                29810000.0  30810000.0   31810000.0  32910000.0  \n",
       "Albania                2881000.0   2881000.0    2881000.0   2881000.0  \n",
       "Andorra                 771000.0    771000.0      77110.0     77310.0  \n",
       "United Arab Emirates   9491000.0   9631000.0    9771000.0   9891000.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population_2= population_2.rename(columns= lambda x: x + '_pop')\n",
    "population_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "136cadaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Population dataset\n",
    "fig, ((ax1, ax2), (ax3, ax4))= plt.subplots(nrows= 2, ncols= 2, figsize= (10,10))\n",
    "ax1.hist(population_2['1999_pop'])\n",
    "ax2.hist(population_2['2006_pop'])\n",
    "ax3.hist(population_2['2013_pop'])\n",
    "ax4.hist(population_2['2019_pop'])\n",
    "ax1.set(title= 'Distribution of Population dataset in 1999' )\n",
    "ax2.set(title= 'Distribution of Population dataset in 2006' )\n",
    "ax3.set(title= 'Distribution of Population dataset in 2013' )\n",
    "ax4.set(title= 'Distribution of Population dataset in 2019' );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48e43f4",
   "metadata": {},
   "source": [
    "# SUMMARY\n",
    "*  Most Countries have less than Average of total population and there are lesser population size of people in 1999\n",
    "*  Over 80% Countries have fewer population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1d119cc9",
   "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>1999_inf</th>\n",
       "      <th>2000_inf</th>\n",
       "      <th>2001_inf</th>\n",
       "      <th>2002_inf</th>\n",
       "      <th>2003_inf</th>\n",
       "      <th>2004_inf</th>\n",
       "      <th>2005_inf</th>\n",
       "      <th>2006_inf</th>\n",
       "      <th>2007_inf</th>\n",
       "      <th>2008_inf</th>\n",
       "      <th>...</th>\n",
       "      <th>2010_inf</th>\n",
       "      <th>2011_inf</th>\n",
       "      <th>2012_inf</th>\n",
       "      <th>2013_inf</th>\n",
       "      <th>2014_inf</th>\n",
       "      <th>2015_inf</th>\n",
       "      <th>2016_inf</th>\n",
       "      <th>2017_inf</th>\n",
       "      <th>2018_inf</th>\n",
       "      <th>2019_inf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>418.00</td>\n",
       "      <td>106.00</td>\n",
       "      <td>197.00</td>\n",
       "      <td>93.90</td>\n",
       "      <td>33.40</td>\n",
       "      <td>42.40</td>\n",
       "      <td>17.10</td>\n",
       "      <td>4.31</td>\n",
       "      <td>19.40</td>\n",
       "      <td>-16.800</td>\n",
       "      <td>...</td>\n",
       "      <td>31.800</td>\n",
       "      <td>7.260</td>\n",
       "      <td>2.840</td>\n",
       "      <td>3.560</td>\n",
       "      <td>-3.520</td>\n",
       "      <td>21.800</td>\n",
       "      <td>22.60</td>\n",
       "      <td>29.10</td>\n",
       "      <td>28.100</td>\n",
       "      <td>15.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>5.64</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.64</td>\n",
       "      <td>5.20</td>\n",
       "      <td>3.16</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.48</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.420</td>\n",
       "      <td>...</td>\n",
       "      <td>2.310</td>\n",
       "      <td>1.040</td>\n",
       "      <td>0.289</td>\n",
       "      <td>1.550</td>\n",
       "      <td>0.564</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.477</td>\n",
       "      <td>-0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>28.70</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.18</td>\n",
       "      <td>3.04</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.46</td>\n",
       "      <td>3.21</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>1.04</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>30.60</td>\n",
       "      <td>10.50</td>\n",
       "      <td>18.40</td>\n",
       "      <td>10.30</td>\n",
       "      <td>13.70</td>\n",
       "      <td>14.90</td>\n",
       "      <td>23.20</td>\n",
       "      <td>15.400</td>\n",
       "      <td>...</td>\n",
       "      <td>23.700</td>\n",
       "      <td>22.300</td>\n",
       "      <td>23.900</td>\n",
       "      <td>40.300</td>\n",
       "      <td>26.600</td>\n",
       "      <td>41.100</td>\n",
       "      <td>26.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>50.600</td>\n",
       "      <td>39.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>-1.37</td>\n",
       "      <td>4.03</td>\n",
       "      <td>2.36</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.28</td>\n",
       "      <td>3.21</td>\n",
       "      <td>4.62</td>\n",
       "      <td>4.28</td>\n",
       "      <td>5.99</td>\n",
       "      <td>2.500</td>\n",
       "      <td>...</td>\n",
       "      <td>4.280</td>\n",
       "      <td>5.350</td>\n",
       "      <td>3.370</td>\n",
       "      <td>2.310</td>\n",
       "      <td>1.210</td>\n",
       "      <td>0.269</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.79</td>\n",
       "      <td>1.460</td>\n",
       "      <td>1.880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           1999_inf  2000_inf  2001_inf  2002_inf  2003_inf  2004_inf  \\\n",
       "country                                                                 \n",
       "Angola       418.00    106.00    197.00     93.90     33.40     42.40   \n",
       "Albania        5.64      3.81      3.64      5.20      3.16      3.30   \n",
       "Andorra       28.70      3.08      3.24      3.18      3.04      3.40   \n",
       "Argentina      1.04     -1.10     30.60     10.50     18.40     10.30   \n",
       "Armenia       -1.37      4.03      2.36      4.60      6.28      3.21   \n",
       "\n",
       "           2005_inf  2006_inf  2007_inf  2008_inf  ...  2010_inf  2011_inf  \\\n",
       "country                                            ...                       \n",
       "Angola        17.10      4.31     19.40   -16.800  ...    31.800     7.260   \n",
       "Albania        2.48      4.39      4.12     2.420  ...     2.310     1.040   \n",
       "Andorra        3.46      3.21      2.27     0.132  ...     0.197     0.175   \n",
       "Argentina     13.70     14.90     23.20    15.400  ...    23.700    22.300   \n",
       "Armenia        4.62      4.28      5.99     2.500  ...     4.280     5.350   \n",
       "\n",
       "           2012_inf  2013_inf  2014_inf  2015_inf  2016_inf  2017_inf  \\\n",
       "country                                                                 \n",
       "Angola        2.840     3.560    -3.520    21.800     22.60     29.10   \n",
       "Albania       0.289     1.550     0.564    -0.632      1.45      1.36   \n",
       "Andorra       0.448    -0.084     0.661     0.348      1.14      1.01   \n",
       "Argentina    23.900    40.300    26.600    41.100     26.00     40.00   \n",
       "Armenia       3.370     2.310     1.210     0.269      2.15      2.79   \n",
       "\n",
       "           2018_inf  2019_inf  \n",
       "country                        \n",
       "Angola       28.100    15.100  \n",
       "Albania       0.477    -0.965  \n",
       "Andorra       1.370     1.060  \n",
       "Argentina    50.600    39.800  \n",
       "Armenia       1.460     1.880  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation_2= inflation_2.rename(columns= lambda x: x+ '_inf')\n",
    "inflation_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "93d19884",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of inflation dataset\n",
    "fig, ((ax1, ax2), (ax3, ax4))= plt.subplots(nrows= 2, ncols= 2, figsize= (10,7))\n",
    "ax1.hist(inflation_2['1999_inf'])\n",
    "ax2.hist(inflation_2['2006_inf'])\n",
    "ax3.hist(inflation_2['2013_inf'])\n",
    "ax4.hist(inflation_2['2019_inf'])\n",
    "ax1.set(title= 'Distribution of inflation dataset in 1999' )\n",
    "ax2.set(title= 'Distribution of inflation dataset 2006' )\n",
    "ax3.set(title= 'Distribution of inflation dataset in 2013' )\n",
    "ax4.set(title= 'Distribution of inflation dataset in 2019' );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4125465d",
   "metadata": {},
   "source": [
    "# Summary\n",
    "> **1999**\n",
    "* No country have less than 0% inflation in 1999\n",
    "* The data is skewed to the right with no outliers\n",
    "* Maximum inflation rate is 2500\n",
    "\n",
    "> **2006**\n",
    "* The minimum inflation rate in 1999 fell from a ratio of 0 to -5 in 2006 \n",
    "* The data is skewed to the right. \n",
    "* There are Ouliers in this dataset\n",
    "* Maximum inflation rate is 25\n",
    "\n",
    "> **2013**\n",
    "* The minimum inflation rate in 2006 fell from a ratio of -5 to -18 in 2013.\n",
    "* Maximum inflation rate is 40\n",
    "* The data is skewed to the right. \n",
    "* There are Ouliers in this dataset\n",
    "\n",
    "> **2019**\n",
    "* The minimum inflation rate in 2019 rose to 13 in 2019. \n",
    "* There are outliers in this dataset \n",
    "* Maximum inflation rate is between 600-700\n",
    "\n",
    "# Conclusion\n",
    "\n",
    "Inflation Rate of most countries decreased significantly over the years especially in between 2006-2013. Then it rose in 2019. The ecomonic rate of 2013 significantly improved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b33f4496",
   "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>1999_inv</th>\n",
       "      <th>2000_inv</th>\n",
       "      <th>2001_inv</th>\n",
       "      <th>2002_inv</th>\n",
       "      <th>2003_inv</th>\n",
       "      <th>2004_inv</th>\n",
       "      <th>2005_inv</th>\n",
       "      <th>2006_inv</th>\n",
       "      <th>2007_inv</th>\n",
       "      <th>2008_inv</th>\n",
       "      <th>...</th>\n",
       "      <th>2010_inv</th>\n",
       "      <th>2011_inv</th>\n",
       "      <th>2012_inv</th>\n",
       "      <th>2013_inv</th>\n",
       "      <th>2014_inv</th>\n",
       "      <th>2015_inv</th>\n",
       "      <th>2016_inv</th>\n",
       "      <th>2017_inv</th>\n",
       "      <th>2018_inv</th>\n",
       "      <th>2019_inv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.5</td>\n",
       "      <td>30.9</td>\n",
       "      <td>27.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>25.7</td>\n",
       "      <td>30.8</td>\n",
       "      <td>42.8</td>\n",
       "      <td>...</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>17.5</td>\n",
       "      <td>15.6</td>\n",
       "      <td>10.9</td>\n",
       "      <td>14.1</td>\n",
       "      <td>17.6</td>\n",
       "      <td>18.9</td>\n",
       "      <td>18.7</td>\n",
       "      <td>20.1</td>\n",
       "      <td>19.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>...</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>21.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>25.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>29.1</td>\n",
       "      <td>35.8</td>\n",
       "      <td>42.2</td>\n",
       "      <td>44.4</td>\n",
       "      <td>47.9</td>\n",
       "      <td>41.1</td>\n",
       "      <td>...</td>\n",
       "      <td>31.9</td>\n",
       "      <td>24.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>21.3</td>\n",
       "      <td>20.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>22.4</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>29.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>23.5</td>\n",
       "      <td>26.9</td>\n",
       "      <td>36.2</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.4</td>\n",
       "      <td>37.7</td>\n",
       "      <td>...</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>27.2</td>\n",
       "      <td>32.6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>26.3</td>\n",
       "      <td>23.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>27.6</td>\n",
       "      <td>28.7</td>\n",
       "      <td>27.4</td>\n",
       "      <td>...</td>\n",
       "      <td>26.5</td>\n",
       "      <td>27.7</td>\n",
       "      <td>27.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>24.5</td>\n",
       "      <td>23.2</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1999_inv  2000_inv  2001_inv  2002_inv  2003_inv  \\\n",
       "country                                                                 \n",
       "Angola                   30.5      30.5      30.5      30.5      30.9   \n",
       "Argentina                17.5      15.6      10.9      14.1      17.6   \n",
       "Armenia                  21.9      22.9      25.4      28.3      29.1   \n",
       "Antigua and Barbuda      29.0      20.5      22.1      23.2      23.5   \n",
       "Australia                26.3      23.5      24.5      26.0      27.2   \n",
       "\n",
       "                     2004_inv  2005_inv  2006_inv  2007_inv  2008_inv  ...  \\\n",
       "country                                                                ...   \n",
       "Angola                   27.6      23.3      25.7      30.8      42.8  ...   \n",
       "Argentina                18.9      18.7      20.1      19.6      16.1  ...   \n",
       "Armenia                  35.8      42.2      44.4      47.9      41.1  ...   \n",
       "Antigua and Barbuda      26.9      36.2      37.4      37.4      37.7  ...   \n",
       "Australia                27.6      27.6      27.6      28.7      27.4  ...   \n",
       "\n",
       "                     2010_inv  2011_inv  2012_inv  2013_inv  2014_inv  \\\n",
       "country                                                                 \n",
       "Angola                   26.4      26.7      26.1      27.5      34.2   \n",
       "Argentina                18.4      16.5      17.3      17.3      17.1   \n",
       "Armenia                  31.9      24.7      22.2      21.3      20.7   \n",
       "Antigua and Barbuda      20.7      22.1      23.7      25.0      23.9   \n",
       "Australia                26.5      27.7      27.9      26.7      26.3   \n",
       "\n",
       "                     2015_inv  2016_inv  2017_inv  2018_inv  2019_inv  \n",
       "country                                                                \n",
       "Angola                   27.2      24.1      21.4      19.8      22.8  \n",
       "Argentina                17.7      18.2      16.0      14.6      13.8  \n",
       "Armenia                  18.0      18.4      22.4      17.4      17.2  \n",
       "Antigua and Barbuda      27.2      32.6      37.0      42.5      55.8  \n",
       "Australia                25.4      24.1      24.5      23.2      22.3  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "investment= investment.rename(columns= lambda x:x + '_inv')\n",
    "investment.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "7c5a598c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of investment dataset\n",
    "fig, ((ax1, ax2), (ax3, ax4))= plt.subplots(nrows= 2, ncols= 2, figsize= (10,8))\n",
    "ax1.hist(investment['1999_inv'])\n",
    "ax2.hist(investment['2006_inv'])\n",
    "ax3.hist(investment['2013_inv'])\n",
    "ax4.hist(investment['2019_inv'])\n",
    "ax1.set(title= 'Distribution of investment dataset in 1999' )\n",
    "ax2.set(title= 'Distribution of investment dataset in 2006' )\n",
    "ax3.set(title= 'Distribution of investment dataset in 2013' )\n",
    "ax4.set(title= 'Distribution of investment dataset in 2019' );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c7d2b6",
   "metadata": {},
   "source": [
    "# SUMMARY\n",
    "> **1999**\n",
    "* The maximum investment rate is 35 \n",
    "* The data is skewed to the left\n",
    "\n",
    "> **2006**\n",
    "* The maximum investment rate increased to 60-70 \n",
    "* The data is skewed to the right\n",
    "* Their are outliers in this dataset\n",
    "\n",
    "> **2013**\n",
    "* the maximum investment rate decreased to 50-60\n",
    "* The data is skewed to the left\n",
    "* Their are outliers in this dataset\n",
    "\n",
    "> **2019**\n",
    "* the maximum investment rate decreased to 50-60\n",
    "* The data is skewed to the left\n",
    "* Their are outliers in this dataset\n",
    "\n",
    "# Conclusion\n",
    "Their is positive increase in the Investment rate of different countries over the years\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "04c0831b",
   "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>1999_tax</th>\n",
       "      <th>2000_tax</th>\n",
       "      <th>2001_tax</th>\n",
       "      <th>2002_tax</th>\n",
       "      <th>2003_tax</th>\n",
       "      <th>2004_tax</th>\n",
       "      <th>2005_tax</th>\n",
       "      <th>2006_tax</th>\n",
       "      <th>2007_tax</th>\n",
       "      <th>2008_tax</th>\n",
       "      <th>2009_tax</th>\n",
       "      <th>2010_tax</th>\n",
       "      <th>2011_tax</th>\n",
       "      <th>2012_tax</th>\n",
       "      <th>2013_tax</th>\n",
       "      <th>2014_tax</th>\n",
       "      <th>2015_tax</th>\n",
       "      <th>2016_tax</th>\n",
       "      <th>2017_tax</th>\n",
       "      <th>2018_tax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>28.70</td>\n",
       "      <td>29.20</td>\n",
       "      <td>15.50</td>\n",
       "      <td>17.2</td>\n",
       "      <td>18.1</td>\n",
       "      <td>17.1</td>\n",
       "      <td>23.4</td>\n",
       "      <td>21.5</td>\n",
       "      <td>25.7</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.73</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.64</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9.62</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.82</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12.4</td>\n",
       "      <td>13.3</td>\n",
       "      <td>12.4</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.6</td>\n",
       "      <td>12.3</td>\n",
       "      <td>12.10</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.10</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>23.10</td>\n",
       "      <td>24.90</td>\n",
       "      <td>23.60</td>\n",
       "      <td>24.4</td>\n",
       "      <td>24.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>24.3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.4</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.20</td>\n",
       "      <td>22.00</td>\n",
       "      <td>23.10</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>26.20</td>\n",
       "      <td>28.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>26.6</td>\n",
       "      <td>26.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.1</td>\n",
       "      <td>25.4</td>\n",
       "      <td>26.1</td>\n",
       "      <td>25.2</td>\n",
       "      <td>25.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>25.9</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.8</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>27.30</td>\n",
       "      <td>26.80</td>\n",
       "      <td>25.70</td>\n",
       "      <td>25.1</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>25.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>25.3</td>\n",
       "      <td>23.8</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>25.8</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.1</td>\n",
       "      <td>24.4</td>\n",
       "      <td>22.80</td>\n",
       "      <td>23.40</td>\n",
       "      <td>24.00</td>\n",
       "      <td>22.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1999_tax  2000_tax  2001_tax  2002_tax  2003_tax  2004_tax  \\\n",
       "country                                                                 \n",
       "Angola        28.70     29.20     15.50      17.2      18.1      17.1   \n",
       "Argentina      9.62      9.32      9.82      12.5      13.1      13.1   \n",
       "Australia     23.10     24.90     23.60      24.4      24.3      24.8   \n",
       "Austria       26.20     28.00     27.00      26.6      26.3      25.5   \n",
       "Belgium       27.30     26.80     25.70      25.1      25.5      25.5   \n",
       "\n",
       "           2005_tax  2006_tax  2007_tax  2008_tax  2009_tax  2010_tax  \\\n",
       "country                                                                 \n",
       "Angola         23.4      21.5      25.7      18.4      16.6      16.1   \n",
       "Argentina      12.9      12.4      13.3      12.4      12.9      12.7   \n",
       "Australia      24.6      24.1      24.3      22.0      20.5      20.4   \n",
       "Austria        25.1      25.4      26.1      25.2      25.3      25.4   \n",
       "Belgium        25.3      24.8      25.3      23.8      24.5      24.9   \n",
       "\n",
       "           2011_tax  2012_tax  2013_tax  2014_tax  2015_tax  2016_tax  \\\n",
       "country                                                                 \n",
       "Angola         14.8      14.2      13.5      11.0      9.73      9.22   \n",
       "Argentina      13.0      12.5      12.6      12.3     12.10     10.90   \n",
       "Australia      21.1      22.0      21.9      21.9     22.20     22.00   \n",
       "Austria        25.9      26.4      26.4      26.8     25.40     25.40   \n",
       "Belgium        25.8      26.2      26.1      24.4     22.80     23.40   \n",
       "\n",
       "           2017_tax  2018_tax  \n",
       "country                        \n",
       "Angola         9.64      9.38  \n",
       "Argentina     10.10     10.60  \n",
       "Australia     23.10     23.30  \n",
       "Austria       25.40     25.60  \n",
       "Belgium       24.00     22.70  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tax_revenue= tax_revenue.rename(columns= lambda x: x +'_tax')\n",
    "tax_revenue.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "89077341",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of tax revenue dataset\n",
    "fig, ((ax1, ax2), (ax3, ax4))= plt.subplots(nrows= 2, ncols= 2, figsize= (10,8))\n",
    "ax1.hist(tax_revenue['1999_tax'])\n",
    "ax2.hist(tax_revenue['2006_tax'])\n",
    "ax3.hist(tax_revenue['2013_tax'])\n",
    "ax4.hist(tax_revenue['2018_tax'])\n",
    "ax1.set(title= 'Distribution of tax revenue dataset in 1999' )\n",
    "ax2.set(title= 'Distribution of tax revenue dataset in 2006' )\n",
    "ax3.set(title= 'Distribution of tax revenue dataset in 2013' )\n",
    "ax4.set(title= 'Distribution of tax revenue dataset in 2019' );"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0773f55",
   "metadata": {},
   "source": [
    "# Summary\n",
    "> **1999**\n",
    "* The maximum revenue is to 40-50 \n",
    "* The data is skewed to the right\n",
    "* Their are outliers in this dataset\n",
    "\n",
    "> **2006**\n",
    "* The maximum investment rate increased to 60-70 \n",
    "* The data is skewed to the right\n",
    "* Their are outliers in this dataset\n",
    "\n",
    "> **2013**\n",
    "* The maximum investment rate decreased to 35-40 \n",
    "* The data is skewed to the right\n",
    "* Their are outliers in this dataset\n",
    "\n",
    "> **2018**\n",
    "* The maximum investment rate increased to 30-35\n",
    "* The data is skewed to the right\n",
    "* Their are outliers in this dataset\n",
    "\n",
    " # Conclusion\n",
    "    There is decrease in Tax Revenue being generated in different countries over the years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784bbdb8",
   "metadata": {},
   "source": [
    "# Merge inflation and population Dataset and see if there is any relationship between them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e99249ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_data_1= pd.merge(population_2, inflation_2, left_index= True, right_index= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "546b1546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999_pop</th>\n",
       "      <th>2000_pop</th>\n",
       "      <th>2001_pop</th>\n",
       "      <th>2002_pop</th>\n",
       "      <th>2003_pop</th>\n",
       "      <th>2004_pop</th>\n",
       "      <th>2005_pop</th>\n",
       "      <th>2006_pop</th>\n",
       "      <th>2007_pop</th>\n",
       "      <th>2008_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2010_inf</th>\n",
       "      <th>2011_inf</th>\n",
       "      <th>2012_inf</th>\n",
       "      <th>2013_inf</th>\n",
       "      <th>2014_inf</th>\n",
       "      <th>2015_inf</th>\n",
       "      <th>2016_inf</th>\n",
       "      <th>2017_inf</th>\n",
       "      <th>2018_inf</th>\n",
       "      <th>2019_inf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>16410000.0</td>\n",
       "      <td>16910000.0</td>\n",
       "      <td>17510000.0</td>\n",
       "      <td>18110000.0</td>\n",
       "      <td>18810000.0</td>\n",
       "      <td>19410000.0</td>\n",
       "      <td>20110000.0</td>\n",
       "      <td>20910000.0</td>\n",
       "      <td>21710000.0</td>\n",
       "      <td>22510000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.800</td>\n",
       "      <td>7.260</td>\n",
       "      <td>2.840</td>\n",
       "      <td>3.560</td>\n",
       "      <td>-3.520</td>\n",
       "      <td>21.800</td>\n",
       "      <td>22.60</td>\n",
       "      <td>29.10</td>\n",
       "      <td>28.100</td>\n",
       "      <td>15.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3131000.0</td>\n",
       "      <td>3121000.0</td>\n",
       "      <td>3110000.0</td>\n",
       "      <td>3091000.0</td>\n",
       "      <td>3061000.0</td>\n",
       "      <td>3031000.0</td>\n",
       "      <td>31000000.0</td>\n",
       "      <td>2971000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.310</td>\n",
       "      <td>1.040</td>\n",
       "      <td>0.289</td>\n",
       "      <td>1.550</td>\n",
       "      <td>0.564</td>\n",
       "      <td>-0.632</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.477</td>\n",
       "      <td>-0.965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>65410.0</td>\n",
       "      <td>67310.0</td>\n",
       "      <td>701000.0</td>\n",
       "      <td>73210.0</td>\n",
       "      <td>76310.0</td>\n",
       "      <td>78910.0</td>\n",
       "      <td>811000.0</td>\n",
       "      <td>82710.0</td>\n",
       "      <td>83910.0</td>\n",
       "      <td>84510.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.448</td>\n",
       "      <td>-0.084</td>\n",
       "      <td>0.661</td>\n",
       "      <td>0.348</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.370</td>\n",
       "      <td>1.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>36910000.0</td>\n",
       "      <td>37310000.0</td>\n",
       "      <td>37710000.0</td>\n",
       "      <td>38110000.0</td>\n",
       "      <td>38510000.0</td>\n",
       "      <td>38910000.0</td>\n",
       "      <td>39310000.0</td>\n",
       "      <td>39710000.0</td>\n",
       "      <td>40110000.0</td>\n",
       "      <td>40510000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.700</td>\n",
       "      <td>22.300</td>\n",
       "      <td>23.900</td>\n",
       "      <td>40.300</td>\n",
       "      <td>26.600</td>\n",
       "      <td>41.100</td>\n",
       "      <td>26.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>50.600</td>\n",
       "      <td>39.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>3071000.0</td>\n",
       "      <td>3051000.0</td>\n",
       "      <td>3031000.0</td>\n",
       "      <td>3021000.0</td>\n",
       "      <td>31000000.0</td>\n",
       "      <td>2981000.0</td>\n",
       "      <td>2961000.0</td>\n",
       "      <td>2931000.0</td>\n",
       "      <td>2911000.0</td>\n",
       "      <td>2891000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.280</td>\n",
       "      <td>5.350</td>\n",
       "      <td>3.370</td>\n",
       "      <td>2.310</td>\n",
       "      <td>1.210</td>\n",
       "      <td>0.269</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.79</td>\n",
       "      <td>1.460</td>\n",
       "      <td>1.880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1999_pop    2000_pop    2001_pop    2002_pop    2003_pop  \\\n",
       "country                                                                 \n",
       "Angola     16410000.0  16910000.0  17510000.0  18110000.0  18810000.0   \n",
       "Albania     3131000.0   3131000.0   3131000.0   3121000.0   3110000.0   \n",
       "Andorra       65410.0     67310.0    701000.0     73210.0     76310.0   \n",
       "Argentina  36910000.0  37310000.0  37710000.0  38110000.0  38510000.0   \n",
       "Armenia     3071000.0   3051000.0   3031000.0   3021000.0  31000000.0   \n",
       "\n",
       "             2004_pop    2005_pop    2006_pop    2007_pop    2008_pop  ...  \\\n",
       "country                                                                ...   \n",
       "Angola     19410000.0  20110000.0  20910000.0  21710000.0  22510000.0  ...   \n",
       "Albania     3091000.0   3061000.0   3031000.0  31000000.0   2971000.0  ...   \n",
       "Andorra       78910.0    811000.0     82710.0     83910.0     84510.0  ...   \n",
       "Argentina  38910000.0  39310000.0  39710000.0  40110000.0  40510000.0  ...   \n",
       "Armenia     2981000.0   2961000.0   2931000.0   2911000.0   2891000.0  ...   \n",
       "\n",
       "           2010_inf  2011_inf  2012_inf  2013_inf  2014_inf  2015_inf  \\\n",
       "country                                                                 \n",
       "Angola       31.800     7.260     2.840     3.560    -3.520    21.800   \n",
       "Albania       2.310     1.040     0.289     1.550     0.564    -0.632   \n",
       "Andorra       0.197     0.175     0.448    -0.084     0.661     0.348   \n",
       "Argentina    23.700    22.300    23.900    40.300    26.600    41.100   \n",
       "Armenia       4.280     5.350     3.370     2.310     1.210     0.269   \n",
       "\n",
       "           2016_inf  2017_inf  2018_inf  2019_inf  \n",
       "country                                            \n",
       "Angola        22.60     29.10    28.100    15.100  \n",
       "Albania        1.45      1.36     0.477    -0.965  \n",
       "Andorra        1.14      1.01     1.370     1.060  \n",
       "Argentina     26.00     40.00    50.600    39.800  \n",
       "Armenia        2.15      2.79     1.460     1.880  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7c49ad58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_data_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0ef38d93",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_1= merged_data_1['1999_pop']\n",
    "y_1= merged_data_1['1999_inf']\n",
    "x_2= merged_data_1['2006_pop']\n",
    "y_2= merged_data_1['2006_inf']\n",
    "x_3= merged_data_1['2013_pop']\n",
    "y_3= merged_data_1['2013_inf']\n",
    "x_4= merged_data_1['2019_pop']\n",
    "y_4= merged_data_1['2019_inf']\n",
    "\n",
    "fig, ((ax1,ax2), (ax3,ax4))= plt.subplots(nrows= 2, ncols=2, figsize= (10,10))\n",
    "ax1.scatter(x_1, y_1)\n",
    "ax2.scatter(x_2, y_2)\n",
    "ax3.scatter(x_3, y_3)\n",
    "ax4.scatter(x_4, y_4)\n",
    "\n",
    "ax1.set(title= ('INFLATION RATE IN RELATION TO POPULATION SIZE IN 1999'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Inflation Rate'))\n",
    "\n",
    "ax2.set(title= ('INFLATION RATE IN RELATION TO POPULATION SIZE IN 2006'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Inflation Rate'))\n",
    "\n",
    "ax3.set(title= ('INFLATION RATE IN RELATION TO POPULATION SIZE IN 2013'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Inflation Rate'))\n",
    "\n",
    "ax4.set(title= ('INFLATION RATE IN RELATION TO POPULATION SIZE IN 2019'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Inflation Rate'));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bec15461",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "* Countries with low Population size have high rate of inflation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "bca22cb1",
   "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>1999_pop</th>\n",
       "      <th>2000_pop</th>\n",
       "      <th>2001_pop</th>\n",
       "      <th>2002_pop</th>\n",
       "      <th>2003_pop</th>\n",
       "      <th>2004_pop</th>\n",
       "      <th>2005_pop</th>\n",
       "      <th>2006_pop</th>\n",
       "      <th>2007_pop</th>\n",
       "      <th>2008_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2009_tax</th>\n",
       "      <th>2010_tax</th>\n",
       "      <th>2011_tax</th>\n",
       "      <th>2012_tax</th>\n",
       "      <th>2013_tax</th>\n",
       "      <th>2014_tax</th>\n",
       "      <th>2015_tax</th>\n",
       "      <th>2016_tax</th>\n",
       "      <th>2017_tax</th>\n",
       "      <th>2018_tax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>16410000.0</td>\n",
       "      <td>16910000.0</td>\n",
       "      <td>17510000.0</td>\n",
       "      <td>18110000.0</td>\n",
       "      <td>18810000.0</td>\n",
       "      <td>19410000.0</td>\n",
       "      <td>20110000.0</td>\n",
       "      <td>20910000.0</td>\n",
       "      <td>21710000.0</td>\n",
       "      <td>22510000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.6</td>\n",
       "      <td>16.1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.2</td>\n",
       "      <td>13.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.73</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.64</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>36910000.0</td>\n",
       "      <td>37310000.0</td>\n",
       "      <td>37710000.0</td>\n",
       "      <td>38110000.0</td>\n",
       "      <td>38510000.0</td>\n",
       "      <td>38910000.0</td>\n",
       "      <td>39310000.0</td>\n",
       "      <td>39710000.0</td>\n",
       "      <td>40110000.0</td>\n",
       "      <td>40510000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.9</td>\n",
       "      <td>12.7</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>12.6</td>\n",
       "      <td>12.3</td>\n",
       "      <td>12.10</td>\n",
       "      <td>10.90</td>\n",
       "      <td>10.10</td>\n",
       "      <td>10.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>191000000.0</td>\n",
       "      <td>19210000.0</td>\n",
       "      <td>19410000.0</td>\n",
       "      <td>19610000.0</td>\n",
       "      <td>19910000.0</td>\n",
       "      <td>20210000.0</td>\n",
       "      <td>20510000.0</td>\n",
       "      <td>20910000.0</td>\n",
       "      <td>21310000.0</td>\n",
       "      <td>21810000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.4</td>\n",
       "      <td>21.1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.9</td>\n",
       "      <td>21.9</td>\n",
       "      <td>22.20</td>\n",
       "      <td>22.00</td>\n",
       "      <td>23.10</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>8071000.0</td>\n",
       "      <td>8110000.0</td>\n",
       "      <td>8131000.0</td>\n",
       "      <td>8181000.0</td>\n",
       "      <td>8221000.0</td>\n",
       "      <td>8251000.0</td>\n",
       "      <td>8291000.0</td>\n",
       "      <td>8311000.0</td>\n",
       "      <td>8341000.0</td>\n",
       "      <td>8371000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>25.9</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.8</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.40</td>\n",
       "      <td>25.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>10310000.0</td>\n",
       "      <td>10310000.0</td>\n",
       "      <td>10410000.0</td>\n",
       "      <td>10410000.0</td>\n",
       "      <td>10510000.0</td>\n",
       "      <td>10510000.0</td>\n",
       "      <td>10610000.0</td>\n",
       "      <td>10710000.0</td>\n",
       "      <td>10810000.0</td>\n",
       "      <td>10910000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.5</td>\n",
       "      <td>24.9</td>\n",
       "      <td>25.8</td>\n",
       "      <td>26.2</td>\n",
       "      <td>26.1</td>\n",
       "      <td>24.4</td>\n",
       "      <td>22.80</td>\n",
       "      <td>23.40</td>\n",
       "      <td>24.00</td>\n",
       "      <td>22.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1999_pop    2000_pop    2001_pop    2002_pop    2003_pop  \\\n",
       "country                                                                  \n",
       "Angola      16410000.0  16910000.0  17510000.0  18110000.0  18810000.0   \n",
       "Argentina   36910000.0  37310000.0  37710000.0  38110000.0  38510000.0   \n",
       "Australia  191000000.0  19210000.0  19410000.0  19610000.0  19910000.0   \n",
       "Austria      8071000.0   8110000.0   8131000.0   8181000.0   8221000.0   \n",
       "Belgium     10310000.0  10310000.0  10410000.0  10410000.0  10510000.0   \n",
       "\n",
       "             2004_pop    2005_pop    2006_pop    2007_pop    2008_pop  ...  \\\n",
       "country                                                                ...   \n",
       "Angola     19410000.0  20110000.0  20910000.0  21710000.0  22510000.0  ...   \n",
       "Argentina  38910000.0  39310000.0  39710000.0  40110000.0  40510000.0  ...   \n",
       "Australia  20210000.0  20510000.0  20910000.0  21310000.0  21810000.0  ...   \n",
       "Austria     8251000.0   8291000.0   8311000.0   8341000.0   8371000.0  ...   \n",
       "Belgium    10510000.0  10610000.0  10710000.0  10810000.0  10910000.0  ...   \n",
       "\n",
       "           2009_tax  2010_tax  2011_tax  2012_tax  2013_tax  2014_tax  \\\n",
       "country                                                                 \n",
       "Angola         16.6      16.1      14.8      14.2      13.5      11.0   \n",
       "Argentina      12.9      12.7      13.0      12.5      12.6      12.3   \n",
       "Australia      20.5      20.4      21.1      22.0      21.9      21.9   \n",
       "Austria        25.3      25.4      25.9      26.4      26.4      26.8   \n",
       "Belgium        24.5      24.9      25.8      26.2      26.1      24.4   \n",
       "\n",
       "           2015_tax  2016_tax  2017_tax  2018_tax  \n",
       "country                                            \n",
       "Angola         9.73      9.22      9.64      9.38  \n",
       "Argentina     12.10     10.90     10.10     10.60  \n",
       "Australia     22.20     22.00     23.10     23.30  \n",
       "Austria       25.40     25.40     25.40     25.60  \n",
       "Belgium       22.80     23.40     24.00     22.70  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data_2= pd.merge(population_2, tax_revenue, left_index= True, right_index= True)\n",
    "merged_data_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c912d033",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged_data_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7b1ac675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_1= merged_data_2['1999_pop']\n",
    "y_1= merged_data_2['1999_tax']\n",
    "x_2= merged_data_2['2006_pop']\n",
    "y_2= merged_data_2['2006_tax']\n",
    "x_3= merged_data_2['2013_pop']\n",
    "y_3= merged_data_2['2013_tax']\n",
    "x_4= merged_data_2['2018_pop']\n",
    "y_4= merged_data_2['2018_tax']\n",
    "\n",
    "fig, ((ax1,ax2), (ax3,ax4))= plt.subplots(nrows= 2, ncols=2, figsize= (15,15))\n",
    "ax1.scatter(x_1, y_1)\n",
    "ax2.scatter(x_2, y_2)\n",
    "ax3.scatter(x_3, y_3)\n",
    "ax4.scatter(x_4, y_4)\n",
    "\n",
    "ax1.set(title= ('TAX REVENUE IN RELATION TO POPULATION SIZE IN 1999'), \n",
    "        xlabel=('POPULATION SIZE'),\n",
    "        ylabel= ('TAX REVENUE'))\n",
    "\n",
    "ax2.set(title= ('TAX REVENUE IN RELATION TO POPULATION SIZE IN 2006'), \n",
    "        xlabel=('POPULATION SIZE'),\n",
    "        ylabel= ('TAX REVENUE'))\n",
    "\n",
    "ax3.set(title= ('TAX REVENUE IN RELATION TO POPULATION SIZE IN 2013'), \n",
    "        xlabel=('POPULATION SIZE'),\n",
    "        ylabel= ('TAX REVENUE'))\n",
    "\n",
    "ax4.set(title= ('TAX REVENUE IN RELATION TO POPULATION SIZE IN 2018'), \n",
    "        xlabel=('POPULATION SIZE'),\n",
    "        ylabel= ('TAX REVENUE'));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "183666d0",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "* Countries with lower population generate more tax than countries with higher population size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "92575b75",
   "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>1999_pop</th>\n",
       "      <th>2000_pop</th>\n",
       "      <th>2001_pop</th>\n",
       "      <th>2002_pop</th>\n",
       "      <th>2003_pop</th>\n",
       "      <th>2004_pop</th>\n",
       "      <th>2005_pop</th>\n",
       "      <th>2006_pop</th>\n",
       "      <th>2007_pop</th>\n",
       "      <th>2008_pop</th>\n",
       "      <th>...</th>\n",
       "      <th>2010_inv</th>\n",
       "      <th>2011_inv</th>\n",
       "      <th>2012_inv</th>\n",
       "      <th>2013_inv</th>\n",
       "      <th>2014_inv</th>\n",
       "      <th>2015_inv</th>\n",
       "      <th>2016_inv</th>\n",
       "      <th>2017_inv</th>\n",
       "      <th>2018_inv</th>\n",
       "      <th>2019_inv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>1.641000e+07</td>\n",
       "      <td>1.691000e+07</td>\n",
       "      <td>1.751000e+07</td>\n",
       "      <td>1.811000e+07</td>\n",
       "      <td>1.881000e+07</td>\n",
       "      <td>1.941000e+07</td>\n",
       "      <td>2.011000e+07</td>\n",
       "      <td>2.091000e+07</td>\n",
       "      <td>2.171000e+07</td>\n",
       "      <td>2.251000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>26.4</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.1</td>\n",
       "      <td>27.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>27.2</td>\n",
       "      <td>24.1</td>\n",
       "      <td>21.4</td>\n",
       "      <td>19.8</td>\n",
       "      <td>22.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>3.691000e+07</td>\n",
       "      <td>3.731000e+07</td>\n",
       "      <td>3.771000e+07</td>\n",
       "      <td>3.811000e+07</td>\n",
       "      <td>3.851000e+07</td>\n",
       "      <td>3.891000e+07</td>\n",
       "      <td>3.931000e+07</td>\n",
       "      <td>3.971000e+07</td>\n",
       "      <td>4.011000e+07</td>\n",
       "      <td>4.051000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>18.4</td>\n",
       "      <td>16.5</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.3</td>\n",
       "      <td>17.1</td>\n",
       "      <td>17.7</td>\n",
       "      <td>18.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>3.071000e+06</td>\n",
       "      <td>3.051000e+06</td>\n",
       "      <td>3.031000e+06</td>\n",
       "      <td>3.021000e+06</td>\n",
       "      <td>3.100000e+07</td>\n",
       "      <td>2.981000e+06</td>\n",
       "      <td>2.961000e+06</td>\n",
       "      <td>2.931000e+06</td>\n",
       "      <td>2.911000e+06</td>\n",
       "      <td>2.891000e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>31.9</td>\n",
       "      <td>24.7</td>\n",
       "      <td>22.2</td>\n",
       "      <td>21.3</td>\n",
       "      <td>20.7</td>\n",
       "      <td>18.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>22.4</td>\n",
       "      <td>17.4</td>\n",
       "      <td>17.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>7.610000e+05</td>\n",
       "      <td>7.721000e+04</td>\n",
       "      <td>7.831000e+04</td>\n",
       "      <td>7.931000e+04</td>\n",
       "      <td>8.031000e+04</td>\n",
       "      <td>8.151000e+04</td>\n",
       "      <td>8.271000e+04</td>\n",
       "      <td>8.410000e+05</td>\n",
       "      <td>8.541000e+04</td>\n",
       "      <td>8.671000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>20.7</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>27.2</td>\n",
       "      <td>32.6</td>\n",
       "      <td>37.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>55.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.910000e+08</td>\n",
       "      <td>1.921000e+07</td>\n",
       "      <td>1.941000e+07</td>\n",
       "      <td>1.961000e+07</td>\n",
       "      <td>1.991000e+07</td>\n",
       "      <td>2.021000e+07</td>\n",
       "      <td>2.051000e+07</td>\n",
       "      <td>2.091000e+07</td>\n",
       "      <td>2.131000e+07</td>\n",
       "      <td>2.181000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>26.5</td>\n",
       "      <td>27.7</td>\n",
       "      <td>27.9</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>24.1</td>\n",
       "      <td>24.5</td>\n",
       "      <td>23.2</td>\n",
       "      <td>22.3</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>United States</th>\n",
       "      <td>2.821000e+09</td>\n",
       "      <td>2.851000e+09</td>\n",
       "      <td>2.871000e+09</td>\n",
       "      <td>2.901000e+09</td>\n",
       "      <td>2.921000e+09</td>\n",
       "      <td>2.951000e+09</td>\n",
       "      <td>2.981000e+09</td>\n",
       "      <td>3.011000e+09</td>\n",
       "      <td>3.031000e+09</td>\n",
       "      <td>3.061000e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>19.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.4</td>\n",
       "      <td>20.8</td>\n",
       "      <td>21.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>21.3</td>\n",
       "      <td>21.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>2.481000e+07</td>\n",
       "      <td>2.511000e+07</td>\n",
       "      <td>2.541000e+07</td>\n",
       "      <td>2.571000e+07</td>\n",
       "      <td>2.611000e+07</td>\n",
       "      <td>2.641000e+07</td>\n",
       "      <td>2.681000e+07</td>\n",
       "      <td>2.721000e+07</td>\n",
       "      <td>2.761000e+07</td>\n",
       "      <td>2.811000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>26.6</td>\n",
       "      <td>25.6</td>\n",
       "      <td>25.5</td>\n",
       "      <td>26.5</td>\n",
       "      <td>26.1</td>\n",
       "      <td>26.4</td>\n",
       "      <td>29.5</td>\n",
       "      <td>37.6</td>\n",
       "      <td>45.2</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>7.991000e+07</td>\n",
       "      <td>8.071000e+07</td>\n",
       "      <td>8.151000e+07</td>\n",
       "      <td>8.231000e+07</td>\n",
       "      <td>8.311000e+07</td>\n",
       "      <td>8.381000e+07</td>\n",
       "      <td>8.461000e+07</td>\n",
       "      <td>8.541000e+07</td>\n",
       "      <td>8.621000e+07</td>\n",
       "      <td>8.711000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>29.8</td>\n",
       "      <td>27.2</td>\n",
       "      <td>26.7</td>\n",
       "      <td>26.8</td>\n",
       "      <td>27.7</td>\n",
       "      <td>26.6</td>\n",
       "      <td>26.6</td>\n",
       "      <td>26.5</td>\n",
       "      <td>26.8</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>1.851000e+06</td>\n",
       "      <td>1.891000e+06</td>\n",
       "      <td>1.941000e+06</td>\n",
       "      <td>1.991000e+06</td>\n",
       "      <td>2.041000e+06</td>\n",
       "      <td>2.091000e+06</td>\n",
       "      <td>2.141000e+06</td>\n",
       "      <td>2.191000e+06</td>\n",
       "      <td>2.251000e+06</td>\n",
       "      <td>2.301000e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>28.7</td>\n",
       "      <td>24.1</td>\n",
       "      <td>27.4</td>\n",
       "      <td>24.3</td>\n",
       "      <td>32.9</td>\n",
       "      <td>23.7</td>\n",
       "      <td>27.9</td>\n",
       "      <td>26.8</td>\n",
       "      <td>31.0</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>4.510000e+08</td>\n",
       "      <td>4.561000e+07</td>\n",
       "      <td>4.621000e+07</td>\n",
       "      <td>4.671000e+07</td>\n",
       "      <td>4.731000e+07</td>\n",
       "      <td>4.791000e+07</td>\n",
       "      <td>4.851000e+07</td>\n",
       "      <td>4.911000e+07</td>\n",
       "      <td>4.981000e+07</td>\n",
       "      <td>5.051000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>19.7</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.2</td>\n",
       "      <td>20.5</td>\n",
       "      <td>20.9</td>\n",
       "      <td>19.2</td>\n",
       "      <td>18.8</td>\n",
       "      <td>17.9</td>\n",
       "      <td>17.6</td>\n",
       "      <td>12.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>123 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         1999_pop      2000_pop      2001_pop      2002_pop  \\\n",
       "country                                                                       \n",
       "Angola               1.641000e+07  1.691000e+07  1.751000e+07  1.811000e+07   \n",
       "Argentina            3.691000e+07  3.731000e+07  3.771000e+07  3.811000e+07   \n",
       "Armenia              3.071000e+06  3.051000e+06  3.031000e+06  3.021000e+06   \n",
       "Antigua and Barbuda  7.610000e+05  7.721000e+04  7.831000e+04  7.931000e+04   \n",
       "Australia            1.910000e+08  1.921000e+07  1.941000e+07  1.961000e+07   \n",
       "...                           ...           ...           ...           ...   \n",
       "United States        2.821000e+09  2.851000e+09  2.871000e+09  2.901000e+09   \n",
       "Uzbekistan           2.481000e+07  2.511000e+07  2.541000e+07  2.571000e+07   \n",
       "Vietnam              7.991000e+07  8.071000e+07  8.151000e+07  8.231000e+07   \n",
       "Vanuatu              1.851000e+06  1.891000e+06  1.941000e+06  1.991000e+06   \n",
       "South Africa         4.510000e+08  4.561000e+07  4.621000e+07  4.671000e+07   \n",
       "\n",
       "                         2003_pop      2004_pop      2005_pop      2006_pop  \\\n",
       "country                                                                       \n",
       "Angola               1.881000e+07  1.941000e+07  2.011000e+07  2.091000e+07   \n",
       "Argentina            3.851000e+07  3.891000e+07  3.931000e+07  3.971000e+07   \n",
       "Armenia              3.100000e+07  2.981000e+06  2.961000e+06  2.931000e+06   \n",
       "Antigua and Barbuda  8.031000e+04  8.151000e+04  8.271000e+04  8.410000e+05   \n",
       "Australia            1.991000e+07  2.021000e+07  2.051000e+07  2.091000e+07   \n",
       "...                           ...           ...           ...           ...   \n",
       "United States        2.921000e+09  2.951000e+09  2.981000e+09  3.011000e+09   \n",
       "Uzbekistan           2.611000e+07  2.641000e+07  2.681000e+07  2.721000e+07   \n",
       "Vietnam              8.311000e+07  8.381000e+07  8.461000e+07  8.541000e+07   \n",
       "Vanuatu              2.041000e+06  2.091000e+06  2.141000e+06  2.191000e+06   \n",
       "South Africa         4.731000e+07  4.791000e+07  4.851000e+07  4.911000e+07   \n",
       "\n",
       "                         2007_pop      2008_pop  ...  2010_inv  2011_inv  \\\n",
       "country                                          ...                       \n",
       "Angola               2.171000e+07  2.251000e+07  ...      26.4      26.7   \n",
       "Argentina            4.011000e+07  4.051000e+07  ...      18.4      16.5   \n",
       "Armenia              2.911000e+06  2.891000e+06  ...      31.9      24.7   \n",
       "Antigua and Barbuda  8.541000e+04  8.671000e+04  ...      20.7      22.1   \n",
       "Australia            2.131000e+07  2.181000e+07  ...      26.5      27.7   \n",
       "...                           ...           ...  ...       ...       ...   \n",
       "United States        3.031000e+09  3.061000e+09  ...      19.1      20.0   \n",
       "Uzbekistan           2.761000e+07  2.811000e+07  ...      26.6      25.6   \n",
       "Vietnam              8.621000e+07  8.711000e+07  ...      29.8      27.2   \n",
       "Vanuatu              2.251000e+06  2.301000e+06  ...      28.7      24.1   \n",
       "South Africa         4.981000e+07  5.051000e+07  ...      19.7      20.0   \n",
       "\n",
       "                     2012_inv  2013_inv  2014_inv  2015_inv  2016_inv  \\\n",
       "country                                                                 \n",
       "Angola                   26.1      27.5      34.2      27.2      24.1   \n",
       "Argentina                17.3      17.3      17.1      17.7      18.2   \n",
       "Armenia                  22.2      21.3      20.7      18.0      18.4   \n",
       "Antigua and Barbuda      23.7      25.0      23.9      27.2      32.6   \n",
       "Australia                27.9      26.7      26.3      25.4      24.1   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "United States            20.4      20.8      21.2      20.5      20.7   \n",
       "Uzbekistan               25.5      26.5      26.1      26.4      29.5   \n",
       "Vietnam                  26.7      26.8      27.7      26.6      26.6   \n",
       "Vanuatu                  27.4      24.3      32.9      23.7      27.9   \n",
       "South Africa             21.2      20.5      20.9      19.2      18.8   \n",
       "\n",
       "                     2017_inv  2018_inv  2019_inv  \n",
       "country                                            \n",
       "Angola                   21.4      19.8      22.8  \n",
       "Argentina                16.0      14.6      13.8  \n",
       "Armenia                  22.4      17.4      17.2  \n",
       "Antigua and Barbuda      37.0      42.5      55.8  \n",
       "Australia                24.5      23.2      22.3  \n",
       "...                       ...       ...       ...  \n",
       "United States            21.1      21.3      21.1  \n",
       "Uzbekistan               37.6      45.2      37.5  \n",
       "Vietnam                  26.5      26.8      26.7  \n",
       "Vanuatu                  26.8      31.0      34.1  \n",
       "South Africa             17.9      17.6      12.4  \n",
       "\n",
       "[123 rows x 42 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data_3= pd.merge(population_2, investment, left_index= True, right_index= True)\n",
    "merged_data_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b8318c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_1= merged_data_3['1999_pop']\n",
    "y_1= merged_data_3['1999_inv']\n",
    "x_2= merged_data_3['2006_pop']\n",
    "y_2= merged_data_3['2006_inv']\n",
    "x_3= merged_data_3['2013_pop']\n",
    "y_3= merged_data_3['2013_inv']\n",
    "x_4= merged_data_3['2019_pop']\n",
    "y_4= merged_data_3['2019_inv']\n",
    "\n",
    "fig, ((ax1,ax2), (ax3,ax4))= plt.subplots(nrows= 2, ncols=2, figsize= (15,15))\n",
    "ax1.scatter(x_1, y_1)\n",
    "ax2.scatter(x_2, y_2)\n",
    "ax3.scatter(x_3, y_3)\n",
    "ax4.scatter(x_4, y_4)\n",
    "\n",
    "ax1.set(title= ('INVESTMENT RATE IN RELATION TO POPULATION SIZE IN 1999'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Investment Rate'))\n",
    "\n",
    "ax2.set(title= ('INVESTMENT RATE IN RELATION TO POPULATION SIZE IN 2006'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Investment Rate'))\n",
    "\n",
    "ax3.set(title= ('INVESTMENT RATE IN RELATION TO POPULATION SIZE IN 2013'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Investment Rate'))\n",
    "\n",
    "ax4.set(title= ('INVESTMENT RATE IN RELATION TO POPULATION SIZE IN 2019'), \n",
    "        xlabel=('Population Size'),\n",
    "        ylabel= ('Investment Rate'));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73b1b1ff",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "* Countries with higher Population size have more investments than country with lower population size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc84b56",
   "metadata": {},
   "source": [
    "# General Conclusion\n",
    "* Population Size of a country always affects economy rate of that country.\n",
    "* Economic Rate of most countries increase over the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a1b019d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}