GDP per capita and its effect on the man life quality.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "\n",
    "# GDP per capita and its effect on the man life quality: Gapminder datasaet Investigation\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "My objective is to identify the impact of [GDP](https://en.wikipedia.org/wiki/Gross_domestic_product) growth on four metrics of human well-being: [Income](https://en.wikipedia.org/wiki/Per_capita_income), [Human Development Index(HDI)](https://en.wikipedia.org/wiki/Human_Development_Index) and [Life Expectancy](https://en.wikipedia.org/wiki/Life_expectancy). In this investigation, I used four datasets from the [Gapminder](https://www.gapminder.org/data/) to identify trends of the change in GDP over time as well as other indicators according to different countries and regions of the world.\n",
    "\n",
    "All data files are arranged by country and have not region file, for that I doanloaded the region file from [github page](https://github.com/lukes/ISO-3166-Countries-with-Regional-Codes).\n",
    "\n",
    "<a id='intro'></a>\n",
    "### Step One: Posing Questions\n",
    "**Research Questions:**\n",
    "\n",
    "**1-** What is the effect of GDP on Income, Human Development Index(HDI) and Life Expectancy? \n",
    "\n",
    "**2-** Are there trends that can be observed between all selected metrics with each other? (correlation matrix)\n",
    "\n",
    "**3-** What are the distributions of each one of the four metrics chosen according to the countries? and according to time (years)?\n",
    "\n",
    "**4-** Have certain regions of the world been growing in the four metrics better than others? \n",
    "\n",
    "**5-** Have certain sampled countries (for example Egypt, USA, etc ... ) of the world been growing in the four metrics better than others?\n",
    "\n",
    "**Datasets Files:**\n",
    "For answering the quesitions above I download the folowing files from Gapminder page:\n",
    "\n",
    "**1-** GDP file: gdppercapita_us_inflation_adjusted.csv\n",
    "\n",
    "**2-** Income file: income_per_person_gdppercapita_ppp_inflation_adjusted.csv\n",
    "\n",
    "**3-** HDI file: human_development_index.csv\n",
    "\n",
    "**4-** Life Expectancy file: life_expectancy_years.csv\n",
    "\n",
    "and one file from github for Regions\n",
    "\n",
    "**5-** World Regions file: world_regions.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing statements for all of the packages that I plan to use.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "### 2nd Step: Data Wrangling\n",
    "\n",
    "**2-1- Gathering and reading data:**\n",
    "After getting the files which from gapminder and github sites I uploaded it to the project folder to read can read in the next cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
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       "      <td>4680.0</td>\n",
       "      <td>4870.0</td>\n",
       "    </tr>\n",
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       "      <th>Algeria</th>\n",
       "      <td>2470.0</td>\n",
       "      <td>2080.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>2130.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>2280.0</td>\n",
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       "      <td>2240.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>2540.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4390.0</td>\n",
       "      <td>4390.0</td>\n",
       "      <td>4460</td>\n",
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       "      <td>4830.0</td>\n",
       "      <td>4830.0</td>\n",
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       "      <td>38200.0</td>\n",
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       "      <td>39100.0</td>\n",
       "      <td>40800.0</td>\n",
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       "               1960    1961    1962    1963    1964    1965    1966    1967  \\\n",
       "country                                                                       \n",
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       "Albania         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
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       "Andorra         NaN     NaN     NaN     NaN     NaN     NaN     NaN     NaN   \n",
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       "\n",
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       "country                       ...                                        \n",
       "Afghanistan     NaN     NaN   ...       445.0    525.0    553    569.0   \n",
       "Albania         NaN     NaN   ...      3780.0   3930.0   4090   4210.0   \n",
       "Algeria      2410.0  2540.0   ...      4390.0   4390.0   4460   4500.0   \n",
       "Andorra         NaN     NaN   ...     43900.0  42000.0  39700  38200.0   \n",
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       "\n",
       "                2012     2013     2014     2015     2016     2017  \n",
       "country                                                            \n",
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       "Albania       4280.0   4330.0   4410.0   4520.0   4680.0   4870.0  \n",
       "Algeria       4560.0   4600.0   4680.0   4760.0   4830.0   4830.0  \n",
       "Andorra      38200.0  39100.0  40800.0  41800.0  43000.0  43900.0  \n",
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       "\n",
       "[5 rows x 58 columns]"
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     },
     "execution_count": 713,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read GDP csv data file and make the 'country' column is index\n",
    "gdp = pd.read_csv('gdppercapita_us_inflation_adjusted.csv', index_col='country')\n",
    "# confirm and display 5 rows to check\n",
    "gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [
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       "      <td>0.295</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.338</td>\n",
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       "      <td>0.434</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.479</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.635</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.577</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.636</td>\n",
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       "      <td>0.705</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.858</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <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",
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       "      <td>0.495</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "              1990   1991   1992   1993   1994   1995   1996   1997   1998  \\\n",
       "country                                                                      \n",
       "Afghanistan  0.295  0.300  0.309  0.305  0.300  0.324  0.328  0.332  0.335   \n",
       "Albania      0.635  0.618  0.603  0.608  0.616  0.628  0.637  0.636  0.646   \n",
       "Algeria      0.577  0.581  0.587  0.591  0.595  0.600  0.609  0.617  0.627   \n",
       "Andorra        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "Angola         NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "              1999  ...    2008   2009   2010   2011   2012   2013   2014  \\\n",
       "country             ...                                                     \n",
       "Afghanistan  0.338  ...   0.434  0.448  0.454  0.463  0.470  0.476  0.479   \n",
       "Albania      0.656  ...   0.721  0.725  0.738  0.752  0.759  0.761  0.762   \n",
       "Algeria      0.636  ...   0.705  0.714  0.724  0.732  0.737  0.741  0.743   \n",
       "Andorra        NaN  ...     NaN    NaN  0.819  0.819  0.843  0.850  0.857   \n",
       "Angola       0.382  ...   0.480  0.488  0.495  0.508  0.523  0.527  0.531   \n",
       "\n",
       "              2015  2016  2107  \n",
       "country                         \n",
       "Afghanistan  0.479   NaN   NaN  \n",
       "Albania      0.764   NaN   NaN  \n",
       "Algeria      0.745   NaN   NaN  \n",
       "Andorra      0.858   NaN   NaN  \n",
       "Angola       0.533   NaN   NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 714,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read GDP csv data file and setting the 'country' column is index\n",
    "hdi = pd.read_csv('human_development_index.csv', index_col='country')\n",
    "# confirm and display 5 rows to check\n",
    "hdi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1806</th>\n",
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       "      <th>1808</th>\n",
       "      <th>1809</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <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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       "  <tbody>\n",
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       "      <th>Afghanistan</th>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>...</td>\n",
       "      <td>1530</td>\n",
       "      <td>1610</td>\n",
       "      <td>1660</td>\n",
       "      <td>1840</td>\n",
       "      <td>1810</td>\n",
       "      <td>1780</td>\n",
       "      <td>1750</td>\n",
       "      <td>1740</td>\n",
       "      <td>1800</td>\n",
       "      <td>1870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>667</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>...</td>\n",
       "      <td>9530</td>\n",
       "      <td>9930</td>\n",
       "      <td>10200</td>\n",
       "      <td>10400</td>\n",
       "      <td>10500</td>\n",
       "      <td>10700</td>\n",
       "      <td>11000</td>\n",
       "      <td>11400</td>\n",
       "      <td>11900</td>\n",
       "      <td>12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>715</td>\n",
       "      <td>716</td>\n",
       "      <td>717</td>\n",
       "      <td>718</td>\n",
       "      <td>719</td>\n",
       "      <td>720</td>\n",
       "      <td>721</td>\n",
       "      <td>722</td>\n",
       "      <td>723</td>\n",
       "      <td>724</td>\n",
       "      <td>...</td>\n",
       "      <td>12600</td>\n",
       "      <td>12900</td>\n",
       "      <td>13000</td>\n",
       "      <td>13200</td>\n",
       "      <td>13300</td>\n",
       "      <td>13500</td>\n",
       "      <td>13700</td>\n",
       "      <td>14000</td>\n",
       "      <td>13800</td>\n",
       "      <td>13700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>1210</td>\n",
       "      <td>1220</td>\n",
       "      <td>1220</td>\n",
       "      <td>...</td>\n",
       "      <td>41700</td>\n",
       "      <td>39000</td>\n",
       "      <td>42000</td>\n",
       "      <td>41900</td>\n",
       "      <td>43700</td>\n",
       "      <td>44900</td>\n",
       "      <td>46600</td>\n",
       "      <td>48200</td>\n",
       "      <td>49800</td>\n",
       "      <td>51500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>618</td>\n",
       "      <td>620</td>\n",
       "      <td>623</td>\n",
       "      <td>626</td>\n",
       "      <td>628</td>\n",
       "      <td>631</td>\n",
       "      <td>634</td>\n",
       "      <td>637</td>\n",
       "      <td>640</td>\n",
       "      <td>642</td>\n",
       "      <td>...</td>\n",
       "      <td>5910</td>\n",
       "      <td>5900</td>\n",
       "      <td>5910</td>\n",
       "      <td>6000</td>\n",
       "      <td>6190</td>\n",
       "      <td>6260</td>\n",
       "      <td>6230</td>\n",
       "      <td>6030</td>\n",
       "      <td>5940</td>\n",
       "      <td>5850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 219 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1800  1801  1802  1803  1804  1805  1806  1807  1808  1809  \\\n",
       "country                                                                   \n",
       "Afghanistan   603   603   603   603   603   603   603   603   603   603   \n",
       "Albania       667   667   667   667   667   668   668   668   668   668   \n",
       "Algeria       715   716   717   718   719   720   721   722   723   724   \n",
       "Andorra      1200  1200  1200  1200  1210  1210  1210  1210  1220  1220   \n",
       "Angola        618   620   623   626   628   631   634   637   640   642   \n",
       "\n",
       "             ...     2009   2010   2011   2012   2013   2014   2015   2016  \\\n",
       "country      ...                                                             \n",
       "Afghanistan  ...     1530   1610   1660   1840   1810   1780   1750   1740   \n",
       "Albania      ...     9530   9930  10200  10400  10500  10700  11000  11400   \n",
       "Algeria      ...    12600  12900  13000  13200  13300  13500  13700  14000   \n",
       "Andorra      ...    41700  39000  42000  41900  43700  44900  46600  48200   \n",
       "Angola       ...     5910   5900   5910   6000   6190   6260   6230   6030   \n",
       "\n",
       "              2017   2018  \n",
       "country                    \n",
       "Afghanistan   1800   1870  \n",
       "Albania      11900  12400  \n",
       "Algeria      13800  13700  \n",
       "Andorra      49800  51500  \n",
       "Angola        5940   5850  \n",
       "\n",
       "[5 rows x 219 columns]"
      ]
     },
     "execution_count": 715,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read Income csv data file and setting the 'country' column is index\n",
    "income_per = pd.read_csv('income_per_person_gdppercapita_ppp_inflation_adjusted.csv', index_col='country') \n",
    "# confirm and display 5 rows to check\n",
    "income_per.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1800</th>\n",
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       "      <th>Afghanistan</th>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.2</td>\n",
       "      <td>28.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>28.1</td>\n",
       "      <td>...</td>\n",
       "      <td>55.7</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.7</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.7</td>\n",
       "      <td>57.8</td>\n",
       "      <td>57.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.4</td>\n",
       "      <td>58.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>35.4</td>\n",
       "      <td>...</td>\n",
       "      <td>75.9</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.7</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.2</td>\n",
       "      <td>77.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>77.7</td>\n",
       "      <td>77.9</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>28.8</td>\n",
       "      <td>...</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.5</td>\n",
       "      <td>76.7</td>\n",
       "      <td>76.8</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.1</td>\n",
       "      <td>77.3</td>\n",
       "      <td>77.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>77.9</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>82.7</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>27.0</td>\n",
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       "      <td>61.7</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.7</td>\n",
       "      <td>64.9</td>\n",
       "      <td>65.2</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 219 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1800  1801  1802  1803  1804  1805  1806  1807  1808  1809  ...   \\\n",
       "country                                                                  ...    \n",
       "Afghanistan  28.2  28.2  28.2  28.2  28.2  28.2  28.1  28.1  28.1  28.1  ...    \n",
       "Albania      35.4  35.4  35.4  35.4  35.4  35.4  35.4  35.4  35.4  35.4  ...    \n",
       "Algeria      28.8  28.8  28.8  28.8  28.8  28.8  28.8  28.8  28.8  28.8  ...    \n",
       "Andorra       NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  ...    \n",
       "Angola       27.0  27.0  27.0  27.0  27.0  27.0  27.0  27.0  27.0  27.0  ...    \n",
       "\n",
       "             2009  2010  2011  2012  2013  2014  2015  2016  2017  2018  \n",
       "country                                                                  \n",
       "Afghanistan  55.7  56.2  56.7  57.2  57.7  57.8  57.9  58.0  58.4  58.7  \n",
       "Albania      75.9  76.3  76.7  77.0  77.2  77.4  77.6  77.7  77.9  78.0  \n",
       "Algeria      76.3  76.5  76.7  76.8  77.0  77.1  77.3  77.4  77.6  77.9  \n",
       "Andorra      82.7  82.7  82.6  82.6  82.6  82.6  82.5  82.5   NaN   NaN  \n",
       "Angola       59.3  60.1  60.9  61.7  62.5  63.3  64.0  64.7  64.9  65.2  \n",
       "\n",
       "[5 rows x 219 columns]"
      ]
     },
     "execution_count": 716,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read Life Expectancy csv data file and setting the 'country' column is index\n",
    "life_exp = pd.read_csv('life_expectancy_years.csv', index_col='country')\n",
    "# confirm and display 5 rows to check\n",
    "life_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>name</th>\n",
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       "      <th>Afghanistan</th>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>4</td>\n",
       "      <td>ISO 3166-2:AF</td>\n",
       "      <td>Asia</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>142.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Åland Islands</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "      <td>8</td>\n",
       "      <td>ISO 3166-2:AL</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>12</td>\n",
       "      <td>ISO 3166-2:DZ</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>AS</td>\n",
       "      <td>ASM</td>\n",
       "      <td>16</td>\n",
       "      <td>ISO 3166-2:AS</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               alpha-2 alpha-3  country-code     iso_3166-2   region  \\\n",
       "name                                                                   \n",
       "Afghanistan         AF     AFG             4  ISO 3166-2:AF     Asia   \n",
       "Åland Islands       AX     ALA           248  ISO 3166-2:AX   Europe   \n",
       "Albania             AL     ALB             8  ISO 3166-2:AL   Europe   \n",
       "Algeria             DZ     DZA            12  ISO 3166-2:DZ   Africa   \n",
       "American Samoa      AS     ASM            16  ISO 3166-2:AS  Oceania   \n",
       "\n",
       "                     sub-region intermediate-region  region-code  \\\n",
       "name                                                               \n",
       "Afghanistan       Southern Asia                 NaN        142.0   \n",
       "Åland Islands   Northern Europe                 NaN        150.0   \n",
       "Albania         Southern Europe                 NaN        150.0   \n",
       "Algeria         Northern Africa                 NaN          2.0   \n",
       "American Samoa        Polynesia                 NaN          9.0   \n",
       "\n",
       "                sub-region-code  intermediate-region-code  \n",
       "name                                                       \n",
       "Afghanistan                34.0                       NaN  \n",
       "Åland Islands             154.0                       NaN  \n",
       "Albania                    39.0                       NaN  \n",
       "Algeria                    15.0                       NaN  \n",
       "American Samoa             61.0                       NaN  "
      ]
     },
     "execution_count": 717,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read 'World Regions' csv data file and setting the 'name' column is index\n",
    "w_reg = pd.read_csv('world_regions.csv', index_col='name')\n",
    "# confirm and display 5 rows to check\n",
    "w_reg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2-2 Assessing the data and Building Intuition**\n",
    "Performing some operations to know number of rows and columns in the dataframe using [.shape], also to inspect data types and look for instances of missing or possibly errant data by using [ .info() ] method and calculating descriptive statistics measurements using [.describe()] method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((190, 58), (187, 28), (193, 219), (187, 219))"
      ]
     },
     "execution_count": 718,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to returns a tuple of the dimensions of the dataframes (rows, col.)\n",
    "gdp.shape, hdi.shape, income_per.shape, life_exp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 190 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 58 columns):\n",
      "1960    88 non-null float64\n",
      "1961    89 non-null float64\n",
      "1962    89 non-null float64\n",
      "1963    89 non-null float64\n",
      "1964    89 non-null float64\n",
      "1965    93 non-null float64\n",
      "1966    96 non-null float64\n",
      "1967    97 non-null float64\n",
      "1968    99 non-null float64\n",
      "1969    99 non-null float64\n",
      "1970    108 non-null float64\n",
      "1971    108 non-null float64\n",
      "1972    108 non-null float64\n",
      "1973    108 non-null float64\n",
      "1974    110 non-null float64\n",
      "1975    114 non-null float64\n",
      "1976    115 non-null float64\n",
      "1977    120 non-null float64\n",
      "1978    120 non-null float64\n",
      "1979    121 non-null float64\n",
      "1980    132 non-null float64\n",
      "1981    136 non-null float64\n",
      "1982    138 non-null float64\n",
      "1983    138 non-null float64\n",
      "1984    140 non-null float64\n",
      "1985    141 non-null float64\n",
      "1986    144 non-null float64\n",
      "1987    147 non-null float64\n",
      "1988    149 non-null float64\n",
      "1989    150 non-null float64\n",
      "1990    161 non-null float64\n",
      "1991    162 non-null float64\n",
      "1992    164 non-null float64\n",
      "1993    165 non-null float64\n",
      "1994    167 non-null float64\n",
      "1995    176 non-null float64\n",
      "1996    178 non-null float64\n",
      "1997    180 non-null float64\n",
      "1998    180 non-null float64\n",
      "1999    181 non-null float64\n",
      "2000    184 non-null float64\n",
      "2001    185 non-null float64\n",
      "2002    186 non-null float64\n",
      "2003    186 non-null float64\n",
      "2004    186 non-null float64\n",
      "2005    186 non-null float64\n",
      "2006    186 non-null float64\n",
      "2007    187 non-null float64\n",
      "2008    187 non-null float64\n",
      "2009    187 non-null float64\n",
      "2010    190 non-null int64\n",
      "2011    187 non-null float64\n",
      "2012    186 non-null float64\n",
      "2013    186 non-null float64\n",
      "2014    186 non-null float64\n",
      "2015    185 non-null float64\n",
      "2016    184 non-null float64\n",
      "2017    183 non-null float64\n",
      "dtypes: float64(57), int64(1)\n",
      "memory usage: 87.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# To display a concise summary of the dataframe, \n",
    "# including the number of non-null values in each column and the datatype of each col.\n",
    "gdp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 187 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 28 columns):\n",
      "1990    143 non-null float64\n",
      "1991    143 non-null float64\n",
      "1992    143 non-null float64\n",
      "1993    143 non-null float64\n",
      "1994    143 non-null float64\n",
      "1995    147 non-null float64\n",
      "1996    147 non-null float64\n",
      "1997    147 non-null float64\n",
      "1998    147 non-null float64\n",
      "1999    150 non-null float64\n",
      "2000    167 non-null float64\n",
      "2001    167 non-null float64\n",
      "2002    167 non-null float64\n",
      "2003    169 non-null float64\n",
      "2004    172 non-null float64\n",
      "2005    181 non-null float64\n",
      "2006    181 non-null float64\n",
      "2007    181 non-null float64\n",
      "2008    181 non-null float64\n",
      "2009    181 non-null float64\n",
      "2010    187 non-null float64\n",
      "2011    187 non-null float64\n",
      "2012    187 non-null float64\n",
      "2013    187 non-null float64\n",
      "2014    187 non-null float64\n",
      "2015    187 non-null float64\n",
      "2016    0 non-null float64\n",
      "2107    0 non-null float64\n",
      "dtypes: float64(28)\n",
      "memory usage: 42.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# To display a concise summary of the dataframe, \n",
    "# including the number of non-null values in each column and the datatype of each col.\n",
    "hdi.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 193 entries, Afghanistan to Zimbabwe\n",
      "Columns: 219 entries, 1800 to 2018\n",
      "dtypes: int64(219)\n",
      "memory usage: 331.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# To display a concise summary of the dataframe, \n",
    "# including the number of non-null values in each column and the datatype of each col.\n",
    "income_per.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 187 entries, Afghanistan to Zimbabwe\n",
      "Columns: 219 entries, 1800 to 2018\n",
      "dtypes: float64(219)\n",
      "memory usage: 321.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# To display a concise summary of the dataframe, \n",
    "# including the number of non-null values in each column and the datatype of each col.\n",
    "life_exp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 249 entries, Afghanistan to Zimbabwe\n",
      "Data columns (total 10 columns):\n",
      "alpha-2                     248 non-null object\n",
      "alpha-3                     249 non-null object\n",
      "country-code                249 non-null int64\n",
      "iso_3166-2                  249 non-null object\n",
      "region                      248 non-null object\n",
      "sub-region                  248 non-null object\n",
      "intermediate-region         107 non-null object\n",
      "region-code                 248 non-null float64\n",
      "sub-region-code             248 non-null float64\n",
      "intermediate-region-code    107 non-null float64\n",
      "dtypes: float64(3), int64(1), object(6)\n",
      "memory usage: 21.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# To display a concise summary of the dataframe, \n",
    "# including the number of non-null values in each column and the datatype of each col.\n",
    "w_reg.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4622.500000</td>\n",
       "      <td>4740.325843</td>\n",
       "      <td>4883.808989</td>\n",
       "      <td>5044.146067</td>\n",
       "      <td>5298.224719</td>\n",
       "      <td>5331.537634</td>\n",
       "      <td>5355.604167</td>\n",
       "      <td>5469.247423</td>\n",
       "      <td>5769.626263</td>\n",
       "      <td>6070.545455</td>\n",
       "      <td>...</td>\n",
       "      <td>13096.508021</td>\n",
       "      <td>12494.850267</td>\n",
       "      <td>13966.078947</td>\n",
       "      <td>12787.668449</td>\n",
       "      <td>12904.198925</td>\n",
       "      <td>12987.623656</td>\n",
       "      <td>13155.446237</td>\n",
       "      <td>13380.529730</td>\n",
       "      <td>13579.945652</td>\n",
       "      <td>13861.781421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6259.530721</td>\n",
       "      <td>6474.995954</td>\n",
       "      <td>6668.398816</td>\n",
       "      <td>6881.558232</td>\n",
       "      <td>7284.774644</td>\n",
       "      <td>7401.639525</td>\n",
       "      <td>7497.344196</td>\n",
       "      <td>7705.354775</td>\n",
       "      <td>7986.131163</td>\n",
       "      <td>8434.129299</td>\n",
       "      <td>...</td>\n",
       "      <td>19030.226639</td>\n",
       "      <td>17930.998978</td>\n",
       "      <td>22366.413612</td>\n",
       "      <td>18167.977557</td>\n",
       "      <td>18000.075075</td>\n",
       "      <td>18028.891032</td>\n",
       "      <td>18253.648558</td>\n",
       "      <td>18648.762614</td>\n",
       "      <td>18890.299898</td>\n",
       "      <td>19122.670759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>213.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>711.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>750.750000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>757.500000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>1340.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1562.500000</td>\n",
       "      <td>1635.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>1830.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1485.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1685.000000</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1690.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4620.000000</td>\n",
       "      <td>4550.000000</td>\n",
       "      <td>4670.000000</td>\n",
       "      <td>4940.000000</td>\n",
       "      <td>5390.000000</td>\n",
       "      <td>5615.000000</td>\n",
       "      <td>5590.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5800.000000</td>\n",
       "      <td>5810.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5147.500000</td>\n",
       "      <td>5540.000000</td>\n",
       "      <td>5380.000000</td>\n",
       "      <td>5330.000000</td>\n",
       "      <td>5710.000000</td>\n",
       "      <td>6040.000000</td>\n",
       "      <td>5655.000000</td>\n",
       "      <td>5700.000000</td>\n",
       "      <td>6405.000000</td>\n",
       "      <td>6850.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14800.000000</td>\n",
       "      <td>13850.000000</td>\n",
       "      <td>13650.000000</td>\n",
       "      <td>13900.000000</td>\n",
       "      <td>14000.000000</td>\n",
       "      <td>14450.000000</td>\n",
       "      <td>14850.000000</td>\n",
       "      <td>14900.000000</td>\n",
       "      <td>15250.000000</td>\n",
       "      <td>15700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27100.000000</td>\n",
       "      <td>27900.000000</td>\n",
       "      <td>27900.000000</td>\n",
       "      <td>28600.000000</td>\n",
       "      <td>30500.000000</td>\n",
       "      <td>29900.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30300.000000</td>\n",
       "      <td>31200.000000</td>\n",
       "      <td>34100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>109000.000000</td>\n",
       "      <td>102000.000000</td>\n",
       "      <td>144000.000000</td>\n",
       "      <td>105000.000000</td>\n",
       "      <td>102000.000000</td>\n",
       "      <td>104000.000000</td>\n",
       "      <td>107000.000000</td>\n",
       "      <td>108000.000000</td>\n",
       "      <td>109000.000000</td>\n",
       "      <td>108000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1960          1961          1962          1963          1964  \\\n",
       "count     88.000000     89.000000     89.000000     89.000000     89.000000   \n",
       "mean    4622.500000   4740.325843   4883.808989   5044.146067   5298.224719   \n",
       "std     6259.530721   6474.995954   6668.398816   6881.558232   7284.774644   \n",
       "min      159.000000    141.000000    132.000000    142.000000    163.000000   \n",
       "25%      711.000000    711.000000    712.000000    708.000000    698.000000   \n",
       "50%     1485.000000   1480.000000   1490.000000   1460.000000   1550.000000   \n",
       "75%     5147.500000   5540.000000   5380.000000   5330.000000   5710.000000   \n",
       "max    27100.000000  27900.000000  27900.000000  28600.000000  30500.000000   \n",
       "\n",
       "               1965          1966          1967          1968          1969  \\\n",
       "count     93.000000     96.000000     97.000000     99.000000     99.000000   \n",
       "mean    5331.537634   5355.604167   5469.247423   5769.626263   6070.545455   \n",
       "std     7401.639525   7497.344196   7705.354775   7986.131163   8434.129299   \n",
       "min      176.000000    164.000000    150.000000    164.000000    166.000000   \n",
       "25%      763.000000    750.750000    737.000000    757.500000    767.000000   \n",
       "50%     1680.000000   1685.000000   1650.000000   1690.000000   1780.000000   \n",
       "75%     6040.000000   5655.000000   5700.000000   6405.000000   6850.000000   \n",
       "max    29900.000000  30000.000000  30300.000000  31200.000000  34100.000000   \n",
       "\n",
       "           ...                 2008           2009           2010  \\\n",
       "count      ...           187.000000     187.000000     190.000000   \n",
       "mean       ...         13096.508021   12494.850267   13966.078947   \n",
       "std        ...         19030.226639   17930.998978   22366.413612   \n",
       "min        ...           230.000000     230.000000     231.000000   \n",
       "25%        ...          1320.000000    1340.000000    1400.000000   \n",
       "50%        ...          4620.000000    4550.000000    4670.000000   \n",
       "75%        ...         14800.000000   13850.000000   13650.000000   \n",
       "max        ...        109000.000000  102000.000000  144000.000000   \n",
       "\n",
       "                2011           2012           2013           2014  \\\n",
       "count     187.000000     186.000000     186.000000     186.000000   \n",
       "mean    12787.668449   12904.198925   12987.623656   13155.446237   \n",
       "std     18167.977557   18000.075075   18028.891032   18253.648558   \n",
       "min       234.000000     236.000000     239.000000     243.000000   \n",
       "25%      1465.000000    1562.500000    1635.000000    1680.000000   \n",
       "50%      4940.000000    5390.000000    5615.000000    5590.000000   \n",
       "75%     13900.000000   14000.000000   14450.000000   14850.000000   \n",
       "max    105000.000000  102000.000000  104000.000000  107000.000000   \n",
       "\n",
       "                2015           2016           2017  \n",
       "count     185.000000     184.000000     183.000000  \n",
       "mean    13380.529730   13579.945652   13861.781421  \n",
       "std     18648.762614   18890.299898   19122.670759  \n",
       "min       227.000000     218.000000     213.000000  \n",
       "25%      1760.000000    1830.000000    1960.000000  \n",
       "50%      5660.000000    5800.000000    5810.000000  \n",
       "75%     14900.000000   15250.000000   15700.000000  \n",
       "max    108000.000000  109000.000000  108000.000000  \n",
       "\n",
       "[8 rows x 58 columns]"
      ]
     },
     "execution_count": 724,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigating the data by calculating the statistical measurements on it using [.describe()] method \n",
    "gdp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "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>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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>183.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4622.500000</td>\n",
       "      <td>4740.325843</td>\n",
       "      <td>4883.808989</td>\n",
       "      <td>5044.146067</td>\n",
       "      <td>5298.224719</td>\n",
       "      <td>5331.537634</td>\n",
       "      <td>5355.604167</td>\n",
       "      <td>5469.247423</td>\n",
       "      <td>5769.626263</td>\n",
       "      <td>6070.545455</td>\n",
       "      <td>...</td>\n",
       "      <td>13096.508021</td>\n",
       "      <td>12494.850267</td>\n",
       "      <td>13966.078947</td>\n",
       "      <td>12787.668449</td>\n",
       "      <td>12904.198925</td>\n",
       "      <td>12987.623656</td>\n",
       "      <td>13155.446237</td>\n",
       "      <td>13380.529730</td>\n",
       "      <td>13579.945652</td>\n",
       "      <td>13861.781421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6259.530721</td>\n",
       "      <td>6474.995954</td>\n",
       "      <td>6668.398816</td>\n",
       "      <td>6881.558232</td>\n",
       "      <td>7284.774644</td>\n",
       "      <td>7401.639525</td>\n",
       "      <td>7497.344196</td>\n",
       "      <td>7705.354775</td>\n",
       "      <td>7986.131163</td>\n",
       "      <td>8434.129299</td>\n",
       "      <td>...</td>\n",
       "      <td>19030.226639</td>\n",
       "      <td>17930.998978</td>\n",
       "      <td>22366.413612</td>\n",
       "      <td>18167.977557</td>\n",
       "      <td>18000.075075</td>\n",
       "      <td>18028.891032</td>\n",
       "      <td>18253.648558</td>\n",
       "      <td>18648.762614</td>\n",
       "      <td>18890.299898</td>\n",
       "      <td>19122.670759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>159.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>239.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>227.000000</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>213.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>711.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>712.000000</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>763.000000</td>\n",
       "      <td>750.750000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>757.500000</td>\n",
       "      <td>767.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1320.000000</td>\n",
       "      <td>1340.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>1465.000000</td>\n",
       "      <td>1562.500000</td>\n",
       "      <td>1635.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1760.000000</td>\n",
       "      <td>1830.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1485.000000</td>\n",
       "      <td>1480.000000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1550.000000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>1685.000000</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1690.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4620.000000</td>\n",
       "      <td>4550.000000</td>\n",
       "      <td>4670.000000</td>\n",
       "      <td>4940.000000</td>\n",
       "      <td>5390.000000</td>\n",
       "      <td>5615.000000</td>\n",
       "      <td>5590.000000</td>\n",
       "      <td>5660.000000</td>\n",
       "      <td>5800.000000</td>\n",
       "      <td>5810.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5147.500000</td>\n",
       "      <td>5540.000000</td>\n",
       "      <td>5380.000000</td>\n",
       "      <td>5330.000000</td>\n",
       "      <td>5710.000000</td>\n",
       "      <td>6040.000000</td>\n",
       "      <td>5655.000000</td>\n",
       "      <td>5700.000000</td>\n",
       "      <td>6405.000000</td>\n",
       "      <td>6850.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14800.000000</td>\n",
       "      <td>13850.000000</td>\n",
       "      <td>13650.000000</td>\n",
       "      <td>13900.000000</td>\n",
       "      <td>14000.000000</td>\n",
       "      <td>14450.000000</td>\n",
       "      <td>14850.000000</td>\n",
       "      <td>14900.000000</td>\n",
       "      <td>15250.000000</td>\n",
       "      <td>15700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27100.000000</td>\n",
       "      <td>27900.000000</td>\n",
       "      <td>27900.000000</td>\n",
       "      <td>28600.000000</td>\n",
       "      <td>30500.000000</td>\n",
       "      <td>29900.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30300.000000</td>\n",
       "      <td>31200.000000</td>\n",
       "      <td>34100.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>109000.000000</td>\n",
       "      <td>102000.000000</td>\n",
       "      <td>144000.000000</td>\n",
       "      <td>105000.000000</td>\n",
       "      <td>102000.000000</td>\n",
       "      <td>104000.000000</td>\n",
       "      <td>107000.000000</td>\n",
       "      <td>108000.000000</td>\n",
       "      <td>109000.000000</td>\n",
       "      <td>108000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               1960          1961          1962          1963          1964  \\\n",
       "count     88.000000     89.000000     89.000000     89.000000     89.000000   \n",
       "mean    4622.500000   4740.325843   4883.808989   5044.146067   5298.224719   \n",
       "std     6259.530721   6474.995954   6668.398816   6881.558232   7284.774644   \n",
       "min      159.000000    141.000000    132.000000    142.000000    163.000000   \n",
       "25%      711.000000    711.000000    712.000000    708.000000    698.000000   \n",
       "50%     1485.000000   1480.000000   1490.000000   1460.000000   1550.000000   \n",
       "75%     5147.500000   5540.000000   5380.000000   5330.000000   5710.000000   \n",
       "max    27100.000000  27900.000000  27900.000000  28600.000000  30500.000000   \n",
       "\n",
       "               1965          1966          1967          1968          1969  \\\n",
       "count     93.000000     96.000000     97.000000     99.000000     99.000000   \n",
       "mean    5331.537634   5355.604167   5469.247423   5769.626263   6070.545455   \n",
       "std     7401.639525   7497.344196   7705.354775   7986.131163   8434.129299   \n",
       "min      176.000000    164.000000    150.000000    164.000000    166.000000   \n",
       "25%      763.000000    750.750000    737.000000    757.500000    767.000000   \n",
       "50%     1680.000000   1685.000000   1650.000000   1690.000000   1780.000000   \n",
       "75%     6040.000000   5655.000000   5700.000000   6405.000000   6850.000000   \n",
       "max    29900.000000  30000.000000  30300.000000  31200.000000  34100.000000   \n",
       "\n",
       "           ...                 2008           2009           2010  \\\n",
       "count      ...           187.000000     187.000000     190.000000   \n",
       "mean       ...         13096.508021   12494.850267   13966.078947   \n",
       "std        ...         19030.226639   17930.998978   22366.413612   \n",
       "min        ...           230.000000     230.000000     231.000000   \n",
       "25%        ...          1320.000000    1340.000000    1400.000000   \n",
       "50%        ...          4620.000000    4550.000000    4670.000000   \n",
       "75%        ...         14800.000000   13850.000000   13650.000000   \n",
       "max        ...        109000.000000  102000.000000  144000.000000   \n",
       "\n",
       "                2011           2012           2013           2014  \\\n",
       "count     187.000000     186.000000     186.000000     186.000000   \n",
       "mean    12787.668449   12904.198925   12987.623656   13155.446237   \n",
       "std     18167.977557   18000.075075   18028.891032   18253.648558   \n",
       "min       234.000000     236.000000     239.000000     243.000000   \n",
       "25%      1465.000000    1562.500000    1635.000000    1680.000000   \n",
       "50%      4940.000000    5390.000000    5615.000000    5590.000000   \n",
       "75%     13900.000000   14000.000000   14450.000000   14850.000000   \n",
       "max    105000.000000  102000.000000  104000.000000  107000.000000   \n",
       "\n",
       "                2015           2016           2017  \n",
       "count     185.000000     184.000000     183.000000  \n",
       "mean    13380.529730   13579.945652   13861.781421  \n",
       "std     18648.762614   18890.299898   19122.670759  \n",
       "min       227.000000     218.000000     213.000000  \n",
       "25%      1760.000000    1830.000000    1960.000000  \n",
       "50%      5660.000000    5800.000000    5810.000000  \n",
       "75%     14900.000000   15250.000000   15700.000000  \n",
       "max    108000.000000  109000.000000  108000.000000  \n",
       "\n",
       "[8 rows x 58 columns]"
      ]
     },
     "execution_count": 725,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigating the data by calculating the statistical measurements on it using [.describe()] method \n",
    "gdp.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
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       "      <th>1806</th>\n",
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       "      <th>1808</th>\n",
       "      <th>1809</th>\n",
       "      <th>...</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>947.782383</td>\n",
       "      <td>948.269430</td>\n",
       "      <td>951.217617</td>\n",
       "      <td>950.911917</td>\n",
       "      <td>952.772021</td>\n",
       "      <td>953.202073</td>\n",
       "      <td>954.305699</td>\n",
       "      <td>953.979275</td>\n",
       "      <td>950.911917</td>\n",
       "      <td>952.202073</td>\n",
       "      <td>...</td>\n",
       "      <td>16176.362694</td>\n",
       "      <td>16444.284974</td>\n",
       "      <td>16704.450777</td>\n",
       "      <td>16866.336788</td>\n",
       "      <td>17010.549223</td>\n",
       "      <td>17210.398964</td>\n",
       "      <td>17432.849741</td>\n",
       "      <td>17611.383420</td>\n",
       "      <td>17908.181347</td>\n",
       "      <td>18241.502591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>508.348372</td>\n",
       "      <td>506.753967</td>\n",
       "      <td>516.692581</td>\n",
       "      <td>511.552526</td>\n",
       "      <td>518.974690</td>\n",
       "      <td>513.492023</td>\n",
       "      <td>514.667191</td>\n",
       "      <td>508.132446</td>\n",
       "      <td>490.318368</td>\n",
       "      <td>492.600302</td>\n",
       "      <td>...</td>\n",
       "      <td>18502.994709</td>\n",
       "      <td>18769.943218</td>\n",
       "      <td>19098.612186</td>\n",
       "      <td>18943.430768</td>\n",
       "      <td>18867.547829</td>\n",
       "      <td>18911.747174</td>\n",
       "      <td>19122.183694</td>\n",
       "      <td>19162.954535</td>\n",
       "      <td>19383.636509</td>\n",
       "      <td>19664.365483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>588.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>616.000000</td>\n",
       "      <td>598.000000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>625.000000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>629.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>608.000000</td>\n",
       "      <td>608.000000</td>\n",
       "      <td>608.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3120.000000</td>\n",
       "      <td>3280.000000</td>\n",
       "      <td>3370.000000</td>\n",
       "      <td>3430.000000</td>\n",
       "      <td>3300.000000</td>\n",
       "      <td>3270.000000</td>\n",
       "      <td>3290.000000</td>\n",
       "      <td>3390.000000</td>\n",
       "      <td>3520.000000</td>\n",
       "      <td>3670.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>847.000000</td>\n",
       "      <td>847.000000</td>\n",
       "      <td>847.000000</td>\n",
       "      <td>847.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>849.000000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>851.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9560.000000</td>\n",
       "      <td>9920.000000</td>\n",
       "      <td>10100.000000</td>\n",
       "      <td>10300.000000</td>\n",
       "      <td>10500.000000</td>\n",
       "      <td>10800.000000</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>11300.000000</td>\n",
       "      <td>11600.000000</td>\n",
       "      <td>12100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>1110.000000</td>\n",
       "      <td>1120.000000</td>\n",
       "      <td>1120.000000</td>\n",
       "      <td>1130.000000</td>\n",
       "      <td>1130.000000</td>\n",
       "      <td>1140.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>22100.000000</td>\n",
       "      <td>21800.000000</td>\n",
       "      <td>22700.000000</td>\n",
       "      <td>23000.000000</td>\n",
       "      <td>23200.000000</td>\n",
       "      <td>24000.000000</td>\n",
       "      <td>24100.000000</td>\n",
       "      <td>24300.000000</td>\n",
       "      <td>24900.000000</td>\n",
       "      <td>25500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4230.000000</td>\n",
       "      <td>4160.000000</td>\n",
       "      <td>4390.000000</td>\n",
       "      <td>4300.000000</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>4240.000000</td>\n",
       "      <td>4270.000000</td>\n",
       "      <td>3910.000000</td>\n",
       "      <td>3480.000000</td>\n",
       "      <td>3430.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>117000.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>129000.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>123000.000000</td>\n",
       "      <td>121000.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>118000.000000</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>121000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 219 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1800         1801         1802         1803         1804  \\\n",
       "count   193.000000   193.000000   193.000000   193.000000   193.000000   \n",
       "mean    947.782383   948.269430   951.217617   950.911917   952.772021   \n",
       "std     508.348372   506.753967   516.692581   511.552526   518.974690   \n",
       "min     350.000000   350.000000   350.000000   350.000000   350.000000   \n",
       "25%     608.000000   608.000000   608.000000   609.000000   609.000000   \n",
       "50%     847.000000   847.000000   847.000000   847.000000   848.000000   \n",
       "75%    1100.000000  1100.000000  1110.000000  1110.000000  1110.000000   \n",
       "max    4230.000000  4160.000000  4390.000000  4300.000000  4500.000000   \n",
       "\n",
       "              1805         1806         1807         1808         1809  \\\n",
       "count   193.000000   193.000000   193.000000   193.000000   193.000000   \n",
       "mean    953.202073   954.305699   953.979275   950.911917   952.202073   \n",
       "std     513.492023   514.667191   508.132446   490.318368   492.600302   \n",
       "min     350.000000   350.000000   350.000000   350.000000   350.000000   \n",
       "25%     609.000000   610.000000   610.000000   610.000000   611.000000   \n",
       "50%     848.000000   849.000000   850.000000   850.000000   851.000000   \n",
       "75%    1120.000000  1120.000000  1130.000000  1130.000000  1140.000000   \n",
       "max    4240.000000  4270.000000  3910.000000  3480.000000  3430.000000   \n",
       "\n",
       "           ...                 2009           2010           2011  \\\n",
       "count      ...           193.000000     193.000000     193.000000   \n",
       "mean       ...         16176.362694   16444.284974   16704.450777   \n",
       "std        ...         18502.994709   18769.943218   19098.612186   \n",
       "min        ...           588.000000     609.000000     614.000000   \n",
       "25%        ...          3120.000000    3280.000000    3370.000000   \n",
       "50%        ...          9560.000000    9920.000000   10100.000000   \n",
       "75%        ...         22100.000000   21800.000000   22700.000000   \n",
       "max        ...        117000.000000  125000.000000  129000.000000   \n",
       "\n",
       "                2012           2013           2014           2015  \\\n",
       "count     193.000000     193.000000     193.000000     193.000000   \n",
       "mean    16866.336788   17010.549223   17210.398964   17432.849741   \n",
       "std     18943.430768   18867.547829   18911.747174   19122.183694   \n",
       "min       616.000000     598.000000     602.000000     623.000000   \n",
       "25%      3430.000000    3300.000000    3270.000000    3290.000000   \n",
       "50%     10300.000000   10500.000000   10800.000000   11000.000000   \n",
       "75%     23000.000000   23200.000000   24000.000000   24100.000000   \n",
       "max    125000.000000  123000.000000  121000.000000  120000.000000   \n",
       "\n",
       "                2016           2017           2018  \n",
       "count     193.000000     193.000000     193.000000  \n",
       "mean    17611.383420   17908.181347   18241.502591  \n",
       "std     19162.954535   19383.636509   19664.365483  \n",
       "min       625.000000     627.000000     629.000000  \n",
       "25%      3390.000000    3520.000000    3670.000000  \n",
       "50%     11300.000000   11600.000000   12100.000000  \n",
       "75%     24300.000000   24900.000000   25500.000000  \n",
       "max    118000.000000  120000.000000  121000.000000  \n",
       "\n",
       "[8 rows x 219 columns]"
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigating the data by calculating the statistical measurements on it using [.describe()] method \n",
    "income_per.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1800</th>\n",
       "      <th>1801</th>\n",
       "      <th>1802</th>\n",
       "      <th>1803</th>\n",
       "      <th>1804</th>\n",
       "      <th>1805</th>\n",
       "      <th>1806</th>\n",
       "      <th>1807</th>\n",
       "      <th>1808</th>\n",
       "      <th>1809</th>\n",
       "      <th>...</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>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.502717</td>\n",
       "      <td>31.461957</td>\n",
       "      <td>31.478804</td>\n",
       "      <td>31.383152</td>\n",
       "      <td>31.459239</td>\n",
       "      <td>31.586413</td>\n",
       "      <td>31.644565</td>\n",
       "      <td>31.598370</td>\n",
       "      <td>31.383152</td>\n",
       "      <td>31.310326</td>\n",
       "      <td>...</td>\n",
       "      <td>70.001070</td>\n",
       "      <td>70.225668</td>\n",
       "      <td>70.659358</td>\n",
       "      <td>71.05615</td>\n",
       "      <td>71.399465</td>\n",
       "      <td>71.622995</td>\n",
       "      <td>71.933690</td>\n",
       "      <td>72.206952</td>\n",
       "      <td>72.422283</td>\n",
       "      <td>72.658152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.814689</td>\n",
       "      <td>3.806303</td>\n",
       "      <td>3.938674</td>\n",
       "      <td>3.962376</td>\n",
       "      <td>3.934674</td>\n",
       "      <td>4.010884</td>\n",
       "      <td>4.110598</td>\n",
       "      <td>3.981247</td>\n",
       "      <td>4.087872</td>\n",
       "      <td>4.040580</td>\n",
       "      <td>...</td>\n",
       "      <td>8.832102</td>\n",
       "      <td>9.050710</td>\n",
       "      <td>8.439841</td>\n",
       "      <td>8.18101</td>\n",
       "      <td>7.996165</td>\n",
       "      <td>7.889169</td>\n",
       "      <td>7.605557</td>\n",
       "      <td>7.414169</td>\n",
       "      <td>7.331040</td>\n",
       "      <td>7.252807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.400000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>19.600000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>32.100000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>47.90000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>50.300000</td>\n",
       "      <td>50.800000</td>\n",
       "      <td>51.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.075000</td>\n",
       "      <td>28.975000</td>\n",
       "      <td>28.900000</td>\n",
       "      <td>28.900000</td>\n",
       "      <td>28.975000</td>\n",
       "      <td>29.075000</td>\n",
       "      <td>29.075000</td>\n",
       "      <td>29.075000</td>\n",
       "      <td>28.975000</td>\n",
       "      <td>28.875000</td>\n",
       "      <td>...</td>\n",
       "      <td>63.400000</td>\n",
       "      <td>63.900000</td>\n",
       "      <td>64.300000</td>\n",
       "      <td>65.00000</td>\n",
       "      <td>65.350000</td>\n",
       "      <td>65.550000</td>\n",
       "      <td>66.050000</td>\n",
       "      <td>66.650000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>67.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.750000</td>\n",
       "      <td>31.650000</td>\n",
       "      <td>31.550000</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>31.550000</td>\n",
       "      <td>31.650000</td>\n",
       "      <td>31.750000</td>\n",
       "      <td>31.750000</td>\n",
       "      <td>31.550000</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>72.600000</td>\n",
       "      <td>72.700000</td>\n",
       "      <td>72.80000</td>\n",
       "      <td>72.900000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>73.300000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>73.700000</td>\n",
       "      <td>74.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.825000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>33.825000</td>\n",
       "      <td>33.625000</td>\n",
       "      <td>33.725000</td>\n",
       "      <td>33.825000</td>\n",
       "      <td>33.925000</td>\n",
       "      <td>33.925000</td>\n",
       "      <td>33.725000</td>\n",
       "      <td>33.625000</td>\n",
       "      <td>...</td>\n",
       "      <td>76.600000</td>\n",
       "      <td>76.700000</td>\n",
       "      <td>76.900000</td>\n",
       "      <td>77.00000</td>\n",
       "      <td>77.200000</td>\n",
       "      <td>77.350000</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>77.650000</td>\n",
       "      <td>77.825000</td>\n",
       "      <td>78.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>42.900000</td>\n",
       "      <td>40.300000</td>\n",
       "      <td>44.400000</td>\n",
       "      <td>44.800000</td>\n",
       "      <td>42.800000</td>\n",
       "      <td>44.300000</td>\n",
       "      <td>45.800000</td>\n",
       "      <td>43.600000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>82.700000</td>\n",
       "      <td>82.800000</td>\n",
       "      <td>82.900000</td>\n",
       "      <td>83.20000</td>\n",
       "      <td>83.400000</td>\n",
       "      <td>83.600000</td>\n",
       "      <td>83.800000</td>\n",
       "      <td>83.900000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 219 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1800        1801        1802        1803        1804        1805  \\\n",
       "count  184.000000  184.000000  184.000000  184.000000  184.000000  184.000000   \n",
       "mean    31.502717   31.461957   31.478804   31.383152   31.459239   31.586413   \n",
       "std      3.814689    3.806303    3.938674    3.962376    3.934674    4.010884   \n",
       "min     23.400000   23.400000   23.400000   19.600000   23.400000   23.400000   \n",
       "25%     29.075000   28.975000   28.900000   28.900000   28.975000   29.075000   \n",
       "50%     31.750000   31.650000   31.550000   31.500000   31.550000   31.650000   \n",
       "75%     33.825000   33.900000   33.825000   33.625000   33.725000   33.825000   \n",
       "max     42.900000   40.300000   44.400000   44.800000   42.800000   44.300000   \n",
       "\n",
       "             1806        1807        1808        1809     ...            2009  \\\n",
       "count  184.000000  184.000000  184.000000  184.000000     ...      187.000000   \n",
       "mean    31.644565   31.598370   31.383152   31.310326     ...       70.001070   \n",
       "std      4.110598    3.981247    4.087872    4.040580     ...        8.832102   \n",
       "min     23.400000   23.400000   12.500000   13.400000     ...       45.400000   \n",
       "25%     29.075000   29.075000   28.975000   28.875000     ...       63.400000   \n",
       "50%     31.750000   31.750000   31.550000   31.500000     ...       72.500000   \n",
       "75%     33.925000   33.925000   33.725000   33.625000     ...       76.600000   \n",
       "max     45.800000   43.600000   43.500000   41.700000     ...       82.700000   \n",
       "\n",
       "             2010        2011       2012        2013        2014        2015  \\\n",
       "count  187.000000  187.000000  187.00000  187.000000  187.000000  187.000000   \n",
       "mean    70.225668   70.659358   71.05615   71.399465   71.622995   71.933690   \n",
       "std      9.050710    8.439841    8.18101    7.996165    7.889169    7.605557   \n",
       "min     32.100000   47.500000   47.90000   48.000000   48.400000   49.600000   \n",
       "25%     63.900000   64.300000   65.00000   65.350000   65.550000   66.050000   \n",
       "50%     72.600000   72.700000   72.80000   72.900000   73.000000   73.300000   \n",
       "75%     76.700000   76.900000   77.00000   77.200000   77.350000   77.500000   \n",
       "max     82.800000   82.900000   83.20000   83.400000   83.600000   83.800000   \n",
       "\n",
       "             2016        2017        2018  \n",
       "count  187.000000  184.000000  184.000000  \n",
       "mean    72.206952   72.422283   72.658152  \n",
       "std      7.414169    7.331040    7.252807  \n",
       "min     50.300000   50.800000   51.100000  \n",
       "25%     66.650000   66.900000   67.100000  \n",
       "50%     73.500000   73.700000   74.050000  \n",
       "75%     77.650000   77.825000   78.025000  \n",
       "max     83.900000   84.000000   84.200000  \n",
       "\n",
       "[8 rows x 219 columns]"
      ]
     },
     "execution_count": 727,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Investigating the data by calculating the statistical measurements on it using [.describe()] method \n",
    "life_exp.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2-3 Cleaning Data**\n",
    "I'll drop (trim) all the dataframes to equal my selected period (1990 - 2015) in the number of rows and columns, then execlude the null values by filling cells by the mean value  of each row. After that I'll search about duplicated data (rows) and drop it if found. For preparing the data frames to the 3rd step (Exploratory Data Analysis), I'll merge and group the necessary dataframes as shown in the next cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>...</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",
       "    </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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>398.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>525.0</td>\n",
       "      <td>553</td>\n",
       "      <td>569.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>634.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>1950.0</td>\n",
       "      <td>1380.0</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>1420.0</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3380.0</td>\n",
       "      <td>3610.0</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>3930.0</td>\n",
       "      <td>4090</td>\n",
       "      <td>4210.0</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>4330.0</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>4520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3550.0</td>\n",
       "      <td>3420.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>3160.0</td>\n",
       "      <td>3220.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3280.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>3460.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4280.0</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>4390.0</td>\n",
       "      <td>4390.0</td>\n",
       "      <td>4460</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>4560.0</td>\n",
       "      <td>4600.0</td>\n",
       "      <td>4680.0</td>\n",
       "      <td>4760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>35800.0</td>\n",
       "      <td>35300.0</td>\n",
       "      <td>34300.0</td>\n",
       "      <td>32800.0</td>\n",
       "      <td>32600.0</td>\n",
       "      <td>32900.0</td>\n",
       "      <td>34200.0</td>\n",
       "      <td>37300.0</td>\n",
       "      <td>38600.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>49700.0</td>\n",
       "      <td>48700.0</td>\n",
       "      <td>43900.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>39700</td>\n",
       "      <td>38200.0</td>\n",
       "      <td>38200.0</td>\n",
       "      <td>39100.0</td>\n",
       "      <td>40800.0</td>\n",
       "      <td>41800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>2050.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>1730.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2740.0</td>\n",
       "      <td>3260.0</td>\n",
       "      <td>3580.0</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>3530</td>\n",
       "      <td>3540.0</td>\n",
       "      <td>3590.0</td>\n",
       "      <td>3710.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>3730.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                1990     1991     1992     1993     1994     1995     1996  \\\n",
       "country                                                                      \n",
       "Afghanistan      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "Albania       1950.0   1380.0   1290.0   1420.0   1550.0   1760.0   1940.0   \n",
       "Algeria       3550.0   3420.0   3400.0   3260.0   3160.0   3220.0   3300.0   \n",
       "Andorra      35800.0  35300.0  34300.0  32800.0  32600.0  32900.0  34200.0   \n",
       "Angola        2050.0   2010.0   1830.0   1350.0   1320.0   1480.0   1630.0   \n",
       "\n",
       "                1997     1998     1999   ...        2006     2007     2008  \\\n",
       "country                                  ...                                 \n",
       "Afghanistan      NaN      NaN      NaN   ...       398.0    440.0    445.0   \n",
       "Albania       1740.0   1910.0   2180.0   ...      3380.0   3610.0   3780.0   \n",
       "Algeria       3280.0   3400.0   3460.0   ...      4280.0   4360.0   4390.0   \n",
       "Andorra      37300.0  38600.0  40000.0   ...     49700.0  48700.0  43900.0   \n",
       "Angola        1700.0   1730.0   1720.0   ...      2740.0   3260.0   3580.0   \n",
       "\n",
       "                2009   2010     2011     2012     2013     2014     2015  \n",
       "country                                                                   \n",
       "Afghanistan    525.0    553    569.0    630.0    634.0    630.0    620.0  \n",
       "Albania       3930.0   4090   4210.0   4280.0   4330.0   4410.0   4520.0  \n",
       "Algeria       4390.0   4460   4500.0   4560.0   4600.0   4680.0   4760.0  \n",
       "Andorra      42000.0  39700  38200.0  38200.0  39100.0  40800.0  41800.0  \n",
       "Angola        3540.0   3530   3540.0   3590.0   3710.0   3750.0   3730.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 728,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drope extraneous columns befor and after the time interval (years) which started 1990 and 2015\n",
    "gdp.drop(gdp.iloc[:, :30], axis=1, inplace=True)\n",
    "gdp.drop(['2016', '2017'], axis=1, inplace=True)\n",
    "# confirm and display 5 rows to check\n",
    "gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
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       "      <th>2013</th>\n",
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       "      <th>2015</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",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>0.295</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.309</td>\n",
       "      <td>0.305</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.324</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.332</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.338</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.433</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.476</td>\n",
       "      <td>0.479</td>\n",
       "      <td>0.479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>0.635</td>\n",
       "      <td>0.618</td>\n",
       "      <td>0.603</td>\n",
       "      <td>0.608</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.636</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.713</td>\n",
       "      <td>0.721</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.738</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.759</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>0.577</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.609</td>\n",
       "      <td>0.617</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.636</td>\n",
       "      <td>...</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.724</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.737</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.745</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.843</td>\n",
       "      <td>0.850</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.858</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>0.382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.480</td>\n",
       "      <td>0.488</td>\n",
       "      <td>0.495</td>\n",
       "      <td>0.508</td>\n",
       "      <td>0.523</td>\n",
       "      <td>0.527</td>\n",
       "      <td>0.531</td>\n",
       "      <td>0.533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1990   1991   1992   1993   1994   1995   1996   1997   1998  \\\n",
       "country                                                                      \n",
       "Afghanistan  0.295  0.300  0.309  0.305  0.300  0.324  0.328  0.332  0.335   \n",
       "Albania      0.635  0.618  0.603  0.608  0.616  0.628  0.637  0.636  0.646   \n",
       "Algeria      0.577  0.581  0.587  0.591  0.595  0.600  0.609  0.617  0.627   \n",
       "Andorra        NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "Angola         NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "              1999  ...     2006   2007   2008   2009   2010   2011   2012  \\\n",
       "country             ...                                                      \n",
       "Afghanistan  0.338  ...    0.415  0.433  0.434  0.448  0.454  0.463  0.470   \n",
       "Albania      0.656  ...    0.703  0.713  0.721  0.725  0.738  0.752  0.759   \n",
       "Algeria      0.636  ...    0.690  0.697  0.705  0.714  0.724  0.732  0.737   \n",
       "Andorra        NaN  ...      NaN    NaN    NaN    NaN  0.819  0.819  0.843   \n",
       "Angola       0.382  ...    0.454  0.468  0.480  0.488  0.495  0.508  0.523   \n",
       "\n",
       "              2013   2014   2015  \n",
       "country                           \n",
       "Afghanistan  0.476  0.479  0.479  \n",
       "Albania      0.761  0.762  0.764  \n",
       "Algeria      0.741  0.743  0.745  \n",
       "Andorra      0.850  0.857  0.858  \n",
       "Angola       0.527  0.531  0.533  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 729,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drope extraneous columns after the time interval (years) which started 1990 and 2015\n",
    "hdi.drop(['2016', '2107'], axis=1, inplace=True)\n",
    "# confirm and display 5 rows to check\n",
    "hdi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
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       "      <th>...</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",
       "    </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>1040</td>\n",
       "      <td>1030</td>\n",
       "      <td>950</td>\n",
       "      <td>818</td>\n",
       "      <td>732</td>\n",
       "      <td>881</td>\n",
       "      <td>904</td>\n",
       "      <td>930</td>\n",
       "      <td>956</td>\n",
       "      <td>982</td>\n",
       "      <td>...</td>\n",
       "      <td>1160</td>\n",
       "      <td>1290</td>\n",
       "      <td>1300</td>\n",
       "      <td>1530</td>\n",
       "      <td>1610</td>\n",
       "      <td>1660</td>\n",
       "      <td>1840</td>\n",
       "      <td>1810</td>\n",
       "      <td>1780</td>\n",
       "      <td>1750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>4560</td>\n",
       "      <td>3230</td>\n",
       "      <td>3010</td>\n",
       "      <td>3320</td>\n",
       "      <td>3620</td>\n",
       "      <td>4130</td>\n",
       "      <td>4530</td>\n",
       "      <td>4070</td>\n",
       "      <td>4460</td>\n",
       "      <td>5100</td>\n",
       "      <td>...</td>\n",
       "      <td>7920</td>\n",
       "      <td>8450</td>\n",
       "      <td>9150</td>\n",
       "      <td>9530</td>\n",
       "      <td>9930</td>\n",
       "      <td>10200</td>\n",
       "      <td>10400</td>\n",
       "      <td>10500</td>\n",
       "      <td>10700</td>\n",
       "      <td>11000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>10200</td>\n",
       "      <td>9870</td>\n",
       "      <td>9820</td>\n",
       "      <td>9400</td>\n",
       "      <td>9130</td>\n",
       "      <td>9300</td>\n",
       "      <td>9510</td>\n",
       "      <td>9460</td>\n",
       "      <td>9800</td>\n",
       "      <td>9970</td>\n",
       "      <td>...</td>\n",
       "      <td>12300</td>\n",
       "      <td>12600</td>\n",
       "      <td>12700</td>\n",
       "      <td>12600</td>\n",
       "      <td>12900</td>\n",
       "      <td>13000</td>\n",
       "      <td>13200</td>\n",
       "      <td>13300</td>\n",
       "      <td>13500</td>\n",
       "      <td>13700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>28400</td>\n",
       "      <td>28000</td>\n",
       "      <td>27200</td>\n",
       "      <td>26000</td>\n",
       "      <td>25900</td>\n",
       "      <td>26100</td>\n",
       "      <td>27200</td>\n",
       "      <td>29700</td>\n",
       "      <td>30800</td>\n",
       "      <td>31900</td>\n",
       "      <td>...</td>\n",
       "      <td>42700</td>\n",
       "      <td>43400</td>\n",
       "      <td>41400</td>\n",
       "      <td>41700</td>\n",
       "      <td>39000</td>\n",
       "      <td>42000</td>\n",
       "      <td>41900</td>\n",
       "      <td>43700</td>\n",
       "      <td>44900</td>\n",
       "      <td>46600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>4390</td>\n",
       "      <td>4210</td>\n",
       "      <td>3790</td>\n",
       "      <td>2760</td>\n",
       "      <td>2770</td>\n",
       "      <td>2970</td>\n",
       "      <td>3210</td>\n",
       "      <td>3370</td>\n",
       "      <td>3500</td>\n",
       "      <td>3510</td>\n",
       "      <td>...</td>\n",
       "      <td>4600</td>\n",
       "      <td>5440</td>\n",
       "      <td>5980</td>\n",
       "      <td>5910</td>\n",
       "      <td>5900</td>\n",
       "      <td>5910</td>\n",
       "      <td>6000</td>\n",
       "      <td>6190</td>\n",
       "      <td>6260</td>\n",
       "      <td>6230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1990   1991   1992   1993   1994   1995   1996   1997   1998  \\\n",
       "country                                                                      \n",
       "Afghanistan   1040   1030    950    818    732    881    904    930    956   \n",
       "Albania       4560   3230   3010   3320   3620   4130   4530   4070   4460   \n",
       "Algeria      10200   9870   9820   9400   9130   9300   9510   9460   9800   \n",
       "Andorra      28400  28000  27200  26000  25900  26100  27200  29700  30800   \n",
       "Angola        4390   4210   3790   2760   2770   2970   3210   3370   3500   \n",
       "\n",
       "              1999  ...     2006   2007   2008   2009   2010   2011   2012  \\\n",
       "country             ...                                                      \n",
       "Afghanistan    982  ...     1160   1290   1300   1530   1610   1660   1840   \n",
       "Albania       5100  ...     7920   8450   9150   9530   9930  10200  10400   \n",
       "Algeria       9970  ...    12300  12600  12700  12600  12900  13000  13200   \n",
       "Andorra      31900  ...    42700  43400  41400  41700  39000  42000  41900   \n",
       "Angola        3510  ...     4600   5440   5980   5910   5900   5910   6000   \n",
       "\n",
       "              2013   2014   2015  \n",
       "country                           \n",
       "Afghanistan   1810   1780   1750  \n",
       "Albania      10500  10700  11000  \n",
       "Algeria      13300  13500  13700  \n",
       "Andorra      43700  44900  46600  \n",
       "Angola        6190   6260   6230  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 730,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drope extraneous columns befor and after the time interval (years) which started 1990 and 2015\n",
    "income_per.drop(income_per.iloc[:, 0:190],  axis=1, inplace=True)\n",
    "income_per.drop(['2016', '2017', '2018'], axis=1, inplace=True)\n",
    "# confirm and display 5 rows to check\n",
    "income_per.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1990</th>\n",
       "      <th>1991</th>\n",
       "      <th>1992</th>\n",
       "      <th>1993</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>...</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",
       "    </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>51.6</td>\n",
       "      <td>51.3</td>\n",
       "      <td>51.4</td>\n",
       "      <td>51.4</td>\n",
       "      <td>50.7</td>\n",
       "      <td>51.1</td>\n",
       "      <td>51.4</td>\n",
       "      <td>51.1</td>\n",
       "      <td>50.1</td>\n",
       "      <td>51.5</td>\n",
       "      <td>...</td>\n",
       "      <td>54.1</td>\n",
       "      <td>54.6</td>\n",
       "      <td>55.2</td>\n",
       "      <td>55.7</td>\n",
       "      <td>56.2</td>\n",
       "      <td>56.7</td>\n",
       "      <td>57.2</td>\n",
       "      <td>57.7</td>\n",
       "      <td>57.8</td>\n",
       "      <td>57.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>73.9</td>\n",
       "      <td>73.9</td>\n",
       "      <td>73.9</td>\n",
       "      <td>73.9</td>\n",
       "      <td>74.0</td>\n",
       "      <td>74.1</td>\n",
       "      <td>74.3</td>\n",
       "      <td>72.5</td>\n",
       "      <td>74.3</td>\n",
       "      <td>74.4</td>\n",
       "      <td>...</td>\n",
       "      <td>75.2</td>\n",
       "      <td>75.4</td>\n",
       "      <td>75.6</td>\n",
       "      <td>75.9</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.7</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.2</td>\n",
       "      <td>77.4</td>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>71.0</td>\n",
       "      <td>71.4</td>\n",
       "      <td>71.7</td>\n",
       "      <td>72.0</td>\n",
       "      <td>72.1</td>\n",
       "      <td>72.3</td>\n",
       "      <td>72.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>73.1</td>\n",
       "      <td>73.5</td>\n",
       "      <td>...</td>\n",
       "      <td>75.6</td>\n",
       "      <td>75.9</td>\n",
       "      <td>76.1</td>\n",
       "      <td>76.3</td>\n",
       "      <td>76.5</td>\n",
       "      <td>76.7</td>\n",
       "      <td>76.8</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.1</td>\n",
       "      <td>77.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>79.5</td>\n",
       "      <td>79.5</td>\n",
       "      <td>79.6</td>\n",
       "      <td>79.8</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.3</td>\n",
       "      <td>80.6</td>\n",
       "      <td>81.0</td>\n",
       "      <td>81.3</td>\n",
       "      <td>81.5</td>\n",
       "      <td>...</td>\n",
       "      <td>82.5</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.7</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.6</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>50.2</td>\n",
       "      <td>50.8</td>\n",
       "      <td>51.0</td>\n",
       "      <td>49.7</td>\n",
       "      <td>51.1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>52.3</td>\n",
       "      <td>52.7</td>\n",
       "      <td>52.8</td>\n",
       "      <td>52.9</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>57.8</td>\n",
       "      <td>58.6</td>\n",
       "      <td>59.3</td>\n",
       "      <td>60.1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>61.7</td>\n",
       "      <td>62.5</td>\n",
       "      <td>63.3</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             1990  1991  1992  1993  1994  1995  1996  1997  1998  1999  ...   \\\n",
       "country                                                                  ...    \n",
       "Afghanistan  51.6  51.3  51.4  51.4  50.7  51.1  51.4  51.1  50.1  51.5  ...    \n",
       "Albania      73.9  73.9  73.9  73.9  74.0  74.1  74.3  72.5  74.3  74.4  ...    \n",
       "Algeria      71.0  71.4  71.7  72.0  72.1  72.3  72.8  73.0  73.1  73.5  ...    \n",
       "Andorra      79.5  79.5  79.6  79.8  80.0  80.3  80.6  81.0  81.3  81.5  ...    \n",
       "Angola       50.2  50.8  51.0  49.7  51.1  52.0  52.3  52.7  52.8  52.9  ...    \n",
       "\n",
       "             2006  2007  2008  2009  2010  2011  2012  2013  2014  2015  \n",
       "country                                                                  \n",
       "Afghanistan  54.1  54.6  55.2  55.7  56.2  56.7  57.2  57.7  57.8  57.9  \n",
       "Albania      75.2  75.4  75.6  75.9  76.3  76.7  77.0  77.2  77.4  77.6  \n",
       "Algeria      75.6  75.9  76.1  76.3  76.5  76.7  76.8  77.0  77.1  77.3  \n",
       "Andorra      82.5  82.7  82.7  82.7  82.7  82.6  82.6  82.6  82.6  82.5  \n",
       "Angola       57.0  57.8  58.6  59.3  60.1  60.9  61.7  62.5  63.3  64.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 731,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drope extraneous columns befor and after the time interval (years) which started 1990 and 2015\n",
    "life_exp.drop(life_exp.iloc[:, 0:190], axis=1, inplace=True)\n",
    "life_exp.drop(['2016', '2017', '2018'], axis=1, inplace=True)\n",
    "# confirm and display 5 rows to check\n",
    "life_exp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>sub-region</th>\n",
       "      <th>intermediate-region</th>\n",
       "      <th>region-code</th>\n",
       "      <th>sub-region-code</th>\n",
       "      <th>intermediate-region-code</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</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>Asia</td>\n",
       "      <td>Southern Asia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>142.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Åland Islands</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Northern Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "      <td>Southern Europe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Northern Africa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>Polynesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 region       sub-region intermediate-region  region-code  \\\n",
       "name                                                                        \n",
       "Afghanistan        Asia    Southern Asia                 NaN        142.0   \n",
       "Åland Islands    Europe  Northern Europe                 NaN        150.0   \n",
       "Albania          Europe  Southern Europe                 NaN        150.0   \n",
       "Algeria          Africa  Northern Africa                 NaN          2.0   \n",
       "American Samoa  Oceania        Polynesia                 NaN          9.0   \n",
       "\n",
       "                sub-region-code  intermediate-region-code  \n",
       "name                                                       \n",
       "Afghanistan                34.0                       NaN  \n",
       "Åland Islands             154.0                       NaN  \n",
       "Albania                    39.0                       NaN  \n",
       "Algeria                    15.0                       NaN  \n",
       "American Samoa             61.0                       NaN  "
      ]
     },
     "execution_count": 732,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first: Drope extraneous columns befor 'region' column.\n",
    "w_reg.drop(w_reg.iloc[:, :4], axis=1, inplace=True)\n",
    "# confirm and display 5 rows to check\n",
    "w_reg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Åland Islands</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Samoa</th>\n",
       "      <td>Oceania</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 region\n",
       "name                   \n",
       "Afghanistan        Asia\n",
       "Åland Islands    Europe\n",
       "Albania          Europe\n",
       "Algeria          Africa\n",
       "American Samoa  Oceania"
      ]
     },
     "execution_count": 733,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Second: Drope extraneous columns after 'region' column.\n",
    "w_reg.drop(w_reg.iloc[:, 1:], axis=1, inplace=True)\n",
    "# confirm and display 5 rows to check\n",
    "w_reg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 734,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 734,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using mean value of each row to fill in missing values in the same row \n",
    "country_mean = gdp.mean(axis=1)\n",
    "for i, col in enumerate(gdp):\n",
    "       gdp.iloc[:, i] = gdp.iloc[:, i].fillna(country_mean)\n",
    "# confirm correction \n",
    "gdp.isnull().any().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last code is taken from [Stackoverflow](https://stackoverflow.com/questions/33058590/pandas-dataframe-replacing-nan-with-row-average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 735,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 735,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using mean value of each row to fill in missing values in the same row \n",
    "country_mean = hdi.mean(axis=1)\n",
    "for i, col in enumerate(hdi):\n",
    "       hdi.iloc[:, i] = hdi.iloc[:, i].fillna(country_mean)\n",
    "# confirm correction \n",
    "hdi.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 736,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 736,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using mean value of each row to fill in missing values in the same row \n",
    "country_mean = income_per.mean(axis=1)\n",
    "for i, col in enumerate(income_per):\n",
    "       income_per.iloc[:, i] = income_per.iloc[:, i].fillna(country_mean)\n",
    "# confirm correction \n",
    "income_per.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 737,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 737,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using mean value of each row to fill in missing values in the same row \n",
    "country_mean = life_exp.mean(axis=1)\n",
    "for i, col in enumerate(life_exp):\n",
    "       life_exp.iloc[:, i] = life_exp.iloc[:, i].fillna(country_mean)\n",
    "# confirm correction \n",
    "life_exp.isnull().any().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 738,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 738,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding duplicated data (rows) \n",
    "sum(gdp.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 739,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 739,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding duplicated data (rows) \n",
    "sum(hdi.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 740,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 740,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding duplicated data (rows) \n",
    "sum(income_per.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 741,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 741,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding duplicated data (rows) \n",
    "sum(life_exp.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging dataframes to get the mean by country** taken from the 4 files in one dataframe to take data of countries found in it (next three steps) and merge it with 'world region' dataframe to add the region column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 742,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>495.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2855.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3909.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>40069.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>2443.076923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        0\n",
       "country                  \n",
       "Afghanistan    495.642857\n",
       "Albania       2855.000000\n",
       "Algeria       3909.615385\n",
       "Andorra      40069.230769\n",
       "Angola        2443.076923"
      ]
     },
     "execution_count": 742,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first: Finding the mean for the four metrics for each country (row) and put it in df\n",
    "gdp_mean_country = gdp.mean(axis=1)\n",
    "gdp_mean_country = gdp_mean_country.to_frame()\n",
    "# Displaying few rows to confirm\n",
    "gdp_mean_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 743,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>495.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2855.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3909.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>40069.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>2443.076923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GDP\n",
       "country                  \n",
       "Afghanistan    495.642857\n",
       "Albania       2855.000000\n",
       "Algeria       3909.615385\n",
       "Andorra      40069.230769\n",
       "Angola        2443.076923"
      ]
     },
     "execution_count": 743,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Second: rename the 0 col. to \"GDP\" (gdp_mean_country)\n",
    "gdp_mean_country.rename(columns={0: 'GDP'}, inplace=True)\n",
    "# Confirming rename\n",
    "gdp_mean_country.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I'll repeat the steps in prevous two cells in the next three dataframes (HDI, Income, Life Expectancy).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 744,
   "metadata": {},
   "outputs": [],
   "source": [
    "hdi_mean_country = hdi.mean(axis=1)\n",
    "hdi_mean_country = hdi_mean_country.to_frame()\n",
    "hdi_mean_country.rename(columns={0: 'HDI'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 745,
   "metadata": {},
   "outputs": [],
   "source": [
    "income_per_mean_country = income_per.mean(axis=1)\n",
    "income_per_mean_country = income_per_mean_country.to_frame()\n",
    "income_per_mean_country.rename(columns={0: 'Income/Person'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 746,
   "metadata": {},
   "outputs": [],
   "source": [
    "life_exp_mean_country = life_exp.mean(axis=1)\n",
    "life_exp_mean_country = life_exp_mean_country.to_frame()\n",
    "life_exp_mean_country.rename(columns={0: 'Life Expectancy'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 747,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Income/Person</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</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>495.642857</td>\n",
       "      <td>0.382808</td>\n",
       "      <td>53.492308</td>\n",
       "      <td>1198.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2855.000000</td>\n",
       "      <td>0.682654</td>\n",
       "      <td>75.042308</td>\n",
       "      <td>6791.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3909.615385</td>\n",
       "      <td>0.663346</td>\n",
       "      <td>74.446154</td>\n",
       "      <td>11279.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>40069.230769</td>\n",
       "      <td>0.841000</td>\n",
       "      <td>81.638462</td>\n",
       "      <td>35173.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>2443.076923</td>\n",
       "      <td>0.462765</td>\n",
       "      <td>55.750000</td>\n",
       "      <td>4376.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>11969.230769</td>\n",
       "      <td>0.782182</td>\n",
       "      <td>75.550000</td>\n",
       "      <td>18826.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>8643.461538</td>\n",
       "      <td>0.772769</td>\n",
       "      <td>74.488462</td>\n",
       "      <td>15738.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>2246.192308</td>\n",
       "      <td>0.670231</td>\n",
       "      <td>71.934615</td>\n",
       "      <td>4677.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>45573.076923</td>\n",
       "      <td>0.905423</td>\n",
       "      <td>80.180769</td>\n",
       "      <td>36300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>42296.153846</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>78.653846</td>\n",
       "      <td>39007.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>3326.538462</td>\n",
       "      <td>0.688667</td>\n",
       "      <td>67.423077</td>\n",
       "      <td>9085.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas</th>\n",
       "      <td>28957.692308</td>\n",
       "      <td>0.787437</td>\n",
       "      <td>72.188462</td>\n",
       "      <td>23684.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>21596.153846</td>\n",
       "      <td>0.794308</td>\n",
       "      <td>73.030769</td>\n",
       "      <td>42853.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>600.000000</td>\n",
       "      <td>0.485115</td>\n",
       "      <td>66.150000</td>\n",
       "      <td>1934.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barbados</th>\n",
       "      <td>14926.923077</td>\n",
       "      <td>0.755654</td>\n",
       "      <td>75.303846</td>\n",
       "      <td>14265.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>4033.076923</td>\n",
       "      <td>0.730381</td>\n",
       "      <td>69.676923</td>\n",
       "      <td>10858.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>40215.384615</td>\n",
       "      <td>0.865115</td>\n",
       "      <td>78.442308</td>\n",
       "      <td>37265.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>3917.307692</td>\n",
       "      <td>0.681615</td>\n",
       "      <td>70.573077</td>\n",
       "      <td>7105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>707.884615</td>\n",
       "      <td>0.414654</td>\n",
       "      <td>59.003846</td>\n",
       "      <td>1700.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>1520.192308</td>\n",
       "      <td>0.591500</td>\n",
       "      <td>67.592308</td>\n",
       "      <td>4477.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>1743.076923</td>\n",
       "      <td>0.610731</td>\n",
       "      <td>68.142308</td>\n",
       "      <td>4759.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>3556.181818</td>\n",
       "      <td>0.723273</td>\n",
       "      <td>74.461538</td>\n",
       "      <td>6662.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>5406.538462</td>\n",
       "      <td>0.615769</td>\n",
       "      <td>54.715385</td>\n",
       "      <td>11363.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>9549.230769</td>\n",
       "      <td>0.687538</td>\n",
       "      <td>72.053846</td>\n",
       "      <td>12365.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brunei</th>\n",
       "      <td>36200.000000</td>\n",
       "      <td>0.826462</td>\n",
       "      <td>75.857692</td>\n",
       "      <td>82676.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulgaria</th>\n",
       "      <td>5202.692308</td>\n",
       "      <td>0.737308</td>\n",
       "      <td>72.530769</td>\n",
       "      <td>11720.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>480.461538</td>\n",
       "      <td>0.370636</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>1160.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burundi</th>\n",
       "      <td>249.192308</td>\n",
       "      <td>0.310192</td>\n",
       "      <td>51.850000</td>\n",
       "      <td>823.653846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>608.043478</td>\n",
       "      <td>0.454885</td>\n",
       "      <td>61.996154</td>\n",
       "      <td>1830.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cameroon</th>\n",
       "      <td>1238.846154</td>\n",
       "      <td>0.460654</td>\n",
       "      <td>56.507692</td>\n",
       "      <td>2590.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Lucia</th>\n",
       "      <td>7409.230769</td>\n",
       "      <td>0.710687</td>\n",
       "      <td>74.269231</td>\n",
       "      <td>10148.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>St. Vincent and the Grenadines</th>\n",
       "      <td>5231.538462</td>\n",
       "      <td>0.702375</td>\n",
       "      <td>70.950000</td>\n",
       "      <td>8326.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sudan</th>\n",
       "      <td>1203.653846</td>\n",
       "      <td>0.414500</td>\n",
       "      <td>63.453846</td>\n",
       "      <td>2743.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Suriname</th>\n",
       "      <td>6901.923077</td>\n",
       "      <td>0.701833</td>\n",
       "      <td>70.073077</td>\n",
       "      <td>11818.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swaziland</th>\n",
       "      <td>3249.230769</td>\n",
       "      <td>0.525846</td>\n",
       "      <td>51.350000</td>\n",
       "      <td>6446.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>45765.384615</td>\n",
       "      <td>0.876385</td>\n",
       "      <td>80.069231</td>\n",
       "      <td>37734.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>68873.076923</td>\n",
       "      <td>0.889654</td>\n",
       "      <td>80.423077</td>\n",
       "      <td>51169.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tajikistan</th>\n",
       "      <td>658.538462</td>\n",
       "      <td>0.577654</td>\n",
       "      <td>66.788462</td>\n",
       "      <td>1879.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tanzania</th>\n",
       "      <td>586.692308</td>\n",
       "      <td>0.429923</td>\n",
       "      <td>55.976923</td>\n",
       "      <td>1747.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thailand</th>\n",
       "      <td>4089.615385</td>\n",
       "      <td>0.664962</td>\n",
       "      <td>73.553846</td>\n",
       "      <td>10860.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>2838.750000</td>\n",
       "      <td>0.555500</td>\n",
       "      <td>65.376923</td>\n",
       "      <td>1571.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Togo</th>\n",
       "      <td>500.961538</td>\n",
       "      <td>0.434192</td>\n",
       "      <td>57.703846</td>\n",
       "      <td>1241.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tonga</th>\n",
       "      <td>3265.000000</td>\n",
       "      <td>0.687538</td>\n",
       "      <td>69.061538</td>\n",
       "      <td>4588.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <td>11890.769231</td>\n",
       "      <td>0.729423</td>\n",
       "      <td>70.792308</td>\n",
       "      <td>22303.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunisia</th>\n",
       "      <td>3263.846154</td>\n",
       "      <td>0.661846</td>\n",
       "      <td>75.096154</td>\n",
       "      <td>8229.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkey</th>\n",
       "      <td>9266.538462</td>\n",
       "      <td>0.670077</td>\n",
       "      <td>74.896154</td>\n",
       "      <td>15600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkmenistan</th>\n",
       "      <td>3436.153846</td>\n",
       "      <td>0.679667</td>\n",
       "      <td>65.388462</td>\n",
       "      <td>7693.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uganda</th>\n",
       "      <td>467.923077</td>\n",
       "      <td>0.405308</td>\n",
       "      <td>52.126923</td>\n",
       "      <td>1211.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ukraine</th>\n",
       "      <td>2653.076923</td>\n",
       "      <td>0.704269</td>\n",
       "      <td>68.723077</td>\n",
       "      <td>7001.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>53500.000000</td>\n",
       "      <td>0.798500</td>\n",
       "      <td>75.192308</td>\n",
       "      <td>87853.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>35884.615385</td>\n",
       "      <td>0.865462</td>\n",
       "      <td>78.407692</td>\n",
       "      <td>33357.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>44757.692308</td>\n",
       "      <td>0.892077</td>\n",
       "      <td>77.296154</td>\n",
       "      <td>45665.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uruguay</th>\n",
       "      <td>9717.692308</td>\n",
       "      <td>0.747538</td>\n",
       "      <td>74.996154</td>\n",
       "      <td>13905.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uzbekistan</th>\n",
       "      <td>1070.730769</td>\n",
       "      <td>0.646813</td>\n",
       "      <td>67.480769</td>\n",
       "      <td>3295.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanuatu</th>\n",
       "      <td>2734.615385</td>\n",
       "      <td>0.588818</td>\n",
       "      <td>62.492308</td>\n",
       "      <td>2718.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Venezuela</th>\n",
       "      <td>12736.400000</td>\n",
       "      <td>0.703077</td>\n",
       "      <td>74.161538</td>\n",
       "      <td>15569.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vietnam</th>\n",
       "      <td>939.230769</td>\n",
       "      <td>0.591577</td>\n",
       "      <td>71.253846</td>\n",
       "      <td>3245.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yemen</th>\n",
       "      <td>1103.538462</td>\n",
       "      <td>0.455846</td>\n",
       "      <td>63.765385</td>\n",
       "      <td>3789.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zambia</th>\n",
       "      <td>1156.692308</td>\n",
       "      <td>0.468654</td>\n",
       "      <td>48.780769</td>\n",
       "      <td>2591.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>1036.307692</td>\n",
       "      <td>0.453846</td>\n",
       "      <td>51.388462</td>\n",
       "      <td>2110.384615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         GDP       HDI  Life Expectancy  \\\n",
       "country                                                                   \n",
       "Afghanistan                       495.642857  0.382808        53.492308   \n",
       "Albania                          2855.000000  0.682654        75.042308   \n",
       "Algeria                          3909.615385  0.663346        74.446154   \n",
       "Andorra                         40069.230769  0.841000        81.638462   \n",
       "Angola                           2443.076923  0.462765        55.750000   \n",
       "Antigua and Barbuda             11969.230769  0.782182        75.550000   \n",
       "Argentina                        8643.461538  0.772769        74.488462   \n",
       "Armenia                          2246.192308  0.670231        71.934615   \n",
       "Australia                       45573.076923  0.905423        80.180769   \n",
       "Austria                         42296.153846  0.846000        78.653846   \n",
       "Azerbaijan                       3326.538462  0.688667        67.423077   \n",
       "Bahamas                         28957.692308  0.787437        72.188462   \n",
       "Bahrain                         21596.153846  0.794308        73.030769   \n",
       "Bangladesh                        600.000000  0.485115        66.150000   \n",
       "Barbados                        14926.923077  0.755654        75.303846   \n",
       "Belarus                          4033.076923  0.730381        69.676923   \n",
       "Belgium                         40215.384615  0.865115        78.442308   \n",
       "Belize                           3917.307692  0.681615        70.573077   \n",
       "Benin                             707.884615  0.414654        59.003846   \n",
       "Bhutan                           1520.192308  0.591500        67.592308   \n",
       "Bolivia                          1743.076923  0.610731        68.142308   \n",
       "Bosnia and Herzegovina           3556.181818  0.723273        74.461538   \n",
       "Botswana                         5406.538462  0.615769        54.715385   \n",
       "Brazil                           9549.230769  0.687538        72.053846   \n",
       "Brunei                          36200.000000  0.826462        75.857692   \n",
       "Bulgaria                         5202.692308  0.737308        72.530769   \n",
       "Burkina Faso                      480.461538  0.370636        54.250000   \n",
       "Burundi                           249.192308  0.310192        51.850000   \n",
       "Cambodia                          608.043478  0.454885        61.996154   \n",
       "Cameroon                         1238.846154  0.460654        56.507692   \n",
       "...                                      ...       ...              ...   \n",
       "St. Lucia                        7409.230769  0.710687        74.269231   \n",
       "St. Vincent and the Grenadines   5231.538462  0.702375        70.950000   \n",
       "Sudan                            1203.653846  0.414500        63.453846   \n",
       "Suriname                         6901.923077  0.701833        70.073077   \n",
       "Swaziland                        3249.230769  0.525846        51.350000   \n",
       "Sweden                          45765.384615  0.876385        80.069231   \n",
       "Switzerland                     68873.076923  0.889654        80.423077   \n",
       "Tajikistan                        658.538462  0.577654        66.788462   \n",
       "Tanzania                          586.692308  0.429923        55.976923   \n",
       "Thailand                         4089.615385  0.664962        73.553846   \n",
       "Timor-Leste                      2838.750000  0.555500        65.376923   \n",
       "Togo                              500.961538  0.434192        57.703846   \n",
       "Tonga                            3265.000000  0.687538        69.061538   \n",
       "Trinidad and Tobago             11890.769231  0.729423        70.792308   \n",
       "Tunisia                          3263.846154  0.661846        75.096154   \n",
       "Turkey                           9266.538462  0.670077        74.896154   \n",
       "Turkmenistan                     3436.153846  0.679667        65.388462   \n",
       "Uganda                            467.923077  0.405308        52.126923   \n",
       "Ukraine                          2653.076923  0.704269        68.723077   \n",
       "United Arab Emirates            53500.000000  0.798500        75.192308   \n",
       "United Kingdom                  35884.615385  0.865462        78.407692   \n",
       "United States                   44757.692308  0.892077        77.296154   \n",
       "Uruguay                          9717.692308  0.747538        74.996154   \n",
       "Uzbekistan                       1070.730769  0.646813        67.480769   \n",
       "Vanuatu                          2734.615385  0.588818        62.492308   \n",
       "Venezuela                       12736.400000  0.703077        74.161538   \n",
       "Vietnam                           939.230769  0.591577        71.253846   \n",
       "Yemen                            1103.538462  0.455846        63.765385   \n",
       "Zambia                           1156.692308  0.468654        48.780769   \n",
       "Zimbabwe                         1036.307692  0.453846        51.388462   \n",
       "\n",
       "                                Income/Person  \n",
       "country                                        \n",
       "Afghanistan                       1198.307692  \n",
       "Albania                           6791.153846  \n",
       "Algeria                          11279.230769  \n",
       "Andorra                          35173.076923  \n",
       "Angola                            4376.153846  \n",
       "Antigua and Barbuda              18826.923077  \n",
       "Argentina                        15738.461538  \n",
       "Armenia                           4677.307692  \n",
       "Australia                        36300.000000  \n",
       "Austria                          39007.692308  \n",
       "Azerbaijan                        9085.769231  \n",
       "Bahamas                          23684.615385  \n",
       "Bahrain                          42853.846154  \n",
       "Bangladesh                        1934.615385  \n",
       "Barbados                         14265.384615  \n",
       "Belarus                          10858.076923  \n",
       "Belgium                          37265.384615  \n",
       "Belize                            7105.000000  \n",
       "Benin                             1700.384615  \n",
       "Bhutan                            4477.692308  \n",
       "Bolivia                           4759.230769  \n",
       "Bosnia and Herzegovina            6662.307692  \n",
       "Botswana                         11363.076923  \n",
       "Brazil                           12365.384615  \n",
       "Brunei                           82676.923077  \n",
       "Bulgaria                         11720.000000  \n",
       "Burkina Faso                      1160.884615  \n",
       "Burundi                            823.653846  \n",
       "Cambodia                          1830.769231  \n",
       "Cameroon                          2590.000000  \n",
       "...                                       ...  \n",
       "St. Lucia                        10148.076923  \n",
       "St. Vincent and the Grenadines    8326.923077  \n",
       "Sudan                             2743.846154  \n",
       "Suriname                         11818.076923  \n",
       "Swaziland                         6446.153846  \n",
       "Sweden                           37734.615385  \n",
       "Switzerland                      51169.230769  \n",
       "Tajikistan                        1879.230769  \n",
       "Tanzania                          1747.692308  \n",
       "Thailand                         10860.384615  \n",
       "Timor-Leste                       1571.153846  \n",
       "Togo                              1241.153846  \n",
       "Tonga                             4588.076923  \n",
       "Trinidad and Tobago              22303.846154  \n",
       "Tunisia                           8229.230769  \n",
       "Turkey                           15600.000000  \n",
       "Turkmenistan                      7693.461538  \n",
       "Uganda                            1211.615385  \n",
       "Ukraine                           7001.538462  \n",
       "United Arab Emirates             87853.846154  \n",
       "United Kingdom                   33357.692308  \n",
       "United States                    45665.384615  \n",
       "Uruguay                          13905.384615  \n",
       "Uzbekistan                        3295.384615  \n",
       "Vanuatu                           2718.076923  \n",
       "Venezuela                        15569.230769  \n",
       "Vietnam                           3245.384615  \n",
       "Yemen                             3789.230769  \n",
       "Zambia                            2591.538462  \n",
       "Zimbabwe                          2110.384615  \n",
       "\n",
       "[182 rows x 4 columns]"
      ]
     },
     "execution_count": 747,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the four dataframes in one dataframe containing the mean for each country\n",
    "merged_data_country = gdp_mean_country\n",
    "merged_data_country = pd.merge(merged_data_country, hdi_mean_country, left_index=True, right_index = True)\n",
    "merged_data_country = pd.merge(merged_data_country, life_exp_mean_country, left_index=True, right_index = True)\n",
    "merged_data_country = pd.merge(merged_data_country, income_per_mean_country, left_index=True, right_index = True)\n",
    "# Next variable (merged_data_not_region) will be used in line chart visualization \n",
    "merged_data_without_region = merged_data_country\n",
    "# Confirm merging\n",
    "merged_data_country.head(200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 748,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Income/Person</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>495.642857</td>\n",
       "      <td>0.382808</td>\n",
       "      <td>53.492308</td>\n",
       "      <td>1198.307692</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2855.000000</td>\n",
       "      <td>0.682654</td>\n",
       "      <td>75.042308</td>\n",
       "      <td>6791.153846</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>3909.615385</td>\n",
       "      <td>0.663346</td>\n",
       "      <td>74.446154</td>\n",
       "      <td>11279.230769</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>40069.230769</td>\n",
       "      <td>0.841000</td>\n",
       "      <td>81.638462</td>\n",
       "      <td>35173.076923</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>2443.076923</td>\n",
       "      <td>0.462765</td>\n",
       "      <td>55.750000</td>\n",
       "      <td>4376.153846</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GDP       HDI  Life Expectancy  Income/Person  region\n",
       "Afghanistan    495.642857  0.382808        53.492308    1198.307692    Asia\n",
       "Albania       2855.000000  0.682654        75.042308    6791.153846  Europe\n",
       "Algeria       3909.615385  0.663346        74.446154   11279.230769  Africa\n",
       "Andorra      40069.230769  0.841000        81.638462   35173.076923  Europe\n",
       "Angola        2443.076923  0.462765        55.750000    4376.153846  Africa"
      ]
     },
     "execution_count": 748,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging with 'World Regions' dataframe to add region column \n",
    "merged_data_country = pd.merge(merged_data_country, w_reg, left_index=True, right_index = True)\n",
    "# Confirm merging\n",
    "merged_data_country.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 749,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Income/Person</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>2159.717785</td>\n",
       "      <td>0.469934</td>\n",
       "      <td>58.928679</td>\n",
       "      <td>4627.469900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Americas</th>\n",
       "      <td>8157.893899</td>\n",
       "      <td>0.691008</td>\n",
       "      <td>73.062997</td>\n",
       "      <td>11706.631300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>11148.231062</td>\n",
       "      <td>0.655503</td>\n",
       "      <td>70.146351</td>\n",
       "      <td>18656.734714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>28189.644220</td>\n",
       "      <td>0.809190</td>\n",
       "      <td>76.387443</td>\n",
       "      <td>27927.567873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>10365.128205</td>\n",
       "      <td>0.656701</td>\n",
       "      <td>67.224786</td>\n",
       "      <td>10048.162393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GDP       HDI  Life Expectancy  Income/Person\n",
       "region                                                          \n",
       "Africa     2159.717785  0.469934        58.928679    4627.469900\n",
       "Americas   8157.893899  0.691008        73.062997   11706.631300\n",
       "Asia      11148.231062  0.655503        70.146351   18656.734714\n",
       "Europe    28189.644220  0.809190        76.387443   27927.567873\n",
       "Oceania   10365.128205  0.656701        67.224786   10048.162393"
      ]
     },
     "execution_count": 749,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the dataframe by region\n",
    "region = merged_data_country.groupby(['region']).mean() \n",
    "# Confirm grouping\n",
    "region.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging dataframes to get the mean by year** taken from the 4 files in one dataframe to take data of the years found in all the 4 dataframes (next three steps) and merging it to get one merged dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 750,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>11109.726369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>11102.896896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>11091.534703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>11108.945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>11248.775144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "1990  11109.726369\n",
       "1991  11102.896896\n",
       "1992  11091.534703\n",
       "1993  11108.945000\n",
       "1994  11248.775144"
      ]
     },
     "execution_count": 750,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first: Finding the mean for the four metrics for each year (column) and put it in df\n",
    "gdp_mean_year = gdp.mean()\n",
    "gdp_mean_year = gdp_mean_year.to_frame()\n",
    "# Displaying few rows to confirm\n",
    "gdp_mean_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 751,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>11109.726369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>11102.896896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>11091.534703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>11108.945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>11248.775144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               GDP\n",
       "1990  11109.726369\n",
       "1991  11102.896896\n",
       "1992  11091.534703\n",
       "1993  11108.945000\n",
       "1994  11248.775144"
      ]
     },
     "execution_count": 751,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Second: rename the 0 col. to \"GDP\" (gdp_mean_year)\n",
    "gdp_mean_year.rename(columns={0: 'GDP'}, inplace=True)\n",
    "# Confirming rename\n",
    "gdp_mean_year.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I'll repeat the steps in prevous two cells in the next three dataframes (HDI, Income, Life Expectancy).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 752,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HDI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>0.599156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>0.600830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>0.602610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>0.605664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>0.608995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           HDI\n",
       "1990  0.599156\n",
       "1991  0.600830\n",
       "1992  0.602610\n",
       "1993  0.605664\n",
       "1994  0.608995"
      ]
     },
     "execution_count": 752,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hdi_mean_year = hdi.mean()\n",
    "hdi_mean_year = hdi_mean_year.to_frame()\n",
    "hdi_mean_year.rename(columns={0: 'HDI'}, inplace=True)\n",
    "hdi_mean_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 753,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income/Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>12045.932642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>11805.652850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>11849.248705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>11863.082902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>12058.181347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Income/Person\n",
       "1990   12045.932642\n",
       "1991   11805.652850\n",
       "1992   11849.248705\n",
       "1993   11863.082902\n",
       "1994   12058.181347"
      ]
     },
     "execution_count": 753,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_per_mean_year = income_per.mean()\n",
    "income_per_mean_year = income_per_mean_year.to_frame()\n",
    "income_per_mean_year.rename(columns={0: 'Income/Person'}, inplace=True)\n",
    "income_per_mean_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 754,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Life Expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>66.193048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>66.281818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>66.381818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>66.370588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>66.253476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Life Expectancy\n",
       "1990        66.193048\n",
       "1991        66.281818\n",
       "1992        66.381818\n",
       "1993        66.370588\n",
       "1994        66.253476"
      ]
     },
     "execution_count": 754,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "life_exp_mean_year = life_exp.mean()\n",
    "life_exp_mean_year = life_exp_mean_year.to_frame()\n",
    "life_exp_mean_year.rename(columns={0: 'Life Expectancy'}, inplace=True)\n",
    "life_exp_mean_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 755,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Income/Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>11109.726369</td>\n",
       "      <td>0.599156</td>\n",
       "      <td>66.193048</td>\n",
       "      <td>12045.932642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>11102.896896</td>\n",
       "      <td>0.600830</td>\n",
       "      <td>66.281818</td>\n",
       "      <td>11805.652850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>11091.534703</td>\n",
       "      <td>0.602610</td>\n",
       "      <td>66.381818</td>\n",
       "      <td>11849.248705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>11108.945000</td>\n",
       "      <td>0.605664</td>\n",
       "      <td>66.370588</td>\n",
       "      <td>11863.082902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>11248.775144</td>\n",
       "      <td>0.608995</td>\n",
       "      <td>66.253476</td>\n",
       "      <td>12058.181347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               GDP       HDI  Life Expectancy  Income/Person\n",
       "1990  11109.726369  0.599156        66.193048   12045.932642\n",
       "1991  11102.896896  0.600830        66.281818   11805.652850\n",
       "1992  11091.534703  0.602610        66.381818   11849.248705\n",
       "1993  11108.945000  0.605664        66.370588   11863.082902\n",
       "1994  11248.775144  0.608995        66.253476   12058.181347"
      ]
     },
     "execution_count": 755,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the four dataframes in one dataframe containing the mean for each year\n",
    "merged_data_year = gdp_mean_year\n",
    "merged_data_year = pd.merge(merged_data_year, hdi_mean_year, left_index=True, right_index = True)\n",
    "merged_data_year = pd.merge(merged_data_year, life_exp_mean_year, left_index=True, right_index = True)\n",
    "merged_data_year = pd.merge(merged_data_year, income_per_mean_year, left_index=True, right_index = True)\n",
    "# Confirm merging\n",
    "merged_data_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 756,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Merging sample countries in one dataframe and \n",
    "# Transpose operation is used to flip the rows with columns\n",
    "egypt_df = merged_data_without_region.loc[\"Egypt\"].to_frame().T\n",
    "sa_df = merged_data_without_region.loc[\"South Africa\"].to_frame().T\n",
    "ger_df = merged_data_without_region.loc[\"Germany\"].to_frame().T\n",
    "uae_df = merged_data_without_region.loc[\"United Arab Emirates\"].to_frame().T\n",
    "chi_df = merged_data_without_region.loc[\"China\"].to_frame().T\n",
    "usa_df = merged_data_without_region.loc['United States'].to_frame().T\n",
    "uk_df = merged_data_without_region.loc[\"United Kingdom\"].to_frame().T\n",
    "brazil_df = merged_data_without_region.loc[\"Brazil\"].to_frame().T\n",
    "aus_df = merged_data_without_region.loc[\"Australia\"].to_frame().T\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The code from** https://pandas.pydata.org/pandas-docs/stable/merging.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 757,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Income/Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Egypt</th>\n",
       "      <td>2077.307692</td>\n",
       "      <td>0.622615</td>\n",
       "      <td>69.319231</td>\n",
       "      <td>7870.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Africa</th>\n",
       "      <td>6408.461538</td>\n",
       "      <td>0.634923</td>\n",
       "      <td>57.792308</td>\n",
       "      <td>10639.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>38750.000000</td>\n",
       "      <td>0.872346</td>\n",
       "      <td>78.334615</td>\n",
       "      <td>37473.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <td>53500.000000</td>\n",
       "      <td>0.798500</td>\n",
       "      <td>75.192308</td>\n",
       "      <td>87853.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2794.538462</td>\n",
       "      <td>0.620577</td>\n",
       "      <td>71.415385</td>\n",
       "      <td>5840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>44757.692308</td>\n",
       "      <td>0.892077</td>\n",
       "      <td>77.296154</td>\n",
       "      <td>45665.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>35884.615385</td>\n",
       "      <td>0.865462</td>\n",
       "      <td>78.407692</td>\n",
       "      <td>33357.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>9549.230769</td>\n",
       "      <td>0.687538</td>\n",
       "      <td>72.053846</td>\n",
       "      <td>12365.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>45573.076923</td>\n",
       "      <td>0.905423</td>\n",
       "      <td>80.180769</td>\n",
       "      <td>36300.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               GDP       HDI  Life Expectancy  Income/Person\n",
       "Egypt                  2077.307692  0.622615        69.319231    7870.000000\n",
       "South Africa           6408.461538  0.634923        57.792308   10639.230769\n",
       "Germany               38750.000000  0.872346        78.334615   37473.076923\n",
       "United Arab Emirates  53500.000000  0.798500        75.192308   87853.846154\n",
       "China                  2794.538462  0.620577        71.415385    5840.000000\n",
       "United States         44757.692308  0.892077        77.296154   45665.384615\n",
       "United Kingdom        35884.615385  0.865462        78.407692   33357.692308\n",
       "Brazil                 9549.230769  0.687538        72.053846   12365.384615\n",
       "Australia             45573.076923  0.905423        80.180769   36300.000000"
      ]
     },
     "execution_count": 757,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenating data of the last cell in one dataframe to be visualized\n",
    "# Insert the sample countries dataframes in a list to be concatenated\n",
    "frames = [egypt_df, sa_df, ger_df, uae_df, chi_df, usa_df, uk_df, brazil_df, aus_df]\n",
    "merged_sample_country = pd.concat(frames)\n",
    "merged_sample_country.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## 3rd Step: Exploratory Data Analysis\n",
    "\n",
    "### Research Question 1 :\n",
    "### What is the effect of GDP on Income, Human Development Index(HDI) and Life expectancy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating the correlation between GDP and the other metrics** to show its effect on each of them where the data is calculated **according to year average for all countries.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 758,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI</th>\n",
       "      <td>0.962689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life Expectancy</th>\n",
       "      <td>0.936365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income/Person</th>\n",
       "      <td>0.997203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GDP\n",
       "GDP              1.000000\n",
       "HDI              0.962689\n",
       "Life Expectancy  0.936365\n",
       "Income/Person    0.997203"
      ]
     },
     "execution_count": 758,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data_year.corr()[\"GDP\"].to_frame() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatter plots to visualize the correlation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 759,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43feda64e0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between 'GDP' and 'Income/Person' which = 0.997203 (positive corr., very strong)\n",
    "merged_data_year.plot(x='GDP', y='Income/Person', kind='scatter', color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 760,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fee6b358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between 'GDP' and 'HDI' which = 0.962689 (positive corr., very strong)\n",
    "merged_data_year.plot(x='GDP', y='HDI', kind='scatter', color='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 761,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fedd84a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between 'GDP' and 'Life Expectancy' which = 0.936365 (positive corr., strong)\n",
    "merged_data_year.plot(x='GDP', y='Life Expectancy', kind='scatter', color='b');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating the correlation between GDP and the other metrics** to show its effect on each of them where the data is calculated **according to countries average for all years.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 762,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI</th>\n",
       "      <td>0.689956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life Expectancy</th>\n",
       "      <td>0.603767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income/Person</th>\n",
       "      <td>0.912654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GDP\n",
       "GDP              1.000000\n",
       "HDI              0.689956\n",
       "Life Expectancy  0.603767\n",
       "Income/Person    0.912654"
      ]
     },
     "execution_count": 762,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data_country.corr()[\"GDP\"].to_frame() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatter plots to visualize the correlation.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 763,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UPMeHcvmDkk6rKF+Zy0YlXTiTdva0eufT+Poi9WvVD77nTlnBag02cyLiwfJORPwQmDPD1z4feKBi/9PAuohYAjwDDOXyIeCZiHg1KcB9GkDSsaQLuL0eWAl8IQewWaQe2OnAscDqXNemaybzaVavTudobrstbZ0ckEzWa2nlD77nTlnBag02I5LWSzop375ImthZF0mLgN8GvpT3BZwM3JirbADOzPdX5X3y46fk+quA6yLi+Yh4BBgFTsi30Yh4OCL2ANflujZdMx0O6++HZcvcoym76ipYvBhOOaV6r6WVP/ge+rSC1RpsziXNtfkAqUfyfeB9M3jdvwf+gv2Ler4ceDYiylf/3A4cme8fCTwOkB/fkev/onzMMROVjyPpHEkjkkZKHi4Yz8NhjXPVVfC+98Hzz8POndV7La38wfe/tRWs1nk2s4FLI+ISSOdbgIPqeUFJvwM8FRFbcuIBgKpUjSkem6i8WgCtmt8dEVeTLgrH4ODg1DngvciX2525UgnOP398+ezZB2bolX/wh4ZSj2bv3ub+4Pvf2gpUa7C5HVgO7Mr7c4F/Ad5Ux2u+GXibpDOAg4H5pJ7OAkmzc+9lEfBErr8dWAxslzQbeBnwdEV5WeUxE5VbPXy53YnVkipcHh57/vkDy/fsGd9rqfzBnzcPdu1Kr9Gsz9//1laQWofRDo6IcqAh3z+knheMiA9FxKKIGCCd4P9WRJwN3AG8PVdbC9yc72/M++THvxVpJupG4KycrXYMsAS4C9gMLMnZbX35NTbW01azSVXLHKuWADAwAPv2jT/+0kvTdmz9/n4YHYXjj3casnWNWoPNzyQdV96RdDywe5L69fhL4IOSRknnZNbn8vXAy3P5B4ELASLifuAG0vmjfwbeHxEv5J7RecAmUrbbDbmuWeNUyxx797vhqKPGB4jK8yHz5sFBB8GVV6ZzI9XSnJ2GbF2o1uVqlpGyusrDUUcA74yIujPS2o2Xq7Fp2bw5BYkdOyauM3aJnsohN5h4SZ9t21LG2s6d+x+bPz+lkS9b1uA3YjYzjV6uZrOk1wKvIZ2Y/4GXq7GeVi1zbKxZs8YnAJTvb9488ZI+d999YKABpyFbx6t1GA1gGfAGYClpouSaYppk1gLTXSKmWqrwnDHznHftSoGjmonSnOfNgwsuGF9/3TqfuLeOVuvaaF8BLgZ+ixR0lgFTdpvMOsJ0l4gpB6blyw9cJWFNlb+/PvCB8QGsPJy2bt34eS27do2f2DlvHhx3HGadrNbU50Hg2KjlBI9ZJ6k8GV8e0hoaSoGkWk9iouv0lErwla+Mr79nD2zdCitWVD9+3boUSMqp06XS+B7PCy94CM06Xq3DaN8DfqnIhpi1xHSWiJksS2zbtjRJczLVjr/gggPn6Hgmv3WpWns2hwPfl3QXB16p822FtMqsWaazRMxk1+kZGIAXXxx/zJw5sHTp1MdXBhPP5LcuVGuw+WiRjTBrmeksETNZYOrvh2uugbVrUxmk59uwYf9zTSeweSa/dZma5tn0As+z6XG1XqGyfM6lMjBVXj6hVErnaCD1aMY+11THm3WYWufZTBpsJO2k+iKWAiIi5tffxPbiYGM1m+mlk33pZesiDZnUGREvbVyTzLrETIe4PERmPWg6kzrNzMzqUmuCgFnrjF1TzENQZh3HwcbaW+UkyOeeAynNPamcUFnJ50PM2pKH0ax9jZ0EuXdvCjITLbs/3WVnzKxpHGysfVWb3V+pcqa/rwFj1tYcbKx9TbWMf+WEyOksO2NmTedgY+1r7Dphc+akgFJtzbDpzM43s6ZzgoC1t7HrhEH1BIDpLDtjZk3nYGPtrxwwykFmoksjewFLs7blYTRrf9PJMuvvT8HIgcasrTjYWONM59LKtdZ1lplZV3CwsemZKEhMp/cxnbrOMjPrCg42VruJgkS13sd731u99zHdnoqzzMy6goNNp5rOkFWjXm+ySyKP7X38/Odw1VXjn2e6PZXJLpPc7M/AzOrmYNOJWrEsy2RBYmAAnn9+/DF/+7fjA0E9PZXVq+HRR+G229J29WovTWPWYRxsOs10hqEa+Zf/VJdE/vCHxx9TrccyWU9lMpVZZk4aMOs4DjadptZhqEb/5T9VkPjjP05llSbqsVTrqUyHkwbMOs6kl4XuJR1zWehSKQWP3bv3l82dm360yz/8tdSZyetPNGmyfDmAyhn80w0ktbahqPdnZtNS62Wh3bPpNLUMQxX5l/9kkyZn2mOZThvqGYozs5Zpes9G0mLgWuCXgBeBqyPiUkmHAdcDA8A24B0R8YwkAZcCZwDPAe+OiLvzc60F/io/9SciYkMuPx74B2Au8E3g/JjijXZMz6Zssh5Gr/zl7wulmbVcO/ds9gF/HhGvA04E3i/pWOBC4PaIWALcnvcBTgeW5Ns5wBUAOThdBLwROAG4SNLCfMwVuW75uJVNeF/NNVkPo1f+8vfSNGYdo+kLcUbEk8CT+f5OSQ8ARwKrgJNytQ3AvwJ/mcuvzT2T70haIOmIXPfWiHgaQNKtwEpJ/wrMj4hv5/JrgTOBW5rx/pqu2l/3pRK8+tWwZQvs2uW//M2s5Vp6zkbSALAUuBN4ZQ5E5YD0ilztSODxisO257LJyrdXKe8+w8Nw1FHw1rem7fDwgVloxx8Pmzbtr+9JkGbWIi0LNpLmATcBfxYRP52sapWyqKO8WhvOkTQiaaTUaT/ApRKsXZtm6v/sZ2m7Zs34+Sd//dcpEP3pn3oSpJm1TEuCjaQ5pEDz1Yj4ei7+cR4eI2+fyuXbgcUVhy8CnpiifFGV8nEi4uqIGIyIwf52HWaaqDeydWtKL660bx+oSqz9+c/h85/3JEgza5mmB5ucXbYeeCAiLql4aCOwNt9fC9xcUb5GyYnAjjzMtglYIWlhTgxYAWzKj+2UdGJ+rTUVz9VZ6pmYWW3ZmGo8CdLMmqgVPZs3A+8CTpZ0T76dAXwKOFXSQ8CpeR9S6vLDwCjwReBPAHJiwMeBzfn2sXKyAHAu8KV8zH/SickBUy3JsnRpChhjSXDwwVM/v1dONrMmakU22v+l+nkVgFOq1A/g/RM81zXANVXKR4Bfm0EzW688MbNyrky5N9Lfn25//ufwqU8deNzs2XDzzXDHHeMfA3jpS9NwWzemQptZ2/IKAu1qooUv581L53AuvhguuWT8cT//OSxeDL/3eymwVJo3Dz73uWJn95uZVdH0no3VqDwxs3KtsaGhlM784ouTn5u56aa0MOa+fQeWv/ACnHGGezRm1nTu2bSjcgba8uX71xrbsiUFn927p04C+OQn07YXVhEws47gYNMqE6U0j52oedttaUmWXbtqf+6+vnRup9aFMT3Z08wK5mDTChOlNFebqLl2bSqfN+/AZIHJVGaaTbV+mK94aWZN4GDTbJOlNFebqLl3byrftWv8xcmqOeig2ofLfMVLM2sSJwg022QpzZOpZU7MnDkpML3udTNvi8/tmFkDuWfTbBOlNA8MpJTll1T5J3nkkQMvGzBvXvXn/tznag80U7XFzKyBHGyardq1Zs4/Hz77WXjDG1Ja81jnnZeGtson/G+6KU3erDR7dppbM9O2OGPNzArgYbRmK5Vg1iz4q7+CH/8Yrrii+kz/Svv2peGxFStSIFi4EA49NJ1nKTvkkPqGv1avTinWvuKlmRXIwaaZhofh7LNhppfibvTwV3n5GzOzgngYrVlKJXjXu+oPNIsrrqbg4S8z6zDu2TTL1q1puZh6HHzw+EmdHv4ysw7iYNMszz47s+OrDZF5+MvMOoSH0ZpheDhdsrleH/6wg4qZdTT3bIpSKqUhrnnzUqAZuwJzrebOTSs4m5l1MAebIgwPp2Vf+vpg587qc2dqNTTkXo2ZdTwPozXa2PXGZhJoIGWZea0yM+twDjaNVl5vrFFqWTfNzKzNOdg02sBAGjqrx6GHji/zWmVm1gUcbBrtJz+pb+jsM5+BO+6AK6/0ZE0z6zpOEGi0u+6aXv2DDoJLL92fcbZsWVpQ05M1zayLONg02iOP1FZPgo99LAWZsQHFkzXNrMs42DRKqQSf+ARcdtnUdefMge9+d3rXnjEz62AONo1QXiGg1ombfX3j1zozM+tiThCYqVIJ1q6d3goBL77oDDMz6ykONjO1dWtKT65VX58zzMys53gYbaYuv7z2un19cM89PldjZj3HPZuZeOAB2Lix9vp79sBNNxXXHjOzNtW1wUbSSkkPShqVdGEhL/LOd07/mE9+0mudmVnP6cpgI2kWcDlwOnAssFrSsQ19kVIJ7rtv+sf19XmtMzPrOV0ZbIATgNGIeDgi9gDXAasa+gpXXFHfcV7rzMx6ULcGmyOBxyv2t+eyxvmP/5i6zuzZcN55XuvMzHpet2ajqUpZjKsknQOcA3DUUUdN7xXe9S7YtKn6Y9dfDwsWwNKlKbB85CNe68zMelq39my2A4sr9hcBT4ytFBFXR8RgRAz2TzcInH02LF48vvxrX4N3vANWrNgfWPr70wKbDjRm1qO6tWezGVgi6RjgR8BZwB82/FUeewy++lX4x3+EN70J3vc+BxQzsyq6MthExD5J5wGbgFnANRFxfyEvdvbZ6WZmZhPqymADEBHfBL7Z6naYmVn3nrMxM7M24mBjZmaFc7AxM7PCOdiYmVnhFDFurmNPklQCHq3j0MOBnzS4OZ3In8N+/iwSfw5Jt38OR0fElHM+HGxmSNJIRAy2uh2t5s9hP38WiT+HxJ9D4mE0MzMrnIONmZkVzsFm5q5udQPahD+H/fxZJP4cEn8O+JyNmZk1gXs2ZmZWOAebGZC0UtKDkkYlXdjq9jSCpMWS7pD0gKT7JZ2fyw+TdKukh/J2YS6XpMvyZ3CvpOMqnmttrv+QpLUV5cdLui8fc5mkatcfaguSZknaKumf8v4xku7M7+l6SX25/KC8P5ofH6h4jg/l8gclnVZR3hHfH0kLJN0o6Qf5e/Gbvfh9kHRB/j/xPUnDkg7uxe9D3SLCtzpupNWk/xN4FdAHfBc4ttXtasD7OgI4Lt9/KfBD4Fjg74ALc/mFwKfz/TOAW0gXrDsRuDOXHwY8nLcL8/2F+bG7gN/Mx9wCnN7q9z3J5/FB4GvAP+X9G4Cz8v0rgXPz/T8Brsz3zwKuz/ePzd+Ng4Bj8ndmVid9f4ANwP/M9/uABb32fSBd6fcRYG7F9+Ddvfh9qPfmnk39TgBGI+LhiNgDXAesanGbZiwinoyIu/P9ncADpP9oq0g/OuTtmfn+KuDaSL4DLJB0BHAacGtEPB0RzwC3AivzY/Mj4tuR/vddW/FcbUXSIuC3gS/lfQEnAzfmKmM/h/LncyNwSq6/CrguIp6PiEeAUdJ3pyO+P5LmA28B1gNExJ6IeJYe/D6QVsmfK2k2cAjwJD32fZgJB5v6HQk8XrG/PZd1jdz1XwrcCbwyIp6EFJCAV+RqE30Ok5Vvr1Lejv4e+Avgxbz/cuDZiNiX9yvb/ov3mx/fketP9/NpN68CSsCX83DilyQdSo99HyLiR8DFwGOkILMD2ELvfR/q5mBTv2rjyl2T2idpHnAT8GcR8dPJqlYpizrK24qk3wGeiogtlcVVqsYUj3X050D6a/444IqIWAr8jDRsNpGu/BzyOalVpKGvXwYOBU6vUrXbvw91c7Cp33ZgccX+IuCJFrWloSTNIQWar0bE13Pxj/OQB3n7VC6f6HOYrHxRlfJ282bgbZK2kYY0Tib1dBbkYRQ4sO2/eL/58ZcBTzP9z6fdbAe2R8Sdef9GUvDpte/DcuCRiChFxF7g68Cb6L3vQ90cbOq3GViSs1H6SCcBN7a4TTOWx5XXAw9ExCUVD20EyhlEa4GbK8rX5CykE4EdeVhlE7BC0sL8V+EKYFN+bKekE/Nrral4rrYRER+KiEURMUD6t/1WRJwN3AG8PVcb+zmUP5+35/qRy8/K2UnHAEtIJ8Q74vsTEf8FPC7pNbnoFOD79Nj3gTR8dqKkQ3I7y59DT30fZqTVGQqdfCNl3vyQlEXy4Va3p0Hv6bdI3fd7gXvy7QzSePPtwEN5e1iuL+Dy/BncBwxWPNd7SSdAR4H3VJQPAt/Lx3yePLm4XW/ASezPRnsV6cdhFPjfwEG5/OC8P5o0psaYAAABwUlEQVQff1XF8R/O7/VBKjKtOuX7A/wGMJK/E98gZZP13PcB+BvgB7mtXyFllPXc96Hem1cQMDOzwnkYzczMCudgY2ZmhXOwMTOzwjnYmJlZ4RxszMyscA42Zi0k6ZWSvibpYUlbJH1b0u9KOknSjrxEzIOS/i2valA+7qOSfiTpnrwK8dta+T7MpjJ76ipmVoQ8OfAbwIaI+MNcdjTwNuAZ4N8j4ndy+W8A35C0OyJuz0+xLiIulvQ64N8lvSIiXhz/Smat556NWeucDOyJiCvLBRHxaER8bmzFiLgH+BhwXpXHHgD2AYcX2FazGXGwMWud1wN3T6P+3cBrxxZKeiNpZepSg9pl1nAONmZtQtLlkr4rafNEVcbsXyDpHtLS9+8MLwdibcznbMxa537g98s7EfF+SYeT1iGrZinpYnZl6yLi4gLbZ9Yw7tmYtc63gIMlnVtRdki1ipLeAPw1aZFLs47jno1Zi0RESDoTWCfpL0jnXH4G/GWu8t8kbSUFoKeAD1Rkopl1FK/6bGZmhfMwmpmZFc7BxszMCudgY2ZmhXOwMTOzwjnYmJlZ4RxszMyscA42ZmZWOAcbMzMr3P8HX8QYRUFqFyQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fed578d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between 'GDP' and 'Income/Person' which = 0.912654 (positive corr., strong)\n",
    "merged_data_country.plot(x='GDP', y='Income/Person', kind='scatter', color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 764,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fec97470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between 'GDP' and 'HDI' which = 0.689956 (weak corr.)\n",
    "merged_data_country.plot(x='GDP', y='HDI', kind='scatter', color='g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 765,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fec7ac50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot relationship between GDP' and 'Life Expectancy' which = 0.603767 (weak corr.)\n",
    "merged_data_country.plot(x='GDP', y='Life Expectancy', kind='scatter', color='c');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  \n",
    "### Are there trends that can be observed between all selected metrics with each other?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 766,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Income/Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962689</td>\n",
       "      <td>0.936365</td>\n",
       "      <td>0.997203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI</th>\n",
       "      <td>0.962689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991441</td>\n",
       "      <td>0.976903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life Expectancy</th>\n",
       "      <td>0.936365</td>\n",
       "      <td>0.991441</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.952900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income/Person</th>\n",
       "      <td>0.997203</td>\n",
       "      <td>0.976903</td>\n",
       "      <td>0.952900</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GDP       HDI  Life Expectancy  Income/Person\n",
       "GDP              1.000000  0.962689         0.936365       0.997203\n",
       "HDI              0.962689  1.000000         0.991441       0.976903\n",
       "Life Expectancy  0.936365  0.991441         1.000000       0.952900\n",
       "Income/Person    0.997203  0.976903         0.952900       1.000000"
      ]
     },
     "execution_count": 766,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The correlation between all metrics on each other according time (years)\n",
    "merged_data_year.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 767,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GDP</th>\n",
       "      <th>HDI</th>\n",
       "      <th>Life Expectancy</th>\n",
       "      <th>Income/Person</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.689956</td>\n",
       "      <td>0.603767</td>\n",
       "      <td>0.912654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDI</th>\n",
       "      <td>0.689956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.902818</td>\n",
       "      <td>0.699108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Life Expectancy</th>\n",
       "      <td>0.603767</td>\n",
       "      <td>0.902818</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.615644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income/Person</th>\n",
       "      <td>0.912654</td>\n",
       "      <td>0.699108</td>\n",
       "      <td>0.615644</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GDP       HDI  Life Expectancy  Income/Person\n",
       "GDP              1.000000  0.689956         0.603767       0.912654\n",
       "HDI              0.689956  1.000000         0.902818       0.699108\n",
       "Life Expectancy  0.603767  0.902818         1.000000       0.615644\n",
       "Income/Person    0.912654  0.699108         0.615644       1.000000"
      ]
     },
     "execution_count": 767,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The correlation between all metrics on each other according countries\n",
    "merged_data_country.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3  \n",
    "### What are the distributions of each one of the four metrics chosen according to the countries ? and according to time (years)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distributions of each one of the four metrics chosen according to the countries for all years.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 768,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f43fec2f400>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f43feb41940>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f43feb11828>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f43feac8c18>]], dtype=object)"
      ]
     },
     "execution_count": 768,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43feb972b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data_country.hist(figsize=(10, 10), color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 769,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f43fea318d0>"
      ]
     },
     "execution_count": 769,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fea04588>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data_country['Income/Person'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 770,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f43fea31128>"
      ]
     },
     "execution_count": 770,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe9bbb38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data_country['GDP'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 771,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f43febde7b8>"
      ]
     },
     "execution_count": 771,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe965710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data_country['HDI'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 772,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f43fe885c18>"
      ]
     },
     "execution_count": 772,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAD6hJREFUeJzt3X+MZWV9x/H3p/yogCCLDGTV4qIhYKNlwXGj0pDiKg1qBOovrFWK6GJasFZRV21FY5tgg13tH8UuCG5bpFoiwail4Ippogk6Cwus/CgVFwTWZVCk/qDy69s/7tk4LrPcc3fnziwP71dyc855znnu+WaS+dxnnrnnnFQVkqQnvt9a6AIkSXPDQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1Ytf5PNn+++9fS5Ysmc9TStIT3rp16+6tqolhx81roC9ZsoSpqan5PKUkPeElub3PcU65SFIjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhoxrxcWSfMlybycx2fyamfiCF1NqqqRXs/+wFdG7mOYa2djoEtSIwx0SWqEgS5JjTDQJakRvQI9yV8m+V6SDUkuTvKUJAcnuTrJrUm+kGT3cRcrSdq2oYGe5JnAu4DJqno+sAtwEvAJYFVVHQLcB5w6zkIlSY+v75TLrsAeSXYF9gQ2AS8DLun2rwFOmPvyJEl9DQ30qroLOAe4g0GQ3w+sA35aVQ93h90JPHO2/klWJJlKMjU9PT03VUuSHqPPlMsi4HjgYOAZwF7AcbMcOutVFlW1uqomq2pyYmLoI/EkSdupz5TLy4EfVNV0VT0EfAl4KbBvNwUD8Czg7jHVKEnqoU+g3wG8OMmeGdwgYzlwI3AV8LrumJOBy8ZToiSpjz5z6Fcz+OfnNcANXZ/VwAeA9yT5H+DpwGfHWKckaYhed1usqrOAs7Zqvg1YNucVSZK2i1eKSlIjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0Y+kzRJIcCX5jR9BzgI8C+wDuA6a79Q1X1tTmvUJLUy9BAr6pbgKUASXYB7gIuBU4BVlXVOWOtUJLUy6hTLsuB71fV7eMoRpK0/YaO0LdyEnDxjO3Tk7wVmALeW1X3bd0hyQpgBcBBBx20vXXqSezwj13B/Q88NPbzLFn51bG+/9P22I3rzjp2rOfQk1vvQE+yO/Aa4INd07nAx4Hqlp8E3rZ1v6paDawGmJycrB2sV09C9z/wEBvPftVCl7HDxv2BIY0y5XIccE1VbQaoqs1V9UhVPQqcBywbR4GSpH5GCfQ3MWO6JcniGftOBDbMVVGSpNH1mnJJsifwCuC0Gc1/l2QpgymXjVvtkyTNs16BXlW/BJ6+VdtbxlKRJGm7eKWoJDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjRj1IdHSvNv7eSt5wZqVC13GDtv7eQBP/GejaudloGun97ObzvYh0VIPTrlIUiMMdElqxNBAT3JokvUzXv+b5N1J9ktyZZJbu+Wi+ShYkjS7oYFeVbdU1dKqWgq8EPglcCmwElhbVYcAa7ttSdICGXXKZTnw/aq6HTgeWNO1rwFOmMvCJEmjGTXQTwIu7tYPrKpNAN3ygLksTJI0mt6BnmR34DXAv49ygiQrkkwlmZqenh61PklST6OM0I8Drqmqzd325iSLAbrlPbN1qqrVVTVZVZMTExM7Vq0kaZtGCfQ38evpFoAvAyd36ycDl81VUZKk0fUK9CR7Aq8AvjSj+WzgFUlu7fadPfflSZL66nXpf1X9Enj6Vm0/ZvCtF0nSTsArRSWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RG9H1I9L5JLklyc5KbkrwkyUeT3JVkffd65biLlSRtW6+HRAOfBi6vqtcl2R3YE/hDYFVVnTO26iRJvQ0N9CT7AEcDfwpQVQ8CDyYZb2WSpJH0mXJ5DjANXJjk2iTnJ9mr23d6kuuTXJBk0fjKlCQN0yfQdwWOBM6tqiOAXwArgXOB5wJLgU3AJ2frnGRFkqkkU9PT03NTtSTpMfoE+p3AnVV1dbd9CXBkVW2uqkeq6lHgPGDZbJ2ranVVTVbV5MTExNxULUl6jKGBXlU/An6Y5NCuaTlwY5LFMw47EdgwhvokST31/ZbLGcBF3TdcbgNOAf4hyVKggI3AaWOpUJLUS69Ar6r1wORWzW+Z+3IkSdvLK0UlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRfS/9lxbUkpVfXegSdtjT9thtoUtQ4wx07fQ2nv2qsZ9jycqvzst5pHFyykWSGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUiF6BnmTfJJckuTnJTUlekmS/JFcmubVbLhp3sZKkbes7Qv80cHlVHQYcDtwErATWVtUhwNpuW5K0QIYGepJ9gKOBzwJU1YNV9VPgeGBNd9ga4IRxFSlJGq7PCP05wDRwYZJrk5yfZC/gwKraBNAtDxhjnZKkIfoE+q7AkcC5VXUE8AtGmF5JsiLJVJKp6enp7SxTkjRMn0C/E7izqq7uti9hEPCbkywG6Jb3zNa5qlZX1WRVTU5MTMxFzZKkWQwN9Kr6EfDDJId2TcuBG4EvAyd3bScDl42lQklSL33vh34GcFGS3YHbgFMYfBh8McmpwB3A68dToiSpj16BXlXrgclZdi2f23IkSdvLK0UlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRvQK9CQbk9yQZH2Sqa7to0nu6trWJ3nleEuVJD2eXg+J7hxTVfdu1baqqs6Zy4IkSdvHKRdJakTfQC/giiTrkqyY0X56kuuTXJBk0RjqkyT11DfQj6qqI4HjgD9PcjRwLvBcYCmwCfjkbB2TrEgylWRqenp6LmqWJM2iV6BX1d3d8h7gUmBZVW2uqkeq6lHgPGDZNvqurqrJqpqcmJiYq7olSVsZGuhJ9kqy95Z14FhgQ5LFMw47EdgwnhIlSX30+ZbLgcClSbYc//mqujzJvyRZymB+fSNw2tiqlCQNNTTQq+o24PBZ2t8ylookSdvFry1KUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjejzkGiSbAR+BjwCPFxVk0n2A74ALGHwkOg3VNV94ylTkjTMKCP0Y6pqaVVNdtsrgbVVdQiwttuWJC2QHZlyOR5Y062vAU7Y8XIkSdurb6AXcEWSdUlWdG0HVtUmgG55wDgKlCT102sOHTiqqu5OcgBwZZKb+56g+wBYAXDQQQdtR4mSpD56jdCr6u5ueQ9wKbAM2JxkMUC3vGcbfVdX1WRVTU5MTMxN1ZKkxxga6En2SrL3lnXgWGAD8GXg5O6wk4HLxlWkJGm4PlMuBwKXJtly/Oer6vIk3wW+mORU4A7g9eMrU5I0zNBAr6rbgMNnaf8xsHwcRUmSRueVopLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDWi7zNFpSeU7oEso/X5xOjnqarRO0ljYqCrSQatnoyccpGkRhjoktSI3oGeZJck1yb5Srf9uSQ/SLK+ey0dX5mSpGFGmUP/C+AmYJ8Zbe+rqkvmtiRJ0vboNUJP8izgVcD54y1HkrS9+k65fAp4P/DoVu1/m+T6JKuS/PbcliZJGsXQQE/yauCeqlq31a4PAocBLwL2Az6wjf4rkkwlmZqent7ReiVJ29BnhH4U8JokG4F/A16W5F+ralMN/Aq4EFg2W+eqWl1Vk1U1OTExMWeFS5J+U0a5ACPJHwBnVtWrkyyuqk0ZXJK3Cvi/qlo5pP80cPuOFCyNyf7AvQtdhLQNz66qoSPiHblS9KIkE0CA9cA7h3XoU5C0EJJMVdXkQtch7YiRRuhSqwx0tcArRSWpEQa6NLB6oQuQdpRTLpLUCEfoktQIA13zKsnPZ2l7Z5K3duuHdTd7uzbJc3u+5zeT3DLjRnHzcn+hJO9Osud8nEvqwykXzaskP6+qpz7O/pXAHlV11gjv+U0G10dMzUGJvXUX201Wld9f107BEboWXJKPJjkzySuBdwNvT3JVt+9PknynG3n/U5JdRnjfy2aM/E9LclG3/s0kn0ry7SQbkizr2vdKckGS73Z/IRzfte+S5JwkN3T3LjojybuAZwBXzaj13O42F99L8rEZdWxM8rEk13TvcVjX/tQkF85439cmOTXJqhl935Hk73fsJ6wnCx9Bp51GVX0tyWeAn1fVOUmeB7wROKqqHkryj8CbgX+epftFSR7o1q+sqvcBK4BvJfkB8F7gxTOO36uqXprkaOAC4PnAh4FvVNXbkuwLfCfJ14G3AgcDR1TVw0n2q6qfJHkPcMyMEfqHu/ZdgLVJfq+qru/23VtVRyb5M+BM4O3AXwP3V9ULAJIsAh4Erk/y/qp6CDgFOG1Hfq568jDQtTNbDrwQ+G730Oc9gHu2ceybt55yqarNST4CXAWcWFU/mbH74u6Y/0qyTxfgxzK4b9GZ3TFPAQ4CXg58pqoe7vrMfJ+Z3pBkBYPfq8XA7wJbAv1L3XId8Efd+suBk2bUex9Akm8Ar05yE7BbVd2wjfNJv8FA184swJqq+uAOvMcLgB8zmB6Zaet/HlV3vtdW1S2/UcTg0+Rx/9mU5GAGI+8XVdV9ST7H4ANhi191y0f49e/dtt73fOBDwM0Mbnwn9eIcunZma4HXJTkAIMl+SZ7dt3M3N34ccARwZhe6W7yxO+b3GUx73A/8J3BGF+AkOaI79grgnUl23VJH1/4zYO9ufR/gF8D9SQ7szjvMFcDpM+pdBFBVVwO/A/wx3V8SUh8GuubbnknunPF6z7YOrKobgb8CrkhyPXAlg6mM2Vw042uLX8/ggSvnAW+rqrsZzKFfsCWsgfuSfBv4DHBq1/ZxYDcGc9gbum0YjJjv6NqvYxC0MLi69D+SXFVV1wHXAt9jMCf/rR4/i78BFnX/mL0OOGbGvi8C39oyDSP14dcW9aSzUF9zHEUGD2NfVVVrF7oWPXE4Qpd2Ikn2TfLfwAOGuUblCF2SGuEIXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXi/wG+UTCOj9bbGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe8c5f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data_country['Life Expectancy'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distributions of each one of the four metrics chosen according to the years for all countries.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 773,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe8fa748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe82ce48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe7e3eb8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe79cf98>]], dtype=object)"
      ]
     },
     "execution_count": 773,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe88ca58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_data_year.hist(figsize=(10, 10), color='r', bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4 \n",
    "### Have certain regions of the world been growing in the four metrics better than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 774,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe7f8048>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe614438>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe5cd390>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe5863c8>], dtype=object)"
      ]
     },
     "execution_count": 774,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe6f8080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "region.plot.bar(figsize=(10, 10),subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 775,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe55d240>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe4bd128>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe4760f0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe42e0b8>], dtype=object)"
      ]
     },
     "execution_count": 775,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe623128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "region.plot.line(figsize=(10, 10),subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 5 \n",
    "### Have certain sampled countries (for example Egypt,USA, etc ... ) of the world been growing in the four metrics better than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 776,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe418b70>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe2fe390>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe2b92e8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe24c4e0>], dtype=object)"
      ]
     },
     "execution_count": 776,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe3be2e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_sample_country.plot.bar(figsize=(10, 10),subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 777,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe190a58>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe098710>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe04f5f8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f43fe008a20>], dtype=object)"
      ]
     },
     "execution_count": 777,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43fe2cb278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_sample_country.plot.line(figsize=(10, 10),subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1-**According to years for all countries there is a strong positive correlation between GDP and all the other metrics.  But..  \n",
    "   \n",
    "**2-**After calculating the correlation matrix according to countries for all years, I can conclude that GDP and Income are still positive strongly correlated and GDP does not affect the other metrics (no correlation) that appear clearly in GDP with Life Expectancy and GDP with HDI plots.\n",
    "\n",
    "**3-**Distributions according to years show a righ skewed in GDP and Income that show the most countries are lower in GDP and income(which clearly appears in the GDP and Income boxplots as outliers). However, both HDI and Life Expectancy are almost left skewed in order to these metrics depend on health and education not economic indicators  .\n",
    "\n",
    "**4-**Europe region is growing in the four metrics over the years and Asia comes after Europe in GDP and Income but Americas come the second in HDI and Life Expectancy and Africa comes in the end of the list of regions in all metrics (as expected)\n",
    "\n",
    "**5-**According to my selected countries, UAE comes the first in GDP and Income however Australia comes first in HDI and Life Expectancy, but Egypt occupy the last one of the list in GDP and Income as shown in previous bar and line charts."
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