{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "import itertools\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(rc={'figure.figsize':(12,8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Life expectancy at birth (years)</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2000</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.786093e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2001</td>\n",
       "      <td>77.3</td>\n",
       "      <td>7.097992e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2002</td>\n",
       "      <td>77.8</td>\n",
       "      <td>6.973681e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2003</td>\n",
       "      <td>77.9</td>\n",
       "      <td>7.564346e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chile</td>\n",
       "      <td>2004</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9.921039e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  Year  Life expectancy at birth (years)           GDP\n",
       "0   Chile  2000                              77.3  7.786093e+10\n",
       "1   Chile  2001                              77.3  7.097992e+10\n",
       "2   Chile  2002                              77.8  6.973681e+10\n",
       "3   Chile  2003                              77.9  7.564346e+10\n",
       "4   Chile  2004                              78.0  9.921039e+10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96 entries, 0 to 95\n",
      "Data columns (total 4 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Country                           96 non-null     object \n",
      " 1   Year                              96 non-null     int64  \n",
      " 2   Life expectancy at birth (years)  96 non-null     float64\n",
      " 3   GDP                               96 non-null     float64\n",
      "dtypes: float64(2), int64(1), object(1)\n",
      "memory usage: 3.1+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('all_data.csv')\n",
    "display(df.head())\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 6 countries\n"
     ]
    }
   ],
   "source": [
    "print(f\"There are {len(df['Country'].unique())} countries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GDP over Time by Country')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for country in df['Country'].unique():\n",
    "#     sns.scatterplot(data=df[df['Country'] == country], x='Year', y='GDP', label=country, loc='upper left')\n",
    "sns.scatterplot(data=df, x='Year', y='GDP', hue='Country')\n",
    "plt.title('GDP over Time by Country')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot clearl shows that the GDP of the USA is much higher than that of other countries and as of 2014 is pretty consistently increasing, with only one dip at 2009 (possibly related to the Great Recession). China clearly has the fastest growing ecvonomy with a harsh increase starting in 2004.\n",
    "Due to the massive values for both China and the USA, it is difficult to see any patterns in the other Countries, we will look at closer plots later on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlogram "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700.975x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, kind=\"reg\", hue='Country')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This carrelogram shows certain trends, such as life expectancy increasing over time pretty steadily for most countries. If you want to look more into carrelograms [The Python Graph Gallery](https://www.python-graph-gallery.com/111-custom-correlogram) is a great place to look at different plots. Despite this carrelogram not being so useful for this dataset, it can come in useful when you have a lot of columns of quantitative data (I'd recommend checking out [Data sets for Quantitative Research](https://libraryguides.missouri.edu/datasets/public-use))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, sharex=True)\n",
    "axes = list(itertools.chain(*axes))\n",
    "for idx, country in enumerate(df['Country'].unique()):\n",
    "    sns.scatterplot(x='Year', y='GDP', data=df[df['Country'] == country], ax=axes[idx])\n",
    "    axes[idx].set_title(f'{country} GDP over time')\n",
    "plt.show()\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code explanation\n",
    "If you haven't seen it before `list(itertools.chain(*axes))` may seem quite confusing, but trust me, it is actually very simple! All this does is take a \"shallow list\" (i.e. `[[1, 2, 3], [2, 1, 1], [1, 1], [2]]`) and converts it into a flat list (i.e. `[1, 2, 3, 2, 1, 1, 1, 1, 2]`). The `list` conversion is necessary to convert it from an `<itertools.chain object>` to a `<list>`.\n",
    "##### What do the results show?\n",
    "Very generally, and not overall, but generally from 2000->2014 GDP has increased. With every single country having a higher GDP in 2014 in comparison to 2000. The country which most clearly follows a different trend to the rest is Zimbabwe, with a decreasing GDP from 2000 -> 2008. The increase after this point may be attributed to the drop of the Zimbabwe Dollar and the acceptance of foreign money."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 864x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 2, sharex=True)\n",
    "axes = list(itertools.chain(*axes))\n",
    "for idx, country in enumerate(df['Country'].unique()):\n",
    "    sns.scatterplot(x='Year', y='Life expectancy at birth (years)', data=df[df['Country'] == country], ax=axes[idx])\n",
    "    axes[idx].set_title(f'{country} Life expectancy over time')\n",
    "plt.show()\n",
    "plt.clf()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, a general increase can be seen with life expectancy increasing for all countries. Similarly to its GDP, Zimbabwe sees a decrease at the start of the 21st century and then a steady increase after that point. Zimbabwe still has the lowest life expectancy of all the countries in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Country', ylabel='Life expectancy at birth (years)'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(y='Life expectancy at birth (years)', x='Country', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [World Bank Data](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD)\n",
    "## A guide to cleaning data\n",
    "I now downloaded World Bank Data for the GDP of many countries, let's look at reusing our other bits of code for this dataset.\n",
    "\n",
    "The dataset begins with:\n",
    "```\n",
    "\"Data Source\",\"World Development Indicators\",\n",
    "\n",
    "\"Last Updated Date\",\"2022-06-30\",\n",
    "\n",
    "```\n",
    "So these lines must be removed.\n",
    "It also adds an extra column at the end because it has a blank `,` at the end of each line, so this column must be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</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>...</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.615084e+09</td>\n",
       "      <td>2.727933e+09</td>\n",
       "      <td>2.791061e+09</td>\n",
       "      <td>2.963128e+09</td>\n",
       "      <td>2.983799e+09</td>\n",
       "      <td>3.092179e+09</td>\n",
       "      <td>3.202235e+09</td>\n",
       "      <td>3.310056e+09</td>\n",
       "      <td>2.496648e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa Eastern and Southern</td>\n",
       "      <td>AFE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2.129059e+10</td>\n",
       "      <td>2.180847e+10</td>\n",
       "      <td>2.370702e+10</td>\n",
       "      <td>2.821004e+10</td>\n",
       "      <td>2.611879e+10</td>\n",
       "      <td>2.968217e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>9.730435e+11</td>\n",
       "      <td>9.839370e+11</td>\n",
       "      <td>1.003679e+12</td>\n",
       "      <td>9.242525e+11</td>\n",
       "      <td>8.823551e+11</td>\n",
       "      <td>1.020647e+12</td>\n",
       "      <td>9.910223e+11</td>\n",
       "      <td>9.975340e+11</td>\n",
       "      <td>9.216459e+11</td>\n",
       "      <td>1.082096e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>5.377778e+08</td>\n",
       "      <td>5.488889e+08</td>\n",
       "      <td>5.466667e+08</td>\n",
       "      <td>7.511112e+08</td>\n",
       "      <td>8.000000e+08</td>\n",
       "      <td>1.006667e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.990732e+10</td>\n",
       "      <td>2.014640e+10</td>\n",
       "      <td>2.049713e+10</td>\n",
       "      <td>1.913421e+10</td>\n",
       "      <td>1.811656e+10</td>\n",
       "      <td>1.875347e+10</td>\n",
       "      <td>1.805323e+10</td>\n",
       "      <td>1.879945e+10</td>\n",
       "      <td>2.011614e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa Western and Central</td>\n",
       "      <td>AFW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1.040414e+10</td>\n",
       "      <td>1.112789e+10</td>\n",
       "      <td>1.194319e+10</td>\n",
       "      <td>1.267633e+10</td>\n",
       "      <td>1.383837e+10</td>\n",
       "      <td>1.486223e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>7.275704e+11</td>\n",
       "      <td>8.207927e+11</td>\n",
       "      <td>8.649905e+11</td>\n",
       "      <td>7.607345e+11</td>\n",
       "      <td>6.905464e+11</td>\n",
       "      <td>6.837487e+11</td>\n",
       "      <td>7.416899e+11</td>\n",
       "      <td>7.945430e+11</td>\n",
       "      <td>7.844457e+11</td>\n",
       "      <td>8.358084e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>AGO</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.249982e+11</td>\n",
       "      <td>1.334016e+11</td>\n",
       "      <td>1.372444e+11</td>\n",
       "      <td>8.721929e+10</td>\n",
       "      <td>4.984049e+10</td>\n",
       "      <td>6.897276e+10</td>\n",
       "      <td>7.779294e+10</td>\n",
       "      <td>6.930910e+10</td>\n",
       "      <td>5.361907e+10</td>\n",
       "      <td>7.254699e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Country Name Country Code     Indicator Name  \\\n",
       "0                        Aruba          ABW  GDP (current US$)   \n",
       "1  Africa Eastern and Southern          AFE  GDP (current US$)   \n",
       "2                  Afghanistan          AFG  GDP (current US$)   \n",
       "3   Africa Western and Central          AFW  GDP (current US$)   \n",
       "4                       Angola          AGO  GDP (current US$)   \n",
       "\n",
       "   Indicator Code          1960          1961          1962          1963  \\\n",
       "0  NY.GDP.MKTP.CD           NaN           NaN           NaN           NaN   \n",
       "1  NY.GDP.MKTP.CD  2.129059e+10  2.180847e+10  2.370702e+10  2.821004e+10   \n",
       "2  NY.GDP.MKTP.CD  5.377778e+08  5.488889e+08  5.466667e+08  7.511112e+08   \n",
       "3  NY.GDP.MKTP.CD  1.040414e+10  1.112789e+10  1.194319e+10  1.267633e+10   \n",
       "4  NY.GDP.MKTP.CD           NaN           NaN           NaN           NaN   \n",
       "\n",
       "           1964          1965  ...          2012          2013          2014  \\\n",
       "0           NaN           NaN  ...  2.615084e+09  2.727933e+09  2.791061e+09   \n",
       "1  2.611879e+10  2.968217e+10  ...  9.730435e+11  9.839370e+11  1.003679e+12   \n",
       "2  8.000000e+08  1.006667e+09  ...  1.990732e+10  2.014640e+10  2.049713e+10   \n",
       "3  1.383837e+10  1.486223e+10  ...  7.275704e+11  8.207927e+11  8.649905e+11   \n",
       "4           NaN           NaN  ...  1.249982e+11  1.334016e+11  1.372444e+11   \n",
       "\n",
       "           2015          2016          2017          2018          2019  \\\n",
       "0  2.963128e+09  2.983799e+09  3.092179e+09  3.202235e+09  3.310056e+09   \n",
       "1  9.242525e+11  8.823551e+11  1.020647e+12  9.910223e+11  9.975340e+11   \n",
       "2  1.913421e+10  1.811656e+10  1.875347e+10  1.805323e+10  1.879945e+10   \n",
       "3  7.607345e+11  6.905464e+11  6.837487e+11  7.416899e+11  7.945430e+11   \n",
       "4  8.721929e+10  4.984049e+10  6.897276e+10  7.779294e+10  6.930910e+10   \n",
       "\n",
       "           2020          2021  \n",
       "0  2.496648e+09           NaN  \n",
       "1  9.216459e+11  1.082096e+12  \n",
       "2  2.011614e+10           NaN  \n",
       "3  7.844457e+11  8.358084e+11  \n",
       "4  5.361907e+10  7.254699e+10  \n",
       "\n",
       "[5 rows x 66 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from io import StringIO\n",
    "\n",
    "csv = ''.join(open('API_NY.GDP.MKTP.CD_DS2_en_csv_v2_4251000/API_NY.GDP.MKTP.CD_DS2_en_csv_v2_4251000.csv', 'r', encoding='utf-8').readlines()[4:])\n",
    "csvFile = StringIO(csv)\n",
    "df2 = pd.read_csv(csvFile).iloc[: , :-1]\n",
    "display(df2.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data needs to be cleaned up again. Can you see why? It's because years are each a seperate column (this is normally useful when training ML models) but we want them to be like a previous dataset. Where we have a column named Year and a column named GDP. How can we do this? \n",
    "\n",
    "We can use the code (adapted from [this stackoverflow answer](https://stackoverflow.com/a/60167500/5210078)) to create a dataset similar to the one found in the previous dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1986</td>\n",
       "      <td>4.055866e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1987</td>\n",
       "      <td>4.877095e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1988</td>\n",
       "      <td>5.966480e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1989</td>\n",
       "      <td>6.955307e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1990</td>\n",
       "      <td>7.648045e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13113</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.758489e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13114</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.811554e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13115</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.928429e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13116</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.805117e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13117</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2021</td>\n",
       "      <td>2.621773e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13118 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country Name Country Code     Indicator Name  Indicator Code  Year  \\\n",
       "0            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1986   \n",
       "1            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1987   \n",
       "2            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1988   \n",
       "3            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1989   \n",
       "4            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD  1990   \n",
       "...            ...          ...                ...             ...   ...   \n",
       "13113     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2017   \n",
       "13114     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2018   \n",
       "13115     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2019   \n",
       "13116     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2020   \n",
       "13117     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD  2021   \n",
       "\n",
       "                GDP  \n",
       "0      4.055866e+08  \n",
       "1      4.877095e+08  \n",
       "2      5.966480e+08  \n",
       "3      6.955307e+08  \n",
       "4      7.648045e+08  \n",
       "...             ...  \n",
       "13113  1.758489e+10  \n",
       "13114  1.811554e+10  \n",
       "13115  1.928429e+10  \n",
       "13116  1.805117e+10  \n",
       "13117  2.621773e+10  \n",
       "\n",
       "[13118 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "years = [str(year) for year in range(1960, 2022)]\n",
    "c = df2.columns.difference(years, sort=False).tolist()\n",
    "display(df2.set_index(c).stack().to_frame().reset_index().rename(columns= {\n",
    "    'level_4': 'Year',\n",
    "    0: 'GDP'\n",
    "}))\n",
    "# .index.to_frame(index=False).rename(columns={len(c): 'Year'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But, how does the above code work? What actually happens here? We will now break down each stage to show how it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "years = [str(year) for year in range(1960, 2022)] # self explanatory, creates a list of years [\"1960\"...\"2022\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n",
      "       '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
      "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
      "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
      "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
      "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
      "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
      "       '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "c = df2.columns.difference(years, sort=False).tolist()\n",
    "print(df2.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above line of code is a bit more difficult to understand, what is it doing? Let's start by looking at, what is `df.columns`? `df.columns` returns a `pd.Index` type which is a list of all the columns. It looks like so:\n",
    "\n",
    "```python\n",
    "Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',\n",
    "       '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',\n",
    "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',\n",
    "       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',\n",
    "       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
    "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',\n",
    "       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',\n",
    "       '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021'],\n",
    "      dtype='object')\n",
    "```\n",
    "\n",
    "The `dtype` property at the end tells us that the `pd.Index` is a list of strings (strings in Python are often called `objects` due to C/numpy naming conventions).\n",
    "\n",
    "Because it is a `pd.Index` type, we can use the [pd.Index.difference](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.difference.html) function, which lets us basically take away from two lists. By default it then attempts to sort the \"array\" so we will tell it not to. \n",
    "\n",
    "The following two pieces of code are virtually equivalent in function:\n",
    "###### Pandas\n",
    "```python\n",
    "import pandas as pd\n",
    "\n",
    "array1 = pd.Index([1, 2, 3, 4])\n",
    "array2 = [3, 2, 4, 5, 6]\n",
    "print(array1.difference(array2, sort=False))\n",
    "```\n",
    "###### Pure explicit python\n",
    "```python\n",
    "array1 = [1, 2, 3, 4]\n",
    "array2 = [3, 2, 4, 5, 6]\n",
    "arr = []\n",
    "for idx, element in enumerate(array1):\n",
    "    if element not in array2:\n",
    "        arr.append(element)\n",
    "print(arr)\n",
    "```\n",
    "\n",
    "\n",
    "So when we do `df2.columns.difference(years, sort=False)`, we are getting a list of column names that aren't a year. So we get the Index: `['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code']`. But we want a python list, so we convert it using the `pd.Index.tolist()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\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>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2020</th>\n",
       "      <th>2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <th>ABW</th>\n",
       "      <th>GDP (current US$)</th>\n",
       "      <th>NY.GDP.MKTP.CD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.615084e+09</td>\n",
       "      <td>2.727933e+09</td>\n",
       "      <td>2.791061e+09</td>\n",
       "      <td>2.963128e+09</td>\n",
       "      <td>2.983799e+09</td>\n",
       "      <td>3.092179e+09</td>\n",
       "      <td>3.202235e+09</td>\n",
       "      <td>3.310056e+09</td>\n",
       "      <td>2.496648e+09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa Eastern and Southern</th>\n",
       "      <th>AFE</th>\n",
       "      <th>GDP (current US$)</th>\n",
       "      <th>NY.GDP.MKTP.CD</th>\n",
       "      <td>2.129059e+10</td>\n",
       "      <td>2.180847e+10</td>\n",
       "      <td>2.370702e+10</td>\n",
       "      <td>2.821004e+10</td>\n",
       "      <td>2.611879e+10</td>\n",
       "      <td>2.968217e+10</td>\n",
       "      <td>3.223912e+10</td>\n",
       "      <td>3.351455e+10</td>\n",
       "      <td>3.652148e+10</td>\n",
       "      <td>4.182834e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>9.730435e+11</td>\n",
       "      <td>9.839370e+11</td>\n",
       "      <td>1.003679e+12</td>\n",
       "      <td>9.242525e+11</td>\n",
       "      <td>8.823551e+11</td>\n",
       "      <td>1.020647e+12</td>\n",
       "      <td>9.910223e+11</td>\n",
       "      <td>9.975340e+11</td>\n",
       "      <td>9.216459e+11</td>\n",
       "      <td>1.082096e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>AFG</th>\n",
       "      <th>GDP (current US$)</th>\n",
       "      <th>NY.GDP.MKTP.CD</th>\n",
       "      <td>5.377778e+08</td>\n",
       "      <td>5.488889e+08</td>\n",
       "      <td>5.466667e+08</td>\n",
       "      <td>7.511112e+08</td>\n",
       "      <td>8.000000e+08</td>\n",
       "      <td>1.006667e+09</td>\n",
       "      <td>1.400000e+09</td>\n",
       "      <td>1.673333e+09</td>\n",
       "      <td>1.373333e+09</td>\n",
       "      <td>1.408889e+09</td>\n",
       "      <td>...</td>\n",
       "      <td>1.990732e+10</td>\n",
       "      <td>2.014640e+10</td>\n",
       "      <td>2.049713e+10</td>\n",
       "      <td>1.913421e+10</td>\n",
       "      <td>1.811656e+10</td>\n",
       "      <td>1.875347e+10</td>\n",
       "      <td>1.805323e+10</td>\n",
       "      <td>1.879945e+10</td>\n",
       "      <td>2.011614e+10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa Western and Central</th>\n",
       "      <th>AFW</th>\n",
       "      <th>GDP (current US$)</th>\n",
       "      <th>NY.GDP.MKTP.CD</th>\n",
       "      <td>1.040414e+10</td>\n",
       "      <td>1.112789e+10</td>\n",
       "      <td>1.194319e+10</td>\n",
       "      <td>1.267633e+10</td>\n",
       "      <td>1.383837e+10</td>\n",
       "      <td>1.486223e+10</td>\n",
       "      <td>1.583259e+10</td>\n",
       "      <td>1.442604e+10</td>\n",
       "      <td>1.488035e+10</td>\n",
       "      <td>1.688209e+10</td>\n",
       "      <td>...</td>\n",
       "      <td>7.275704e+11</td>\n",
       "      <td>8.207927e+11</td>\n",
       "      <td>8.649905e+11</td>\n",
       "      <td>7.607345e+11</td>\n",
       "      <td>6.905464e+11</td>\n",
       "      <td>6.837487e+11</td>\n",
       "      <td>7.416899e+11</td>\n",
       "      <td>7.945430e+11</td>\n",
       "      <td>7.844457e+11</td>\n",
       "      <td>8.358084e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <th>AGO</th>\n",
       "      <th>GDP (current US$)</th>\n",
       "      <th>NY.GDP.MKTP.CD</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.249982e+11</td>\n",
       "      <td>1.334016e+11</td>\n",
       "      <td>1.372444e+11</td>\n",
       "      <td>8.721929e+10</td>\n",
       "      <td>4.984049e+10</td>\n",
       "      <td>6.897276e+10</td>\n",
       "      <td>7.779294e+10</td>\n",
       "      <td>6.930910e+10</td>\n",
       "      <td>5.361907e+10</td>\n",
       "      <td>7.254699e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                   1960  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  2.129059e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  5.377778e+08   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.040414e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1961  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  2.180847e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  5.488889e+08   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.112789e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1962  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  2.370702e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  5.466667e+08   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.194319e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1963  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  2.821004e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  7.511112e+08   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.267633e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1964  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  2.611879e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  8.000000e+08   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.383837e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1965  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  2.968217e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.006667e+09   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.486223e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1966  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  3.223912e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.400000e+09   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.583259e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1967  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  3.351455e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.673333e+09   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.442604e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1968  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  3.652148e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.373333e+09   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.488035e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                                   1969  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  4.182834e+10   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.408889e+09   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  1.688209e+10   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD           NaN   \n",
       "\n",
       "                                                                           ...  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code  ...   \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  ...   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  ...   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  ...   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  ...   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  ...   \n",
       "\n",
       "                                                                                   2012  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  2.615084e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  9.730435e+11   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.990732e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  7.275704e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  1.249982e+11   \n",
       "\n",
       "                                                                                   2013  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  2.727933e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  9.839370e+11   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  2.014640e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  8.207927e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  1.334016e+11   \n",
       "\n",
       "                                                                                   2014  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  2.791061e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  1.003679e+12   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  2.049713e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  8.649905e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  1.372444e+11   \n",
       "\n",
       "                                                                                   2015  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  2.963128e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  9.242525e+11   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.913421e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  7.607345e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  8.721929e+10   \n",
       "\n",
       "                                                                                   2016  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  2.983799e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  8.823551e+11   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.811656e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  6.905464e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  4.984049e+10   \n",
       "\n",
       "                                                                                   2017  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  3.092179e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  1.020647e+12   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.875347e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  6.837487e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  6.897276e+10   \n",
       "\n",
       "                                                                                   2018  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  3.202235e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  9.910223e+11   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.805323e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  7.416899e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  7.779294e+10   \n",
       "\n",
       "                                                                                   2019  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  3.310056e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  9.975340e+11   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  1.879945e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  7.945430e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  6.930910e+10   \n",
       "\n",
       "                                                                                   2020  \\\n",
       "Country Name                Country Code Indicator Name    Indicator Code                 \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD  2.496648e+09   \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  9.216459e+11   \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD  2.011614e+10   \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  7.844457e+11   \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  5.361907e+10   \n",
       "\n",
       "                                                                                   2021  \n",
       "Country Name                Country Code Indicator Name    Indicator Code                \n",
       "Aruba                       ABW          GDP (current US$) NY.GDP.MKTP.CD           NaN  \n",
       "Africa Eastern and Southern AFE          GDP (current US$) NY.GDP.MKTP.CD  1.082096e+12  \n",
       "Afghanistan                 AFG          GDP (current US$) NY.GDP.MKTP.CD           NaN  \n",
       "Africa Western and Central  AFW          GDP (current US$) NY.GDP.MKTP.CD  8.358084e+11  \n",
       "Angola                      AGO          GDP (current US$) NY.GDP.MKTP.CD  7.254699e+10  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df2.set_index(c).head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `df2.set_index(c)` creates a new DataFrame where the old index of `1, 2, 3, 4, 5......` is replaced by the columns `['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code']`. This is called a [pd.MultiIndex](https://pandas.pydata.org/docs/user_guide/advanced.html) because there are multiple columns being used as an index. If you run `print(df2.set_index(c).index[0])` you will see that the index for the first row is `('Aruba', 'ABW', 'GDP (current US$)', 'NY.GDP.MKTP.CD')` This tells us that the first row has a Country Name of Aruba, Country Code of ABW, Indicator Name of \"GDP (current US$)\" and an Indicator Code of `NY.GDP.MKTP.CD`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country Name  Country Code  Indicator Name     Indicator Code      \n",
       "Aruba         ABW           GDP (current US$)  NY.GDP.MKTP.CD  1986    4.055866e+08\n",
       "                                                               1987    4.877095e+08\n",
       "                                                               1988    5.966480e+08\n",
       "                                                               1989    6.955307e+08\n",
       "                                                               1990    7.648045e+08\n",
       "                                                               1991    8.720670e+08\n",
       "                                                               1992    9.586592e+08\n",
       "                                                               1993    1.083240e+09\n",
       "                                                               1994    1.245810e+09\n",
       "                                                               1995    1.320670e+09\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df2.set_index(c).stack().head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then use [stack](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.stack.html) to extend the aforementioned `pd.MultiIndex`. The function converts the current columns into a new Index. You should see from the above that the first row now has some more subrows (it now has more dimensions), so the country Aruba also has the rows `[\"1960\"...\"2022\"]`. It also converts the dataframe to a `pd.Series` so we will now convert it back to a dataframe: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Aruba</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">ABW</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">GDP (current US$)</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">NY.GDP.MKTP.CD</th>\n",
       "      <th>1986</th>\n",
       "      <td>4.055866e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>4.877095e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>5.966480e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>6.955307e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>7.648045e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Zimbabwe</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">ZWE</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">GDP (current US$)</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">NY.GDP.MKTP.CD</th>\n",
       "      <th>2017</th>\n",
       "      <td>1.758489e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.811554e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.928429e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.805117e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2.621773e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13118 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                            0\n",
       "Country Name Country Code Indicator Name    Indicator Code                   \n",
       "Aruba        ABW          GDP (current US$) NY.GDP.MKTP.CD 1986  4.055866e+08\n",
       "                                                           1987  4.877095e+08\n",
       "                                                           1988  5.966480e+08\n",
       "                                                           1989  6.955307e+08\n",
       "                                                           1990  7.648045e+08\n",
       "...                                                                       ...\n",
       "Zimbabwe     ZWE          GDP (current US$) NY.GDP.MKTP.CD 2017  1.758489e+10\n",
       "                                                           2018  1.811554e+10\n",
       "                                                           2019  1.928429e+10\n",
       "                                                           2020  1.805117e+10\n",
       "                                                           2021  2.621773e+10\n",
       "\n",
       "[13118 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df2.set_index(c).stack().to_frame())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
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       "      <th>0</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1986</td>\n",
       "      <td>4.055866e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1987</td>\n",
       "      <td>4.877095e+08</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1988</td>\n",
       "      <td>5.966480e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1989</td>\n",
       "      <td>6.955307e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>ABW</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>1990</td>\n",
       "      <td>7.648045e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13113</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2017</td>\n",
       "      <td>1.758489e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13114</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.811554e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13115</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2019</td>\n",
       "      <td>1.928429e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13116</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.805117e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13117</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>GDP (current US$)</td>\n",
       "      <td>NY.GDP.MKTP.CD</td>\n",
       "      <td>2021</td>\n",
       "      <td>2.621773e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13118 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country Name Country Code     Indicator Name  Indicator Code level_4  \\\n",
       "0            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD    1986   \n",
       "1            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD    1987   \n",
       "2            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD    1988   \n",
       "3            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD    1989   \n",
       "4            Aruba          ABW  GDP (current US$)  NY.GDP.MKTP.CD    1990   \n",
       "...            ...          ...                ...             ...     ...   \n",
       "13113     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD    2017   \n",
       "13114     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD    2018   \n",
       "13115     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD    2019   \n",
       "13116     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD    2020   \n",
       "13117     Zimbabwe          ZWE  GDP (current US$)  NY.GDP.MKTP.CD    2021   \n",
       "\n",
       "                  0  \n",
       "0      4.055866e+08  \n",
       "1      4.877095e+08  \n",
       "2      5.966480e+08  \n",
       "3      6.955307e+08  \n",
       "4      7.648045e+08  \n",
       "...             ...  \n",
       "13113  1.758489e+10  \n",
       "13114  1.811554e+10  \n",
       "13115  1.928429e+10  \n",
       "13116  1.805117e+10  \n",
       "13117  2.621773e+10  \n",
       "\n",
       "[13118 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df2.set_index(c).stack().to_frame().reset_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resetting the index converts all the MultiIndex back into columns. We now have a DataFrame like the previous dataset. We can then just rename the last two columns to make the names more clear.\n",
    "\n",
    "What can you do now? I hand it on to you to now analyse this cleaned data! Perhaps try and change the previous lines of codes to work on this huge new dataset! Which countries are similar to Zimbabwe?"
   ]
  }
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