exercises_2024-06-06.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "title: 'Walkthroughs and Exercises for *Data Analysis in Python*'\n",
    "author: \"Dr. Chester Ismay\"\n",
    "format: html\n",
    "engine: knitr\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Display all columns\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intro: Foundations of Data Analysis with Python\n",
    "\n",
    "## Walkthrough #1: Setting Up the Python Environment\n",
    "\n",
    "If you haven't already installed Python, Jupyter, and the necessary packages, there are instructions on the course repo in the README to do so [here](https://github.com/ismayc/oreilly-data-analysis-with-python/blob/main/README.md). \n",
    "\n",
    "If you aren't able to do this on your machine, you may want to check out [Google Colab](https://colab.research.google.com/). It's a free service that allows you to run Jupyter notebooks in the cloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.32.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# For plotly to load directly in Jupyter notebook\n",
    "import plotly.offline as pyo\n",
    "pyo.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #1: Setting Up the Python Environment\n",
    "\n",
    "By completing this exercise, you will be able to  \n",
    "- Import necessary Python packages  \n",
    "- Check for successful package loading  \n",
    "\n",
    "\n",
    "Follow the instructions above in Walkthrough #1 to check for correct installation \n",
    "of necessary packages. We'll wait a few minutes to make sure as many of you are\n",
    "set up as possible. Please give a thumbs up in the pulse check if you are ready\n",
    "to move on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Module 1: Data Wrangling with Pandas\n",
    "\n",
    "## Walkthrough #2: Loading and Inspecting Data with Pandas\n",
    "\n",
    "### Import data from a CSV or from an Excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\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>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2010</td>\n",
       "      <td>24087.950</td>\n",
       "      <td>13.255</td>\n",
       "      <td>2.078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.768</td>\n",
       "      <td>32.285</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>975.851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code      country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "0    ABW        Aruba  2010      24087.950         13.255           2.078   \n",
       "1    ABW        Aruba  2015      27126.620         21.411           0.475   \n",
       "2    ABW        Aruba  2020      21832.920         -7.521          -1.338   \n",
       "3    AFG  Afghanistan  2010        631.490         59.699           2.179   \n",
       "4    AFG  Afghanistan  2015        711.337         22.223          -0.662   \n",
       "..   ...          ...   ...            ...            ...             ...   \n",
       "556  ZMB       Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB       Zambia  2020        981.311         36.030          16.350   \n",
       "558  ZWE     Zimbabwe  2010        975.851            NaN           3.045   \n",
       "559  ZWE     Zimbabwe  2015       1425.010            NaN          -2.410   \n",
       "560  ZWE     Zimbabwe  2020       1385.040            NaN         557.210   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "0                 NaN             10.600      NaN      NaN   \n",
       "1                 NaN              7.298      NaN      NaN   \n",
       "2                 NaN             13.997      NaN      NaN   \n",
       "3              30.269                NaN    9.768   32.285   \n",
       "4              18.427                NaN  -11.585   15.309   \n",
       "..                ...                ...      ...      ...   \n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "558               NaN                NaN      NaN      NaN   \n",
       "559               NaN                NaN      NaN      NaN   \n",
       "560               NaN                NaN      NaN      NaN   \n",
       "\n",
       "            income_group  \n",
       "0            High income  \n",
       "1            High income  \n",
       "2            High income  \n",
       "3             Low income  \n",
       "4             Low income  \n",
       "..                   ...  \n",
       "556  Lower middle income  \n",
       "557  Lower middle income  \n",
       "558  Lower middle income  \n",
       "559  Lower middle income  \n",
       "560  Lower middle income  \n",
       "\n",
       "[561 rows x 11 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data from a CSV file\n",
    "economies = pd.read_csv(\"economies.csv\")\n",
    "\n",
    "# Or load the data from an Excel file\n",
    "economies = pd.read_excel(\"economies.xlsx\")\n",
    "economies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform an initial exploration of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>TKM</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>4439.200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.447</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.613</td>\n",
       "      <td>1.391</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>COG</td>\n",
       "      <td>Congo</td>\n",
       "      <td>2020</td>\n",
       "      <td>2185.580</td>\n",
       "      <td>17.652</td>\n",
       "      <td>2.386</td>\n",
       "      <td>21.423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.785</td>\n",
       "      <td>-12.600</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>MOZ</td>\n",
       "      <td>Mozambique</td>\n",
       "      <td>2015</td>\n",
       "      <td>570.129</td>\n",
       "      <td>3.835</td>\n",
       "      <td>3.551</td>\n",
       "      <td>41.249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.988</td>\n",
       "      <td>1.352</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>NGA</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2010</td>\n",
       "      <td>2328.430</td>\n",
       "      <td>20.843</td>\n",
       "      <td>13.742</td>\n",
       "      <td>17.291</td>\n",
       "      <td>5.092</td>\n",
       "      <td>10.732</td>\n",
       "      <td>22.920</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>TUR</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>2020</td>\n",
       "      <td>8548.180</td>\n",
       "      <td>26.665</td>\n",
       "      <td>12.279</td>\n",
       "      <td>31.483</td>\n",
       "      <td>13.110</td>\n",
       "      <td>-17.990</td>\n",
       "      <td>5.381</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>ETH</td>\n",
       "      <td>Ethiopia</td>\n",
       "      <td>2015</td>\n",
       "      <td>702.767</td>\n",
       "      <td>31.409</td>\n",
       "      <td>9.569</td>\n",
       "      <td>39.417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.229</td>\n",
       "      <td>26.602</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>IRQ</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>2015</td>\n",
       "      <td>5044.620</td>\n",
       "      <td>18.568</td>\n",
       "      <td>1.393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>BRN</td>\n",
       "      <td>Brunei</td>\n",
       "      <td>2020</td>\n",
       "      <td>26089.280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.937</td>\n",
       "      <td>40.553</td>\n",
       "      <td>6.822</td>\n",
       "      <td>7.525</td>\n",
       "      <td>-2.055</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2020</td>\n",
       "      <td>31982.230</td>\n",
       "      <td>28.223</td>\n",
       "      <td>-2.074</td>\n",
       "      <td>25.126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-13.652</td>\n",
       "      <td>-18.151</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2015</td>\n",
       "      <td>16692.650</td>\n",
       "      <td>17.223</td>\n",
       "      <td>8.666</td>\n",
       "      <td>17.477</td>\n",
       "      <td>7.525</td>\n",
       "      <td>-7.564</td>\n",
       "      <td>-8.145</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code               country  year  gdp_percapita  gross_savings  \\\n",
       "498  TKM          Turkmenistan  2010       4439.200            NaN   \n",
       "110  COG                 Congo  2020       2185.580         17.652   \n",
       "352  MOZ            Mozambique  2015        570.129          3.835   \n",
       "372  NGA               Nigeria  2010       2328.430         20.843   \n",
       "512  TUR                Turkey  2020       8548.180         26.665   \n",
       "163  ETH              Ethiopia  2015        702.767         31.409   \n",
       "241  IRQ                  Iraq  2015       5044.620         18.568   \n",
       "77   BRN                Brunei  2020      26089.280            NaN   \n",
       "14   ARE  United Arab Emirates  2020      31982.230         28.223   \n",
       "526  URY               Uruguay  2015      16692.650         17.223   \n",
       "\n",
       "     inflation_rate  total_investment  unemployment_rate  exports  imports  \\\n",
       "498           4.447               NaN                NaN   23.613    1.391   \n",
       "110           2.386            21.423                NaN  -10.785  -12.600   \n",
       "352           3.551            41.249                NaN   -1.988    1.352   \n",
       "372          13.742            17.291              5.092   10.732   22.920   \n",
       "512          12.279            31.483             13.110  -17.990    5.381   \n",
       "163           9.569            39.417                NaN    2.229   26.602   \n",
       "241           1.393               NaN                NaN      NaN      NaN   \n",
       "77            1.937            40.553              6.822    7.525   -2.055   \n",
       "14           -2.074            25.126                NaN  -13.652  -18.151   \n",
       "526           8.666            17.477              7.525   -7.564   -8.145   \n",
       "\n",
       "            income_group  \n",
       "498  Upper middle income  \n",
       "110  Lower middle income  \n",
       "352           Low income  \n",
       "372  Lower middle income  \n",
       "512  Upper middle income  \n",
       "163           Low income  \n",
       "241  Upper middle income  \n",
       "77           High income  \n",
       "14           High income  \n",
       "526          High income  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "economies.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2010</td>\n",
       "      <td>24087.950</td>\n",
       "      <td>13.255</td>\n",
       "      <td>2.078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.768</td>\n",
       "      <td>32.285</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  code      country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "0  ABW        Aruba  2010      24087.950         13.255           2.078   \n",
       "1  ABW        Aruba  2015      27126.620         21.411           0.475   \n",
       "2  ABW        Aruba  2020      21832.920         -7.521          -1.338   \n",
       "3  AFG  Afghanistan  2010        631.490         59.699           2.179   \n",
       "4  AFG  Afghanistan  2015        711.337         22.223          -0.662   \n",
       "\n",
       "   total_investment  unemployment_rate  exports  imports income_group  \n",
       "0               NaN             10.600      NaN      NaN  High income  \n",
       "1               NaN              7.298      NaN      NaN  High income  \n",
       "2               NaN             13.997      NaN      NaN  High income  \n",
       "3            30.269                NaN    9.768   32.285   Low income  \n",
       "4            18.427                NaN  -11.585   15.309   Low income  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of the DataFrame\n",
    "economies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 561 entries, 0 to 560\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   code               561 non-null    object \n",
      " 1   country            561 non-null    object \n",
      " 2   year               561 non-null    int64  \n",
      " 3   gdp_percapita      558 non-null    float64\n",
      " 4   gross_savings      490 non-null    float64\n",
      " 5   inflation_rate     555 non-null    float64\n",
      " 6   total_investment   490 non-null    float64\n",
      " 7   unemployment_rate  312 non-null    float64\n",
      " 8   exports            509 non-null    float64\n",
      " 9   imports            506 non-null    float64\n",
      " 10  income_group       561 non-null    object \n",
      "dtypes: float64(7), int64(1), object(3)\n",
      "memory usage: 48.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Display the information about the DataFrame\n",
    "economies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>561.000000</td>\n",
       "      <td>558.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>555.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>509.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>13447.838281</td>\n",
       "      <td>20.641665</td>\n",
       "      <td>9.762438</td>\n",
       "      <td>25.348976</td>\n",
       "      <td>8.894619</td>\n",
       "      <td>-0.844275</td>\n",
       "      <td>0.813121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.086126</td>\n",
       "      <td>18481.107981</td>\n",
       "      <td>10.813159</td>\n",
       "      <td>103.013164</td>\n",
       "      <td>23.546022</td>\n",
       "      <td>5.605188</td>\n",
       "      <td>17.817279</td>\n",
       "      <td>15.644724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>231.549000</td>\n",
       "      <td>-10.331000</td>\n",
       "      <td>-3.900000</td>\n",
       "      <td>0.521000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>-80.939000</td>\n",
       "      <td>-59.381000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1842.815000</td>\n",
       "      <td>14.129000</td>\n",
       "      <td>0.731000</td>\n",
       "      <td>18.449250</td>\n",
       "      <td>5.252250</td>\n",
       "      <td>-8.528000</td>\n",
       "      <td>-8.253000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>5049.830000</td>\n",
       "      <td>20.536000</td>\n",
       "      <td>2.507000</td>\n",
       "      <td>22.808000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.334000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>16509.697500</td>\n",
       "      <td>26.819750</td>\n",
       "      <td>5.406000</td>\n",
       "      <td>27.644750</td>\n",
       "      <td>10.772000</td>\n",
       "      <td>8.033000</td>\n",
       "      <td>9.348000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>116921.110000</td>\n",
       "      <td>59.699000</td>\n",
       "      <td>2355.150000</td>\n",
       "      <td>363.411000</td>\n",
       "      <td>32.050000</td>\n",
       "      <td>159.103000</td>\n",
       "      <td>84.555000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "count   561.000000     558.000000     490.000000      555.000000   \n",
       "mean   2015.000000   13447.838281      20.641665        9.762438   \n",
       "std       4.086126   18481.107981      10.813159      103.013164   \n",
       "min    2010.000000     231.549000     -10.331000       -3.900000   \n",
       "25%    2010.000000    1842.815000      14.129000        0.731000   \n",
       "50%    2015.000000    5049.830000      20.536000        2.507000   \n",
       "75%    2020.000000   16509.697500      26.819750        5.406000   \n",
       "max    2020.000000  116921.110000      59.699000     2355.150000   \n",
       "\n",
       "       total_investment  unemployment_rate     exports     imports  \n",
       "count        490.000000         312.000000  509.000000  506.000000  \n",
       "mean          25.348976           8.894619   -0.844275    0.813121  \n",
       "std           23.546022           5.605188   17.817279   15.644724  \n",
       "min            0.521000           0.900000  -80.939000  -59.381000  \n",
       "25%           18.449250           5.252250   -8.528000   -8.253000  \n",
       "50%           22.808000           7.400000    1.000000    1.334000  \n",
       "75%           27.644750          10.772000    8.033000    9.348000  \n",
       "max          363.411000          32.050000  159.103000   84.555000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display summary statistics of the DataFrame\n",
    "economies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code                   0\n",
       "country                0\n",
       "year                   0\n",
       "gdp_percapita          3\n",
       "gross_savings         71\n",
       "inflation_rate         6\n",
       "total_investment      71\n",
       "unemployment_rate    249\n",
       "exports               52\n",
       "imports               55\n",
       "income_group           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data\n",
    "economies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "??economies.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "code                  object\n",
       "country               object\n",
       "year                   int64\n",
       "gdp_percapita        float64\n",
       "gross_savings        float64\n",
       "inflation_rate       float64\n",
       "total_investment     float64\n",
       "unemployment_rate    float64\n",
       "exports              float64\n",
       "imports              float64\n",
       "income_group          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check data types\n",
    "economies.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #2: Loading and Inspecting Data with Pandas\n",
    "\n",
    "By completing this exercise, you will be able to use `pandas` to  \n",
    "- Import data from a CSV or from an Excel file   \n",
    "- Perform an initial exploration of the data  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the populations data from an Excel file\n",
    "\n",
    "\n",
    "# Inspection methods for populations DataFrame\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Checking for missing data and data types for populations DataFrame\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Walkthrough #3: Cleaning and Preparing Data with Pandas\n",
    "\n",
    "### Handle missing data\n",
    "\n",
    "#### Remove rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2010</td>\n",
       "      <td>4097.83</td>\n",
       "      <td>20.023</td>\n",
       "      <td>3.615</td>\n",
       "      <td>31.318</td>\n",
       "      <td>14.000</td>\n",
       "      <td>10.473</td>\n",
       "      <td>-9.316</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2015</td>\n",
       "      <td>3953.61</td>\n",
       "      <td>15.804</td>\n",
       "      <td>1.868</td>\n",
       "      <td>26.237</td>\n",
       "      <td>17.100</td>\n",
       "      <td>5.272</td>\n",
       "      <td>0.076</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020</td>\n",
       "      <td>5286.68</td>\n",
       "      <td>13.255</td>\n",
       "      <td>1.603</td>\n",
       "      <td>22.845</td>\n",
       "      <td>12.500</td>\n",
       "      <td>-28.951</td>\n",
       "      <td>-21.446</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2010</td>\n",
       "      <td>10412.97</td>\n",
       "      <td>17.323</td>\n",
       "      <td>10.461</td>\n",
       "      <td>17.706</td>\n",
       "      <td>7.750</td>\n",
       "      <td>13.701</td>\n",
       "      <td>39.414</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>2020</td>\n",
       "      <td>8554.64</td>\n",
       "      <td>17.798</td>\n",
       "      <td>42.015</td>\n",
       "      <td>16.845</td>\n",
       "      <td>11.364</td>\n",
       "      <td>-13.124</td>\n",
       "      <td>-10.722</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>VNM</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2015</td>\n",
       "      <td>2582.39</td>\n",
       "      <td>26.444</td>\n",
       "      <td>0.631</td>\n",
       "      <td>27.339</td>\n",
       "      <td>2.330</td>\n",
       "      <td>9.713</td>\n",
       "      <td>15.426</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>VNM</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>2020</td>\n",
       "      <td>3498.98</td>\n",
       "      <td>28.603</td>\n",
       "      <td>3.222</td>\n",
       "      <td>26.444</td>\n",
       "      <td>3.300</td>\n",
       "      <td>2.822</td>\n",
       "      <td>2.948</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2010</td>\n",
       "      <td>7311.74</td>\n",
       "      <td>18.012</td>\n",
       "      <td>4.264</td>\n",
       "      <td>19.513</td>\n",
       "      <td>24.875</td>\n",
       "      <td>7.718</td>\n",
       "      <td>10.794</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2015</td>\n",
       "      <td>5731.73</td>\n",
       "      <td>16.300</td>\n",
       "      <td>4.575</td>\n",
       "      <td>20.918</td>\n",
       "      <td>25.350</td>\n",
       "      <td>2.925</td>\n",
       "      <td>5.443</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2020</td>\n",
       "      <td>5067.15</td>\n",
       "      <td>14.602</td>\n",
       "      <td>3.268</td>\n",
       "      <td>12.426</td>\n",
       "      <td>29.175</td>\n",
       "      <td>-10.280</td>\n",
       "      <td>-16.615</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>282 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code       country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "9    ALB       Albania  2010        4097.83         20.023           3.615   \n",
       "10   ALB       Albania  2015        3953.61         15.804           1.868   \n",
       "11   ALB       Albania  2020        5286.68         13.255           1.603   \n",
       "15   ARG     Argentina  2010       10412.97         17.323          10.461   \n",
       "17   ARG     Argentina  2020        8554.64         17.798          42.015   \n",
       "..   ...           ...   ...            ...            ...             ...   \n",
       "541  VNM       Vietnam  2015        2582.39         26.444           0.631   \n",
       "542  VNM       Vietnam  2020        3498.98         28.603           3.222   \n",
       "552  ZAF  South Africa  2010        7311.74         18.012           4.264   \n",
       "553  ZAF  South Africa  2015        5731.73         16.300           4.575   \n",
       "554  ZAF  South Africa  2020        5067.15         14.602           3.268   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "9              31.318             14.000   10.473   -9.316   \n",
       "10             26.237             17.100    5.272    0.076   \n",
       "11             22.845             12.500  -28.951  -21.446   \n",
       "15             17.706              7.750   13.701   39.414   \n",
       "17             16.845             11.364  -13.124  -10.722   \n",
       "..                ...                ...      ...      ...   \n",
       "541            27.339              2.330    9.713   15.426   \n",
       "542            26.444              3.300    2.822    2.948   \n",
       "552            19.513             24.875    7.718   10.794   \n",
       "553            20.918             25.350    2.925    5.443   \n",
       "554            12.426             29.175  -10.280  -16.615   \n",
       "\n",
       "            income_group  \n",
       "9    Upper middle income  \n",
       "10   Upper middle income  \n",
       "11   Upper middle income  \n",
       "15   Upper middle income  \n",
       "17   Upper middle income  \n",
       "..                   ...  \n",
       "541  Lower middle income  \n",
       "542  Lower middle income  \n",
       "552  Upper middle income  \n",
       "553  Upper middle income  \n",
       "554  Upper middle income  \n",
       "\n",
       "[282 rows x 11 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows with any missing values\n",
    "economies_cleaned_any = economies.dropna(how='any')\n",
    "economies_cleaned_any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2010</td>\n",
       "      <td>24087.950</td>\n",
       "      <td>13.255</td>\n",
       "      <td>2.078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.768</td>\n",
       "      <td>32.285</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>975.851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code      country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "0    ABW        Aruba  2010      24087.950         13.255           2.078   \n",
       "1    ABW        Aruba  2015      27126.620         21.411           0.475   \n",
       "2    ABW        Aruba  2020      21832.920         -7.521          -1.338   \n",
       "3    AFG  Afghanistan  2010        631.490         59.699           2.179   \n",
       "4    AFG  Afghanistan  2015        711.337         22.223          -0.662   \n",
       "..   ...          ...   ...            ...            ...             ...   \n",
       "556  ZMB       Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB       Zambia  2020        981.311         36.030          16.350   \n",
       "558  ZWE     Zimbabwe  2010        975.851            NaN           3.045   \n",
       "559  ZWE     Zimbabwe  2015       1425.010            NaN          -2.410   \n",
       "560  ZWE     Zimbabwe  2020       1385.040            NaN         557.210   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "0                 NaN             10.600      NaN      NaN   \n",
       "1                 NaN              7.298      NaN      NaN   \n",
       "2                 NaN             13.997      NaN      NaN   \n",
       "3              30.269                NaN    9.768   32.285   \n",
       "4              18.427                NaN  -11.585   15.309   \n",
       "..                ...                ...      ...      ...   \n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "558               NaN                NaN      NaN      NaN   \n",
       "559               NaN                NaN      NaN      NaN   \n",
       "560               NaN                NaN      NaN      NaN   \n",
       "\n",
       "            income_group  \n",
       "0            High income  \n",
       "1            High income  \n",
       "2            High income  \n",
       "3             Low income  \n",
       "4             Low income  \n",
       "..                   ...  \n",
       "556  Lower middle income  \n",
       "557  Lower middle income  \n",
       "558  Lower middle income  \n",
       "559  Lower middle income  \n",
       "560  Lower middle income  \n",
       "\n",
       "[561 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows only if all values are missing\n",
    "economies_cleaned_all = economies.dropna(how='all')\n",
    "economies_cleaned_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
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       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
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       "      <td>Low income</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
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       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020</td>\n",
       "      <td>580.817</td>\n",
       "      <td>27.132</td>\n",
       "      <td>5.607</td>\n",
       "      <td>16.420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.424</td>\n",
       "      <td>2.892</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2010</td>\n",
       "      <td>3641.440</td>\n",
       "      <td>34.833</td>\n",
       "      <td>14.480</td>\n",
       "      <td>28.197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.266</td>\n",
       "      <td>-21.656</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2015</td>\n",
       "      <td>4354.920</td>\n",
       "      <td>28.491</td>\n",
       "      <td>9.159</td>\n",
       "      <td>34.202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-19.515</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2015</td>\n",
       "      <td>5731.730</td>\n",
       "      <td>16.300</td>\n",
       "      <td>4.575</td>\n",
       "      <td>20.918</td>\n",
       "      <td>25.350</td>\n",
       "      <td>2.925</td>\n",
       "      <td>5.443</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2020</td>\n",
       "      <td>5067.150</td>\n",
       "      <td>14.602</td>\n",
       "      <td>3.268</td>\n",
       "      <td>12.426</td>\n",
       "      <td>29.175</td>\n",
       "      <td>-10.280</td>\n",
       "      <td>-16.615</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2010</td>\n",
       "      <td>1456.050</td>\n",
       "      <td>37.405</td>\n",
       "      <td>8.500</td>\n",
       "      <td>29.878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.476</td>\n",
       "      <td>32.492</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code       country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "3    AFG   Afghanistan  2010        631.490         59.699           2.179   \n",
       "4    AFG   Afghanistan  2015        711.337         22.223          -0.662   \n",
       "5    AFG   Afghanistan  2020        580.817         27.132           5.607   \n",
       "6    AGO        Angola  2010       3641.440         34.833          14.480   \n",
       "7    AGO        Angola  2015       4354.920         28.491           9.159   \n",
       "..   ...           ...   ...            ...            ...             ...   \n",
       "553  ZAF  South Africa  2015       5731.730         16.300           4.575   \n",
       "554  ZAF  South Africa  2020       5067.150         14.602           3.268   \n",
       "555  ZMB        Zambia  2010       1456.050         37.405           8.500   \n",
       "556  ZMB        Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB        Zambia  2020        981.311         36.030          16.350   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "3              30.269                NaN    9.768   32.285   \n",
       "4              18.427                NaN  -11.585   15.309   \n",
       "5              16.420                NaN  -10.424    2.892   \n",
       "6              28.197                NaN   -3.266  -21.656   \n",
       "7              34.202                NaN    6.721  -19.515   \n",
       "..                ...                ...      ...      ...   \n",
       "553            20.918             25.350    2.925    5.443   \n",
       "554            12.426             29.175  -10.280  -16.615   \n",
       "555            29.878                NaN   19.476   32.492   \n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "\n",
       "            income_group  \n",
       "3             Low income  \n",
       "4             Low income  \n",
       "5             Low income  \n",
       "6    Lower middle income  \n",
       "7    Lower middle income  \n",
       "..                   ...  \n",
       "553  Upper middle income  \n",
       "554  Upper middle income  \n",
       "555  Lower middle income  \n",
       "556  Lower middle income  \n",
       "557  Lower middle income  \n",
       "\n",
       "[506 rows x 11 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows with missing values in specific columns\n",
    "economies_cleaned_subset = economies.dropna(subset=['exports', 'imports'])\n",
    "economies_cleaned_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2010</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Low income</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>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code      country  year         income_group\n",
       "0    ABW        Aruba  2010          High income\n",
       "1    ABW        Aruba  2015          High income\n",
       "2    ABW        Aruba  2020          High income\n",
       "3    AFG  Afghanistan  2010           Low income\n",
       "4    AFG  Afghanistan  2015           Low income\n",
       "..   ...          ...   ...                  ...\n",
       "556  ZMB       Zambia  2015  Lower middle income\n",
       "557  ZMB       Zambia  2020  Lower middle income\n",
       "558  ZWE     Zimbabwe  2010  Lower middle income\n",
       "559  ZWE     Zimbabwe  2015  Lower middle income\n",
       "560  ZWE     Zimbabwe  2020  Lower middle income\n",
       "\n",
       "[561 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove columns with any missing values\n",
    "economies_no_missing_columns = economies.dropna(axis=1)\n",
    "\n",
    "# Display the DataFrame after removing columns with missing values\n",
    "economies_no_missing_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replace missing values with specific value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2010</td>\n",
       "      <td>24087.950</td>\n",
       "      <td>13.255</td>\n",
       "      <td>2.078</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.600</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.298</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>0.000</td>\n",
       "      <td>13.997</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.269</td>\n",
       "      <td>0.000</td>\n",
       "      <td>9.768</td>\n",
       "      <td>32.285</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>975.851</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.045</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>0.000</td>\n",
       "      <td>557.210</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code      country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "0    ABW        Aruba  2010      24087.950         13.255           2.078   \n",
       "1    ABW        Aruba  2015      27126.620         21.411           0.475   \n",
       "2    ABW        Aruba  2020      21832.920         -7.521          -1.338   \n",
       "3    AFG  Afghanistan  2010        631.490         59.699           2.179   \n",
       "4    AFG  Afghanistan  2015        711.337         22.223          -0.662   \n",
       "..   ...          ...   ...            ...            ...             ...   \n",
       "556  ZMB       Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB       Zambia  2020        981.311         36.030          16.350   \n",
       "558  ZWE     Zimbabwe  2010        975.851          0.000           3.045   \n",
       "559  ZWE     Zimbabwe  2015       1425.010          0.000          -2.410   \n",
       "560  ZWE     Zimbabwe  2020       1385.040          0.000         557.210   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "0               0.000             10.600    0.000    0.000   \n",
       "1               0.000              7.298    0.000    0.000   \n",
       "2               0.000             13.997    0.000    0.000   \n",
       "3              30.269              0.000    9.768   32.285   \n",
       "4              18.427              0.000  -11.585   15.309   \n",
       "..                ...                ...      ...      ...   \n",
       "556            42.791              0.000  -11.407    0.696   \n",
       "557            34.514              0.000    1.143    2.635   \n",
       "558             0.000              0.000    0.000    0.000   \n",
       "559             0.000              0.000    0.000    0.000   \n",
       "560             0.000              0.000    0.000    0.000   \n",
       "\n",
       "            income_group  \n",
       "0            High income  \n",
       "1            High income  \n",
       "2            High income  \n",
       "3             Low income  \n",
       "4             Low income  \n",
       "..                   ...  \n",
       "556  Lower middle income  \n",
       "557  Lower middle income  \n",
       "558  Lower middle income  \n",
       "559  Lower middle income  \n",
       "560  Lower middle income  \n",
       "\n",
       "[561 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace missing values with a specific value (e.g., 0 for numerical columns, 'Unknown' for categorical columns)\n",
    "economies_fill_value = economies.fillna({\n",
    "    'gdp_percapita': 0,\n",
    "    'gross_savings': 0,\n",
    "    'inflation_rate': 0,\n",
    "    'total_investment': 0,\n",
    "    'unemployment_rate': 0,\n",
    "    'exports': 0,\n",
    "    'imports': 0,\n",
    "    'income_group': 'Unknown'\n",
    "})\n",
    "\n",
    "# Display the DataFrame after replacing missing values with specific values\n",
    "economies_fill_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be extended to replace missing values with the mean, median, or mode of the column too, but that's beyond the scope of this course.\n",
    "\n",
    "### Convert a column to a different data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 561 entries, 0 to 560\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   code               561 non-null    object \n",
      " 1   country            561 non-null    object \n",
      " 2   year               561 non-null    object \n",
      " 3   gdp_percapita      558 non-null    float64\n",
      " 4   gross_savings      490 non-null    float64\n",
      " 5   inflation_rate     555 non-null    float64\n",
      " 6   total_investment   490 non-null    float64\n",
      " 7   unemployment_rate  312 non-null    float64\n",
      " 8   exports            509 non-null    float64\n",
      " 9   imports            506 non-null    float64\n",
      " 10  income_group       561 non-null    object \n",
      "dtypes: float64(7), object(4)\n",
      "memory usage: 48.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Change year to be a string instead of an integer\n",
    "economies_char_year = economies.astype({'year': 'str'})\n",
    "\n",
    "# Display the information on the DataFrame with year as a string\n",
    "economies_char_year.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 561 entries, 0 to 560\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   code               561 non-null    object \n",
      " 1   country            561 non-null    object \n",
      " 2   year               561 non-null    int64  \n",
      " 3   gdp_percapita      558 non-null    float64\n",
      " 4   gross_savings      490 non-null    float64\n",
      " 5   inflation_rate     555 non-null    float64\n",
      " 6   total_investment   490 non-null    float64\n",
      " 7   unemployment_rate  312 non-null    float64\n",
      " 8   exports            509 non-null    float64\n",
      " 9   imports            506 non-null    float64\n",
      " 10  income_group       561 non-null    object \n",
      "dtypes: float64(7), int64(1), object(3)\n",
      "memory usage: 48.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Change the year of string type back to integer\n",
    "economies_int_year = economies_char_year.astype({'year': 'int'})\n",
    "\n",
    "# Display the information on the DataFrame with year as a string\n",
    "economies_int_year.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>975.851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code   country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "556  ZMB    Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB    Zambia  2020        981.311         36.030          16.350   \n",
       "558  ZWE  Zimbabwe  2010        975.851            NaN           3.045   \n",
       "559  ZWE  Zimbabwe  2015       1425.010            NaN          -2.410   \n",
       "560  ZWE  Zimbabwe  2020       1385.040            NaN         557.210   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "558               NaN                NaN      NaN      NaN   \n",
       "559               NaN                NaN      NaN      NaN   \n",
       "560               NaN                NaN      NaN      NaN   \n",
       "\n",
       "         income_category  \n",
       "556  Lower middle income  \n",
       "557  Lower middle income  \n",
       "558  Lower middle income  \n",
       "559  Lower middle income  \n",
       "560  Lower middle income  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the 'income_group' column to 'income_category'\n",
    "economies_renamed = economies.rename(columns={'income_group': 'income_category'})\n",
    "economies_renamed.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing a DataFrame’s index\n",
    "\n",
    "#### Set the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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></th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <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 rowspan=\"3\" valign=\"top\">ABW</th>\n",
       "      <th>2010</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>24087.950</td>\n",
       "      <td>13.255</td>\n",
       "      <td>2.078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>Aruba</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AFG</th>\n",
       "      <th>2010</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.768</td>\n",
       "      <td>32.285</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ZMB</th>\n",
       "      <th>2015</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">ZWE</th>\n",
       "      <th>2010</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>975.851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "code year                                                              \n",
       "ABW  2010        Aruba      24087.950         13.255           2.078   \n",
       "     2015        Aruba      27126.620         21.411           0.475   \n",
       "     2020        Aruba      21832.920         -7.521          -1.338   \n",
       "AFG  2010  Afghanistan        631.490         59.699           2.179   \n",
       "     2015  Afghanistan        711.337         22.223          -0.662   \n",
       "...                ...            ...            ...             ...   \n",
       "ZMB  2015       Zambia       1310.460         40.103          10.107   \n",
       "     2020       Zambia        981.311         36.030          16.350   \n",
       "ZWE  2010     Zimbabwe        975.851            NaN           3.045   \n",
       "     2015     Zimbabwe       1425.010            NaN          -2.410   \n",
       "     2020     Zimbabwe       1385.040            NaN         557.210   \n",
       "\n",
       "           total_investment  unemployment_rate  exports  imports  \\\n",
       "code year                                                          \n",
       "ABW  2010               NaN             10.600      NaN      NaN   \n",
       "     2015               NaN              7.298      NaN      NaN   \n",
       "     2020               NaN             13.997      NaN      NaN   \n",
       "AFG  2010            30.269                NaN    9.768   32.285   \n",
       "     2015            18.427                NaN  -11.585   15.309   \n",
       "...                     ...                ...      ...      ...   \n",
       "ZMB  2015            42.791                NaN  -11.407    0.696   \n",
       "     2020            34.514                NaN    1.143    2.635   \n",
       "ZWE  2010               NaN                NaN      NaN      NaN   \n",
       "     2015               NaN                NaN      NaN      NaN   \n",
       "     2020               NaN                NaN      NaN      NaN   \n",
       "\n",
       "                  income_group  \n",
       "code year                       \n",
       "ABW  2010          High income  \n",
       "     2015          High income  \n",
       "     2020          High income  \n",
       "AFG  2010           Low income  \n",
       "     2015           Low income  \n",
       "...                        ...  \n",
       "ZMB  2015  Lower middle income  \n",
       "     2020  Lower middle income  \n",
       "ZWE  2010  Lower middle income  \n",
       "     2015  Lower middle income  \n",
       "     2020  Lower middle income  \n",
       "\n",
       "[561 rows x 9 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set unique combinations of 'code' and 'year' as the index\n",
    "economies_indexed = economies.set_index(['code', 'year'])\n",
    "economies_indexed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reset the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>year</th>\n",
       "      <th>country</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>2010</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>24087.950</td>\n",
       "      <td>13.255</td>\n",
       "      <td>2.078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>2015</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>2020</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>2010</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.768</td>\n",
       "      <td>32.285</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>2015</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>2015</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>2020</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>2010</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>975.851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>2015</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>2020</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code  year      country  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "0    ABW  2010        Aruba      24087.950         13.255           2.078   \n",
       "1    ABW  2015        Aruba      27126.620         21.411           0.475   \n",
       "2    ABW  2020        Aruba      21832.920         -7.521          -1.338   \n",
       "3    AFG  2010  Afghanistan        631.490         59.699           2.179   \n",
       "4    AFG  2015  Afghanistan        711.337         22.223          -0.662   \n",
       "..   ...   ...          ...            ...            ...             ...   \n",
       "556  ZMB  2015       Zambia       1310.460         40.103          10.107   \n",
       "557  ZMB  2020       Zambia        981.311         36.030          16.350   \n",
       "558  ZWE  2010     Zimbabwe        975.851            NaN           3.045   \n",
       "559  ZWE  2015     Zimbabwe       1425.010            NaN          -2.410   \n",
       "560  ZWE  2020     Zimbabwe       1385.040            NaN         557.210   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "0                 NaN             10.600      NaN      NaN   \n",
       "1                 NaN              7.298      NaN      NaN   \n",
       "2                 NaN             13.997      NaN      NaN   \n",
       "3              30.269                NaN    9.768   32.285   \n",
       "4              18.427                NaN  -11.585   15.309   \n",
       "..                ...                ...      ...      ...   \n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "558               NaN                NaN      NaN      NaN   \n",
       "559               NaN                NaN      NaN      NaN   \n",
       "560               NaN                NaN      NaN      NaN   \n",
       "\n",
       "            income_group  \n",
       "0            High income  \n",
       "1            High income  \n",
       "2            High income  \n",
       "3             Low income  \n",
       "4             Low income  \n",
       "..                   ...  \n",
       "556  Lower middle income  \n",
       "557  Lower middle income  \n",
       "558  Lower middle income  \n",
       "559  Lower middle income  \n",
       "560  Lower middle income  \n",
       "\n",
       "[561 rows x 11 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset the index\n",
    "economies_reset = economies_indexed.reset_index()\n",
    "economies_reset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering rows based on conditions\n",
    "\n",
    "#### Conditions on a single column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2010</td>\n",
       "      <td>24087.95</td>\n",
       "      <td>13.255</td>\n",
       "      <td>2.078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>27126.62</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>21832.92</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2010</td>\n",
       "      <td>35064.26</td>\n",
       "      <td>31.330</td>\n",
       "      <td>0.878</td>\n",
       "      <td>27.121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.540</td>\n",
       "      <td>0.405</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2015</td>\n",
       "      <td>37380.57</td>\n",
       "      <td>30.540</td>\n",
       "      <td>4.070</td>\n",
       "      <td>25.639</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.055</td>\n",
       "      <td>2.488</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>SWE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2015</td>\n",
       "      <td>51274.28</td>\n",
       "      <td>27.743</td>\n",
       "      <td>0.704</td>\n",
       "      <td>24.429</td>\n",
       "      <td>7.400</td>\n",
       "      <td>4.995</td>\n",
       "      <td>5.031</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>SWE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2020</td>\n",
       "      <td>51796.38</td>\n",
       "      <td>29.755</td>\n",
       "      <td>0.657</td>\n",
       "      <td>24.518</td>\n",
       "      <td>8.292</td>\n",
       "      <td>-5.191</td>\n",
       "      <td>-5.358</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010</td>\n",
       "      <td>48403.30</td>\n",
       "      <td>15.466</td>\n",
       "      <td>1.637</td>\n",
       "      <td>18.743</td>\n",
       "      <td>9.608</td>\n",
       "      <td>12.137</td>\n",
       "      <td>13.128</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015</td>\n",
       "      <td>56848.51</td>\n",
       "      <td>20.141</td>\n",
       "      <td>0.121</td>\n",
       "      <td>21.163</td>\n",
       "      <td>5.275</td>\n",
       "      <td>0.421</td>\n",
       "      <td>5.175</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2020</td>\n",
       "      <td>63415.99</td>\n",
       "      <td>17.802</td>\n",
       "      <td>1.248</td>\n",
       "      <td>20.988</td>\n",
       "      <td>8.108</td>\n",
       "      <td>-12.970</td>\n",
       "      <td>-9.285</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code               country  year  gdp_percapita  gross_savings  \\\n",
       "0    ABW                 Aruba  2010       24087.95         13.255   \n",
       "1    ABW                 Aruba  2015       27126.62         21.411   \n",
       "2    ABW                 Aruba  2020       21832.92         -7.521   \n",
       "12   ARE  United Arab Emirates  2010       35064.26         31.330   \n",
       "13   ARE  United Arab Emirates  2015       37380.57         30.540   \n",
       "..   ...                   ...   ...            ...            ...   \n",
       "475  SWE                Sweden  2015       51274.28         27.743   \n",
       "476  SWE                Sweden  2020       51796.38         29.755   \n",
       "528  USA         United States  2010       48403.30         15.466   \n",
       "529  USA         United States  2015       56848.51         20.141   \n",
       "530  USA         United States  2020       63415.99         17.802   \n",
       "\n",
       "     inflation_rate  total_investment  unemployment_rate  exports  imports  \\\n",
       "0             2.078               NaN             10.600      NaN      NaN   \n",
       "1             0.475               NaN              7.298      NaN      NaN   \n",
       "2            -1.338               NaN             13.997      NaN      NaN   \n",
       "12            0.878            27.121                NaN    7.540    0.405   \n",
       "13            4.070            25.639                NaN    3.055    2.488   \n",
       "..              ...               ...                ...      ...      ...   \n",
       "475           0.704            24.429              7.400    4.995    5.031   \n",
       "476           0.657            24.518              8.292   -5.191   -5.358   \n",
       "528           1.637            18.743              9.608   12.137   13.128   \n",
       "529           0.121            21.163              5.275    0.421    5.175   \n",
       "530           1.248            20.988              8.108  -12.970   -9.285   \n",
       "\n",
       "    income_group  \n",
       "0    High income  \n",
       "1    High income  \n",
       "2    High income  \n",
       "12   High income  \n",
       "13   High income  \n",
       "..           ...  \n",
       "475  High income  \n",
       "476  High income  \n",
       "528  High income  \n",
       "529  High income  \n",
       "530  High income  \n",
       "\n",
       "[121 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where 'gdp_percapita' is greater than 20,000\n",
    "economies_high_gdp = economies[economies['gdp_percapita'] > 20000]\n",
    "economies_high_gdp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
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       "      <td>21832.92</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>2010</td>\n",
       "      <td>35064.26</td>\n",
       "      <td>31.330</td>\n",
       "      <td>0.878</td>\n",
       "      <td>27.121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.540</td>\n",
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       "      <th>13</th>\n",
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       "      <td>37380.57</td>\n",
       "      <td>30.540</td>\n",
       "      <td>4.070</td>\n",
       "      <td>25.639</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.488</td>\n",
       "      <td>High income</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2015</td>\n",
       "      <td>16692.65</td>\n",
       "      <td>17.223</td>\n",
       "      <td>8.666</td>\n",
       "      <td>17.477</td>\n",
       "      <td>7.525</td>\n",
       "      <td>-7.564</td>\n",
       "      <td>-8.145</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2020</td>\n",
       "      <td>15777.96</td>\n",
       "      <td>12.873</td>\n",
       "      <td>9.756</td>\n",
       "      <td>14.319</td>\n",
       "      <td>10.350</td>\n",
       "      <td>-28.231</td>\n",
       "      <td>-12.544</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010</td>\n",
       "      <td>48403.30</td>\n",
       "      <td>15.466</td>\n",
       "      <td>1.637</td>\n",
       "      <td>18.743</td>\n",
       "      <td>9.608</td>\n",
       "      <td>12.137</td>\n",
       "      <td>13.128</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015</td>\n",
       "      <td>56848.51</td>\n",
       "      <td>20.141</td>\n",
       "      <td>0.121</td>\n",
       "      <td>21.163</td>\n",
       "      <td>5.275</td>\n",
       "      <td>0.421</td>\n",
       "      <td>5.175</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2020</td>\n",
       "      <td>63415.99</td>\n",
       "      <td>17.802</td>\n",
       "      <td>1.248</td>\n",
       "      <td>20.988</td>\n",
       "      <td>8.108</td>\n",
       "      <td>-12.970</td>\n",
       "      <td>-9.285</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code               country  year  gdp_percapita  gross_savings  \\\n",
       "0    ABW                 Aruba  2010       24087.95         13.255   \n",
       "1    ABW                 Aruba  2015       27126.62         21.411   \n",
       "2    ABW                 Aruba  2020       21832.92         -7.521   \n",
       "12   ARE  United Arab Emirates  2010       35064.26         31.330   \n",
       "13   ARE  United Arab Emirates  2015       37380.57         30.540   \n",
       "..   ...                   ...   ...            ...            ...   \n",
       "526  URY               Uruguay  2015       16692.65         17.223   \n",
       "527  URY               Uruguay  2020       15777.96         12.873   \n",
       "528  USA         United States  2010       48403.30         15.466   \n",
       "529  USA         United States  2015       56848.51         20.141   \n",
       "530  USA         United States  2020       63415.99         17.802   \n",
       "\n",
       "     inflation_rate  total_investment  unemployment_rate  exports  imports  \\\n",
       "0             2.078               NaN             10.600      NaN      NaN   \n",
       "1             0.475               NaN              7.298      NaN      NaN   \n",
       "2            -1.338               NaN             13.997      NaN      NaN   \n",
       "12            0.878            27.121                NaN    7.540    0.405   \n",
       "13            4.070            25.639                NaN    3.055    2.488   \n",
       "..              ...               ...                ...      ...      ...   \n",
       "526           8.666            17.477              7.525   -7.564   -8.145   \n",
       "527           9.756            14.319             10.350  -28.231  -12.544   \n",
       "528           1.637            18.743              9.608   12.137   13.128   \n",
       "529           0.121            21.163              5.275    0.421    5.175   \n",
       "530           1.248            20.988              8.108  -12.970   -9.285   \n",
       "\n",
       "    income_group  \n",
       "0    High income  \n",
       "1    High income  \n",
       "2    High income  \n",
       "12   High income  \n",
       "13   High income  \n",
       "..           ...  \n",
       "526  High income  \n",
       "527  High income  \n",
       "528  High income  \n",
       "529  High income  \n",
       "530  High income  \n",
       "\n",
       "[180 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where 'income_group' is 'High income'\n",
    "economies_high_income = economies[economies['income_group'] == 'High income']\n",
    "economies_high_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>631.490</td>\n",
       "      <td>59.699</td>\n",
       "      <td>2.179</td>\n",
       "      <td>30.269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.768</td>\n",
       "      <td>32.285</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020</td>\n",
       "      <td>580.817</td>\n",
       "      <td>27.132</td>\n",
       "      <td>5.607</td>\n",
       "      <td>16.420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.424</td>\n",
       "      <td>2.892</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2010</td>\n",
       "      <td>3641.440</td>\n",
       "      <td>34.833</td>\n",
       "      <td>14.480</td>\n",
       "      <td>28.197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.266</td>\n",
       "      <td>-21.656</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2015</td>\n",
       "      <td>4354.920</td>\n",
       "      <td>28.491</td>\n",
       "      <td>9.159</td>\n",
       "      <td>34.202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-19.515</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2015</td>\n",
       "      <td>5731.730</td>\n",
       "      <td>16.300</td>\n",
       "      <td>4.575</td>\n",
       "      <td>20.918</td>\n",
       "      <td>25.350</td>\n",
       "      <td>2.925</td>\n",
       "      <td>5.443</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2020</td>\n",
       "      <td>5067.150</td>\n",
       "      <td>14.602</td>\n",
       "      <td>3.268</td>\n",
       "      <td>12.426</td>\n",
       "      <td>29.175</td>\n",
       "      <td>-10.280</td>\n",
       "      <td>-16.615</td>\n",
       "      <td>Upper middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2010</td>\n",
       "      <td>1456.050</td>\n",
       "      <td>37.405</td>\n",
       "      <td>8.500</td>\n",
       "      <td>29.878</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.476</td>\n",
       "      <td>32.492</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>490 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code       country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "3    AFG   Afghanistan  2010        631.490         59.699           2.179   \n",
       "4    AFG   Afghanistan  2015        711.337         22.223          -0.662   \n",
       "5    AFG   Afghanistan  2020        580.817         27.132           5.607   \n",
       "6    AGO        Angola  2010       3641.440         34.833          14.480   \n",
       "7    AGO        Angola  2015       4354.920         28.491           9.159   \n",
       "..   ...           ...   ...            ...            ...             ...   \n",
       "553  ZAF  South Africa  2015       5731.730         16.300           4.575   \n",
       "554  ZAF  South Africa  2020       5067.150         14.602           3.268   \n",
       "555  ZMB        Zambia  2010       1456.050         37.405           8.500   \n",
       "556  ZMB        Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB        Zambia  2020        981.311         36.030          16.350   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "3              30.269                NaN    9.768   32.285   \n",
       "4              18.427                NaN  -11.585   15.309   \n",
       "5              16.420                NaN  -10.424    2.892   \n",
       "6              28.197                NaN   -3.266  -21.656   \n",
       "7              34.202                NaN    6.721  -19.515   \n",
       "..                ...                ...      ...      ...   \n",
       "553            20.918             25.350    2.925    5.443   \n",
       "554            12.426             29.175  -10.280  -16.615   \n",
       "555            29.878                NaN   19.476   32.492   \n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "\n",
       "            income_group  \n",
       "3             Low income  \n",
       "4             Low income  \n",
       "5             Low income  \n",
       "6    Lower middle income  \n",
       "7    Lower middle income  \n",
       "..                   ...  \n",
       "553  Upper middle income  \n",
       "554  Upper middle income  \n",
       "555  Lower middle income  \n",
       "556  Lower middle income  \n",
       "557  Lower middle income  \n",
       "\n",
       "[490 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where total_investment is not NaN\n",
    "non_null_investment = economies[economies['total_investment'].notna()]\n",
    "non_null_investment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conditions on multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
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       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>BFA</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>2010</td>\n",
       "      <td>648.365</td>\n",
       "      <td>20.194</td>\n",
       "      <td>-0.608</td>\n",
       "      <td>21.990</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.547</td>\n",
       "      <td>13.986</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>TCD</td>\n",
       "      <td>Chad</td>\n",
       "      <td>2010</td>\n",
       "      <td>895.354</td>\n",
       "      <td>25.871</td>\n",
       "      <td>-2.110</td>\n",
       "      <td>34.388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.488</td>\n",
       "      <td>17.218</td>\n",
       "      <td>Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code       country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "4    AFG   Afghanistan  2015        711.337         22.223          -0.662   \n",
       "42   BFA  Burkina Faso  2010        648.365         20.194          -0.608   \n",
       "486  TCD          Chad  2010        895.354         25.871          -2.110   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports income_group  \n",
       "4              18.427                NaN  -11.585   15.309   Low income  \n",
       "42             21.990                NaN   54.547   13.986   Low income  \n",
       "486            34.388                NaN   -5.488   17.218   Low income  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where inflation_rate is less than 0 and income_group is 'Low income'\n",
    "deflation_low_income = economies[(economies['inflation_rate'] < 0) & (economies['income_group'] == 'Low income')]\n",
    "deflation_low_income.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
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       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
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       "      <th>income_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2010</td>\n",
       "      <td>56459.80</td>\n",
       "      <td>23.105</td>\n",
       "      <td>2.863</td>\n",
       "      <td>26.369</td>\n",
       "      <td>5.208</td>\n",
       "      <td>5.717</td>\n",
       "      <td>15.507</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>AUS</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2015</td>\n",
       "      <td>51484.05</td>\n",
       "      <td>21.608</td>\n",
       "      <td>1.485</td>\n",
       "      <td>25.880</td>\n",
       "      <td>6.050</td>\n",
       "      <td>6.533</td>\n",
       "      <td>1.962</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2010</td>\n",
       "      <td>46955.17</td>\n",
       "      <td>25.463</td>\n",
       "      <td>1.693</td>\n",
       "      <td>22.608</td>\n",
       "      <td>4.817</td>\n",
       "      <td>13.131</td>\n",
       "      <td>11.970</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>AUT</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2015</td>\n",
       "      <td>44267.81</td>\n",
       "      <td>25.531</td>\n",
       "      <td>0.808</td>\n",
       "      <td>23.806</td>\n",
       "      <td>5.742</td>\n",
       "      <td>3.049</td>\n",
       "      <td>3.630</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>2010</td>\n",
       "      <td>44448.17</td>\n",
       "      <td>24.751</td>\n",
       "      <td>2.334</td>\n",
       "      <td>23.127</td>\n",
       "      <td>8.308</td>\n",
       "      <td>8.484</td>\n",
       "      <td>7.171</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BEL</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>2015</td>\n",
       "      <td>41147.26</td>\n",
       "      <td>25.011</td>\n",
       "      <td>0.620</td>\n",
       "      <td>23.628</td>\n",
       "      <td>8.483</td>\n",
       "      <td>1.368</td>\n",
       "      <td>1.053</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2010</td>\n",
       "      <td>47627.35</td>\n",
       "      <td>19.918</td>\n",
       "      <td>1.777</td>\n",
       "      <td>23.482</td>\n",
       "      <td>8.075</td>\n",
       "      <td>6.678</td>\n",
       "      <td>13.788</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>CAN</td>\n",
       "      <td>Canada</td>\n",
       "      <td>2015</td>\n",
       "      <td>43626.47</td>\n",
       "      <td>20.326</td>\n",
       "      <td>1.125</td>\n",
       "      <td>23.822</td>\n",
       "      <td>6.942</td>\n",
       "      <td>3.419</td>\n",
       "      <td>0.751</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>CHE</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2010</td>\n",
       "      <td>77433.67</td>\n",
       "      <td>39.762</td>\n",
       "      <td>0.688</td>\n",
       "      <td>25.178</td>\n",
       "      <td>3.516</td>\n",
       "      <td>12.184</td>\n",
       "      <td>8.003</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>CHE</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2015</td>\n",
       "      <td>85240.79</td>\n",
       "      <td>35.012</td>\n",
       "      <td>-1.142</td>\n",
       "      <td>24.747</td>\n",
       "      <td>3.178</td>\n",
       "      <td>3.972</td>\n",
       "      <td>4.561</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2010</td>\n",
       "      <td>42379.84</td>\n",
       "      <td>25.810</td>\n",
       "      <td>1.119</td>\n",
       "      <td>20.066</td>\n",
       "      <td>6.967</td>\n",
       "      <td>14.047</td>\n",
       "      <td>12.648</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>DEU</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2015</td>\n",
       "      <td>41107.22</td>\n",
       "      <td>28.344</td>\n",
       "      <td>0.680</td>\n",
       "      <td>19.743</td>\n",
       "      <td>4.633</td>\n",
       "      <td>4.878</td>\n",
       "      <td>5.361</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>DNK</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2010</td>\n",
       "      <td>58177.16</td>\n",
       "      <td>24.639</td>\n",
       "      <td>2.198</td>\n",
       "      <td>18.076</td>\n",
       "      <td>7.733</td>\n",
       "      <td>2.940</td>\n",
       "      <td>0.541</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>DNK</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>2015</td>\n",
       "      <td>53478.50</td>\n",
       "      <td>28.876</td>\n",
       "      <td>0.226</td>\n",
       "      <td>20.631</td>\n",
       "      <td>6.283</td>\n",
       "      <td>3.577</td>\n",
       "      <td>4.561</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>FIN</td>\n",
       "      <td>Finland</td>\n",
       "      <td>2010</td>\n",
       "      <td>46647.00</td>\n",
       "      <td>23.536</td>\n",
       "      <td>1.686</td>\n",
       "      <td>22.048</td>\n",
       "      <td>8.500</td>\n",
       "      <td>6.182</td>\n",
       "      <td>6.304</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>FIN</td>\n",
       "      <td>Finland</td>\n",
       "      <td>2015</td>\n",
       "      <td>42867.09</td>\n",
       "      <td>20.748</td>\n",
       "      <td>-0.156</td>\n",
       "      <td>21.686</td>\n",
       "      <td>9.575</td>\n",
       "      <td>0.392</td>\n",
       "      <td>1.963</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>FRA</td>\n",
       "      <td>France</td>\n",
       "      <td>2010</td>\n",
       "      <td>42178.58</td>\n",
       "      <td>21.317</td>\n",
       "      <td>1.737</td>\n",
       "      <td>21.946</td>\n",
       "      <td>9.250</td>\n",
       "      <td>8.695</td>\n",
       "      <td>8.893</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>GBR</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2015</td>\n",
       "      <td>45053.49</td>\n",
       "      <td>12.697</td>\n",
       "      <td>0.040</td>\n",
       "      <td>17.720</td>\n",
       "      <td>5.375</td>\n",
       "      <td>2.799</td>\n",
       "      <td>5.412</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>HKG</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>2015</td>\n",
       "      <td>42325.35</td>\n",
       "      <td>24.859</td>\n",
       "      <td>2.995</td>\n",
       "      <td>21.541</td>\n",
       "      <td>3.307</td>\n",
       "      <td>-1.368</td>\n",
       "      <td>-1.776</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>IRL</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>2010</td>\n",
       "      <td>48758.01</td>\n",
       "      <td>16.023</td>\n",
       "      <td>-1.619</td>\n",
       "      <td>17.217</td>\n",
       "      <td>14.583</td>\n",
       "      <td>6.289</td>\n",
       "      <td>0.678</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>IRL</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>2015</td>\n",
       "      <td>61951.73</td>\n",
       "      <td>29.966</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>25.576</td>\n",
       "      <td>9.950</td>\n",
       "      <td>38.212</td>\n",
       "      <td>32.303</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>ISL</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>2010</td>\n",
       "      <td>43293.02</td>\n",
       "      <td>7.814</td>\n",
       "      <td>5.400</td>\n",
       "      <td>13.909</td>\n",
       "      <td>8.317</td>\n",
       "      <td>0.954</td>\n",
       "      <td>2.319</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>ISL</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>2015</td>\n",
       "      <td>53227.63</td>\n",
       "      <td>25.034</td>\n",
       "      <td>1.634</td>\n",
       "      <td>19.404</td>\n",
       "      <td>4.500</td>\n",
       "      <td>8.948</td>\n",
       "      <td>13.542</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>JPN</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2010</td>\n",
       "      <td>45135.80</td>\n",
       "      <td>26.427</td>\n",
       "      <td>-0.721</td>\n",
       "      <td>22.590</td>\n",
       "      <td>5.058</td>\n",
       "      <td>24.861</td>\n",
       "      <td>11.285</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>LUX</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2010</td>\n",
       "      <td>106177.04</td>\n",
       "      <td>23.984</td>\n",
       "      <td>2.795</td>\n",
       "      <td>17.885</td>\n",
       "      <td>5.808</td>\n",
       "      <td>10.373</td>\n",
       "      <td>12.172</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>LUX</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>2015</td>\n",
       "      <td>102625.13</td>\n",
       "      <td>24.394</td>\n",
       "      <td>0.060</td>\n",
       "      <td>19.324</td>\n",
       "      <td>6.626</td>\n",
       "      <td>4.701</td>\n",
       "      <td>3.399</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>MAC</td>\n",
       "      <td>Macao</td>\n",
       "      <td>2010</td>\n",
       "      <td>51135.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.809</td>\n",
       "      <td>13.247</td>\n",
       "      <td>2.825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>MAC</td>\n",
       "      <td>Macao</td>\n",
       "      <td>2015</td>\n",
       "      <td>69666.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.561</td>\n",
       "      <td>25.330</td>\n",
       "      <td>1.825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>NLD</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2010</td>\n",
       "      <td>51165.82</td>\n",
       "      <td>27.156</td>\n",
       "      <td>0.929</td>\n",
       "      <td>20.222</td>\n",
       "      <td>4.995</td>\n",
       "      <td>9.764</td>\n",
       "      <td>8.552</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>NLD</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2015</td>\n",
       "      <td>45302.80</td>\n",
       "      <td>28.774</td>\n",
       "      <td>0.216</td>\n",
       "      <td>22.475</td>\n",
       "      <td>6.891</td>\n",
       "      <td>7.444</td>\n",
       "      <td>14.883</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>2010</td>\n",
       "      <td>87356.19</td>\n",
       "      <td>36.282</td>\n",
       "      <td>2.419</td>\n",
       "      <td>25.425</td>\n",
       "      <td>3.794</td>\n",
       "      <td>0.556</td>\n",
       "      <td>8.416</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>NOR</td>\n",
       "      <td>Norway</td>\n",
       "      <td>2015</td>\n",
       "      <td>74115.16</td>\n",
       "      <td>35.521</td>\n",
       "      <td>2.171</td>\n",
       "      <td>27.476</td>\n",
       "      <td>4.531</td>\n",
       "      <td>4.342</td>\n",
       "      <td>1.935</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>QAT</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>2010</td>\n",
       "      <td>69796.14</td>\n",
       "      <td>48.177</td>\n",
       "      <td>-2.389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.090</td>\n",
       "      <td>-1.425</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>QAT</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>2015</td>\n",
       "      <td>66346.96</td>\n",
       "      <td>45.569</td>\n",
       "      <td>0.939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.827</td>\n",
       "      <td>-9.173</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>SGP</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2010</td>\n",
       "      <td>47236.67</td>\n",
       "      <td>50.592</td>\n",
       "      <td>2.824</td>\n",
       "      <td>27.658</td>\n",
       "      <td>2.175</td>\n",
       "      <td>17.788</td>\n",
       "      <td>16.307</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>SGP</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>2015</td>\n",
       "      <td>55645.61</td>\n",
       "      <td>44.046</td>\n",
       "      <td>-0.523</td>\n",
       "      <td>25.353</td>\n",
       "      <td>1.900</td>\n",
       "      <td>4.968</td>\n",
       "      <td>3.381</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>SMR</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>2010</td>\n",
       "      <td>60426.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.413</td>\n",
       "      <td>19.613</td>\n",
       "      <td>4.945</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>SMR</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>2015</td>\n",
       "      <td>43068.69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.146</td>\n",
       "      <td>12.086</td>\n",
       "      <td>9.183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>SWE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2010</td>\n",
       "      <td>52658.79</td>\n",
       "      <td>28.799</td>\n",
       "      <td>1.901</td>\n",
       "      <td>22.962</td>\n",
       "      <td>8.575</td>\n",
       "      <td>11.107</td>\n",
       "      <td>11.816</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>SWE</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2015</td>\n",
       "      <td>51274.28</td>\n",
       "      <td>27.743</td>\n",
       "      <td>0.704</td>\n",
       "      <td>24.429</td>\n",
       "      <td>7.400</td>\n",
       "      <td>4.995</td>\n",
       "      <td>5.031</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2010</td>\n",
       "      <td>48403.30</td>\n",
       "      <td>15.466</td>\n",
       "      <td>1.637</td>\n",
       "      <td>18.743</td>\n",
       "      <td>9.608</td>\n",
       "      <td>12.137</td>\n",
       "      <td>13.128</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2015</td>\n",
       "      <td>56848.51</td>\n",
       "      <td>20.141</td>\n",
       "      <td>0.121</td>\n",
       "      <td>21.163</td>\n",
       "      <td>5.275</td>\n",
       "      <td>0.421</td>\n",
       "      <td>5.175</td>\n",
       "      <td>High income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    code         country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "24   AUS       Australia  2010       56459.80         23.105           2.863   \n",
       "25   AUS       Australia  2015       51484.05         21.608           1.485   \n",
       "27   AUT         Austria  2010       46955.17         25.463           1.693   \n",
       "28   AUT         Austria  2015       44267.81         25.531           0.808   \n",
       "36   BEL         Belgium  2010       44448.17         24.751           2.334   \n",
       "37   BEL         Belgium  2015       41147.26         25.011           0.620   \n",
       "87   CAN          Canada  2010       47627.35         19.918           1.777   \n",
       "88   CAN          Canada  2015       43626.47         20.326           1.125   \n",
       "90   CHE     Switzerland  2010       77433.67         39.762           0.688   \n",
       "91   CHE     Switzerland  2015       85240.79         35.012          -1.142   \n",
       "129  DEU         Germany  2010       42379.84         25.810           1.119   \n",
       "130  DEU         Germany  2015       41107.22         28.344           0.680   \n",
       "138  DNK         Denmark  2010       58177.16         24.639           2.198   \n",
       "139  DNK         Denmark  2015       53478.50         28.876           0.226   \n",
       "165  FIN         Finland  2010       46647.00         23.536           1.686   \n",
       "166  FIN         Finland  2015       42867.09         20.748          -0.156   \n",
       "171  FRA          France  2010       42178.58         21.317           1.737   \n",
       "181  GBR  United Kingdom  2015       45053.49         12.697           0.040   \n",
       "214  HKG       Hong Kong  2015       42325.35         24.859           2.995   \n",
       "234  IRL         Ireland  2010       48758.01         16.023          -1.619   \n",
       "235  IRL         Ireland  2015       61951.73         29.966          -0.050   \n",
       "243  ISL         Iceland  2010       43293.02          7.814           5.400   \n",
       "244  ISL         Iceland  2015       53227.63         25.034           1.634   \n",
       "258  JPN           Japan  2010       45135.80         26.427          -0.721   \n",
       "309  LUX      Luxembourg  2010      106177.04         23.984           2.795   \n",
       "310  LUX      Luxembourg  2015      102625.13         24.394           0.060   \n",
       "315  MAC           Macao  2010       51135.16            NaN           2.809   \n",
       "316  MAC           Macao  2015       69666.75            NaN           4.561   \n",
       "378  NLD     Netherlands  2010       51165.82         27.156           0.929   \n",
       "379  NLD     Netherlands  2015       45302.80         28.774           0.216   \n",
       "381  NOR          Norway  2010       87356.19         36.282           2.419   \n",
       "382  NOR          Norway  2015       74115.16         35.521           2.171   \n",
       "426  QAT           Qatar  2010       69796.14         48.177          -2.389   \n",
       "427  QAT           Qatar  2015       66346.96         45.569           0.939   \n",
       "444  SGP       Singapore  2010       47236.67         50.592           2.824   \n",
       "445  SGP       Singapore  2015       55645.61         44.046          -0.523   \n",
       "456  SMR      San Marino  2010       60426.24            NaN           2.413   \n",
       "457  SMR      San Marino  2015       43068.69            NaN           0.146   \n",
       "474  SWE          Sweden  2010       52658.79         28.799           1.901   \n",
       "475  SWE          Sweden  2015       51274.28         27.743           0.704   \n",
       "528  USA   United States  2010       48403.30         15.466           1.637   \n",
       "529  USA   United States  2015       56848.51         20.141           0.121   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports income_group  \n",
       "24             26.369              5.208    5.717   15.507  High income  \n",
       "25             25.880              6.050    6.533    1.962  High income  \n",
       "27             22.608              4.817   13.131   11.970  High income  \n",
       "28             23.806              5.742    3.049    3.630  High income  \n",
       "36             23.127              8.308    8.484    7.171  High income  \n",
       "37             23.628              8.483    1.368    1.053  High income  \n",
       "87             23.482              8.075    6.678   13.788  High income  \n",
       "88             23.822              6.942    3.419    0.751  High income  \n",
       "90             25.178              3.516   12.184    8.003  High income  \n",
       "91             24.747              3.178    3.972    4.561  High income  \n",
       "129            20.066              6.967   14.047   12.648  High income  \n",
       "130            19.743              4.633    4.878    5.361  High income  \n",
       "138            18.076              7.733    2.940    0.541  High income  \n",
       "139            20.631              6.283    3.577    4.561  High income  \n",
       "165            22.048              8.500    6.182    6.304  High income  \n",
       "166            21.686              9.575    0.392    1.963  High income  \n",
       "171            21.946              9.250    8.695    8.893  High income  \n",
       "181            17.720              5.375    2.799    5.412  High income  \n",
       "214            21.541              3.307   -1.368   -1.776  High income  \n",
       "234            17.217             14.583    6.289    0.678  High income  \n",
       "235            25.576              9.950   38.212   32.303  High income  \n",
       "243            13.909              8.317    0.954    2.319  High income  \n",
       "244            19.404              4.500    8.948   13.542  High income  \n",
       "258            22.590              5.058   24.861   11.285  High income  \n",
       "309            17.885              5.808   10.373   12.172  High income  \n",
       "310            19.324              6.626    4.701    3.399  High income  \n",
       "315            13.247              2.825      NaN      NaN  High income  \n",
       "316            25.330              1.825      NaN      NaN  High income  \n",
       "378            20.222              4.995    9.764    8.552  High income  \n",
       "379            22.475              6.891    7.444   14.883  High income  \n",
       "381            25.425              3.794    0.556    8.416  High income  \n",
       "382            27.476              4.531    4.342    1.935  High income  \n",
       "426               NaN                NaN   31.090   -1.425  High income  \n",
       "427               NaN                NaN   -1.827   -9.173  High income  \n",
       "444            27.658              2.175   17.788   16.307  High income  \n",
       "445            25.353              1.900    4.968    3.381  High income  \n",
       "456            19.613              4.945      NaN      NaN  High income  \n",
       "457            12.086              9.183      NaN      NaN  High income  \n",
       "474            22.962              8.575   11.107   11.816  High income  \n",
       "475            24.429              7.400    4.995    5.031  High income  \n",
       "528            18.743              9.608   12.137   13.128  High income  \n",
       "529            21.163              5.275    0.421    5.175  High income  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where gdp_percapita is greater than 40,000 and year is less than or equal to 2016\n",
    "top_gdp_2010_2015 = economies[(economies['gdp_percapita'] > 40000) & (economies['year'] <= 2016)]\n",
    "top_gdp_2010_2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #3: Cleaning and Preparing Data with Pandas\n",
    "\n",
    "By completing this exercise, you will be able to use `pandas` to  \n",
    "- Handle missing data  \n",
    "- Convert a column to a different data type   \n",
    "- Rename a column  \n",
    "- Change a DataFrame’s index  \n",
    "- Filter a DataFrame  \n",
    "\n",
    "### Handle Missing Data\n",
    "\n",
    "#### Remove rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows with any missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows only if all values are missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rows with missing values in specific columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns with any missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replace missing values with specific value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing values with a specific value (e.g., 0 for numerical columns, \n",
    "# 'Unknown' for categorical columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert a Column to a Different Data Type and Rename a Column\n",
    "\n",
    "#### Convert a Column to a Different Data Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the 'year' column to string type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert it back to integer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rename a Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the 'fertility_rate' column to 'fertility'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change a DataFrame’s Index and Filter a DataFrame\n",
    "\n",
    "#### Change a DataFrame’s Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the 'country_code' column as the index\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filter a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the DataFrame to include only rows where the 'continent' is 'Asia'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the DataFrame to include only rows where the 'year' is 2020\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter the DataFrame to include only rows where the 'fertility_rate' is greater than 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Walkthrough #4: Transforming and Aggregating Data with Pandas\n",
    "\n",
    "### Grouping data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Functions\n",
    "\n",
    "#### Applying a function element-wise with `map()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_group\n",
       "High income            33781.737556\n",
       "Low income               688.904493\n",
       "Lower middle income     2329.609629\n",
       "Not classified          7805.646667\n",
       "Upper middle income     6679.059320\n",
       "Name: gdp_percapita, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert income_group to uppercase using map()\n",
    "economies.groupby('income_group')['gdp_percapita'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying a function across columns with `apply()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_percapita</th>\n",
       "      <th>gross_savings</th>\n",
       "      <th>inflation_rate</th>\n",
       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>exports</th>\n",
       "      <th>imports</th>\n",
       "      <th>income_group</th>\n",
       "      <th>gdp_percapita_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "      <td>-0.195148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020</td>\n",
       "      <td>580.817</td>\n",
       "      <td>27.132</td>\n",
       "      <td>5.607</td>\n",
       "      <td>16.420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.424</td>\n",
       "      <td>2.892</td>\n",
       "      <td>Low income</td>\n",
       "      <td>-0.183485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2015</td>\n",
       "      <td>4354.920</td>\n",
       "      <td>28.491</td>\n",
       "      <td>9.159</td>\n",
       "      <td>34.202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-19.515</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2020</td>\n",
       "      <td>5067.150</td>\n",
       "      <td>14.602</td>\n",
       "      <td>3.268</td>\n",
       "      <td>12.426</td>\n",
       "      <td>29.175</td>\n",
       "      <td>-10.280</td>\n",
       "      <td>-16.615</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>-0.115948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>-0.251171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>-0.028049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>374 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code       country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "1    ABW         Aruba  2015      27126.620         21.411           0.475   \n",
       "2    ABW         Aruba  2020      21832.920         -7.521          -1.338   \n",
       "4    AFG   Afghanistan  2015        711.337         22.223          -0.662   \n",
       "5    AFG   Afghanistan  2020        580.817         27.132           5.607   \n",
       "7    AGO        Angola  2015       4354.920         28.491           9.159   \n",
       "..   ...           ...   ...            ...            ...             ...   \n",
       "554  ZAF  South Africa  2020       5067.150         14.602           3.268   \n",
       "556  ZMB        Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB        Zambia  2020        981.311         36.030          16.350   \n",
       "559  ZWE      Zimbabwe  2015       1425.010            NaN          -2.410   \n",
       "560  ZWE      Zimbabwe  2020       1385.040            NaN         557.210   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "1                 NaN              7.298      NaN      NaN   \n",
       "2                 NaN             13.997      NaN      NaN   \n",
       "4              18.427                NaN  -11.585   15.309   \n",
       "5              16.420                NaN  -10.424    2.892   \n",
       "7              34.202                NaN    6.721  -19.515   \n",
       "..                ...                ...      ...      ...   \n",
       "554            12.426             29.175  -10.280  -16.615   \n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "559               NaN                NaN      NaN      NaN   \n",
       "560               NaN                NaN      NaN      NaN   \n",
       "\n",
       "            income_group  gdp_percapita_change  \n",
       "1            High income                   NaN  \n",
       "2            High income             -0.195148  \n",
       "4             Low income                   NaN  \n",
       "5             Low income             -0.183485  \n",
       "7    Lower middle income                   NaN  \n",
       "..                   ...                   ...  \n",
       "554  Upper middle income             -0.115948  \n",
       "556  Lower middle income                   NaN  \n",
       "557  Lower middle income             -0.251171  \n",
       "559  Lower middle income                   NaN  \n",
       "560  Lower middle income             -0.028049  \n",
       "\n",
       "[374 rows x 12 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate percentage change in gdp_percapita across rows for 2015 to 2020\n",
    "# Filter rows for the years 2015 and 2020\n",
    "economies_focused = economies[economies['year'].isin([2015, 2020])].copy()\n",
    "\n",
    "# Calculate percentage change in gdp_percapita across columns for 2015 to 2020\n",
    "gdp_pct_change = economies_focused.groupby('code')['gdp_percapita'].apply(lambda x: x.pct_change(fill_method=None))\n",
    "\n",
    "# Reset the index of the result to align it with the original DataFrame\n",
    "gdp_pct_change = gdp_pct_change.reset_index(level=0, drop=True)\n",
    "\n",
    "# Assign the calculated percentage change back to the DataFrame\n",
    "economies_focused['gdp_percapita_change'] = gdp_pct_change\n",
    "economies_focused"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/t9/1zhtjchs09z7bj7wn8gkbwt40000gp/T/ipykernel_80470/3616364814.py:6: FutureWarning:\n",
      "\n",
      "The default fill_method='ffill' in SeriesGroupBy.pct_change is deprecated and will be removed in a future version. Either fill in any non-leading NA values prior to calling pct_change or specify 'fill_method=None' to not fill NA values.\n",
      "\n"
     ]
    },
    {
     "data": {
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       "      <th>country</th>\n",
       "      <th>year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>27126.620</td>\n",
       "      <td>21.411</td>\n",
       "      <td>0.475</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>21832.920</td>\n",
       "      <td>-7.521</td>\n",
       "      <td>-1.338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
       "      <td>-0.195148</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>711.337</td>\n",
       "      <td>22.223</td>\n",
       "      <td>-0.662</td>\n",
       "      <td>18.427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.585</td>\n",
       "      <td>15.309</td>\n",
       "      <td>Low income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020</td>\n",
       "      <td>580.817</td>\n",
       "      <td>27.132</td>\n",
       "      <td>5.607</td>\n",
       "      <td>16.420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.424</td>\n",
       "      <td>2.892</td>\n",
       "      <td>Low income</td>\n",
       "      <td>-0.183485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2015</td>\n",
       "      <td>4354.920</td>\n",
       "      <td>28.491</td>\n",
       "      <td>9.159</td>\n",
       "      <td>34.202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.721</td>\n",
       "      <td>-19.515</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>ZAF</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>2020</td>\n",
       "      <td>5067.150</td>\n",
       "      <td>14.602</td>\n",
       "      <td>3.268</td>\n",
       "      <td>12.426</td>\n",
       "      <td>29.175</td>\n",
       "      <td>-10.280</td>\n",
       "      <td>-16.615</td>\n",
       "      <td>Upper middle income</td>\n",
       "      <td>-0.115948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2015</td>\n",
       "      <td>1310.460</td>\n",
       "      <td>40.103</td>\n",
       "      <td>10.107</td>\n",
       "      <td>42.791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.407</td>\n",
       "      <td>0.696</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>ZMB</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2020</td>\n",
       "      <td>981.311</td>\n",
       "      <td>36.030</td>\n",
       "      <td>16.350</td>\n",
       "      <td>34.514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.143</td>\n",
       "      <td>2.635</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>-0.251171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>1425.010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>1385.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>557.210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lower middle income</td>\n",
       "      <td>-0.028049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>374 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    code       country  year  gdp_percapita  gross_savings  inflation_rate  \\\n",
       "1    ABW         Aruba  2015      27126.620         21.411           0.475   \n",
       "2    ABW         Aruba  2020      21832.920         -7.521          -1.338   \n",
       "4    AFG   Afghanistan  2015        711.337         22.223          -0.662   \n",
       "5    AFG   Afghanistan  2020        580.817         27.132           5.607   \n",
       "7    AGO        Angola  2015       4354.920         28.491           9.159   \n",
       "..   ...           ...   ...            ...            ...             ...   \n",
       "554  ZAF  South Africa  2020       5067.150         14.602           3.268   \n",
       "556  ZMB        Zambia  2015       1310.460         40.103          10.107   \n",
       "557  ZMB        Zambia  2020        981.311         36.030          16.350   \n",
       "559  ZWE      Zimbabwe  2015       1425.010            NaN          -2.410   \n",
       "560  ZWE      Zimbabwe  2020       1385.040            NaN         557.210   \n",
       "\n",
       "     total_investment  unemployment_rate  exports  imports  \\\n",
       "1                 NaN              7.298      NaN      NaN   \n",
       "2                 NaN             13.997      NaN      NaN   \n",
       "4              18.427                NaN  -11.585   15.309   \n",
       "5              16.420                NaN  -10.424    2.892   \n",
       "7              34.202                NaN    6.721  -19.515   \n",
       "..                ...                ...      ...      ...   \n",
       "554            12.426             29.175  -10.280  -16.615   \n",
       "556            42.791                NaN  -11.407    0.696   \n",
       "557            34.514                NaN    1.143    2.635   \n",
       "559               NaN                NaN      NaN      NaN   \n",
       "560               NaN                NaN      NaN      NaN   \n",
       "\n",
       "            income_group  gdp_percapita_change  \n",
       "1            High income                   NaN  \n",
       "2            High income             -0.195148  \n",
       "4             Low income                   NaN  \n",
       "5             Low income             -0.183485  \n",
       "7    Lower middle income                   NaN  \n",
       "..                   ...                   ...  \n",
       "554  Upper middle income             -0.115948  \n",
       "556  Lower middle income                   NaN  \n",
       "557  Lower middle income             -0.251171  \n",
       "559  Lower middle income                   NaN  \n",
       "560  Lower middle income             -0.028049  \n",
       "\n",
       "[374 rows x 12 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way?\n",
    "# Filter rows for the years 2015 and 2020\n",
    "economies_focused2 = economies[economies['year'].isin([2015, 2020])].copy()\n",
    "\n",
    "# Calculate percentage change in gdp_percapita across rows for each country\n",
    "economies_focused2['gdp_percapita_change'] = economies_focused2.groupby('code')['gdp_percapita'].pct_change()\n",
    "\n",
    "economies_focused2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying a Function to Groups with `groupby()` and `agg()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>gdp_percapita</th>\n",
       "      <th>inflation_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High income</th>\n",
       "      <td>29529.305</td>\n",
       "      <td>0.8595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low income</th>\n",
       "      <td>631.490</td>\n",
       "      <td>5.0490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle income</th>\n",
       "      <td>2012.150</td>\n",
       "      <td>4.4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not classified</th>\n",
       "      <td>10568.100</td>\n",
       "      <td>121.7380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper middle income</th>\n",
       "      <td>6083.870</td>\n",
       "      <td>2.7645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     gdp_percapita  inflation_rate\n",
       "income_group                                      \n",
       "High income              29529.305          0.8595\n",
       "Low income                 631.490          5.0490\n",
       "Lower middle income       2012.150          4.4370\n",
       "Not classified           10568.100        121.7380\n",
       "Upper middle income       6083.870          2.7645"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the median gdp_percapita and inflation_rate for each income_group\n",
    "median_values = economies.groupby('income_group').agg({\n",
    "    'gdp_percapita': 'median',\n",
    "    'inflation_rate': 'median'\n",
    "})\n",
    "median_values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">gdp_percapita</th>\n",
       "      <th colspan=\"3\" halign=\"left\">inflation_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
       "      <th>2020</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_group</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>High income</th>\n",
       "      <td>33265.256167</td>\n",
       "      <td>33484.692333</td>\n",
       "      <td>34595.264167</td>\n",
       "      <td>2.168550</td>\n",
       "      <td>0.910950</td>\n",
       "      <td>0.666333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low income</th>\n",
       "      <td>736.990261</td>\n",
       "      <td>685.146565</td>\n",
       "      <td>644.576652</td>\n",
       "      <td>5.915000</td>\n",
       "      <td>7.187591</td>\n",
       "      <td>14.530182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle income</th>\n",
       "      <td>2151.058283</td>\n",
       "      <td>2399.781453</td>\n",
       "      <td>2437.989151</td>\n",
       "      <td>5.778264</td>\n",
       "      <td>4.951170</td>\n",
       "      <td>18.002566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not classified</th>\n",
       "      <td>11158.180000</td>\n",
       "      <td>10568.100000</td>\n",
       "      <td>1690.660000</td>\n",
       "      <td>28.187000</td>\n",
       "      <td>121.738000</td>\n",
       "      <td>2355.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper middle income</th>\n",
       "      <td>6463.234694</td>\n",
       "      <td>6919.517551</td>\n",
       "      <td>6654.425714</td>\n",
       "      <td>4.251592</td>\n",
       "      <td>3.186125</td>\n",
       "      <td>3.886408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    gdp_percapita                             inflation_rate  \\\n",
       "year                         2010          2015          2020           2010   \n",
       "income_group                                                                   \n",
       "High income          33265.256167  33484.692333  34595.264167       2.168550   \n",
       "Low income             736.990261    685.146565    644.576652       5.915000   \n",
       "Lower middle income   2151.058283   2399.781453   2437.989151       5.778264   \n",
       "Not classified       11158.180000  10568.100000   1690.660000      28.187000   \n",
       "Upper middle income   6463.234694   6919.517551   6654.425714       4.251592   \n",
       "\n",
       "                                              \n",
       "year                       2015         2020  \n",
       "income_group                                  \n",
       "High income            0.910950     0.666333  \n",
       "Low income             7.187591    14.530182  \n",
       "Lower middle income    4.951170    18.002566  \n",
       "Not classified       121.738000  2355.150000  \n",
       "Upper middle income    3.186125     3.886408  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pivot table of gdp_percapita and inflation_rate by income_group and year\n",
    "pd.pivot_table(\n",
    "    economies,\n",
    "    values=['gdp_percapita', 'inflation_rate'],\n",
    "    index=['income_group'],\n",
    "    columns=['year'],\n",
    "    aggfunc='mean'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing categorical data \n",
    "\n",
    "#### Using cross-tabulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>year</th>\n",
       "      <th>2010</th>\n",
       "      <th>2015</th>\n",
       "      <th>2020</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High income</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low income</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lower middle income</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Not classified</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper middle income</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year                 2010  2015  2020\n",
       "income_group                         \n",
       "High income            60    60    60\n",
       "Low income             24    24    24\n",
       "Lower middle income    53    53    53\n",
       "Not classified          1     1     1\n",
       "Upper middle income    49    49    49"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show counts of income_group by year\n",
    "pd.crosstab(economies['income_group'], economies['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By getting group counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_group\n",
       "High income            180\n",
       "Lower middle income    159\n",
       "Upper middle income    147\n",
       "Low income              72\n",
       "Not classified           3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the occurrences of each income_group\n",
    "economies['income_group'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #4: Transforming and Aggregating Data with Pandas\n",
    "\n",
    "By completing this exercise, you will be able to use `pandas` to\n",
    "- Aggregate data effectively by grouping it  \n",
    "- Transform data by applying functions element-wise, across rows/columns, or to groups  \n",
    "- Create summary tables  \n",
    "- Analyze categorical data using cross-tabulation and counts  \n",
    "\n",
    "\n",
    "### Grouping Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group data by continent and calculate the mean life expectancy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying Functions\n",
    "\n",
    "#### Applying a function element-wise with `map()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert continent to uppercase using map()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying a function across columns with `apply()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter rows for the years 2015 and 2020\n",
    "\n",
    "\n",
    "# Calculate percentage change in population size across columns for 2015 to 2020\n",
    "\n",
    "\n",
    "# Reset the index of the result to align it with the original DataFrame\n",
    "\n",
    "\n",
    "# Assign the calculated percentage change back to the DataFrame\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying a function to groups with `groupby()` and `agg()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the median fertility rate and life expectancy for each continent\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a pivot table of fertility rate and life expectancy by continent and year\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing Categorical Data\n",
    "\n",
    "#### Using Cross-Tabulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a cross-tabulation of continent and year\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Getting Group Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the occurrences of each region\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 2: Data Visualization Basics with Matplotlib and Seaborn\n",
    "\n",
    "## Walkthrough #5: Creating Basic Plots with Matplotlib\n",
    "\n",
    "### Line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for a specific country\n",
    "afg_data = economies[economies['code'] == 'AFG']\n",
    "\n",
    "# Line plot of gdp_percapita over the years\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(afg_data['year'], afg_data['gdp_percapita'],\n",
    "        marker='o', linestyle='-', color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QktSgQQNJUnZ2tsrKyhQfH+/UtGnTRk2bNlVWVpYkKSsrSx06dHACjiQlJCTI4/Fo7969Ts2Zy6isqVxGaWmpsrOzvWr8/PwUHx/v1JxLSUmJPB6P1w0AANjpgkNORUWFxo8fr549e6p9+/aSpPz8fAUEBKh+/fpetWFhYcrPz3dqzgw4lfMr5/1UjcfjUXFxsb7++muVl5efs6ZyGeeSlpamkJAQ5xYZGVn1FQcAADXCBYec5ORk7dmzR6+//np19nNJpaamqrCw0LkdOnTI1y0BAIBLpNaFPGjcuHFavXq1Nm3apCZNmjjTw8PDVVpaquPHj3vtzSkoKFB4eLhT83+vgqq8+urMmv97RVZBQYHcbreCg4Pl7+8vf3//c9ZULuNcAgMDFRgYWPUVBgAANU6V9uQYYzRu3DgtX75c69evV/Pmzb3mx8TEqHbt2lq3bp0zLTc3V3l5eYqLi5MkxcXFaffu3V5XQWVkZMjtdisqKsqpOXMZlTWVywgICFBMTIxXTUVFhdatW+fUAACAK1uV9uQkJydr8eLFevPNN1WvXj3n/JeQkBAFBwcrJCRESUlJSklJUYMGDeR2u/XQQw8pLi5OPXr0kCT1799fUVFRGjlypGbMmKH8/HxNmTJFycnJzl6WMWPGaM6cOZo4caLuu+8+rV+/XkuXLtWaNWucXlJSUjRq1Ch17dpV3bt31/PPP6+ioiLde++91fXaAACAGqxKIWfevHmSpBtvvNFr+quvvqp77rlHkvTcc8/Jz89PQ4cOVUlJiRISEvTiiy86tf7+/lq9erXGjh2ruLg41a1bV6NGjdITTzzh1DRv3lxr1qzRhAkTNGvWLDVp0kQvv/yyEhISnJphw4bp2LFjmjp1qvLz89WpUyelp6efdTIyAAC4Ml3UODk1HePk2IlxcuzFODkApF9onBwAAIDLFSEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwUpVDzqZNmzR48GBFRETI5XJpxYoVXvPvueceuVwur9uAAQO8ar799luNGDFCbrdb9evXV1JSkk6ePOlVs2vXLvXu3VtBQUGKjIzUjBkzzupl2bJlatOmjYKCgtShQwe99dZbVV0dAABgqSqHnKKiIkVHR2vu3Lk/WjNgwAB99dVXzu0f//iH1/wRI0Zo7969ysjI0OrVq7Vp0yY98MADznyPx6P+/furWbNmys7O1jPPPKPp06drwYIFTs2WLVt0xx13KCkpSTt27NCQIUM0ZMgQ7dmzp6qrBAAALOQyxpgLfrDLpeXLl2vIkCHOtHvuuUfHjx8/aw9PpY8//lhRUVH64IMP1LVrV0lSenq6Bg0apMOHDysiIkLz5s3TY489pvz8fAUEBEiSJk+erBUrVmjfvn2SpGHDhqmoqEirV692lt2jRw916tRJ8+fPP6/+PR6PQkJCVFhYKLfbfQGvwI9zPe6q1uXh/JlpF/xf+rywbX3nUm5btqvvXOr3LOxzvp/fl+ScnI0bNyo0NFStW7fW2LFj9c033zjzsrKyVL9+fSfgSFJ8fLz8/Pz0/vvvOzV9+vRxAo4kJSQkKDc3V999951TEx8f7/W8CQkJysrK+tG+SkpK5PF4vG4AAMBO1R5yBgwYoP/93//VunXr9Je//EWZmZkaOHCgysvLJUn5+fkKDQ31ekytWrXUoEED5efnOzVhYWFeNZX3f66mcv65pKWlKSQkxLlFRkZe3MoCAIDLVq3qXuDw4cOdf3fo0EEdO3bUddddp40bN6pfv37V/XRVkpqaqpSUFOe+x+Mh6AAAYKlLfgn5r371KzVs2FD79++XJIWHh+vo0aNeNadPn9a3336r8PBwp6agoMCrpvL+z9VUzj+XwMBAud1urxsAALDTJQ85hw8f1jfffKPGjRtLkuLi4nT8+HFlZ2c7NevXr1dFRYViY2Odmk2bNqmsrMypycjIUOvWrXX11Vc7NevWrfN6royMDMXFxV3qVQIAADVAlUPOyZMnlZOTo5ycHEnSgQMHlJOTo7y8PJ08eVKPPvqotm7dqi+++ELr1q3TrbfeqhYtWighIUGS1LZtWw0YMED333+/tm3bpvfee0/jxo3T8OHDFRERIUm68847FRAQoKSkJO3du1dLlizRrFmzvA41/f73v1d6erpmzpypffv2afr06dq+fbvGjRtXDS8LAACo6aoccrZv367OnTurc+fOkqSUlBR17txZU6dOlb+/v3bt2qVbbrlFrVq1UlJSkmJiYvTuu+8qMDDQWcaiRYvUpk0b9evXT4MGDVKvXr28xsAJCQnRO++8owMHDigmJkYPP/ywpk6d6jWWzvXXX6/FixdrwYIFio6O1htvvKEVK1aoffv2F/N6AAAAS1zUODk1HePk2IlxcuzFODl2YpwcVJVPx8kBAADwNUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwUpVDzqZNmzR48GBFRETI5XJpxYoVXvONMZo6daoaN26s4OBgxcfH69NPP/Wq+fbbbzVixAi53W7Vr19fSUlJOnnypFfNrl271Lt3bwUFBSkyMlIzZsw4q5dly5apTZs2CgoKUocOHfTWW29VdXUAAIClqhxyioqKFB0drblz555z/owZMzR79mzNnz9f77//vurWrauEhASdOnXKqRkxYoT27t2rjIwMrV69Wps2bdIDDzzgzPd4POrfv7+aNWum7OxsPfPMM5o+fboWLFjg1GzZskV33HGHkpKStGPHDg0ZMkRDhgzRnj17qrpKAADAQi5jjLngB7tcWr58uYYMGSLph704ERERevjhh/XII49IkgoLCxUWFqaFCxdq+PDh+vjjjxUVFaUPPvhAXbt2lSSlp6dr0KBBOnz4sCIiIjRv3jw99thjys/PV0BAgCRp8uTJWrFihfbt2ydJGjZsmIqKirR69Wqnnx49eqhTp06aP3/+OfstKSlRSUmJc9/j8SgyMlKFhYVyu90X+jKc+7V53FWty8P5M9Mu+L/0eWHb+s6l3LZsV9+51O9Z2Mfj8SgkJORnP7+r9ZycAwcOKD8/X/Hx8c60kJAQxcbGKisrS5KUlZWl+vXrOwFHkuLj4+Xn56f333/fqenTp48TcCQpISFBubm5+u6775yaM5+nsqbyec4lLS1NISEhzi0yMvLiVxoAAFyWqjXk5OfnS5LCwsK8poeFhTnz8vPzFRoa6jW/Vq1aatCggVfNuZZx5nP8WE3l/HNJTU1VYWGhczt06FBVVxEAANQQtXzdwC8pMDBQgYGBvm4DAAD8Aqp1T054eLgkqaCgwGt6QUGBMy88PFxHjx71mn/69Gl9++23XjXnWsaZz/FjNZXzAQDAla1aQ07z5s0VHh6udevWOdM8Ho/ef/99xcXFSZLi4uJ0/PhxZWdnOzXr169XRUWFYmNjnZpNmzaprKzMqcnIyFDr1q119dVXOzVnPk9lTeXzAACAK1uVQ87JkyeVk5OjnJwcST+cbJyTk6O8vDy5XC6NHz9ef/rTn7Ry5Urt3r1bd999tyIiIpwrsNq2basBAwbo/vvv17Zt2/Tee+9p3LhxGj58uCIiIiRJd955pwICApSUlKS9e/dqyZIlmjVrllJSUpw+fv/73ys9PV0zZ87Uvn37NH36dG3fvl3jxo27+FcFAADUeFU+J2f79u3q27evc78yeIwaNUoLFy7UxIkTVVRUpAceeEDHjx9Xr169lJ6erqCgIOcxixYt0rhx49SvXz/5+flp6NChmj17tjM/JCRE77zzjpKTkxUTE6OGDRtq6tSpXmPpXH/99Vq8eLGmTJmiP/zhD2rZsqVWrFih9u3bX9ALAQAA7HJR4+TUdOd7nf2FYMwN32GcHHsxTo6dGCcHVeWTcXIAAAAuF4QcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAVqrl6wYAALiUXI+7fN3CFctMMz59fvbkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlao95EyfPl0ul8vr1qZNG2f+qVOnlJycrGuuuUZXXXWVhg4dqoKCAq9l5OXlKTExUXXq1FFoaKgeffRRnT592qtm48aN6tKliwIDA9WiRQstXLiwulcFAADUYJdkT067du301VdfObfNmzc78yZMmKBVq1Zp2bJlyszM1JEjR3T77bc788vLy5WYmKjS0lJt2bJFr732mhYuXKipU6c6NQcOHFBiYqL69u2rnJwcjR8/XqNHj9batWsvxeoAAIAaqNYlWWitWgoPDz9remFhof7nf/5Hixcv1k033SRJevXVV9W2bVtt3bpVPXr00DvvvKOPPvpI//73vxUWFqZOnTrpySef1KRJkzR9+nQFBARo/vz5at68uWbOnClJatu2rTZv3qznnntOCQkJP9pXSUmJSkpKnPsej6ea1xwAAFwuLsmenE8//VQRERH61a9+pREjRigvL0+SlJ2drbKyMsXHxzu1bdq0UdOmTZWVlSVJysrKUocOHRQWFubUJCQkyOPxaO/evU7NmcuorKlcxo9JS0tTSEiIc4uMjKyW9QUAAJefag85sbGxWrhwodLT0zVv3jwdOHBAvXv31okTJ5Sfn6+AgADVr1/f6zFhYWHKz8+XJOXn53sFnMr5lfN+qsbj8ai4uPhHe0tNTVVhYaFzO3To0MWuLgAAuExV++GqgQMHOv/u2LGjYmNj1axZMy1dulTBwcHV/XRVEhgYqMDAQJ/2AAAAfhmX/BLy+vXrq1WrVtq/f7/Cw8NVWlqq48ePe9UUFBQ45/CEh4efdbVV5f2fq3G73T4PUgAA4PJwyUPOyZMn9dlnn6lx48aKiYlR7dq1tW7dOmd+bm6u8vLyFBcXJ0mKi4vT7t27dfToUacmIyNDbrdbUVFRTs2Zy6isqVwGAABAtYecRx55RJmZmfriiy+0ZcsW3XbbbfL399cdd9yhkJAQJSUlKSUlRRs2bFB2drbuvfdexcXFqUePHpKk/v37KyoqSiNHjtTOnTu1du1aTZkyRcnJyc6hpjFjxujzzz/XxIkTtW/fPr344otaunSpJkyYUN2rAwAAaqhqPyfn8OHDuuOOO/TNN9+oUaNG6tWrl7Zu3apGjRpJkp577jn5+flp6NChKikpUUJCgl588UXn8f7+/lq9erXGjh2ruLg41a1bV6NGjdITTzzh1DRv3lxr1qzRhAkTNGvWLDVp0kQvv/zyT14+DgAAriwuY4zxdRO+4vF4FBISosLCQrnd7mpdtutxV7UuD+fPTLu0/6XZtr5zKbct29V3eM/a61Jt2/P9/Oa3qwAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJVqfMiZO3eurr32WgUFBSk2Nlbbtm3zdUsAAOAyUKNDzpIlS5SSkqJp06bpww8/VHR0tBISEnT06FFftwYAAHysRoecv/71r7r//vt17733KioqSvPnz1edOnX0yiuv+Lo1AADgY7V83cCFKi0tVXZ2tlJTU51pfn5+io+PV1ZW1jkfU1JSopKSEud+YWGhJMnj8VR/g6eqf5E4P5dke56Jbeszl3Tbsl19hvesvS7Vtq1crjHmJ+tqbMj5+uuvVV5errCwMK/pYWFh2rdv3zkfk5aWpscff/ys6ZGRkZekR/hGyNMhvm4Blwjb1k5sV3td6m174sQJhYT8+HPU2JBzIVJTU5WSkuLcr6io0LfffqtrrrlGLpfLh51dXjwejyIjI3Xo0CG53W5ft4Nqwna1F9vWXmzbczPG6MSJE4qIiPjJuhobcho2bCh/f38VFBR4TS8oKFB4ePg5HxMYGKjAwECvafXr179ULdZ4brebN5WF2K72Ytvai217tp/ag1Opxp54HBAQoJiYGK1bt86ZVlFRoXXr1ikuLs6HnQEAgMtBjd2TI0kpKSkaNWqUunbtqu7du+v5559XUVGR7r33Xl+3BgAAfKxGh5xhw4bp2LFjmjp1qvLz89WpUyelp6efdTIyqiYwMFDTpk0769Aeaja2q73YtvZi214cl/m5668AAABqoBp7Tg4AAMBPIeQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg7OyxdffKH169f7ug0AAM5bjR4nB7+MU6dOaeLEifr222/Vvn17hYaG+rolVIOCggJ98cUXatWqla6++mpftwOgCoqKihQQEKDatWv7upXLGnty8LOCgoL061//Wjk5OdqyZYskqby83Mdd4WLs3btXN998s+bPn699+/b5uh1UM4Y/s9vevXvVp08fvfnmmyorK/N1O5c1BgPEeRs5cqQ2bNigjz76SG63W8YYfr29BtqzZ4969+6tpKQkDR8+XF27dvV1S6hG+/fv1+uvv66DBw/qzjvvVI8ePRQcHOzrtlCNbrvtNr355ptq1qyZnnvuOQ0ePFj+/v6+buuyxJ4cnNP+/fu1aNEi5eXlOdMmTpyoBg0a6LHHHlNZWRkBpwb65ptvNHr0aD344IN69tlnvQJOSUmJvv/+e0nsCaipdu7cqV69eum9997Ttm3bNGTIEG3fvt3XbaGapaSkaNCgQYqIiNBdd92lFStWqKKiwtdtXZYIOThLcXGxkpKSNHLkSD344INauXKlJCkqKkq33HKL1q9frx07dkgSb6wa5tixYyouLtaQIUOcae+//76ef/559ezZU8OGDdPKlSsJsDXQzp07df311yspKUkrV67Uzp071aFDB61du1anT5/mELNFIiIiVFxcrMmTJ+vhhx8m6PwEQg7OUrt2bQ0ePFhdunRRz549ddddd2ny5MnKzc3V9OnTFRAQoKeeekqS5Ofnx7f+GuTLL7/UoUOHnD+GL730kiZMmKA33nhDLVq0kJ+fn0aNGqX333/fx52iKvLy8tS1a1dNmDBBf/7zn53tGxYWpr1796pnz54aM2aMMjMzfdwpLkRxcbFXgLnuuus0aNAgPfLII0pJSdEDDzygkSNHEnTOgZCDs9SqVUujR49WcXGx6tSpo7179+rAgQN67LHH9Oyzz2rOnDl699139dJLL0kS3/ovc4cOHdIbb7whSerXr5+6d++uG264Qe3atdO4ceM0ePBgvfjii3r99df19NNPKyQkRLt37/Zx16iKjz76SI0bN9bOnTsl/fDL1U8//bRWrlypmJgYde7cWdnZ2XrooYeUm5vr425RFbt27VJMTIz+8Ic/aPny5c70Bx54QC1atNCWLVs0a9YsDR8+XHfffbdWrlzJXrszGcAYc/DgQbN48WKvaVu2bDHNmzc3mzdvNoWFhWb58uWma9eupnXr1qZjx46me/fuZt++fT7qGOejpKTE3H///aZdu3bmb3/7mzN97ty55oUXXjCffPKJV/2RI0dMTEyM+ec///lLt4qLcOrUKbNmzRrTunVrM3jwYPP000+b0NBQs2bNGqdm8eLFJiAgwLz55ps+7BRVNXToUONyuUzfvn1NgwYNzD333GNeeeUVU1FRYcaPH28SEhKc2t/+9rfG5XKZlStX+rDjywsh5wpXUVFhysrKzO23324iIiLMLbfcYo4ePWrKysqMMcakpqaacePGGY/H4zzmd7/7nQkPDzcNGzY0+fn5vmod52n79u1mxIgRpmfPnmbhwoXO9PLy8rNqH3vsMdOmTRtz6NChX7JFVINTp06ZVatWmejoaONyuUxGRoYxxpjvv//eGGPM559/btq0aWPeeustX7aJKqqoqDA33HCDadu2rVm0aJEZO3asGThwoOncubP561//aoKDg83q1aud+gkTJpiPP/7Yhx1fXgg5MMYYc/ToUfP666+bqKgoExoaambOnGmOHDliPvroIxMVFeX1jdAYYzZu3Gjy8vJ81C3OR0VFhfPvnTt3mvvuu890797dLFmy5KzanJwc8/DDD5sGDRqYHTt2/IJd4kJ8/vnn5oUXXjCTJ082WVlZzpeQU6dOmZUrV5r27dt7fcM3xpjJkyeb1q1bmy+//NIXLeMiVFRUmPbt25u4uDiTmZlpTp06ZaZNm2ZuvfVW43K5zMaNG33d4mWLkHMFO3jwoHn55ZfNK6+8Yvbu3WuM+eHb/dixY02HDh1Mly5dTHZ2tpkxY4Zp0qQJe21qiOLiYmcvTWlpqTN93LhxJigoyMTFxZm///3vzvSXXnrJREdHm169epldu3b94v2ianJycsx//Md/mN69e5tGjRqZkJAQ8+KLLzp7X0tKSszq1atN69atTXx8vDHGmLS0NBMcHEyArQG+++47k5uba9LT082nn35qjh496szr2LGjadWqldm+fbsxxpjjx4+fdcgZ3gg5V6idO3eaJk2amLi4ONOgQQOTmJhoPvzwQ2f+qlWrzMiRI43L5TJJSUnmuuuuMxMnTjTFxcU+7Bo/5+DBg6Zr165mw4YNXgEnLS3NNGzY0Lz44ovmrrvuMr169fIKOunp6earr77yRcuogl27dpk6deqY6dOnmxMnTpiKigrTsWNH07lzZ3P69GmnrqSkxKxatcq0a9fOXHXVVSYoKMj5YMTla/fu3aZ3796mdevWpl69eiY4ONgMGTLE6zyqTp06mZYtW5qtW7c6X2bO3GsLb4ScK1BOTo4JDg42kydPNsXFxebdd981jRo1OuuQVGlpqVm2bJlp166dcblcpmvXrl7n5uDy1KJFC9O6dWuzefNmY4wxM2bMMA0aNDBr1641xvyw/e+44w5zww03mJdeesmXraIKDh8+bFwul7nzzju9pg8ePNjUr1/fHD582Gt6cXGx+de//mX69etncnJyfslWcQH27Nlj3G63SUlJMZmZmeaTTz4xM2bMMC1btjRNmjTxujCkc+fOpl27dmbTpk0EnJ/BzzpcYT7++GNFR0dr8uTJeuKJJ5zp/fv3V+vWreVyudS0aVM98sgjzrxPPvlEb775pm699Va1atXKF23jZxhjVFZWpoCAAElSbGysTp48qRtuuEFLly7V0qVLddNNNzn1u3bt0h/+8AeVl5dryZIlcrvdvmodVdCxY0eVl5drwYIF6tmzp2bOnKlHH31UYWFhuummm7R3714NHjxYt9xyi1q1aqWQkBAVFRWpbt26vm4dP+HEiRMaMmSI2rVrp9mzZ3vNe/vtt/X444/rxIkTeuWVVxQbGyvph7FyrrnmGm3atElBQUG+aLtm8HHIwi9s1qxZxuVymX/961/OtKeeesq4XC5z1113mfj4eONyuczo0aO9HneuK3FwecjNzTXjxo0zt912m3nqqaec6b179zYul8s899xzzrQzt+Pu3bvP+vaPy09FRYUpKSlx7nfv3t20bdvWjB071jRs2NBkZGSYgwcPmuPHj5vp06eb22+/3bhcLpOYmGhOnDjhw85xvo4cOWI6dOhg3nnnHWPMD+/TMw8//vOf/zR169b1ei8b88MJ6PhphJwrUGpqqqldu7ZJT083f/nLX0yDBg2cQ1VFRUXmiSeeMEFBQeaDDz5gV+hlLicnxzRq1MgMGTLEDB8+3NSuXdsr6PTs2dNcd9115t1333UCDoG15qhKgD3Thg0bzGefffYLdYmLlZOTY2rVqmX+/e9/e00/8+/v4MGDTf/+/Y0xxiv04qfV8vWeJPxyysvL5e/vr6eeekqnT5/WwIED5e/vr/T0dPXr10/GGNWpU0dt2rRReHi4GjRowGjGl7Fdu3YpLi7Oayj/hg0b6ujRo/J4PHK73dq8ebP69u2rkSNHatGiRerRo4f8/BjovCbYuXOnfv3rX6tnz54KCgrStGnTJEmpqanatGmTevXqpTlz5qhr1666/vrrnZ9YcblcuvHGG33bPKqkTp068vf319atW9WvXz9VVFTIz8/P6+9v7dq1nfdu5WFp/Dz+2llu3759euyxx3Tw4EGvN8yMGTP0pz/9SeXl5fruu+8k/f+fZ9i+fbsaN26skJAQn/SMn3fo0CH169dPN998s/785z9L+uF3xI4dO6aNGzcqNjZW8fHxWrVqlTZs2KBmzZrp5ptv1gcffODjznE+KgPs/fffr+XLl2vRokV68MEHnQArSZs3b1ZkZKRGjhyprVu3qqKigi8lNcT333+vr7/+Whs2bNCXX36pli1b6qGHHlJaWpq2bt0qPz8/56cZysvLVVFRIX9/f3Xu3FmS+L3AqvDxniRcQqWlpaZbt27G5XKZli1bmkceeeSsgeBSUlJM7dq1zaJFi4wxxkydOtXUqVOHqzEucwcOHDDdunUzt9xyi3MVVVpamqlTp4558sknzcsvv2zatm1rrr32WnPw4EFjjDH9+vUzn376qS/bxnnIy8szDRs2NP/1X//lNX3YsGGmU6dOpk2bNqZfv37O0P033HCDufrqq83WrVt90S6qKDc319x9992mTZs2JigoyLjdbnPnnXeaOXPmmMTERFOvXj2Tnp5uTp48aYz5YcTqadOmmUaNGjEmzgXg6irLPfPMM6pVq5bat2+v9957T7Nnz9agQYN0/fXXa+zYsXK5XJo+fbpmzJihXr16acuWLcrMzFRMTIyvW8fP+PTTT/W73/1OAQEBCg0N1cqVK/W3v/1N/fv3l/TDL1Nfe+21mj17tsaNG+fjbnG+vvjiC/3mN79R48aNNXHiRPXs2VNPP/20nnzySaWmpqpx48aaOXOmiouLlZmZqaZNmyo+Pl7z589XixYtfN0+fsKuXbs0YMAA3XrrrerRo4diY2P1yiuvaPXq1TLGaNiwYdq3b5+WLl2qbt26qW7dugoKCtKOHTu0Zs0adenSxderUPP4OGThEtuwYYNxu93mgw8+MMb8cBb/9OnTTXBwsImNjTULFiwwn3zyiZk5c6apVauW14CAuPzl5uaaX//61yY4ONg8++yzxpgfTlYsLS01hw8fNtHR0WbZsmXOdNQMn3zyiRkwYIC55ZZbzOjRo01oaKgzzpExPwz66HK5zAsvvODDLlEVO3fuNHXq1DGpqanO6NSVFi9ebGJjY01sbKzZsmWLefXVV01SUpL5zW9+Y2bNmmX279/vo65rPkLOFeCRRx4xI0aMcEYrHjZsmGnTpo25++67TZ8+fUzt2rXNP/7xD3P8+HEfd4oLsX//ftO/f38zcOBAs2nTJmf6H//4R9O8eXN+Y6yGIsDa41yHICt/HLnS/PnzTUhIiFmwYIExxnhdQo4Lx4nHV4DY2Fh9/vnnCggI0OjRo7Vx40a98cYbeu2117RgwQLNmDFDHTp04ETjGuq6667TnDlzZIzRn//8Z+3YsUMzZszQM888o3/+85+KjIz0dYu4AK1atdK8efPUu3dvrVu3Tu+++65cLpdq166t//7v/5bH43EGhuOE48tbeXm5mjdvrpKSEm3evFnSD9usVq1azknEDz74oKKjo/X2228783HxOCfnCnHDDTdo8+bNCg8P11tvvaXo6Ghft4Rq9umnnyolJUXbtm3Td999p6ysLM6tskDluVfGGKWlpSkjI0PTpk3Tli1bnKttcPk7cztOmTJFvXr1kiTnsn9J6tu3ryIiIrRo0SJftmoV9uRYrjLDTpo0SS1atNDcuXMVHR3NJYgWatmypZ599ln16NFDO3bsIOBYomXLlpo9e7Zq166tAQMGaMqUKdq8eTMBp4ap3I4ul0t/+tOf9N5770n6YY9NRUWFDh8+rODgYOfCAf5GVw9CjuUqvyHExMSooqJC2dnZXtNhl9atW+uNN95Qu3btfN0KqhEB1g5nBp0nn3zSOXTl5+enOXPm6MiRI+rXr58k/kZXFw5XXUH+/ve/a8yYMVq/fr26d+/u63YAVFFZWZlq167t6zZwkc51CLIy9HAqQfUi5FxBvvzyS911113629/+piZNmvi6HQC4YnEO3S+DkHOFOXXqlIKCgnzdBgBc8XJzczVx4kQ99dRTHGK+RAg5AAD4CIcgLy1CDgAAsBJXVwEAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAK/0/i7TQPfgmPgQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for Caribbean countries and the year 2020\n",
    "caribbean_countries = ['ABW', 'BHS', 'BRB', 'DOM']\n",
    "data_2020_caribbean = economies[(economies['year'] == 2020) & (economies['code'].isin(caribbean_countries))]\n",
    "\n",
    "# Bar chart of gdp_percapita for different Caribbean countries in 2020\n",
    "plt.bar(data_2020_caribbean['code'], \n",
    "        data_2020_caribbean['gdp_percapita'], \n",
    "        color='g')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding labels and titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v3r3Av/14bhUYGOjw2svLK0XXTnjfrYnb7fz000+8+uqrREVFJWr+uHjxosMokGLFimGz2RzOKV68OGA+7s6XL1+Kr5vgww8/5M0332T37t3Exsba9xcuXPi270v4ft3uXpNKiJ5++mk+//xzoqKiePDBB9m/fz/btm1zGBq/d+9eDMPg/vvvT7LcW5sV7rvvPofRZylRqFAhgoODKVOmjD1xSfhc3K6J4eLFi+TIkYNr164xfvx4IiMjOXbsmEPydmuSCknX58SJE+nUqRMhISFUqVKFpk2b0rFjx2T7Nd3O4cOHAShRokSiY6VKlWLlypVcuXKFgICA28bkCrd+H4sWLYqHh4e9ac2V3++7/Xwn+O/39W7cf//9PPzww/bXLVq0wGazMWXKFLp27Uq5cuXsx8LCwvjggw+Ij49n//79jB07ln/++Qc/P7/bxnbrzwRJOSU7kkj27NnJnz9/khNZJfThuZuOwwnlFStWzGG/p6enww+J1CpXrlyy70/463zevHlJJgi39kHx9fXFw+PODzwDAwMpUKBAiif72r9/Pw0aNKBkyZJMnjyZkJAQfHx8+Prrr3nrrbcSza/hbPPnz6dz5848/vjjDB48mODgYDw9PRk/fry9j1NySpUqBcBvv/3G448/nuQ5CXMulS5d2r7vkUcewd/fn0WLFvHggw+yaNEiPDw8HOa3iY+Px2az8c033yR6GgKJny7+9ynLvUio70mTJlGxYsUkz0m49nPPPUdkZKS9c3P27Nmx2Wy0bt06ye9bUjE+9dRT1K5dm2XLlrFq1SomTZrEhAkTWLp0KU2aNHHKPd2Os+ottW795eyq7/e9fL7B7N93/vz5FF0rNRo0aMC7777L999/75DsBAQEOPzMqlmzJpUrV2bYsGFJTomQEFvu3LmdHmNmoWRHktSsWTNmzZrFzz//TPXq1Z1S5rx58wCIiIhwSnkpkTAvRXBw8D0lVElp3rw5M2fOJCoqivDw8Nue++WXXxITE8Py5cvtTThAsqNF9u3bh2EYDr8s/vrrLwD7CKukJPeX32effUaRIkVYunSpwzmvvvrqbeMGqFWrFkFBQSxYsICXX345yV9SH330EWDWSYKAgACaN2/O4sWLmTx5Mp9++im1a9d2mPuoaNGiGIZB4cKF7U+u3CHhcxEYGHjHz8Vnn31Gp06dePPNN+37rl+/nqgz7Z3kz5+f3r1707t3b06fPk3lypUZO3ZsqpOdhHmt9uzZk+jY7t27yZ07t8NTHXfau3evw5OUffv2ER8fb//Muur7fS+fb4CSJUs6jN50lps3bwJJPyX/r/Lly9O+fXvef/99Bg0a5PAzAsyRpR4eHm79P5LRqM+OJOnFF1/E39+frl27curUqUTHU/vId8GCBcyaNYvw8HAaNGjgrDDvKCIigsDAQMaNG+fwaDvBP//8c9dlv/jiiwQEBNC9e/ck62j//v28/fbbAPYE4dYmkMjIyCTLPn78uEOfpejoaD766CMqVqx42yasgICAJJtWkrr+5s2biYqKut0tAuDv78+gQYPYs2ePfTTQf3311VfMnTuXiIgIHnjgAYdjTz/9NMePH2fWrFn8+uuvPP300w7HW7RogaenJ6NGjUr0mTIMg7Nnz94xvrtRpUoVihYtyhtvvJHkL6L/fi48PT0TxfbOO+8kmhk3OXFxcYm+J8HBwRQoUOCOs4QnJX/+/FSsWJEPP/zQIeHauXMnq1atsk9sZ4Vb+/C98847APaEzlXf73v5fIM5HcLOnTvv6vtxOwnN8xUqVLjjuS+++CKxsbFMnjw50bFt27ZRpkyZu57wUPRkR5Jx//33s2DBAtq0aUOJEiXsMygbhsHBgwdZsGABHh4eSfZv+eyzz8iaNSs3btywz6D8008/UaFCBRYvXuzW+wgMDGT69Ol06NCBypUr07p1a/LkycORI0f46quvqFmzJu++++5dlV20aFEWLFjA008/TalSpRxmUN64cSOLFy+mc+fOADRq1AgfHx8eeeQRnnnmGS5fvswHH3xAcHAwJ06cSFR28eLF6datG1u2bCFv3rzMmTOHU6dOJZscJahSpQqffvopAwcOpFq1amTNmpVHHnmE5s2bs3TpUp544gmaNWvGwYMHmTFjBqVLl77jX50AL730Etu3b2fChAlERUXRsmVLsmTJwo8//sj8+fMpVaoUH374YaL3JcxXNGjQIDw9PRMN5S9atCivvfYaQ4cO5dChQzz++ONky5aNgwcPsmzZMnr27MmgQYPuGF9qeXh4MGvWLJo0aUKZMmXo0qUL9913H8eOHWP9+vUEBgbaf1E1b96cefPmkT17dkqXLk1UVBRr1qxJ8bQIly5domDBgjz55JNUqFCBrFmzsmbNGrZs2eLwtCg1Jk2aRJMmTQgPD6dbt25cu3aNd955h+zZsyc5/01qLVmyhN27dyfan9DvKDkHDx7k0UcfpXHjxkRFRTF//nzatm1r/2Xvqu/3vX6+H3vsMcaMGcOGDRto1KhRouNz5szh22+/TbT/v/No/fLLL8yfPx8wv+dr165lyZIlPPjgg0mWeavSpUvTtGlT+zQJCZ+v2NhY+9xNcg/cPPpL0pl9+/YZzz77rFGsWDHDz8/PyJIli1GyZEmjV69exo4dOxzOTRh+mvDl5+dnFCxY0GjevLkxZ84c4/r164nKT2oG5ZRKzWyv69evNyIiIozs2bMbfn5+RtGiRY3OnTsbW7duvedY/vrrL6NHjx5GWFiY4ePjY2TLls2oWbOm8c477zjc8/Lly43y5csbfn5+RlhYmDFhwgRjzpw5BmAcPHjQfl5oaKjRrFkzY+XKlUb58uUNX19fo2TJkonuM6mh55cvXzbatm1rBAUFGYB9GHp8fLwxbtw4IzQ01PD19TUqVapkrFixwujUqVOKZ1yOi4szIiMjjZo1axqBgYGGn5+fUaZMGWPUqFG3nZW3Xbt2BmA8/PDDyZ6zZMkSo1atWkZAQIAREBBglCxZ0ujTp4+xZ88e+zlJzTybUgl1eqvt27cbLVq0MHLlymX4+voaoaGhxlNPPWWsXbvWfs758+eNLl26GLlz5zayZs1qREREGLt37zZCQ0ONTp062c9LbphyTEyMMXjwYKNChQpGtmzZjICAAKNChQopmsX5dp/xNWvWGDVr1jSyZMliBAYGGo888oixa9cuh3NSO7NxwvWS+/rhhx8Mw0h+6PmuXbuMJ5980siWLZuRI0cOo2/fvsa1a9cSXedev9+3Dj13xue7fPnyRrdu3Rz2JXxPk/s6evRokkPPvby8jCJFihiDBw82Ll26lCj25O7ru+++S1S333zzjQEYe/fuTdF9SNJshnGPXdBFxKnCwsIoW7asfYI+EXG9efPm0adPH44cOZLkxH5Wefzxx7HZbCmeikOSpj47IiKS6bVr145ChQqlau4wV/vzzz9ZsWIFY8aMsTqUdE99dkREJNPz8PBI8VQS7lKqVCn7iC65N3qyIyIiIhma+uyIiIhIhqYnOyIiIpKhKdkRERGRDE0dlDHXazl+/DjZsmXTQmsiIiLphGEYXLp0iQIFCtx2XUMlO5hT899uVlARERFJu44ePZrkjP4JlOwA2bJlA8zKCgwMdFq5sbGxrFq1ikaNGuHt7e20csWR6tl9VNfuoXp2D9Wze7iynqOjowkJCbH/Hk+Okh3+XSk6MDDQ6cmOv78/gYGB+o/kQqpn91Fdu4fq2T1Uz+7hjnq+UxcUdVAWERGRDE3JjoiIiGRoSnZEREQkQ7M02QkLC8NmsyX66tOnDwD16tVLdKxXr14OZRw5coRmzZrh7+9PcHAwgwcP1loiIiIiYmdpB+UtW7YQFxdnf71z504aNmxIq1at7Pt69OjB6NGj7a/9/f3t23FxcTRr1ox8+fKxceNGTpw4QceOHfH29mbcuHHuuQkRERFJ0yxNdvLkyePw+vXXX6do0aLUrVvXvs/f3598+fIl+f5Vq1axa9cu1qxZQ968ealYsSJjxoxhyJAhjBw5Eh8fH5fGLyIiImlfmumzc+PGDebPn0/Xrl0dhpB9/PHH5M6dm7JlyzJ06FCuXr1qPxYVFUW5cuXImzevfV9ERATR0dH88ccfbo1fRERE0qY0M8/O559/zoULF+jcubN9X9u2bQkNDaVAgQL89ttvDBkyhD179rB06VIATp486ZDoAPbXJ0+eTPZaMTExxMTE2F9HR0cD5lwAsbGxzrole1nOLFMSUz27j+raPVTP7qF6dg9X1nNKy7QZhmE4/ep3ISIiAh8fH7788stkz1m3bh0NGjRg3759FC1alJ49e3L48GFWrlxpP+fq1asEBATw9ddf06RJkyTLGTlyJKNGjUq0f8GCBQ59gkRERCTtunr1Km3btuXixYu3nRQ4TTzZOXz4MGvWrLE/sUlOjRo1AOzJTr58+fj5558dzjl16hRAsv18AIYOHcrAgQPtrxOmm27UqJHTZ1BevXo1DRs21OycLqR6dp90V9dxcdh+/BFOnID8+TFq1QJPT6ujuqN0V8/plOrZPVxZzwktM3eSJpKdyMhIgoODadas2W3P27FjBwD58+cHIDw8nLFjx3L69GmCg4MBWL16NYGBgZQuXTrZcnx9ffH19U2039vb2yUfeFeVK45Uz+6TLup66VLo3x/+/vvffQULwttvQ4sW1sWVCuminjMA1bN7uKKeU1qe5R2U4+PjiYyMpFOnTnh5/Zt77d+/nzFjxrBt2zYOHTrE8uXL6dixI3Xq1KF8+fIANGrUiNKlS9OhQwd+/fVXVq5cySuvvEKfPn2STGZEJJNYuhSefNIx0QE4dszcf4enyCKSsVie7KxZs4YjR47QtWtXh/0+Pj6sWbOGRo0aUbJkSV544QVatmzp0KfH09OTFStW4OnpSXh4OO3bt6djx44O8/KISCYTF2c+0UmqO2LCvuefN88TkUzB8masRo0akVQf6ZCQEDZs2HDH94eGhvL111+7IjQRSY9++CHxE53/Mgw4etQ8r149t4UlItax/MmOiIhTnTjh3PNEJN1TsiMiGcv/BzA47TwRSfeU7IhIxlK7NuTMeftzvL2hQAH3xCMillOyIyIZy9mzcKdZVWNjoWpVWLjQPTGJiKWU7IhIxmEY0Ls3XLoEoaFw332Ox0NC4P33oVYt85w2baB7d7hyxZp4RcQtlOyISMbx6aewZAl4ecHnn8Phw7B+PSxYYP578CD07GluDx8ONhvMng3VqsHvv1sdvYi4iJIdEckYTp6EPn3M7VdegYoVzaUh6tUzn+DUq/fvUhFeXjB6NKxda3ZU/vNPqF4dZsxIen4eEUnXlOyISPpnGNCrF5w7ZyY5w4al7H0PPQS//gpNmsD16/Dss9CqFVy44MpoRcTNlOyISPq3YAF88YU5yurDD81/UypPHlixAt5803zfkiVmwhQV5bJwRcS9lOyISPp2/Dg895y5/eqr8P+181LFwwMGDoSffoIiRcy+PrVrw+uvQ3y8c+MVEbdTsiMi6ZdhwDPPwPnzUKUKDBlyb+VVqwbbt0Pr1ubaWUOHQuPGZn8gEUm3lOyISPr10UdmE5SPD8yda3Y8vleBgWaz2OzZkCULrF4NFSrAqlX3XraIWELJjoikT8eOmaubA4waBWXLOq9smw26doWtW6FcOTh9GiIi4KWX7jxhoYikOUp2RCT9MQzo0QMuXjSHjA8a5JrrlC4Nmzebo7QAJkyAOnXM+XpEJN1QsiMi6U9kJHzzDfj6Oq/5KjlZssB778Fnn0H27LBpE1SqZL4WkXRByY6IpC9HjsCAAeb2mDFQqpR7rtuyJezYAeHh5hOlVq3MuX2uXXPP9UXkrinZEZH0wzDMtayio+GBB8zh4u4UFgYbNpijtGw2c52t6tXhjz/cG4eIpIqSHRFJP2bNMkdH+fmZzVcJyz+4k7c3jBsHK1dC3rywc6c5ZH3WLC01IZJGKdkRkfTh8OF/n+SMGwclSlgbT8OG5lITjRqZTVk9ephrcF28aG1cIpKIkh0RSfvi482h4JcvQ82a0K+f1RGZ8uY1O0pPmGB2kv70U7Pz8s8/Wx2ZiPyHkh0RSfvefx/WrTNHRkVGWtN8lRwPD3jxRfjhB7NPz8GDZkL2xhtaakIkjVCyIyJp28GDMHiwuf3663D//dbGk5wHHjCXmnjySbh504y5WTNzQkIRsZSSHRFJuxKar65cMSfz69vX6ohuLygIFi0yn0T5+cG335pLTaxda3VkIpmakh0RSbveew+++w4CAszmK4908CPLZoOePWHLFnMG5pMnzc7Mr7xiPvEREbdLBz85RCRT2rfv31XMJ0yAIkWsjSe1ypY1E54ePcwh6WPHQr165qSIIuJWSnZEJO1JaL66ehUeeujftanSG39/mDkTFi40V1P/6SeoWBGWLbM6MpFMRcmOiKQ977xjjm7KmhXmzEkfzVe38/TTZuflatXg/Hlo0cLsf3T9utWRiWQK6fwniIhkOH/9ZS7HAObw7bAwS8NxmiJF4Mcf/x1ZNm0a1KgBu3dbG5dIJqBkR0TSjrg46NLFnJH44YfNjr4ZiY8PTJxoTkSYJw/89htUqWIufaGlJkRcRsmOiKQdU6bAxo2QLZu51pTNZnVErtG4sbnURP36Zr+kLl2gQwe4dMnqyEQyJCU7IpI27N4NL79sbk+eDKGh1sbjavnzw6pV5igtT0/4+GOoXBm2bbM6MpEMx9JkJywsDJvNluirT58+nDt3jueee44SJUqQJUsWChUqRL9+/bh4yyJ7Sb1/4cKFFt2RiNyVuDjo3BliYiAiArp1szoi9/D0hGHDYMMGCAkxh9uHh+MxdaqatUScyMvKi2/ZsoW4uDj76507d9KwYUNatWrF8ePHOX78OG+88QalS5fm8OHD9OrVi+PHj/PZZ585lBMZGUnjxo3tr4OCgtx1CyLiDG++CZs3Q/bsGbv5Kjk1a8KOHdC9OyxbhuegQdSoWtXswJw/v9XRiaR7liY7efLkcXj9+uuvU7RoUerWrYvNZmPJkiX2Y0WLFmXs2LG0b9+emzdv4uX1b+hBQUHky5fPbXGLiBPt2gXDh5vbb70FBQtaG49VcuaEJUtg+nSMgQPJt3UrRtWqsGAB1K1rdXQi6Zqlyc5/3bhxg/nz5zNw4EBsyfxVd/HiRQIDAx0SHYA+ffrQvXt3ihQpQq9evejSpUuyZQDExMQQExNjfx0dHQ1AbGwssbGxTrgb7OX9919xDdWz+zi9rm/exLNjRzxu3CC+SRPi2rWDzP597NGDm5Urc/PJJ8l27BhG/frEDxtG/Msvp63V3jMA/exwD1fWc0rLtBlG2mgYXrRoEW3btuXIkSMUKFAg0fEzZ85QpUoV2rdvz9ixY+37x4wZQ/369fH392fVqlW8+uqrTJw4kX79+iV7rZEjRzJq1KhE+xcsWIC/v79zbkhE7uj+zz6j9Pz53AgIYP3UqVzPlcvqkNIMz+vXKffBB4T+fxHRM2XKsG3AAK7nzm1xZCJpx9WrV2nbtq39YUhy0kyyExERgY+PD19++WWiY9HR0TRs2JCcOXOyfPlyvL29ky1nxIgRREZGcvTo0WTPSerJTkhICGfOnLltZaVWbGwsq1evpmHDhreNWe6N6tl9nFrXv/+O1wMPYIuN5eacORjt2zsnyAzgv/Xss3gxnn37Yrt8GSNnTuJmzcJo3tzqEDME/exwD1fWc3R0NLlz575jspMmmrEOHz7MmjVrWLp0aaJjly5donHjxmTLlo1ly5bdsaJq1KjBmDFjiImJwdfXN8lzfH19kzzm7e3tkg+8q8oVR6pn97nnuo6NNRfIjI2FRx7Bq3PnzNcpOQW8vb3x6tQJHnwQWrfG9ssveLVoAf37m4ujJvMzTlJHPzvcwxX1nNLy0sQ8O5GRkQQHB9OsWTOH/dHR0TRq1AgfHx+WL1+On5/fHcvasWMHOXLkSDbREZE04PXX4ZdfIEcOeP99JTp3cv/95mSLAwaYr99+G8LDYe9ea+MSSScsf7ITHx9PZGQknTp1cuh4nJDoXL16lfnz5xMdHW3vSJwnTx48PT358ssvOXXqFA888AB+fn6sXr2acePGMWjQIKtuR0Tu5NdfYcwYc/vddzW0OqV8fc3JFuvXN+ck2r7dnIRw+nRQE6DIbVme7KxZs4YjR47QtWtXh/2//PILmzdvBqBYsWIOxw4ePEhYWBje3t5MmzaNAQMGYBgGxYoVY/LkyfTo0cNt8YtIKty4Yf6ijo2FJ56ANm2sjij9ad7cTBjbtTMnI+zQAdasMRPHrFmtjk4kTbI82WnUqBFJ9ZGuV69ekvv/q3Hjxg6TCYpIGjdunDl5Xq5c5hMJNV/dnfvug7VrzaUmRo2CDz+EqCj49FOoWNHq6ETSnDTRZ0dEMoFffjF/OQNMmwZ581obT3rn6QkjRsD69Wby89df5ozL776rpSZEbqFkR0RcL6H56uZNePJJeOopqyPKOOrUMZu1HnnErOfnnjObCM+dszoykTRDyY6IuN6YMfD775AnD7z3npqvnC1XLvjiC3OUlo+PuV2xIvz4o9WRiaQJSnZExLW2boXx483t6dPNhEecz2aDfv3Mvjv33w9Hj5prar32mrmqvEgmpmRHRFwnJgY6dTJ/2T79NLRsaXVEGV/lyrBtmzlKKz7eXGS1YUM4ftzqyEQso2RHRFxn5EhzVfPgYLPjrLhHtmzw0UfmKK2AALMTc4UK8M03VkcmYgklOyLiGj//DBMnmtszZoAWsHS/jh3NpzwVK8KZM9C0KQwaZHZkFslElOyIiPNdv242X8XHm5PfPfGE1RFlXiVKmP14nnvOfP3mm1CzJuzfb21cIm6kZEdEnG/ECNi9G/Llg6lTrY5G/PzM78Pnn5vrkW3dCpUqwcKFVkcm4hZKdkTEuTZuhDfeMLdnzoScOa2NR/712GPmnDy1asGlS+ZyHd27w5UrVkcm4lJKdkTEea5dgy5dzBl8O3Y0J7qTtCUkxOywPHy4OVx99myoVs2cB0kkg1KyIyLO88or5rIFBQrAlClWRyPJ8fKC0aPN9bXy54c//4Tq1c2O5FpqQjIgJTsi4hw//ghvvWVuf/CB2TdE0raHHjKbtZo0MTuVP/sstGoFFy5YHZmIUynZEZF7d+XKv81XXbqYQ5wlfciTB1asMEdpeXvDkiXmUPWoKKsjE3EaJTsicu+GDYN9+6BgQZg82epoJLU8PGDgQPjpJyhSBA4fhtq14fXXzekDRNI5JTsicm82bPh3ePmsWRAUZGk4cg+qVYPt26F1a3OJj6FDoXFjOHnS6shE7omSHRG5e5cvQ9eu5naPHhARYW08cu8CA2HBAnOUVpYssHq1udTEqlVWRyZy15TsiMjde+klOHAAChX6d24dSf9sNjOJ3bYNypWD06fNRPallyA21uroRFJNyY6I3J1162DaNHN79mzziYBkLKVKwebN5igtgAkToE4dOHjQ2rhEUknJjoik3qVL/zZf9eoFDz9sbTziOlmywHvvwWefmf2xNm0yl5r47DOrIxNJMSU7IpJqHkOHmiN2wsL+XdlcMraWLc3Oy+HhcPGiOR9Pr17mrNkiaZySHRFJlTw7duA5c6b5Ys4cyJbN2oDEfcLCzNF3Q4ea/Xref9+cefmPP6yOTOS2lOyISMpFR1Px3XfN7T59zBl4JXPx9oZx42DlSsibF3buNIesz5qlpSYkzVKyIyIp5vnii/ifOYNRpIg54ZxkXg0bmktNNGpkNmX16GGuon7xotWRiSSiZEdEUmblSjzmzAEg7oMPIGtWiwMSy+XNC998Y47S8vKCTz81Oy///LPVkYk4ULIjInd24QJ06wbA/ubNMWrXtjYeSTs8PODFF+GHH8w+PQcPQs2a5rxLWmpC0gglOyJyZwMHwrFjGMWK8WeHDlZHI2nRAw+Yo7VatYKbN2HwYGjWzJyQUMRiSnZE5Pa++goiI8FmI+6DD4jz9bU6IkmrgoLMpqz33wc/P/j2W3OpibVrrY5MMjklOyKSvPPnzY6nAAMGYNSsaW08kvbZbNCzJ2zZAqVLm4uINmwIr7xiPvERsYCSHRFJ3vPPw4kTULw4vPaa1dFIelK2rJnw9OhhDkkfOxbq1YMjR6yOTDIhS5OdsLAwbDZboq8+ffoAcP36dfr06UOuXLnImjUrLVu25NSpUw5lHDlyhGbNmuHv709wcDCDBw/mpv56ELl3y5fDRx+ZHVA//NBcNkAkNfz9YeZMWLjQXDvtp5+gYkVYtszqyCSTsTTZ2bJlCydOnLB/rV69GoBWrVoBMGDAAL788ksWL17Mhg0bOH78OC1atLC/Py4ujmbNmnHjxg02btzIhx9+yNy5cxkxYoQl9yOSYZw9C888Y26/8ILZ+VTkbj39tNl5uXp1s2m0RQvo2xeuX7c6MskkLE128uTJQ758+exfK1asoGjRotStW5eLFy8ye/ZsJk+eTP369alSpQqRkZFs3LiRTZs2AbBq1Sp27drF/PnzqVixIk2aNGHMmDFMmzaNGzduWHlrIulbv35mX4uSJWH0aKujkYygSBFzePrgwebradOgRg3YvdvauCRTSDN9dm7cuMH8+fPp2rUrNpuNbdu2ERsby8P/WU25ZMmSFCpUiKioKACioqIoV64cefPmtZ8TERFBdHQ0f2itFpG7s2wZLFjwb/OVn5/VEUlG4eNjLhz7zTeQJw/89htUqQJz52qpCXEpL6sDSPD5559z4cIFOnfuDMDJkyfx8fEhKCjI4by8efNy8uRJ+zn/TXQSjiccS05MTAwxMTH219HR0QDExsYSGxt7r7dil1CWM8uUxFTPTnTmDF69emED4gYNIr5SJfhPvaqu3SPD13ODBrB1K55duuCxbh106UL8qlXEvfuuWxeWzfD1nEa4sp5TWmaaSXZmz55NkyZNKFCggMuvNX78eEaNGpVo/6pVq/D393f69RL6IolrqZ7vXZU33qDg6dNEFyrEhmrViP/66yTPU127R4av5z59uD9/fkp+8gken3zC1e++Y+sLL3CxWDG3hpHh6zmNcEU9X716NUXnpYlk5/Dhw6xZs4alS5fa9+XLl48bN25w4cIFh6c7p06dIl++fPZzfr5lDZaE0VoJ5yRl6NChDBw40P46OjqakJAQGjVqRGBgoDNuCTAzztWrV9OwYUO8vb2dVq44Uj07h+2zz/D68UcMT0/8P/2UxlWqJDpHde0emaqeH3mE+B49sHXsSNYjR6g7dCjx48cT/9xz5pw9LpSp6tlCrqznhJaZO0kTyU5kZCTBwcE0a9bMvq9KlSp4e3uzdu1aWrZsCcCePXs4cuQI4eHhAISHhzN27FhOnz5NcHAwYGaOgYGBlC5dOtnr+fr64pvELLDe3t4u+cC7qlxxpHq+B6dPm52SAdvQoXjdYfSV6to9Mk09161rjtbq3h3bsmV4DhqE53ffmTN3587t8stnmnq2mCvqOaXlWd5BOT4+nsjISDp16oSX17+5V/bs2enWrRsDBw5k/fr1bNu2jS5duhAeHs4D//9B3KhRI0qXLk2HDh349ddfWblyJa+88gp9+vRJMpkRkSQYBvTuDWfOQPnyMHy41RFJZpQzJyxZYo7S8vWFFSvMpSY2bLA6MskALE921qxZw5EjR+jatWuiY2+99RbNmzenZcuW1KlTh3z58jk0dXl6erJixQo8PT0JDw+nffv2dOzYkdEaKiuScosWmb9kvLzMUTE+PlZHJJmVzWYm3ps3Q4kScPw41K8PI0dCXJzV0Uk6ZnkzVqNGjTCSGXLo5+fHtGnTmDZtWrLvDw0N5etkOlGKyB2cPGn+cgF4+WWoVMnaeETAfKKzbRs895zZlDVqFKxfDx9/DAULWh2dpEOWP9kREYsYBjz7LJw7Z07hP2yY1RGJ/CsgAObMgfnzIWtW+P57Mwn68kurI5N0SMmOSGb1ySfw+efg7a3mK0m72rUzOy9Xrmwm5o8+ai5Q+5+50kTuRMmOSGZ04oS5NhHAiBHmX8wiaVWxYrBxIwwYYL5++20ID4e9e62NS9INJTsimY1hmIt8nj9v/rU8ZIjVEYncma8vTJ5sjtLKlevfpz3z51sdmaQDSnZEMpt588x+D97e5tpXml9E0pNmzeDXX825eS5fhg4doHNnc1skGUp2RDKTY8fskwcyahSULWttPCJ34777YO1a8zOcsGBtlSqwY4fVkUkapWRHJLMwDOjZEy5ehGrVYPBgqyMSuXuenmZ/s/XrzeTnr7+gRg14912toC6JKNkRySzmzoWvvzb7Psyda04iKJLe1aljNms98gjcuGHOzfPEE+bILZH/U7IjkhkcPWoO1wUYPRpus3acSLqTKxd88YU5SsvHx9yuWBF+/NHqyCSNULIjktEZBvToAdHR8MAD8MILVkck4nw2m9kfLSoK7r/fTPDr1oXXXtNSE6JkRyTDmz0bVq4EPz+z+crT0+qIRFyncmVzqYkOHSA+3lzYtmFDc50tybSU7IhkZIcPw8CB5vbYsebiiiIZXbZs8NFH5iitgACzE3OFCvDNN1ZHJhZRsiOSURkGdOsGly5BzZrQv7/VEYm4V8eO5lOeihXhzBlo2hQGDTI7MkumomRHJKN6/31zLpIsWcwFFdV8JZlRiRJmP57nnjNfv/mmmfzv329tXOJWSnZEMqKDB82/YAHGj4fixa2NR8RKfn4wdaq58G2OHLB1K1SqhO3TT62OTNxEyY5IRhMfbzZfXbkCtWv/+xetSGb32GPmnDy1asGlS3h16EDFd981/69IhqZkRySjmT7d7JDp7w+RkeZ0+iJiCgkx/38MH45hsxG6Zg1e4eHw++9WRyYupJ+CIhnJ/v3w4ovm9oQJULSotfGIpEVeXjB6NHErV3I9Rw5su3dD9eowY4aWmsiglOyIZBTx8dC1K1y9CvXqQe/eVkckkqYZ9eqxfsoU4ps0gevX4dlnoVUruHDB6tDEyZTsiGQU774L339vzisyZ46ar0RS4Eb27MQtW2aO0vL2hiVLzKHqUVFWhyZOpJ+GIhnB3r3w0kvm9htvQOHC1sYjkp54eJiTb/70ExQpYk7GWbs2vP66+cRU0j0lOyLpXVwcdOkC165BgwbwzDNWRySSPlWrBtu3Q+vW5v+roUMhIgJOnrQ6MrlHSnZE0ru33zb/Is2a1VwHy2azOiKR9CswEBYsMP8vZckCa9aYS02sWmV1ZHIPlOyIpGd79sDLL5vbkydDaKi18YhkBDab2dl/2zYoVw5Onzaf8Lz0EsTGWh2d3AUlOyLpVVwcdO5sjiJp1Ai6d7c6IpGMpVQp2LzZHKUF5nQOdeqYM5RLuqJkRyS9mjwZNm0yH7vPmqXmKxFXyJIF3nsPPvsMgoLM/3OVKsHixVZHJqmgZEckPdq1C4YPN7ffesucFVZEXKdlS7Pzcng4XLwITz0FvXqZAwMkzVOyI5Le3LxpNl/FxECTJuZILBFxvbAw2LDBHKVls8H775szL//xh9WRyR0o2RFJb954A7ZsgezZ4YMP1Hwl4k7e3jBunDk6K29e2LnTHLI+a5aWmkjDlOyIpCc7d8Krr5rbU6fCffdZG49IZvXww+YK6o0amU1ZPXpAmzZmE5ekOUp2RNKL2Fiz+erGDWjeHDp0sDoikcwtb1745htzlJaXF3z6qdl5+eefrY5MbmF5snPs2DHat29Prly5yJIlC+XKlWPr1q324zabLcmvSZMm2c8JCwtLdPz111+34nZEXGfCBHPejxw5zL4Car4SsZ6HB7z4Ivzwg9mn5+BBqFnTbG7WUhNphqXJzvnz56lZsybe3t5888037Nq1izfffJMcOXLYzzlx4oTD15w5c7DZbLRs2dKhrNGjRzuc99xzz7n7dkRc57ffYPRoc/udd6BAAWvjERFHDzxgjtZq1cocRDB4MDRrZk5IKJbzsvLiEyZMICQkhMjISPu+wrcsYJgvXz6H11988QUPPfQQRYoUcdifLVu2ROeKZAixsdCpk/nv449D27ZWRyQiSQkKMpuyHn4Y+veHb781l5qYP99ct04sc1fJztatW1m0aBFHjhzhxo0bDseWLl2a4nKWL19OREQErVq1YsOGDdx333307t2bHj16JHn+qVOn+Oqrr/jwww8THXv99dcZM2YMhQoVom3btgwYMAAvr6RvLyYmhpiYGPvr6OhoAGJjY4l14lTgCWU5s0xJLKPXs8eYMXju2IGRMyc3p041/2q0SEav67RC9eweLqvnLl2gWjW82rXD9uefGA0bEj9kCPEjRph9ezIZV36eU1qmzTBSN1Zu4cKFdOzYkYiICFatWkWjRo3466+/OHXqFE888YTDU5o78fPzA2DgwIG0atWKLVu20L9/f2bMmEGnTp0SnT9x4kRef/11jh8/bn8vwOTJk6lcuTI5c+Zk48aNDB06lC5dujB58uQkrzty5EhGjRqVaP+CBQvw9/dPcfwirpb9wAHqDB6MR1wcW194gWO1a1sdkoikkGdMDGVnzSJs9WoAzpYqxbaBA7mWJ4/FkWUcV69epW3btly8eJHAwMBkz0t1slO+fHmeeeYZ+vTpQ7Zs2fj1118pXLgwzzzzDPnz508yiUiOj48PVatWZePGjfZ9/fr1Y8uWLURFRSU6v2TJkjRs2JB33nnntuXOmTOHZ555hsuXL+Pr65voeFJPdkJCQjhz5sxtKyu1YmNjWb16NQ0bNsTb29tp5YqjDFvPN27gFR6O7fffiX/iCeIWLrS8U3KGres0RvXsHu6qZ9uiRXj27o0tOhojRw7i3n8f4/HHXXa9tMaV9RwdHU3u3LnvmOyk+nna/v37adasGWAmK1euXMFmszFgwADq16+fqmQnf/78lC5d2mFfqVKlWLJkSaJzf/jhB/bs2cOnn356x3Jr1KjBzZs3OXToECVKlEh03NfXN8kkyNvb2yUfeFeVK44yXD2PGQO//w65c+MxYwYePj5WR2SX4eo6jVI9u4fL67ldO3OZiTZtsP38M15PPQV9+pgjtv7TSpHRuaKeU1peqkdj5ciRg0uXLgFw3333sXPnTgAuXLjA1atXU1VWzZo12bNnj8O+v/76i9DQ0ETnzp49mypVqlChQoU7lrtjxw48PDwIDg5OVTwiaca2beYsrWAuQqjPskj6VqSIOTx98GDz9bRpUKMG7N5tbVyZRKqTnTp16rD6/+2PrVq1on///vTo0YM2bdrQIJW9zQcMGMCmTZsYN24c+/btY8GCBcycOZM+ffo4nBcdHc3ixYvp3r17ojKioqKYMmUKv/76KwcOHODjjz9mwIABtG/f3mEIu0i6ERNjjr6KizMXG2zVyuqIRMQZfHxg4kRzIsI8ecwpJapUgblztdSEi6W6Gevdd9/l+vXrALz88st4e3uzceNGWrZsySuvvJKqsqpVq8ayZcsYOnQoo0ePpnDhwkyZMoV27do5nLdw4UIMw6BNmzaJyvD19WXhwoWMHDmSmJgYChcuzIABAxg4cGBqb00kbRg1ylxYMDjY/OtPRDKWxo3NpSY6dIC1a83RW2vWwPTpkC2b1dFlSKlOdnLmzGnf9vDw4KWXXrqnAJo3b07z5s1ve07Pnj3p2bNnkscqV67Mpk2b7ikGkTRjyxZzpmSAGTMgd25r4xER18ifH1auNP+/jxgBH38MmzfDwoXm0x5xqlQ3Y3l6enI6iRkhz549i6enp1OCEsmUrl83m6/i482JA594wuqIRMSVPD1h2DDYsAEKFYJ9+8yOzFOmqFnLyVKd7CQ3Uj0mJgafNDRaRCTdefVV+PNPc3HBqVOtjkZE3KVmTdixw/wDJzYWBgyARx+FM2esjizDSHEz1tT///C12WzMmjWLrFmz2o/FxcXx/fffU7JkSedHKJIZREWZw1DBXOQzVy5r4xER98qRA5YsMfvtDBwIK1aYS00sWAB161odXbqX4mTnrbfeAswnOzNmzHBosvLx8SEsLIwZM2Y4P0KRjO7aNejc2Wy+6tABHnvM6ohExAo2G/TubT7pefpp2LMH6teH4cPNL3UVuWspTnYOHjwIwEMPPcTSpUs1rFvEWYYPh7/+Mjssvv221dGIiNUqVDDn2nruOYiMNEdorl9vdmIuWNDq6NKlVPfZWb9+vRIdEWf56SdIWMPtgw/MR9kiIgEBMGeOuWJ61qzw/fdmEvTll1ZHli6l6MnOwIEDGTNmDAEBAXecvya5xTdF5BZXr5rNV4Zh/vv/ZVhEROzatTNnWm7d2nza8+ij0L+/OWQ9iWWPJGkpSna2b99uX0Z9+/btyZ5ns3iRQpF0Zdgwc6jpfffB//vEiYgkUqwYbNwIL71k/qx4+23zSc+nn8L991sdXbqQomRn/fr1SW6LyF36/vt/h5fPmgVBQZaGIyJpnI+P2eTdoIE5H9f27VC5sjl6q317q6NL81LdZ+e/jh49ytGjR50Vi0jmcOWKOT28YUD37ubU8SIiKdGsmbnURN26cPmyOYKzc2dzW5KV6mTn5s2bDB8+nOzZsxMWFkZYWBjZs2fnlVdesTd1ichtvPQSHDgAISHw5ptWRyMi6c1995lrao0aBR4e8OGH5hITO3ZYHVmalepk57nnnmPmzJlMnDiR7du3s337diZOnMjs2bPp16+fK2IUyTjWr4d33zW3Z8+GwEBr4xGR9MnT01xTa/16czj6X3+ZHZnffVdLTSQh1QuBLliwgIULF9KkSRP7vvLlyxMSEkKbNm2YPn26UwMUyTAuX4auXc3tZ56Bhg2tjUdE0r86dcwnOl27wvLl5tw8a9aYw9b/s3B3ZpfqJzu+vr6EhYUl2l+4cGGtjSVyOy++CIcOQWgoTJpkdTQiklHkygWff26O0vLxgS++gIoV4ccfrY4szUh1stO3b1/GjBlDTEyMfV9MTAxjx46lb9++Tg1OJMNYs8YcNQHmX1zZslkbj4hkLDYb9OtnrrN3//1w9KjZifm11yAuzuroLJfqZqzt27ezdu1aChYsSIUKFQD49ddfuXHjBg0aNKBFixb2c5cuXeq8SEXSq+ho6NbN3O7d21zrRkTEFSpXNicf7NMH5s0zl6NZt86ciblAAaujs0yqk52goCBatmzpsC8kJMRpAYlkOIMGwZEjULiwOeupiIgrZcsGH30EDz9s/oG1fr251MRHH8F/+ttmJqlOdiIjI10Rh0jGtGqVueYVmAv6Zc1qbTwiknl07AgPPGCuoL5jBzRtCi+8AOPGmX17MpF7mlRQRG7j4sV/m6/69TPbz0VE3Kl4cbMfz3PPma/ffBNq1oT9+62Ny81S/WQH4LPPPmPRokUcOXKEGzduOBz75ZdfnBKYSLo3cCD8/TcULWr+JSUiYgU/P3N5mgYNzNnbt26FSpVg5kxzgdFMINVPdqZOnUqXLl3Imzcv27dvp3r16uTKlYsDBw44zL0jkql9/bU56spmM5uvAgKsjkhEMrvHHjOXmqhVCy5dgjZtzCVrrlyxOjKXS3Wy89577zFz5kzeeecdfHx8ePHFF1m9ejX9+vXj4sWLrohRJH05fx569DC3n38eate2NBwREbuQELPD8ogR5h9js2dDtWrw++9WR+ZSqU52jhw5woMPPghAlixZuHTpEgAdOnTgk08+cW50IunRgAFw/LjZVv7aa1ZHIyLiyMvLXFdr7VrInx/+/BOqV4cZMzLsUhOpTnby5cvHuXPnAChUqBCbNm0C4ODBgxgZtJJEUuzLL81F+Tw8YO5c8Pe3OiIRkaQ99JDZrNW0KVy/Ds8+C61awYULVkfmdKlOdurXr8/y5csB6NKlCwMGDKBhw4Y8/fTTPPHEE04PUCTdOHcOevY0twcOhPBwa+MREbmTPHnMP9LefBO8vWHJEnOpiagoqyNzqlSPxpo5cybx8fEA9OnTh1y5crFx40YeffRRnnnmGacHKJJu9OsHJ09CyZIwerTV0YiIpIyHh/kHWu3a5uisAwfM7ddeM9f080j/s9SkOtnx8PDA4z833rp1a1pnkqFrIsn6/HP4+ON/m6+yZLE6IhGR1KlWDbZvh1694JNPYOhQs1/PvHmQL5/V0d2TFKdre/fupU2bNkRHRyc6dvHiRdq2bcuBAwecGpxIunDmDCQ81XzxRahRw9p4RETuVmCg+Yfb7NnmH21r1phLTaxaZXVk9yTFyc6kSZMICQkhMDAw0bHs2bMTEhLCpEmTnBqcSLrw3HNw+jSULg0jR1odjYjIvbHZoGtXc0HRcuXMn28REfDSSxAba3V0dyXFyc6GDRto1apVssefeuop1q1b55SgRNKNJUtg4ULw9DSbr3x9rY5IRMQ5SpWCzZvNUVpgLmRcpw4cPGhtXHchxcnOkSNHCA4OTvZ47ty5OXr0aKoDOHbsGO3btydXrlxkyZKFcuXKsXXrVvvxzp07Y7PZHL4aN27sUMa5c+do164dgYGBBAUF0a1bNy5fvpzqWERS5Z9//v0h8NJLZnu3iEhGkiULvPcefPYZBAXBpk3mUhOLF1sdWaqkONnJnj07+2+zcNi+ffuSbOK6nfPnz1OzZk28vb355ptv2LVrF2+++SY5cuRwOK9x48acOHHC/nXr5IXt2rXjjz/+YPXq1axYsYLvv/+englDgEVcpU8fM+EpVw6GD7c6GhER12nZ0lw5PTzcXOT4qafMjszXrlkdWYqkeDRWnTp1eOedd6hfv36Sx6dOnUrtVE6LP2HCBEJCQoiMjLTvK1y4cKLzfH19yZdMT/A///yTb7/9li1btlC1alUA3nnnHZo2bcobb7xBgQIFUhWTSIosWmT+ZePlpeYrEckcQkNhwwZ49VV4/XV4/3346SezKb9MGauju60UJztDhw4lPDycJ598khdffJESJUoAsHv3biZOnMjKlSvZuHFjqi6+fPlyIiIiaNWqFRs2bOC+++6jd+/e9EhYV+j/vvvuO4KDg8mRIwf169fntddeI1euXABERUURFBRkT3QAHn74YTw8PNi8eXOSEx3GxMQQExNjf50wwiw2NpZYJ3a+SijLmWVKYm6v51On8OrdGxsQN2QI8eXKpdtOe6mlz7R7qJ7dQ/V8l0aNwlanDp6dO2PbuROjWjXi3noLo0sXs3PzLVxZzykt02akYo2HFStW0LVrV86ePeuwP1euXMyaNYtHH300VUH6+fkBMHDgQFq1asWWLVvo378/M2bMoFOnTgAsXLgQf39/ChcuzP79+xk2bBhZs2YlKioKT09Pxo0bx4cffsiePXscyg4ODmbUqFE8m9Cn4j9GjhzJqFGjEu1fsGAB/preX27HMKg2YQIFNm3iYlgYGyZNwvD2tjoqERG3871wgcpTphC8YwcAf9eqxa/PPsvNgAC3xXD16lXatm3LxYsXb9uVJlXJDsC1a9f49ttv2bdvH4ZhULx4cRo1anRXSYKPjw9Vq1Z1eCLUr18/tmzZQlQyU1UfOHCAokWLsmbNGho0aHBXyU5ST3ZCQkI4c+ZMqvsd3U5sbCyrV6+mYcOGeOsXosu4s55tCxfi1bEjhpcXN6OizPknMhF9pt1D9eweqmcniI/HY/JkPEaMwHbzJkbhwsTNn4+RMGAjLo64775j5+rVlG3YEM969czRq04SHR1N7ty575jspHoG5SxZsjhtDaz8+fNTunRph32lSpViyZIlyb6nSJEi5M6dm3379tGgQQPy5cvH6dOnHc65efMm586dS7afj6+vL75J9LHw9vZ2yQfeVeWKI5fX84kT0L8/ALYRI/D+T9NpZqPPtHuont1D9XyPhg6F+vWhdWtsBw/iVbcujB8PYWEwYADef/9NVYDJk6FgQXj7bWjRwimXTun3zdIFL2rWrJnoicxff/1FaGhosu/5+++/OXv2LPnz5wcgPDycCxcusG3bNvs569atIz4+nhqayVacxTDMWZLPn4fKlc2h5iIiYqpRw1xqolUruHkTBg82t//+2/G8Y8fgySdh6VK3hmdpsjNgwAA2bdrEuHHj2LdvHwsWLGDmzJn06dMHgMuXLzN48GA2bdrEoUOHWLt2LY899hjFihUjIiICMJ8ENW7cmB49evDzzz/z008/0bdvX1q3bq2RWOI88+ebKwN7e5ujr/RXoIiIo6Ag+PRTmD49+XMSes48/zzExbkjKsDiZKdatWosW7aMTz75hLJlyzJmzBimTJlCu3btAPD09OS3337j0UcfpXjx4nTr1o0qVarwww8/ODRDffzxx5QsWZIGDRrQtGlTatWqxcyZM626Lclojh83VzQHczmIcuUsDUdEJM2y2aBkydufYxhw9Cj88IN7YiKVfXZu3rzJggULiIiIIG/evE4JoHnz5jRv3jzJY1myZGHlypV3LCNnzpwsWLDAKfGIODAM6NkTLlyAqlXNhT5FRCR5J0449zwnSNWTHS8vL3r16sX169ddFY9I2vLhh/DVV+DjY257pbpPv4hI5vL/PrVOO88JUt2MVb16dXb8f0y9SIb299/20VeMHm2uai4iIrdXu7Y56iqJCQYBc39IiHmem6T6z9TevXszcOBAjh49SpUqVQi4ZfKg8uXLOy04EcsYBnTvDtHR5iiDF16wOiIRkfTB09McXv7kk2Zi89/p/BISoClTnDrfzp2kOtlp3bo1YE7+l8Bms2EYBjabjTg39q4WcZk5c2DlSnPNq7lz1XwlIpIaLVqYK6X37+84/LxgQTPRcdI8OymV6p/gBw8edEUcImnHkSMwYIC5PXbsnUcWiIhIYi1awGOPcXP9enZ88w0VmzTB66GH3PpEJ0Gqk53bTfgnku4ZBnTrBpcuwYMPmnNBiIjI3fH0xKhbl2NXrlChbl1LEh24y3l25s2bR82aNSlQoACHDx8GYMqUKXzxxRdODU7E7WbOhDVrwM8PIiMt+48pIiLOk+pkZ/r06QwcOJCmTZty4cIFex+doKAgpkyZ4uz4RNzn0CEYNMjcHj8eihe3NBwREXGOVCc777zzDh988AEvv/wynv/5q7dq1ar8/vvvTg1OxG3i483mq8uXzeGQ/+mALyIi6Vuqk52DBw9SqVKlRPt9fX25cuWKU4IScbsZM2DdOvD3N0dieVi6koqIiDhRqn+iFy5cOMlJBb/99ltKlSrljJhE3OvAAXOFXoDXX4dixayNR0REnCrVo7EGDhxInz59uH79OoZh8PPPP/PJJ58wfvx4Zs2a5YoYRVwnPh66dIGrV6FuXejTx+qIRETEyVKd7HTv3p0sWbLwyiuvcPXqVdq2bUuBAgV4++237RMOiqQb06bB999DQICar0REMqhUJzsxMTE8/vjjtGvXjqtXr3L58mWCg4NdEZuIa+3bB0OGmNuTJkGRItbGIyIiLpHiP2P/+ecfmjRpQtasWQkMDOSBBx7gxIkTSnQkfYqLg86d4do1qF8fnnnG6ohERMRFUpzsDBkyhB07djB69GjeeOMNLly4QPfu3V0Zm4jrTJ0KP/0EWbPC7NlqvhIRycBS3Iy1evVq5s6dS0REBADNmzenVKlSxMTE4Ovr67IARZzur79g2DBz+803ISzM0nBERMS1Uvzn7PHjx6lQoYL99f3334+vry8nTpxwSWAiLpHQfHX9OjRsCD16WB2RiIi4WKqe3Xvesk6Qp6cnhmE4NSARl3rrLYiKgmzZYNYssNmsjkhERFwsxc1YhmFQvHhxbP/55XD58mUqVaqEx3/6O5w7d865EYo4y59/wiuvmNtvvQWFClkbj4iIuEWKk53IyEhXxiHiWjdvms1XMTHQuDF07Wp1RCIi4iYpTnY6derkyjhEXOvNN+HnnyF7dvjgAzVfiYhkIhpvKxnfH3/AiBHm9ttvQ8GC1sYjIiJupWRHMrbYWOjUCW7cgGbNoGNHqyMSERE3U7IjGdvEibBtGwQFwcyZar4SEcmElOxIxvX77zBqlLn9zjtQoIC18YiIiCVStRBodHQ0mzdv5saNG1SvXp08efK4Ki6Re5PQfBUbC489Bu3aWR2RiIhYJMXJzo4dO2jatCmnTp3CMAyyZcvGokWL7MtHiKQp48fD9u2QMyfMmKHmKxGRTCxVC4EWLlyYH3/8kW3bttGgQQP69u3rythE7s6OHTBmjLn97ruQL5+l4YiIiLVS/GRn27ZtrFq1isqVKwMwZ84ccubMSXR0NIGBgS4LUCRVbtwwm69u3oQWLaB1a6sjEhERi6X4yc65c+co+J/5SYKCgggICODs2bP3FMCxY8do3749uXLlIkuWLJQrV46tW7cCEBsby5AhQyhXrhwBAQEUKFCAjh07cvz4cYcywsLCsNlsDl+vv/76PcUl6dTYsfDbb5A7N0yfruYrERFJXQflXbt2cfLkSftrwzD4888/uXTpkn1f+fLlU1ze+fPnqVmzJg899BDffPMNefLkYe/eveTIkQOAq1ev8ssvvzB8+HAqVKjA+fPn6d+/P48++qg9IUowevRoevxnBets2bKl5tYkI9i+3Ux2AN57D4KDrY1HRETShFQlOw0aNEi0ynnz5s2x2WwYhoHNZiMuLi7F5U2YMIGQkBCHdbcKFy5s386ePTurV692eM+7775L9erVOXLkCIX+s5BjtmzZyKe+GZmWR2wsXl27QlwctGplfomIiJCKZqyDBw9y4MABDh48mOgrYf+BAwdSdfHly5dTtWpVWrVqRXBwMJUqVeKDDz647XsuXryIzWYjKCjIYf/rr79Orly5qFSpEpMmTeLmzZupikXStxKffortjz8gTx6YNs3qcEREJA1J8ZOd0NBQp1/8wIEDTJ8+nYEDBzJs2DC2bNlCv3798PHxSXLh0evXrzNkyBDatGnj0Cm6X79+VK5cmZw5c7Jx40aGDh3KiRMnmDx5cpLXjYmJISYmxv46OjoaMPsIxcbGOu3+EspyZpmSWNymTRRbuhSAm++8gxEUZM6vI06nz7R7qJ7dQ/XsHq6s55SWaTNubZe6g7179/LFF19w6NAhbDYbhQsX5vHHH6dIkSKpDtLHx4eqVauyceNG+75+/fqxZcsWoqKiHM6NjY2lZcuW/P3333z33Xe3HQE2Z84cnnnmGS5fvoyvr2+i4yNHjmRUwsy6/7FgwQL8/f1TfR9iHY8bN6j7wgsEHj3K37Vrs+2FF6wOSURE3OTq1au0bduWixcv3jYvSFWyM378eEaMGEF8fDzBwcEYhsE///yDp6cn48aNY9CgQakKMjQ0lIYNGzJr1iz7vunTp/Paa69x7Ngx+77Y2FieeuopDhw4wLp168iVK9dty/3jjz8oW7Ysu3fvpkSJEomOJ/VkJyQkhDNnzjh1GH1sbCyrV6+mYcOGeHt7O61c+ZfHsGF4vvEG14OCiP/tN7zVb8ul9Jl2D9Wze6ie3cOV9RwdHU3u3LnvmOykuBlr/fr1vPLKKwwfPpz+/fvbR0ydO3eOKVOm8NJLL1G9enXq1KmT4iBr1qzJnj17HPb99ddfDk1mCYnO3r17Wb9+/R0THTBne/bw8CA4mdE4vr6+ST7x8fb2dskH3lXlZnqbNsH/myp/ffZZKufLp3p2E32m3UP17B6qZ/dwRT2ntLwUJzszZsyge/fujBw50mF/zpw5GT16NCdPnmT69OmpSnYGDBjAgw8+yLhx43jqqaf4+eefmTlzJjNnzgTMROfJJ5/kl19+YcWKFcTFxdmHvufMmRMfHx+ioqLYvHkzDz30ENmyZSMqKooBAwbQvn17e0ImGdC1a9C5M8THE9+2LSdr1LA6IhERSaNSPBrr559/pkOHDske79ChA5s2bUrVxatVq8ayZcv45JNPKFu2LGPGjGHKlCm0+/+ijceOHWP58uX8/fffVKxYkfz589u/Evr5+Pr6snDhQurWrUuZMmUYO3YsAwYMsCdMkkGNGAF79kD+/MQl0xFdREQEUvFk59SpU4SFhSV7vHDhwg4TDqZU8+bNad68eZLHwsLCEs3rc6vKlSunOsmSdG7jRnjzTXN75kxzsU8REZFkpPjJzvXr1/Hx8Un2uLe3Nzdu3HBKUCLJunrVbL4yDHMNrGQSZRERkQSpmkF51qxZZM2aNclj/10yQsRlXn4Z9u6FAgVgyhSroxERkXQgxclOoUKF7ji78X+XbxBxuh9+gLffNrdnzYJbZtEWERFJSoqTnUOHDrkwDJE7uHIFunQxm6+6dYMmTayOSERE0okU99kRsdTQobB/PxQs+G/nZBERkRRI8ZOda9eusXbtWvvIqaFDhzrMQuzp6cmYMWPw8/NzfpSSuX33Hbzzjrk9ezZkz25pOCIikr6kONn58MMP+eqrr+zJzrvvvkuZMmXIkiULALt376ZAgQIMGDDANZFK5nT5MnTtam737AmNGlkbj4iIpDspbsb6+OOP6dmzp8O+BQsWsH79etavX8+kSZNYtGiR0wOUTG7IEDh4EEJD4Y03rI5GRETSoRQnO/v27aNcuXL2135+fnh4/Pv26tWrs2vXLudGJ5nb2rXw3nvm9uzZkC2btfGIiEi6lOJmrAsXLjj00fnnn38cjsfHxzscF7kn0dH/Nl89+yw0aGBtPCIikm6l+MlOwYIF2blzZ7LHf/vtNwoWLOiUoEQYPBiOHIGwMJg40epoREQkHUtxstO0aVNGjBjB9evXEx27du0ao0aNolmzZk4NTjKpVavMNa8AIiMhmVm7RUREUiLFzVjDhg1j0aJFlChRgr59+1K8eHEA9uzZw7vvvsvNmzcZNmyYywKVTOLiReje3dx+7jmoV8/ScEREJP1LcbKTN29eNm7cyLPPPstLL71kX43cZrPRsGFD3nvvPfLmzeuyQCWTeOEFOHoUihaF8eOtjkZERDKAVC0EWrhwYb799lvOnTvHvn37AChWrBg5c+Z0SXCSyXzzjTnqymYzm68CAqyOSEREMoBUJTsJcubMSfXq1Z0di2RmFy5Ajx7mdv/+ULu2peGIiEjGobWxJG0YMACOHYP774exY62ORkREMhAlO2K9FStg7lyz+WruXPD3tzoiERHJQJTsiLXOnTPXvAIYOBAefNDaeEREJMNRsiPW6t8fTpyAEiVgzBiroxERkQxIyY5Y54svYP588PAwm6+yZLE6IhERyYCU7Ig1zp6FZ54xtwcPhgcesDYeERHJsJTsiDWeew5OnYLSpWHkSKujERGRDEzJjrjf0qXwySfg6Wk2X/n5WR2RiIhkYEp2xL3++Qd69TK3hwyBatWsjUdERDI8JTviXn37mglP2bIwYoTV0YiISCagZEfcZ9Ei88vTEz78EHx9rY5IREQyASU74h6nTkHv3ub2sGFQubK18YiISKahZEdczzDMROfsWShfHl55xeqIREQkE1GyI6736afmCCwvL7P5ysfH6ohERCQTUbIjrnXyJPTpY24PHw4VK1oajoiIZD6WJzvHjh2jffv25MqViyxZslCuXDm2bt1qP24YBiNGjCB//vxkyZKFhx9+mL179zqUce7cOdq1a0dgYCBBQUF069aNy5cvu/tW5FaGYQ4zP3cOKlWCoUOtjkhERDIhS5Od8+fPU7NmTby9vfnmm2/YtWsXb775Jjly5LCfM3HiRKZOncqMGTPYvHkzAQEBREREcP36dfs57dq1448//mD16tWsWLGC77//np4JK2mLdT7+2Fz/ytvbnDzQ29vqiEREJBPysvLiEyZMICQkhMjISPu+woUL27cNw2DKlCm88sorPPbYYwB89NFH5M2bl88//5zWrVvz559/8u2337JlyxaqVq0KwDvvvEPTpk154403KFCggHtvSkzHj0O/fub2q6+aHZNFREQsYGmys3z5ciIiImjVqhUbNmzgvvvuo3fv3vTo0QOAgwcPcvLkSR5++GH7e7Jnz06NGjWIioqidevWREVFERQUZE90AB5++GE8PDzYvHkzTzzxRKLrxsTEEBMTY38dHR0NQGxsLLGxsU67v4SynFlmumAYePbogcf588RXrkzcwIHgwjrItPVsAdW1e6ie3UP17B6urOeUlmlpsnPgwAGmT5/OwIEDGTZsGFu2bKFfv374+PjQqVMnTp48CUDevHkd3pc3b177sZMnTxIcHOxw3MvLi5w5c9rPudX48eMZNWpUov2rVq3C39/fGbfmYPXq1U4vMy0LWbeOyl9/TZyXFxs6d+bSqlVuuW5mq2crqa7dQ/XsHqpn93BFPV+9ejVF51ma7MTHx1O1alXGjRsHQKVKldi5cyczZsygU6dOLrvu0KFDGThwoP11dHQ0ISEhNGrUiMDAQKddJzY2ltWrV9OwYUO8M0t/lb//xivhezdyJLUT1sFyoUxZzxZRXbuH6tk9VM/u4cp6TmiZuRNLk538+fNTunRph32lSpViyZIlAOTLlw+AU6dOkT9/fvs5p06douL/hzDny5eP06dPO5Rx8+ZNzp07Z3//rXx9ffFNYqkCb29vl3zgXVVumpMweeDFi1C9Op5DhuDp5b6PWKap5zRAde0eqmf3UD27hyvqOaXlWToaq2bNmuzZs8dh319//UVoaChgdlbOly8fa9eutR+Pjo5m8+bNhIeHAxAeHs6FCxfYtm2b/Zx169YRHx9PjRo13HAXYhcZCd9+a655NXeuOYmgiIiIxSz9bTRgwAAefPBBxo0bx1NPPcXPP//MzJkzmTlzJgA2m43nn3+e1157jfvvv5/ChQszfPhwChQowOOPPw6YT4IaN25Mjx49mDFjBrGxsfTt25fWrVtrJJY7HTkCAwaY26+9BqVKWRuPiIjI/1ma7FSrVo1ly5YxdOhQRo8eTeHChZkyZQrt2rWzn/Piiy9y5coVevbsyYULF6hVqxbffvstfn5+9nM+/vhj+vbtS4MGDfDw8KBly5ZMnTrVilvKnAwDuneH6GgID/836REREUkDLG9naN68Oc2bN0/2uM1mY/To0YwePTrZc3LmzMmCBQtcEZ6kxAcfwOrV4OdnNmV5elodkYiIiJ3ly0VIOnf4MLzwgrk9bhyUKGFtPCIiIrdQsiN3Lz4eunaFy5ehVq1/Z0wWERFJQ5TsyN17/31Ytw6yZFHzlYiIpFlKduTuHDgAgweb26+/DsWKWRuPiIhIMpTsSOolNF9duQJ16kDfvlZHJCIikiwlO5J6770HGzZAQIDZfOWhj5GIiKRd+i0lqbNvHwwZYm5PnAhFilgbj4iIyB0o2ZGUi4+HLl3g6lV46CFwwyKfIiIi90rJjqTc1Knw44+QNSvMmaPmKxERSRf020pS5q+/YNgwc/uNNyAszNJwREREUkrJjtxZXJzZfHXtGjz8MPTsaXVEIiIiKaZkR+5syhTYuBGyZYPZs8FmszoiERGRFFOyI7e3eze8/LK5PXkyFCpkbTwiIiKppGRHkhcXB507Q0wMRERAt25WRyQiIpJqSnYkeW++CZs3Q/bsMGuWmq9ERCRdUrIjSdu1C4YPN7enTIGCBS0NR0RE5G4p2ZHEbt6ETp3gxg1o1szcFhERSaeU7EhiEyfC1q0QFATvv6/mKxERSdeU7Iij33+HkSPN7alT4b77LA1HRETkXinZkX/Fxpqjr2Jj4dFHoX17qyMSERG5Z0p25F+vvw6//AI5csCMGWq+EhGRDEHJjph27IDRo83td9+F/PktDUdERMRZlOyIOeqqc2dzFNYTT0CbNlZHJCIi4jRKdgTGjYNff4VcuWD6dDVfiYhIhqJkJ7P75RcYO9bcfu89yJvX2nhEREScTMlOZhYT82/z1ZNPwlNPWR2RiIiI0ynZyczGjDHn1cmTx3yqIyIikgEp2cmstm41h5qD2U8nTx5r4xEREXERJTuZUUyMud5VXBy0bg0tW1odkYiIiMso2cmMRo40VzUPDoZ33rE6GhEREZeyNNkZOXIkNpvN4atkyZIAHDp0KNGxhK/Fixfby0jq+MKFC626pbRv82ZzoU8wZ0nOndvaeERERFzMy+oAypQpw5o1a+yvvbzMkEJCQjhx4oTDuTNnzmTSpEk0adLEYX9kZCSNGze2vw4KCnJdwOnZ9evm6Kv4eGjXzpxAUEREJIOzPNnx8vIiX758ifZ7enom2r9s2TKeeuopsmbN6rA/KCgoyTLkFiNGwO7dkC+fuaK5iIhIJmB5n529e/dSoEABihQpQrt27Thy5EiS523bto0dO3bQrVu3RMf69OlD7ty5qV69OnPmzMEwDFeHnf5s3AhvvGFuz5wJOXNaG4+IiIibWPpkp0aNGsydO5cSJUpw4sQJRo0aRe3atdm5cyfZsmVzOHf27NmUKlWKBx980GH/6NGjqV+/Pv7+/qxatYrevXtz+fJl+vXrl+x1Y2JiiImJsb+Ojo4GIDY2ltjYWKfdX0JZzizzrly9ilenTtgMg/j27Ylr3BisjsmJ0kw9ZwKqa/dQPbuH6tk9XFnPKS3TZqShxyAXLlwgNDSUyZMnOzzBuXbtGvnz52f48OG88MILty1jxIgRREZGcvTo0WTPGTlyJKNGjUq0f8GCBfj7+9/9DaRRZebModjy5VzLmZP1U6cSe0szoIiISHp09epV2rZty8WLFwkMDEz2vDSV7ABUq1aNhx9+mPHjx9v3zZs3j27dunHs2DHy3GHyu6+++ormzZtz/fp1fH19kzwnqSc7ISEhnDlz5raVlVqxsbGsXr2ahg0b4u3t7bRyU8P200941q+PzTC4+cUXGLd07s4I0kI9Zxaqa/dQPbuH6tk9XFnP0dHR5M6d+47JjuUdlP/r8uXL7N+/nw4dOjjsnz17No8++ugdEx2AHTt2kCNHjmQTHQBfX98kj3t7e7vkA++qcu/oyhXo0QMMA7p2xevRR90fgxtZVs+ZkOraPVTP7qF6dg9X1HNKy7M02Rk0aBCPPPIIoaGhHD9+nFdffRVPT0/atGljP2ffvn18//33fP3114ne/+WXX3Lq1CkeeOAB/Pz8WL16NePGjWPQoEHuvI20a9gw2LcPChaEyZOtjkZERMQSliY7f//9N23atOHs2bPkyZOHWrVqsWnTJocnOHPmzKFgwYI0atQo0fu9vb2ZNm0aAwYMwDAMihUrxuTJk+nRo4c7byNt2rDh3+Hls2ZB9uzWxiMiImIRS5OdlMx0PG7cOMaNG5fkscaNGztMJij/d/kydO1qbvfoARER1sYjIiJiIcvn2REXeOklOHAAChX6d24dERGRTErJTkazbh1Mm2Zuz54NThxdJiIikh4p2clILl36t/mqVy94+GFr4xEREUkDlOxkJIMHw+HDEBb278rmIiIimZySnYxi9Wp4/31ze84cuGW5DRERkcxKyU5GEB0NCctr9O0LDz1kbTwiIiJpiJKdjOCFF+DoUShSBF5/3epoRERE0hQlO+ndt9+akwYCREZCQIC18YiIiKQxSnbSswsXoHt3c7t/f6hTx9JwRERE0iIlO+nZwIFw7BgUKwbJzDItIiKS2SnZSa+++spstrLZYO5c8Pe3OiIREZE0SclOenT+vLnmFcCAAVCzprXxiIiIpGFKdtKj/v3hxAkoXhxee83qaERERNI0JTvpzfLlMG8eeHjAhx9ClixWRyQiIpKmKdlJT86ehWeeMbcHDYIHHrA2HhERkXRAyU560q8fnDwJpUrBqFFWRyMiIpIuKNlJL5YtgwULzOaruXPBz8/qiERERNIFJTvpwZkz0KuXuT1kCFSvbm08IiIi6YiSnfSgb184fRrKlIFXX7U6GhERkXRFyU5at3gxfPopeHqao698fa2OSEREJF1RspOWnT4NvXub20OHQpUq1sYjIiKSDinZSasMw0x0zpyB8uVh+HCrIxIREUmXlOykVYsWwZIl4OVljr7y8bE6IhERkXRJyU5adPLkv81Xr7wClSpZG4+IiEg6pmQnrTEMc5j5uXNQsSIMG2Z1RCIiIumakp20ZsEC+OIL8PY2m6+8va2OSEREJF1TspOWnDgBzz1nbo8YARUqWBuPiIhIBqBkJ60wDHORz/PnzSHmQ4ZYHZGIiEiGoGQnrZg3D7780hx1peYrERERp1GykxYcO2auaA4wciSULWtpOCIiIhmJkh2rGQb06AEXL0K1ajB4sNURiYiIZCiWJjsjR47EZrM5fJUsWdJ+vF69eomO90pY/fv/jhw5QrNmzfD39yc4OJjBgwdz8+ZNd9/K3Zs7F775xlzzau5ccxJBERERcRrLf7OWKVOGNWvW2F973fLLvkePHowePdr+2t/f374dFxdHs2bNyJcvHxs3buTEiRN07NgRb29vxo0b5/rg79XRo/D88+b2mDFQurSl4YiIiGRElic7Xl5e5MuXL9nj/v7+yR5ftWoVu3btYs2aNeTNm5eKFSsyZswYhgwZwsiRI/FJy0ssGAZ07w7R0fDAAzBwoNURiYiIZEiWJzt79+6lQIEC+Pn5ER4ezvjx4ylUqJD9+Mcff8z8+fPJly8fjzzyCMOHD7c/3YmKiqJcuXLkzZvXfn5ERATPPvssf/zxB5WSWWYhJiaGmJgY++vo6GgAYmNjiY2Nddq9JZSVVJm22bPxWrUKw8+Pmx98APHx5pek2u3qWZxLde0eqmf3UD27hyvrOaVl2gzDMJx+9RT65ptvuHz5MiVKlODEiROMGjWKY8eOsXPnTrJly8bMmTMJDQ2lQIEC/PbbbwwZMoTq1auzdOlSAHr27Mnhw4dZuXKlvcyrV68SEBDA119/TZMmTZK87siRIxk1alSi/QsWLHBoJnOVLKdP81D//nhfu8bOLl3Y/9hjLr+miIhIRnP16lXatm3LxYsXCQwMTPY8S5OdW124cIHQ0FAmT55Mt27dEh1ft24dDRo0YN++fRQtWvSuk52knuyEhIRw5syZ21ZWasXGxrJ69WoaNmyId8K8OYaBZ5MmeKxbR/yDDxK3di14ejrtmplRkvUsLqG6dg/Vs3uont3DlfUcHR1N7ty575jsWN6M9V9BQUEUL16cffv2JXm8Ro0aAPZkJ1++fPz8888O55w6dQrgtv2AfH198fX1TbTf29vbJR94h3JnzIB16yBLFjzmzsXDz8/p18usXPX9k8RU1+6henYP1bN7uKKeU1pemppn5/Lly+zfv5/8+fMneXzHjh0A9uPh4eH8/vvvnD592n7O6tWrCQwMpHRaHNl08CAMGmRujx8P999vbTwiIiKZgKXJzqBBg9iwYQOHDh1i48aNPPHEE3h6etKmTRv279/PmDFj2LZtG4cOHWL58uV07NiROnXqUL58eQAaNWpE6dKl6dChA7/++isrV67klVdeoU+fPkk+ubFUfDx07QpXrkDt2v8u+CkiIiIuZWkz1t9//02bNm04e/YsefLkoVatWmzatIk8efJw/fp11qxZw5QpU7hy5QohISG0bNmSV155xf5+T09PVqxYwbPPPkt4eDgBAQF06tTJYV6eNGP6dPjuO/D3h8hI8EhTD9VEREQyLEuTnYULFyZ7LCQkhA0bNtyxjNDQUL7++mtnhuUccXHYNmzgvu+/x3bx4r/LQEycCEWLWhubiIhIJpKmOihnGEuXQv/+eP39N1X/u79MGXj2WauiEhERyZTUluJsS5fCk0/C338nPrZrF3z+udtDEhERycyU7DhTXBz0728uBZGc5583zxMRERG3ULLjTD/8kPQTnQSGYS7++cMP7otJREQkk1Oy40wnTjj3PBEREblnSnacKZnJEO/6PBEREblnSnacqXZtKFgQbLakj9tsEBJiniciIiJuoWTHmTw94e23ze1bE56E11OmaOFPERERN1Ky42wtWsBnn8F99znuL1jQ3N+ihTVxiYiIZFKaVNAVWrSAxx7j5vr17PjmGyo2aYLXQw/piY6IiIgFlOy4iqcnRt26HLtyhQp16yrRERERsYiasURERCRDU7IjIiIiGZqSHREREcnQlOyIiIhIhqZkR0RERDI0JTsiIiKSoSnZERERkQxNyY6IiIhkaEp2REREJEPTDMqAYRgAREdHO7Xc2NhYrl69SnR0NN7e3k4tW/6lenYf1bV7qJ7dQ/XsHq6s54Tf2wm/x5OjZAe4dOkSACEhIRZHIiIiIql16dIlsmfPnuxxm3GndCgTiI+P5/jx42TLlg2bzea0cqOjowkJCeHo0aMEBgY6rVxxpHp2H9W1e6ie3UP17B6urGfDMLh06RIFChTAwyP5njl6sgN4eHhQsGBBl5UfGBio/0huoHp2H9W1e6ie3UP17B6uqufbPdFJoA7KIiIikqEp2REREZEMTcmOC/n6+vLqq6/i6+trdSgZmurZfVTX7qF6dg/Vs3ukhXpWB2URERHJ0PRkR0RERDI0JTsiIiKSoSnZERERkQxNyY6IiIhkaEp27mD8+PFUq1aNbNmyERwczOOPP86ePXsczrl+/Tp9+vQhV65cZM2alZYtW3Lq1CmHc/r160eVKlXw9fWlYsWKSV7rt99+o3bt2vj5+RESEsLEiRNddVtpjrvq+bvvvuOxxx4jf/78BAQEULFiRT7++GNX3lqa4s7Pc4J9+/aRLVs2goKCnHw3aZc769kwDN544w2KFy+Or68v9913H2PHjnXVraUp7qznlStX8sADD5AtWzby5MlDy5YtOXTokIvuLO1xRl3/+uuvtGnThpCQELJkyUKpUqV4++23E13ru+++o3Llyvj6+lKsWDHmzp17z/Er2bmDDRs20KdPHzZt2sTq1auJjY2lUaNGXLlyxX7OgAED+PLLL1m8eDEbNmzg+PHjtGjRIlFZXbt25emnn07yOtHR0TRq1IjQ0FC2bdvGpEmTGDlyJDNnznTZvaUl7qrnjRs3Ur58eZYsWcJvv/1Gly5d6NixIytWrHDZvaUl7qrnBLGxsbRp04batWs7/V7SMnfWc//+/Zk1axZvvPEGu3fvZvny5VSvXt0l95XWuKueDx48yGOPPUb9+vXZsWMHK1eu5MyZM0mWk1E5o663bdtGcHAw8+fP548//uDll19m6NChvPvuu/ZzDh48SLNmzXjooYfYsWMHzz//PN27d2flypX3dgOGpMrp06cNwNiwYYNhGIZx4cIFw9vb21i8eLH9nD///NMAjKioqETvf/XVV40KFSok2v/ee+8ZOXLkMGJiYuz7hgwZYpQoUcL5N5EOuKqek9K0aVOjS5cuTok7vXF1Pb/44otG+/btjcjISCN79uzODj/dcFU979q1y/Dy8jJ2797tstjTE1fV8+LFiw0vLy8jLi7Ovm/58uWGzWYzbty44fwbSQfuta4T9O7d23jooYfsr1988UWjTJkyDuc8/fTTRkRExD3Fqyc7qXTx4kUAcubMCZiZamxsLA8//LD9nJIlS1KoUCGioqJSXG5UVBR16tTBx8fHvi8iIoI9e/Zw/vx5J0WffriqnpO7VsJ1MhtX1vO6detYvHgx06ZNc17A6ZSr6vnLL7+kSJEirFixgsKFCxMWFkb37t05d+6cc28gnXBVPVepUgUPDw8iIyOJi4vj4sWLzJs3j4cffhhvb2/n3kQ64ay6vvXnb1RUlEMZYP4uvNef80p2UiE+Pp7nn3+emjVrUrZsWQBOnjyJj49Pov4IefPm5eTJkyku++TJk+TNmzdRGQnHMhNX1vOtFi1axJYtW+jSpcu9hJwuubKez549S+fOnZk7d26mX2DRlfV84MABDh8+zOLFi/noo4+YO3cu27Zt48knn3TmLaQLrqznwoULs2rVKoYNG4avry9BQUH8/fffLFq0yJm3kG44q643btzIp59+Ss+ePe37kvtdGB0dzbVr1+46Zq16ngp9+vRh586d/Pjjj1aHkqG5q57Xr19Ply5d+OCDDyhTpoxLr5UWubKee/ToQdu2balTp47Ty05vXFnP8fHxxMTE8NFHH1G8eHEAZs+eTZUqVdizZw8lSpRw+jXTKlfW88mTJ+nRowedOnWiTZs2XLp0iREjRvDkk0+yevVqbDab06+Zljmjrnfu3Mljjz3Gq6++SqNGjZwYXdL0ZCeF+vbty4oVK1i/fj0FCxa078+XLx83btzgwoULDuefOnWKfPnypbj8fPnyJRohkPA6NeWkd66u5wQbNmzgkUce4a233qJjx473Gna64+p6XrduHW+88QZeXl54eXnRrVs3Ll68iJeXF3PmzHHWbaR5rq7n/Pnz4+XlZU90AEqVKgXAkSNH7i34dMTV9Txt2jSyZ8/OxIkTqVSpEnXq1GH+/PmsXbuWzZs3O+s20gVn1PWuXbto0KABPXv25JVXXnE4ltzvwsDAQLJkyXLXcSvZuQPDMOjbty/Lli1j3bp1FC5c2OF4lSpV8Pb2Zu3atfZ9e/bs4ciRI4SHh6f4OuHh4Xz//ffExsba961evZoSJUqQI0eOe7+RNM5d9QzmsMZmzZoxYcIEh8enmYG76jkqKoodO3bYv0aPHk22bNnYsWMHTzzxhNPuJ61yVz3XrFmTmzdvsn//fvu+v/76C4DQ0NB7vIu0z131fPXqVTw8HH9denp6AubTtczAWXX9xx9/8NBDD9GpU6ckp0gIDw93KAPM34Wp/Tmf1A3IbTz77LNG9uzZje+++844ceKE/evq1av2c3r16mUUKlTIWLdunbF161YjPDzcCA8Pdyhn7969xvbt241nnnnGKF68uLF9+3Zj+/bt9tFXFy5cMPLmzWt06NDB2Llzp7Fw4ULD39/feP/99916v1ZxVz2vW7fO8Pf3N4YOHepwnbNnz7r1fq3irnq+VWYbjeWueo6LizMqV65s1KlTx/jll1+MrVu3GjVq1DAaNmzo1vu1irvqee3atYbNZjNGjRpl/PXXX8a2bduMiIgIIzQ01OFaGZkz6vr333838uTJY7Rv396hjNOnT9vPOXDggOHv728MHjzY+PPPP41p06YZnp6exrfffntP8SvZuQMgya/IyEj7OdeuXTN69+5t5MiRw/D39zeeeOIJ48SJEw7l1K1bN8lyDh48aD/n119/NWrVqmX4+voa9913n/H666+76S6t56567tSpU5LH69at676btZA7P8//ldmSHXfW87Fjx4wWLVoYWbNmNfLmzWt07tw50yTv7qznTz75xKhUqZIREBBg5MmTx3j00UeNP//80013aj1n1PWrr76aZBmhoaEO11q/fr1RsWJFw8fHxyhSpIjDNe6W7f83ISIiIpIhqc+OiIiIZGhKdkRERCRDU7IjIiIiGZqSHREREcnQlOyIiIhIhqZkR0RERDI0JTsiIiKSoSnZERERkQxNyY6IpAuGYfDwww8TERGR6Nh7771HUFAQf//9twWRiUhap2RHRNIFm81GZGQkmzdv5v3337fvP3jwIC+++CLvvPOOwyrMzvDfhXlFJP1SsiMi6UZISAhvv/02gwYN4uDBgxiGQbdu3WjUqBGVKlWiSZMmZM2albx589KhQwfOnDljf++3335LrVq1CAoKIleuXDRv3txhtfBDhw5hs9n49NNPqVu3Ln5+fnz88cdW3KaIOJnWxhKRdOfxxx/n4sWLtGjRgjFjxvDHH39QpkwZunfvTseOHbl27RpDhgzh5s2brFu3DoAlS5Zgs9koX748ly9fZsSIERw6dIgdO3bg4eHBoUOHKFy4MGFhYbz55ptUqlQJPz8/8ufPb/Hdisi9UrIjIunO6dOnKVOmDOfOnWPJkiXs3LmTH374gZUrV9rP+fvvvwkJCWHPnj0UL148URlnzpwhT548/P7775QtW9ae7EyZMoX+/fu783ZExMXUjCUi6U5wcDDPPPMMpUqV4vHHH+fXX39l/fr1ZM2a1f5VsmRJAHtT1d69e2nTpg1FihQhMDCQsLAwAI4cOeJQdtWqVd16LyLiel5WByAicje8vLzw8jJ/hF2+fJlHHnmECRMmJDovoRnqkUceITQ0lA8++IACBQoQHx9P2bJluXHjhsP5AQEBrg9eRNxKyY6IpHuVK1dmyZIlhIWF2ROg/zp79ix79uzhgw8+oHbt2gD8+OOP7g5TRCyiZiwRSff69OnDuXPnaNOmDVu2bGH//v2sXLmSLl26EBcXR44cOciVKxczZ85k3759rFu3joEDB1odtoi4iZIdEUn3ChQowE8//URcXByNGjWiXLlyPP/88wQFBeHh4YGHhwcLFy5k27ZtlC1blgEDBjBp0iSrwxYRN9FoLBEREcnQ9GRHREREMjQlOyIiIpKhKdkRERGRDE3JjoiIiGRoSnZEREQkQ1OyIyIiIhmakh0RERHJ0JTsiIiISIamZEdEREQyNCU7IiIikqEp2REREZEMTcmOiIiIZGj/AzeZDi9wS0G5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for a specific country\n",
    "liberia_data = economies[economies['code'] == 'LBR']\n",
    "\n",
    "# Line plot of gdp_percapita over the years with labels and titles\n",
    "plt.plot(liberia_data['year'], liberia_data['gdp_percapita'],\n",
    "        marker='o', linestyle='-', color='r')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP Per Capita')\n",
    "plt.title('GDP Per Capita Over Years for Liberia (LBR)')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting axes and tick marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart of gdp_percapita for different Caribbean countries in 2020 with \n",
    "# adjusted axes and tick marks\n",
    "\n",
    "# Bar chart of gdp_percapita for different Caribbean countries in 2020\n",
    "plt.bar(data_2020_caribbean['code'], \n",
    "        data_2020_caribbean['gdp_percapita'], \n",
    "        color='g')\n",
    "plt.xlabel('Country Code')\n",
    "plt.ylabel('GDP Per Capita')\n",
    "plt.title('GDP Per Capita for Different Caribbean Countries in 2020')\n",
    "\n",
    "\n",
    "# Adjust axes\n",
    "plt.ylim(0, max(data_2020_caribbean['gdp_percapita']) + 5000)\n",
    "\n",
    "\n",
    "# Adjust tick marks\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(range(0, int(max(data_2020_caribbean['gdp_percapita']) + 5000), 5000))\n",
    "\n",
    "plt.grid(axis='y')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #5: Creating Basic Plots with Matplotlib\n",
    "\n",
    "By completing this exercise, you will be able to use `matplotlib` to  \n",
    "- Create line plots and bar charts  \n",
    "- Add labels and titles  \n",
    "- Adjust axes and tick marks  \n",
    "\n",
    "### Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for India\n",
    "\n",
    "\n",
    "# Line plot of fertility rate over the years\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for selected Asian countries and the year 2020\n",
    "# ['CHN', 'IND', 'IDN', 'PAK', 'BGD']\n",
    "\n",
    "\n",
    "\n",
    "# Bar chart of population size for selected Asian countries in 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Labels and Titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for Nigeria\n",
    "\n",
    "\n",
    "# Line plot of life expectancy over the years with labels and titles\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting Axes and Tick Marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for selected African countries ('NGA', 'ETH', 'EGY', 'ZAF', 'DZA')\n",
    "# and the year 2020\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Bar chart of fertility rate for selected African countries in 2020 with \n",
    "# adjusted axes and tick marks\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Adjust axes\n",
    "\n",
    "\n",
    "# Adjust tick marks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Walkthrough #6: Data Visualization Techniques with Seaborn\n",
    "\n",
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select only the numeric columns\n",
    "numeric_cols = economies.select_dtypes(include=['float64', 'int64']).columns\n",
    "numeric_economies = economies[numeric_cols]\n",
    "\n",
    "# Calculate correlation matrix\n",
    "corr_matrix = numeric_economies.corr()\n",
    "\n",
    "# Create heatmap\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f')\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pair plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pair plot of some numeric columns\n",
    "sns.pairplot(economies, vars=['gdp_percapita', 'gross_savings', 'inflation_rate', 'total_investment'])\n",
    "plt.suptitle('Pair Plot of Numerical Columns', y=1) # at the top of the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Violin plot of income_group against gdp_percapita\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.violinplot(x='income_group', y='gdp_percapita', data=economies)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing Seaborn plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot with customization\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x='code', y='gdp_percapita', hue='code', \n",
    "            data=data_2020_caribbean, palette='viridis')\n",
    "plt.xlabel('Country Code')\n",
    "plt.ylabel('GDP Per Capita')\n",
    "plt.title('GDP Per Capita for Different Caribbean Countries in 2020')\n",
    "\n",
    "# Customizing axes and tick marks\n",
    "plt.ylim(0, max(data_2020_caribbean['gdp_percapita']) + 5000)\n",
    "plt.xticks(rotation=60)\n",
    "plt.yticks(range(0, int(max(data_2020_caribbean['gdp_percapita']) + 5000), 5000))\n",
    "\n",
    "plt.grid(axis='y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #6: Data Visualization Techniques with Seaborn\n",
    "\n",
    "By completing this exercise, you will be able to use `seaborn` to  \n",
    "- Create heatmaps  \n",
    "- Design pair plots and violin plots  \n",
    "- Customize Seaborn plots  \n",
    "\n",
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only the numeric columns\n",
    "\n",
    "\n",
    "\n",
    "# Calculate correlation matrix\n",
    "\n",
    "\n",
    "# Create heatmap\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pair Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pair plot of fertility rate, life expectancy, and population size\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Violin plot of fertility rate by continent\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing Seaborn Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for selected European countries ('DEU', 'FRA', 'ITA', 'ESP', 'GBR')\n",
    "# and the year 2020\n",
    "\n",
    "\n",
    "\n",
    "# Bar plot with customization\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Customizing axes and tick marks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# Module 3: Interactive Data Visualization with Plotly\n",
    "\n",
    "## Walkthrough #7: Interactive Charts and Dashboards with Plotly\n",
    "\n",
    "### Basic interactive chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "year=%{x}<br>gdp_percapita=%{y}<extra></extra>",
         "legendgroup": "",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "type": "scatter",
         "x": [
          2010,
          2015,
          2020
         ],
         "xaxis": "x",
         "y": [
          4894.37,
          5204.83,
          5095.64
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
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   "source": [
    "# Filter data for a specific country\n",
    "jam_data = economies[economies['code'] == 'JAM']\n",
    "\n",
    "# Create an interactive line chart\n",
    "fig = px.line(jam_data,\n",
    "              x='year',\n",
    "              y='gdp_percapita',\n",
    "              title='GDP Per Capita Over Years for Jamaica (JAM)')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding interactive elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Create an interactive scatter plot\n",
    "fig = px.scatter(economies, x='gdp_percapita', y='gross_savings',\n",
    "                 color='income_group', \n",
    "                 hover_name='code',\n",
    "                 title=\"GDP Per Capita vs. Gross Savings\",\n",
    "                 labels={'gdp_percapita': 'GDP Per Capita',\n",
    "                         'gross_savings': 'Gross Savings (%)'})\n",
    "\n",
    "# Add hover, zoom, and selection tools\n",
    "fig.update_traces(marker=dict(size=10), selector=dict(mode='markers'))\n",
    "fig.update_layout(hovermode='closest')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Designing a simple dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Filter data for the year 2020\n",
    "data_2020 = economies[economies['year'] == 2020]\n",
    "\n",
    "# Create a subplot figure with 1 row and 2 columns\n",
    "fig = make_subplots(rows=1, cols=2,\n",
    "                    subplot_titles=('GDP Per Capita Over Years for Jamaica',\n",
    "                                    'GDP Per Capita for Different Countries in 2020'))\n",
    "\n",
    "# Line chart of GDP Per Capita for Jamaica\n",
    "jam_data = economies[economies['code'] == 'JAM']\n",
    "line_chart = go.Scatter(x=jam_data['year'], y=jam_data['gdp_percapita'],\n",
    "                        mode='lines+markers', name = \"Jamaica\")\n",
    "fig.add_trace(line_chart, row=1, col=1)\n",
    "\n",
    "# Bar chart of GDP Per Capita for different countries in 2020\n",
    "bar_chart = go.Bar(x=data_2020['code'], y=data_2020['gdp_percapita'],\n",
    "                   name='2020')\n",
    "fig.add_trace(bar_chart, row=1, col=2)\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(title_text=\"Simple Dashboard with Multiple Charts\", showlegend=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #7: Interactive Charts and Dashboards with Plotly\n",
    "\n",
    "By completing this exercise, you will be able to use `plotly` to  \n",
    "- Create a basic interactive chart  \n",
    "- Add interactive elements: hover, zoom, and selection tools  \n",
    "- Design a simple dashboard with multiple charts  \n",
    "\n",
    "### Basic Interactive Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for a specific country (Brazil)\n",
    "\n",
    "# Create an interactive line chart (Fertility Rate Over Years)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Interactive Elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an interactive scatter plot\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Add hover, zoom, and selection tools\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Designing a Simple Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter data for the year 2020\n",
    "\n",
    "# Create a subplot figure with 1 row and 2 columns\n",
    "\n",
    "\n",
    "\n",
    "# Line chart of Life Expectancy for Brazil\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Bar chart of Life Expectancy for South American countries in 2020\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Update layout to add a title and hide the legend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Walkthrough #8: Creating a Dynamic Data Report\n",
    "\n",
    "### Selecting relevant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select relevant data for the year 2020 and specific columns\n",
    "selected_data = economies[economies['year'] == 2020][['code', 'gdp_percapita', 'gross_savings', 'inflation_rate', 'income_group']]\n",
    "selected_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a dynamic report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a subplot figure with 3 rows\n",
    "fig = make_subplots(rows=3, cols=1, \n",
    "                    subplot_titles=('GDP Per Capita vs. Gross Savings', \n",
    "                                    'GDP Per Capita by Country and Income Group', \n",
    "                                    'Gross Savings by Country and Income Group'))\n",
    "\n",
    "# Add scatter plot\n",
    "fig.add_trace(go.Scatter(x=selected_data['gdp_percapita'], y=selected_data['gross_savings'], \n",
    "                         mode='markers', \n",
    "                         marker=dict(color=selected_data['income_group'].astype('category').cat.codes), \n",
    "                         text=selected_data['code'], name='Scatter'), \n",
    "              row=1, col=1)\n",
    "\n",
    "# Add bar chart\n",
    "fig.add_trace(go.Bar(x=selected_data['code'], y=selected_data['gdp_percapita'], \n",
    "                     marker=dict(color=selected_data['income_group'].astype('category').cat.codes), name='Bar'), \n",
    "              row=2, col=1)\n",
    "\n",
    "# Add another scatter plot\n",
    "fig.add_trace(go.Scatter(x=selected_data['code'], y=selected_data['gross_savings'], \n",
    "                         mode='markers', \n",
    "                         marker=dict(color=selected_data['income_group'].astype('category').cat.codes), text=selected_data['code'], name='Scatter'), \n",
    "              row=3, col=1)\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(title_text='Dynamic Data Report for Economic Indicators (2020)', showlegend=False, height=900)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding contextual text and summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.io as pio\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Create a subplot figure with 3 rows\n",
    "fig = make_subplots(rows=3, cols=1, \n",
    "                    subplot_titles=('GDP Per Capita vs. Gross Savings', \n",
    "                                    'GDP Per Capita by Country and Income Group', \n",
    "                                    'Gross Savings by Country and Income Group'))\n",
    "\n",
    "# Add scatter plot\n",
    "fig.add_trace(go.Scatter(x=selected_data['gdp_percapita'], y=selected_data['gross_savings'], \n",
    "                         mode='markers', \n",
    "                         marker=dict(color=selected_data['income_group'].astype('category').cat.codes), \n",
    "                         text=selected_data['code'], name='Scatter'), \n",
    "              row=1, col=1)\n",
    "\n",
    "# Add bar chart\n",
    "fig.add_trace(go.Bar(x=selected_data['code'], y=selected_data['gdp_percapita'], \n",
    "                     marker=dict(color=selected_data['income_group'].astype('category').cat.codes), name='Bar'), \n",
    "              row=2, col=1)\n",
    "\n",
    "# Add another scatter plot\n",
    "fig.add_trace(go.Scatter(x=selected_data['code'], y=selected_data['gross_savings'], \n",
    "                         mode='markers', \n",
    "                         marker=dict(color=selected_data['income_group'].astype('category').cat.codes), \n",
    "                         text=selected_data['code'], name='Scatter'), \n",
    "              row=3, col=1)\n",
    "\n",
    "# Update layout\n",
    "fig.update_layout(\n",
    "    title_text='Dynamic Data Report for Economic Indicators (2020)', \n",
    "    showlegend=False, \n",
    "    height=900,\n",
    "    annotations=[\n",
    "        go.layout.Annotation(\n",
    "            text='''This report presents key economic indicators for various countries in 2020, categorized by income group. ''', \n",
    "            xref='paper', yref='paper', x=0.5, y=1, showarrow=False, font=dict(size=14)\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Add summaries below each subplot\n",
    "fig.add_annotation(text='The scatter plot reveals a positive correlation between GDP per Capita and Gross Savings, especially for high-income countries.', xref='paper', yref='paper', x=0, y=0.75, showarrow=False, font=dict(size=12))\n",
    "fig.add_annotation(text='The bar chart shows that high-income countries generally have higher GDP per Capita compared to low-income countries.', xref='paper', yref='paper', x=0, y=0.30, showarrow=False, font=dict(size=12))\n",
    "fig.add_annotation(text='The scatter plot indicates no clear relationship between income group and gross savings.', xref='paper', yref='paper', x=0, y=-0.1, showarrow=False, font=dict(size=12))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise #8: Creating a Dynamic Data Report\n",
    "\n",
    "By completing this exercise, you will be able to use `pandas` and `plotly` to  \n",
    "- Select relevant data  \n",
    "- Build a dynamic report  \n",
    "- Add contextual text and summaries  \n",
    "\n",
    "### Selecting Relevant Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select relevant data for the year 2020 and specific columns (country_code, fertility_rate, life_expectancy, continent)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a Dynamic Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Create a subplot figure with 3 rows and subplot titles\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Adding a scatter plot trace to the figure\n",
    "# - x-axis: 'fertility_rate' from the selected population data\n",
    "# - y-axis: 'life_expectancy' from the selected population data\n",
    "# - mode: 'markers' to display points\n",
    "# - marker color: based on the 'continent' category codes, to differentiate points by continent\n",
    "# - text: 'country_code' to show country codes on hover\n",
    "# - name: 'Scatter' to label this trace\n",
    "# The trace is added to the first row and first column of the subplot grid\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Adding a bar chart trace to the figure\n",
    "# - x-axis: 'country_code' from the selected population data\n",
    "# - y-axis: 'fertility_rate' from the selected population data\n",
    "# - marker color: based on the 'continent' category codes, to differentiate bars by continent\n",
    "# - name: 'Bar' to label this trace\n",
    "# The trace is added to the second row and first column of the subplot grid\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Adding a scatter plot trace to the figure\n",
    "# - x-axis: 'country_code' from the selected population data\n",
    "# - y-axis: 'life_expectancy' from the selected population data\n",
    "# - mode: 'markers' to display points\n",
    "# - marker color: based on the 'continent' category codes, to differentiate points by continent\n",
    "# - name: 'Scatter' to label this trace\n",
    "# The trace is added to the third row and first column of the subplot grid\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Update layout to include title, hide legend, and set height to 900\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding Contextual Text and Summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy over plot creation code from previous\n",
    "\n",
    "# Updating the layout of the figure\n",
    "# - Setting the title text of the entire figure\n",
    "# - Disabling the legend display\n",
    "# - Setting the height of the figure to 900 pixels\n",
    "# - Adding an annotation to provide additional information about the report\n",
    "#   - Annotation text: A brief description of the report content\n",
    "#   - xref and yref: Setting the reference frame to 'paper' coordinates (relative to the entire figure)\n",
    "#   - x and y: Positioning the annotation at the center top of the figure\n",
    "#   - showarrow: Disabling the arrow for the annotation\n",
    "#   - font: Setting the font size of the annotation text\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Add summaries below each subplot\n",
    "# Add summaries below each subplot\n",
    "fig.add_annotation(text='A negative correlation between Fertility Rate and Life Expectancy.', xref='paper', yref='paper', x=0, y=0.75, showarrow=False, font=dict(size=12))\n",
    "fig.add_annotation(text='Fertility rates vary significantly across countries, with African countries generally exhibiting higher fertility rates.', xref='paper', yref='paper', x=0, y=0.30, showarrow=False, font=dict(size=12))\n",
    "fig.add_annotation(text='Life expectancy varies across countries and continents, with European tending to have higher life expectancies.', xref='paper', yref='paper', x=0, y=-0.1, showarrow=False, font=dict(size=12))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4: Real-World Data Analysis Project\n",
    "\n",
    "## Walkthrough #9: Interactive Charts and Dashboards with Plotly\n",
    "\n",
    "### Selecting a Dataset\n",
    "\n",
    "#### Questions to Ask:\n",
    "1. **What industry problem or area of interest does the dataset align with?**\n",
    "   - Is the dataset relevant to economic analysis, market research, policy planning, or another industry?\n",
    "2. **Does the dataset provide sufficient complexity and scope for a thorough analysis?**\n",
    "   - Does it include multiple variables and data points across different time periods and categories (e.g., income groups, countries)?\n",
    "3. **What specific questions or hypotheses do we want to explore with this dataset?**\n",
    "   - Are we interested in comparing economic indicators across countries, understanding the impact of GDP per capita on other variables, or identifying trends over time?\n",
    "\n",
    "#### Example:\n",
    "- **Dataset**: The `economies` dataset.\n",
    "- **Industry Problem**: Understanding economic disparities between countries and the impact of economic indicators on overall economic health.\n",
    "- **Specific Questions**:\n",
    "  - How do GDP per capita and gross savings vary across different income groups?\n",
    "  - How has the inflation rate changed over time for specified income groups?\n",
    "\n",
    "### Applying Cleaning, Transforming, and Analysis Techniques\n",
    "\n",
    "#### Questions to Ask:\n",
    "1. **What cleaning steps are necessary to prepare the data for analysis?**\n",
    "   - Are there any missing values that need to be handled? Are there any inconsistencies in data types?\n",
    "2. **What transformations are required to make the data analysis-ready?**\n",
    "   - Do we need to create new columns, filter specific rows, or aggregate data by certain categories?\n",
    "3. **How can we analyze the data to uncover patterns, trends, or anomalies?**\n",
    "   - What statistical methods or visualizations can we use to explore relationships between variables?\n",
    "\n",
    "#### Example:\n",
    "- **Cleaning**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "  # Handle missing values\n",
    "  economies_cleaned = economies.fillna({\n",
    "      'gdp_percapita': economies['gdp_percapita'].mean(),\n",
    "      'gross_savings': economies['gross_savings'].mean(),\n",
    "      'inflation_rate': economies['inflation_rate'].mean(),\n",
    "      'total_investment': economies['total_investment'].mean(),\n",
    "      'unemployment_rate': economies['unemployment_rate'].mean(),\n",
    "      'exports': economies['exports'].mean(),\n",
    "      'imports': economies['imports'].mean()\n",
    "  })\n",
    "\n",
    "  # Convert categorical variables to category type\n",
    "  economies_cleaned['income_group'] = economies_cleaned['income_group'].astype('category')\n",
    "  economies_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Transforming**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "  # Create new columns for analysis\n",
    "  economies_cleaned['gdp_growth'] = economies_cleaned.groupby('code')['gdp_percapita'].pct_change()\n",
    "  economies_cleaned"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Analyzing**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "  import seaborn as sns\n",
    "  import matplotlib.pyplot as plt\n",
    "\n",
    "  plt.clf()\n",
    "\n",
    "  # Analyze the relationship between GDP per capita and gross savings\n",
    "  sns.scatterplot(data=economies_cleaned, x='gdp_percapita', y='gross_savings', hue='income_group')\n",
    "  plt.title('GDP Per Capita vs. Gross Savings by Income Group')\n",
    "  plt.show()\n",
    "  plt.clf()\n",
    "\n",
    "  # Analyze the trend of inflation rate over time for all classified income groups\n",
    "  classified_data = economies_cleaned[economies_cleaned['income_group'] != 'Not classified']\n",
    "  sns.lineplot(data=classified_data, x='year', y='inflation_rate', \n",
    "               hue='income_group', errorbar=None)\n",
    "  plt.title('Inflation Rate Over Time by Specified Income Group')\n",
    "  plt.legend(title='Income Group', labels=classified_data['income_group'].unique())\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial Findings and Interpretation\n",
    "\n",
    "#### Questions to Ask:\n",
    "1. **What do the initial findings tell us about the data?**\n",
    "   - Are there any notable patterns or trends in the data? Are there any unexpected results?\n",
    "2. **How do these insights relate to the problem defined earlier?**\n",
    "   - Do the findings help us understand economic disparities between countries? Do they provide insights into the impact of certain economic indicators?\n",
    "3. **What hypotheses can we test based on the initial results?**\n",
    "   - Can we test hypotheses about the relationship between GDP per capita and other economic indicators? Can we refine our analysis to explore these hypotheses further?\n",
    "\n",
    "#### Example:\n",
    "- **Initial Findings**:\n",
    "  - **GDP per Capita vs. Gross Savings**: The scatter plot shows that high-income countries generally have higher GDP per capita and gross savings. There seems to be a slight positive correlation between these two indicators.\n",
    "  - **Inflation Rate Over Time**: The line plot indicates that inflation rates vary significantly over time and across different income groups. Low and lower middle income countries tend to experience higher volatility in inflation rates.\n",
    "\n",
    "- **Interpretation**:\n",
    "  - These findings suggest that economic health, as measured by GDP per capita and gross savings, is strongly influenced by the income group of a country. High-income countries appear to have more stable and higher economic performance.\n",
    "  - The volatility in inflation rates among low-income countries may indicate economic instability, which could be a key area for policy intervention.\n",
    "\n",
    "- **Hypotheses**:\n",
    "  - **Hypothesis 1**: High-income countries have a higher average GDP per capita and gross savings compared to low-income countries.\n",
    "  - **Hypothesis 2**: Low-income countries experience greater volatility in inflation rates compared to high-income countries.\n",
    "\n",
    "- **Next Steps**:\n",
    "  - Conduct further analysis to test these hypotheses, using statistical methods to confirm the observed patterns.\n",
    "  - Explore other economic indicators to gain a more comprehensive understanding of economic disparities and trends.\n",
    "\n",
    "By following these steps, you can effectively select, clean, transform, and analyze the `economies` dataset to gain valuable insights and address common industry problems or research questions.\n",
    "\n",
    "\n",
    "## Walkthrough #10: Finalizing and Presenting Your Data Analysis Project\n",
    "\n",
    "### Integrate Feedback to Refine the Analysis\n",
    "\n",
    "#### Questions to Ask:\n",
    "1. **What feedback have you received from peers, stakeholders, or mentors?**\n",
    "   - Is there feedback on the clarity of the analysis, choice of visualizations, or the comprehensiveness of the analysis?\n",
    "2. **How can you incorporate this feedback into your analysis?**\n",
    "   - Are there additional variables that need to be analyzed? Do you need to clean the data further or adjust the visualizations?\n",
    "3. **What new questions or hypotheses have emerged from the feedback?**\n",
    "   - Does the feedback suggest new directions for the analysis or areas that need more focus?\n",
    "\n",
    "#### Example:\n",
    "- **Feedback**:\n",
    "  - Peers suggested that the analysis should also consider the impact of unemployment rates.\n",
    "  - Stakeholders requested more clarity on the relationship between GDP per capita and inflation rates across different income groups.\n",
    "\n",
    "- **Refining the Analysis**:\n",
    "  - Additional data needs to be found to meet the request for more clarity. Or maybe a further drilldown on specific countries would be helpful?\n",
    "  \n",
    "### Finalize the Presentation with Impactful Visuals and Narrative\n",
    "\n",
    "#### Questions to Ask:\n",
    "1. **What are the key insights from the analysis that need to be highlighted?**\n",
    "   - What are the most important findings that should be communicated to the audience?\n",
    "2. **How can you create impactful visuals that clearly convey these insights?**\n",
    "   - What types of charts or visualizations best represent the data and findings?\n",
    "3. **What narrative will you use to guide the audience through the presentation?**\n",
    "   - How will you structure the presentation to tell a compelling story with the data?\n",
    "\n",
    "#### Example:\n",
    "- **Key Insights**:\n",
    "  - High-income countries have higher GDP per capita and gross savings.\n",
    "  - There is a positive correlation between GDP per capita and gross savings.\n",
    "  - Low-income countries experience greater volatility in inflation rates.\n",
    "  - Unemployment rates vary significantly across income groups.\n",
    "\n",
    "- **Impactful Visuals**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "  import plotly.express as px\n",
    "\n",
    "  # Bar chart of GDP per Capita by Country and Income Group\n",
    "  bar_fig = px.bar(economies_cleaned, x='code', y='gdp_percapita', color='income_group',\n",
    "                   title='GDP Per Capita by Country and Income Group (2020)',\n",
    "                   labels={'gdp_percapita': 'GDP Per Capita', 'code': 'Country Code'})\n",
    "  bar_fig.show()\n",
    "\n",
    "  # Scatter plot of GDP per Capita vs. Gross Savings by Income Group\n",
    "  scatter_fig = px.scatter(economies_cleaned, x='gdp_percapita', y='gross_savings', color='income_group',\n",
    "                           hover_name='code', title='GDP Per Capita vs. Gross Savings (2020)',\n",
    "                           labels={'gdp_percapita': 'GDP Per Capita', 'gross_savings': 'Gross Savings (%)'})\n",
    "  scatter_fig.show()\n",
    "\n",
    "  # Scatter plot of GDP per Capita vs. Unemployment Rate by Income Group\n",
    "  scatter_fig_2 = px.scatter(economies_cleaned, x='gdp_percapita', y='unemployment_rate', color='income_group',\n",
    "                             hover_name='code', title='GDP Per Capita vs. Unemployment Rate (2020)',\n",
    "                             labels={'gdp_percapita': 'GDP Per Capita', 'unemployment_rate': 'Unemployment Rate (%)'})\n",
    "  scatter_fig_2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Narrative**:\n",
    "  - **Introduction**: Introduce the dataset and the industry problem. Explain why understanding economic indicators across different income groups is important.\n",
    "  - **Key Findings**: Present the key findings using the visualizations created. Highlight the relationship between GDP per capita, gross savings, inflation rates, and unemployment rates.\n",
    "  - **Detailed Analysis**: Dive deeper into each key finding, providing more context and interpretation. Explain the significance of the trends and patterns observed in the data.\n",
    "  - **Conclusion**: Summarize the insights and discuss potential implications for policy or business decisions. Suggest areas for further research or analysis based on the findings.\n",
    "\n",
    "### Rehearse the Presentation\n",
    "\n",
    "#### Questions to Ask:\n",
    "1. **How will you structure your presentation to ensure a smooth flow?**\n",
    "   - What order will you present the visualizations and insights? How will you transition between different sections?\n",
    "2. **How will you engage your audience and ensure they understand the key points?**\n",
    "   - What techniques will you use to highlight important information and keep the audience's attention?\n",
    "3. **What potential questions or feedback might you receive, and how will you address them?**\n",
    "   - How will you prepare for questions about the data, analysis methods, or findings?\n",
    "\n",
    "#### Example:\n",
    "- **Structuring the Presentation**:\n",
    "  - Start with an overview of the dataset and the industry problem.\n",
    "  - Move on to the key findings, using the most impactful visualizations to illustrate each point.\n",
    "  - Provide a detailed analysis of each finding, explaining the significance and implications.\n",
    "  - Conclude with a summary of insights and suggestions for further research.\n",
    "\n",
    "- **Engaging the Audience**:\n",
    "  - Use clear and concise language to explain complex concepts.\n",
    "  - Highlight key points using annotations or callouts on the visualizations.\n",
    "  - Encourage questions and interaction to keep the audience engaged.\n",
    "\n",
    "- **Preparing for Questions**:\n",
    "  - Anticipate common questions about the data sources, cleaning methods, and analysis techniques.\n",
    "  - Prepare explanations for any limitations of the data or analysis.\n",
    "  - Be ready to discuss potential next steps and areas for further research based on the findings.\n",
    "\n",
    "By following these steps, you can effectively integrate feedback, finalize your presentation with impactful visuals and narrative, and rehearse to ensure a smooth and engaging delivery.\n"
   ]
  }
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