exercises_2025-01-08.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "title: 'Walkthroughs and Exercises for *Data Analysis in Python*'  \n",
    "author: \"Dr. Chester Ismay\"  \n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Display all columns\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# Display all outputs from each cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "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": 2,
   "metadata": {},
   "outputs": [],
   "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.io as pio\n",
    "pio.renderers.default = 'iframe'"
   ]
  },
  {
   "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.\n",
    "\n",
    "---\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": 5,
   "metadata": {},
   "outputs": [
    {
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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",
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       "      <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",
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       "    <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]"
      ]
     },
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       "      <td>ABW</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>10.600</td>\n",
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       "      <td>ABW</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>7.298</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
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       "      <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",
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       "      <td>...</td>\n",
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       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data from a CSV file\n",
    "economies = pd.read_csv(\"economies.csv\")\n",
    "economies\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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of the DataFrame\n",
    "economies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display summary statistics of the DataFrame\n",
    "economies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing data\n",
    "economies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_code</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>fertility_rate</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>size</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2010</td>\n",
       "      <td>1.941</td>\n",
       "      <td>75.404</td>\n",
       "      <td>100341</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>North America</td>\n",
       "      <td>Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.972</td>\n",
       "      <td>75.683</td>\n",
       "      <td>104257</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>North America</td>\n",
       "      <td>Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020</td>\n",
       "      <td>1.325</td>\n",
       "      <td>75.723</td>\n",
       "      <td>106585</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>North America</td>\n",
       "      <td>Caribbean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2010</td>\n",
       "      <td>6.099</td>\n",
       "      <td>60.851</td>\n",
       "      <td>28189672</td>\n",
       "      <td>The Islamic Republic of Afghanistan</td>\n",
       "      <td>UN member</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>5.405</td>\n",
       "      <td>62.659</td>\n",
       "      <td>33753499</td>\n",
       "      <td>The Islamic Republic of Afghanistan</td>\n",
       "      <td>UN member</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Southern and Central Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_code      country  year  fertility_rate  life_expectancy      size  \\\n",
       "0          ABW        Aruba  2010           1.941           75.404    100341   \n",
       "1          ABW        Aruba  2015           1.972           75.683    104257   \n",
       "2          ABW        Aruba  2020           1.325           75.723    106585   \n",
       "3          AFG  Afghanistan  2010           6.099           60.851  28189672   \n",
       "4          AFG  Afghanistan  2015           5.405           62.659  33753499   \n",
       "\n",
       "                   official_state_name  sovereignty      continent  \\\n",
       "0                                Aruba  Netherlands  North America   \n",
       "1                                Aruba  Netherlands  North America   \n",
       "2                                Aruba  Netherlands  North America   \n",
       "3  The Islamic Republic of Afghanistan    UN member           Asia   \n",
       "4  The Islamic Republic of Afghanistan    UN member           Asia   \n",
       "\n",
       "                      region  \n",
       "0                  Caribbean  \n",
       "1                  Caribbean  \n",
       "2                  Caribbean  \n",
       "3  Southern and Central Asia  \n",
       "4  Southern and Central Asia  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 645 entries, 0 to 644\n",
      "Data columns (total 10 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   country_code         645 non-null    object \n",
      " 1   country              645 non-null    object \n",
      " 2   year                 645 non-null    int64  \n",
      " 3   fertility_rate       627 non-null    float64\n",
      " 4   life_expectancy      623 non-null    float64\n",
      " 5   size                 645 non-null    int64  \n",
      " 6   official_state_name  645 non-null    object \n",
      " 7   sovereignty          645 non-null    object \n",
      " 8   continent            645 non-null    object \n",
      " 9   region               645 non-null    object \n",
      "dtypes: float64(2), int64(2), object(6)\n",
      "memory usage: 50.5+ KB\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>645.000000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>623.000000</td>\n",
       "      <td>6.450000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.727907</td>\n",
       "      <td>71.553996</td>\n",
       "      <td>3.429149e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.085651</td>\n",
       "      <td>1.386750</td>\n",
       "      <td>8.118422</td>\n",
       "      <td>1.346457e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>0.837000</td>\n",
       "      <td>45.596000</td>\n",
       "      <td>1.024100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.670000</td>\n",
       "      <td>65.742000</td>\n",
       "      <td>7.550310e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.216000</td>\n",
       "      <td>73.004000</td>\n",
       "      <td>6.292731e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>3.537500</td>\n",
       "      <td>77.720695</td>\n",
       "      <td>2.301265e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>7.485000</td>\n",
       "      <td>85.497561</td>\n",
       "      <td>1.411100e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  fertility_rate  life_expectancy          size\n",
       "count   645.000000      627.000000       623.000000  6.450000e+02\n",
       "mean   2015.000000        2.727907        71.553996  3.429149e+07\n",
       "std       4.085651        1.386750         8.118422  1.346457e+08\n",
       "min    2010.000000        0.837000        45.596000  1.024100e+04\n",
       "25%    2010.000000        1.670000        65.742000  7.550310e+05\n",
       "50%    2015.000000        2.216000        73.004000  6.292731e+06\n",
       "75%    2020.000000        3.537500        77.720695  2.301265e+07\n",
       "max    2020.000000        7.485000        85.497561  1.411100e+09"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "country_code            0\n",
       "country                 0\n",
       "year                    0\n",
       "fertility_rate         18\n",
       "life_expectancy        22\n",
       "size                    0\n",
       "official_state_name     0\n",
       "sovereignty             0\n",
       "continent               0\n",
       "region                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "country_code            object\n",
       "country                 object\n",
       "year                     int64\n",
       "fertility_rate         float64\n",
       "life_expectancy        float64\n",
       "size                     int64\n",
       "official_state_name     object\n",
       "sovereignty             object\n",
       "continent               object\n",
       "region                  object\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the populations data from an Excel file\n",
    "populations = pd.read_excel(\"populations.xlsx\")\n",
    "\n",
    "# Inspection methods for populations DataFrame\n",
    "populations.head()\n",
    "populations.info()\n",
    "populations.describe()\n",
    "\n",
    "\n",
    "\n",
    "# Checking for missing data and data types for populations DataFrame\n",
    "populations.isnull().sum()\n",
    "populations.dtypes"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "    .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>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
       "      <th>inflation_rate</th>\n",
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       "      <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>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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows with any missing values\n",
    "economies_cleaned_any = economies.dropna(how='any')\n",
    "economies_cleaned_any\n",
    "economies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>NaN</td>\n",
       "      <td>10.600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>High income</td>\n",
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       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "      <td>ABW</td>\n",
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       "      <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",
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       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
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       "      <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",
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       "    <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",
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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",
       "    </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": 14,
     "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\n",
    "economies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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": 15,
     "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": 16,
   "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>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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove columns with any missing values\n",
    "# Display the DataFrame after removing columns with missing values\n",
    "economies.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replace missing values with specific value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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>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>0.0</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>0.0</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>0.0</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>0.0</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>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</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          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",
       "556            42.791                0.0  -11.407    0.696   \n",
       "557            34.514                0.0    1.143    2.635   \n",
       "558             0.000                0.0    0.000    0.000   \n",
       "559             0.000                0.0    0.000    0.000   \n",
       "560             0.000                0.0    0.000    0.000   \n",
       "\n",
       "            income_group  \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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace missing values with a specific value \n",
    "# (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",
    "\n",
    "# Display the DataFrame after replacing missing values \n",
    "# with specific values (shows the last few rows)\n",
    "economies_fill_value.tail()"
   ]
  },
  {
   "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": 18,
   "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": 19,
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0meconomies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmapper\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Renamer | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Renamer | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Renamer | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Axis | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Level | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'IgnoreRaise'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'DataFrame | None'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Rename columns or index labels.\n",
       "\n",
       "Function / dict values must be unique (1-to-1). Labels not contained in\n",
       "a dict / Series will be left as-is. Extra labels listed don't throw an\n",
       "error.\n",
       "\n",
       "See the :ref:`user guide <basics.rename>` for more.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "mapper : dict-like or function\n",
       "    Dict-like or function transformations to apply to\n",
       "    that axis' values. Use either ``mapper`` and ``axis`` to\n",
       "    specify the axis to target with ``mapper``, or ``index`` and\n",
       "    ``columns``.\n",
       "index : dict-like or function\n",
       "    Alternative to specifying axis (``mapper, axis=0``\n",
       "    is equivalent to ``index=mapper``).\n",
       "columns : dict-like or function\n",
       "    Alternative to specifying axis (``mapper, axis=1``\n",
       "    is equivalent to ``columns=mapper``).\n",
       "axis : {0 or 'index', 1 or 'columns'}, default 0\n",
       "    Axis to target with ``mapper``. Can be either the axis name\n",
       "    ('index', 'columns') or number (0, 1). The default is 'index'.\n",
       "copy : bool, default True\n",
       "    Also copy underlying data.\n",
       "\n",
       "    .. note::\n",
       "        The `copy` keyword will change behavior in pandas 3.0.\n",
       "        `Copy-on-Write\n",
       "        <https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html>`__\n",
       "        will be enabled by default, which means that all methods with a\n",
       "        `copy` keyword will use a lazy copy mechanism to defer the copy and\n",
       "        ignore the `copy` keyword. The `copy` keyword will be removed in a\n",
       "        future version of pandas.\n",
       "\n",
       "        You can already get the future behavior and improvements through\n",
       "        enabling copy on write ``pd.options.mode.copy_on_write = True``\n",
       "inplace : bool, default False\n",
       "    Whether to modify the DataFrame rather than creating a new one.\n",
       "    If True then value of copy is ignored.\n",
       "level : int or level name, default None\n",
       "    In case of a MultiIndex, only rename labels in the specified\n",
       "    level.\n",
       "errors : {'ignore', 'raise'}, default 'ignore'\n",
       "    If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,\n",
       "    or `columns` contains labels that are not present in the Index\n",
       "    being transformed.\n",
       "    If 'ignore', existing keys will be renamed and extra keys will be\n",
       "    ignored.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "DataFrame or None\n",
       "    DataFrame with the renamed axis labels or None if ``inplace=True``.\n",
       "\n",
       "Raises\n",
       "------\n",
       "KeyError\n",
       "    If any of the labels is not found in the selected axis and\n",
       "    \"errors='raise'\".\n",
       "\n",
       "See Also\n",
       "--------\n",
       "DataFrame.rename_axis : Set the name of the axis.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "``DataFrame.rename`` supports two calling conventions\n",
       "\n",
       "* ``(index=index_mapper, columns=columns_mapper, ...)``\n",
       "* ``(mapper, axis={'index', 'columns'}, ...)``\n",
       "\n",
       "We *highly* recommend using keyword arguments to clarify your\n",
       "intent.\n",
       "\n",
       "Rename columns using a mapping:\n",
       "\n",
       ">>> df = pd.DataFrame({\"A\": [1, 2, 3], \"B\": [4, 5, 6]})\n",
       ">>> df.rename(columns={\"A\": \"a\", \"B\": \"c\"})\n",
       "   a  c\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6\n",
       "\n",
       "Rename index using a mapping:\n",
       "\n",
       ">>> df.rename(index={0: \"x\", 1: \"y\", 2: \"z\"})\n",
       "   A  B\n",
       "x  1  4\n",
       "y  2  5\n",
       "z  3  6\n",
       "\n",
       "Cast index labels to a different type:\n",
       "\n",
       ">>> df.index\n",
       "RangeIndex(start=0, stop=3, step=1)\n",
       ">>> df.rename(index=str).index\n",
       "Index(['0', '1', '2'], dtype='object')\n",
       "\n",
       ">>> df.rename(columns={\"A\": \"a\", \"B\": \"b\", \"C\": \"c\"}, errors=\"raise\")\n",
       "Traceback (most recent call last):\n",
       "KeyError: ['C'] not found in axis\n",
       "\n",
       "Using axis-style parameters:\n",
       "\n",
       ">>> df.rename(str.lower, axis='columns')\n",
       "   a  b\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6\n",
       "\n",
       ">>> df.rename({1: 2, 2: 4}, axis='index')\n",
       "   A  B\n",
       "0  1  4\n",
       "2  2  5\n",
       "4  3  6\n",
       "\u001b[0;31mFile:\u001b[0m      ~/.virtualenvs/my_new_env/lib/python3.13/site-packages/pandas/core/frame.py\n",
       "\u001b[0;31mType:\u001b[0m      method"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For getting information about a particular method\n",
    "?economies.rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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>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_category  \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": 23,
     "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing a DataFrame’s index\n",
    "\n",
    "#### Set the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></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": 24,
     "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": 25,
   "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>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": 25,
     "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": 26,
   "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",
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       "      <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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where 'gdp_percapita' is greater than 20,000\n",
    "economies[ economies['gdp_percapita'] > 20000 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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",
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       "      <th>total_investment</th>\n",
       "      <th>unemployment_rate</th>\n",
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       "      <th>income_group</th>\n",
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       "  <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",
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       "      <td>High income</td>\n",
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       "    <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>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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where 'income_group' is 'High income'\n",
    "economies[ economies['income_group'] == 'High income' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "  </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": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
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       "\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>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": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "ename": "NameError",
     "evalue": "name 'NaN' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[29], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m economies[\u001b[38;5;241m~\u001b[39meconomies[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal_investment\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39misnull()]\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Doesn't work unfortunately\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m economies[economies[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtotal_investment\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[43mNaN\u001b[49m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'NaN' is not defined"
     ]
    }
   ],
   "source": [
    "# Filter rows where total_investment is not NaN\n",
    "economies[economies['total_investment'].notna()]\n",
    "\n",
    "# Also valid\n",
    "economies[~economies['total_investment'].isnull()]\n",
    "\n",
    "# Doesn't work unfortunately\n",
    "economies[economies['total_investment'] != NaN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conditions on multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <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>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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where inflation_rate is less than 0 and income_group is 'Low income'\n",
    "economies[ (economies['inflation_rate'] < 0) & (economies['income_group'] == 'Low income') ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>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>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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter rows where gdp_percapita is greater than 40,000 and \n",
    "# year is less than or equal to 2016\n",
    "economies[ (economies['gdp_percapita'] > 40000) & (economies['year'] <= 2016) ] "
   ]
  },
  {
   "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 (e.g., 'fertility_rate', 'life_expectancy')"
   ]
  },
  {
   "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": 34,
   "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>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>income_group_upper</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>LOWER MIDDLE INCOME</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 12 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   income_group_upper  \n",
       "0            High income          HIGH INCOME  \n",
       "1            High income          HIGH INCOME  \n",
       "2            High income          HIGH INCOME  \n",
       "3             Low income           LOW INCOME  \n",
       "4             Low income           LOW INCOME  \n",
       "..                   ...                  ...  \n",
       "556  Lower middle income  LOWER MIDDLE INCOME  \n",
       "557  Lower middle income  LOWER MIDDLE INCOME  \n",
       "558  Lower middle income  LOWER MIDDLE INCOME  \n",
       "559  Lower middle income  LOWER MIDDLE INCOME  \n",
       "560  Lower middle income  LOWER MIDDLE INCOME  \n",
       "\n",
       "[561 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert income_group to uppercase using map()\n",
    "economies_plus = economies.copy()\n",
    "\n",
    "economies_plus['income_group_upper'] = economies_plus['income_group'].map(str.upper)\n",
    "economies_plus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying a Function to Groups with `groupby()` and `agg()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "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": 38,
   "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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pivot table of mean gdp_percapita and inflation_rate by income_group and year\n",
    "pivot_table = pd.pivot_table(\n",
    "    economies,\n",
    "    values=['gdp_percapita', 'inflation_rate'],\n",
    "    aggfunc='mean',\n",
    "    index=['income_group'],\n",
    "    columns=['year']\n",
    ")\n",
    "pivot_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing categorical data \n",
    "\n",
    "#### Using cross-tabulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show counts of income_group by year\n",
    "cross_tab = pd.crosstab(economies['income_group'], economies['year'])\n",
    "cross_tab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By getting group counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the occurrences of each income_group\n",
    "income_group_counts = economies['income_group'].value_counts()\n",
    "income_group_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 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 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": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for a specific country (Afghanistan)\n",
    "afg_data = economies[economies['code'] == 'AFG']\n",
    "\n",
    "# Line plot of gdp_percapita over time\n",
    "plt.plot(afg_data['year'], afg_data['gdp_percapita'],\n",
    "         marker='o', linestyle='-', color='blue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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'], data_2020_caribbean['gdp_percapita'], color='green');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding labels and titles"
   ]
  },
  {
   "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",
    "liberia_data = economies[economies['code'] == 'LBR']\n",
    "\n",
    "# Line plot of gdp_percapita over the years with labels and titles\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(liberia_data['year'], liberia_data['gdp_percapita'], 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')\n",
    "plt.grid(True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting axes and tick marks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
    "plt.bar(data_2020_caribbean['code'], data_2020_caribbean['gdp_percapita'], color='purple')\n",
    "plt.xlabel('Country Code')\n",
    "plt.ylabel('GDP Per Capita')\n",
    "\n",
    "# Adjust axes\n",
    "plt.ylim(0, 35000)\n",
    "\n",
    "# Adjust tick marks\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.grid(axis='y');"
   ]
  },
  {
   "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",
    "\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",
    "# Need to convert year back to an integer?\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": 55,
   "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_cols\n",
    "numeric_economies = economies[numeric_cols]\n",
    "# numeric_economies\n",
    "\n",
    "# Calculate correlation matrix\n",
    "corr_matrix = numeric_economies.corr()\n",
    "corr_matrix\n",
    "\n",
    "# Create heatmap\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pair plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(economies, vars=['gdp_percapita', 'gross_savings', 'inflation_rate', 'total_investment']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "sns.violinplot(x='income_group', y='gdp_percapita', data=economies);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizing Seaborn plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bar plot with customization\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Customizing axes and tick marks\n"
   ]
  },
  {
   "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": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_59.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter data for a specific country\n",
    "usa_data = economies[economies['code'] == \"USA\"]\n",
    "\n",
    "# Create an interactive line chart\n",
    "fig = px.line(usa_data, x='year', y='gdp_percapita', title='GDP Per Capita Over Years for the US')\n",
    "fig.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding interactive elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe\n",
       "    scrolling=\"no\"\n",
       "    width=\"100%\"\n",
       "    height=\"545px\"\n",
       "    src=\"iframe_figures/figure_60.html\"\n",
       "    frameborder=\"0\"\n",
       "    allowfullscreen\n",
       "></iframe>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an interactive scatter plot\n",
    "fig = px.scatter(economies, x='gdp_percapita', y='gross_savings', color='income_group',\n",
    "                 hover_name='code', title='GDP Per Capita vs. Gross Savings',\n",
    "                 labels={'gdp_percapita': 'GDP Per Capita', '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",
    "\n",
    "fig.show();"
   ]
  },
  {
   "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",
    "\n",
    "# Create a subplot figure with 1 row and 2 columns\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Line chart of GDP Per Capita for Afghanistan\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Bar chart of GDP Per Capita for different countries in 2020\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Update layout\n"
   ]
  },
  {
   "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",
    "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": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 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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